Clarion
University
Clarion, PA
Proceedings of the 2012 Pennsylvania Economic Association Conference
Proceedings of the 2012 Pennsylvania Economic Association Conference
PROCEEDINGS OF THE
PENNSYLVANIA ECONOMIC
ASSOCIATION
2012 CONFERENCE
May 31-June 2, 2012
Clarion University
Clarion, Pennsylvania
William K. Bellinger, Editor
Dickinson College
Visit the Pennsylvania Economic Association Home Page at
http://aux.edinboro.edu/pea/index.html
Proceedings of the 2012 Pennsylvania Economic Association Conference
Pennsylvania Economic Association: 2011-2012
Executive Board
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President: Orhan Kara, West Chester University
President-Designate: Tracy Miller, Grove City College
Vice President, Program: William Bellinger, Dickinson College
Vice President, Publicity: Sandra Trejos, Clarion University
Vice President, Membership: Natalie Reaves, Rowan University
Secretary: Stephanie Brewer, Indiana University of Pennsylvania
Treasurer: Steven Andelin, Penn State - Schuylkill
Co-Editors, Pennsylvania Economic Review: Thomas Tolin & Orhan Kara, West
Chester University
Webmaster: Michael Hannan, Edinboro University of Pennsylvania
Immediate Past President: James Jozefowicz, Indiana University of Pennsylvania
Board of Directors
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Ron Baker, Millersville University
Charles Bennett, Gannon University
Deborah Gougeon, University of Scranton
Kosin Isariyawongse, Edinboro University
John McCollough, Penn State - Lehigh Valley
Brian O'Roark, Robert Morris University
Mark Schweitzer, Federal Reserve Bank of Cleveland
Yaya Sissoko, Indiana University of Pennsylvania
Luke Tilley, Federal Reserve Bank of Philadelphia
Roger White, Franklin and Marshall College
Ex-Officio Directors
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Thomas O. Armstrong,
Pennsylvania Department of
Community & Economic
Development
Gerald Baumgardner,
Pennsylvania College of
Technology
David Culp, Slippery Rock
University
Donald Dale, Muhlenberg College
Robert D'Intino, Rowan
University
James Dunn, Edinboro University
Andrew Economopoulos, Ursinus
College
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Joseph Eisenhauer, Wright State
University
Mark Eschenfelder, Robert
Morris University
Andrew Hill, Federal Reserve
Bank of Philadelphia
Elizabeth Hill, Penn State-Mont
Alto
Mehdi Hojjat, Neuman College
Tahereh Hojjat, DeSales
University
Ioannis N. Kallianiotis,
University of Scranton
Donna Kish-Goodling,
Muhlenberg College
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Richard Lang, Federal Reserve
Bank of Philadelphia (retired)
Daniel Y. Lee, Shippensburg
University
Robert Liebler, King's College
Johnnie B. Linn III, Concord
University
Patrick Litzinger, Robert Morris
College
Stanley G. Long, University of
Pittsburgh/Johnstown
Jacquelynne McLellan, Frostburg
State University
Lawrence Moore, Potomac State
College of West Virginia
University
Gayle Morris, Edinboro
University
Heather O'Neill, Ursinus College
Brian O’Roark, Robert Morris
University
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Abdul Pathan, Pennsylvania
College of Technology
William F. Railing, Gettysburg
College
Margarita M. Rose, King's
College
William Sanders, Clarion
University
John A. Sinisi, Penn State
University-Schuylkill
Brian Sloboda, US Postal Service
Kenneth Smith, Millersville
University of Pennsylvania
Lynn Smith, Clarion University
Osman Suliman, Millersville
University
Paul Woodburne, Clarion
University
Bijou Yang-Lester, Drexel
University
David Yerger, Indiana University
of Pennsylvania
Proceedings of the 2012 Pennsylvania Economic Association Conference
Editor’s Introduction and Acknowledgements
The papers published in this volume were presented at the 2012 Annual Conference of the
Pennsylvania Economic Association held at Clarion University of Pennsylvania from May 31
to June 2, 2012. The program lists all scheduled presenters, session chairs and discussants.
Only the papers and comments submitted according to the manuscript guidelines are included
in the Proceedings. Each research paper listed in the Table of Contents and Author Index can
be accessed directly by clicking on the listing.
The 2012 Conference was apparently a great success. Participants gathered from across
Pennsylvania, several other states, and multiple nations in order to share their research.
Faculty, other professionals, and both graduate and undergraduate students participated in the
conference. The conference also featured three general presentations; “Stratification
Economics” by Dr. Sue Stockly, a presentation on the regional economy by Mark
Schweitzer, Research Director at the Federal Reserve Bank of Cleveland, and a session on
teaching economics with Excel by Dr. Humberto Barreto.
I particularly wish to thank Dr. Sandra Trejos of Clarion University for her time and energy
in coordinating all local arrangements. We also owe special thanks to the Federal Reserve
Bank of Cleveland and Dr. James Pesek, Dean of the College of Business at Clarion University of
Pennsylvania for their support of this conference. Additional thanks go to the PEA board for
their advice and effort in making the conference a success. Lastly, thanks to all of the
participants who contributed interesting ideas and a friendly atmosphere to the conference.
Proceedings of the 2012 Pennsylvania Economic Association Conference
Table of Contents
Conference Agenda
page 1
RISK AVERSION AND BUSINESS CYCLES: AN EMPIRICAL ANALYSIS
Cristian Pardo
page 16
RESPONSIVENESS OF THE U.S. TRADE FLOWS TO CHANGES IN CHINESE CURRENCY
Orhan Kara
page 37
RECENT PENNSYLVANIA JOB TRENDS: EFFECTS OF SHALE? (2012)
Jay Bryson, Tim Quinlan and Joe Seydl
page 48
MULTIGENERATIONAL DISCOUNTING: MERGING
INTERGENERATIONAL EQUITY AND INDIVIDUAL TIME PREFERENCE
William Bellinger
page 55
AID EFFECTIVENESS IN SUB-SAHARAN AFRICA
Yaya Sissoko and Niloufer Sohrabji
page 66
ECONOMIC ANALYSIS OF ALTERNATIVE WAYS TO REFORM MEDICAL MALPRACTICE
Tracy C. Miller
page 85
DOES A FIRM’S DIVIDEND INITIATION AFFECT ITS RISK?
Henry F. Check, Jr., John S. Walker and Karen L. Randall
page 90
THE BUREAU OF MOTOR FUEL TAXES COMPLIANCE STRATEGY:
PENNSYLVANIA DEPARTMENT OF REVENUE
Thomas O. Armstrong, Daniel Meuser, James Dehnert, and Nic Banting
page 101
A PANEL STUDY ANALYSIS OF ECONOMIC GROWTH IN SOUTH EAST ASIA
Tai McNaughton
page 117
THE DODD-FRANK “WALL STREET REFORM” ACT OF 2010: IS OUR
FINANCIAL SYSTEM MORE STABLE NOW?
Adora D. Holstein
page 125
GOVERNMENT POLICY AND RESULTANT EFFECT ON NICHE INDUSTRIES:
THE CASE OF USPS “EVERY DOOR DIRECT MAIL”
Brenda Ponsford and William R. Hawkins
page 145
FOREIGN AID EFFECTS ON GROWTH IN LATIN AMERICA
Tai McNaughton
page 148
OFFSETS IN THE DEFENSE TRADE:
COUNTERING COMPARATIVE ADVANTAGE INERNATIONAL BUSINESS
Brenda Ponsford and William R. Hawkins
page 156
ACADEMIC PERFORMANCE IN GRADUATE MANAGERIAL ECONOMICS
Rod D. Raehsler
page 164
DECOMPOSING RECENT MONEY SUPPLY CHANGES
WITH IMPLICATIONS FOR CURRENT FED POLICY
Richard Robinson and Marwan El Nasser
page 177
Proceedings of the 2012 Pennsylvania Economic Association Conference
WORLD OIL PRICES: ECONOMIC IMPACT AND ECONOMETRIC FORECAST
Carrie R. Williams
page 187
ADVANTAGEOUS SELECTION IN HEALTH INSURANCE
Paul Sangrey
page 196
THE EFFECT OF RELIGION AND EMPOWERMENT OF WOMEN ON FERTILITY
Denae A. Heath and Cameron D. McConnell
page 202
THE EFFECT OF HUMAN DEVELOPMENT LEVEL ON THE RELATIONSHIP BETWEEN
ENERGY CONSUMPTION AND ECONOMIC GROWTH: A CHAOS THEORY APPROACH
Ezatollah Abbasian and Maysam Nasrindoost
page 208
LIVING IN KEYNES’S LONG RUN: THE EFFECTS OF THE
OVERUSE OF ECONOMIC STIMULUS
David Nugent
page 216
DISCUSSANT COMMENT ON LIVING IN KEYNES'S LONG RUN:
THE EFFECTS OF THE OVERUSE OF ECONOMIC STIMULUS
Michael J. Hannan
page 222
KANTIAN MARKETS, BOYCOTTS, AND EFFICIENCY
Richard Robinson
page 223
THE ECONOMIC IMPACT OF ALVERNIA UNIVERSITY
Tufan Tiglioglu, Lisa Cooper, Bari Dzomba, Rachel Gifford, and Joseph Hess
page 232
A STUDY OF FACTORS AFFECTING THE ECONOMIC FEASIBILITY
OF THE IMPLEMENTATION OF TORREFACTION TECHNOLOGY
BY THE PENNSYLVANIA WOOD PELLET INDUSTRY
Robert F. Brooker and Harry R. Diz
page 243
THE COLLEGE EDUCATED AND THE PUBLIC/PRIVATE SALARY DIFFERENTIAL:
CAN OCCUPATION EXPLAIN DIFFERENCES?
Mary Ellen Benedict, Michael Bajic, and David McClough
page 251
Author Index
Proceedings of the 2012 Pennsylvania Economic Association Conference
page 267
Pennsylvania Economic Association
2012 CONFERENCE AGENDA
THURSDAY, May 31, 2012
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4:00 pm - 9:00 pm Registration (Moore Hall)
5:00 pm - 8:00 pm Board of Directors Dinner/Meeting
(Eagle Commons 107-108)
6:00 pm - 10:00 pm Reception (Moore Hall)
FRIDAY, June 1, 2012
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8:00 am – noon, 2 pm – 4 pm Registration (Still Hall Lobby)
7:30 am - 10:30 am Continental Breakfast (Still Hall Lobby)
9:00 am - 10:15 am Concurrent Sessions (Still Hall)
10:30 am - 11:45 pm Concurrent Sessions (Still Hall)
12 noon - 1:45 pm Luncheon with Speaker- “Stratification Economics"
Dr. Sue Stockly (Gemmell Complex-Multipurpose Room)
2:15 pm - 3:30 pm Concurrent Sessions (Still Hall)
3:45 pm - 4:45 pm Fed Lecture: Mark Schweitzer, Research Director at The Federal
Reserve Bank of Cleveland (Still Hall 112)
5 pm - 8 pm Fed Sponsored Reception (Still Hall Lobby)
SATURDAY, June 2, 2012
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7:30 am - 10:30 am Registration & Continental Breakfast (Still Hall Lobby)
9:00 am - 10:15 am Concurrent Sessions (Still Hall)
10:30 am - 11:30 am Plenary Session- Teaching Economics with Excel by Dr.
Humberto Barreto (Still Hall 112)
11:45-12:45 General Membership Meeting (Still Hall 112)
12:45 am Closing
Proceedings of the 2012 Pennsylvania Economic Association Conference
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FRIDAY, June 1, 2012
Conference Registration 8:00 a.m. – noon, 2-4 p.m., Still Hall Lobby
7:30 a.m. – 10:30 a.m. Continental Breakfast(Still Hall Lobby)
Sessions F1: Friday, June 1, 2012, 9:00 a.m. – 10:15 a.m.
Session F1A: Development Economics
Location: Still Hall 111
Chair: Orhan Kara, West Chester University
The effect of human development level on the relationship between energy consumption and economic growth: a
chaos theory approach
Ezatollah Abbasian Bu-Ali Sina University
Is Foreign Debt a Threat or a Promise to Least Developed Countries?
Evelyn Wamboye
Pennsylvania State University
Aid Effectiveness in Sub-Saharan Africa
Yaya Sissoko
Indiana University of Pennsylvania
Niloufer Sohrabji
Simmons College
Discussants:
Morteza Sameti, University of Isfahan, Iran
Kiril Tochkov, Texas Christian University
Orhan Kara, West Chester University
Session F1B: Student Session I
Location: Still Hall 102
Chair: Tracy Miller Grove City College
A Dissimilarity of the Federal Reserve and the European Central Bank Mandates and an Investigation of the ZeroBound Rule during the Recent Global Financial Crisis of 2008
Emily Abbondanza
Edinboro University
Industrial Composition of U.S. Counties as a Determent of Income: A Case Study of Pennsylvania
Deepra Yusuf
Franklin & Marshall College
Advantageous Selection in Health Insurance
Paul Sangrey
Grove City College
Discussants:
Tracy Miller, Grove City College
Thomas Armstrong, PA Department of Revenue
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Session F1C: Women and Labor
Location: Still Hall 202
Chair: Mary Ellen Benedict, Bowling Green University
Models for Forecasting Local Employment Trends in Pennsylvania
Sandra McPherson
Millersville University
Determinants of Youth Unemployment in the UAE
Samer Kherfi
American University of Sharjah
Informal social networks and household wellbeing in rural Ethiopia
Abera Birhanu Demeke
University Canada West
Tax Incentives, Education and Migration: The Case of Puerto Rico
Carlos Liard
Central Connecticut State University
Discussants:
Jay Bryson, Wells Fargo
Kevin Quinn, Bowling Green State University
Mary Ellen Benedict, Bowling Green University
Session F1D: Microeconomics in an International Context
Location: Still Hall 203
Chair: Ronald Baker, Millersville University of Pennsylvania
Elasticity Estimation for the Turkish Manufacturing Industry
Zeynep Deniz Dervisen
Kadir Has University
Cournot and Bertrand Competition when Advertising Rotates Demand: The Case of Honda and Scion
Kosin Isariyawongse
Edinboro University of Pennsylvania
Ecological Efficiency and Economic Development in Latin America
SandraTrejos
Clarion University of PA
Discussants:
Ronald Baker, Millersville University of Pennsylvania
Thomas Andrews, West Chester University
Soloman Kone, City University of New York
Session F1E: Macroeconomics
Location: Still Hall 205
Chair: Michael Hannon, Edinboro University of Pennsylvania
Risk Aversion and Business Cycles: An Empirical Analysis
Cristian Pardo
Saint Joseph's University
Using Options to Measure Monetary Policy Credibility
Timothy Kearney
Misericordia University
The Volcker Rule, Wall Street Reform, and the Subprime Financial Crisis
Adora Holstein and Nicole Baird
Robert Morris University
Changing Patterns of Credit Usage from the Survey of Consumer Expenditures: An Exploratory Study of
Type and Level of Credit Before and After 9/11
Heather Kirkwood-Mazik Clarion University of Pennsylvania
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Discussants:
Richard Robinson
Shuang Feng
Michael Hannan
Marwan El Nasser
SUNY-Fredonia
Edinboro University of PA
Edinboro University of PA
SUNY-Fredonia
Session F1F: The Economics of Education
Location: Still Hall 206
Chair: Lynn Smith, Clarion University
What Distinguishes Winners from Losers? An Analysis of School Choice in Massachusetts
Soma Ghosh
Albright College
Financial Aid, College Tuition and Student Retention
Frederick Tannery
Slippery Rock University
The Economic Impact of Alvernia University
Tufan Tiglioglu
Alvernia University
Discussants:
Lynn Smith, Clarion University of Pennsylvania
Liang Ding, Alvernia University
William Bellinger, Dickinson College
Sessions F2: Friday, June 1, 2012 10:30 a.m. – 11:45 a.m.
Session F2A: Health Economics
Location: Still Hall 111
Chair: David McClough, Ohio Northern University
The Effect of Safe Water, Sanitation, and Human Capital on Child Nutrition and Health: A Quantile Regressions
Approach
Divya Balasubramaniam
St. Joseph's University
Economic Analysis of Alternative ways to reform medical malpractice
Tracy Miller
Grove City College
The Economic Costs of Racial Disparities in Breast Cancer
Kemi Oyewole
Spelman College
Discussants:
Samer Kherfi, American University of Sharjah
Charles Telly, SUNY Fredonia
David McClough, Ohio Northern University
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Session F2B: Student Session II, Empirical Analysis
Location: Still Hall 102
Chair: Rod Raehsler Clarion University
A Panel Study Analysis of Economic Growth in Southeast Asia
McNaughton, Tai
Clarion University
The Determinants of Flight Delays
Nadejda Sergheeva
Villanova University
World oil Prices: Economic Impact and Econometric Forecast
Williams, Carrie
Clarion University
Discussants:
Wenting Yu
Rod Raehsler
Texas Christian University
Clarion University
Session F2C: Sustainability in the Business Curriculum
Location: Still Hall 202
Chair: David Culp, Slippery Rock University
A Matrix Model for Integrating Sustainability into the Business Curriculum: A Case Study
David Culp
Slippery Rock University
Green Accounting: Measuring and Reporting the Triple Bottom Line
Anna Lusher
Slippery Rock University
How a Sustainable Business Accelerator Can Enhance Delivery
of Sustainability in the Business Curriculum
John Golden
Slippery Rock University
Discussants:
Robert Leibler, Kings College
Robert F. Brooker, Gannon University
Steven Andelin, Penn State University-Schuylkill
Session F2D: International Macro Topics
Location: Still Hall 203
Chair: Lei Zhang, Edinboro University of PA
Convergence and Persistence of Prices within the European Union
Olena Ogrokhina
University Of Houston
Interest Rates and the Demand for Credit in Ghana
Eric Fosu Oteng-Abayie
Kwame Nkrumah University
Is Foreign Debt a Threat or a Promise to Least Developed Countries?
Evelyn Wamboye
Pennsylvania State University
Discussants:
Lei Zhang, Edinboro University of PA
Cristian Pardo, Saint Joseph's University
Xuebing Yang, Penn State University-Altoona
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Session F2E: Public Economics
Location: Still Hall 205
Chair: Michael Hannan , Edinboro University of PA
The Bureau of Motor Fuels Tax Compliance Strategy: Pennsylvania Department of Revenue
Thomas Armstrong
PA Department of Revenue
Can Responsibility Centered Budgeting Provide Healing from Budget Cuts?
Robert Balough
Clarion University of PA
Tax Incentives and Capital Spending Revisited
Arthur Schiller Casimir
Western New England University
Discussants:
Heather Kirkwood-Mazik, Clarion University of Pennsylvania
Michael Hannan , Edinboro University of PA
John Walker, Kutztown University
Session F2F: Miscellaneous Topics
Location: Still Hall 206
Chair: Roger White, Franklin and Marshall
Does Economic Freedom Mean Freedom to Grow?
Ranajoy Ray-Chaudhuri
The Ohio State University
Sources and Management of Stress Among Non-Academic Staff in Public Universities in
Ghana: The Case of Kwame Nkrumah University of Science and Technology
Henry Kofi Mensah Kwame Nkrumah University of Science and Technology
The Reciprocal Relation of Marginal Cost of Public Funds: Inequality Aversion and Economic
Growth in Iran
Morteza Sameti
University of Isfahan, Iran
Estimation Window and the Power of Event Study
Liang Ding and Tufan Tiglioglu
Alvernia University
Discussants:
Roger White, Franklin & Marshall
David Nugent, Not Affiliated
Ezatollah Abbasian Bu-Ali Sina University
Abera Birhanu Demeke , University Canada West
Proceedings of the 2012 Pennsylvania Economic Association Conference
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LUNCHEON AND SPEAKER
12:00 Noon – 1:45 P.M.
“Stratification Economics"
Dr. Sue Stockly
Gemmell Complex-Multipurpose Room)
Dr. Sue K. Stockly is an Associate Professor of Economics at Eastern New Mexico
University. A native New Mexican, she completed a B.A. and an M.B.A. at the College
of Santa Fe. She received a Ph.D. in Economics from the University of Texas at Austin
in 1999. After working as an associate economist with the RAND Corporation for five
years, she returned to New Mexico where she teaches Economics and Business
Research Methods. Her current research interests include stratification economics,
regional economic development, teaching of economics, and minority student
performance in higher education.
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Sessions F3: Friday, June 1, 2012 2:15 p.m. – 3:30 p.m.
Student Poster Session:
Location: Still Hall Lobby
Nobel Laureate James Meade
Kathleen Coen
Clarion University
Antoine-Augustin Cournot
Matthew Bauer
Clarion University
Jeremy Bentham
Samantha Myler
Clarion University
Session F3A: Resource Economics
Location: Still Hall 111
Chair: Sandra McPherson, Millersville University
Recent Pennsylvania Job Trends: Effects of Shale?
Jay Bryson, Tim Quinlan and Joe Seydl, Wells Fargo
Anthracite Production in Northeast Pennsylvania
Steven Andelin
Pennsylvania State University
Determinants of Distributed Photovoltaic Installations in the United States
Thomas Andrews
West Chester University
A study of factors affecting the economic feasibility of the implementation of torre faction technology by the
Pennsylvania wood pellet industry
Robert F. Brooker
Gannon University
Discussants:
Sandra McPherson, Millersville University
Arthur Schiller Casimir, Western New England University
Tufan Tiglioglu, Alvernia University
Steven Andelin, Penn State University-Schuylkill
Session F3B: Student Session III
Location: Still Hall 102
Chair: Sandra Trejos, Clarion University
Small Business and the United States Economy: The Key to Job Creation and Growth
Miranda Mease
Clarion University
Sheltering Our Poor: Natural Disasters and the Economies of Bangladesh and Haiti
Mary Jo Milford
Clarion University
Militarization and Economic Development in Brazil
Braden Picardi
Clarion University
Discussants:
Sandra Trejos Clarion University
Anna Lusher, Slippery Rock University
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Session F3C: Economic Education
Location: Still Hall 202
Chair: Abdul Pathan, PA College of Technology
Academic Performance in Graduate Managerial Economics
Rod Raehsler
Clarion University
An Outline of Some Thoughts on Teaching Money, Banking, and Monetary Economics in Ex-Soviet
Countries in Transition
Marwan El Nasser SUNY-Fredonia
Testing Procedures
Robert Liebler
King's College
Approaches to Lecture Preparation Presentation and Assessing Student Learning for Economics
Abdul Pathan
PA College of Technology
Discussants:
David Culp, Slippery Rock University
Robert Balough, Clarion University of PA
Soma Ghosh, Albright College
John Golden, Slippery Rock University
Session F3D: Microeconomics
Location: Still Hall 203
Chair: Kosin Isariyawongse, Edinboro University of Pennsylvania
Comparing the effectiveness of leadership gifts and matching funds in the provision of public goods under
alternative laboratory methodologies
Ronald Baker
Millersville University of Pennsylvania
Multigenerational Discounting: Merging Intergenerational Equity and Individual Time Preference
William Bellinger
Dickinson College
Kantian Markets, Boycotts, and Efficiency
Richard Robinson
SUNY Fredonia
Discussants:
Kosin Isariyawongse, Edinboro University of Pennsylvania
Zeynep Deniz Dervisen, Kadir Has University
Brenda Ponsford, Clarion University
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Session F3E: Miscellaneous Topics
Location: Still Hall 205
Chair: Tracy Miller, Grove City College
The College Educated and the Public/Private Salary Differential: Can Occupation Explain Differences?
Mary Ellen Benedict and Michael Bajic, Bowling Green State University"
David McClough
Ohio Northern University
Innovation by London's Water Companies: Internalizing public health externalities
Nicola Tynan
Dickinson College
Adam Smith on Education
Kevin Quinn
Bowling Green State University
Smith’s Invisible Hand is Exonerated by Hayek’s Concept in the Fatal Conceit
Charles Telly
SUNY Fredonia
Discussants:
Lynn Smith, Clarion University
Tracy Miller, Grove City College
Charles Telly, SUNY Fredonia
Kevin Quinn, Bowling Green State University
Session F3F: International Economics
Location: Still Hall 206
Chair: Kiril Tochkov Texas Christian University
Responsiveness of the U.S. Trade Flows to Changes in Chinese Currency
Orhan Kara
West Chester University
Impact of the Global Financial Crisis the ECOWAS Countries
Yaya Sissoko
Indiana University of Pennsylvania
Soloman Kone
City University of New York
The Transforming Dragon: Labor Productivity Change in China's Three Economic Sectors
Wenting Yu
Texas Christian University
Discussants:
Xuebing Yang, Penn State University-Altoona
Adora Holstein, Robert Morris University
Ranajoy Ray-Chaudhuri, The Ohio State University
3:45 p.m. – 4:45 p.m. Still Hall 112
Presentation on the Regional Economy, Mark Schweitzer, Research Director at the
Federal Reserve Bank of Cleveland
5 –8 P.M. Still Hall Lobby
Reception hosted by the Federal Reserve Bank of Cleveland
Proceedings of the 2012 Pennsylvania Economic Association Conference
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SATURDAY, June 2, 2012 7:30 – 10:30 A.M.
Conference Registration & Continental Breakfast:
Still Hall Lobby
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Sessions S1: Saturday, June 2, 2012 9:00 a.m. – 10:15 a.m.
Session S1A: International Trade
Location: Still Hall 111
Chair: Shuang Feng, Edinboro University of PA
Regional Supplier Associations as the Producer of Transitional Public Goods in Latin America and the Caribbean
Sunita Mondal
University of Pittsburgh
Losing Comparative Advantage? Calibrating the DFS Model for US Trade, 1970-2008
Roger White
Franklin & Marshall College
An Eaton-Kortum Openness Index
Xuebing Yang
Penn State University at Altoona
Discussants:
Ronald Baker, Millersville University of Pennsylvania
Orhan Kara, West Chester University
Shuang Feng, Edinboro University of PA
Session S1B: Monetary Economics
Location: Still Hall 102
Chair: Robert Balough, Clarion University of PA
Decomposing Recent Money Supply Changes with Implications for Current Fed Policy
Richard Robinson and Marwan El Nasser, SUNY at Fredonia
The Bank Lending Channel: a FAVAR Analysis
Lei Zhang
Edinboro University of PA
Living in Keynes's Long Run: The Effects of the Overuse of Economic Stimulus
David Nugent
Not Affiliated
Discussants:
Timothy Kearney, Misericordia University
Robert Balough, Clarion University of PA
Michael Hannan, Edinboro University of PA
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Session S1C: Student Session IV
Location: Still Hall 202
Chair: Abdul Pathan, Pennsylvania College of Technology
Impacts of the Recession on Women: Analysis of a Local Economy
Deepra Yusuf
Franklin and Marshall College
The Effect of Immigration on State Wages
Jennifer Johnson
Indiana University of Pennsylvania
Foreign Aid and Its Effect on Human Functioning In Africa
Nathan Wolbert
Clarion University
Why Do Dragons Breathe Fire? The Economic Growth and Development of Eastern Asia
Michael Bartley
Clarion University
Discussants:
Abdul Pathan, Pennsylvania College of Technology
Lynn Smith, Clarion University
Session S1D: Miscellaneous Topics
Location: Still Hall 203
Chair: William Bellinger, Dickinson College
Government Policy and Resultant Effect on Niche Industries
Brenda Ponsford
Clarion University
William R. Hawkins U.S. House of Representatives
Can a Change in Dividend Policy Affect Risk?
John Walker
Kutztown University
Offsets in the Defense Trade:
Brenda Ponsford
Clarion University
William R. Hawkins U.S. House of Representatives
Discussants:
William Bellinger, Dickinson College
Thomas Andrews, West Chester University
Nicola Tynan, Dickinson College
Session S1E: Student Session V
Location: Still Hall 205
Chair: Yaya Sissoko, Indiana University of PA
Promised Land? The Impact of Promise Scholarship Programs on Housing Prices
Michael LeGower
University of Pittsburgh
Analysis of the Time Series Properties of U.S. Unemployment
Kristen Workley
Clarion University
Recovery From Current Recession: Is It Different From Previous Recessions?
Yang Yang
Clarion University
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Discussants:
Heather Kirkwood-Mazik, Clarion University
Yaya Sissoko, Indiana University of PA
Session S1F: Student Session VI (Development)
Location: Still Hall 206
Chair: Sandra Trejos Clarion University
Religion and Empowerment of Women in Brazil
Deane Heath
Clarion University
The Effects of Political Stability and Disease in Sub-Saharan Africa
Jared Bruce
Clarion University
Leadership, Democracy and Economic Development in Africa
Chris Myers
Clarion University
Foreign Aid Effects on Growth in Latin America
McNaughton, Tai
Clarion University
Discussants:
Sandra Trejos, Clarion University
Rod Raehsler, Clarion University
10:30 am - 11:30 am Plenary Session, Still Hall 112
Dr. Humberto Barreto, Teaching Economics with Excel
11:45-12:45 General Membership Meeting
Still Hall 112
This Annual Business Meeting of the General Membership of the Pennsylvania Economic Association is open to
the entire membership of the PEA, including all registrants at the conference. Door prizes will be awarded.
SATURDAY, June 2, 2012 CLOSING
Program Author & Participant Index
First Name
Last Name
E-mail
Sessions
Ezatollah
Abbasian
abbasian@basu.ac.ir
F1A, F2F
Emily
Abbondanza
abbondanzaemily@yahoo.com
F1B
Steven
Andelin
sla7@psu.edu
F2C, F3A
Thomas
Andrews
tandrews@wcupa.edu
F1D, F3A, S1D
Thomas
Armstrong
thoarmstro@state.pa.us
F1B, F2E
Ronald
Baker
ronald.baker@millersville.edu
F1D, F3D, S1A
Divya
Balasubramaniam
dbalasub@sju.edu
F2A
Robert
Balough
balough@clarion.edu
F2E, F3C, S1B
Michael
Bartley
m.c.bartley@eagle.clarion.edu
S1C
Matthew
Bauer
m.j.Bauer1@eagle.clarion.edu
Poster Sess.
Proceedings of the 2012 Pennsylvania Economic Association Conference
13
William
Bellinger
bellinge@dickinson.edu
F3D, S1D
Mary Ellen
Benedict
mbenedi@bgsu.edu
F1C, F3E
Robert
Brooker
brooker@gannon.edu
F2C, F3A
Jared
Bruce
j.m.bruce@eagle.clarion.edu
S1F
Jay
Bryson
jay.bryson@wellsfargo.com
F1C, F3A
Arthur Schiller
Casimir
scasimir@wne.edu
F2E, F3A
Kathleen
Coen
K.A.Coen@eagle.clarion.edu
Poster Sess.
David
Culp
david.culp@sru.edu
F2C, F3C
Abera Birhanu
Demeke
abera.birhanu@yahoo.com
F1C, F2F
Zeynep
Deniz Dervisen
zeynepdeniz.dervisen@khas.edu.tr
F1D, F3D
Liang
Ding
liang.ding@alvernia.edu
F1F, F2F
Marwan
El Nasser
Marwan.ElNasser@fredonia.edu
F1E, F3C, S1B
Shuang
Feng
sfeng@edinboro.edu
F1E, S1A
Soma
Ghosh
sghosh@alb.edu
F1F, F3C
John
Golden
john.golden@sru.edu
F2C, F3C
Deborah
Gougeon
gougeond1@scranton.edu
Michael
Hannon
hannan@edinboro.edu
F1E, F2E, S1B
Deane
Heath
D.A.Heath@eagle.clarion.edu
S1F
Adora
Holstein
holstein@rmu.edu
F1E, F3F
Kosin
Isariyawongse
kisariyawongse@edinboro.edu
F1D, F3D
Jennifer
Johnson
jennifermjohnson0811@hotmail.com
S1C
Orhan
Kara
dearorhankara@gmail.com
F1A, F3F, S1A
Timothy
Kearney
tkearney@misericordia.edu
F1E, S1B
Samer
Kherfi
skherfi@aus.edu
F1C, F2A
Heather
Kirkwood-Mazik
hmazik@clarion.edu
F1E, F2E, S1E
Soloman
Kone
skone@lagcc.cuny.edu
F1D, F3F
Michael
LeGower
mjl88@pitt.edu
S1E
Carlos
Liard
liardcaf@ccsu.edu
F1C, F2D
Robert
Liebler
robertliebler@kings.edu
F2C, F3C
Anna
Lusher
anna.lusher@sru.edu
F2C, F3B
David
McClough
d-mcclough@onu.edu
F2A, F3E
Tai
McNaughton
t.k.mcnaughton@eagle.clarion.edu
F2B, F3B
Sandra
McPherson
smcpherson@millersville.edu
F1C, F3A
Miranda
Mease
M.E.Mease@eagle.clarion.edu
F3B
Henry Kofi
Mensah
henbil25@yahoo.com
F2F
Mary Jo
Milford
m.j.milford@eagle.clarion.edu
F3B
Tracy
Miller
tcmiller@gcc.edu
F1B, F2A, F3E
Sunita
Mondal
sum46@pitt.edu
S1A
Chris
Myers
Samantha
Myler
S.L.Myler@eagle.clarion.edu
Poster Sess.
David
Nugent
davidanugent@hotmail.com
F2F, S1B
S1F
Proceedings of the 2012 Pennsylvania Economic Association Conference
14
Olena
Ogrokhina
oogrokhi@mail.uh.edu
F2D
Eric Fosu
Oteng-Abayie
efoteng-abayie.socs@knust.edu.gh
F2D
Kemi
Oyewole
koyewole@gmail.com
F2A
Cristian
Pardo
cpardo@sju.edu
F1E, F2D
Abdul
Pathan
apathan@pct.edu
F3C, S1C
Braden
Picardi
B.C.Picardi@eagle.clarion.edu
F3B
Brenda
Ponsford
bponsford@clarion.edu
F3D, S1D
Kevin
Quinn
kquinn@bgsu.edu
F1C, F2A, F3E
Rod
Raehsler
rraehsler@clarion.edu
F2B, F3C, S1F
Ranajoy
Ray-Chaudhuri
ray-chaudhuri.4@buckeyemail.osu.edu
F2F, F3F
Richard
Robinson
Richard.Robinson@fredonia.edu
F1E, F3D, S1B
Morteza
Sameti
msameti@gmail.com
F1A, F2F
Paul
Sangrey
Sangreypm1@gcc.edu
F1B
Arthur Casimir
Schiller
scasimir@wne.edu
F1E, F3A
Nadejda
Sergheeva
nsergh01@villanova.edu
F2B
Joseph
Seydl
joseph.seydl@wellsfargo.com
F3A
Yaya
Sissoko
ysissoko@iup.edu
F1A, F3F, S1E
Lynn
Smith
lsmith@clarion.edu
F3E, S1C
Frederick
Tannery
frederick.tannery@sru.edu
S2G
Charles
Telly
tabak@fredonia.edu
F2A, F3E
Tufan
Tiglioglu
tufan.tiglioglu@alvernia.edu
F2F, F3A,F1F
Sandra
Trejos
strejos@clarion.edu
F1D, F3B, S1F
Kiril
Tochkov
k.tochkov@tcu.edu
F1A, F3F
Nicola
Tynan
tynann@dickinson.edu
F3E, S1D
John
Walker
walker@kutztown.edu
F2E, S1D
Evelyn
Wamboye
efw10@psu.edu
F1A, F2D
Roger
White
roger.white@fandm.edu
F2F, S1A
Carrie
Williams
C.R.Williams1@eagle.clarion.edu
F2B
Nathan
Wolbert
n.a.wolbert1@eagle.clarion.edu
S1C
Kristen
Workley
K.M.Workley@eagle.clarion.edu
S1E
Yang
Yang
morningyang@yahoo.com
S1E
Xuebing
Yang
xyang@psu.edu
F2D, F3F, S1A
Wenting
Yu
wentingyu@tcu.edu
F2B, F3F
Deepra
Yusuf
deepra.yusuf@fandm.edu
F1B, S1C
Lei
Zhang
lzhang@edinboro.edu
F2D, S1B
Proceedings of the 2012 Pennsylvania Economic Association Conference
15
RISK AVERSION AND BUSINESS CYCLES: AN EMPIRICAL ANALYSIS
Cristian Pardo
Department of Economics
Saint Joseph's University
Philadelphia, PA 19131
ABSTRACT
Private entrepreneurs usually lack access to complete riskpooling for their idiosyncratic risk. Consequently, risk-averse
entrepreneurs internalize volatility and demand a private
equity premium, which is capable of amplifying business
cycles due to its sensitivity to shocks and subsequent impact
on investment and output. Therefore, economies with larger
private entrepreneurial sectors should present higher
volatility. I test this prediction by (1) conducting a reducedform analysis that shows that volatility is negatively
associated with the importance of the corporate vs. privatelyheld sectors; and (2) estimating the model's structural
parameters, where positive risk-aversion coefficients should
be found where private entrepreneurs are predominant.
INTRODUCTION
Differences in business cycle fluctuations in emerging
markets relative to developed economies have been
extensively studied by economists. Prasad, Agenor and
McDermott (1999), among many others, provide empirical
evidence supporting much higher average output volatility in
emerging economies than in industrialized economies.1 In the
theoretical arena, numerous models rely on financial
imperfections as a primary stylized fact to motivate this
discussion, where low levels of development in financial
markets observed in emerging markets are often cited. For
instance, Calvo and Reinhart (2000) and Chang and Velasco
(2000) focus on the role of “dollarized liabilities." Namely,
due to currency mismatching, real exchange rate
depreciations may negatively impact firms' and banks'
balance sheets by asymmetrically increasing the value of
outstanding debt relative to revenues.
Another stylized fact commonly analyzed is the presence of a
more important privately-held sector relative to corporations
in emerging markets. Market capitalization as a fraction of
GDP, for instance, is about 100 percent or more in highincome countries, while 35 percent or less in low and middleincome countries (The World Bank, 2010). The fact that
privately-owned firms tend to rely more on debt rather than
equity to finance their investments may play an additional
role in creating frictions in emerging economies. Bernanke,
Gertler and Gilchrist (1999), for example, study the impact of
information asymmetries in the borrower-lender relationship.
The authors show that the agency problems that arise from
the positive probability of costly default imply that lenders
optimally charge entrepreneurs an external finance premium.
This premium is endogenous to firms' balance sheet in that a
higher reliance on external funds raises the aforementioned
agency costs. As business cycles affect entrepreneurs' net
worth, the resulting external finance premium is countercyclical and may become a mechanism that magnifies the
impact of real shocks over time.
Pardo (2010) examines additional stylized facts about
emerging markets, including that private entrepreneurial
activity in particular is also often very volatile. An illustrative
example is the Chilean case. While it has boasted one of the
most robust financial systems in its region, its economy still
reacted strongly to the effects of the Asian crisis of the
1990's. A reason often cited for this response was that the
entrepreneurial sector moved quickly from an early-to-mid
1990's boom euphoria to a deep depression in the following
years. Evidence like this may make it worthwhile to further
examine facts and assumptions about the private
entrepreneurial sector.
In general, the simplifying assumption of risk neutrality on
agents makes sense in some cases. Gale and Hellwig (1985),
for instance, point out that “risk neutrality is not an
unreasonable assumption to make in the case of investors
since it can be justified as a consequence of risk-pooling."
That is, by investing large amounts of funds and thus taking
advantage of economies of scale, lenders tend to successfully
maintain highly diversified portfolios that allow them to
significantly reduce the exposure to risk. However, the
authors also emphasize the fact that the risk-neutrality
assumption “makes less sense in the case of entrepreneurs."
As shown by Moskowitz and Vissing-Jørgensen (2002), the
high concentration of ownership of privately-held companies
and their importance in their owners' portfolios, leave private
entrepreneurs highly vulnerable to project-specific,
uninsurable risks. That is, the lack of access to complete riskpooling for their idiosyncratic risks leaves private
entrepreneurs with no other option but to internalize the cost
of volatility. Therefore, assuming risk neutrality (that is,
assuming that risk can be ignored) seems to be a stronger
assumption in this case.
Pardo (2010) builds on Bernanke et al. (1999) to show that
introducing risk aversion among private entrepreneurs
modifies the optimal relationship with lenders mainly by
Proceedings of the 2012 Pennsylvania Economic Association Conference
16
incorporating a private equity premium, or the positive risk
premium that risk-averse entrepreneurs demand due to the
stochastic nature of the uninsurable part of their investment
returns. This premium may lead to further magnifying the
aggregate effects of real shocks. The mechanism works as
follows: a real shock that decreases entrepreneurial profits
and net worth, reduces entrepreneurs' minimum guaranteed
level of consumption (the insurable part of the entrepreneur's
returns). Consequently, their effective risk aversion and so
the private equity premium rise. In response to the increased
internal and external costs, entrepreneurs increase the rental
rate of capital to final goods firms, producing a contraction in
the supply of capital and thus additional impact of shocks on
investment and production.
Finally, Pardo (2012) extends the previous model into a small
open economy framework. In this context, following Chang
and Velasco (2000), shocks not only affect entrepreneurs' net
worth directly, but also indirectly through the increase in the
value of debt following the corresponding real exchange rate
adjustment. As wealth falls, the aforementioned private
equity premium rises, therefore generating the known
amplifying impacts of shocks over time. Consequently, a
direct implication of this model is that economies where the
private entrepreneurial sector is a relatively important actor in
the financial market (for instance, where family-owned
businesses are predominant), and therefore the economy as a
whole is more vulnerable to uninsurable risk, all else equal,
should present higher output volatility than economies where
the corporate sector, whose ownership structure is highly
diversified at all levels, is more important.
Considerable debate has taken place among economists about
the role of entrepreneurial activity in affecting economic
growth. Arguments about the impact of entrepreneurship on
important factors affecting long-run growth often discuss
innovation, productivity and knowledge spillovers (van Stel,
Carree and Thurik, 2005). Much less attention has been given
to the relationship between entrepreneurship and output
growth volatility. Apart from Pardo (2012), another exception
is Rampini (2004), who provides a theoretical framework in
which entrepreneurial activity is procyclical and produces
amplification and propagation of shocks. In the empirical
arena, however, to the best of my knowledge there have been
no studies examining the relationship between the ownership
structure of the real sector and output volatility.
Entrepreneurial activity is a plausible index of the economic
importance of entrepreneurship. The Global Entrepreneurship
Monitor (GEM) builds the Total Entrepreneurial Activity rate
(TEA), which measures the “relative amount of nascent
entrepreneurs and business owners of young firms for a range
of countries" (van Stel et al., 2005). Figure 1 illustrates the
simple direct (unconditional) correlation between total
entrepreneurial activity, as measured by the TEA index, and
output volatility, as measured by the standard deviation of the
per capita real GDP growth, for 46 countries. At least as
preliminary evidence, there seems to be a positive correlation
between those two variables.2
The objective of this paper is to empirically test whether the
volatility-inducing frictions that risk aversion introduces are
more likely to be present the greater the relative size of the
private entrepreneurial sector is. I test this prediction through
two alternative approaches. First, I examine the statistical
significance of the correlation between GDP growth volatility
and the ownership structure of the productive sector through
a simple reduced-form analysis. In particular, using
international data, I find that output volatility is negatively
associated with the relative importance of the corporate
sector in the financial market, all else equal. The investmentto-capital ratio (as a measure of financial leverage), indices of
financial structure and development plus other measures of
cross-country sources of uncertainty are used as control
variables.
The reduced-form approach's main limitation in this case is
that a resulting statistical relationship between output
volatility and ownership structure may not provide empirical
significance of the magnitude of the impact imposed by
private entrepreneurs in promoting sharper business cycle
volatility. Consequently, I also conduct a structural analysis
through which instead of using a measure of ownership of the
real sector as a proxy for the relevance of entrepreneurial risk
aversion, I estimate the model's risk-aversion coefficient (γ)
that is consistent with an economy's observed fluctuation of
output. That is, I estimate the structural parameters of the
dynamic model that I introduce in Section 2 using observed
data for the model's main variables; namely output,
investment and consumption. Intuitively, if an economy is
mostly composed of corporations (such as the U.S.) and so
the importance of frictions imposed by risk-averse
entrepreneurs is relatively small, we should see this economy
behave more closely to one with risk neutral agents (that is,
the estimated γ should not be significantly different from
zero). Conversely, an economy where private entrepreneurs
are predominant and so risk aversion is likely to impose
stronger impacts, a positive coefficient of risk aversion
should be found.
The outline of this paper is as follows. The next section
describes the dynamic model and its theoretical implications;
the following two sections introduce a reduced-form
empirical analysis using international data and then conducts
a structural empirical analysis of the risk-aversion
assumption. The last section 4 provides some concluding
remarks.
THE SUPPLY OF CAPITAL, AGGREGATE EFFECTS
AND DYNAMICS
Proceedings of the 2012 Pennsylvania Economic Association Conference
17
The theoretical framework is based on Pardo (2012), who
builds upon Cespedes, Chang and Velasco (2004) and
Bernanke et al. (1999). In order to focus this paper's attention
on the model's empirical implication, which is that GDP
growth volatility, all else equal, should be associated with the
ownership structure of the productive sector, this section
briefly describes the model in which frictions between riskaverse entrepreneurs and lenders may lead to the
aforementioned aggregate effects, while leaving further
discussion and a more detailed description of the model in 5.
The first step is to introduce the context in which an
international lender and a domestic borrower optimally
interact and, as a consequence of asymmetric information and
entrepreneurial risk aversion, a risk premium arises. The
contract between entrepreneurs and lenders, which eventually
yields the economy's capital supply, involves the following
features: (i) entrepreneurs produce capital goods (such as
plant and equipment) that domestic firms rent in order to
produce final goods; (ii) entrepreneurs (or capitalists) finance
their capital investments using both their internal net worth
and borrowed funds denominated in foreign currency; (iii)
competitive risk-neutral international lenders provide
financing to entrepreneurs for which they receive a
contracted interest rate; and (iv) the contractual relationship
between borrowers and lenders is subject to informational
frictions in that the effective profitability of capital
production is ex-ante unknown and ex-post observed only by
entrepreneurs, unless lenders pay an auditing cost
(Townsend, 1979).
The optimal contract implies the following equilibrium
results: (i) lenders charge a premium that is higher than the
opportunity cost of lending due to the agency costs that arise
from costly entrepreneurial bankruptcy, denoted the external
finance premium; (ii) risk-averse entrepreneurs seek selfinsurance against low state of nature; (iii) hedging is
incomplete as entrepreneurs optimally and willingly accept
some uncertainty in order to maintain some information as
private, namely the upper side of the distribution of capital
returns; (iv) risk-averse entrepreneurs internalize the utility
associated with facing uncertain returns and for which they
require a premium on top of the pecuniary borrowing costs,
denoted the private equity premium; (v) the capital rental rate
that entrepreneurs charge captures both the external finance
premium and the private equity premium; (vi) all else equal, a
risk-averse entrepreneur requires a higher expected capital
return than a risk-neutral entrepreneur, or equivalently, given
a capital rental rate, an entrepreneur is willing to supply less
capital to final goods firms; and (vii) any drop in
entrepreneurial net worth, say as a consequence of an adverse
shock, affects both premiums: the external finance premium
due to increased agency costs as entrepreneurs rely more on
external rather than internal funds, and the private equity
premium, as the entrepreneurs' minimum guaranteed
consumption level falls and so their effective level of risk
aversion increases.
The next step is to introduce the dynamics and aggregate
effects of the derived supply of capital in the context of an
open economy dynamic general equilibrium framework. The
main elements that this model comprises include: (i) the
domestic economy produces a final good using labor and
capital supplied by workers and entrepreneurs, respectively;
(ii) domestic firms maximize profits by optimally choosing
capital, labor and output; (iii) the firm optimality conditions
yield the economy's demand for capital, which together with
the supply of capital, determine the economy's equilibrium
capital investment and rental rate; (iv) workers maximize
utility over consumption and leisure subject to a budget
constraint; (v) the monetary authority uses its policy
instruments to maintain the domestic price level constant
while letting the exchange rate freely fluctuate; and (vi) the
model closes by imposing domestic and external market
clearing conditions.
The predicted effects of an unexpected shock on the economy
are as follows. Say that an unexpected decrease in the foreign
demand for domestic goods takes place. The drop in exports
produces a decrease in output demand and, in particular, in
planned capital investment demand. The corresponding drop
in domestic expenditure produces an instantaneous real
depreciation of the local currency, which reduces the
entrepreneur's net worth by increasing the domestic currency
value of debt repayment, which is denominated in foreign
currency. As a consequence, both the external finance
premium and the private equity premium increase, further
impacting planned investment decisions and the real
exchange rate. That is, the shock together with
entrepreneurial risk aversion should produce a magnified
short-term effect on capital investment and output.
The predicted implication suggests that business cycles
should be more volatile in economies where non-publicly
traded private firms are relatively more important than in
economies where publicly-traded corporations are relatively
more important, all else equal. This result arises from the fact
that risk aversion and thus the private equity premium are
more relevant to privately-held companies than to, say,
corporations, which are usually owned by multiple large
investors. Recall that given the observed high concentration
of ownership of privately-held companies and their
importance in the owners' portfolios, private entrepreneurs
become highly vulnerable to project-specific risks and thus
are forced to internalize the cost of volatility more
(Moskowitz and Vissing-Jørgensen, 2002).
EMPIRICAL EVIDENCE:
REDUCED-FORM APPROACH
Proceedings of the 2012 Pennsylvania Economic Association Conference
18
This model, like others that examine the impact of financial
frictions on business cycles, predict that more leveraged
economies tend to present stronger business cycle
fluctuations, as higher reliance on debt rather than internal
wealth translates into a higher external finance premium. In
addition, the model in this paper particularly stresses the role
played by risk-averse entrepreneurs in that the private equity
premium may also be responsible for creating even sharper
business cycle fluctuations.
This section attempts to empirically test this model's main
predictions. In particular, using international data, I look at
the reduced-form cross-sectional association between
business cycle volatility and the ownership structure of the
economy's capital-producing sector, controlling for the
financial development, leverage and other sources of
uncertainty. Specifically, I test whether business cycle
volatility, all else equal, is more pronounced where the
privately-owned private sector represents a larger fraction of
their economic activity of a country, relative to where the
corporate sector predominates.
The Data
I use the standard deviation of per capita GDP growth to
measure business cycle volatility.3 I obtain data for this
variable from the International Financial Statistics (IFS) of
the International Monetary Fund. To capture leverage, I
employ the investment to capital ratio estimated by
Albuquerque and Wang (2008) (QK/PN).4 In order to account
for the relative importance of the privately-owned sector, I
use cross-country data on a financial structure definition
constructed by Levine (2002). This measure, called Structuresize (STSIZE), is an index of the relative size of stock
markets vs. bank credit to the private sector. The main
principle behind this measure is that an economy will have a
larger index the greater the importance of the corporate sector
relative to the privately-owned sector in its financial
structure. Therefore, this model predicts a negative
coefficient on this variable. This measure, however, harbors
some limitations. For instance, even though this index
measures the relative importance of the two markets, it can
only serve as a proxy for the relative importance of the flow
of new equity and credit to businesses. No clear bias can be
ex-ante identified as consequence of this measurement error.
In addition, due to its role of facilitating the optimal
allocation of funds and risk pooling, the different levels of
development of financial markets should in theory imply
differences in how risk is managed and diversified, thus also
affecting GDP growth volatility. To this goal, I have included
an index of financial development as an explanatory variable
both by itself and multiplied by the financial structure
variables in order to explore an interaction effect. I use as the
index of financial development the ratio of the stock market
liquidity to costs in the banking sector as a measure of
efficiency in the financial market, as in Levine (2002).
Empirically, however, there is no clear evidence of a
significant link between financial development and economic
growth volatility. Some studies have found that financial
development does dampen GDP growth volatility (Wahid and
Jalil, 2010; Denizer, Iyigun and Owen, 2002), while others
have obtained no robust relationship (Beck, Lundberg and
Majnoni, 2006). Consequently, there is no unambiguous exante way that differences in financial development may affect
GDP growth volatility in the context of this model.
As in previous studies, I control for other cross-country
sources of uncertainty. To account for aggregate monetary
uncertainty, I use the average inflation rate for the period
1985-2006 (INFAVE) and inflation volatility for the same
period (INFSTD). In terms of uncertainty associated with
international trade, I use the volatility of the real exchange
rate (RERSTD) and the degree of openness of the economy,
as measured by the share of the sum of exports and imports to
GDP (TRADE). All these series come from either the
International Financial Statistics (IFS) of the IMF or the
World Development Indicators (WDI) of the World Bank.
Finally, in order to account for the uncertainty caused by
government institutions, as in Albuquerque and Wang (2008),
I employ a measure of government spending volatility
(GOVSTD), an index of corruption (CORRUPT) and an
index of government stability (GOVSTAB).5 The first series
is obtained from the IFS while the last two series come from
the International Country Risk Guide (ICRG) of the Political
Risk Service Group.
Results
Table 1 shows the results from estimating the relationship
between output growth volatility, firm leverage (QK/PN) and
the measure of relative importance of the corporate sector in
the financial market (STSIZE), while controlling for different
sets of exogenous sources of volatility. In regression (1), I
examine the impact of leverage and the relative size of the
corporate sector on volatility, initially controlling only for
monetary shock variables: the average inflation level
(INFAVE) and its volatility (INFSTD). Only the estimated
coefficients for the inflation variables have the predicted sign
and statistical significance. Regression (2) include the
volatility induced by openness to international markets, as
captured by the volatility of the real exchange rate
(RERSTD) and the trade to GDP ratio (TRADE).
Regressions (3) additionally considers the volatility induced
by the government. In both regressions (2) and (3), the
coefficients for the relative importance of the corporate sector
have the expected sign and statistical significance.
In regressions (4) and (5), I split the sample in order to track
Proceedings of the 2012 Pennsylvania Economic Association Conference
19
groups with high and low entrepreneurial importance base on
their “Total Entrepreneurial Activity" index (TEA)
separately. Results are not unambiguous. While the volatilityreducing impact of the relative importance of the corporate
sector is stronger for countries with lower entrepreneurial
activity (STSIZE1) than those with higher entrepreneurial
activity (STSIZE2), the difference of these values, however,
is not statistically significant. The lack of significance maybe
partially explained by the low number of observations.
Finally, regression (6) considers the impact of differences in
financial development and GDP growth volatility across
countries, either directly or through its interaction with the
financial structure variable. As commonly found in the
literature, results do not show evidence of a significant role
played by capital markets in affecting growth volatility.
The above results suggest that the volatility of GDP growth is
indeed negatively associated with the relative importance of
the corporate sector in the financial market, holding overall
leverage, financial development and other sources of
volatility constant. That is, these results are consistent with
this model's main prediction that a relatively larger
entrepreneurial sector should increase volatility, all else
equal.6 In addition, results are also consistent with the more
standard prediction that more leverage should significantly
increase volatility.
The main limitations of the reduced-form approach is that the
existence of a statistical relationship between volatility and
ownership structure does not indicate how strong the role of
entrepreneurial risk aversion is in promoting sharper business
cycles fluctuations. This shortcoming can be addressed
through a structural empirical analysis.
EMPIRICAL EVIDENCE:
STRUCTURAL APPROACH
Given the reduced-form analysis' limitation in providing clear
empirical evidence about the magnitude of the role of
entrepreneurial risk aversion in affecting business cycles, this
section attempts to estimate the coefficient of risk aversion (
γ ) that would be consistent with observed fluctuations in
output and other variables experienced by individual
economies. A positive coefficient would signal that
entrepreneurial risk aversion is indeed a necessary feature of
the model in order to replicate the observed path of real
variables. A coefficient not significantly different from zero,
on the other hand, would imply that risk aversion is irrelevant
in explaining a country's business cycle.
Consequently, the objective of this section is two-fold: (i) to
estimate the main structural parameters of the model, in
particular the risk-aversion coefficients ( γ ) that are
consistent with 12 economies' observed paths of real
variables such as output, consumption and investment:
Argentina, Brazil, Canada, Chile, France, Germany, Japan,
South Korea, Mexico, Thailand, the U.K. and the U.S.7; (ii)
to provide cross-country comparisons for consideration.
As results in Section 3.1 suggest, the role of risk-averse
entrepreneurs in causing sharper business cycle fluctuations
is likely to be more significant in economies where the
privately-owned private sector represents a relatively larger
fraction of the real sector. In contrast, for countries like the
U.S., where the corporate sector is more important, we should
observe this economy behave more closely to one with riskneutral agents. Therefore, the null hypotheses are (i) that the
estimated risk-aversion coefficient is significantly greater
than zero, and (ii) that it is higher for countries in which
privately-held economies predominate like, say, Mexico,
Chile, Thailand or Korea, when compared to corporateintensive economies like the U.S.
In short, the way this estimation procedure works is the
following. The model's non-standard parameters are initially
calibrated using observed data on each economy. Calibration
details are presented in 7. Subsequently, the mathematical
package DYNARE, which is commonly used for simulation
and estimation of rational expectation models, solves the
dynamic model presented in 5 and obtains the policy function
of its choice variables, given the realization of the stochastic
shocks and an initial choice of parameters values. Then, using
Bayesian methods, the package uses real data in order to
estimate the value of select parameters that best match the
observed path followed by some of the model's main
variables.
The following section introduces the dataset and the
parameters to be estimated.
The Data
Besides the risk-aversion coefficient (γ), the parameters I
estimate in this section are the share of domestic goods in
total expenditure (θ) and the three parameters that are initially
obtained through calibration: the default cost rate (μ), the
entrepreneurial saving rate (δ) and the variance of capital
returns (σω2).
Given that the model has three shock parameters (the
multifactor productivity parameter, A; the international riskfree rate, ρ; and foreign demand for domestic goods, X), only
three data vectors can be used in order to avoid stochastic
singularity (Juillard, 2004). As a result, I use time series on
real output (Y), consumption (C) and physical capital
investment (K) to conduct the estimations.8 Data for France,
Germany, Japan and the U.K. come from Eurostat. Data for
Brazil, Korea and Mexico were obtained from the IMF's
International Financial Statistics (IFS). In addition, I obtain
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Argentinean data from Argentina's Instituto Nacional de
Estadística y Censos (INDEC), Chilean data from the Central
Bank of Chile, Canadian data from OECD.Stat Extracts,
Thailand's data from Office of The National Economic and
Social Development Board, Thailand, and U.S. data comes
the U.S. Bureau of Economic Analysis (BEA).
The frequency of data is quarterly and, in order to facilitate
cross-country comparison, the data range from 1995.1 to
2011.4 for all 12 economies. In addition, I use the HodrickPrescott filter in order to extract the trend of all series and
then transform the series in terms of the percentage
deviations from their trends.
Priors
The structural estimation procedure requires setting prior
assumptions for the parameters' distribution, mean and
standard deviation. Most of the mean values were obtained or
chosen in the calibration procedure presented in 7 (see Table
4 for a summary of results). For the risk-aversion coefficient
(γ), for instance, I use the normal distribution around the
initially assumed mean value of 2 for all countries. The
standard deviation is set high enough in order to allow for
higher flexibility in the estimation. For the expenditure share
of domestic goods in home consumption parameter (θ), I also
assume a normal distribution with a prior mean of 0.6 and
standard deviation of 0.3 for all countries.9
Mean and standard deviation values for the remaining
variables to be estimated were set according to calibration
and specific to each country. The bankruptcy cost parameter
(μ) and the variance of capital returns (σω2) were assumed to
follow an inverse gamma distribution in order to ensure
positive estimates. The standard deviations for these variables
were set at values equal to roughly one half of their prior
means. Finally, the entrepreneurial saving rate (δ) follows a
normal distribution with rather low standard deviations
(between 0.05 and 0.1), in order to avoid the possibility of
saving rates greater than one.
With respect to the shock parameters, I use uniform
distribution for their standard deviations (σ) and persistence
parameters (φ). Their lower and upper bounds were set
loosely equal to 0 and 1, respectively.
The first four columns of Table 2 summarize the parameters'
prior distribution, mean and standard deviation.
Results
The last three columns of Table 2 present the parameter
estimations computed using the Bayesian method. The first
column contains the estimated mean, while the last two show
the 5th and 95th percentiles of the posterior distributions. The
bottom of each table contains the value of the marginal
likelihood, computed with the Laplace approximation.
Turning to the risk-aversion coefficient, as observed in Table
2, while the posterior values for γ are positive for Argentina,
Brazil, Canada, Chile, Germany, Korea, Mexico and
Thailand, they are not statistically different from zero for the
cases of France, Japan, the U.S. and the U.K. One may have
not expected Canada and Germany to belong to the group
with positive γ parameters; however one may also notice that
their values are statistically lower than the values for γ of any
country in that group. In general, the fact that the posterior γ
is statistically higher for countries like Argentina, Brazil,
Chile, Korea, Mexico and Thailand than for Canada, France,
Germany, the U.K. and the U.S. suggests that this model
requires a higher risk-aversion coefficient in order to explain
the responses to shocks followed by some economies than for
others.
Similarly, one may observe that not all the estimated mean
values for γ can be statistically distinguished among
countries. For instance, though France presents the lowest
mean value for γ, its estimate is not statistically different
from those found for Japan, the U.S. or the U.K.
Equivalently, though Korea presents the highest mean values
for γ, its estimate is not statistically different from those
found for Argentina or Brazil. In addition, the estimate of γ
for Japan cannot be statistically distinguished from any of the
countries in the group of economies with positive γ, except
for Korea.
The result above supports the idea that entrepreneurial risk
neutrality may be an assumption that more closely
reassembles economies that are more corporate-intensive
than predominantly privately-held. Put differently,
entrepreneurial risk aversion is realistic and statistically
significant in explaining output fluctuations for economies
that are more heavily composed of privately-held firms, but
not so much for economies where the corporate sector is
relatively more important. That is, these results suggest that
risk aversion on the part of entrepreneurs not only makes
sense in theory for some economies, but also presents
empirical relevance.
The values of the other coefficients are mostly close to their
corresponding prior values, which came from the calibration
procedures described in 7. One exception is the coefficient on
the share of domestic goods in consumption (θ), which tends
to be greater than the uniformly assumed value of 0.6 for
developed countries and lower for less developed countries.
This result is consistent with the empirical evidence that bias
towards domestic goods weakens in emerging markets
relative to developed countries. Wang and Chen (2004)
discuss and provide evidence that issues like lack of trust of
goods manufactured in developing countries and the
Proceedings of the 2012 Pennsylvania Economic Association Conference
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documented consumption home bias among OECD countries
are behind this result.
With respect to the shock variables, the estimated standard
deviations and persistence parameters tend to differ with
respect to the shock and across countries. In general,
however, shocks are on average stronger in emerging
economies than in more advanced economies (as captured by
their standard deviations σX, σρ and σA), however less
persistent (as given by their autocorrelation coefficients φX,
φρ and φA. The difference in volatility and persistence of
shocks towards stronger but less persistent values for less
developed economies is also consistent with previous
literature. For instance, Mendoza (1995) finds that terms of
trade shocks are on average almost three times more volatile
however less persistent in emerging markets relative to
developed countries.
The parameter μ is one that seemingly departs from
previously estimates values. For instance, Bernanke et al.
(1999) and Carlstrom and Fuerst (1997) find that for the
United States, μ = 0.12 and μ = 0.20, respectively, while I
found from both calibration and estimation than its value is
considerably lower. One of the factors that may explain this
difference is that in both Bernanke et al. (1999) and
Carlstrom and Fuerst (1997), μ represents costs of
bankruptcy, while in this model μ captures the costs of
default. Given that bankruptcy implies default while default
does not necessarily imply bankruptcy, it follows that the
costs of default should be lower than those of bankruptcy.
CONCLUDING REMARKS
Evidence has shown that economies can still respond strongly
to shocks even in the absence of noticeable flaws in their
financial and/or productive sectors. A possible answer to this
apparent puzzle may lie in the role of private entrepreneurs.
This sector has evidenced signals of rapid transitions from
euphoria during booms to deep depression in even mild
recessions in some economies. Consequently, their behavior
towards the exposure to risk may be an additional source of
amplification of shocks in an economy.
Risk aversion on the part of private entrepreneurs implies that
entrepreneurs internalize the cost of the uncertainty of their
investment returns, for which they require an extra premium:
the private equity premium. A microeconomic analysis of the
optimal relationship between lenders and entrepreneurs under
asymmetric information shows that the rate at which
entrepreneurs rent capital to final goods firms (R)
incorporates this premium. As a consequence, a positive
premium translates into a more constrained capital supply.
may respond to shocks as changes in net worth would impact
their effective level of risk aversion in the opposite direction.
The corresponding effect on the private equity premium
further impacts the economy's capital supply and thus
production.
A corollary to this result is the fact that higher output
volatility should be more prevalent in countries where private
entrepreneurs are likely to be relatively more important actors
of an economy. That is, economies with a relatively higher
share of privately-held companies, all else equal, should
exhibit sharper business cycles than economies with a private
sector composed more importantly of publicly traded
companies. Put differently, two otherwise similar economies
subject to similar real shocks may respond differently to these
shocks due to differences in the ownership structure of the
real sector.
This paper empirically tests the above implication and finds
evidence to suggest the hypothesis that risk aversion may
matter. I first approach this issue by conducting a simple
reduced-form regression analysis to examine the crosscountry empirical connection between GDP growth volatility
and the general ownership structure of the real sector. Results
suggest that a statistically significant relationship exists when
controlling for other relevant variables, like financial
development, leverage and other cross-country sources of
uncertainty.
To provide further evidence on the magnitude of the role of
entrepreneurial risk aversion in affecting business cycles
fluctuations, I also execute an empirical analysis of the
structural parameters of the model. In particular, I estimate
the risk aversion coefficient that would generate business
cycles which best match the observed real fluctuations.
Empirical results for 12 countries signal that the risk-aversion
assumption is macroeconomically more relevant for small
open economies like Argentina, Brazil, Chile, Mexico, Korea
and Thailand (which present relatively stronger privatelyheld sectors) than for Canada, France, Germany, the U.K. and
the U.S. (where the corporate sector is relatively more
important).
In terms of policy implications, improvements in information
technology and transparency in the privately-owned sector
that could alleviate agency costs may reduce the importance
of asymmetric information in generating volatility. In
addition, entrepreneurial risk aversion will play a lesser role
as more established businesses become public. Therefore,
policies encouraging the participation of investors in the
ownership of firms, for instance through private equity firms,
may help reduce the volatility associated with entrepreneurial
risk aversion.
In addition, this premium may further amplify shocks in
general equilibrium. Specifically, entrepreneurial activity
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Figure 1: Scatter plot and linear fit of the volatility of GDP growth on total entrepreneurial activity.
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Table 1: OLS Estimates of Standard Deviation of Per Capita Real GDP Growth
Variable
QK/PN
STSIZE
(1)
-6.1030
12.065
-0.0514
0.0501
(2)
3.1022**
1.2218
-0.0093**
0.0038
(3)
4.700**
1.5297
-0.0108**
0.0038
STSIZE1
(4)
5.6532
2.2419
INFAVE
0.2770**
0.0402
1.2297**
0.1012
0.7893**
0.0461
0.0836
0.1019
0.0003
0.0009
0.0002*
0.0001
0.7535**
0.0550
0.2753**
0.1336
0.0004
0.0009
0.0002*
0.0001
0.0026
0.0055
-0.3378
0.3256
0.0075
0.0050
0.6324**
0.1135
0.5384
0.3169
0.0017
0.0014
0.0002*
0.0001
-0.0192
0.0099
-0.5655
0.3433
0.0200*
0.0083
-0.0093
0.0093
1.3930**
0.8700
-0.1047
0.8218
0.001
0.0018
3.6E-6
0.0004
0.0145
0.0103
0.3571
1.7343
0.0025
0.0102
0.6140
1.3061
34
0.9939
-0.3378**
0.1250
28
0.9822
-0.5652**
0.1656
28
0.9835
-0.5677**
0.2325
14
0.9955
-0.7808**
0.3421
14
0.7044
RERSTD
TRADE
GOVSTAB
GOVSTD
CORRUPT
FDEV
STSIZE*FDEV
Intercept
# of Obs.
Adjusted R2
(6)
3.9861**
1.9727
-0.0144**
0.0066
-0.0131**
0.0048
STSIZE2
INFSTD
(5)
5.9117*
3.0158
0.7349**
0.0857
0.3201*
0.1899
0.0004
0.0009
0.0002*
0.0001
0.0005
0.0070
-0.2655
0.3712
0.0073
0.0053
0.0033
0.0051
0.0021
0.0050
-0.4800**
0.2197
28
0.9820
Notes: Variables are the capital to net worth ratio (QK/PN), the measure of the relative importance of the corporate sector
(STSIZE), the volatility of inflation (INFSTD), average inflation (INFAVE), the volatility of the real exchange rate
(RERSTD), trade openness (TRADE), an index of government stability (GOVSTAB), the volatility of real government
spending (GOVSTD), the degree of corruption (CORRUPT), and an index of efficiency of the domestic financial market
(FDEV). Numbers below the coefficient estimates correspond to the standard errors.
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Table 2: Bayesian Estimation
ARGENTINA
Prior
Parameter
Distrib.
Mean Std. dev Mean
γ
normal
2
2
2.7859
σ2ω
inv gamma 0.009 0.0044
0.0077
μ
inv gamma 0.028
0.014
0.0274
δ
gamma
0.947
0.05
0.8431
θ
normal
0.6
0.05
0.6684
φX
uniform
0.5
0.2887
0.6522
φA
uniform
0.5
0.2887
0.1455
φρ
uniform
0.5
0.2887
0.2809
σX
uniform
0.5
0.2887
0.2919
σA
uniform
0.5
0.2887
0.0586
σρ
uniform
0.5
0.2887
0.0151
Marginal Likelihood:
345.39
BRAZIL
Prior
Parameter
Distrib.
Mean Std. dev
γ
normal
2
3
σ2ω
inv gamma 0.055 0.0276
μ
inv gamma 0.775 0.3875
δ
gamma
0.52
0.05
θ
normal
0.6
0.3
φX
uniform
0.5
0.2887
φA
uniform
0.5
0.2887
φρ
uniform
0.5
0.2887
σX
uniform
0.5
0.2887
σA
uniform
0.5
0.2887
σρ
uniform
0.5
0.2887
Marginal Likelihood:
375.43
CANADA
Prior
Parameter
Distrib.
Mean Std. dev
γ
normal
2
3
σ2ω
inv gamma 0.055 0.0276
μ
inv gamma 0.775 0.3875
δ
gamma
0.52
0.05
θ
normal
0.6
0.3
φX
uniform
0.5
0.2887
φA
uniform
0.5
0.2887
φρ
uniform
0.5
0.2887
σX
uniform
0.5
0.2887
σA
uniform
0.5
0.2887
σρ
uniform
0.5
0.2887
Marginal Likelihood:
662.56
Posterior
Confidence interval
1.9004
3.9440
0.0049
0.0102
0.0187
0.0340
0.7976
0.8924
0.6423
0.7063
0.6098
0.6873
0.0232
0.2810
0.0790
0.4858
0.2578
0.3435
0.0516
0.0663
0.0133
0.0167
Mean
2.5528
0.0812
0.6583
0.4276
0.5472
0.2375
0.2785
0.3492
0.1393
0.0349
0.0062
Posterior
Confidence interval
1.8430
3.4458
0.0695
0.0927
0.3889
0.8846
0.3739
0.4876
0.4773
0.6285
0.1687
0.3136
0.2025
0.3528
0.1552
0.5505
0.1193
0.1621
0.0295
0.0393
0.0033
0.0096
Mean
0.8780
0.1240
0.5409
0.4505
0.8584
0.6853
0.9633
0.7214
0.1337
0.0052
0.0030
Posterior
Confidence interval
0.8364
0.9153
0.1208
0.1277
0.5215
0.5649
0.4461
0.4548
0.8450
0.8674
0.6701
0.7017
0.9483
0.9847
0.6716
0.7625
0.1244
0.1460
0.0047
0.0057
0.0025
0.0036
Proceedings of the 2012 Pennsylvania Economic Association Conference
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CHILE
Prior
Parameter
Distrib.
Mean Std. dev
γ
normal
2
1
σ2ω
inv gamma 0.007 0.0035
μ
inv gamma 0.018 0.0092
δ
gamma
0.959
0.05
θ
normal
0.6
0.3
φX
uniform
0.5
0.2887
φA
uniform
0.5
0.2887
φρ
uniform
0.5
0.2887
σX
uniform
0.5
0.2887
σA
uniform
0.5
0.2887
σρ
uniform
0.5
0.2887
Marginal Likelihood:
399.34
FRANCE
Prior
Parameter
Distrib.
Mean Std. dev
γ
normal
2
4
σ2ω
inv gamma 0.01
0.0048
μ
inv gamma 0.029 0.0146
δ
gamma
0.939
0.05
θ
normal
0.6
0.3
φX
uniform
0.5
0.2887
φA
uniform
0.5
0.2887
φρ
uniform
0.5
0.2887
σX
uniform
0.5
0.2887
σA
uniform
0.5
0.2887
σρ
uniform
0.5
0.2887
Marginal Likelihood:
721.64
GERMANY
Prior
Parameter
Distrib.
Mean Std. dev
γ
normal
2
3
σ2ω
inv gamma 0.042 0.0208
μ
inv gamma 0.059 0.0296
δ
gamma
0.915
0.05
θ
normal
0.6
0.3
φX
uniform
0.5
0.2887
φA
uniform
0.5
0.2887
φρ
uniform
0.5
0.2887
σX
uniform
0.5
0.2887
σA
uniform
0.5
0.2887
σρ
uniform
0.5
0.2887
Marginal Likelihood:
599.62
Mean
1.9325
0.0057
0.0635
0.9353
0.6257
0.6912
0.2463
0.0553
0.2035
0.0175
0.0310
Posterior
Confidence interval
0.9202
3.0959
0.0037
0.0074
0.0256
0.0887
0.8902
0.9850
0.4984
0.7474
0.5973
0.7934
0.0753
0.4331
0.0003
0.1217
0.1505
0.2615
0.0149
0.0197
0.0263
0.0358
Mean
-0.2102
0.0168
0.0469
0.9483
0.9667
0.9563
0.7402
0.8195
0.2437
0.0047
0.0044
Posterior
Confidence interval
-0.5691
0.0535
0.0160
0.0178
0.0440
0.0484
0.9414
0.9540
0.9605
0.9736
0.9357
0.9692
0.6981
0.7868
0.7811
0.8603
0.2050
0.2710
0.0041
0.0054
0.0037
0.0052
Mean
0.8205
0.0405
0.0424
0.9001
0.6868
0.6410
0.7979
0.5944
0.0685
0.0084
0.0052
Posterior
Confidence interval
0.7550
0.9037
0.0352
0.0460
0.0313
0.0515
0.8838
0.9173
0.6679
0.7015
0.6239
0.6604
0.7066
0.8763
0.4271
0.7053
0.0588
0.0777
0.0075
0.0093
0.0045
0.0059
Proceedings of the 2012 Pennsylvania Economic Association Conference
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JAPAN
Prior
Parameter
Distrib.
Mean Std. dev
γ
normal
2
4
σ2ω
inv gamma 0.008 0.0038
μ
inv gamma 0.02
0.01
δ
gamma
0.961
0.05
θ
normal
0.6
0.3
φX
uniform
0.5
0.2887
φA
uniform
0.5
0.2887
φρ
uniform
0.5
0.2887
σX
uniform
0.5
0.2887
σA
uniform
0.5
0.2887
σρ
uniform
0.5
0.2887
Marginal Likelihood:
643.23
KOREA
Prior
Parameter
Distrib.
Mean Std. dev
γ
normal
2
2
σ2ω
inv gamma 0.008 0.0042
μ
inv gamma 0.039 0.0193
δ
gamma
0.924
0.05
θ
normal
0.6
0.3
φX
uniform
0.5
0.2887
φA
uniform
0.5
0.2887
φρ
uniform
0.5
0.2887
σX
uniform
0.5
0.2887
σA
uniform
0.5
0.2887
σρ
uniform
0.5
0.2887
Marginal Likelihood:
275.91
MEXICO
Prior
Parameter
Distrib.
Mean Std. dev
γ
normal
2
4
σ2ω
inv gamma 0.006 0.0031
μ
inv gamma 0.015 0.0075
δ
gamma
0.967
0.05
θ
normal
0.6
0.3
φX
uniform
0.5
0.2887
φA
uniform
0.5
0.2887
φρ
uniform
0.5
0.2887
σX
uniform
0.5
0.2887
σA
uniform
0.5
0.2887
σρ
uniform
0.5
0.2887
Marginal Likelihood:
393.38
Mean
0.9690
0.0068
0.0208
0.9196
0.7167
0.7156
0.7008
0.6579
0.0544
0.0097
0.0054
Posterior
Confidence interval
-0.4808
2.2994
0.0041
0.0095
0.0103
0.0348
0.8530
0.9615
0.6239
0.8095
0.6208
0.8187
0.5985
0.8184
0.4766
0.7976
0.0390
0.0700
0.0085
0.0111
0.0047
0.0065
Mean
3.9768
0.0073
0.0306
0.8696
0.3667
0.1559
0.0552
0.7975
0.2897
0.0534
0.0238
Posterior
Confidence interval
3.2552
4.8196
0.0062
0.0081
0.0174
0.0473
0.8249
0.9171
0.2895
0.4796
0.0636
0.2640
0.0004
0.0956
0.7070
0.8854
0.2475
0.3218
0.0468
0.0628
0.0203
0.0285
Mean
2.6745
0.0069
0.0179
0.8884
0.8574
0.8310
0.0838
0.3178
0.2408
0.0308
0.0328
Posterior
Confidence interval
2.2630
3.0079
0.0050
0.0092
0.0136
0.0229
0.8168
0.9314
0.8253
0.8881
0.7959
0.8682
0.0102
0.1652
0.1565
0.4718
0.1891
0.3051
0.0274
0.0349
0.0279
0.0362
Proceedings of the 2012 Pennsylvania Economic Association Conference
27
THAILAND
Prior
Parameter
Distrib.
Mean Std. dev
γ
normal
2
1
σ2ω
inv gamma 0.014 0.0071
μ
inv gamma 0.053 0.0264
δ
gamma
0.902
0.05
θ
normal
0.6
0.3
φX
uniform
0.5
0.2887
φA
uniform
0.5
0.2887
φρ
uniform
0.5
0.2887
σX
uniform
0.5
0.2887
σA
uniform
0.5
0.2887
σρ
uniform
0.5
0.2887
Marginal Likelihood:
338.43
U.K.
Prior
Parameter
Distrib.
Mean Std. dev
γ
normal
2
4
σ2ω
inv gamma 0.007 0.0033
μ
inv gamma 0.013 0.0067
δ
gamma
0.973
0.05
θ
normal
0.6
0.3
φX
uniform
0.5
0.2887
φA
uniform
0.5
0.2887
φρ
uniform
0.5
0.2887
σX
uniform
0.5
0.2887
σA
uniform
0.5
0.2887
σρ
uniform
0.5
0.2887
Marginal Likelihood:
659.46
U.S.
Prior
Parameter
Distrib.
Mean Std. dev
γ
normal
2
3
μ
inv gamma 0.02
0.0101
σ2ω
inv gamma 0.005 0.0024
δ
gamma
0.948
0.05
φA
uniform
0.5
0.2887
σA
uniform
0.5
0.2887
Marginal Likelihood:
190.14
Mean
1.5726
0.0112
0.0372
0.8822
0.4703
0.5507
0.4518
0.0563
0.2136
0.0377
0.0209
Posterior
Confidence interval
0.9820
2.3990
0.0079
0.0145
0.0234
0.0480
0.8122
0.9558
0.4085
0.5452
0.4983
0.6070
0.2849
0.6767
0.0035
0.0921
0.1862
0.2460
0.0346
0.0417
0.0194
0.0236
Mean
-0.0299
0.0048
0.021
0.9579
0.7311
0.7318
0.8345
0.5564
0.0813
0.0052
0.0051
Posterior
Confidence interval
-0.8155
0.9308
0.0044
0.0052
0.0197
0.0222
0.9411
0.9753
0.6736
0.8040
0.6567
0.8063
0.7130
0.9361
0.5124
0.6078
0.0627
0.1041
0.0045
0.0060
0.0042
0.0058
Mean
-0.1481
0.0159
0.0039
0.9711
0.0028
0.0075
Posterior
Confidence interval
-1.0452
0.4725
0.0125
0.0200
0.0035
0.0046
0.9511
1.0020
0.0001
0.0050
0.0039
0.0124
Note: Dependent variables: Y, K and C. The intervals correspond to the 5th and 95th percentiles of the posterior
distributions.
Proceedings of the 2012 Pennsylvania Economic Association Conference
28
Table 3: Calibration Parameter Values and Sources
Country
Argentina
Argentina
Parameter
Default probability
Risk premium
Value
3%
2.85%
Argentina
Brazil
Brazil
Brazil
Canada
Canada
Canada
Capital to net worth ratio
Default probability
Risk premium
Capital to net worth ratio
Default probability
Risk premium
Capital to net worth ratio
1.646
4%
36.62%
2.7
7%
1.521%
1.463
Chile
Default probability
Chile
Chile
France
France
France
Germany
Germany
Germany
Japan
Japan
Japan
Korea
Korea
Korea
Mexico
Mexico
Mexico
Thailand
Thailand
Thailand
U.K.
U.K.
Risk premium
Capital to net worth ratio
Default probability
Risk premium
Capital to net worth ratio
Default probability
Risk premium
Capital to net worth ratio
Default probability
Risk premium
Capital to net worth ratio
Default probability
Risk premium
Capital to net worth ratio
Default probability
Risk premium
Capital to net worth ratio
Default probability
Risk premium
Capital to net worth ratio
Default probability
Risk premium
2.12%
1.612
6%
3.145%
1.818
9%
6.727%
1.314
4%
2.13%
1.481
2.72%
3.5%
2.1
4.74%
1.747%
1.424
8.7%
5.369%
1.988
3%
1.499%
U.K.
U.S.
U.S.
U.S.
Capital to net worth ratio
Default probability
Risk premium
Capital to net worth ratio
1.202
2%
2%
2
9%
Sources
Lizarazo (2011)
Ministerio de Economia (Argentina);
Glen and Mondragon-Velez (2011)
Cespedes, Gonzalez and Molina (2010)
Souza-Sobrinho (2010)
World Development Indicators (WDI)
Terra (2009)
Aretz and Pope (2007)
World Development Indicators (WDI)
Goyal and Wang (2011)
Calvo, Drexler, Flores and Pacheco
(2009)
Central Bank of Chile
Fernandez (2005)
Aretz and Pope (2007)
World Development Indicators (WDI)
Mefteh and Oliver (2007)
Aretz and Pope (2007)
World Development Indicators (WDI)
Niu (2009)
Aretz and Pope (2007)
World Development Indicators (WDI)
Bartram, Brown and Fehle (2009)
Gertler, Gilchrist and Natalucci (2007)
Gertler et al. (2007)
Gertler et al. (2007)
Rocha and Garcia (2004)
World Development Indicators (WDI)
Cespedes et al. (2010)
Sandstrom (2006)
World Development Indicators (WDI)
Chorruk and Worthington (2010)
Aretz and Pope (2007)
UK Debt Management Offfice;
Bank of England
Niu (2009)
Bernanke et al. (1999); Fisher (1998)
Bernanke et al. (1999)
Bernanke et al. (1999)
Proceedings of the 2012 Pennsylvania Economic Association Conference
29
Table 4: Calibration.
Parameter
α
γ
β
Υ
θ
Common Parameters
Description
share of capital to output
coefficient of risk aversion
discount factor
labor supply elasticity parameter
domestic goods to total expenditure ratio
Value
0.35
2
0.99
4/3
0.6
Country Parameters
Parameter
µ
δ
σω
Description
default cost rate
entrepreneur saving rate
volatility
Argentina
0.0285
0.9471
0.0936
µ
δ
σω
default cost rate
entrepreneur saving rate
volatility
France
0.0293
0.9390
0.0980
µ
δ
σω
default cost rate
entrepreneur saving rate
volatility
Mexico
0.0148
0.9676
0.0781
Parameter
κ
H(ŵ)
R − (1 + ρ)
QK/Y
QC/Y
SX/Y
x
κ
H(ŵ)
R − (1 + ρ)
QK/Y
QC/Y
SX/Y
x
κ
H(ŵ)
R − (1 + ρ)
QK/Y
QC/Y
SX/Y
x
Country
Brazil
Canada
0.7750
0.0127
0.5205
0.9698
0.2350
0.0876
Country
Germany Japan
0.0592
0.0200
0.9147
0.9610
0.2039
0.0875
Country
Thailand
U.K.
0.0528
0.0134
0.9020
0.9726
0.1193
0.0814
Table 5: Implied steady state values
Description
Value
Argentina
Brazil
Canada
capital to net worth ratio
1.647
2.694
1.437
default rate
2.999%
4.017% 6.923%
overall risk premium
2.849%
36.603% 1.537%
capital to output ratio
33.70%
25.50% 34.13%
consumption to output ratio
65.00%
65.00% 65.00%
exports to output ratio
40.78%
45.70% 40.52%
insurance rate
476.38%
43.62% 88.32%
France
Germany Japan
capital to net worth ratio
1.860
1.306
1.463
default rate
6.053%
8.975% 4.025%
overall risk premium
3.086%
6.756% 2.178%
capital to output ratio
33.62%
32.48% 33.93%
consumption to output ratio
65.00%
65.00% 65.00%
exports to output ratio
40.83%
41.51% 40.64%
insurance rate
79.25%
81.93% 81.30%
Mexico
Thailand
U.K.
capital to net worth ratio
1.445
2.032
1.187
default rate
4.769%
8.800% 2.969%
overall risk premium
1.717%
5.230% 1.554%
capital to output ratio
34.07%
32.94% 34.12%
consumption to output ratio
65.00%
65.00% 65.00%
exports to output ratio
40.56%
41.24% 40.53%
insurance rate
83.94%
75.55% 84.47%
Chile
0.0185
0.9594
0.0842
Korea
0.0385
0.9244
0.0916
U.S.
0.0193
0.9600
0.0705
Chile
1.617
7.93%
2.14%
33.94%
65.0%
40.64%
83.88%
Korea
2.1
2.72%
3.5%
33.49%
65.0%
40.91%
70.67%
U.S.
2
2.15%
1.98%
34.11%
65.0%
40.54%
75.98%
Proceedings of the 2012 Pennsylvania Economic Association Conference
30
ENDNOTES
1
See also Backus and Kehoe (1992).
The limitation of this index is that it is measured in absolute
terms and not relative to the importance of the corporate sector.
That is, a country with a higher entrepreneurial activity index
does not necessarily have lower participation corporate sector
participation in the productive sector. In section 3.1, I use a
different variable that overcomes this limitation.
3
Data are for the period 1985-2006 and are for the following
sources Argentina, Australia, Austria, Belgium, Brazil, Canada,
Chile, Colombia, Denmark, Finland, France, Germany, Greece,
India, Ireland, Israel, Italy, Japan, Malaysia, Mexico,
Netherlands, New Zealand, Norway, Pakistan, Peru, Philippines,
Portugal, South Africa, Spain, Sweden, Switzerland, Thailand,
the United Kingdom and the United States.
4
These authors use real GDP data from the World Development
Indicators of the World Bank and follow King and Levine (1994)
to estimate this variable.
5
This index measures both the government’s ability to carry out
its declared program(s), and its ability to stay in office.
6
A potential reverse causality situation in which GDP growth
volatility would favor private entrepreneurial activity over
corporate activity is not a concern. Given corporations' greater
ability to manage and diversify risk over private entrepreneurs, a
resulting higher GDP growth volatility would make private
entrepreneurial activity relatively less important rather than more
predominant. As common in the entrepreneurial activity
literature, the existence of idiosyncratic uninsurable risk may
affect this sector and in turn cause amplified output responses to
shocks, but the resulting increased aggregate volatility does not
translate into higher idiosyncratic risk (Rampini, 2004; Angeletos
and Calvet, 2006).
7
I include these countries as representative economies with
different degrees of openness, income level, geographic location
and degree of participation of publicly-traded businesses in
physical capital formation.
8
Given its size and lower degree of openness, I estimate a closedeconomy version of the model for the case of the U.S. Since there
are no export or international interest rate shocks in a closedeconomy framework, only technological shocks can be
considered. Consequently, only one variable series can be used to
estimate the model; in this case, I used capital investment ( K )
because it is the variable that is most responsive to shocks in this
model.
9
See Cespedes et al. (2004).
2
APPENDIX 1
The Model
This section briefly describes the optimal contract between
an international lender and a domestic borrower in order to
examine the risk premium that arises from asymmetric
information and entrepreneurial risk aversion. As in
Bernanke et al. (1999), entrepreneurs produce capital
goods (such as plant and equipment) that domestic firms
employ to manufacture final goods. The effective amount
of finished capital, however, after entrepreneur j’s purchase
of Kjt+1 units of raw capital, is given by ωjKjt+, where ωj is a
stochastic variable with a known distribution, Et(ωj) = 1
and variance σω2.
The entrepreneur finances the investment using both his
internal net worth (N) and funds borrowed from
international lenders denominated in foreign currency (B),
St Bt j+1 = Pt K K t j+1 − Pt N t j
(1)
where net worth is measured in terms of the domestic good
priced P, PK is the price of capital, and S is the nominal
exchange rate. All but borrowing (B) are denominated in
local currency.
Competitive international lenders charge a contracted gross
interest rate Zjt+1. The contract is subject to informational
frictions in that the ex-post realization of ωj is private
information and idiosyncratic to each entrepreneur.
Assuming costly state verification as in Townsend (1979),
lenders can only observe the return to capital by paying an
auditing cost. This framework ensures that in non-default
states, the lender optimally does not monitor and thus the
entrepreneur maintains the true realization of ωj as private
information and repays the lender the contracted interest
rate. Therefore, profits for the entrepreneur in high states
from producing capital are ωjRt+1Kjt+1 – St+1Zjt+1Bjt+1, where
Rt+1 is the nominal rental rate denominated in domestic
currency that final goods firms pay to entrepreneurs for
renting capital.
In low states of nature where default takes place, however,
the lender does monitor and states become observable by
both parties. In such a case, the risk-neutral lender
optimally purchases the project from the risk-averse
entrepreneur at some fixed price equal to, say, a fraction
xjt+1 ≤ 1 of his initial net worth PtNjt. That is, the optimal
contract insures the risk-averse entrepreneur with positive
consumption in all states. Therefore, entrepreneur j
maximizes the expected utility:
ωˆ j
xtj Pt N t j
U
(
) dH (ω ) +
∫0
S t +1
(2)
∞
ω j Rt +1 K t j+1 − S t +1 Z t j+1 Bt j+1
) dH (ω )
∫ωˆ jU (
S t +1
where St+1 also denotes the price of entrepreneurial
consumption, assuming for simplicity that they only
consume imports.
Let ω̂ j be the cut-off value of ωj such that the entrepreneur
is indifferent between defaulting or not. That is, ω̂ j solves
St+1Zjt+1Bjt+1 = ω̂ j Rt+1Kjt+1 – xjtPtNjt and the entrepreneur's
expected utility can be re-expressed as
Proceedings of the 2012 Pennsylvania Economic Association Conference
31
ωˆ j
∫
0
U(
which imply the following optimal relationship:
xtj Pt N t j
) dH (ω ) +
S t +1
xtj Pt N t j + (ω j − ωˆ j ) Rt +1 K t j+1
) dH (ω )
∫ωˆ jU (
S t +1
(3)
∞
Note that the entrepreneur obtains x tPtN t regardless of ω .
In addition, if ωj > ω̂ j the entrepreneur receives an extra
random return ωjRt+1Kjt+1 and repays a fixed amount ω̂ j
Rt+1Kjt+1 to the lender. Note also that xtPtNjt is the price at
which it would be optimal for the entrepreneur not to face
risk and thus sell his business to investors. Since the
entrepreneur would agree to take that payment only when
the state of nature is “low enough," then xjt can be
interpreted as an insurance rate against idiosyncratic risk.
j
j
j
Risk-neutral international lenders are willing to take part in
this contract only when their participation constraint holds:
ωˆ j
∫
0
[
∞ ω
ˆ j Rt +1 K t j+1
(1 − µ ) ωRt +1 K t j+1
] dH (ω ) + ∫ˆ j [
] dH (ω )
ω
S t +1
S t +1
(4)
xtj Pt N t j
j
−
≥ (1 + ρ t +1 ) Bt +1
S t +1
That is, the lender participates only when the expected
returns from lending net of default costs (as given by
auditing costs and captured by the parameter μ) are not
lower than the opportunity cost of funds, where ρ is the
international risk-free interest rate. In equilibrium, due to
default costs, lenders charge a external finance premium.
The optimal contract is given by the choice of Kjt+1, ω̂ j and
xjt that maximizes the entrepreneur's expected utility
subject to the lender participation constraint. The
optimality conditions are:
∞
∫ω (ω − ωˆ ) dH (ω ) R
t +1 =
ˆ
′
E (U ( ) |ω < ω ) (1 + ρ t +1 ) Pt N t S t +1 xt Pt N t
−
[1 +
][
] (5)
E (U ′( ) |ω > ωˆ )
K t +1
St
K t +1
Rt +1
−
Cov{U ′( ), ω}
E (U ′( ) |ω > ωˆ )
∞
xt Pt N t
+ [1 − H (ωˆ )]∫ˆ ωRt +1 dH (ω ) =
ω
K t +1
∞
S (1 + ρ t +1 ) Pt N t
[1 − H (ωˆ )]∫ˆ ωˆ Rt +1 dH (ω ) + t +1
ω
St
K t +1
[1 − H (ωˆ ) − µωˆ h(ωˆ )]
−
⋅ Cov{U ′(⋅), ωRt +1} + ϕ
E (U ′(⋅) |ω > ωˆ )
(8)
Equation (8) captures the entrepreneur's optimal
investment decision that in equilibrium equates the
entrepreneur's expected marginal revenues (left hand side)
and expected marginal costs (right hand side). Revenues
are the sum of the insurance guaranteed in all states of
nature, and the expected net return in non-default states.
Marginal costs, on the other hand, consist of the
combination of four terms. The first two are, respectively,
the unit cost of capital repayment in non-default states and
the entrepreneur's opportunity cost of funds. The third
term, the covariance between capital return and the
entrepreneur's marginal utility of consumption, captures
the drop in utility that risk-averse entrepreneurs experience
from facing uncertain returns. This additional cost that for
which entrepreneurs demand a premium corresponds to the
private equity premium (the fourth term φ is negligible).
Equation (8) also captures the economy's supply of capital
as a function of its price (R) and the other predetermined
variables. In general equilibrium, the entrepreneurs' capital
supply and final goods firms' capital demand yield the
equilibrium R and the economy's optimal capital
investment (K).
In terms of the general equilibrium framework, the
domestic economy produces a good through a standard
constant returns to scale Cobb-Douglas production
function, employing capital (K) supplied by entrepreneurs
and employing labor (L) supplied by workers,
ˆ
[1 +
1
E (U ′( ) | ω < ωˆ )
]=
E (U ′( ) | ω > ωˆ ) [1 − H (ωˆ )] − µωˆ h(ωˆ )
j
ωˆ j (1 − µ ) ωR K
xtj Pt N t j
t +1 t +1
=∫ [
] dH (ω ) +
0
St +1
St +1
∞ ω
ˆ j Rt +1 K t j+1
j
∫ωˆ j[ St +1 ] dH (ω ) − (1 + ρt +1 ) Bt +1
(6)
(7)
Yt = At K tα L1t−α
(9)
where At is a aggregate multifactor productivity shock
parameter.
Domestic firms maximize profits by optimally choosing
capital, labor and output. The standard optimality
conditions determine the economy's demand for capital and
labor:
Rt K t = αPtYt
Wt Lt = (1 − α ) PtYt
(10)
(11)
where Wt is the nominal wage rate, which is denominated
Proceedings of the 2012 Pennsylvania Economic Association Conference
32
in local currency. Firm's profits are zero in equilibrium.
The representative worker
consumption and leisure,
maximizes
utility
N t = δ {( Rt /Pt ) K t − (1 + ρ t )( S t /Pt ) Bt
over
ωˆ
− µ ∫ ω ( Rt /Pt ) K t dH (ω )}
(17)
0
St CtE =
1
log Ct − Lυt
υ
subject to the budget constraint
Wt Lt = Pt CtH + Pt F CtF
where Ct = [θθ(1 – θ)(1 – θ)]-1(CtH)θ(CtF)(1 – θ) is a CES
aggregate of domestic goods (CH) and foreign goods (CF),
υ > 0 determines the elasticity of labor supply, θ is the
weight of domestic goods in total consumption and PtF is
the domestic price of imports. Assuming that the law of
one price holds and the price of the foreign good is the
numeraire (PF = 1), then PF = S.
Consumption cost minimization and the definition of
aggregate consumption Ct, produce the economy's labor
supply and demand for consumption goods
1−δ
Pt N t
δ
Finally, the model closes by imposing domestic and
external market clearing conditions. For the money market,
the monetary authority uses its policy instruments to
maintain a constant domestic price level while letting the
exchange rate freely fluctuate. The goods market
equilibrium condition imply that:
PtYt = θQt ( K t +1 + Ct ) + St X t
(19)
That is, proceeds from selling output can be consumed,
invested in capital or exported, where Xt is exports, which
are exogenous to the model.
The stochastic behavior of the model is driven by three
structural shocks
At = φ A At −1 + ε tA
Wt
= Lυt −1
Qt Ct
Qt Ct = Wt Lt
(18)
ρ
ρ t +1 = φρ ρ t + ε t
(12)
X t = φ X X t −1 + ε
(13)
(20)
(21)
X
t
(22)
where εtA, εtρ and εtX are independent and identically
distributed innovations with εtA ~ N(μi, σi2), i = {A, ρ, X}.
where
Qt = Ptθ S t1−θ
(14)
Assuming that capital production uses domestic and
foreign goods in proportions θ and 1 – θ, respectively, then
Qt also represents the domestic price of capital production
(i.e., PK = Q). Then, equation (1) can be re-expressed as:
Pt N t + St Bt +1 = Qt K t +1
Therefore, the rational expectations stochastic dynamic
general equilibrium is defined by equations (5), (6), (7),
(9), (10), (11), (14), (13), (12), (15), (17) and (19), along
with the stochastic processes in equations (20), (21) and
(22), that solve for Yt, Lt, Kt+1, Qt, St, Rt, ω̂ j , Wt, xt, Nt, Bt+1
and Ct,.
(15)
From equations (3) and (4), the entrepreneurial sector
aggregate equity (Vt+1) accrued from renting capital to final
goods firms is:
ωˆ
Vt +1 = Rt +1 K t +1 − (1 + ρ t +1 ) S t +1 Bt +1 − µ ∫ ωRt +1 K t +1 dH (ω )
0
(16)
Following an overlapping generations framework as in
Pardo (2010), entrepreneurs consume fraction 1 - δ of Vt,
and save and invest the rest in a risky project that yields
aggregate equity Vt+1 in the following period. As a result,
entrepreneurial net worth and consumption (PE) at period t
evolve respectively as:
APPENDIX 2
Calibration
This section presents the values of the parameters used to
solve the dynamic model. I select most parameters in a
standard manner. For instance, following Bernanke et al.
(1999), the share of capital in production (α) is set at 0.35,
the discount factor β is 0.99 and the labor supply elasticity
is equal to 3 (which implies a value for the parameter υ
equal to 4/3). In addition, the idiosyncratic return ω is
assumed to be log-normally distributed with a mean of one
and standard deviation of σω. As in Cespedes et al. (2004),
the expenditure on domestic goods in total consumption, θ,
is set at 0.6. Finally, as in most dynamic macroeconomic
models, I initially set the constant relative risk-aversion
Proceedings of the 2012 Pennsylvania Economic Association Conference
33
coefficient (γ) equal to 2.
I select the remaining parameters so that they imply steadystate values that match previously estimated measures for
the 12 economies under study. In particular, I choose the
values of the volatility of returns σω, the default cost rate μ
and the entrepreneurial saving rate δ so that the steadystate values of the default rate H(ω), the overall risk
premium R – (1 + ρ) and the ratio of capital to net worth
QK/PN match previous estimates. Table 3 presents the
values for each of those parameters found in the literature
and their sources. To match such values, Table 4
summarizes the values for σω, μ and δ that I obtained from
the calibration procedure. Table 5 displays the resulting
calibrated steady state values of the model's main
variables, which are used for the structural estimation of
the model.
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Proceedings of the 2012 Pennsylvania Economic Association Conference
36
RESPONSIVENESS OF THE U.S. TRADE FLOWS TO CHANGES IN CHINESE CURRENCY
Orhan Kara
Department of Economics and Finance
West Chester University
West Chester, PA 19383
ABSTRACT
This study examines the trade flows between the U.S.
and China to find the effect of changes in the
renminbi on the U.S. trade deficit. The conclusions
reveal that the U.S. imports from China are more
sensitive to changes in the U.S. income than the U.S.
exports to China to the changes in the Chinese
income level. Furthermore, a one percent
appreciation of the renminbi leads to an about 0.82
percent decrease in the value of the U.S. imports
from China and an about two percent increase in the
value of the U.S. exports to China.
INTRODUCTION
While encouraging a weaker dollar, Krugman (2011)
stated the following: “But sensible policy makers
have long known that sometimes a weaker currency
means a stronger economy, and have acted on that
knowledge. Switzerland, for example, has intervened
massively to keep the franc from getting too strong
against the euro. Israel intervened ever more
forcefully to weaker the shekel.” Since the U.S. has
high trade deficit, especially with China, the
argument for a weaker dollar by Krugman (2011)
was against China.
China pegged her currency to the U.S. dollar and then
to the British pound during the 1950s and 1960s until
the breakdown of Bretton Woods Agreement
(Eichengreen, 2006). Then it pegged to a basket of
currencies. China started a dual exchange rate policy
consisting of an official rate and market rate (Jin,
2009). In trade, China used the market rate (1 dollar
= 2.8 yuan) to promote exports and the official rate
was used for non-trade transactions, mainly
remittance and tourism (Eichengreen, 2006; Jin,
2009). However, China abandoned the dual exchange
rate system and fixed the renminbi at the prevailing
market rate of 8.7 to the dollar and allowed to float in
a narrow margin ( Eichengreen, 2006). On July 21,
2005 the Chinese government implemented a new
currency reform, pegging the renminbi a basket of
eleven currencies that were important in terms of
their shares in Chinese trade (Sun, 2010).
With a tightly controlled exchange rate system and
export promoting strategies, China has become the
leading exporter of the world (Chen, Rau, and Chiu;
2011). Due to a growing high volume of trade flows
between the U.S. and China, the U.S. trade deficit has
increased over the years. Several public officials and
commentators speculated that China has been
engaging in currency manipulation and that various
proposals appeared to take action against China
(Staiger and Sykes, 2010). Specifically China has
been accused of keeping her currency undervalued
which led to the increase of China’s export. Scholars
examining the degree of undervaluation of Chinese
currency estimated that the renminbi (RMB), the
Chinese currency, is undervalued as high as fifty
percent (Tyers and Zhang; 2011). Under the political
pressure, the U.S. government has entered exchange
negotiations with China (Cao, Cao, Prasad, and Shen;
2011). Recently, several proposals in Washington has
been put forward, ranging from insisting that the
Treasury Department designate China as a ‘currency
manipulator’ and take this matter to the International
monetary Fund, having the U.S. trade Representative
file a complaint to the World trade organization, and
imposing antidumping and countervailing duties on
China’s exports to the U.S. (Staiger and Sykes,
2010). However, there are other studies arguing that
the Chinese currency is not undervalued and
revaluing the RMB would not reduce the U.S. trade
deficit with China (McKinnon and Schabl; 2009,
2008).
Therefore, this study investigates the trade flows
between China and the U.S., focusing on the effect of
exchange rates. Specifically, this study examines the
short run and long run effect of changes in dollar
renminbi exchange rate on trade flows by estimating
export and import demand equations. To that end, the
next section reviews the literature. Then, the model
and methodology are explained. The section after that
presents the results. The final section concludes the
study.
LITERATURE REVIEW
China’s export promoting growth strategy led to a
remarkable economic development over thirty years
Proceedings of the 2012 Pennsylvania Economic Association Conference
37
with a 9.8% average growth rate (Xu, 2010). This
reduced the number of people living below the
poverty level and increased prosperity of the middle
class. Although this great success story also brought
many problems as the trade deficits of several
countries with China increased, particularly the U.S.
As a result, rich literature has been developed . The
main criticism is that China has manipulated her
currency with keeping low at the expense of her
trading partners.
With respect to currency manipulation, many studies
tried to estimate how undervalued the Chinese
currency, renminbi. According to Cheung, Chinn,
and Fujii (2010), renminbi was undervalued as much
as fifty percent with the pre-2005 international
Comparison program benchmark data. Cheung et al.
(2010) also presented other estimates undertaken by
different researches. For example, Cline and
Williamson (2010) found that Chinese currency was
thirty three percent undervalued. Similarly, Wang
and Hu (2010) estimated a 17.5 percent
undervaluation while Subramanian (2010) reported a
value of thirty one percent undervalued renminbi.
Goldstein and Lardy (2009) argued that renminbi was
undervalued twenty one percent and in another study
Goldstein (2004) found that the renminbi was
undervalued by 15-30 percent. An estimate of twenty
three percent undervalued renminbi was found by
Coudert and Couharde (2004). While Tenengauzer
(2010) found ten percent undervalued renminbi,
Stupnytska, Stolper, and Meechan (2009) estimated
only 2.56% undervaluation. On the other hand,
Bergsten (2010) argued that the renminbi was
undervalued by forty percent against dollar, and
about twenty five percent on a trade weighted basis.
However, there are also studies that the renminbi is
actually overvalued or no evidence of undervalued
renminbi. For instance, Goh and Kim (2006),
employing a data set from 1972 to 2002, found no
evidence that the renminbi was significantly
undervalued. Wang (2004) claimed that the renminbi
was actually overvalued. Similarly, Funke and Rahn (
2005) claimed that the renminbi was not substantially
undervalued. McKinnon and Schnabl (2008) also
argued that the Chinese currency was not
undervalued. Hu and Chen (2010) found that the
Chinese currency was overvalued by 13.4 percent.
Hoover, Cheung et al. (2010) estimated an
overvaluation of 16.8 percent based on the real U.S.
exchange rate and thirty six percent on M trade
weighted exchange rate basis.
Although no agreed upon conclusion has been
reached as to whether the Chinese currency is
undervalued and how much it was undervalued, some
researchers (for example Krugman, 2011) suggest the
Chinese government appreciate her currency. In
addition, the U.S. government has been pressuring
for appreciation. After finding that the renminbi was
undervalued, Goldstein (2006) suggested a ten to
twenty five percent appreciation. According to Stager
and Sykes (2010) Paul Krugman and C. Fred
Bergsten claimed that the renminbi must appreciate
twenty five to forty percent to restore global
imbalances. Furthermore, the U.S. government has
been pressuring and negotiating with the Chinese
government to increase the value of renminbi and the
negotiations are likely to continue (Cao, Cao, Prasad,
and Shen, 2011). However, studies are also divided if
an appreciation of the renminbi is going to help to
balance global imbalances in general and the U.S.
trade deficit in particular.
Bahmani-Oskooee and Wang (2006) investigated the
short run and long run effect of depreciation of the
renminbi on the trade balance of China with thirteen
trading partners and concluded that depreciation had
a favorable impact on the trade balance with the U.S.
in the long run implying an appreciation would
deteriorate the trade balance, there was no short run
effect. Similarly, Thorbecke (2006) found that an
appreciation of renminbi would reduce the trade
deficit of the U.S. with China and that an
appreciation of the Chinese currency would also lead
to a generalized appreciation of Asian currencies that
reduce the U.S. trade deficit further. Moreover,
Chen, Rau, and Chiu (2011) examined the
determinants of China’s exports to the U.S. and Japan
and empirically tested the response to the real
appreciation of the renminbi with quarterly data on
seventy one industries for 1997 to 2007. They
concluded that appreciation of the Chinese currency
had negative effect on the exports to the U.S.
On the other hand, several studies provided
compelling arguments, both theoretically and
empirically, that an appreciation of renminbi would
not lead to an improvement in the U.S. trade balance.
For example, the Chinese currency appreciated about
twenty percent between 2005 and 2008, leading to an
increase in the imports of China; however, the U.S.
imports from China also increased at a much faster
rate (Cheung et al.,2010). Another argument suggests
that even a forty percent appreciation in renminbi
would not reduce the U.S. trade deficit since the U.S.
will replace the imports from China with goods from
other countries as was the case when the Japanese
Proceedings of the 2012 Pennsylvania Economic Association Conference
38
currency appreciated, the U.S. replaced imports from
Japan with imports from other countries (Woo,
2008). Witte (2009) noted that there was a significant
deflationary trend in Chinese exports during the
appreciation of the renminbi after 2005, due to the
high market share of Chinese exporters, and that
appreciation of the renminbi ‘resulting from a more
flexible exchange rate regime, would have a little
impact on the Chinese export prices to the U.S.
Likewise, Zhang, Fung and Kummar (2006) offered
several explanations as to why the appreciation of
renminbi would not solve the U.S. trade deficit.
Among the explanations were China’s exports to the
U.S. were necessity goods, that the U.S. trade deficit
was due to the fiscal deficit and low saving rate, and
the U.S. have been increasingly importing more
manufacturing goods as the country moved to a
service oriented economy. Furthermore, Chinese
government officials vehemently denied the
allegations that the renminbi was undervalued by the
government (Stranger and Sykes, 2010: Moosa,
2011). Chinese government policies in terms of
monetary and exchange rate were also supported by
academics. For instance, McKinnon and Schnabl
(2008) and McKinnon, Lee, and Wang (2010) argued
in favor of Chinese currency policy and its monetary
policies to control inflation and sustaining favorable
economic growth.
Given the controversies surrounding the Chinese
currency and calls for appreciation of renminbi, only
did few studies estimate the effect of the renminbi
appreciation
on
the
U.S.
trade
deficit
(Thorbecke,2006). Thorbecke (2006) estimated that a
ten percent appreciation of the renminbi in 2005, the
gap between exports would have been reduced to 1.4
percent of the U.S. GDP from 1.6 percent. As
Thorbecke (2006) pointed out other studies reported
similar results. For example, ten percent valuation of
the renminbi would cause a fifteen billion dollar
decline in the Chinese trade surplus (Park ,2004).
Kamada and Takagawa (2005) asserted that a ten
percent increase in the value of renminbi would
decrease the Chinese trade surplus by 0.5 percent of
Chinese GDP. Similarly, Marquez and Schindler
(2007) concluded that ten percent appreciation of the
renminbi decreased the Chinese exports by one
percentage point while the effect on the imports was
negligible. Finally, the price elasticities of exports
and imports were estimated around one (Thorbecke
,2006).
In summary, several studies provided evidence of
misalignment of the renminbi. Estimates of the
amount of undervaluation were as high as fifty
percent; whereas, some claimed overvaluation of the
currency up to thirty six percent. The Chinese
government refused any currency manipulation,
supporter by academics while the U.S. government
and several scholars urged an appreciation of the
renminbi. Even the renminbi is appreciated by some
amount in response to calls, the evidence as to how
much appreciation would improve the U. S. trade
deficit is scarce and no agreed upon conclusion has
been reached. Therefore, this study aims at providing
further evidence by empirically estimating the U.S.
export and import demand functions to determine the
effect of changes in the renminbi on the U.S. trade
deficit with China.
THE MODEL AND METHODOLOGY
To determine the effect of exchange rate on exports
and imports, we develop two equations: import and
export demand functions that are applied to the data
for the U.S and China trade flows. The import and
export demand functions adopted in this study are
conventional in the sense that they were also
employed by previous studies. In formulating the
import demand function we follow BahmaniOskooee and Kara (2008) and use the following log
linear formulation:
 PM 
ln M td = α + β ln Yt + φ ln  + ϕ ln Et
 PD 
(1)
Where Md is demand for imports, Y is national
income, PM is price of imports, PD is price of
domestic goods, and E is exchange rate. We would
expect an estimate of β to be positive indicating that
at high level of income we import more. However, if
the increase in income is due to an increase in
production of import substitute goods, a country may
import less yielding a negative estimate of β. An
increase in import price relative to domestic price
levels is expected to depress the import volume
resulting in a negative estimate for ᶲ. Finally, since
the exchange rate is defined as number of units of
foreign currency per unit of domestic currency, a
decrease in E or a depreciation of the domestic
currency is expected to reduce the import volume,
thus estimate of ᵠ is expected to be negative.
Equation (1) indicates the long-run relationship
among the variables of import demand function.
However, since we are also interested in short run
response of imports to changes in the exchange rate,
we need to introduce the short-run dynamics into
equation (1). In order to do so, we follow Pesaran et
al. (1996, 1999, & 2009) and express the equation in
Proceedings of the 2012 Pennsylvania Economic Association Conference
39
an Autoregressive Distributed Lag (ARDL) format as
follows.
n
n
i =0
i =0
∆ ln M td = α + ∑ β i ∆ ln Yt −i + ∑ γ i ∆ ln(
n
+ ∑θ i ∆ ln M td−i + δ1 ln Yt −1 + δ 2 ln(
i =1
n
PM
)t −i + ∑ λi ∆ ln Et −i
PD
i =0
PM
) t −1 + δ 3 ln Et −1
PD
(2)
+ δ 4 ln M t −1 + ut
Again, the variables are defined as before. The error
term, ut, is assumed iid(0,σu2 ).
The export demand function is structurally very
similar to the import demand function and is assumed
to be the function of income level of a country’s
trading partner, export prices of the country, export
prices of the country’s trading partners, and exchange
rate. As formulated by Bahmani-Oskooee and Kara
(2008), the export demand equation in log linear form
as follows:
ln X td = a + b ln YWt + c ln(
PX
) t + d ln Et (3)
PXW
Where Xd is demand for exports, YW is income level
of trading partner (China), PX is export prices of the
U.S., PXW is export prices of other countries, and E
is exchange rate. We assume that the Chinese
demand for the U.S. exports (Xd) has a positive
relation with the income (YW). However, this
relation could be negative if the increase in the
income is due to an increase in import substitute
goods in China. Exports are expected to have a
negative relation with the relative price of a country's
export price over world export price (PX/PXW).
Finally, given the definition of exchange rate, E, if
currency depreciation is to stimulate exports, we
would expect an estimate of d to be negative.
However, equation (3) only provides the long-run
estimates. Since we are interested in short run
response, we incorporate short-run dynamics into
equation (3) and express it in ARDL.
m
m
PX
∆ ln X td = α + ∑ β i ∆ ln YWt −i + ∑ γ i ∆ ln(
) t −i
PXW
i =0
i =0
m
m
i =0
i =1
+ ∑ λi ∆ ln Et −i + ∑ φi ∆ ln X td−i + θ1 ln YWt −1
PX
+ θ 2 ln(
) t −1 + θ 3 ln Et −1 + θ 4 ln X t −1 + vt
(4)
PXW
In equation (4), the variables are defined the same as
before. The error term is assumed to be iid(0,σv2 ).
Due to a relative ease in estimation, we use ARDL
approach developed by Pesaran et al. (1996, 2009).
First, we construct two null hypotheses (stating the
non-existence of cointegration) to determine the
cointegration among variables in each equation and
test them according to the test statistics obtained from
the models.
H1: δ1≠0, δ2≠0, δ3≠0, δ4≠0 (5)
H0: δ1=δ2=δ3=δ4=0
H0: θ1=θ2=θ3=θ4=0 H1: θ1≠0, θ2≠0, θ3≠0, θ4≠0 (6)
After, we make a decision, we proceed to the second
stage of the ARDL method and estimate equations
(2) and (4). In this stage, we estimate four models
based on the following criteria: the r bar square
criterion, Akaika information criterion (AIC),
Schwarz Bayesian criterion (SBC), and HannanQuinn criterion (HQC). Then we select one model
based on examining diagnostic statistics.
The data for this study cover the period from 2000 to
2011. All data were collected on a quarterly basis,
ranging from the first quarter of 2000 to the last
quarter of 2011 (2000Q1-2011Q4). Part of data used
in this study was extracted from the “IMF Financial
Statistics and IMF Direction of Trade Statistics,
online”. The International Monetary Fund, (IMF),
collects data on countries for economic variables on
an annual, quarterly, and monthly basis.
RESULTS
The error-correction models were estimated as
outlined by equations (2) & (4) using quarterly data
over 2000Q1-2011Q4 period for trade flows between
the United States and China. W first tested for the
joint hypotheses in equations (5) and (6). Since we
have quarterly data, we imposed four lags. As the
testing can be executed based on; a) no intercept, no
trend variable, b) intercept and no trend variable, and
c) intercept and trend variables, we performed three F
tests. In most cases the F-statistic was significant. For
the case of intercept and no trend variable, the F
values were much higher than upper bound critical
value at 95% significance level, which indicated the
existence of cointegration and our estimates are based
on intercept and no trend equations (Table 1).
Since the variables in both models were cointegrated,
we moved to the second stage of estimation and
estimated equation (2) by imposing eight maximum
lags on each first differenced variable. Using the
ARDL Approach, ARDL(6,6,0,6) was selected based
on Hannan-Quinn Criterion, since it gave the best
results. Table 2 reports the long run estimates along
with ARDL estimates. Long run estimates confirm
our expected signs.
All of the variables are
statistically significant, except for price variable. The
biggest effect on imports comes from income,
followed by the exchange rate. Although it is not
statistically significant, the effect of price is relatively
Proceedings of the 2012 Pennsylvania Economic Association Conference
40
small. Since we have the log of variables, the
coefficients can also be interpreted as elasticities.
Therefore, the U.S. imports are income elastic, but
they are close to unit elastic with respect to prices
and exchange rate. The estimated coefficient for the
exchange rate suggests that a one percent
depreciation in the Chinese currency leads to about
0.8 percent increase in the imports. In other words,
ten percent appreciation of the renminbi reduces the
U.S. imports from China by eight percent. If the
Chinese government answers the calls by the critics
and decrease the value of its currency against the
dollar, its exports to the U.S. would go down by
about twenty percent, all else will stay constant.
ARDL estimates indicate that some of the lagged
variables are statistically significant. As indicated
with changing signs in the lags, the variables adjust
over time to long run values. Diagnostic tests show
the robustness of the estimates. Under the variables
column in the table, the number in parenthesis
indicates the lags and L means log of the variable,
which is the same in the following tables in the study.
Table 3 shows the error correction estimates. Again,
under the variables column in the table, the symbol Δ
in front of the variables refers to the differencing. For
example, ΔLE means one period difference. The
number after the variable refers to the further
differencing in the variables. For instance, ΔLE1
indicates one period difference of ΔLE, and so on,
which is the same in the remaining tables. Ecm(-1)
is the error correction coefficient, which means how
fast the economy returns to the equilibrium (long run
values) once it is shocked. After a shock (change in
exchange rates), an adjustment process takes place
during which the economy returns to its long-run
equilibrium values. The expected sign of the error
correction coefficient is negative, indicating that
when the short- run values overshoot the long-run
equilibrium values, the adjustment is downward, as
expected or vice versa (Greene, 2008).
Tables 4 and 5 present the results from the export
equation. ARDL(6,8,0,5) was selected based on RBar Criterion, since it provided the best results. The
long run estimates along with ARDL estimates are
given in Table 4. Similar to the import case, long run
estimates confirm our expected signs. Again, all of
the variables are statistically significant, except for
price variable. In terms of the magnitudes of the
estimated coefficients, the biggest effect on exports
comes from income, followed by the exchange rate,
although the values are very similar. The estimated
coefficient for the exchange rate suggests that a one
percent depreciation in the Chinese currency leads to
about 2 percent decrease in the U.S. exports to China,
or a ten percent appreciation of the renminbi
increases the U.S. exports to China by about twenty
percent, which is about the same effect that an
increase in Chinese GDP would have. Compared to
the imports from China to the U.S., responsiveness of
the U.S. exports to China is twice as much to the
changes in the exchange rate between the renminbi
and dollar, and one third to the changes in the
income. ARDL estimates illustrate that changing
signs in the lags shows that variables adjust over time
to long run values. Error correction estimates for the
exports are given in table 5. The error correction
coefficient in the export case is again statistically
significant and negative, indicating that when the
short- run values overshoot the long-run equilibrium
values, the adjustment is downward.
We have examined the diagnostic statistics (given in
tables 2-5) for the models in this study as well as
visual examination of the plot of residuals and fitted
values. In order to check the structural stability of
the models, we performed two additional tests:
cumulative sum (CUSUM) and cumulative sum of
squares (CUSUM of squares) of residuals. These
tests were obtained by examining the plots of
cumulative sum and cumulative sum of squares
against time. In all four four cases of the model, all
of them pass the CUSUM and CSUM of squares tests
that the graphs do not cross the straight lines at 95
percent level. This is what we expected if we
consider the diagnostic tests that were robust.
CONCLUSIONS
As the U.S. trade deficit has increased over the years
with China, several public officials and
commentators speculated that China has been
engaging in currency manipulation and that various
proposals appeared to take action against China.
Although there seems to be some consensus that
Chinese currency is undervalued, previous research
often produced mixed result as to how much the
currency is undervalued and some research even
suggested overvaluation of the renminbi. However,
the evidence as to how much appreciation would
improve the U. S. trade deficit is scarce and no
agreed upon conclusion has been reached. This study
investigated the trade flows between the U.S. and
China and estimated the effect of changes in the
renminbi on the U.S. trade deficit with China. The
main findings of this study can be summarized as
follows. First, the U.S. imports from China are more
sensitive to changes in the U.S. income than the U.S.
exports to China to the Chinese income level. In
Proceedings of the 2012 Pennsylvania Economic Association Conference
41
particular, a one percent increase in the U.S. income
increases imports from China by about 6.9 percent
while a one percent increase in the Chinese GDP
raises the U.S. exports to China by about 2.1 percent.
Second, the U.S. imports from China are less
responsive to the changes in the renminbi-dollar
exchange rates. It is found that a one percent
appreciation of the renminbi leads to about a 0.82
percent decrease in the value of the U.S. imports
from China. Third, the U.S. exports to China are
more responsive to the changes in the renminbi.
Specifically, a one percent appreciation of the
Chinese currency causes a two percent increase in the
value of the U.S. exports to China. Finally, the
renminbi should appreciate about fifty six percent in
order to eliminate the U.S. trade deficit with China
based on 2011 trade flows. Moreover, a ten percent
appreciation (as some scholars call for) would reduce
the U.S. trade deficit with China by 28.5 percent and
forty percent appreciation of the renminbi (as
suggested by researchers and politicians) decreases it
by
approximately
seventy
percent.
TABLE 1. F-TEST RESULTS FOR COINTEGRATION
F-values*
8.3768
8.2151
Import Equation
Export Equation
*. Critical values for F-Test at 95%(intercept, No Trend): Lower Bound=2.850 & Upper Bound= 4.049
Variables
LY
LPMPD
LE
INPT
TABLE 2. LONG-RUN COEFFICIENT ESTIMATES FOR IMPORT
Coefficient
Standard Error
T-Ratio [P-value]
6.8971
0.2917
23.6416 [.000]
-0.6077
0.2129
-2.8543 [.011]
-0.8177
0.0753
-10.8547 [.000]
-5.1525
1.4503
-3.5527 [.002]
ARDL Estimates
LM(-1)
LM(-2)
LM(-3)
LM(-4)
LM(-5)
LM(-6)
LY
LY(-1)
LY(-2)
LY(-3)
LY(-4)
LY(-5)
LY(-6)
LPMPD
LE
LE(-1)
LE(-2)
LE(-3)
LE(-4)
LE(-5)
LE(-6)
INPT
Coefficient
Standard Error
T-Ratio [P-value]
0.1451
-0.1306
-0.3336
0.5120
-0.4642
-0.3110
2.4640
4.4160
1.9379
1.4961
0.3293
-2.6426
2.9133
-0.9617
3.9383
-1.6512
-2.0906
1.6906
-0.7509
-4.8780
2.4479
-8.1534
0.1877
0.1260
0.1194
0.1454
0.1484
0.1786
1.4614
2.1505
2.0992
1.8894
1.9226
1.9744
1.1850
0.3599
1.2687
2.1690
2.1711
2.2402
2.0957
2.2625
1.5830
3.5668
.77272
-1.0369
-2.7928
3.5218
-3.1279
-1.7412
1.6861
2.0534
.92315
.79182
.17129
-1.3384
2.4586
-2.6720
3.1043
-.76130
-.96291
.75467
-.35828
-2.1560
1.5464
-2.2859
R-Bar-Squared
F-Stat.
Equation Log-likelihood
DW-statistic
[.450]
[.313]
[.012]
[.002]
[.006]
[.099]
[.109]
[.055]
[.368]
[.439]
[.866]
[.197]
[.024]
[.016]
[.006]
[.456]
[.348]
[.460]
[.724]
[.045]
[.139]
[.035]
0.9948
354.1942
102.2746
2.0135
Proceedings of the 2012 Pennsylvania Economic Association Conference
42
TABLE 3. ERROR CORRECTION ESTIMATES FOR IMPORTS
Coefficient
Variables
ΔLM1
ΔLM2
ΔLM3
ΔLM4
ΔLM5
ΔLY
ΔLY1
ΔLY2
ΔLY3
ΔLY4
ΔLY5
ΔLPMPD
ΔLE
ΔLE1
ΔLE2
ΔLE3
ΔLE4
ΔLE5
ecm(-1)
0.7275
0.5968
0.2633
0.7752
0.3110
2.4640
-4.0340
-2.0961
-0.6000
-0.2707
-2.9133
-0.9617
3.9383
3.5810
1.4904
3.1810
2.4301
-2.4479
-1.5824
R-Bar-Squared
F-Stat.
Equation Log-likelihood
DW-statistic
Variables
LYW
LPXPXW
LE
INPT
LX(-1)
LX(-2)
LX(-3)
LX(-4)
LX(-5)
LX(-6)
LYW
LYW(-1)
LYW(-2)
LYW(-3)
LYW(-4)
LYW(-5)
LYW(-6)
LYW(-7)
Standard Error
0.3148
0.3376
0.2631
0.1615
0.1786
1.4614
2.3600
2.0117
1.6652
1.5515
1.1850
0.3599
1.2687
1.5445
1.4820
1.4047
1.4268
1.5830
0.4052
T-Ratio [Pvalue]
2.3107
1.7680
1.0004
4.8001
1.7412
1.6861
-1.7093
-1.0420
-.36035
-.17448
-2.4586
-2.6720
3.1043
2.3186
1.0056
2.2646
1.7033
-1.5464
-3.9056
0.9540
43.7122
102.2746
2.0135
TABLE 4. LONG-RUN COEFFICIENT ESTIMATES FOR EXPORTS
Coefficient
Standard Error
T-Ratio [Pvalue]
31.2098
2.0576
0.0659
-1.5567
-0.2960
0.1902
8.7183
1.9903
0.2283
12.9838
9.4101
0.7248
ARDL Estimates
Coefficient
Standard Error
T-Ratio [Pvalue]
1.3391
0.2360
0.1763
-3.0484
-0.5450
0.1788
.17617
0.0347
0.1971
-3.5186
-0.6719
0.1910
-.42478
-0.0787
0.1853
-2.7762
-0.3877
0.1397
2.9823
2.9482
0.9886
1.1727
1.2734
1.0859
-.94614
-1.0676
1.1283
-1.3301
-1.6244
1.2213
.37886
0.5275
1.3923
-.19913
-0.2200
1.1050
2.1380
2.4221
1.1329
2.0530
2.5130
1.2241
Proceedings of the 2012 Pennsylvania Economic Association Conference
43
LYW(-8)
-1.8083
LPXPXW
-0.7142
LE
-4.7293
LE(-1)
4.0603
LE(-2)
0.8726
LE(-3)
3.7070
LE(-4)
-7.3483
LE(-5)
8.2392
INPT
22.7020
R-Bar-Squared
F-Stat.
Equation Log-likelihood
DW-statistic
1.1368
0.4865
2.2919
4.0018
4.1307
4.0832
3.9137
2.5720
4.4326
-1.5907
-1.4679
-2.0635
1.0146
.21125
.90789
-1.8776
3.2035
5.1216
0.98889
158.0987
77.6229
2.0439
TABLE 5. ERROR CORRECTION ESTIMATES FOR EXPORTS
Coefficient
Standard Error T-Ratio
value]
ΔLX1
1.6485
0.37784
4.3630
ΔLX2
1.1036
0.33926
3.2528
ΔLX3
1.1383
0.26104
4.3606
ΔLX4
0.4664
0.18104
2.5762
ΔLX5
0.38771
0.13966
2.7762
ΔLYW
2.9482
0.98857
2.9823
ΔLYW1
-0.74232
1.2661
-.58630
ΔLYW2
-1.8099
1.386
-1.3058
ΔLYW3
-3.4343
1.6874
-2.0353
ΔLYW4
-2.9068
2.1062
-1.3801
ΔLYW5
-3.1269
1.8793
-1.6639
ΔLYW6
-0.70474
1.494
-.47172
ΔLYW7
1.8083
1.1368
1.5907
ΔLPXPXW
-0.71418
0.48652
-1.4679
ΔLE
-4.7293
2.2919
-2.0635
ΔLE1
-5.4705
2.8801
-1.8995
ΔLE2
-4.5979
2.5826
-1.7804
ΔLE3
-0.8909
2.6717
-.33346
ΔLE4
-8.2392
2.572
-3.2035
ecm(-1)
-2.4125
0.44563
-5.4138
R-Bar-Squared
0.78943
F-Stat.
8.4104
Equation Log-likelihood
77.6229
DW-statistic
2.0439
Variables
Proceedings of the 2012 Pennsylvania Economic Association Conference
[P-
44
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Proceedings of the 2012 Pennsylvania Economic Association Conference
47
RECENT PENNSYLVANIA JOB TRENDS: EFFECTS OF SHALE? (2012)
Jay Bryson, Tim Quinlan and Joe Seydl*
Wells Fargo Securities, LLC
ABSTRACT
This paper examines employment trends in Pennsylvania,
with an emphasis on investigating the effects that natural gas
drilling has had on the commonwealth’s labor market in
recent years. We consider county-level employment data and
find that natural gas drilling is having a noticeable impact on
employment in the counties in which most of the drilling is
occurring. We also find statistical evidence to suggest that
the employment effects of natural gas drilling in the state are
beginning to spill over into counties in which drilling is not
occurring. By constructing a regression based on our
estimates of the spillover employment effects related to
natural gas drilling, we then forecast employment growth for
the overall commonwealth under three different scenarios: an
optimistic scenario, a pessimistic scenario and a midpoint
scenario. Under our midpoint scenario, which is predicated
on what may be the most realistic set of assumptions,
statewide employment would increase by about 570,000 jobs
by the end of 2020, roughly equivalent to the job creation
experience in Pennsylvania during the 1990s. Of the 570,000
new jobs created by 2020, about 250,000 would be related to
the spillover effects of natural gas drilling. Whether the extra
boost to employment that is tied to natural gas exploration
and production in Pennsylvania is “worth it” depends on the
reader’s assessments of the economic, environmental and
social issues that are involved.
INTRODUCTION
Following the deepest downturn in the post-World War II
era, economic expansion has taken hold in the United States
again. Real Gross Domestic Product (GDP) has grown for 11
consecutive quarters, and recent data suggest that the
expansion remains intact in the current quarter. Nationwide
employment is also growing again, although it lags behind
output. Whereas real GDP has finally surpassed its previous
peak, nonfarm payrolls in the United States have, at the time
of this writing, retraced less than half of the 8.8 million jobs
that were shed between early 2008 and early 2010.
Although we do not yet have Gross State Product data for
Pennsylvania for 2011, available indicators suggest that the
commonwealth’s economy grew during 2011 and that the
expansion has continued into the current year. For example,
the Pennsylvania Coincident Index, which is published by the
Federal Reserve Bank of Philadelphia, is currently expanding
at a pace faster than that at any point during the 2003-2007
expansion (Figure 1).
Recent gains in nonfarm employment in Pennsylvania are
also consistent with continued economic expansion. Since
reaching a bottom in February 2010, nearly 160,000 jobs
have been added in the commonwealth. This 2.8 percent
increase in Pennsylvania payrolls is in line with the job
growth experienced at the national level (Figure 2).
Interestingly, job growth in the commonwealth relative to
that at the national level has been much stronger in the
current cycle than what had been the case in the 2003-2007
cycle, during which job growth in Pennsylvania lagged
considerably behind job growth for the country as a whole.
PENNSYLVANIA EMPLOYMENT: SECTORAL
AND REGIONAL CHARACTERISTICS
So what sectors are creating jobs in the Keystone State?
Figure 3 shows that the gains have been generally broad
based.1 The sector comprised of education and healthcare
employs 20 percent of the commonwealth’s workers and has
accounted for one-third of all the jobs that have been created
in the state since employment bottomed in February 2010.
Trade, transportation and utilities, professional and business
services, manufacturing, leisure and hospitality, and
construction have also generated jobs over the past two years.
Notably, the natural resources and mining sector, which
employs less than 1 percent of the commonwealth’s workers,
has accounted for 8 percent of the overall job growth in
Pennsylvania. In other words, the natural resources and
mining sector, which includes the shale gas industry, is
currently an outperformer in terms of employment growth.
Since drilling in the Marcellus formation began in earnest
during the past decade, there has been much speculation, both
in the popular media and in academia, about the employment
effects from the shale industry in Pennsylvania. Not
surprisingly, some studies have found significant
employment effects from the shale industry, while other
studies have been less effusive.2 Before turning to our formal
econometric analysis, we undertake a quick visual
examination of the employment experience in the
commonwealth over the past few years.
To look at the local impact of the shale gas industry, we
divide Pennsylvania into 14 counties that account for 90
percent of the shale gas wells drilled to date in the Keystone
State and the commonwealth’s other 53 counties. We call the
former group primary shale (PS) counties and the latter group
we refer to as other counties (Figure 5). Between mid-2003
and early 2008, the period that marked the last cyclical
employment upturn in Pennsylvania, there was little material
Proceedings of the 2012 Pennsylvania Economic Association Conference
48
difference between the employment outcomes in these two
groups of counties, especially once employment gains really
started to gather pace in 2004 (Figure 4).3 However, in the
current cycle, there has been a clear divergence in the two
time series. Nonfarm employment in the PS counties rose
nearly 5 percent between its trough in August 2009 and June
2011, while employment in the other 53 counties rose only
1.6 percent. Furthermore, employment in the PS counties has
surpassed its previous cyclical peak, unlike the situation in
the other Pennsylvania counties or in the nation at large.
These 14 PS counties are generally remote and account for
only 10 percent of the commonwealth’s employment base
and 11 percent of its population of 12.7 million people.
Therefore, it may seem reasonable to assume that
employment in the PS counties has not had a significant
effect on job growth in the other counties. A quick glance at
Figure 4 also seems to lend some visual credence to this
supposition. However, we wish to formally test the
hypothesis that employment in the PS counties has not had a
statistically significant effect on employment in the other
counties. We now turn to our econometric analysis of
employment growth in Pennsylvania.
PENNSYLVANIA EMPLOYMENT:
AN ECONOMETRIC APPROACH
The 53 counties that are not PS counties account for
90 percent of the commonwealth’s jobs. Therefore, it seems
reasonable that employment in these other counties would be
related to more than just employment in the PS counties.
Specifically, employment in these other 53 counties should
be positively correlated with the national labor market as
Figure 2 suggests.
We performed cointegration tests on U.S. nonfarm payrolls,
employment in the PS counties and employment in the other
counties for our monthly data sample, which spans
January 1990 to June 2011. A Johansen test indicated the
existence of one cointegrating vector among these three
variables for the entire sample. However, the residuals of an
error correction model (ECM) showed significant volatility in
late 1999, which could indicate the presence of one or more
series breaks in the time-series data. Therefore, we limited
our sample to January 2000 through June 2011, and subdivided this sample into two periods (January 2000 until
December 2005, and January 2006 until June 2011) because
annual time-series data indicate that shale gas production
began to ramp up in Pennsylvania around 2005-2006.4 For
the January 2000 to December 2005 sub-period, a Johansen
test was not able to reject the null hypothesis of no
cointegration among the three variables at the 95 percent
confidence level (Table 1). However, the variables appear to
be cointegrated in the January 2006 to June 2011 time period
(Table 2).
Because there is evidence to suggest that U.S. nonfarm
payrolls, employment in the PS counties and employment in
the other Pennsylvania counties have been cointegrated
recently (i.e., during the January 2006 to June 2011 subperiod), we estimated the following ECM for this sub-period:
∆ log(other ) t =
− 0.0001 0.271∆ log( ps ) t 0.467∆ log(us ) t 0.315errort −1
+
+
−
+ εt
( −1.063)
( 4.936)
(5.033)
( −2.822)
R2 = 0.81
DW
=
2.08
where (ps)t = employment in primary shale counties in
Pennsylvania
(other)t = employment in other counties in Pennsylvania
(us)t = U.S. nonfarm employment
(error)t-1 = error from the first-stage regression
Our model results suggest that, everything else equal, a one
percentage point change in employment growth in the PS
counties between January 2006 and June 2011, a period
during which shale gas exploration and production was
ramping up significantly, was associated with a 0.271
percentage point change in employment growth in the other
53 Pennsylvania counties. In other words, we can reject the
null hypothesis that employment in the PS counties has not
had a statistically significant effect on employment in the
commonwealth’s other 53 counties. In addition, a one
percentage point change in U.S. nonfarm payrolls growth
was associated with a 0.467 percentage point change in
employment growth in the other 53 counties. We then use
these model results to make projections about employment in
the Keystone State until the end of 2020.
However, we first need to make some assumptions regarding
growth in the explanatory variables over the next few years.
Recognizing that recent growth rates of employment in the
PS counties may not be indicative of future trends, we
considered three scenarios for our forecast: an optimistic
scenario, a pessimistic scenario and a midpoint scenario.
In our optimistic scenario, we assume that employment in the
PS counties will grow over the next eight years at the rapid
2.9 percent per annum rate experienced between August
2009, when employment in the PS counties troughed after the
last downturn, and June 2011. In addition, we assume that
growth in U.S. nonfarm payrolls reverts to the 1.6 percent
annual pace witnessed during the 2003-2008 expansion.
Using these assumptions and the above regression results, we
were able to produce a forecast of employment in the
commonwealth’s other 53 counties. We then added this
forecast to our trend estimate of employment in the PS
counties to generate a forecast for total payrolls in
Pennsylvania. This forecast is shown as the optimistic
scenario in Figure 6..
Proceedings of the 2012 Pennsylvania Economic Association Conference
49
Under this optimistic scenario, we project that nonfarm
payrolls in the Keystone State would grow by 825,000 by the
end of 2020 (Figure 8). This 15 percent increase from the
current level represents a 1.5 percent per annum rise, nearly
twice as fast as the 0.8 percent average annual growth rate
that was achieved in Pennsylvania between 2003 and 2008.
Employment in the PS counties would shoot up by more than
165,000 by the end of 2020, a remarkable 32 percent increase
for counties that historically have been among the
commonwealth’s less prosperous areas.5
The commonwealth’s other 53 counties would also
experience strong job growth. Employment in these counties
would increase by more than 650,000 positions by the end of
2020 due to two components. First, the general U.S.
macroeconomic expansion, which we proxy by the
1.6 percent per annum increase in U.S. nonfarm payrolls,
would lead to an increase in jobs in the other counties in
excess of 250,000 positions. The second component reflects
very strong growth in the PS counties, which induces an
extraordinary 400,000 jobs in the other counties over the next
eight years. As the example of Williamsport shows, strong
growth in PS counties could generate demand for leisure and
hospitality jobs in other counties that are located near the PS
counties. In addition, employment in other supporting
industries likely would be induced in other counties by
continued strong growth in the PS counties. We refer
interested readers to the appendix, where we discuss the
recent employment experience in Williamsport in more
detail.
However, the trajectory of recent history may not be
sustainable for our entire forecast period, because natural gas
prices have receded to their lowest levels in a decade
(Figure 7). Consequently, some producers have announced
plans to freeze production. For example, Chesapeake Energy
recently announced plans to reduce production and to halve
the number of dry gas drilling rigs this year.6 The unusually
warm winter this year has clearly played a role in reducing
the price for natural gas, as individuals have needed less gas
to heat their households and businesses. That said, the
downward trend in natural gas prices has been in place for
much longer than what unseasonably warm winter weather
this year would explain. The risk is that if gas prices remain
depressed due to factors beyond just warmer weather, our
optimistic scenario could prove to be, well, way too
optimistic.
Therefore, we consider a pessimistic scenario. Under this
scenario, we assume that job growth in the PS counties will
revert to its 2003-2008 trend, which coincided with the last
cyclical employment upturn in Pennsylvania but which also
largely preceded the significant increase in shale gas
production in the commonwealth. During this period,
employment growth in the PS counties equaled only 0.8
percent per annum. We also assume that U.S. nonfarm
payrolls will continue to grow at the relatively sluggish
1.2 percent annualized pace that has prevailed since national
employment bottomed in February 2010. Using these
assumed employment growth rates along with our ECM
estimates we produce the pessimistic scenario that is shown
in Figure 9.
Whereas a remarkable 825,000 jobs were created in
Pennsylvania under our optimistic scenario, the pessimistic
scenario leads to an increase of only 325,000 statewide
positions by the end of 2020. The employment gain in the PS
counties shrinks to less than 45,000 positions, almost onefourth of the jobs created under the optimistic scenario. The
level of employment in the other counties increases by only
285,000 jobs. Not only does the sluggish nature of U.S.
economic growth lead to slower job creation in the Keystone
State, but the induced rise in employment in the other
counties from economic growth in the PS counties also falls
to a bit more than 100,000 jobs from the 400,000 positions
that were created under the more optimistic scenario.
As with most things in life, the “truth” probably lies
somewhere between these two scenarios. Expecting that the
recent breakneck pace of job creation in the PS counties will
continue for the next eight years does not seem very credible,
especially in light of the sharp decline in natural gas prices.
On the other hand, it seems likely that U.S. employment
growth will eventually strengthen from its slow pace of the
past two years. Perhaps our pessimistic scenario is too
gloomy. Therefore, we considered a midpoint scenario in
which employment in the PS counties grows at a pace that is
the average of its 2003-2008 and its 2009-2011 growth rates
(i.e., we assumed that employment in these 14 counties will
grow 1.9 percent per annum). We also assumed that U.S.
nonfarm payrolls would grow at an annual average rate of 1.3
percent, the midpoint between our pessimistic and optimistic
scenarios, over the next eight years.
Under this scenario, which may be the most realistic of our
three forecasts, total employment in Pennsylvania would rise
by roughly 570,000 jobs by the end of 2020 (Figure 6).
Approximately 100,000 of these positions would be created
in the PS counties, and about 250,000 jobs (out of a total of
470,000) would be induced in the other 53 counties by strong
growth in the PS counties. On a statewide basis, employment
would rise by 1.1 percent per annum, which would be just
shy of the 1.2 percent annual average growth rate that was
achieved during the long expansion of the 1990s.
CONCLUSION
Economic growth in Pennsylvania appears to be positive at
present, which is consistent with the expansion that has taken
hold in the overall U.S. economy. Furthermore, growth in
nonfarm payrolls in the Keystone State has kept pace over
the past two years with employment growth in the overall
national economy. There is plenty of anecdotal evidence to
suggest that the shale gas industry is helping to boost
Proceedings of the 2012 Pennsylvania Economic Association Conference
50
Under our optimistic scenario, in which employment in PS
counties continues to grow at its robust pace of the past two
years and U.S. nonfarm payroll growth reverts to its 20032008 trend, we estimate that overall employment in
Pennsylvania will soar by about 825,000 jobs between now
and the end of 2020, a remarkable 15 percent increase from
its current level. Under more pessimistic assumptions,
however, employment in Pennsylvania would increase by
only 325,000 jobs over the next eight years, a disappointing
0.6 percent per annum increase. Our midpoint scenario leads
to an increase in statewide employment of roughly 570,000
jobs over the next eight years, which would be roughly
equivalent to the total job creation enjoyed during the 1990s.
We are sometimes asked if shale gas exploration, drilling and
production in Pennsylvania is “worth it.” In other words, do
the benefits of shale gas outweigh the costs? Our midpoint
scenario projects that there would be 250,000 more statewide
jobs at the end of 2020 than there would be under our
pessimistic scenario, which assumes slow economic growth
in the PS counties as well as in the overall national economy.
Of this 250,000 increase, about 60,000 more jobs would be
created directly in the PS counties and roughly 140,000 more
would be induced in the other counties by the stronger
growth in the PS counties. In other words, the stronger
growth rate we assumed for the PS counties under the
midpoint scenario relative to the pessimistic scenario would
lead to 200,000 more jobs across the commonwealth, which
represents a 3 percent boost to employment, at the end of
2020.
The 3 percent extra boost to employment that we referenced
above, even if accrued over an eight-year period, is clearly a
benefit. That said, shale gas exploration, drilling and
production could potentially entail significant environmental
costs. In addition, there may be some social negative
externalities (e.g., there have been anecdotes of families
becoming priced out of the rental market in shale regions).
As economists, we believe that we have the expertise to
address economic issues, and we hope that the analysis in this
paper will help to enlighten the debate about the desirability
of shale gas exploration, drilling and production in
Pennsylvania. Because we do not have expertise in
environmental and other social sciences, however, we need to
leave an analysis of these issues to others. Thus, taking
economic, environmental and social issues into
consideration, we leave it up to our readers to decide whether
the benefits of shale gas outweigh the costs.
FIGURES AND TABLES
Figure 1
Pennsylvania Coincident Index
Three-Month Percent Change
4.0%
4.0%
Pennsylvania: Mar @ 1.2%
3.0%
3.0%
2.0%
2.0%
1.0%
1.0%
0.0%
0.0%
-1.0%
-1.0%
-2.0%
-2.0%
-3.0%
-3.0%
80
82 84
88 90
86
92
94
96 98
02 04
00
06
08 10
12
Figure 2
Nonfarm Payrolls: PA vs. US
Index, 100=Feb. 2010, From The Establishment Survey
108
108
United States: Apr @ 102.9
Pennsylvania: Mar @ 102.8
106
106
104
104
102
102
100
100
98
98
03
04
05
06
07
08
09
10
11
12
Figure 3
PA Employment Growth By Sector
Percent, Since Feb. 2010 Trough, As of Dec. 2011
35%
Percent of Job Growth Accounted For By Sector
employment in Pennsylvania, and “hard” data confirm that
job growth in the commonwealth’s 14 primary shale (PS)
counties has indeed been strong over the past two years.
Moreover, our statistical analysis shows that, everything else
equal, growth in employment in the commonwealth’s 53
other counties has been associated directly with job growth in
the PS counties. Significant drilling activity and gas
production in the PS counties has probably increased demand
for services such as leisure and hospitality, and engineering
and surveying in adjacent counties.
45-Degree Line
Ed/Health Svcs.
30%
25%
Trade/Trans/Ut
20%
15%
Leis/Hosp
Mfg.
10%
Prof/Bus Svcs.
Construction
Nat. Resources & Mining
Other Svcs.
5%
Info. Svcs.
0%
0%
5%
10%
15%
20%
25%
30%
35%
Size of Sector - Percent of Total Payrolls
Proceedings of the 2012 Pennsylvania Economic Association Conference
51
Figure 4
Table 2: Cointegration Tests
(January 2006 to June 2011)
Employment Growth in PA
Index, 100=July 2003, SA, Quarterly Census of Employ. & Wages
106
106
Cointegrating
Vectors
Primary Shale Counties: Jun @ 105.0
Other Counties: Jun @ 100.2
104
Eigenvalue
Trace
Statistic
Prob
None
0.240
31.976
0.028
≤1
0.187
13.890
0.086
≤2
0.003
0.225
0.635
104
102
102
100
100
98
2003
98
2005
2007
2009
2011
Figure 5
Williamsport
ERIE
WARREN
MCKEAN
POTTER
CRAWFORD
FOREST
ELK
VENANGO
CLINTON
CLARION
LYCOMING
JEFFERSON
BEAVER
MIFFLIN
INDIANA
CAMBRIA
SNYDER
SOMERSET
CARBON
NORTH_
UMBERLAND
H
RT
NO
SCHUYLKILL
DAUPHIN
LEBANON
CUMBERLAND
FAYETTE
MONROE
BEDFORD
BERKS
M
O
N
TG
LANCASTER
FULTON
FRANKLIN
CHESTER
ADAMS
N
TO
MP
HA
LEHIGH
PERRY
HUNTINGDON
WASHINGTON
GREENE
PIKE
LUZERNE
JUNIATA
BLAIR
ALLEGHENY
WESTMORELAND
LACKAWANNA
Montour
UNION
CENTRE
ARMSTRONG
WYOMING
COLUMBIA
CLEARFIELD
BUTLER
SUSQUEHANNA
WAYNE
SULLIVAN
CAMERON
MERCER
LAWRENCE
Pittsburgh
BRADFORD
TIOGA
YORK
BUCKS
O
M
ER
Y
DELA- PH
WARE
IA
PH
EL
AD
IL
Primary Shale Counties
Other Counties
Table 1: Cointegration Tests
(January 2000 to December 2005)
Cointegrating
Vectors
Eigenvalue
Trace
Statistic
Prob
None
0.232
28.157
0.076
≤1
0.097
9.155
0.351
≤2
0.025
1.799
0.180
Proceedings of the 2012 Pennsylvania Economic Association Conference
52
Figure 6
Figure 9
Pennsylvania Employment Projections
Job Growth in Pessimistic Scenario
Millions of Jobs, Seasonally Adjusted
6.3
6.2
Optimistic Scenario: 2020 @ 6.24 Million
Midpoint Scenario: 2020 @ 5.98 Million
6.2
6.1
Pessimistic Scenario: 2020 @ 5.74 Million
Total Nonfarm: 2011 @ 5.41 Million
6.1
6.0
6.0
5.9
5.9
5.8
5.8
5.7
5.7
5.6
5.6
5.5
5.5
5.4
5.4
5.3
5.3
5.2
2003
2008
2010
2013
2015
2018
Induced Statewide Change from PS Counties: 2020 @ 106.6K
Primary Shale Counties: 2020 @ 42.3K
800
600
400
400
200
200
0
2012
0
2014
2016
2018
2020
Figure 10
Williamsport Employment Growth By Sector
Natural Gas
Henry Hub Spot, Dollars per MMBTU
Percent, Since Feb. 2010 Trough, As of Dec. 2011
$16
$14
$14
$12
$12
$10
$10
$8
$8
$6
$6
$4
40%
Percent of Job Growth Accounted For By Sector
$16
$4
$2
$2
2007
2008
2009
2010
2011
Leisure & Hospitality
30%
20%
Manufacturing
10%
Edu. & Health
Services
Government
Trans. & Utilities
0%
$0
2006
Nat. Resources,
Mining & Construction
0%
Natural Gas: May @ $2.28
$0
2005
800
600
2020
Figure 7
1,000
Trend Growth in Other Counties: 2020 @ 177.9K
5.2
2005
Thousands of Jobs Created (Cumulative), Seasonally Adjusted
1,000
6.3
10%
20%
45-Degree Line
30%
40%
Size of Sector - Percent of Total Payrolls
2012
Figure 8
ENDNOTES
Job Growth in Optimistic Scenario
1,000
Thousands of Jobs Created (Cumulative), Seasonally Adjusted
1,000
Trend Growth in Other Counties: 2020 @ 258.5K
Induced Statewide Change from PS Counties: 2020 @ 398.6K
800
Primary Shale Counties: 2020 @ 167.8K
800
600
600
400
400
200
200
* We would like to thank Tom Murphy from Penn
University and Matthew Conlan and David Tameron
Wells Fargo Securities for helpful discussions about
gas production in Pennsylvania. We also thank Azhar
from Wells Fargo Securities for econometric support.
State
from
shale
Iqbal
1
0
2012
0
2014
2016
2018
2020
Sectors that lie above the 45-degree line in Figure 3 have
contributed more to employment growth than their weight in
the overall workforce. For expositional simplicity, we have
omitted sectors in which employment has contracted from
Figure 3. Over the past two years, employment in the
financial services industry, which accounts for 5 percent of
Pennsylvania’s nonfarm payrolls, has contracted nearly
2 percent, while public sector employment (13 percent of
payrolls) has plunged nearly 18 percent.
2 Considine, Watson and Blumsack (2010) estimate that the
industry would create 111,000 jobs in 2011. However,
Herzenberg (2011), and Timothy Kelsey et al (2011) find the
employment-generating effects to be significantly smaller.
Proceedings of the 2012 Pennsylvania Economic Association Conference
53
3
All county-level employment data considered in this report
henceforward is obtained from The U.S. Department of
Labor’s Quarterly Census of Employment and Wages.
4
According to data obtained from the Pennsylvania
Department of Environmental Protection.
5
The number of jobs in these 14 counties would swell to
nearly 700,000 at the end of 2020 from about 530,000 in June
2011.
6
See “CHK: Positive – More Shifting Away from Dry Gas”,
Wells Fargo Securities, January 23, 2012.
7 As was the case with Figure 3, we have omitted sectors in
which employment has contracted since February 2010 in
Figure 10. In the case of Williamsport, the only sector in
which jobs have declined since February 2010 is the “other
services” sector, where employment is down only 0.2
percent.
APPENDIX
Williamsport is the county seat of Lycoming County, which
is one of our PS counties. Since the statewide trough in
employment in February 2010, payrolls in the Williamsport
Metropolitan Statistical Area (MSA) have risen 2.9 percent,
which is above the 2.4 percent gain witnessed at the broader
state level. The increase in jobs in the natural resources,
mining and construction sector has been especially
impressive, surging by more than 25 percent since February
2010. Despite accounting for only 5 percent of the
employment base in Williamsport, the natural resources,
mining and construction sector has been responsible for
nearly 40 percent of the total job gains witnessed in the
metropolitan area since February 2010 (Figure 10).7 The
leisure and hospitality sector is also having an outsized effect
on overall employment growth in Williamsport. This sector,
which makes up only 8 percent of the MSA’s employment
base, has accounted for 35 percent of the job growth in
Williamsport since February 2010. Shale gas drilling in
Lycoming County and in some of the nearby PS counties is
likely contributing to additional demand in the city’s hotels
and restaurants, translating into 13 percent job growth in the
leisure and hospitality sector over the past two years.
REFERENCES
Considine, Timothy, Watson, Robert and Blumsack, Seth,
“The Economic Impacts of the Pennsylvania Marcellus Shale
Natural Gas Play: An Update,” (May 24, 2010)
Herzenberg, Stephen “Drilling Deeper into Job Claims: The
Actual Contribution of Marcellus Shale to Pennsylvania Job
Growth,” (June 2011)
Kelsey, Timothy, Shields, Martin, Ladlee, James, and Ward,
Melissa, “Economic Impacts of Marcellus Shale in
Pennsylvania: Employment and Income in 2009,” (August
2011)
Proceedings of the 2012 Pennsylvania Economic Association Conference
54
MULTIGENERATIONAL DISCOUNTING:
MERGING INTERGENERATIONAL EQUITY AND INDIVIDUAL TIME PREFERENCE
William Bellinger
Department of Economics
Dickinson College
Carlisle, PA 17013
ABSTRACT
The goal of this paper is to present a model that combines
as distinct elements the intergenerational and intertemporal dimensions of present value discounting. The
main components of the model are a basic social welfare
function defined across overlapping annual cohorts, and an
individual decision making model based on common
assumptions of individual time preference. The most
unique aspect of the model is the resultant adjustment to a
new long run equilibrium caused by the implementation of
a long-lived public policy decision.
INTRODUCTION
The adjustment process mentioned above may benefit from
more explanation. When a long lived policy with public net
benefits begins in the same year (year 0) for all living
cohorts, the length of time the average cohort experiences
net benefits gradually rises throughout the first lifetime of
net benefits as cohorts living in year 0 are replaced with
future cohorts who will experience the policy’s net benefits
over their entire lifetimes. This gradual lessening of
immediacy produces discounting over one lifetime even
under utilitarian social welfare and restrictive steady state
assumptions.
While neither the basic structure nor individual
components of this model are new (Bellinger 1991; Kula
1981, 1988), the analysis of the adjustment process created
by a new policy over the first subsequent lifetime has some
unique implications for the relationship between private
and public good investments and the related distinction
between private and social rates of discount. In addition to
this adjustment process, the implications of the model for
intergenerational equity and social welfare will also be
explored. Considerable additional development of the
model is needed and future goals for this project will be
discussed in the conclusion.
COMPONENTS OF THE MULTIGENERATIONAL
VALUE MODEL
Before presenting the full model, its two main components
should be briefly discussed. With regard to the time
preference of individual decision makers, discussion
generally begins with the common discounted utility model
of Samuelson (1937), and the closely related present value
discounting formula, which will be referred to as an
exponential discounting model in the following discussion.
While the discounted utility model has been subjected to
several technical and behavioral criticisms (Frederick, et.
al, pp. 355-365), the most familiar controversy involving
the pattern of individual time preference comes from the
behavioral economics literature, where many experiments
have found that individual time preference follows a
pattern that has come to be known as hyperbolic
discounting (Frederick, et. al.).
While several formulas expressive of this discounting
pattern have been proposed, (Frederick, et. al, pp. 360n),
the main characteristic of hyperbolic discounting is that the
typical individual’s marginal rate of discount has been
found to decline with a lengthening of the time frame of
the analysis. This concept currently lacks an accepted
theoretical foundation and is unlikely to apply to decisions
related to financial or real capital investment. However, the
model of time preference developed in this paper will have
something to say about this concept. The impact of
hyperbolic individual time preference will be explored
later in this paper.
The social welfare and equity dimensions of long term
policy issues involve considering the well-being of future
as well as currently living cohorts. In some senses this is
not fully feasible, as is widely known. Generations not yet
born cannot be active participants in present contractual or
political decisions (Georgescu-Roegen, 1975, Beckerman,
p. 199-200). Therefore the inclusion of future generations
in a long run public goods decision necessarily involves a
more abstract decision rule based on broader ethical
considerations. In this paper we combine a utilitarian
social welfare function with a net benefit decision rule for
both current and future cohorts.
THE MULTIGENERATIONAL VALUE MODEL
The presentation of this model begins by considering its
basic assumptions and decision framework. The
multigenerational value (MV) model is based on
overlapping annual cohorts who analyze issues in different
time frames. Each annual cohort includes N individuals.
All individuals will have equal tastes with the usual
properties, equal incomes unless otherwise noted, and
equal lifetimes of L years. This analysis further assumes
that policy alternatives will be in force T+1 (t=0,...,T)
Proceedings of the 2012 Pennsylvania Economic Association Conference
55
years and that G cohorts (g=(-L+1),...,G) will live within a
project's lifetime. All individuals will have equal tastes
with the usual properties, equal incomes unless otherwise
noted, and equal lifetimes of L years.
There is a natural distinction to be drawn between those
involved in an initial decision in year 0 and those not yet
born who will have at least an implicit decision to reverse
or continue the original policy. Future cohorts (g=1,...,G)
are annual cohorts born in years 1 to T. The initial
decision-makers include all cohorts alive at the time (t=0)
of the initial policy decision, with birth years from -L+1 to
0. For convenience this group will be referred to as
generation 0, The initial decision group's population equals
(L-t)N for any year prior to t=L, starting with LN persons
in year zero and declining to zero in year t=L and beyond
as older cohorts die off.
Each cohort alive during the duration of a policy, whether
part of the original or future set of cohorts, is assumed to
make a decision on that policy. Current decision making
cohorts in generation 0 may choose to implement or not
implement the policy. Future generations decide whether
to alter or continue the policy. Because each generation
lives for many years its private decision will be based on
the present value of net benefits over its lifetime
discounted to its decision-making period. Because
different generations make their decisions at different
times, however, they will discount their net benefits to
different "present" values. 1 All those involved in the initial
decision will discount their net benefits to the date of
implementation (t=0). I will assume that future cohorts
(generations 1-T) make their decisions during their birth
year, when they first receive net benefits. 2
Each policy produces net benefits Bt in year t. Each living
generation receives Bgt net benefits in year t, where Σg Bgt
= Bt. Generational share coefficients (αgt), which must
sum to one, may be defined as follows.
G
G
G
Σ αgt = Σ Bgt/Bt = Σ Ngt.(Bgt/Ngt)/Bt = 1
g=1
g=1
g=1
(1)
where Ngt = the population of cohort g in year t and Bgt/Ngt
= the per capita share of benefits for cohort g. This share
coefficient will vary with the type of investment and, under
broader assumptions, with the generation's population. For
example, a purely private investment by a member of any
cohort in generation 0 implies zero benefits and/or a zero
share for all future generations. Also, equal per-capita
benefits imply that a cohort’s benefit share will be
proportional to its population. Equally dividing Bt implies
equal shares of 1/L for each living cohort, and therefore for
all living generations other than 0. Generation 0's share
(while living) equals (L-t)/L, the number of cohorts living
in any year t between 0 and L. Assuming for simplicity
that its cohorts are equally weighted, generation 0's
decision equals the sum of the choices of L cohorts from
year 0 to their death, judged in current time.
1
MV0 = U(Bt/L)/(1+r)0 + Σ U(Bt/L)/(1+r)t +...+
t=0
L-1
L-1
Σ U(Bt/L)/(1+r)t = Σ U[Bt.(L-t)/L]/(1+r)t
t=0
t=0
(2)
where the first addend is the net benefit stream for the
oldest cohort (–L+1), the last addend equals the benefit
stream for the cohort born in year 0, MV0 = the
multigenerational value for all cohorts in generation 0,
U(Bt/L) = each cohort's utility from its share (Bt/L) of net
benefits, 3 r is a fixed private discount rate, L-1 is the final
year of life for the youngest cohort in generation 0, and (Lt)/L is generation 0's benefit share (α0t) in each year prior
to L. Similarly, a future cohort (g>0) values its
proportional share of the benefit stream as
g+L-1
MVg = Σ U(Bt/L)/(1+r)t-g
t=g
(3)
Society maximizes social welfare over a set of feasible
policies. The social welfare function (W) will be defined in
weighted linear form. The weight equals the social value
of each generation's per capita utility, and is specified as λg
= (1/1+τ)g, where τ is society's discount rate for the per
capita utility of future generations. 4 This function is
assumed to be constructed by an impartial observer with
perfect information. 5 This function takes the form
G
G
W = Σ λg U(αgt.Bt) = Σ U(αgt.Bt)/(1+τ)g
g=0
g=0
(4)
where all symbols are defined above. For a utilitarian
social welfare function in a steady state world with fixed
tastes, incomes, and population, τ = 0, λg =1, and the social
welfare function reduces to
G
Σ U(αgt.Bt)
g=0
(5)
the common equally weighted utilitarian social welfare
function.
The multigenerational value for year t (MVt) is then
derived by adding the weighted benefits of all living
Proceedings of the 2012 Pennsylvania Economic Association Conference
56
cohorts.
t
MVt= Σ U(αgt.Bt)/(1+r)t-g(1+τ)g
g=max{t-L,0}
(6)
The total multigenerational value (MV) of a project may
then be obtained by summing over years 0 to T. 6
T
T G
MV = Σ MVt = Σ Σ U(αgt.Bt)/(1+r)t-g(1+τ)g
t=0
t=0 g=0
(7)
Summing over T years makes the asymmetric minimum
value for g in (7) redundant. If benefits are equally
divided, as in the public goods case, (7) becomes
L-1
MV= Σ U[Bt.(L-t)/L]/(1+r)t +
t=0
T G
Σ Σ U(Bt/L)/(1+r)tg (1+τ)g,
t=1g=1
(8)
where the first addend refers to the decision of the cohorts
in generation zero and the second to cohorts 1-G. In
interpreting the second addend note that a generation born
in year t ≥ 0 will experience benefits from year t to t+L.
Equation (8) represents the general form of the
multigenerational value (MV) model for policy analysis.
For the public goods case, utilitarianism implies the
following multigenerational value model. Combining the
social welfare function from equation (5) with the
exponential individual time preference assumption
produces the following model of social time preference:
MVU
Now let us evaluate the time preference pattern of this
utilitarian model. Let us first consider years beyond one
lifetime (t > L). Beyond year L all generations are single
cohorts with equal share coefficients of 1/L. The social
value for year t equals
t
MVt = Σ U(Bt/L)/(1+r)t-g= U(Bt/L)/(1+r)L +...+
g=t-L
U(Bt/L)/(1+r)0.
(10)
Similarly for year t+1,
t+1
MVt+1 = Σ U(Bt+1/L)/(1+r)t+1-g =
g=t+1-L
U(Bt+1/L)/(1+r)L +..+ U(Bt+1/L)/(1+r)0.
(11)
If Bt+1 equals Bt, MVt+1 minus MVt equals zero. This
proves that if Bt is fixed, utilitarian MVt is constant after
one lifetime.
L-1
= Σ U[Bt.(L-t)/L]/(1+r)t +
t=0
T G
Σ Σ U(Bt/L)/(1+r)t-g,
t=1 g=1
utilitarian optimality may be identified via an "overtaking"
rule (von Weizsäcker, 1965), a "catching up" rule (Gale,
1967), or by the assumption of a rate of discount based on
the probability of world extinction (Dasgupta and Heal,
1979, pp. 273-81), although the last approach implies that
expected population declines over time. For a review, see
Dasgupta and Heal (1979, Ch. 9). The interpretation of
these maximums that is least problematic is to assume an
arbitrary finite maximum for G, and a corresponding value
for T of G+L, the final year of net benefits for the final
cohort being considered. Under this set of values the social
value of each year greater than L will be constant until the
policy analysis ends in year G+L.
(9)
where MVU is utilitarian multigenerational value, the first
addend refers to generation 0, and the second addend sums
all future cohorts.
Before analyzing and displaying the pattern of social time
preference exhibited by this model, the meanings of the
maximum values of G and T should be discussed briefly.
If the policy under consideration is infinite, and annual net
benefits are positive, the utilitarian form will produce an
infinite net present value. However, alternative criteria
developed under utilitarianism may also be applied to MV
discounting in this context. In an infinite time horizon,
For years within one lifetime of the original policy
decision (t < L), utilitarian discounting takes place at a
slower rate than present value given equal interest rates.
The social value for any year t < L is shown below:
t
MVt<L = Σ U(αgt.Bt)/(1+r)t-g = U[Bt.(L-t)/L]/(1+r)t
g=0
t
(12)
+ Σ U(Bt/L)/(1+r)t-g,
g=1
where the right hand portion of (12) divides the decisions
of generation 0 from those of other living generations.
Since future generations discount to their birth year (or
implicit decision year), future generations place a higher
Proceedings of the 2012 Pennsylvania Economic Association Conference
57
present value on their net benefits for a given year than
does generation zero, implying that MVt > PVt for all t > 0.
Combining these results, utilitarian principles and
restrictive steady state assumptions imply that the social
value of each unit of net benefits will converge after L-1
years to a fixed value < 1.
DISCOUNTING PATTERNS FOR PUBLIC GOODS
IN THE MV MODEL
Social rates of discount become both most relevant and
most controversial in the context of long-lived public or
quasi-public policy issues. Long run environmental
decisions provide the context in which this issue has been
most actively discussed during recent decades (Lind, et.al;
Portney and Weyant).
In this model the decision context involves multiple
dimensions related to the overlapping cohorts, public net
benefits, and steady state assumptions for income, tastes,
and population. Into this constantly replicating and stable
age and income distribution we introduce a policy that
begins in year 0 for all living cohorts (for an 80 year
lifespan, g = -79 through 0), and continues indefinitely
with constant annual social net benefits. For every living
generation the policy’s net present value will extend over
its remaining lifespan. Each generation unborn in year
zero will receive net benefits from the policy over their
entire lives.
The practical effect of this distinction between current and
future cohorts is that over the first human lifetime of the
policy’s existence the average length of the net benefit
stream rises until it reaches the full lifetime L for all living
cohorts after year L. As noted above, for the public goods
case this lengthening of the average net benefit stream
produces a quasi-hyperbolic valuation pattern over one
lifetime followed by a constant mean present value
produced by the overlapping cohorts associated with future
generations.
g+L
U(B0g) = 1/L Σ U(B0t)/(1+r)t-g,
t=g
(13)
where g = the birth year of future cohort g and U(B0g) is
the present value of policy A0 to cohort g. Alternatively,
cohort g may adopt its best alternative (Aîg) with PV
g+L-1
U(Bîg) =1/L Σ [U(Bît)/(1+r)t-g] - 1/L U(Rît)
t=g
(14)
where Bîg = the discounted net benefits of Aî to generation
g, and 1/L Rît equals the cost to generation g of transferring
resources from policy A0 to Aî. Generation g will reject
policy A0 if
g+L-1
Σ [U(Bît) - U(B0t)]/(1+r)t-g > U(Rît).
t=g
(15)
The existence of reversal costs will have significant
implications for the efficiency of PV discounting, as we
will explore later.
The next step in the exploration of the model is to explore
the discounting patterns it produces under various
assumptions. The basic discounting pattern produced by
this model in the public goods context adds a
superstructure associated with the cohorts in generation 0
to the long run pattern produced by the social welfare
function. In the case of a steady state economy with equal
income and population size over time, this model produces
a quasi-hyperbolic discounting pattern over one lifetime
(years 0 to L-1) followed by a constant present value
beyond year L. This long run pattern can vary with
assumptions about the social welfare function or the long
run behavior of population, income, or cumulative risk, as
will be discussed later in the paper.
The decision process associated with this type of problem
involves the effects of such policies on future as well as
present generations, but also the degree to which there is a
potential decision rule for future generations regarding
such a policy. As can be seen in equations (8) and (9),
those cohorts living at the initiation of the policy
(generation 0) will choose to implement a policy if the net
present value over their remaining lifetimes is positive.
The net welfare gains for future generations depend on the
net benefits of the original policy, those of the next best
alternative, and any transactions costs associated with a
policy modification.
The Effect of the Length of Life
Retaining the initial policy A0 produces the following
present value for any future cohort:
The aggregate MV discounting pattern produced in the
bottom row is of interest for two related reasons. First,
The first step in this section involves displaying the full
discounting pattern for a very short lifespan. This step
helps to clarify the pattern of net present value in the MV
model, and to identify the sources of the distinction in
present values between a common exponential PV model
and the MV model. Table I displays the pattern of present
value with a 5 year lifespan for each cohort, an infinitely
lived policy whose net benefits begin in year 0, and an
exponential individual discounting pattern with a 5%
discount rate.
Proceedings of the 2012 Pennsylvania Economic Association Conference
58
despite the assumption of an exponential discounting
pattern for each individual cohort, the aggregate present
value shows a time pattern more consistent with a
hyperbolic form. A comparison of the exponential
discounting pattern for cohort 0 and the MV values in
Table I reveals this distinction. One hypothesis produced
by this model is that individuals with a strong sense of
altruism (i.e.: for other cohorts) may display a more
hyperbolic individual discounting pattern than those who
have more selfish tastes. Secondly, under the assumed
static economy, all discounting ends after one lifetime in
the MV model, a pattern that is partially consistent with the
long run discounting pattern behavior of a utilitarian social
welfare function in a steady state. There are several wellknown justifications for a degree of discounting across
generations, including increasing mean incomes,
increasing risk or uncertainty, and a cumulative long run
risk of annihilation, all of which can be included in this
model through the generational discount factor λ (see
equation 9).
notably, the distinctiveness of exponential and hyperbolic
individual discounting streams is significantly reduced in
this framework (See Table III below). The clear result
displayed in Table III is that the overlapping cohort model
substantially dampens the difference in present value
created by exponential and hyperbolic discount factors.
While the future value in year 100 for the exponential
discount factor is only.0456 (about 1/20th) that of the
hyperbolic formula, the corresponding ratio in the
overlapping cohorts model is .63. However, under either
formula hyperbolic individual time preference lessens the
degradation of present value over time. The distinction
between private and social discounting in the public goods
case offers a potentially interesting hypothesis regarding
the causes of the hyperbolic pattern. If an individual is
perfectly altruistic with no distinction between her welfare
and that of others, private time preference may be shown to
equal social time preference. In this case the MV model
implies a positive relation between a person’s altruism and
the hyperbolic pattern of her time preference.
We next consider the sensitivity of the model to the
assumptions of lifespan. Under the restrictive assumptions
outlined above, differing assumptions regarding the
average lifespan have two effects on the present value
pattern. First, longer lifespans mean that the substitution
of future for current cohorts takes place more gradually,
which implies (rather obviously) that long run social
equilibrium will reappear more slowly. Second, and far
more importantly, the long run equilibrium present value,
one it is reached, will be inversely related to the length of
life. This finding implies that an increasing average
lifespan over the long run creates another source for long
run social discounting.
MULTIGENERATIONAL VALUE AND
INTERGENERATIONAL EQUITY ISSUES
Two of several unanswered questions in this draft relate to
the relationship between this model and the basic social
welfare and individual time preference models from which
it is derived. These are (1) As L approaches infinity, does
MV approach PV, and (2) As L approaches 0, does MV
approach one, a constant social present value. Both are
likely results which would help to place this model firmly
in its broader context.
Exponential Versus Hyperbolic Individual Discounting
As noted above, one area of controversy related to
discounting is the accuracy of the traditional assumption of
exponential individual time preference. Considerable
behavioral research has found that individuals tend to value
the distant future more highly than an exponential present
value model. While several formulas are capable of
producing this pattern, the relatively basic formula used
below is PVh = 1/(1+rt), where r is the discount rate and t
is time. The MV model offers some surprising implications
for the role of hyperbolic individual time preference. Most
The equity implications of this model are somewhat
narrow due to its reliance on the familiar utilitarian social
welfare construct. However, the inclusion of individual as
well as intergenerational discount factors raises some
potentially interesting observations related to equity. By
including intra-generational and inter-generational
dimensions this model suggests a useful equity criterion,
which will be labeled "moral consistency". Moral
consistency requires that the same equity assumption be
applied to all distributional dimensions unless the
difference can be explicitly justified. Recall that for any
year t > L,
t
(16)
MVt = Σ αgt.U(Bt)/(1+τ)g(1+r)t-g.
g=t-L
Note from (16) that if τ = r, MV is equal to the present
value model PV. Unless one can justify discounting future
generations at rate r, PV will be inequitable. For any other
value of τ including 0, MV is morally consistent over time
while PV is not. However, it is also well known that a
degree of generational discounting may be consistent with
utilitarian social welfare maximization in some contexts.
If per capita income grows over the lifetime of the project,
and if marginal utility of income declines, a degree of
positive generational discounting may be morally
consistent.
Moreover, the MV model violates moral consistency in
one dimension. Specifically, utilitarian MV does not
weigh net benefits equally across contemporaneous
generations in a given year. To take an extreme example,
Proceedings of the 2012 Pennsylvania Economic Association Conference
59
assume that the marginal utility of net benefits is constant,
assume τ=0, and give all of Bt to the oldest living
generation (g=t-L) by setting its share coefficient (αt-L)
equal to 1. This maximizes the exponent of (1+r) and
therefore minimizes MVt.
MVt = U(Bt)/(1+0)t-L(1+r)L.
(17)
Conversely, if the share (αt) of the newborn generation
(g=t) equals 1, MVt is maximized. While diminishing
marginal utility may avert such a corner solution,
contemporaneous generations are not valued equally under
utilitarian MV. This result is peripherally related to the
finding of Dasgupta, et. al., p. 64) that an individual’s
decision regarding a long term policy is not consistent over
time. However, (16) and (17) imply that if τ = r, as in the
long run present value case, the distribution among living
generations does not affect MVt. This result verifies that
PV and utilitarian welfare are consistent in a crosssectional context both within and across living generations,
despite their inter-temporal inconsistency. It also implies
that MV will not be morally consistent for decisions
involving the distribution of benefits across generations in
a single time period.
Because this overlapping cohorts approach retains a degree
of discounting, critics content that it also retains some
inequity (Price 1987, p. 500; Thompson 1988, pp. 171-72).
While I have established that this complaint has validity
for cross-sectional comparisons, I defend MV's equity over
time. First, analyzing every decision in its own time frame
eliminates one source of bias against future generations.
Moreover, given a constant population and average
income, the MV model is consistent with the equal
weighing of each individual, and therefore with utilitarian
equity. Furthermore, the MV model allows more explicit
consideration of the efficiency costs of zero discounting.
Early defenders of discounting often argued that zero
discounting sacrifices efficiency by ignoring foregone
interest (Baumol, 1968; Mikesell, 1977, pp. 18-21).
Within the traditional formulation, this debate has been
confused because single generation analyses (Marglin,
1963; Baumol, 1968) consider only r, while most multiple
generation models (Dasgupta, 1974; Brown, 1987)
consider only τ. Since both groups claim to be analyzing
the discount rate, confusion has been unavoidable.
The debate can be repeated here more explicitly. MV
produces zero discounting if both the interest rate (r) and
the generational discounting parameter (τ) equal zero.
Setting only τ equal to zero provides equal generational
weights, and produces the utilitarian MV discounting
pattern. However, setting the intra-generational discount
rate (r) to zero has no effect on the weights of different
generations. It merely forces the analyst to ignore an
important cost. Those who insist on equal outcomes, as
opposed to fair process, are welcome to disagree.
In conclusion, while the MV model is more equitable than
PV in the inter-temporal dimension, discounting is not
inherently unfair to future generations, and
multigenerational discounting is not morally consistent in
all contexts. Therefore, Kula's claim (1988) of moral
superiority for multigenerational discounting is subdued
considerably in this modification of his model.
MULTIGENERATIONAL VALUE AND
ALLOCATIVE EFFICIENCY
In this section I explore the conditions under which present
value may be consistent with the maximization of social
welfare, thereby defining more carefully those cases for
which PV is suboptimal and MV discounting may be
appropriate. First, if a given policy is at least equally
preferred to any alternative by all generations, then any
choice of non-negative generational weights (λg > 0) will
lead to a socially optimum choice. This policy criterion
will be labeled multigenerational Pareto optimality
(MPO). 7 Multigenerational Pareto optimality is extremely
stringent, since an MPO policy has no preferred alternative
for any generation. Yet some potentially common cases
exist which may meet the MPO criterion.
One such case is purely private investment. For example,
if all net benefits go to an individual in generation zero, its
share coefficient equals 1 and the social welfare function
reduces to
L-1
W = λ0 U(B0) = λ0 Σ U(Bt)/(1+r)t.
t=0
(18)
A private investment that provides positive net benefits to
one individual in generation 0, all else equal, is a net gain
to generation 0, and thus a net welfare gain to society if
generation zero's social weight (λ0) is positive. A
comparison of equation (18) with the public goods case
developed above indicates that the MV model implies
different social discount rates for inter-generationally
private and public goods. 8 Specifically, for private goods
the social (MV) and private rates of discount are identical,
while only PV generational weights (τ=r) produce this
result for public goods.
Also, a policy decision by generation 0 may be MPO
despite intergenerational externalities if the initial decision
may be costlessly reversed. The existence of reversal costs
(Rît) is a crucial factor in determining the efficiency of
PV. 9 This argument involves revisiting the decision
process for future generations in equations (13) through
(15) above. Equations (13) through (15) imply that if
policy A0 is perfectly reversible (R=0), future generations
Proceedings of the 2012 Pennsylvania Economic Association Conference
60
will shift from policy A0 to policy Aî whenever the net
benefits of the change are greater than 0. A selfish (PV)
decision by generation zero will not restrict the decisions
of future generations, and therefore will be MPO. Another
case which may approximate costless policy modification
involves liquid private bequests, which the recipient may
invest as she sees fit. Therefore, the use of present value
by generation zero may be MPO given costless
reversibility. On the other hand, these equations imply that
with positive reversal costs future generations may accept a
policy which would be suboptimal for them in the absence
of the original policy decision, thus violating the MPO
criterion.
More generally, when an initial policy is MPO, either
through the provision of optimal benefits to each
generation or through zero reversal costs, then many
common equity assumptions are consistent with the
maximization of social welfare, including the equity
assumption implicit in PV. When a policy is not MPO,
generational equity becomes crucial, and MV may be
preferable.
CONCLUSION
The multigenerational value (MV) approach offered in this
paper reconsiders the theoretical basis and applicability of
social discounting in the context of overlapping cohorts.
For public goods in a steady state world, the utilitarianbased MV model proposing that positive discounting
should be limited to one human lifetime. Also, a few
principles are suggested for applying MV. First, MV has
different implications for analyzing public and private
goods. Analyzing any type of good requires the proper
application of MV. For private goods, PV is the
appropriate form of MV. Secondly, multigenerational
analysis assumes that different generations make decisions
in different time frames. Therefore, each element of any
decision sequence should be analyzed in its own time
frame, and only over its effective length.
Some new hypotheses arise from this analysis of the
model. These include the possibility that individual time
preference will be hyperbolic for highly altruistic
individuals and the finding of a negative relation between
lifespan and long run social present value. Finally, the
explicit distinction between discount rates for private and
long-lived public goods is also relatively unique to this
approach.
equity. Rather, it is a generalization of both aspects of
inter-temporal welfare. Multigenerational discounting, and
particularly the MV approach, is both a blessing and a
curse in that it forces (or allows) analysts to face complex
conceptual and empirical issues which traditional analysis
sweeps under the rug. The applicability of MV to grey
areas such as quasi-public goods and relatively brief time
frames depends on our ability to address difficult empirical
and conceptual issues. At this point it would be premature
to prejudge our ability to do so. At a minimum, however,
MV has many potential policy applications involving longlived public goods such as nuclear waste disposal,
infrastructure, and wilderness development.
Intergenerational equity is an important factor in weighing
the net benefits of such policies, and the recommendations
of the multigenerational value model may be quite
different from those of present value.
This paper represents a first step in more fully considering
the implications of the much older paper that originally
produced this model. The explicit presentation of
valuation patterns under different lifespan and individual
time preference assumptions is a useful addition.
However, several important aspects of the model cannot be
easily explored in discrete time, suggesting that a dynamic
equilibrium analysis of these concepts is warranted. I
suspect, but cannot yet prove, that this model with
overlapping cohorts contains long run equilibrium
properties that may solve problems arising from other
types of time-based social welfare functions. For example,
the policy choice related to long run equilibrium is likely to
prove consistent with a median cohort voting model,
lessening the problem of inconsistent individual decisions
over time. This paper already demonstrates the much less
significant role for alternative individual time preference
patterns in the public goods context. The constant value
under long run steady state equilibrium beyond one
lifetime, while not equal to 1, can provide an explicit
definition of the numeraire value for social welfare that
varies with lifespan as well as discounting assumptions. .
Reintroducing and analyzing the generational discount
factor λ will permit the integration of the role of expected
population, changes in per capita income, uncertainty, and
other determinants of the social welfare discount factor
into the model, producing a more or less complete model
of individual and social time preference. Such a model
could prove beneficial in bringing an increased degree of
consistency to the analysis of time preference in a
multitude of contexts.
More generally, multigenerational value is neither a
rejection of present value nor a political compromise
between discounting and utilitarian intergenerational
Proceedings of the 2012 Pennsylvania Economic Association Conference
61
ENDNOTES
i. This approach differs subtly from Kula's. He (1981) assumes different time frames because of different birthdates. I
assume different time frames because of different decision periods. While insignificant in the public goods case, this
distinction provides considerable guidance toward the analysis of sequential decisions and other applications later in the
paper.
ii. Alternative decision periods are possible. For example, each generation could make its policy decision at its age of
majority. Generation 0 would then include all adult cohorts, and each "future" cohort would make its decision and begin
receiving net benefits upon reaching adulthood. In the utilitarian case considered later, this assumption produces social
discounting over one adult lifetime, rather than an entire lifespan. This distinction doesn't affect the model's general
characteristics.
iii. The assumption of equal tastes removes the need to identify the utility function by generation.
iv. This weighted linear social welfare function has the advantage of being directly comparable with the present value
discounting formula under discrete time. For the purposes of this paper, this advantage outweighs any lack of generality.
v. A version of the impartial observer was introduced by Adam Smith (1976, pp. 162, 352). A more idealized version of
the impartial observer is described by Rawls (1971, pp. 186-87). While this observer is generally associated with
utilitarianism, it also can be applied to the Rawlsian case (Dasgupta and Heal, 1979, pp. 271-72).
vi. Alternatively one could determine the value of net benefits for each generation and then sum across the generations.
vii. More formally, any policy alternative Ai ε A , where A is the set of all feasible policy alternatives, will be MPO if its
net benefits Big > Bjg for all Aj ε A and all generations g.
viii. This result is superficially similar to that of Marglin, but differs in some important respects. First, Marglin bases his
analysis only on the preferences of currently living generations (1963, pp. 97) and, secondly, that his model is based
largely on cross sectional and intergenerational altruism by generation 0. For effective criticisms of Marglin's approach,
see Tullock (1964) and Baumol (1968).
ix. Reversal costs in this context refer to the dollar costs of resource transfer and to any decision making costs associated
with the political process for public goods. Irreversibility may be defined in at least the following three ways. Policy Ai
will be irreversible if (1) Rig = 4 for all i and g, if (2) Rîg > Bîg - B0g for all g, or if (3) Rig is "large". The first definition is
very strong, the second defines all multi-generationally optimal policies as irreversible, and the third is somewhat vague.
The weak definition (2) is most useful in this context.
Proceedings of the 2012 Pennsylvania Economic Association Conference
62
APPENDIX: PRESENT VALUE PATTERNS
Table 1: MV for a 5 Year Lifespan
Cohort
birth year
Yr. 0
-4
1
-3
1
0.95238
-2
1
0.95238
0.90703
-1
1
0.95238
0.90703
0.863838
0
1
0.95238
0.90703
0.863838
0.822702
1
0.95238
0.907029
0.863838
0.822702
1
0.952381
0.907029
0.863838
0.822702
1
0.952381
0.907029
0.863838
0.822702
1
0.952381
0.907029
0.863838
1
0.952381
0.907029
1
0.952381
Year 1
1
Year 2
2
Year 3
3
Year 4
4
5
Year 5
6
Year 6
7
Year 7
1
Σ PVit
Mean PVit
=MVt
5
4.80952
4.67347
4.587086
4.545951
4.545951
4.545951
4.545951
1
0.96191
0.93469
0.917417
0.90919
0.90919
0.90919
0.90919
Yr 80
Yr 100
Lifespan
yr 0
Table 2: MV Present Values for Different Lifespans
yr 1
yr 25
Yr 50
Yr 65
65 years
1
0.967766
0.413939
0.316369
0.31017
0.31017
0.31017
80 years
1
0.964881
0.391695
0.2734
0.259878
0.257456
0.257456
100 years
1
0.962381
0.372417
0.236161
0.216292
0.21
0.198479
Table III: PV and MV with Exponential and Hyperbolic Discounting Patterns
Discounting
Pattern
yr 0
yr 1
yr 25
Yr 50
Yr 65
Yr 80
Yr 100
PV 5%
PV 5%
Hyperbolic
1
0.952381
0.295303
0.087204
0.041946
0.020177
0.0076
0.952381
0.444444
0.285714
0.235294
0.2
0.16667
MV, L = 80
MV, L = 80
Hyperbolic
1
0.964881
0.391695
0.2734
0.259878
0.257456
0.257456
1
0.964881
0.511802
0.424846
0.410676
0.407409
0.407409
Proceedings of the 2012 Pennsylvania Economic Association Conference
63
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Proceedings of the 2012 Pennsylvania Economic Association Conference
65
AID EFFECTIVENESS IN SUB-SAHARAN AFRICA
Yaya Sissoko
Department of Economics
Indiana University of Pennsylvania
Indiana, PA 15705
Niloufer Sohrabji
Simmons College
300 The Fenway
Boston, MA 02115
ABSTRACT
There is considerable debate on the potential benefits and
costs of aid. On the one hand, aid provides much needed
resources to improve development efforts and alleviate
poverty. On the other hand, aid can create dependencies. This
paper analyzes the effectiveness of aid for countries in subSaharan Africa.
Using a sample of eleven countries including Botswana,
Cameroon, Cote d'Ivoire, Ethiopia, Ghana, Kenya, Nigeria,
Senegal, South Africa, Tanzania and Togo we estimate a
modified version of Barro’s (1991) growth model. Following
the literature we use GMM estimation for panel data of our
sample countries from 1975 to 2006. We find that aid is a
positive and statistically significant determinant of growth for
our sample. As expected however, it cannot be the sole
component of a growth strategy as its effectiveness is
complicated by other factors such as FDI flows and political
regimes. Using our empirical results and case studies we
analyze how aid has helped in some cases and not in others.
obstacles to aid effectiveness, we study the impact of aid on
growth in Botswana, Cameroon, Cote d'Ivoire, Ethiopia,
Ghana, Kenya, Nigeria, Senegal, South Africa, Tanzania and
Togo. Our sample includes large and small countries with
varying levels of trade and foreign investment flows and
different levels of political stability. This paper uses
generalized method of moments (GMM) for panel data from
1975 to 2006 and combines these results with case study
analysis to shed light on the importance of aid and other
factors in determining growth. We find that aid has a positive
(and statistically significant) impact on growth. However,
large aid flows alone are not likely to be beneficial for
countries that have low levels of FDI and/or have more
autocratic regimes.
The paper is organized as follows: the next section provides
background on our sample countries. Section 3 describes the
framework for estimating aid effectiveness which is followed
by an analysis of the results. Section 5 is a comparative case
study analysis of the impact of aid in our sample countries
and the last section concludes.
INTRODUCTION
BACKGROUND
The paper analyzes the effectiveness of aid in countries in
sub-Saharan Africa. There is considerable debate on the
beneficial and harmful effects of aid. On the one hand, Sachs
et al. (2004) highlight the need for aid to reduce poverty. Aid
can also support large scale investment in infrastructure for
long-term growth and development benefits. However, aid
funds can be wasted which leads others to argue that it does
not promote growth (Easterly 2003; Collier, 2007; and
Williamson, 2008). Moreover, in some cases not only is aid
not helpful, but can create dependencies which actually hurt
the country. The potential benefit to countries from aid is
related to effective governance and strength of institutions
(Radelet et al. 2005).
Countries in sub-Saharan Africa (SSA) are among the
poorest nations. As expected, they are also major recipients
of aid. Of the approximately $ 129 billion in official
development assistance (ODA) in 2010 from Development
Assistance Committee (DAC) of OECD, $ 26.5 billion was
given to SSA countries (OECD website). Tables 1 and 2
provide current macroeconomic information and aid data for
our sample countries.
Despite the debate on its effectiveness, aid flows continue.
With challenges of high debt and low growth and
development, countries in sub-Saharan Africa are major
recipients of aid. Unfortunately, the region has witnessed
failures in effective aid utilization. To understand the
Overall, the most distressed economies in our sample are
Côte d'Ivoire and Ethiopia. Côte d'Ivoire has a relatively low
GDP per capita, high unemployment1 and the biggest debt
burden (table 1). Moreover, it is the only country in our
sample to experience negative growth in 2011 (table 1).
Ethiopia is also a concern with a relatively low GDP per
capita, high inflation and large debt burden (table 1). With a
40% unemployment rate and a 48.5% debt to GDP ratio
Kenya also faces considerable challenges. The same is true
Proceedings of the 2012 Pennsylvania Economic Association Conference
66
for Senegal with 48% unemployment rate and a 33.2% debt
to GDP ratio.
Botswana, Ghana and South Africa are the most successful in
our sample. Ghana’s GDP per capita is one of the higher ones
in our sample and with a growth of 13.5% (table 1) it is one
of the fastest growing countries in the world. The most
prosperous countries in our sample (based on GDP per
capita) are Botswana and South Africa (table 1).
Expectedly, Botswana has the least aid inflows (table 2).
However, despite its high GDP per capita and status as a
major emerging market,2 South Africa continues to have
substantial aid inflows. Other major recipients are Kenya and
Nigeria. These four countries are not part of the Heavily
Indebted Poor Countries (HIPC) Initiative unlike the rest of
the countries in the sample. Cameroon, Ethiopia, Ghana,
Senegal, Tanzania and Togo have reached the Completion
Point of the HIPC Initiative and Côte d'Ivoire is at the
Decision Point.3 Despite their participation in the HIPC
Initiative, Ethiopia and Tanzania are significant recipients of
ODA from OECD DAC (table 2). For both countries, the
IDA and the U.S. are major donors (table 2).
Aid from OECD (and elsewhere) have been and remain high
to countries in SSA. As noted above, some countries have
experienced macroeconomic successes while others have
continued to struggle. Both the successes and failures could
be, but are not necessarily related to, aid. To analyze the link
between aid and growth we discuss the framework to
measure aid effectiveness.
FRAMEWORK FOR ESTIMATING AID
EFFECTIVENESS
Barro (1991) provides a model for real GDP growth which
includes factors such as the real GDP per capita for initial
period, investment, education, civil wars and aid. Our model
is a modified version of Barro’s (1991) where we also
include money supply (based on Alvi, Mukherjee and
Shukralla, 2008 and Armah and Nelson, 2008). We also,
explore the importance of globalization by including both
foreign direct investment and a measure of openness (the sum
of exports and imports). The basic model to be estimated is
as follows:
grthit = βo + β1 GDPPC + β2 DI + β3 Openit + β4 FDIit
+ β5 MSit + β6 PIit + β7 Aidit + εit
(1)
where grth = rate of growth of real Gross Domestic Product
(GDP) per capita, GDPPC = GDP per capita of initial year of
each period, DI = domestic investment as a percentage of
GDP, Open = exports and imports as a percentage of GDP,
FDI = foreign direct investment as a percentage of GDP, MS
= money supply as a percentage of GDP, PI = polity index
(which measures extent of democracy), Aid = foreign aid as a
percentage of Gross National Income (GNI). All variables are
expected to have a positive impact on growth except aid
which is ambiguous given the debate over aid effectiveness.
In addition to the above, similar to Alvi, Mukherjee and
Shukralla (2008) and Armah and Nelson (2008) we also
examine the importance of interaction between the polity
variable and aid and compare our results to theirs. The
equation to be estimated is:
grthit = βo + β1 GDPPC + β2 DI + β3 Openit
+ β4 FDIit + β5 MSit + β6 PIit
+ β7 Aidit + β8 Aidit ∗ PIit + εit
(2)
Given our focus on the role of globalization, specifically
trade, we also examine the interaction between aid and trade
as it impacts growth. This allows us to analyze if aid and
trade are complementing each other in the pursuit of growth.
The equation to be estimated is given as:
grthit = βo + β1 GDPPC + β2 DI + β3 Openit
+ β4 FDIit + β5 MSit + β6 PIit
+ β7 Aidit + β8 Aidit ∗ PIit + β9 Aidit
∗ Openit + εit
(3)
Similar to the literature, this paper uses Generalized Method
of Moments (GMM) estimation. As noted by Alvi,
Mukherjee and Shukralla (2008), this technique “removes
country specific effects by taking first differences and makes
use of lagged values of the dependent variable and
predetermined variables as instruments.” Aid is considered
endogenous and its lagged value4 as well as lagged inflation5
are used as instruments.
Our results based on GMM estimation are discussed in the
following section.
DATA AND RESULTS
We use annual data from 1975 to 2006 for our eleven
countries (Botswana, Cameroon, Côte d'Ivoire, Ethiopia,
Ghana, Kenya, Nigeria, Senegal, South Africa, Tanzania and
Togo). The series used are real and nominal GDP, domestic
investment, exports, imports, FDI, money supply (M2),
polity index, consumer price index (CPI) and aid. Growth
and GDP per capita are based on real GDP (GDP per capita
is expressed as a natural log). Domestic investment, sum of
exports and imports, FDI and money supply are expressed as
percentages of nominal GDP and aid is expressed as a
percentage of nominal GNI. CPI is used to compute inflation
which is an instrument for aid. The polity index ranges from 10 to 10 with the former being strongly autocratic and the
latter being strongly democratic. All data except the polity
index is from the World Bank Indicators database from the
World Bank. The polity index series is from Integrated
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Network for Societal Conflict Research database from the
Center for Systemic Peace.
We estimate three growth equations as shown in equations
(1), (2) and (3). The results are displayed in table 3. The
Hausman J-test is a test for over-identifying restrictions.
Rejection of the null would indicate that the equation is
misspecified. As can be seen from the results in table 3, the
GMM estimated equations are correctly specified.6
The estimation results for equation 1 show that FDI, political
stability and aid are statistically significant determinants of
growth. When an interaction term is added for polity index
and aid (equation 2), initial GDP, FDI, money supply and aid
are statistically significant but the individual impact of the
polity index is not statistically significant. When the
interaction term for aid and trade is included (equation 3)
trade, FDI, aid and the interactive variable for trade and aid
are statistically significant determinants of growth. A
comparison of the results suggests that these interactive terms
do not add significant information to the aid-growth relation
and thus, we keep our first specification.
Based on the results, we can conclude is that aid is associated
with higher growth for our sample. This differs from Alvi,
Mukherjee and Shukralla (2008) but is similar to the
conclusion reached by Armah and Nelson (2008) and
Gomanee, Girma and Morrissey (2005). Both studies focus
on SSA as we do, although Gomanee, Girma and Morrissey
(2005) use a different estimation technique and Armah and
Nelson (2008) have different specifications than ours. While
the econometric analysis is supportive of increasing aid to
SSA, one concern about the estimation is that the impact of
aid is assumed to be uniform for all countries in our sample.
To more fully explore the econometric results in the context
of our countries we examine macroeconomic trends of the
main determinants of growth. Through this we aim to shed
light on effectiveness of aid for the specific countries in our
sample.
CASE STUDY ANALYSIS
The above econometric work shows the importance of FDI
flows, political regimes and aid in growth. Aid in turn is
affected by the inflationary environment. We thus examine
these macroeconomic trends to more fully explore our
econometric results. Figures 1 through 11 map out GDP per
capita, aid as a percentage of GNI, inflation and FDI as a
percentage of GDP data for our sample countries.
Information about political trends is presented in table 4.
Botswana’s GDP per capita has been on a steady upward
trajectory since 1975 (figure 1A) and is currently the most
prosperous country in our sample based on GDP (PPP) per
capita (table 1). Botswana was a major recipient of aid in the
1970s and 1980s but has tapered off as the country has
become prosperous (figure 1B). Currently, Botswana
receives the least aid from the OECD DAC in our sample
(table 2). For most of the three decades, Botswana’s inflation
rate has been in the double digits (figure 1C) but has been
relatively stable which has probably helped the aid-growth
relation. GDP has also been helped by FDI in the 1980s
(figure 1D). While FDI declined in the 1990s, there has been
some recovery in the 2000s (figure 1D). Botswana’s polity
index has ranged from 6-8 over the sample period reflecting a
stable democratic environment (table 4). This coupled with
significant aid and a moderate level of FDI flows in the
1970s and 1980s, have contributed to Botswana’s overall
success. It has led to dependence on aid today (figure 1B and
table 2).
Cameroon’s GDP per capita was increasing in the 1970s but
saw a decline in the mid-1990s and has steadied at a lower
level compared to the highs of the 1980s (figure 2A). This
suggests that aid has not been as effective which can partially
be explained by the volatile inflationary environment (figure
2C). GDP has been further hampered by unstable FDI flows
(figure 2D). Moreover, the political environment is not
supportive of higher growth as Cameroon has suffered from
strongly to weakly autocratic regimes in the sample period
(table 4). After the declining rates in the 1980s (which are
likely related to rising gross national income) aid has once
again increased and given an improved inflationary
environment (currently its inflation rate of 3.4% in 2011 is
the lowest among the countries in our sample (table 1) could
be beneficial. But without an increase in FDI flows and no
change in the political climate, rising aid may not be
sufficient to help Cameroon’s growth prospects.
Côte d’Ivoire’s GDP per capita is on a downward trajectory
(figure 3A). It is the only country in our sample with a
negative GDP growth rate (table 1). Aid had jumped
considerably in the 1990s, but has reduced in the 2000s
(figure 3B). The high inflation levels seen in the 1970s may
have hindered the aid-growth relationship although that has
stabilized considerably in the 2000s (figure 3C). With higher
FDI flows since the 1990s (figure 3D) and a shift to a more
democratic regime in 2000 (table 4), Côte d’Ivoire could
have reversed the trend. Unfortunately, the move to a more
democratic system was short-lived (table 4). Overall, Côte
d’Ivoire’s growth forecast looks weak.
Ethiopia’s GDP per capita has been steady but very low for
the entire period (figure 4A). Aid has been substantial for
most of the period (figure 4B) and the country is one of the
major recipients of aid from OECD DAC (table 2). There is
considerable volatility in inflation observed in Ethiopia
(figure 4C) with high levels as well as several deflationary
periods. At 28.8% it has the highest inflation rate of our
sample countries (table 1). This may explain why despite
rising aid, GDP remains low. The one bright spot for
Ethiopia is that after years of practically non-existent levels,
Proceedings of the 2012 Pennsylvania Economic Association Conference
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FDI flows have picked up in the 2000s although they remain
at very modest levels (figure 4D). A move to a more
democratic regime in the early 1990s was also a step in the
right direction. Unfortunately, the country has a very weak
form of democratic government (table 4) which is not as
beneficial for growth.
there is some stability in the 2000s (9C and 9D). In addition,
aid has been very modest for South Africa which suggests
that it is not a prime determining factor of GDP. With
improvements in FDI flows and inflation rates and a strong
and stable democratic regime it is not surprising that South
Africa has emerged as a member of the BRICS.
Ghana’s GDP per capita is on an upward trajectory for the
last three decades (figure 5A) and in terms of GDP per capita
(PPP) is currently ranked only below South Africa in our
sample (table 1). After modest levels in the 1970s and early
1980s, aid has been increasing for the country (figure 5B).
Ghana’s inflation rate has been falling but still remained high
and has been steady in the 2000s (figure 5C). FDI flows have
increased in the 1990s and 2000s although they remain a
small percentage of GDP (figure 5D). Since the 2000s,
Ghana’s political regime has become more democratic (table
4). Improvements (modest as they may be) in all these factors
lead to brighter prospects for Ghana’s future.
In Tanzania’s case aid, inflation and FDI flows have
improved since the 1990s (figures 10B, 10C and 10D). This
may explain the improvement in GDP (10A). Despite this,
Tanzania has not been as successful as some of the other
countries with less aid. This may be explained by its political
system which has remained autocratic for the entire sample
period.
Kenya’s GDP per capita has been somewhat volatile over the
sample period (figure 6A). Aid has declined in the late 1990s
and 2000s (figure 6B). The volatility in inflation may have
hurt the aid-growth relation while the stability in the 2000s
has helped Kenya (figure 6C). The shift to a more democratic
regime in the 2000s (table 4) was also beneficial. One
problem is the low level of FDI flows since the 1970s (figure
6D).
Nigeria’s GDP per capita has been steady over the sample
period (figure 7A). Nigeria had several years of low aid flows
(7B) and that coupled with a high inflationary environment
did not help GDP. In recent years, Nigeria has become a
major recipient of aid (figure 7B) and was a top three
recipient of OECD DAC aid in 2010 (table 2). This could be
beneficial given that inflation rates have stabilized in the
2000s (figure 7C). FDI has seen some highs and lows but
appears to have stabilized in the 2000s as well (figure 7D).
After some instability, Nigeria is back to a more democratic
regime although it is only moderately democratic (table 4).
Overall, the 2000s have some modest improvements which
could help Nigeria.
Senegal’s GDP per capita has been steady for the sample
period (figure 8A). The relatively high levels of aid were
hindered by a volatile inflationary environment (figures 8B
and 8C) which explains Senegal’s GDP trajectory. FDI flows
were also volatile in earlier decades (figure 8D) further
hurting GDP for the country. The 2000s may see some
improvement because aid remains strong and both inflation
and FDI has stabilized (figures 8B, 8C and 8D). Moreover,
Senegal has become strongly democratic in the 2000s (table
4).
South Africa has had a relatively high GDP per capita (figure
9A). After some instability in inflation rates and FDI flows,
Togo’s GDP per capita shows a stagnating economy (figure
11A). This is despite relatively high aid in the 1970s and
1980s (figure 11B). There is some volatility in the
inflationary environment including deflationary episodes
(figure 11C) which may explain the lack of success of aid.
Moreover, FDI flows have not always been reliable, although
there is improvement in the late 1990s and early 2000s
(figure 11D). The political system has contributed to Togo’s
struggles with growth (table 4).
CONCLUSION
In this paper we examine the effectiveness of aid in
promoting growth for countries in SSA. Using panel data in a
GMM framework for our sample of eleven countries, we find
that aid is beneficial to growth.
However, there are complicating factors to this general
conclusion. Our econometric analysis also shows that
political regimes and FDI flows are statistically significant
contributors to growth. The importance of regimes cannot be
over-emphasized. Donors are more reluctant to aid autocratic
regimes. But even if funding continues, aid is not beneficial
if it lines the pockets of potentially corrupt autocratic leaders.
Moreover, aid flows in these cases may be more “projectsupport” than “budget-support”. If aid is in the form of the
latter, governments can invest where they perceive the
greatest need based on a comprehensive overview of the
requirements of the country. Fungible aid is more flexible
and can be more beneficial. However, fear of more autocratic
governments may lead to a shift to “project-support” aid
which while helpful in some ways, may not be as beneficial
to overall growth. Thus, an improved political climate may
be as important as aid flows in promoting growth.
Another benefit for countries is from FDI flows. There are
many benefits associated with FDI flows. Poorer countries
may be more reliant on flows from abroad than from
domestic sources. Secondly, FDI flows may also come with
technical expertise and “good business practices”. However,
foreign investors, particularly foreign direct investors require
Proceedings of the 2012 Pennsylvania Economic Association Conference
69
increased stability (both economic and political). Moreover,
they need incentives. The kind of commitment needed to
attract and maintain FDI flows requires political stability and
good use of aid flows. In turn, this could help improve aid
flows and lead to better use of aid for development projects
to support foreign investment.
countries. It also clarifies why some countries have had
successes with promoting growth through aid.
What we can thus conclude is that an over-reliance on aid as
a driver of growth will not be beneficial to countries. It can
ultimately only be one piece rather than the entire strategy to
promote growth. If a country pursues the latter it does show
why aid has lead to dependencies and has been harmful to
TABLES
Table 1: Macroeconomic Indicators (2011 estimates)
Country
GDPPC PPP
Real GDP
Growth
Unemployment
Rate
Inflation
Rate
Public
Debt
Botswana
$ 16,300
6.2%
7.5%1
7.8%
20.3%
Cameroon
$ 2,300
3.8%
30%
3.4%
16.2%
2
5.2%
65.8%
Côte d'Ivoire
$ 1,600
-5.8%
NA
Ethiopia
$ 1,100
7.5%
NA
Ghana
$ 3,100
13.5%
28.8%
42.3%
3
8.8%
38.7%
4
11%
Kenya
$ 1,700
5.3%
40%
11.0%
48.5%
Nigeria
$2,600
6.9%
21%
10.8%
17.6%
5
Senegal
$ 1,900
4.0%
48%
3.4%
33.2%
South Africa
$ 11,000
3.4%
23.9%
5.0%
35.6%
Tanzania
$ 1,500
6.1%
NA
11.1%
36.9%
$ 900
3.8%
NA
4.5%
NA
Togo
Notes: NA indicates data is not available.
1
Unemployment rate for Botswana is a 2007 estimate.
2
There are no statistics on unemployment for Côte d'Ivoire, but it is estimated that it could be as high as 40-50%.
3
Ethiopia’s unemployment rate is a 2000 estimate.
4
Ghana’s unemployment rate is a 2008 estimate.
5
Senegal’s unemployment rate is a 2007 estimate.
Source: CIA World Factbook.
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Table 2: Official Development Assistance (ODA) from OECD DAC Countries (2010)
Country
ODA (millions)
Major Donors
Botswana
$ 157
U.S., EU Institutions
Cameroon
$ 538
France, Germany
Côte d'Ivoire
$ 848
France, IDA
Ethiopia
$ 3,539
IDA, U.S.
Ghana
$1,694
IDA, U.S.
Kenya
$ 1,631
U.S., IDA
Nigeria
$ 2,069
IDA, U.S.
Senegal
$ 931
France, IDA
South Africa
$ 1,032
U.S. EU Institutions
Tanzania
$ 2,961
IDA, U.S.
$ 421
France, Switzerland
Togo
Notes: ODA figures are for aid in 2010 from OECD DAC countries. IDA refers to International Development Association
(World Bank Fund). GDP per capita (PPP) and real GDP growth data are 2011 estimates.
Sources: OECD, World Bank.
Table 3: GMM Results for Growth
Variable
GMM (1)
GMM (2)
*
GMM (3)
GDPPC
0.66
(0.23)
0.84
(0.03)
0.56
(0.22)
DI
0.01
(0.88)
0.01
(0.83)
0.01
(0.59)
Open
0.02
(0.21)
0.02
(0.22)
0.08*
(0.03)
FDI
0.42*
(0.00)
0.38*
(0.01)
0.36*
(0.01)
MS
-0.08
(0.36)
-0.07
(0.38)
-0.06
(0.39)
PSI
0.20*
(0.00)
0.10
(0.29)
0.08
(0.54)
Aid
0.15*
(0.04)
0.19*
(0.01)
0.64*
(0.02)
0.02+
(0.16)
0.02
(0.20)
Aid*PSI
-0.01*
(0.08)
Aid*Open
J test (p-value)
0.98
0.95
0.69
Notes: J-Test is test for over-identification of instruments. Rejection of the null hypothesis indicates that the instruments are not
appropriate and the model is misspecified. *, ** and *** indicates that the variables are statistically significant at 5%, 10% and 15%
respectively. + indicates that while the variable is not statistically significant at usual levels of significant, it is still an important
determinant of growth.
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Table 4: Polity Index (1975-2006)
Country
Max
Min
Trends
Botswana
8
6
Botswana is one of the most consistently democratic regimes in our sample
with an increasing polity score from 6 to 8.
Cameroon
-4
-8
In the early 1990s, Cameroon went from being relatively strongly autocratic
(-8) to relatively weakly autocratic (-4)
Côte d'Ivoire
4
-8
In 2000, Côte D'Ivoire shifted to a democratic government since 2003 has
been classified as being in an interregnum period.
Ethiopia
1
-8
Between 1991 and 1994, Ethiopia attempted to transition from an autocratic
government to a more democratic government. However, since 1995 until the
end of the sample, Ethiopia’s score has not exceeded 1 which is an extremely
weak form of democracy.
Ghana
8
-7
Ghana has had long history of autocratic governments. There was a brief
period of democracy in the late 1970s but that quickly reversed to an
autocratic government which remained in place until the 1990s. By the 2000s,
Ghana’s government has become more democratic.
Kenya
8
-7
Kenya’s trajectory has been somewhat similar to Ghana. After extremely
autocratic governments for most of the sample period, there is a shift to a
more democratic government in the 2000s.
Nigeria
7
-7
Nigeria shifted to a more democratic government in 1978 which lasted for a
few years but went back to a more autocratic regime by 1984. Again, there
was a shift in 1998 and currently Nigeria’s polity score is 4 (weak to
moderately democratic)
Senegal
8
-6
Senegal started off with an autocratic regime which weakened in the 1980s
and become strongly democratic in the 2000s (scores of 8 since 2000).
South Africa
9
4
Apartheid notwithstanding, South Africa has had a democratic regime for the
entire sample period (the only country besides Botswana). Since 1994 (end of
apartheid), South Africa has had a strongly democratic regime (polity score
of 9).
Tanzania
-1
-6
Tanzania has had an autocratic government for the entire period. Since 1995
the country has had a weakly autocratic government (polity score of -1)
Togo
-2
-7
Following a strongly autocratic regime until 1990, Togo has had varying
degrees of weak autocratic regimes since then, with a current polity score of 4.
Source: Data from Center of Systemic Peace, Integrated Network for Societal Conflict Research database.
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Figure 1B: Botswana's Aid as a Percentage of GNI
14
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
12
10
Percentage
6
4
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
2005
2003
2001
1999
1997
1995
1993
1991
1987
1989
1985
1983
1981
1979
1977
0
Figure 1C: Botswana's Inflation Rate
Figure 1D: Botswana's FDI as a Percentage of GDP
20
18
16
14
12
10
8
6
4
2
0
15
5
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
-5
1977
0
1975
Percentage
10
1975
Percentage
8
2
1975
U.S. dollars
Figure 1A: Botswana's Real GDP Per Capita
-10
-15
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Figure 2B: Cameroon's Aid as a Percentage of GNI
7
35
6
30
5
25
4
2005
2003
2001
1999
1997
1993
1995
2005
2003
2001
1999
1997
1995
1993
1991
-1
1989
0
1987
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
-10
1
1985
5
0
2
1983
10
3
1981
15
1979
20
1977
Percentage
40
-5
1991
Figure 2D: Cameroon's FDI as a Percentage of GDP
1975
Figure 2C: Cameroon's Inflation Rate
Percentage
1975
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
0
1989
200
1987
400
1985
600
1983
Percentage
U.S. dollars
800
1981
1000
10
9
8
7
6
5
4
3
2
1
0
1979
1200
1977
Figure 2A: Cameroon's Real GDP Per Capita
-2
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Figure 3A: Côte D'Ivoire's Real GDP Per Capita
Figure 3B: Côte D'Ivoire's Aid as a Percentage of GNI
25
1000
20
800
15
Percentage
600
400
10
5
200
25
3
20
2
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
0
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
-1
1985
0
1983
5
1981
10
1
1979
15
1977
Percentage
4
-5
1979
1975
2005
2003
2001
1999
1997
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
1995
Figure 3D: Côte D'Ivoire's FDI as a Percentage of GDP
30
1975
Percentage
Figure 3C: Côte D'Ivoire's Inflation Rate
1977
0
0
1975
U.S. dollars
1200
-2
-3
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Figure 4A: Ethiopia's Real GDP Per Capita
Figure 4B: Ethiopia's Aid as a Percentage of GNI
25
160
140
20
100
Percentage
U.S. dollars
120
80
60
40
15
10
5
20
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1975
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
Figure 4C: Ethiopia's Inflation Rate
1977
0
0
Figure 4D: Ethiopia's FDI as a Percentage of GDP
7
40
6
5
1
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2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
-1
1983
0
1981
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
2
1979
-20
1979
-10
1977
0
3
1977
10
4
1975
Percentage
20
1975
Percentage
30
76
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
0
-1
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2003
2001
1999
40
1997
60
1995
4
1993
120
1991
5
1989
140
1987
Figure 5C: Ghana's Inflation Rate
1985
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
0
1981
0
1983
2
1979
50
1981
100
1977
250
1979
150
1975
200
U.S. dollars
300
1977
80
Percentage
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
U.S. dollars
350
1975
Percentage
Figure 5A: Ghana's Real GDP Per Capita
Figure 5B: Ghana's Aid as a Percentage of GNI
18
16
14
12
10
8
6
4
Figure 5D: Ghana's FDI as a Percentage of GDP
100
3
2
20
1
0
77
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
-0.2
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2003
2001
2003
1999
1989
1987
1985
1983
2001
0
1997
0.2
1999
0.4
1995
0.6
1997
0.8
1993
1
1995
1.2
1991
1.4
1993
Figure 6D: Kenya's FDI to GDP
1991
1989
1987
1985
Figure 6C: Kenya's Inflation Rate
1981
340
1983
360
1979
12
1981
420
1977
14
1979
380
1975
400
Percentage
440
1977
50
45
40
35
30
25
20
15
10
5
0
Percentage
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
U.S. dollars
460
1975
Percentage
Figure 6A: Kenya's Real GDP Per Capita
Figure 6B: Kenya's Aid as a Percentage of GNI
18
16
10
8
6
4
2
0
78
Figure 7B: Nigeria's Aid as a Percentage of GNI
12
500
450
400
350
300
250
200
150
100
50
0
10
Percentage
8
6
4
Figure 7C: Nigeria's Inflation Rate
2005
2001
2003
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1975
1977
0
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
2
1975
U.S. dollars
Figure 7A: Nigeria's Real GDP Per Capita
Figure 7D: Nigeria's FDI as a Percentage of GDP
10
80
70
8
Percentage
50
40
30
20
6
4
2
10
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2001
1999
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1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
-2
1975
0
0
1975
Percentage
60
79
Figure 8A: Senegal's Real GDP Per Capita
Figure 8B: Senegal's Aid as a Percentage of GNI
18
16
500
14
400
12
Percentage
300
200
10
8
6
4
100
2
5
30
4
2005
2003
2001
1999
1995
1997
1993
1991
1989
1987
1985
1983
3
Percentage
20
15
10
5
2
1
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
-1
1977
0
0
1975
Percentage
25
-5
1981
Figure 8D: Senegal's FDIas a Percentage of GDP
35
-10
1979
1975
2005
2003
2001
1999
1997
1995
1993
1991
1987
1989
1985
1983
1981
1979
1977
1975
Figure 8C: Senegal's Inflation Rate
1977
0
0
1975
U.S. dollars
600
-2
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Figure 9A: South Africa's Real GDP Per Capita
Figure 9B: South Africa's Aid as a Percentage of GNI
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
4000
3500
2500
35
6
30
5
25
4
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
2005
2003
2001
1999
1997
1995
-1
1993
0
1991
0
1989
5
1987
2
1
1985
10
3
1983
15
1981
Percentage
7
20
1981
1975
Figure 9D: South Africa's FDI as a Percentage of GDP
40
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Percentage
Figure 9C: South Africa's Inflation Rate
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
0
1979
500
1979
1000
1977
1500
1975
2000
1977
Percentage
U.S. dollars
3000
-2
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Figure 10A: Tanzania's Real GDP Per Capita
Figure 10B: Tanzania's Aid as a Percentage of GNI
35
30
300
25
250
20
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1975
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
0
1981
0
1979
5
1977
50
Figure 10C: Tanzania's Inflation Rate
Figure 10D: Tanzania's FDI as a Percentage of GDP
40
7
35
6
30
5
Percentage
25
20
15
10
5
4
3
2
1
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2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
0
1975
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
0
1975
Percentage
10
1983
100
15
1981
150
1979
200
1977
Percentage
350
1975
U.S. dollars
400
82
Figure 11A: Togo's Real GDP Per Capita
Figure 11B: Togo's Aid as a Percentage of GNI
18
400
16
350
14
300
12
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
Figure 11D: Togo's FDI as a Percentage of GDP
12
10
Percentage
8
6
4
2
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
-2
1975
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
0
1975
45
40
35
30
25
20
15
10
5
0
-5
-10
1975
2005
2003
2001
1999
1997
1995
1993
1991
1989
1985
1987
1983
1981
1979
1977
0
1975
2
0
1985
4
50
Figure 11C: Togo's Inflation Rate
Percentage
6
1983
100
8
1981
150
10
1979
200
1977
Percentage
U.S. dollars
250
-4
ENDNOTES
1
While the unemployment rate is not available, it is estimated to be between 40-50% in 2011.
2
Since 2011, South Africa is part of the BRICS.
3
World Bank Annual Meetings Report, 2011.
4
Lagged values have been used as an instrument of aid by Alvi, Mukherjee and Shukralla (2008) and Armah and
Nelson (2008) and others. Also, Gomanee, Girma and Morrisey (2005) who do not use the same estimation
technique because they find that instruments are not necessary, also use lagged aid as a variable in their growth
regression and note that “this can be interpreted as an instrument (in the spirit of Hansen and Tarp).”
5
Armah and Nelson (2008) use a lagged policy variable among others as an instrument for aid. This variable
includes a budget variable, trade openness and inflation. Since our paper focuses on globalization as a separate
indicator of growth we leave it out here. In addition, we do not have budget surplus/deficit data and thus our policy
variable is based solely on inflation.
6
The Sargan test which is a special case of the J-test is often reported in studies. We conducted Sargan tests for each
specification and the results (not reported here) reinforce the conclusion of appropriate instruments.
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Ali, A. M., H. S. Isse, and W. Peek. 2009. A sensitivity analysis approach on the effect of foreign aid on growth.
Journal of Applied Business and Economics. 10(3): 97-110.
Alvi, E., D. Mukherjee, and E. K. Shukralla. 2008. Foreign aid, growth, policy and reform. Economics Bulletin.
15(6): 1-9.
Armah, S. and C. Nelson. 2008. Is foreign aid beneficial for Sub-Saharan Africa? A Panel Data Analysis.
Working Paper.
Barro, R. J. 1991. Economic growth in a cross-section of countries. The Quarterly Journal of Economics. 106(2):
407-43.
Burnside, C. and D. Dollar. 1997. Aid, policies and growth. American Economic Review. 90(4): 847-68.
Center of Systemic Peace, Integrated Network for Societal Conflict Research database.
Clemens, M.A, S. Radalet, R. Bhavnani, and S. Bazzi. 2004. Counting chickens when they hatch: The short-term
effect of aid on growth. Center for Global Development Working Paper No. 44.
Collier, P. 2007. The bottom billion why the poorest countries are falling behind and what we can do about it?
Oxford University Press.
Dalgaard, C-J., H. Hansen, and F. Tarp. 2004. On the empirics of foreign aid and growth. Economic Journal.
114(496): 191-216.
Djankov, S., J. G. Montalvo, and M. Reynal-Querol. 2006. Does foreign aid help? Cato Journal. 26(1), Winter
issue.
Easterly, W. 2003. Can foreign aid buy growth? Journal of Economic Perspectives. 17(3): 23-48.
Gomanee, K., S. Girma, and O. Morrissey. 2005. Aid and growth in Sub-Saharan Africa accounting for
transmission mechanisms.
United Nations University-World Institute for Development Economics Research
(UNU-WIDER) Research Paper No. 2005/60.
Hansen, H. and F. Tarp. 2001. Aid effectiveness disputed. Journal of International Development. 12(3): 375-98.
Hansen, H. and F. Tarp. 2001. Aid and growth regressions. Journal of Development Economics. 64: 547-70.
OECD, International Development Statistics Database.
Radelet, S. 2006. A primer on foreign aid. Center for Global Development Working Paper No. 92.
Rajan, R. and A. Subramanian. 2008. Aid and growth: What does the cross country evidence really show? Review
of Economics and Statistics. 90(4): 643-65.
Williamson C. R. 2008. Foreign aid and human development: The impact of foreign aid to the health sector.
Southern Economic Journal. 75(1). World Bank, World Development Indicators Database.
Proceedings of the 2012 Pennsylvania Economic Association Conference
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EOECONOMIC ANALYSIS OF ALTERNATIVE WAYS TO REFORM MEDICAL MALPRACTICE
Tracy C. Miller
Grove City College
100 Campus Dr. # 3018
Grove City, Pa 16127
ABSTRACT
It is generally believed that malpractice liability raises the
cost of health care. A number of reforms to the malpractice
liability system have been considered and some have been
implemented in selected states. In spite of its problems,
however, malpractice liability plays an important role in
promoting safe and high quality health care. Rather than
imposing uniform limits on malpractice liability, a better
approach is to combine a system of privately developed best
practice guidelines with legal reforms facilitating binding
early offers from providers and contractual agreements
between patients and providers to limit liability.
INTRODUCTION
One cause of high medical costs not addressed by health care
reform is malpractice liability. The high cost of malpractice
liability has resulted in a variety of state laws being passed to
cap damage payments, or make it more difficult to find a
health care provider liable. It is hard to dispute the assertion
that the malpractice liability system needs to be reformed.
Meaningful reform, however, should not reduce or eliminate
malpractice liability for patients who value the protection it
provides enough to pay the resulting higher prices for health
care. Instead reform should make it possible for patients and
physicians to make contractual agreements to limit liability
and make it easier for defendants to settle negligence claims
by offering immediate payments for actual damages.
Malpractice is defined as “unreasonable” or negligent
treatment of a patient by medical personnel responsible for
health care (Wright, Godfrey, & Eberhardt, 1988). Evidence
of damage or injury is necessary. Malpractice purportedly
raises health care costs in two ways-- through the cost of
insurance premiums that physicians pay to protect themselves
from the risk of large judgments, and through the extra costs
of diagnosis and treatment incurred by doctors to defend their
decisions in case they are sued.
The central elements of malpractice are based on common
law, which gradually evolved as part of legal practice,
beginning in England in the eleventh century (Wright,
Godfrey, & Eberhardt, 1988). Negligence is defined as “the
omission to do something which a reasonable man, guided by
those ordinary considerations which ordinarily regulate
human affairs, would do: or the doing of something which a
reasonable and prudent man would not do” (Harrison, Worth,
& Carlucci, 1985, p. 42)
In Australia, USA and the UK, a doctor is liable if he or she
is more likely than not to have acted negligently. In the US
and UK compliance with customary practice is an acceptable
defense (Kessler, Summerton and Graham). Australia takes a
different approach- judges are the ultimate arbiter of the
standard of negligence. Some states in the US allow judges
to reject customary practice as a defense in some
circumstances.
A malpractice crisis arose in the mid-1970s, exemplified by
insurance unavailability for some physicians and high costs
for others (Wright, Godfrey, & Eberhardt, 1988). Similar
crises arose in the 1980s and in the early 2000s. In response
to the crisis in the 1970s, most state legislatures responded by
enacting tort reform laws. Reforms that have been
implemented include patient compensation funds, limits on
noneconomic damages, statutes of limitations, limitations on
contingent fees, and absolute dollar limits on compensation
that a patient could recover for a single injury or death. In
some states, providers are liable for all damages up to the
limit. In other states, a patient compensation fund is
responsible for recoveries above a specified amount, such as
$100,000. Arbitration systems & pretrial screening panels
were also adopted with the purpose of directing the handling
of claims away from courts and eliminating frivolous claims.
THE CASE AGAINST LIMITS ON MALPRACTICE
AWARDS
Laws to limit malpractice liability have been challenged in a
number of state courts, and some of the laws have been
struck down. Some judges reject compensation limits on the
grounds of equal protection- they give unequal advantage to
health-care providers (Wright, Godfrey, & Eberhardt, 1988).
Courts have also argued that caps are regressive. In striking
down a $350,000 cap on noneconomic damages in Wisconsin
in 2005, the court noted that the cap had the greatest impact
on severely injured children (Nelson, Morrisey, & Kilgore,
2007). Since economic damages include lost income, they are
lower for children, so caps on noneconomic damages reduce
the incentive of filing lawsuits on behalf of injured children.
Courts have also thrown out damage caps on the grounds that
evidence is inconclusive about their effectiveness.
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In addition to the distributional impacts of malpractice
liability and laws to limit it, malpractice liability impacts
health care in three ways. It influences the incentives of
health care providers to make mistakes, to practice defensive
medicine, and to provide treatment and procedures of
questionable medical value. Limiting liability can save costs
by reducing defensive medicine but it can lead to increases in
the frequency of errors and in the cost of excessive medical
care, sometimes referred to as offensive medicine (Avraham,
2011).
Some would argue that because most health care providers
have liability insurance, the threat of being sued has little
impact on physician behavior and health care outcomes.
Although malpractice insurance protects physicians from
legal expenses, through experience rating of premiums, it
creates incentives for physicians to practice safer medicine.
Insurers reward physicians for passing certification tests,
adopting new techniques, or purchasing safer equipment,
while penalizing physicians with more claims filed against
them, with substance abuse problems, or with other personal
characteristics that might increase their risk of being
negligent.
The conventional wisdom is that malpractice insurance has
less experience rating than other lines of insurance (Danzon,
2000, Sloan, 1990). Svorny (2011) disputes the conventional
wisdom, noting that experience rating is hard to observe
because different insurance companies specialize in insuring
providers with different levels of risk. Thus individual
companies may not charge different premiums to physicians
in the same specialty, but those whose risks are higher pay
more. Some companies only insure physicians with spotless
records. Physicians whose applications are rejected by lowpriced insurance companies must turn to surplus-lines
carriers and pay between 150 and 500 percent of the prices in
standard markets (Svorny, 2011, p. 7).
CRITICAL ASSESSMENT OF THE US
MALPRACTICE SYSTEM
Compared to identifying the negligent party in auto accidents
and workplace injuries, medical negligence is hard to identify
because of the fact that patients seeking treatment are already
ill or injured and because treatment has different effects on
different patients (Danzon, 2000). This opens the door for
decisions that reflect cognitive biases of judges and juries.
Evidence suggests that courts compensate injured victims
when their harm is large, even if negligence is not involved
(Avraham, 2011).
Critics of malpractice point out that a very small percentage
of victims of negligence file malpractice cases. Malpractice
cases that are filed have very high transactions cost as a share
of payouts to victims. According to Studdert, Mello, and
Gawande (2006), for every dollar spent on patient
compensation, 54 cents went to administrative expenses.
Malpractice liability contributes significantly to health care
costs by raising the cost of practicing medicine, particularly
in selected specialty areas. Obstetricians and gynecologists
bear some of the highest liability costs, which have resulted
in some giving up their practice and others moving away
from high-risk areas. Obstetricians can be held liable for
injuries sustained at birth for up to 21 years (Wright,
Godfrey, & Eberhardt, 1988).
Caps result in high costs of a different kind. With
pregnancies, caps resulted in less care being taken but may
result in more procedures, particularly if procedures are
risky, as is the case with Caesarian sections, as well as
induction and stimulation of labor (Currie & Macleod, 2008).
Caps on noneconomic damages also reduce the incentive for
filing lawsuits on behalf of children, married women and the
elderly, for whom economic damages are generally smaller,
thus resulting in their families bearing the costs of medical
injuries that they sustain.
Although it has its flaws, a careful assessment reveals that the
malpractice system works better than many critics believe.
When cases aren’t filed, it is often due to small damages,
either because the patient fully recovers or is very old
(Svorny, 2011). In evaluating a sample of cases raised against
a large hospital between 1977 and 1989, Farber and White
(1991) found that most of the cases that go to trial, which are
a small proportion of the total, are won by defendants, but
that the vast majority of cases were dropped, dismissed or
settled out of court. Their results imply that the discovery
process in malpractice cases serves the purpose of enabling
poorly informed plaintiffs to gather information about
whether negligence is likely. This results in a substantial
percentage of cases being dropped by plaintiffs or dismissed
by the judge. Plaintiffs received damage payments in one
fourth of the cases where the hospital rated care quality as
good, but in 89 percent of the cases where hospitals rated
care quality as bad (Farber & White, 1991).
The high transactions cost of malpractice cases do not seem
so large when compared to the losses from injuries that may
be prevented as doctors and hospitals seek to take precautions
to keep their insurance premiums down. Farber and White
(1991) noted that the expected liability of the hospital in their
study was 25 times as high in cases involving negligence
(where the hospital reported bad care) than in cases that it did
not (reported by the hospital as good care). Thus a strong
incentive exists to provide high quality care so as to avoid
liability. Thus limits on liability may increase negligence
and could even increase the number of malpractice cases.
Proceedings of the 2012 Pennsylvania Economic Association Conference
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If most victims of malpractice do not file lawsuits, as asserted
by Localio et al. (1991), then high awards may be warranted.
If the probability of being sued is low even if a physician is
guilty of negligence, then his incentive to be careful may be
too low unless the cost when he is found guilty is
considerably greater than the damage caused by his actions.
SUGGESTIONS FOR REFORM
Proposals to reform medical liability have included a variety
of measures. A common approach is to limit compensation
to loss of earning capacity & reasonable medical expenses
and related monetary costs. This would eliminate a major
share of compensation that goes for pain, suffering, or
punitive damages.
Another possible reform is to eliminate or reduce contingent
fees. Lawyer fees are contingent if they depend on case
outcomes. Courts in the UK and Australia do not allow
contingent fees, but they do allow conditional fees that vary
with the outcome of the case (Kessler, Summerton and
Graham, 2006). Some states have set limits on contingent
fees. Eliminating contingent and conditional fees may
discourage lawyers from filing suits where claims are weak
or frivolous. At the same time, contingent fees enable some
risk averse patients to file valid claims that they might not
choose to file if they had to pay hourly fees regardless of the
outcome of the case (Danzon, 2000, p. 1375).
The problem with the system of medical liability is not that it
results in very large payouts for those guilty of negligence.
The problem is the cost of defending cases where the
physician was not negligent. This includes the costs of
defensive medicine whose primary purpose is to protect the
physician from liability if he or she is sued. Although, caps
on malpractice awards may reduce the incidence of cases
where the plaintiff has a low probability of winning, caps will
also discourage some cases being brought where there was
negligence.
Defensive practices added an estimated $500,000 per year of
life saved for heart disease patients according to one study
(Kessler and McClellan, 1996). Doctors practice defensive
medicine because they bear “uninsured, nonmonetary costs
of liability” (Kessler, Summerton and Graham, 2006). These
include lost time, harm to their reputations and the emotional
impacts of responding to claims. Health insurance
reimbursement encourages more defensive medicine, since
doctors and patients bear less of the cost. Rather than putting
caps on malpractice liability, defensive medicine can be
limited by other means, such as by managed care
organizations not reimbursing unnecessary defensive
diagnoses and treatment.
One promising reform that could reduce spending on
defensive medicine is to make evidence of compliance with
written clinical practice guidelines an acceptable defense
against negligence. Medical guidelines have been developed
by a variety of organizations over the years, with a dramatic
increase in the number of guidelines being produced
beginning in the 1990s (Avraham, 2011, p. 8). The Agency
for Health Care Policy and Research played an important role
in this. Although guidelines seem to have had limited effect
on physician behavior so far, if implemented with adequate
incentives they have the potential to reduce the use of costly
defensive medicine and provide physicians grounds for an
affirmative defense if they are sued.
Existing guidelines, many of which have been developed by
government agencies, are problematic. Government agencies
do not have an incentive to keep guidelines up-to-date so that
they are consistent with the latest evidence about best
practices. Outdated guidelines could themselves give health
care providers cover for not using the most up-to-date
procedures for treating a particular patient. Nevertheless,
guidelines could play a beneficial role in discouraging
defensive medicine if they were designed by private firms.
With appropriate changes in legal infrastructure, a private
regulation regime could permit firms to develop guidelines
and be held accountable for the consequences of those
guidelines in terms of promoting safe treatment of patients
(Avraham, 2011).
Another way to save costs is to make it easier to settle cases
quickly. In some cases, both plaintiffs and defendants could
benefit by a reform that trades off reduced compensation for
greater access to compensation. One way to accomplish this
would be to reform liability rules to favor binding early
offers made by defendants. This approach was proposed by a
2006 Department of Health and Human Services study
(Hersch, O'Connell, & Viscusi, 2006). It involves a statute
that would give a health care provider the option to offer an
injured patient, within 180 days after a claim is filed, periodic
payments of the patient’s net economic losses as they accrue.
The statute would provide criteria for how much
compensation would be required in a way similar to workers’
compensation statutes (O'Connell, 2007). The plaintiff
would have a choice between accepting the early offer or
declining it in favor of litigation. If he or she declined the
early offer, however, the plaintiff could only win if gross
negligence were proven beyond a reasonable doubt. The
advantage to the plaintiff of accepting the early offer would
be reduced uncertainty, delays, and transactions costs
compared to full-scale tort claims.
While a reform facilitating cases to be settled with early
offers will reduce the number of cases resulting in large
payouts for noneconomic damages, the impact on the
incentive to file lawsuits is unclear. Fewer lawyers will earn
the high fees from large payouts, but contingent fee lawyers
will collect some fees in a larger percentage of cases.
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87
Damage caps clearly reduce the benefits to some plaintiffs of
filing a lawsuit, while early offer reform creates the
opportunity for gains to both the defendant and the plaintiff
compared to the status quo. A statute to reform liability to
include an early offer reform was first proposed in the 1980s
(O'Connell, 2007). It is not as appealing to defendants as
rules limiting claims and is opposed by the plaintiff’s bar
because it would reduce spending on litigation. If enough
consumers, health care providers and insurance companies
can be convinced that early offer reform could potentially
benefit everyone but the lawyers, perhaps it will be
implemented in some states.
Early offer reform preserves the essential feature of the
malpractice liability system so that patients are fully
protected against the costs they bear due to provider
negligence. Although it could have similar effects to caps on
noneconomic damages in ordinary negligence cases, it
permits plaintiffs to reject early offers and proceed with
lawsuits seeking noneconomic damages in cases involving
gross negligence.
In addition to damage caps in some states, liability of
physicians is also limited by a variety of other provisions.
Government employed physicians are shielded from
malpractice claims by the Federal Torts Claims Act (Svorny,
2011). Joint Underwriting Associations, which operated in 13
states in 2007, act as insurers of last resort for physicians
who cannot obtain insurance in private markets. By not
exposing physicians to the full costs of liability for their
actions, such arrangements increase the risk of patients being
harmed.
much that patient values the protection he or she receives.
Instead some have argued that courts should honor contracts
that patients could agree to in advance that limit how much
liability the health care provider would incur (Cannon, 2010).
This would make is possible for some patients to pay less for
health care in exchange for a smaller award in the case of
negligence. Under our current system where third parties pay
most medical expenses, individual patients would not benefit
much from such agreements. Thus an important step to
improving the malpractice system is for patients to pay a
greater share of their own health care expenses. Then many
people could benefit directly from reforms that allow
negotiation between patients and health care providers over
malpractice limits. Such negotiated limits should not apply
to gross negligence, which is clearly provable in only a small
percentage of cases (Hersch, O'Connell, & Viscusi, 2006).
Many would object to patients negotiating away the right to
hold health care providers liable because of concerns that
uninformed patients would be taken advantage of. It is
unlikely that courts would ever uphold any and all contracts
to limit liability. Nevertheless, a menu of limitations
established by third parties, such as health insurance
providers, could play an important role in reducing the costs
of health care in exchange for less protection in the event of
negligence. As Cannon points out, “we have struck a balance
that demands greater protection from simple negligence than
many patients would prefer, that is uniform and inescapable”.
Because the existing liability system raises the cost of health
care for everyone, some get too much protection from
negligence and not enough health care.
CONCLUSION
Liability of health care providers combined with malpractice
insurance could play an even bigger role than it does now in
promoting safe, efficient, and low cost health care. This
could be accomplished by eliminating state licensing
requirements and relying on companies that provide liability
insurance to certify that health care professionals are
competent and do quality work. Liability insurers have
incentives to do a more comprehensive job of regularly
evaluating the quality of care and the competence of
physicians than do licensing boards. “Malpractice
underwriters review physicians annually”, evaluating claims
histories, investigating physicians for behavior that increases
their risks of negligence and providing information and
advice to the medical community (Svorny, 2011). Without
licensing, arbitrary education and training standards would
no longer restrict the supply of health care providers. Instead
liability insurance companies would provide the information
patients need to choose physicians that provide safe and high
quality care.
One problem with malpractice liability is that the court
system, which is a government monopoly, gives about the
same level of protection to every patient regardless of how
Rather than capping malpractice damages for everyone,
moving toward a free market in health care along with
reforms to our legal system would enable individual patients
and their families to choose in advance the level of protection
from risk that they are willing to pay for. This would make
health care more affordable while permitting those who value
a high level of protection against negligence the freedom to
pay more for such protection.
Liability for malpractice gives health care providers an
incentive to pursue measures to reduce risk. This is the cost
poorly informed patients pay for safer medicine and the right
to be compensated in case of negligence. The best way to
reform liability for medical malpractice is to permit mutually
beneficial arrangements, such as binding early offers and
agreements between patients and providers to limit liability.
Reforms such as caps on liability take away an important
protection that many patients value. Additional reforms such
as requiring the loser to pay all court costs also make sense.
Nevertheless, the key is to preserve the protection against
negligence of the existing system, while opening the door for
alternative arrangements that would make it possible to
Proceedings of the 2012 Pennsylvania Economic Association Conference
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reduce health care costs for those who cannot afford the high
costs of the existing system.
REFERENCES
Avraham, R. (2011). Clinical Practice Guidelines: The
Warped Incentives in the U.S. Healthcare System. American
Journal of Law and Medicine, 37(1), 7-40.
Cannon, M. (2010, November 12). Reforming Medical
Malpractice Liability Through Contract. Retrieved November
9, 2011, from Cato Institute.
Congressional Budget Office. (2004, January 8). Limiting
Tort Liability for Medical Malpractice. Retrieved May 9,
2012, from Congressional Budget Office.
Currie, J., & Macleod, W. B. (2008, May). First do no harm?
Tort Reform and Birth Outcomes. Quarterly Journal of
Economics, 123(2), 795-830.
Danzon, P. M. (2000). Liability for Medical Malpractice. In
A. J. Culyer, & J. P. Newhouse, Handbook of Health
Economics,Volume 1 (pp. 1339-1404). Amsterdam: Elsevier.
Farber, H., & White, M. (1991). Medical Malpractice: An
Empirical Examination of the Litigation Process. The RAND
Journal of Economics, 22(1), 199-217.
Harrison, L., Worth, M., & Carlucci, M. (1985). The
development of the principle of medical malpractice in the
United States". Perspectives in Biology and Medicine, pp.
41-72 cited in Wrght, Fedorvich and Close.
Hersch, J., O'Connell, J., & Viscusi, K. (2006, June 5).
Evaluation of Early Offer Reform of Medical Malpractice
Claims: Final Report. Retrieved May 25, 2012, from
Department of Health and Human Services:
http://aspe.hhs.gov/daltcp/reports/2006/medmalcl.pdf
Kessler, D., & McClellan, M. (1996). Do doctors practice
defensive medicine. Quarterly Journal of Economics, 111(2),
353-90.
Kessler, D., Summerton, N., & Graham, J. (2006). Effects of
the medical liability system in Australia, the UK, and the
USA. Lancet, 368, 240-246.
Localio, A. R., Lawthers, A., Brennan, T., Laird, N., Hebert,
L., Peterson, L., et al. (1991). Relation between Malpractice
Claims and Adverse Events Due to Negligence. New
England Journal of Medicine 325, 245-251.
Nelson, L., Morrisey, M., & Kilgore, M. (2007). Damages
Caps in Medical Malpractice Cases. Milbank Quarterly,
85(2), 259-286.
O'Connell, J. (2007). Binding Early Offers versus Caps for
Medical Malpractice Claims. Milbank Quarterly, 85(2), 287296.
Sloan, F. (1990, May). Experience Rating: Does It make
Sense of Medical Malpractice Insurance? American
Economic Review, 80(2), 128-133.
Studdert, D., Mello, A., & Gawande, T. (2066). Claims,
Errors, and Compensation Payouts in Medical Malpractice
Litigation. New England Journal of Medicine, 354, 20242033.
Svorny, S. (2011). Could Mandatory Caps on Medical
Malpractice Damages Harm Consumers. Washington, DC:
Cato Institute.
Wright, J. E., Godfrey, G. M., & Eberhardt, T. C. (1988,
September-October). Analyzing Caps on Malpractice
Monetary Awards, Part I: Historical and Social Perspectives.
Nursing Economics, 6(5), 257-260.
Proceedings of the 2012 Pennsylvania Economic Association Conference
89
DOES A FIRM’S DIVIDEND INITIATION AFFECT ITS RISK?
Henry F. Check, Jr.
Penn State Lehigh Valley (retired)
4014 Autumn Ridge Road
Bethlehem, PA 18017
John S. Walker
Department of Business Administration
Kutztown University of Pennsylvania
Kutztown, PA 19530
Karen L. Randall
1159 Hagues Mill Road
Amber, PA 19002
ABSTRACT
The Jobs and Growth Tax Relief Reconciliation Act of 2003
(JGTRRA) was signed into law by President Bush on May
28, 2003. Under the JGTRRA corporate dividends are no
longer taxed as ordinary income; instead, they are taxed
preferentially like capital gains and in some cases not taxed
at all.
This study examines the stock performance of a sample of 18
firms which initiated cash dividends immediately after
JGTRRA was enacted. We find that the risk of these firms’
stock, as measured by the variance of returns, decreased in
absolute terms after dividends were initiated.
Since this was a period of generally declining stock market
volatility, we also compared our sample firms’ variances to
the market as a whole and found that a majority of the firms’
risk declined relative to the market as well.
Finally, we compared the firms’ systematic risk pre- and
post-dividend initiation, as quantified by the Capital Asset
Pricing Model’s (CAPM) beta measure, and found that a
majority of the firms’ systematic risk changed. When
systematic risk (i.e., the firm’s beta) changed, roughly half of
the firms in our sample experienced higher betas and half
saw lower betas post dividend initiation.
The application of this study is to suggest to corporations
dividend policy strategies which would reduce their cost of
capital, and to suggest to investors equity-screening strategies
which would increase their rate of return.
This work is especially timely as the sunset provision of
JGTRRA will restore the ordinary income treatment of
dividends after December 31, 2012, unless extended by
Congress, and corporations with huge market capitalizations
(specifically Apple and Google) are considering initiating
cash dividends.
INTRODUCTION
Companies decide to pay dividends for various reasons and
have a number of constraints on those payments. For
example, firms are precluded from retaining earnings
indefinitely; if they can’t find viable investments and
continue to retain earnings, they could face an accumulated
earnings tax. Thus, even high-growth companies should
eventually pay a dividend when their need for capital
diminishes. For companies paying dividends, or initiating
dividend payments, the amount of dividends paid to
shareholders might be limited by loan covenants that require
debt servicing and/or liquidation prior to dividend payments.
Standard textbook discussions of dividend theory examine
dividend decisions using various models. For example,
Graham, Smart, and Megginson (2010) discuss (1) the
agency cost/contracting model, (2) the signaling model, and
(3) the catering model of dividend payout. The agency
cost/contracting model recognizes conflicts that can arise
between a firm’s managers and shareholders. The model
explains the desirability of dividend payments as a way to
help resolve conflicts between managers who want to retain
earnings to fund perquisites and negative NPV (“pet”)
projects and shareholders who want to receive a portion of
the firm’s earnings in order to reduce agency costs.
The signaling model explains that dividends and share
repurchases are a way for management to “signal” the
marketplace that the firm is sufficiently “strong” to utilize
cash flow in this manner. Healy and Palepu (1988) find that
dividend-initiating firms experience higher earnings growth
than industry peers within the first year following initiation.
These higher earnings translate into abnormal returns for
investors. Their findings are consistent with the signaling
model, as the results indicate that investors can interpret
announcements of dividend initiations as managers’ forecasts
of future earnings changes.
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The catering theory suggests that firms will pay dividends
when shareholders are paying a premium for stocks that are
paying a dividend. Conversely, the theory suggests that firms
will not pay a dividend when shareholders are paying a
discount for shares that are paying a dividend. Baker and
Wurgler (2004a, 2004b) provide analysis and data that
support this more recent theory on dividend policy. While
research on the catering theory is more current, it is closely
related to what other finance textbooks term the “clientele
effect.” Brigham and Ehrhardt (2008) observe that different
groups of stockholders prefer different dividend payout
policies; for example, certain shareholder groups (clienteles)
may prefer higher payouts.
While catering theory appears to be one of the most recent
additions to the various theories on dividend policy, Graham,
Smart, and Megginson (p. 494) observe that “it does not
describe why dividends are paid in the first place.” The focus
of our research is not to explain why a firm decides to initiate
a dividend. Instead, we look at the impact dividend initiation
has on a firm’s risk profile using a combination of three
different risk metrics and a statistical/econometric
methodology that is not used in prior research looking at
dividend initiation, to the best of our knowledge. We look at
firms that initiated dividend payments shortly after the
passage of the Jobs and Growth Tax Recovery Reconciliation
Act of 2003 (JGTRRA). We find that a firm’s total risk is
reduced as a result of dividend initiation, which can be one
benefit of dividend initiation. However, we find
inconsistency in the impact to a firm’s systematic risk.
Because an investor is capable of diversifying away firm
specific risk but not systematic risk, changes in systematic
risk are more noteworthy.
RELEVANT RESEARCH
The scholars who first come to mind when you think about
seminal work on dividend policy are Lintner (1956) and
Modigliani and Miller (1958 and 1959). Lintner uses the term
“progressive, continuing partial adaptation” to describe
firms’ behavior to increase dividends by only a fraction of
what the firm’s current financial performance will support. If
we apply this conservative approach to dividend initiation,
then a firm’s management will only decide to initiate a
dividend when the free cash flow is sufficient to maintain
that dividend for the foreseeable future. This view is
consistent with signaling theory as management’s decision
about increasing the firm’s dividend conveys valuable
information to investors.
Modigliani and Miller (1958 and 1959) introduce two
historic irrelevancy propositions and argue that the value of
the firm is independent of capital structure and dividend
policy. Their papers apply, many have since argued,
unrealistic specific assumptions, such as, “…all capital
market participants, inside managers and outside investors
alike, have the same information about the firm’s cash flows”
(p. 105, Miller, 1988). One benefit of M&M’s work is that it
gives future researchers a reference point. Our research finds
that dividend initiation reduces total firm risk, and investors
can learn this information at the time that a firm announces
that it will begin paying a dividend. Thus, material,
asymmetric information about risk can exist prior to the
dividend initiation announcement.
Past researchers who have looked at the impact to risk from
dividend initiation include Venkatesh (1989) and Dyl and
Weigand (1998). Venkatesh is one of the first to look at the
volatility of returns after initiation and finds decreases in the
post-initiation volatility as measured by standard deviation.
He feels that the decrease is only in the firm-specific
volatility as opposed to systematic volatility, which he shows
by examining firm betas. He finds the betas to be stable while
the standard deviations are unstable. He explains that risk is
reduced post dividend initiation because investors can now
observe dividend declarations, not just earnings
announcements. Consequently, after initiation, investors give
less weight to information cues other than dividends and
earnings, whereas in the pre-dividend initiation period, they
may have reacted to other information more strongly.
Nearly ten years later, Dyl and Weigand find that dividend
initiation leads to a decrease in total risk and systematic risk
in the year following the dividend announcement. They view
a firm’s dividend initiation announcement as providing
investors valuable information about the firm’s risk and, thus,
term this the “risk information hypothesis.” According to this
view, the initiation of dividends is a signal to markets that a
firm’s earnings and cash flows have become fundamentally
less risky, and therefore beta is lower. Firms are expected to
have fewer surprises after dividend initiation. If the earnings
volatility decreases, then the volatility of returns will
decrease as well. The D&W study finds lower betas over the
three year period following dividend initiation. We believe
our research makes an important extension to Venkatesh’s
and D&W’s research as we consider changes to a firm’s risk
relative to the risk of the overall stock market. D&W only
looked at total and systematic risk.
Grullon, Michaely, and Swaminathan (2002) examine the
relationship between changes to dividends and changes in
systematic risk. While D&W and our research look at
dividend initiation, GM&S exclude companies that are
initiating dividends. Nevertheless, we believe it is noteworthy
to report that GM&S find that systematic risk declines around
the time of dividend increases. In contrast to the consistent
pattern of lower total and relative risk that we observe, we
find inconsistent changes to systematic risk in the case of
dividend initiation.
The focus of our research is to consider the impact of
dividend initiation to a firm’s risk. Looking at price behavior
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pre- and post-initiation and the wealth effect to shareholders
are logical extensions to our research. Jin (2000) finds that
there is an “apparent heterogeneity” in how stock prices react
to dividend initiation announcements. Her research observes
that there can be both benefits and costs to dividend
initiation. For example, initiation of dividends can help to
mitigate agency costs. In contrast, dividend initiation could
be a signal that management expects fewer growth
opportunities for the firm. A more extensive discussion of the
literature, which is beyond the score of this paper, finds that
the impact from dividend initiation to share price and
stockholders wealth is inconsistent. Yet, it is relevant to
mention these inconsistencies as our analysis of systematic
risk also produced inconsistent results.
Our research provides some new insights into the impact to
risk from dividend initiation, but it also adds empirical results
to the extant literature that are consistent with some and
inconsistent with other researchers’ findings. Moreover, our
study focuses on firms that initiated dividend payout soon
after the Jobs and Growth Tax Relief Reconciliation Act of
2003 (JGTRRA). We have not proven or asserted any
significance to the timing of firms’ decisions to pay
dividends to the JGTRRA. However, we reference the
JGTRRA as part of our timeline because our dataset contains
firms that implemented their dividend initiations shortly after
the passage of this act.
METHODOLOGY AND RESULTS
Our process began with an Internet search for firms which
had initiated cash dividends in the 2003–2004 time frame.
We found 25 such firms. We then obtained daily and monthly
stock prices from Yahoo! Finance. (Clayton, Jahera and
Schmidt (2008) has certified Yahoo! Finance as a data source
suitable for academic research.) That search resulted in seven
firms being dropped from our sample because they were no
longer publicly traded. Four firms were acquired by other
firms (American Power Conversion Corporation, Phelps
Dodge Corporation, Reebok International Limited, and
Tektronix), two firms were taken private (Clear Channel
Communications, Inc. and Harrah’s Entertainment), and one
firm was broken up and sold off (Cendant Corporation).
Table 1 shows our 18 sample firms.
We also obtained from Yahoo! Finance daily and monthly
S&P 500 Index prices, and daily and monthly 10-year
Treasury yields for our market and risk-free return measures,
respectively. The initial observation for each firm was the
first day that firm was publicly traded or January 1, 1997,
whichever date was later. Final observations were the last
day the firm was publicly traded or December 31, 2008,
whichever was earlier. This time frame brackets the dividend
initiation dates for our sample firms and avoids much of the
market turmoil of the recession. Dividend initiation dates
were obtained as part of the original Internet search.
We first tested whether each firm’s total risk decreased
following the initiation of dividends. From the stock prices,
which were adjusted for dividends and stock splits, stock
returns and variances were calculated. The hypotheses tested
are:
In words, the null hypothesis says that the risk of the firm
before dividend initiation is either the same as or less than the
risk of the firm after dividend initiation; the alternate
hypothesis says that the risk of the firm before dividend
initiation is greater than the risk of the firm after dividend
initiation. This is an F-test where the before/after variance
ratio for each firm is compared to a critical F value. The
critical F value is different for each firm as the degrees of
freedom vary with the number of before/after dividend
initiation observations.
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92
Table 2 presents the results of this hypothesis test using daily
data. Notice every firm exhibited a statistically significant
decrease in risk. Table 5 in the Appendix shows comparable
results using monthly data.
constructed by subtracting and adding a margin of error, E, to
the after and before risk ratios to obtain the LCLs and UCLs.
The endpoints of the confidence intervals are calculated as:
For clarity, the confidence interval method of hypothesis
testing is shown in Figure 1. We used a 95% confidence
coefficient to construct our confidence intervals. If the upper
limit of the post-dividend 95% confidence interval is less
than the lower limit of the pre-dividend 95% confidence
interval, we can reject the null hypothesis of the postdividend variance ratio being greater than or equal to the predividend variance ratio, meaning that the firm is less risky
relative to the market after dividend initiation.
We next test whether each firm’s total risk per unit of market
risk decreases following the initiation of dividends. As done
with the stock price data, we calculate market returns and
variances. This enabled us to then calculate the ratio of firmto-market variance for each firm before and after the
dividend initiation announcement. The hypotheses tested are:
In words, the null hypothesis says that the risk of the firm
relative to the market before dividend initiation is either
equal to or less than the risk of the firm relative to the market
after dividend initiation. The alternate hypothesis says that
the risk of the firm relative to the market before dividend
initiation is greater than the risk of the firm relative to the
market after dividend initiation.
Figure 1 – Confidence Interval Method of Hypothesis
Testing
Table 3 presents the results of this process using daily data.
For example, consider the first company, Analog Devices.
The firm’s LCLbefore (9.74) is greater than the UCLafter (6.59)
confirming that the confidence interval for relative risk prior
to dividend initiation is higher than the risk interval after
initiation. Note that all sample firms evidenced a statistically
significant decrease in relative risk. Table 6 in the Appendix
shows comparable results using monthly data.
The second set of hypotheses is tested using the confidence
interval approach. The endpoints of the confidence intervals
are denoted as LCL for the lower confidence limit and UCL
for the upper confidence limit. The confidence intervals are
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93
positive alpha means a stock is under priced; a negative alpha
means a stock is overpriced.
In detail, our dummy variable regression model is:
The above discussion and the first two sets of hypotheses
tested pertain to total risk. However, the relevant risk
measure for investors is systematic risk, the risk that remains
after an investor’s portfolio is fully diversified. One measure
of systematic risk is the Capital Asset Pricing Model’s
(CAPM) beta measure. Beta is calculated as the slope of a
linear regression where the dependent variable is the excess
return of the stock and the independent variable is the excess
return of the market. Excess returns are returns which have
the risk-free rate of interest netted out. This regression line is
referred to as the Characteristic Line. The hypotheses tested
are:
Table 4 summarizes the results of the dummy variable
regression using daily data. Table 7, in the Appendix,
presents these results more completely. Notice that 13 of the
18 sample firms exhibited a statistically significant change in
systematic risk; six decreased and seven increased. Tables 8
and 9 in the Appendix show comparable results using
monthly data.
In words, the null hypothesis says that the stock’s systematic
risk is unchanged; the alternate hypothesis says that the
stock’s systematic risk has changed.
Changes in betas can be tested in a variety of ways such as a
difference of means, a change in the correlation coefficient,
and the Chow Test that compares sums of squares. We
utilized a dummy variable regression method since that
allowed us to test the intercept, alpha, as well as the slope,
beta, simultaneously. Nonzero alphas are especially
interesting to investors since they represent a “free lunch.” A
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94
trading days, different starting and ending days of the week,
and different seasons, and might involve different ends of
quarters and ends of years. Our results from the monthly data
in no way contradict the daily data results. They are merely
less significant.
We find that systematic risk, as quantified by beta, exhibited
a statistically significant change for 13 of our 18 sample
firms. It is somewhat perplexing that the change observed
was a nearly equal number of increases and decreases (seven
up, six down). To our knowledge this effect has not been
previously published. A priori it might be expected that beta
should decrease when a firm initiates dividends since that
initiation could be taken as a signal to investors that the firm
is confident enough in its future cash flows to commit
themselves to continuing dividend payments. However, the
initiation of dividends could alternatively be a signal that the
firm is moving from the growth phase to the maturity phase
of its lifecycle. The payment of dividends might mean that
the firm can no longer reinvest earnings in projects that
would earn more than their rate of return on equity.
SUMMARY AND CONCLUSIONS
The analysis above demonstrates quite conclusively that
initiating dividends reduces a firm’s total risk and affects its
systematic risk. These results are consistent with prior
research that examined other timeframes. However, we
believe that we are the first to examine these effects on a
relative basis. Just as we judge a firm’s stock performance
relative to other firms in its industry or to the market as a
whole, the riskiness of a firm’s return should be compared to
a suitable benchmark. Simply demonstrating a decrease in
risk following dividend initiation leaves open the possibility
that the market as a whole was becoming less volatile during
the period studied. This is in fact what occurred during the
period covered by our study.
Thus it becomes necessary to examine relative risk. We
confirm that the decrease in total risk caused by initiating
dividends is larger than the decrease in market risk that
occurred independently and concurrently. That relative
decrease was statistically significant in all 18 firms in our
sample using daily stock price data.
The changes in the risk measures that we studied—i.e.,
absolute risk, relative risk, and systematic risk—were all
stronger using daily data compared to monthly data. We
attribute this to the smaller sample sizes and to the additional
variability introduced by the somewhat arbitrary assigning of
months. Different months contain different numbers of
We posit that the change in beta caused by a dividend
initiation is affected by the timing of that initiation. If the
market feels that the timing is appropriate, beta will decrease.
If the market feels that the initiation is early or late, beta will
increase. Such an increase could reflect the market’s
judgment that sub-prime projects had been accepted or
additional fertile opportunities for diversification of earnings
sources were not being cultivated. This effect is an
opportunity for future research. An event study methodology
could be used to compare the change in beta, decrease or
increase, with subsequent changes in the firm’s earnings.
REFERENCES
Baker, Malcolm, and Jeffrey Wurgler. 2004a. “A Catering
Theory of Dividends,” Journal of Finance, vol. 59, no. 3
(June):1125–1165.
Baker, Malcolm, and Jeffrey Wurgler. 2004b. “Appearing
and Disappearing Dividends: The Link to Catering
Incentives,” Journal of Financial Economics, vol. 73, no. 2
(August):271–288.
Brigham, Eugene F. and Michael C. Ehrhardt. 2008.
Financial Management: Theory and Practice. 12th ed.
Mason, OH: Thomson Higher Education.
Clayton Ronnie J., John S. Jahera, Jr., and Bill Schmidt.
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THE BUREAU OF MOTOR FUEL TAXES COMPLIANCE STRATEGY:
PENNSYLVANIA DEPARTMENT OF REVENUE
Thomas O. Armstrong*
Daniel Meuser
James Dehnert
Nic Banting
Commonwealth of Pennsylvania
Department of Revenue, Harrisburg, PA 17128
ABSTRACT
In August 2011, the Pennsylvania Governor’s
Transportation Advisory Commission’s Final
Report stated that Pennsylvania’s transportation
infrastructure is in need of repair and
reinvestment. The Motor License Fund provides
state financial resources for the transportation
infrastructure,
primarily
for
the
Commonwealth’s highway program. The Fund’s
revenues consist of fuel taxes, license and
registration fees, fines and penalities. The Motor
License Fund, budgeted at $2.4 billion for FY
2012-13, is projected a nominal increase on
average for five years of only 1.05%, where the
Report argues for initiatives to enhance Motor
License Fund revenue collections.
Initiatives for improving tax compliance which
enhance revenues without increasing the current
tax burden on compliant taxpayers can advance
economic efficiency. The Bureau of Motor Fuel
Taxes has proposed the Compliance Strategy
which
will
provide
the
technological
infrastructure to increase revenue, reduce costs,
and advance tax adminstration of the
Commonwealth’s motor fuel laws. The Strategy
focuses on automation of tax registration,
processing and risk management, while
educating the taxpayer base of their
responsibilities. The Strategy, consisting of 11
components, is projected to take about five years
to implement with a significant return on
investment. Evasion of motor fuel taxes is an
economic
problem
necessitating
the
implementation of the Compliance Strategy to
reduce evasion and provide added revenue to
meet on-going transportation challenges.
I.
INTRODUCTION
The optimal taxation literature argues that tax
reform is of critical economic importance where
one of its objectives is tax simplicity
(Armstrong, 2002).
Tax simplicity is the
constructing of tax systems with relatively low
compliance costs and administrative burdens
which elicit a high degree of voluntary
compliance, therefore a low degree of tax
evasion (Armstrong and Brehouse, 2001).
Motor fuels tax evasion occurs when businesses
deliberately fail to comply with their tax
obligations (Franzoni, 1999). Fuel tax evasion
becomes greater due to the complexity of the
fuel distribution system and differences in state
taxes imposed upon fuel. The greater the tax
evasion the less financial resoures supporting
Pennsylvania’s transporation infrastructure in
need of repair and reinvestment (Transportation
Funding Advisory Commission, August 2011).
Generally, the motor fuel tax is an indirect tax
that is collected from citizens by vendors selling
or distributing the fuel. From Denison and Eger
(March/April 2000), most fuel tax evasion
occurs after the tax is collected from individuals
and before the tax is remitted to the state.
Figure 1 provides the key elements of the
petroleum process. Crude petroleum is produced
domestically or imported to the refinery.
Pennsylvania has minor reserves of crude oil.
Although Pennsylvania is credited with drilling
the first commercial oil well in 1859, the State’s
current production is minimal, with output
derived primarily from stripper wells that
produce less than 10 barrels per day (Department
of Community and Economic Development,
October 22, 2009).
Most petroleum is imported into Pennsylvania
which processes the crude into diesel and
gasoline products. Pennsylvania is the leading
petroleum refining State in the Northeast.
Pennsylvania’s large-scale petroleum refineries
are located along the Delaware River near
Philadelphia and process crude oil shipped from
overseas. These refineries supply the Northeast
markets.
Proceedings of the 2012 Pennsylvania Economic Association Conference
101
Figure 1: Petroleum Distribution System
Domestically Produced
Crude Oil
Imported Crude Oil
Refinery
Imported
Fuel
Bulk Terminals at
the "rack"
Exported
Fuel
Distributor
Wholesaler
Bulk Stations
Retailer
Consumers:
Private, Public
Source: Denison and Eger (March/April 2000)
The rack is the point of first distibution of the
refined fuel. The rack is commonly associated
with the terminals of the manufacturer, refiner,
or producers. The rack is a mechanism of
delivering fuel into a means of transport other
than by pipeline or vessel. From the rack, the
refined petroleum is sold to the distributor
wholesaler or exported. Wholesalers sell fuel to
retailers where it is sold to consumers.
The fuel tax can be levied at several points in the
distribution process, with the tax incidence being
passed down the chain to the end consumer
(Denison and Eger, March/April 2000). Fuel tax
is generally collected and reported at one of three
points in the distribution chain: terminal
rack/import, wholesale level, and retail level.
The fuel tax is administered like a sales tax
where the wholesaler in Pennsylvania is
responsible for collecting and remitting the tax to
the government. Pennsylvania’s liquid fuels and
fuels tax rates are the gas tax at 31.2 cents per
gallon and diesel tax at 38.1 cents per gallon.
For fuel taxes, there are users who are exempt
from tax, and there are specific uses that are
exempt.
For Pennsylvania, there are tax
exemptions for political subdivisions when such
liquid fuels and fuels are delivered to this
Commonwealth, a political subdivision, a
volunteer fire company, a volunteer ambulance
service, a volunteer rescue squad, a second class
county port authority or a nonpublic school not
operated for profit (75 Pa.C.S. § 9004(e)(4)). A
political subdivision is any county, city,
borough, incorporated town, township, school
district, vocational school district and county
institution district (1 Pa.C.S. § 1991).
As the point of taxation moves up the
distribution chain, fuel tax exemption becomes
more difficult to administer where refunds
become more common for tax exempt fuel tax
users (Weimer, et.al., 2008).
Eliminating
refunds for exempt uses substantially reduces the
potential for refund fraud.
Federal motor fuel taxes are collected at the rack.
The IRS provides Terminal Control Numbers
(TCN), if a terminal is registered as a part of the
taxable fuel bulk delivery system. Currently,
Pennsylvania has 700 registered wholesale
distributor licensees and 68 registered TCNs
(racks).
Over the years, the point of taxation has moved
up the distribution chain where the Federation of
Tax Administrators (FTA) as of January 27,
2012, reported 23 states tax at the rack or first
receiver below the rack.1 By imposing the tax at
a higher point in the distribution chain offers a
potential for decreasing fraud, mainly by
reducing the number of taxpayers. Loss of
motor fuel tax represents a significant loss of
funding to every state, especially since each state
Proceedings of the 2012 Pennsylvania Economic Association Conference
102
receives a share of federal-aid highway dollars
based on their consumption.
in Pennsylvania (federal tax 24.4 cents plus PA
state tax 38.1 cents), this could mean $5,000
additional profit to the distributor.
To understand the magnitude and significance of
the evasion, it is necessary to understand the
profit collected by the distributor. The profit on
a gallon of fuel is usually a few cents. If taxes
can be evaded, the profit can be as much as 45
cents per gallon higher (24.4 cents federal diesel
tax per gallon plus 20 cents average State tax).
Thus, one truckload of fuel, 8,000 gallons, could
potentially yield about $3,600 in additional
profits if both Federal and State diesel taxes are
evaded (45 cents x 8,000 gallons). As mentioned
in Pennsylvania, the tax per gallon of diesel is
38.1 cents and 31.2 cents per gallon of gasoline.
Considering 62.5 cents diesel fuel tax per gallon
Tax evasion incentives increase when states have
differential tax rates. Table 1 provides gas and
diesel tax rates of Pennsylvania and surrounding
states. The Table shows Pennsylvania’s fuel
rates are higher than the surrounding states. This
results in Pennsylvania having greater difficulty
to track fuel to its ultimate destination into the
Commonwealth where the fuel would be taxed in
one state but evaded for payment to
Pennsylvania. Better tracking movements of
fuel using electronic filing and payments will
reduce evasion and enhance compliance.
TABLE 1: STATE MOTOR FUEL TAX RATES
State
Gasoline*
Diesel Fuel*
Delaware
23.00
22.00
Maryland
23.50
24.25
New Jersey
14.50
17.50
New York
25.80
24.05
Ohio
28.00
28.00
Pennsylvania
31.20
38.10
Virginia
17.50
17.50
West Virginia
33.40
33.40
* Federation of Tax Administrators, January 2012.
The purpose of the paper is to provide
information on the proposed Bureau of Motor
Fuel Taxes Compliance Strategy.
The
Compliance Strategy consists of 11 components.
As each component is implemented, improved
tax compliance will enhance motor fuel revenues
over and above the nominal average increase of
1.5 percent over five years; thereby, helping to
support Pennsylvania’s transportation resource
needs. Section 2 provides a brief background of
the Bureau of Motor Fuel Taxes. Section 3
details the 11 components of the Compliance
Strategy where some components can be
implemented administratively while other
components
require
legislative
statutory
authority. The final Section concludes the paper.
II.
BUREAU OF MOTOR FUEL
TAXES
From Armstrong and Meuser (2011), the Bureau
of Motor Fuel Taxes is responsible for licensing
and bonding activities for applicants within
registration guidelines for the various Motor
Fuels and Motor Carrier Road Tax/International
Fuel Tax Agreement (IFTA).2 Furthermore, the
Bureau directs the collection programs for
delinquent accounts for these various taxes. In
addition,
the
Bureau
conducts
investigations/cursory audits of suspect files;
files criminal complaints for violations of tax
acts; and issues and files citations for infractions
of tax laws. The Bureau is budgeted to collect
$2.4 billion for FY 2012-13 for the Motor
License Fund--one of the Commonwealth’s
special funds.
The Liquid Fuels and Fuels Tax, the state’s
excise tax, is a permanent trust fund tax of 12
cents per gallon or a fractional part is imposed on
all liquid fuels and fuels used or sold and
Proceedings of the 2012 Pennsylvania Economic Association Conference
103
delivered by distributors in Pennsylvania. The
tax is imposed on the ultimate consumer, but the
distributor is liable for collecting and remitting
the tax. Payments and reports are due on or
before the 20th of the month following the month
of tax collection. Returns filed annually are
approximately 7,000 by paper (80%), e-TIDES
(18%), or Telefile (2%).3 Payment for the
Liquid Fuels and Fuels Tax are by credit card
(2%), EFT (80%), or by paper check (18%).4
The Commonwealth received for FY 2010 was
about $752 million.
On the same tax return as the state excise tax, the
Oil Company Franchise Tax is imposed on all
taxable liquid fuels and fuels on a cents-pergallon equivalent basis and is remitted by
distributors of liquid fuels and fuels. The rate is
19.2 cents-per-gallon on all liquid fuels and 26.1
cents-per-gallon on all fuels used or sold and
delivered
by
distributors
within
the
Commonwealth. Payments and reports are due
from distributors on or before the 20th of the
month following the month of fuel sales.
Returns filed annually were about 750 by paper
(75%), e-TIDES (23%), or Telefile (2%).
Payment for the Oil Company Franchise Tax is
by credit card (2%), EFT (80%) or by paper
check (18%). The Commonwealth received for
FY 2010 was about $1.3 billion.
The Alternative Fuels Tax is imposed on
alternative fuels used to propel vehicles on
public highways.
Alternative fuels include
natural gas, compressed natural gas, liquid
propane gas and liquefied petroleum gas,
alcohols, gasoline-alcohol mixtures containing at
least 85% alcohol by volume, hydrogen,
hythane, electricity, and any other fuel not
taxable as liquid fuels or fuels. Each alternative
fuel is converted to a gasoline gallon equivalent
where the basis of this conversion is statutorily
set at 114,500 Btu. The tax rate applied to the
gasoline gallon equivalent equals the current
liquid fuels tax and oil company franchise tax
applicable to one gallon of gasoline.5 Alternative
fuels dealer-users are required to remit this tax.
Reports and payments are due on or before the
20th of each month for fuel sold or used in the
preceding month. Returns filed annually are
approximately 960 for by paper (100%).
Payment for the Alternative Fuels Tax is by EFT
(80%) or by paper check (20%).
The
Commonwealth received for FY 2010 was about
$550,000.
Pennsylvania joined the International Fuel Tax
Agreement (IFTA) effective January 1, 1996.
This agreement provides for base state reporting
of fuel taxes for operators of qualified motor
vehicles used in interstate operations.6 Qualified
motor vehicles operated in Pennsylvania
intrastate activities only are subject to fuel
taxation under the Motor Fuels Road Tax. The
Motor Fuels Road Tax is equivalent to the rate
per gallon in effect on liquid fuels, fuels, or
alternative fuels, plus an oil company franchise
tax component. IFTA payments and reports are
due on or before the last day of April, July,
October, and January for the quarter ending the
last day of the preceding month. Motor Carriers
Road Tax reports are filed annually. Almost
67,000 returns are filed annually by paper
(100%). Payment is by EFT (10%) or by paper
check (90%). The Commonwealth received
revenues of over $42 million for 2010.
Table 2 provides the Motor License Fund five
year projections. For the current fiscal year, the
FY 2012-13 is budgeted at $2.4 billion and is
projected a nominal increase on average for five
years of only 1.05%.
Table 2: Motor License Fund Five Year Revenue Projections*
Fiscal Year
Revenues
2012-13 budget
2,433,560,000
2013-14 estimated
2,474,680,000
2014-15 estimated
2,493,140,000
2015-16 estimated
2,512,100,000
2016-17 estimated
2,537,620,000
Average Annual
1.05%
*Governor's Executive Budget 2012-2013, C2.6
Proceedings of the 2012 Pennsylvania Economic Association Conference
104
In August 2011, the Pennsylvania Governor’s
Transportation Advisory Commission’s Final
Report stated that Pennsylvania’s transportation
infrastructure is in need of repair and
reinvestment where projected increases over time
fall short of funding requirements. The Report
argues for initiatives to enhance Motor License
Fund revenue collections. The Bureau of Motor
Fuel Taxes has proposed the Compliance
Strategy which will provide the technological
infrastructure to increase revenue, reduce costs,
and advance tax adminstration of the
Commonwealth’s motor fuel laws.
III.
of staff reductions and compliance service and
enforcement enhancements, revenue agencies
have considerable incentives to automate these
processes through greater use of technology.
From the Organisation for Economic Cooperation and Development, OECD, (January 28,
2009), the key benefits of accelerating the slow
pace of technology innovations are the
following:
•
•
•
•
•
COMPLIANCE STRATEGY
For Pennsylvania to maximize receipt of motor
fuel taxes, the Bureau of Motor Fuel Taxes
Compliance Strategy has been developed to
enhance enforcement resources and use those
resources more efficiently.
The underlying
theme of the strategy is to compile data
electronically into a single data source so that
instantaneous verification and validation that
taxes have been paid can occur. Paper-based
processes associated with tax returns and
payments processing have consumed significant
usage of a revenue agency resources (Armstrong
and Meuser, 2012). With an economic climate
Faster collection of government revenue
Improved data accuracy and elimination
of reverse workflows
Reduced paperwork for taxpayers
Faster crediting of tax refunds
Faster capture of taxpayer data for a
range of administrative purposes.
The data currently required by the Bureau of
Motor Fuel Taxes due to technology and process
limitations is not to be maximized for tax
collection efforts. Therefore, a compliance gap
will exist for fuel taxes where, on average, it is
estimated at six percent at all state levels.7
Figure 2 below provides a description of the fuel
landscape to verify purchases and cross match
into a single data source.
Figure 2: Complete Verification of Fuel Landscape
Distributor
Sales
Distributor
Purchases
Other States
Imports
The fuel landscape, shown in Figure 2,
highlights all the sources of fuel transactions to
which data can be verified. The flow of fuel
from the terminals at the rack to retail, in-state to
out-of-state, from data reported to data gathered
through inspection and investigation.
The
sources of fuel transaction can be linked to
Figure 1, the Petroleum Distribution System.
The
Compliance
Strategy
focuses
on
consolidating this data into a single data source
for detailed analysis.
Transporter
Report
Single Data
Source
FuelCAP
Case
Terminal
Operator
Reports
Fuel transactions can be traced from the terminal
(rack) all the way to consumption using the BOL
(Bill of Lading) number. The BOL number
remains consistent no matter who handles the
fuel. The goal of the compliance strategy is to
capture the fuel transactions from all those that
handle the fuel and then reconcile their records.
For instance, a load of fuel will be reported on a
distributors disbursement schedule when it is
sold, the same load will be reported on the
buyers receipt schedule and then again on the
transporters monthly report. By capturing and
Proceedings of the 2012 Pennsylvania Economic Association Conference
105
storing electronically all this data it will make it
easier to identify unreported loads of fuel.
Ultimately the goal is to increase the Motor Fund
annual revenue by cost reductions which are
easily measurable and will be defined throughout
the document or revenue increases which can be
realized by several methods and approaches:
•
•
•
•
•
•
•
•
Increase compliant revenue
Collect outstanding delinquent
liabilities
Identify true delinquencies through
accurate audit and examination
Reduce administrative costs
Reduce enforcement costs
Reduce the number of cases that go
through the appeal/legal process
Reduce the number of taxpayers
Preventative enforcement
The Motor Fuel Tax Compliance Strategy is
comprised of 11 components each designed to
lower administrative costs or increase motor
fuels revenue:
A.
Mandate Liquid Fuels and Fuels Tax EFile
B.
Distributor Classification Clarification
C.
Reduce Mandatory E- Payment
Threshold from $20,000 to $500
D.
Increase LFFT Bond Based on
Taxpayer Risk
E.
Institute IFTA bonding based on
Taxpayer Risk
F.
Operation AAA (Avoid Audit
Assessment)
G.
Automated LFFT Audits/Examination CROSS-MATCHING
H.
Mandating E-File Transporter Reports
I.
Mandating IFTA E-File
J.
Online licensing – LFFT, IFTA,
Transporters, Alt Fuels (includes refund
applications)
K.
Move Point of Taxation to the rack
Below is a brief description of each of the
compliance strategies as proposed by the
Department of Revenue’s Bureau of Motor Fuel
Taxes.
A. Mandate Liquid Fuels and Fuels Tax
E-file
One of the methods to evade motor fuel taxes is
the filing of false information. Paper filing is not
only costly to administer, but is more difficult to
detect fuel tax evasion due to false information
provisions. Currently, approximately 30% out of
the 700 required monthly filers utilize the
Department’s E-Tides filing system.
This
mandate would take that number 100%. It
would no longer be acceptable for taxpayers to
file paper tax returns that often exceed thousands
of pages in transactions. Many of the large
distributors have avoided sending their tax
returns electronically to Pennsylvania despite
doing the same for the feds and other states. It is
unlikely to cause resistance but merely close a
loophole for the Bureau. The data would be
uploaded to the Department and then easily
accessible to the Department’s examiners and
auditors. Weimer, et.al., (2008) report that many
states have moved to require all fuel taxpayers
file their returns electronically.
Currently the business process does not allow the
release of the necessary funds from Revenue to
the Pennsylvania Department of Transportation
(PennDOT) until the tax return and payment are
processed.
This can be lengthy when an
electronic payment is made but the paper tax
return is waiting to be processed. This process
can take approximately 30 days; however, if both
electronic payment and electronic tax return are
received timely this would only take 48 hours.
All Liquid Fuel and Fuels Tax (LFFT)
transactional tax data will now be electronically
captured, detailed line items will be available to
easily examine and queried. This will now give
the Bureau of Motor Fuel Taxes the platform by
which to provide detailed analysis while
increasing the efficiency of our auditors and
examiners. In addition, this may also cause
voluntary compliance revenue increases under
the premise that all data is now instantly
accessible to auditors and data accuracy can now
be verified on a large scale.
There is a projected $100,000 per year cost
savings as a result of:
•
•
•
•
•
Identify true delinquencies through
accurate audit and examination,
Reduce administrative costs,
Reduce enforcement costs,
Reduce the number of cases that go
through the appeal/legal process,
Preventative enforcement,
Proceedings of the 2012 Pennsylvania Economic Association Conference
106
•
•
Accurate PennDOT cost analysis, and
Reduce audit costs.
B. Distributor Classification
Clarification
One method of reducing tax evasion is to reduce
the number of taxpayers responsible for
remitting the tax which permits more thorough
audits without increasing auditing resources
(Denison and Eger, March/April 2000). There
are approximately 600 class 1-5 distributors.
These companies are permitted to buy tax free
fuel and sell to other registered distributors. Tax
evasion most frequently occurs when a
distributor purchases tax free fuel and collects
the tax on the sale but fails to accurately report it
to the department. If the current classification
language was clarified and rewritten to exclude
many unqualified existing companies; then by
having less companies in this category, tax
evasion can be reduced.
The form that determines Distributor eligibility
is Rev 543 and has not been revised since 2004.
This form is policy set forth by the Secretary of
Revenue. If the department adopted a less
subjective approach to issuing class 1 licenses
and reduced number of filers by 20%, the risk of
tax evasion would be significantly lowered. The
Bureau conducts approximately 100 pre-license
investigations each year on new companies
applying for a license. It is estimated that each
pre-license investigation cost’s $1,000 in
enforcement and administrative resources.
Existing guidelines for Distributor classification
are ambiguous and lead to companies filing
multiple applications and cause an unnecessarily
long licensing process and increased costs.
Taxpayer service and resolution is also estimated
at $1,000 per year in resource costs. By reducing
the number of filer’s employees would spend
less time servicing, examining and auditing these
companies. The auditing department would
increase its percentage of filers that are audited
each year.
If accounts were reduced from 600 to 500 the
department would realize $100,000 per year cost
savings as a result of:
•
•
•
Reduction in number of taxpayers,
Identify true delinquencies through
accurate audit and examination,
Reduce administrative costs,
•
•
•
Reduce enforcement costs,
Reduce the number of cases that go
through the appeal/legal process, and
Preventative enforcement.
C. Reduce Mandatory E-Payment
Threshold from $20,000 to $500
As stated previously, the Department of Revenue
only releases the funds to PennDOT when both
the tax return and payment have been processed.
Paper checks take longer to process and can
often delay the LFFT funding process. Many
hours are spent researching illegible and
incomplete checks. Checks sent in error often
require taxpayer calls and hours wasted with
resubmission processes. In addition, the risk of
payments being lost, moved or incorrectly
applied is greatly reduced if the payments are
electronic. The cost of storage and processing is
lower with electronic payments than paper
checks.
This initiative will reduce mandatory electronic
payment threshold from $20,000 to $500.
Currently, about 70% of LFFT payments are
made via EFT (ACD Debit or ACH Credit) – by
moving this threshold it would be far closer to
100%. The result is almost 100% of payments
would be processed 48 hours after the timely due
date. The projected savings is about $5,000 per
year as the result of the following:
•
•
•
Identify true delinquencies through
accurate audit and examination,
Reduce administrative costs, and
Reduce enforcement costs.
D. LFFT - Increase Bond Based on
Taxpayer Risk
States impose bonding requirements on the
wholesaler to provide additional protection
against fuel tax fraud.
Bonding enhances
protection where bonds in most states are at least
equal to the value of three months of their tax
remittance due. As mentioned by Denison and
Eger (March/April 2000), three months is
ordinary sufficient time for detection of fradulent
activity.
In Pennsylvania, a bond amount is calculated by
taking the number of gallons sold x tax rate x
1.5. For new companies estimated gallons are
used and then recalculated each March. In the
Proceedings of the 2012 Pennsylvania Economic Association Conference
107
event that a distributor defaults on a tax payment
or is caught in fraudulent activity, under
common practice it takes a minimum of three
months before a license is suspended or
cancelled. By changing the bond calculation
from 1.5 to 3, the Commonwealth’s interests are
further protected.
Now, taxpayer risk can be defined as likelihood
that the taxpayer will result in an account
receivable due to inability to pay or tax evasion.
High risk accounts share consistent traits and can
usually be identified at the time of licensing.8
Only companies with low risk of evasion or
default will have the privilege of buying tax free
fuel. This will reduce the opportunities for
companies to evade tax by paying their tax owed
upon purchase.
E. IFTA – Institute Bonding Based on
Taxpayer Risk
Unlike LFFT, bonding is not currently
implemented for the IFTA; however, the statute
allows for it. Currently, a loophole in the law
allows a company to run up liabilities under their
Employee Identification Number (EIN), refuse to
pay, and then reopen a new account under the
same name, owner, address but different EIN –
typically a wife’s, child’s or other relative.
Qualified accounts and those who refuse to
adhere to the laws would be identified as high
risk; thereby, requiring a bond based on taxpayer
risk. The Commonwealth’s interests would be
protected by the number of dubious accounts
being reduced. If a company becomes fraudulent
and unresponsive, the bond will be recalled to
cover outstanding tax liabilities. If no bonds are
instituted, the Commonwealth continues to run
the risk of evasion and increased accounts
receivables.
This concept would not change the licensing
process for all filers; however if it was
discovered upon licensing that the owner,
address, or company has a previous liability with
the Bureau, a bond would be exercised due to the
obvious risk. Further risk calculations could
include fleet size, non-compliance with other
states, record keeping compliance and
outstanding liens against owners.
The expected increase in revenue is projected at
$1 million per year attributed to:
•
•
•
•
•
•
Identify true delinquencies through
accurate audit and examination,
Reduce administrative costs,
Reduce enforcement costs,
Reduce the number of cases that go
through the appeal/legal process,
Preventative enforcement, and
Reduce accounts receivables.
F. Operation AAA (Avoid Audit
Assessment - Enforcement &
Education)
Nagan (1990) proposes that one of the methods
to reduce tax noncompliance is to increase
visibility. Increasing visibility can occur with
enhancing education and outreach to motor fuel
taxpayers.
Currently, there are 1,200 new
accounts being created and 1,200 accounts
cancelled each year. It is very important to
ensure that all tax records are accurately kept by
all those susceptible to audit. Information needs
to be provided to taxpayers including what data
is required and when it must be filed. Many of
the audit findings are reoccurring and can be
avoided
with
effective
education
and
enforcement by the Bureau of Motor Fuel Taxes
staff.
The purpose of Operation AAA is to educate
high audit risk accounts and heavily enforce the
records keeping requirements of the Bureau.
From the Bureau’s perspective, understanding
the taxpayer base means that the Bureau will
have to condense and simplify our record
keeping requirement instructions. In addition,
Motor Fuels must also have a comprehensive
system of calling and visiting high audit risk
accounts. The final piece is visiting already
audited companies to ensure compliance to avoid
repeat audit.
At present in excess of 95% of the IFTA
accounts receivable listing is a result of IFTA
audits.
By reducing the IFTA accounts
receivables, the Bureau will be able to allocate
more time to preventative enforcement.
Furthermore, by showing a history of education
and enforcement with the taxpayer if the audit
was to come under appeal, there would be
opportunity for the company to claim they were
not
properly
informed
of
their
tax
responsibilities.
Proceedings of the 2012 Pennsylvania Economic Association Conference
108
Operation AAA is expected to gain about
$150,000 per year as the result of the following:
•
•
•
•
•
Identify true delinquencies through
accurate audit and examination,
Reduce the number of cases that go
through the appeal/legal process,
Preventative enforcement,
Reduce accounts receivables, and
Citation Issuance.
G. Automated LFFT Computer
Audits/Examination CROSSMATCHING
Weimer, et. al., (2008) report that most states
agree that all information should be filed
electronically for cross checking of fuel reports.
Currently, less than 1% of transactions are
verified between distributors on a monthly basis.
By moving towards an automated crossmatch
system, all transactional disparities will be
instantly recognizable.
This will not only
generate accurate liabilities but will indirectly
increase the accuracy of filers who will soon
learn that every transaction is being scrutinized.
This component exponentially increases our
ability to identify that all taxes have been paid.
It will give a holistic view of all the fuel
transactions in Pennsylvania that can be verified
by independent sources.
This is the single most important element of the
Compliance Strategy. This is contingent on
100% LFFT E-file (A) and 100% Transporter Efile (I) and is not possible until that has been
completed.
Once the tax data is stored
electronically, three main types of evasion can be
investigated on an automatic and uniform level:
1.
2.
3.
Sales between distributors can be
reconciled, i.e. receipt on one taxpayer
schedule is a disbursement on another
(basically an audit).
Imports can be reconciled with the
schedule data received from other
states.9
Data gathered at the retail level via Fuel
Composition and Accountability
Program (FuelCAP) cases thereby can
be reconciled against the distributor
sales.
According to a leading fuel tax system company
(ACS) which implements a comparable system
‘Vista’;
“The Michigan Department of Treasury
implemented VISTA/FT in 2003 with the goal of
increasing motor fuel tax collections by $4M.
The system provided the state with tools which
enabled them to exceed their prediction of
collection by more than $20M in increased
revenues. Their 2004 goal was $12.1M and they
realized more than $20M in increased
collections, and an additional $11M in
compliance improvements. Michigan’s published
results include increased revenue of $32.5M in
the first year of operation, with an additional
$15.9M obtained in the second year. As of the
end of 2009 Michigan’s Return on Investment
(ROI) has been well over $25 to $1.”
Once implemented, it is expected to generate
over $50 million over 2 years. This is the result
of:
•
•
•
•
•
•
•
•
•
Increase in compliant revenue,
Increase delinquent collections,
Reduce accounts receivable,
Identify true delinquencies through
accurate audit and examination,
Reduce administrative costs,
Reduce enforcement costs,
Reduce the number of cases that go
through the appeal/legal process,
Preventative enforcement, and
Reduce audit costs.
H. Mandate E-file IFTA
IFTA requires trucks operating in interstate
commerce to report their mileage in all member
jurisdictions to their home state or province
(hereafter referred to as states). They also report
all tax paid on fuel by state. The tax due to each
state is calculated and compared to the tax paid
to each state and appropriate payments or
refunds by state are made (Weimer, et. al., 2008).
With approximately 11,000 filers, IFTA is the
larger of the two taxpayer bases for Bureau
administration
responsibility.
The
administration process is 100% paper filing and
paper payments.
This initiative would
implement a new submissions system and
process enabling the user to input the states
traveled in, the miles and the mpg and tax due
would be calculated. The system would also
enable online payment through ACH debit,
credit or credit card. If a refund was due then it
Proceedings of the 2012 Pennsylvania Economic Association Conference
109
could be direct deposited into the taxpayer’s
account.
IFTA only contributes less than 2% of the
overall income for the Bureau however it absorbs
approximately
70%
of
the
Bureau’s
administrative and enforcement resources. By
moving towards electronic submission many of
these resources could be deployed towards
Liquid Fuels and Fuels Tax operations which
contribute over 97% of the Bureau’s revenue
source. Mandating e-filing of IFTA returns and
payments would reduce administration and
enforcement costs and provide accurate data for
reporting, thereby automated examination and
audits would then be possible. A significant
stakeholder outreach program would be
necessary to ensure accurate compliance.
The expected increase in revenues as the result
of the e-file mandate corresponding with an
electronic submission platform is about $100,000
per year. The expected increase can be attributed
to the following:
•
•
•
•
•
•
•
•
Reduce enforcement costs,
Reduce administrative costs,
Identify true delinquencies through
accurate audit and examination,
Reduce the number of cases that go
through the appeal/legal process,
Preventative enforcement,
Reduce accounts receivables,
Citation Issuance,
Increase compliant revenue, and
Increase delinquent collections.
I.
Mandate E-File Transporter Reports
•
There are approximately 600 Fuel Transporters
registered in Pennsylvania. These reports list all
the fuel transported in and out of Pennsylvania
listing both the buyer and seller. Many of
department’s resources are consumed by the
manual administrative processes ensuring that
the 600 companies file on time and keep their
account information up to date. After the
mandate is complete and automation is deployed,
the resources can be focused on analyzing data
discrepancies.
This is often the missing link in many fuel tax
investigations. The line item data is not currently
captured however would be essential to the
FuelCAP program, computerized audits, and
examination. FuelCAP (Fuel Accountability and
Composition) program is an enforcement tool
whereby the Bureau verifies timely and accurate
submission of liquid fuel and fuel taxes in order
to prevent evasion schemes and the associated
resources needed in the recovery. This is
achieved by gathering raw data at the retail/end
user levels and following the physical trail of
fuel back to suppliers to form a complete picture
of all associated transactions.
By mandating E-filing transporter reports, this
data source would be used to reconcile fuel tax
transactions to ensure the tax has been paid. By
capturing all this data electronically and
combined with component ‘G’ this will now
become a key data source for independent
verification that taxes were paid. The result of
this initiative is an expected increase in revenues
of about $500,000 per year. The gains would
include the following:
•
•
•
•
•
•
•
•
•
J.
Reduce enforcement costs,
Reduce administrative costs,
Identify true delinquencies through
accurate audit and examination,
Reduce the number of cases that go
through the appeal/legal process,
Preventative enforcement,
Reduce accounts receivables,
Citation Issuance,
Increase compliant revenue, and
Increase delinquent collections.
Online Licensing (LFFT, ALT Fuels,
Transporters and IFTA)
This component of the Compliance Strategy
would consist of a public facing interface
whereby the taxpayer can fill in an application
for a license or refund online for Liquid Fuel and
Fuels Tax (LFFT), Alternative Fuels (ALT
Fuels), transporters and IFTA. Currently, 100%
of applications for licenses and refunds are still
received as paper forms. If this data would be
received in an electronic format, security and
completeness of checks can be automated. Error
checks would ensure that data is valid therefore
increasing the accuracy of enforcement efforts.
In addition, not only would the department be
able to reduce costs via automation, the taxpayer
would see the turnaround time for a refund or a
license application reduced.
Clerical and
administrative resources can be reassigned to
Proceedings of the 2012 Pennsylvania Economic Association Conference
110
more in depth analysis of the newly captured
data. Clean, fast accurate data – fewer wasted
mailings, expedited license issuance and
increased customer service.
and the oil company franchise tax,
which at the present time is 19.2 cents
per gallon.10
•
It is expected once the online licensing
component
has
been
implemented,
approximately $100,000 per year is expected in
increased revenues. In addition, other additional
impacts are the following:
•
•
•
Reduce administrative costs,
Reduce enforcement costs, and
Increase delinquent collections.
K. Move Point of Taxation to the Rack
The incentive for tax evasion increases when
competitors can undercut prices and attract
customers from compliant competitors. This tax
evasion is more likely to occur when fuel tax is
levied at the wholesale distribution or retail level
(Denison and Egar, March/April 2000). The
Department of Revenue collects taxes at the
wholesale distributor level. The point of taxation
for both liquid fuels and fuels is on the sale of
product from a registered distributor to a nonregistered distributor. Imposing the liquid fuel
tax at a higher point in the distribution chain, to
the terminal, or “rack” as it is commonly referred
to, will offer the potential of reducing instances
of fraud, mainly by decreasing the number of
taxpayers to collect fuel taxes by the Bureau.
Weimer, et. al. (2008) mentions taxing at the
terminal rack is generally widely accepted as one
key measure a government can take towards
increasing motor fuel excise compliance.
This component of the legislative initiative
would move the liquid fuel and fuels point of
taxation from the registered distributor level to
the terminal, or rack, which is a higher point in
the fuel distribution system. The rack is a
mechanism of delivering fuel into a means of
transport other than pipeline or vessel. This
changes the imposition and collection of taxes
from the wholesale level to the rack, resulting in
the reduction of collection points. Collecting the
tax at the rack does not change the motor fuel tax
rates, only how and where it is collected.
Pennsylvania’s liquid fuels and fuels tax rates
are:
•
Gas tax is 31.2 cents per gallon, which
is the sum of the fixed liquid fuels tax
of 12 cents per gallon (state excise tax),
Diesel tax is 38.1 cents per gallon,
which is the sum of the fixed fuels tax
of 12 cents per gallon (state excise tax),
and the oil company franchise tax,
which at present time is 26.1 cents per
gallon.
At the federal level, the point of taxation for
gasoline was moved to the terminal rack in 1986.
In 1990, administrative regulations were
tightened requiring the gas tax imposition at the
point of the import, removal from the terminal or
refinery, or the point of sale of any unregistered
entity. The point of taxation was also moved to
the rack at the federal level for diesel in 1994.
The IRS provides Terminal Control Numbers
(TCN), if a terminal is registered as a part of the
taxable fuel bulk delivery system. Currently,
Pennsylvania has 700 registered wholesale
distributor licensees and 68 registered TCNs
(racks). Taxing at the rack means the tax is due
from the consumer, but it is remitted to
Pennsylvania by the supplier, or in rare cases, by
the distributor. The tax is pre-collected by the
supplier before going all the way down the
supply chain.
Imposing the liquid fuel tax at a higher point in
the distribution chain offers a potential for
eliminating fraud, mainly by reducing the
number of taxpayers. Loss of motor fuel tax
represents a significant loss of funding to every
state, especially since each state receives a share
of federal-aid highway dollars based on their
consumption. Weimer, et.al., (2008) report that
many states have seen increased revenues after
moving the point of taxation up the fuel supply
chain.
All purchases from the rack (in
Pennsylvania) would include the Pennsylvania
tax. Weimer, et.al., (2008) state that taxation at
the rack makes the most sense for states,
including Pennsylvania, with refineries and
terminals.
In instances where gallons were exported and
reported to another state, Pennsylvania would
refund the tax paid. Additionally, political
subdivisions, currently exempt under the current
statute such as school districts, municipalities,
and townships would be exempt from paying the
tax or included in the refund process. By
Proceedings of the 2012 Pennsylvania Economic Association Conference
111
exempting users of fuel; and thus not having to
pay the tax, reduces the potential for refund fraud
Weimer, et.al., (2008).11
Following the passage of legislation, it is
estimated that 24 months would afford the time
necessary for the Department of Revenue and the
Bureau of Motor Fuel Taxes to successfully
implement such an extensive change to our
motor fuel tax law.12 Therefore, if legislation on
Title 75 of the Vehicle Code were passed by July
of 2013, it is a fair assumption that the
Department could be fully prepared to “go live”
no later than July of 2015 with electronic
reporting, electronic processing, and employee
training/redeployment in place by that juncture.
The resulting outcomes of moving the point of
taxation to the rack is the following:
•
•
•
•
•
•
•
The Pennsylvania point of tax would be
consistent with the point of taxation of
the federal government, which is at the
rack.
Stakeholders already reporting to the
federal government would already have
those processes in place when reporting
to Pennsylvania.
Pennsylvania would see a decrease in
our number of licensed distributors, and
tax return processing would see a
significant drop in bureau work hours.
The tax examination and refund
verification process could be automated
The
if all data was electronic.13
automation would require a new tax
processing and submission engine.
Mandate that all payments must be
electronic and all refunds will be issued
by electronic fund transfer.14
Mailing costs would decrease as the
result of the elimination of mailing
monthly tax returns to associated
taxpayers.15
Licensing and bonding hours will be
reduced.
It is expected that an increase in revenues once
all facets are fully implemented to be about $20
to $25 million annually, though quantifying such
an increase is very difficult to assess. Additional
expected results are the following:
•
Reduction in number of taxpayers,
•
•
•
•
•
IV.
Identify true delinquencies through
accurate audit and examination,
Reduce administrative costs,
Reduce enforcement costs,
Reduce the number of cases that go
through the appeal/legal process, and
Preventative enforcement.
CONCLUSION
Denison and Egar (March/April 2000) classify
four basic categories to evade motor fuel taxes:
1) failure to file information, 2) filing of false
information, 3) filing of false exemptions, and 4)
failure to pay assessed taxes. The Department of
Revenue Bureau of Motor Fuel Taxes
Compliance Strategy addresses the four
categories by providing outreach and education
to taxpayers to address their tax obligations;
providing greater electronic methods for filing
and payment along with cross-matching ability
for the Bill of Lading sales reciepts; and moving
the point of taxation at a higher level of the
distribution chain to the rack. Components of
the Compliance Strategy are under consideration
by the administration.
ENDNOTES
* The authors would like to thank Sally Fishel
and discussant for their assistance and
comments. The conclusions do not necessarily
reflect the positions of the Pennsylvania
Department of Revenue. All possible errors are
the author’s.
1. Based on the data reported by the FTA Motor
Fuel Tax Uniformity Committee E-Commerce
Subcommittee Survey, January 27, 2012; seven
states point of taxation are position holder at the
rack, nine states are exchange receiver at the
rack, seven states are first receiver below the
rack, four states are importation into the state
(first receipt into storage), and 23 states point of
taxation are at the distributor stage.
2. IFTA, International Fuel Tax Agreement, is an
agreement between states that simplifies the
reporting of fuel taxes by interstate haulers by
establishing a uniform system for administering
and collecting taxes. Motor carriers choose a
base jurisdiction in which to register and file a
single return with a single payment to their base
jurisdiction. The base jurisdiction processes the
IFTA return for net fuel taxes and forwards fund
Proceedings of the 2012 Pennsylvania Economic Association Conference
112
to, or requests refunds from, each jurisdiction.
By 1996, all 50 states and 9 Canadian provinces
were IFTA members (Weimer, et.al., 2008).
3. E-TIDES, Electronic Tax Information and
Data Exchange System, is an Internet filing
system that allows electronic filing of returns,
payments and/or extension requests. Motor Fuel
Taxes, Corporation Taxes, Sales and Use Tax,
and Employer Withholding Taxes use e-TIDES.
The taxpayer can also purchase software to file
online. Telefile is the process where taxpayers
and businesses can file simple tax returns over
the telephone.
4. EFT (electronic funds transfer) is a method of
payment
whereby
the
taxpayers
will
electronically pay from their bank account to
settle their Commonwealth of Pennsylvania
liability. This can be initiated by the taxpayer or
Alternative Fuel
Ethanol
Methanol
Propane/LPG
E-85
M-85
Compressed Natural
Gas (CNG)
Liquefied Natural Gas
(LNG)
Electricity
the Department depending on the agreed terms.
Credit Card payments are taxpayer initiated form
of electronic payment, the difference from EFT
being that it is not directly linked to a bank
account but instead the taxpayers credit card.
5. The point of taxation for the Alternative Fuels
tax is at the retail or end-user level for alternative
fuels. This is due to the fact that most alternative
fuels have many uses other than as a fuel for
propelling a vehicle on the public highways;
therefore, until they are placed into a vehicle
they do not qualify as an alternative fuel.
Alternative fuels are taxed at the rate of the
Commonwealth’s Liquid Fuels and Fuels Tax,
plus the Oil Company Franchise Tax using a
gasoline gallon equivalent calculation using a
BTU conversion factor for each alternative fuel.
The Department publishes revised tax rates each
December for the following calendar year.
2012 Tax Rates for Alternative Fuels
Amount Equivalent
Rate of
to One Gallon of
Conversion
Gasoline @
(BTU/gal of
114,500 BTU per
alternative fuel)
gallon
76,400
1.4999
56,560
2.024
83,500
1.371
80,460
1.423
65,350
1.752
Tax Rate
per Gallon
of
Alternative
Fuel
$0.208
$0.154
$0.228
$0.219
$0.178
29,000@3,000 PSI
3.948
$0.079
66,640
1.718
$0.182
3,412BTU/KWH
33.558 KWH
0.0093/KWH
6. Qualified motor vehicles include those used, designed, or maintained for the transportation of persons or property which: a)
have two axles and a registered or gross weight greater than 26,000 pounds, or b) have three or more axles regardless of weight,
or c) are operated as a vehicle combination exceeding 26,000 pounds.
7. Currently, there is no tax gap analysis for Pennsylvania’s Motor Fuels taxes.
8. With over 100 new companies applying for licenses every year the Commonwealth is constantly exposed to threat of
potential tax evasion. Identified factors for high risk taxpayers that increase the likelihood of tax evasion are the following:
• Home state: If the company is out of state, enforcement and collection powers/efforts are substantially reduced. It is
also very difficult to conduct a thorough pre-license interview over the phone as there would be no on site visit.
• Years of residency: If the company is based in PA, how long have they been in existence? Companies new to PA that
has no record of compliance are obviously a higher risk.
• Value of company’s physical assets: If a company has zero physical assets, if a liability is incurred there will be
nothing to lien or leverage.
• Credit Score of applicant: Credit score is frequently used by insurance companies, banks and the majority of financial
institutions to evaluate the risk of potential members.
Proceedings of the 2012 Pennsylvania Economic Association Conference
113
•
•
•
•
•
Principals–criminal activity in background check: The Department has the ability through the Pennsylvania Office of
the Attorney General to perform background checks on the principals before licensing. The issue was particularly
relevant when a Distributor’s principal ended up on the Interpol watch list after they left the country.
Number of years in business: The high rate of attrition means new companies possess a higher risk of default.
Outstanding liabilities in another state: Established liabilities maybe subjective without knowing all the circumstances
but if activity is shown in another state then it would be prudent to check with our sister agency in that state for
flagrant activity.
Only retail distribution: Smaller distributers who are simply selling to a few retail outlets should not qualify for a
license. Allowing these companies to purchase tax free fuel leaves us more susceptible to tax evasion.
How many times have they applied? Companies who continually apply after multiple license denials should be flagged
as potential high risk account due to the constant manipulation of data on their application.
9. Bootlegging is fuel purchased in a state with a low-tax rate, such as New Jersey at $0.145 without filling out the proper
export documentation; it is then exported to a border state with a higher rate, Pennsylvania at $0.312, and sold at retail stations
without remitting the tax in the high-tax state. Import and export information can be analyzed more efficiently with electronic
reporting. Furthermore, false claim of exports can be detected through fuel tracking systems with electronic reporting.
10. The total state and federal taxes per gallon of gasoline in Pennsylvania is the Liquid Fuels Tax at $0.12; Oil Company
Franchise Tax at $0.192; Underground Storage Indemnification Fund at $0.011; and the Federal Excise Tax at $0.18.
11. Diesel fuel dyed red is not subject to highway use tax. If used on public roads and did not pay the federal and state diesel
fuel tax, tax evasion is the result. Fuel to be used for exempt or off-road use is currently dyed red per IRS standards. The dye is
usually added at the terminal with dye injectors that should be temper proof.
12. Rack legislation would require an overhaul of Title 75 of the Vehicle Code, Chapter 90, perhaps as much as 50% of the
current statutory revisions would either need to be amended or replaced. Furthermore, regulations will be reviewed and
possibly repealed or replaced in order to be consistent with the new law. Other related chapters of Title 75 will be reviewed in
order to ensure no incompatibility issues. For example, enforcement statutes may or may not need review and revision such as
Chapter 94.
13. Daisy chain tax evasion schemes occur where several fallacious purchases of fuel occur without remitting tax payments
where one of dummy companies purchasing the fuel, known as the burn company, dissolves along with any tax liability.
Weimer, et. al., (2008) report that states that have moved the point of taxation to the rack deter this scheme.
14. Moving the point of taxation to the rack may result in fuel previously not taxed would now be taxed and subject to a refund.
The Title 75 statutory changes would attempt to maintain exemptions within the statute.
15. Pennsylvania’s current fuel tax is on the distributor; thus payment to the state is delayed, allowing for interest free use of the
funds, known as the float. Title 75 changes would address this issue to maintain the float; thereby not increasing costs to the
distributors.
Proceedings of the 2012 Pennsylvania Economic Association Conference
115
REFERENCES
Armstrong, Thomas O. and Daniel Meuser 2011. "Increasing Innovation in Tax Administration Collections: Pennsylvania
Department of Revenue," Pennsylvania Economic Association Proceedings, 80-92.
Armstrong, Thomas.
Proceedings. 1-10.
2002.
“State Taxation Reform Proposals for Pennsylvania,” Pennsylvania Economic Association
IBID and Jason R. Brehouse. 2001. "State Tax Simplification: State Nuisance and Obsolete Provision Tax Repeals, Including
Proper Placement within the Tax Reform Code," Pennsylvania Economic Association Proceedings, 171-175.
Department of Community and Economic Development, Commonwealth of Pennsylvania. October 22, 2009. “Pennsylvania’s
Initiative for Energy and the Environment Plan for Support and Investment,” Harrisburg, PA.
Denison, Dwight V. and Robert J. Eger III. March/April 2000. “Tax Evasion from a Policy Perspective: The Case of the
Motor Fuels Tax,” Public Administration Review. 60;2, 163-172.
Franzoni, Luigi Alverto. 1999. “Tax Evasion and Tax Compliance,” Encyclopedia of Law & Economics. Edward Elgar and
the University of Ghent.
OECD. January 28, 2009. Forum on Tax Administration, “Tax Administration in OECD and Selected Non-OECD Countries:
Comparative Information Series”, 1-215.
Pennsylvania Office of the Budget. February 7, 2012. “Governor’s Executive Budget 2012-13,” Harrisburg, PA.
Transportation Funding Advisory Commission-Final Report. August 2011. Pennsylvania Governor’s Transportation Funding
Advisory Commission.
Weimer, Mark; Balducci, Patrick; Fathelrahman, Eihab; Whitmore, Susan; and Anthony Rufolo. 2008. “National Cooperative
Highway Research Program: Identifying and Quantifying Rates of State Motor Fuel Tax Evasion,” Transportation Research
Board, Washington, D.C.
Proceedings of the 2012 Pennsylvania Economic Association Conference
116
A PANEL STUDY ANALYSIS OF ECONOMIC GROWTH IN SOUTH EAST ASIA
Tai McNaughton
Clarion University of Pennsylvania
Clarion, PA 16214
ABSTRACT
Identifying factors relevant to economic growth has been the
centerpiece of economic development and studies in
economic history for decades. Rostow (1960) represents one
of the first authors to develop a progressive discussion of
how economic growth occurs in all economies by
identifying five stages of growth. These views sparked a
great deal of research in economic development to
determine underlying factors influencing economic growth.
This paper will look at several economies in Southeast Asia
simultaneously to identify the variables that have been
determining economic development in the region. By
identifying these variables, future growth can be
extrapolated.
]
Theories of how to achieve growth have differed
throughout the years. An export led growth hypothesis is
one of the ways that is said to create regional growth. The
economies of South East Asia are well known for using a
growth policy that focuses on export expansion.
Furthermore, these countries also show a lot of potential for
growth. They could even have the underlying factors to
become just as advanced as Japan. Therefore, it is very
crucial that these countries grow to reach their full potential.
However, literature varies on the actual effectiveness of
export expansion policies. This study attempts to determine
the variables that determine growth in the economies of
Cambodia, Indonesia, Laos, Malaysia, Philippines,
Singapore, Thailand, and Vietnam. This study shows that
increases in Foreign Direct Investment, Gross Capital
Investment, and Life Expectancy will result in an increase in
the standard of living across all of the countries is South
East Asia. The volume of Exports of Goods and Services is
statistically insignificant in describing growth while
Manufacturing has a negative relationship with growth.
foreign direct investment in Southeast Asia and other
developing countries over the past one hundred years. FDI is
said to be desirable by other countries because it not only is a
growth enhancer due to capital accumulation, but it also
helps boost productivity and output growth.
Hsiao (2006) concluded after looking at the results of other
studies that there are positive effects of “FDI on transitional
and long-run economic growth through capital accumulation
and technical or knowledge transfers” (1083). An article by
Thomsen (1999) confirms this as he agrees that FDI leads
export oriented growth. He also suggests that FDI should be
judged by quality instead of quantity. However, Hsiao (2006)
points out that FDI inflows could be attracted to growing
economies and therefore FDI and economic growth can run
bi-directionally.
Manufacturing
According to the article by Rasiah and Hing (2006),
attempts to analyze the effects of industrialization on growth
could not be completed thoroughly without breaking down
the different forms of industrialization to actually show what
helps growth the most. According to an article by
Jongwanich (2010), within a country it is important to
distinguish the difference in exports to tell how much growth
will occur. Jonhwanich (2010) breaks exports down into
“total merchandise exports, manufacturing exports, and
exports of machinery and transport equipment” (21). This
allows for comparison among the different types of exports.
A study completed in 1967 showed that in manufacturing
there was a positive and strong elasticity of change with
growth (Rasiah, 2009). Due to government intervention,
manufacturing soon became a huge industry across the
resource rich Southeast Asia countries. Rasiah and Hing
(2009) also make it a point to label the countries of Southeast
Asia with transitional economies.
LITERATURE REVIEW
Exports of Goods and Services
Foreign Direct Investment
According to Rasiah and Hing (2009), foreign
ownership led export-oriented industrialization in Southeast
Asia. In the basic understandings of economics, it is said that
industrialization drives increasing returns. Much like Rasiah
and Hing’s article that dealt with foreign direct investment
(FDI), an article by de Mello Jr and Fukasku (2000)
examined the actual relationship between foreign direct
investment and foreign trade. They detail the huge increase in
An article by Trindade (2005) shows the effects of exportoriented growth and export enhancing policies in two of the
countries in my sample of eight. In an article by Ghirmay,
Grabowski and Sharma (2001), the way growth and exports
are connected were analyzed. They stated that there have
been many different studies where the goal was to figure out
whether export expansion leads to growth or how exports
affect economic growth. According to Ghirmay, Grabowski,
and Sharma (2001), a part of economic theory leads to the
Proceedings of the 2012 Pennsylvania Economic Association Conference
117
belief that “export expansion is believed to promote
economic growth via two paths: by improving efficiency in
the allocation of productive resources and by increasing the
volume of productive resources through capital
accumulation” (689). They concluded that a time series study
of a country by country approach would be more effective
than a study of how growth is effected across different
countries. There is a casual effect between export growth and
output growth by looking at studies where growth is
regressed on exports.
A lot of studies have been carried out to prove the belief that
export growth improves economic growth by improving
economic efficiency (Ghirmay, 2001). This has been one of
the major underlying beliefs for trade promotion. However,
some economists differ from this belief and instead feel that
there has to be a minimum level of development before
benefits from trade are actually shown to help the economy.
This is where Ghirmay (2001) comes to the conclusion that
exports are leading to accumulation rather than actually
improving the efficiency in an economy.
Life Expectancy and Education
Booth (2004) feels that globalization and trade open a
country up to the outside world and therefore improve the
situation the country is in. Booth shows this by referencing
the mass amounts of growth over the past 100 years in
Southeast Asia and adds in the incredible increases in life
expectancy rates and literacy rates. She then compares this to
other countries that were just as undeveloped as these Asian
countries. Thompsen (1999) confirms the importance of
education as he talks about education in an investment in
human capital sense.
DATA AND METHODS
A time series, panel study regression was run in TSP with the
purpose to see if Economic Growth measured in PPP
Converted GDP Per Capita at constant prices is dependent on
Foreign Direct Investment as a percent of GDP, Real Gross
Capital Formation, Average Life Expectancy at birth, Volume
of exports of Goods and Services, and Manufacturing as a
percent of GDP. Due to incomplete Education data it was not
used in this study. The natural log of PPP converted GDP Per
Capita as well as the natural log of Gross Capital Formation
were also substituted in but produced the similar final results.
All of these variables were collected for the South East Asian
countries of Cambodia, Indonesia, Laos, Malaysia,
Philippines, Singapore, Thailand, and Vietnam. The data for
these countries begins in 1980. Myanmar and Brunei did not
have enough data in order for those two countries to be
included into the data analysis.
The following is the regression model actually used:
(1)
Y (PERGDP) = β0 + β1 (FDI) + β2 (GROSS CAP
FORMATION) + β3 (LIFEEXP) + β4 (VOLUME EXPORTS)
+ β5 (MANU)
Where: Economic Growth (Y) = f(FDI, Gross Capital
Formation, Life Expectancy, Volume of Exports, and
Manufacturing)
When using time series data across countries in a panel study,
it must be determine whether differences across countries can
be captured by differences in the constant term β0 (fixed
effects approach) or whether the constant terms for each
country are randomly distributed (random effects approach).
This approach to panel estimation is outlined in Greene
(2000).
Table 1 lists the variables, how they were measured, their
source, and the years they were collected for.
The graphs included visually show the distributions of the
data across the various countries. It should be noted that a
few of the variables do have data that goes back further in
time than 1980. While the years are not included in the study,
it is important to show the long-term trend to help with the
qualitative forecast located at the end.
Graph 1 shows the Economic Growth levels, measured in
GDP PPP per capita. Each of the eight countries’ data is
represented. In comparison, Singapore has achieved a higher
level of growth than all 7 other countries.
Graph 2 shows the level of Foreign Direct Investment, net
inflows. Singapore has a higher level of FDI percent of their
GDP. There is no clear trend line across all of the countries.
Graph 3 shows the volume of Savings or Gross Capital
Formation volume. There is an overall increasing trend in
gross capital formation across all countries.
Graph 4 shows the variable for health across all countries in
the region. The overall trend is an increase in life expectancy
as time increases.
Graph 5 shows the Percentage of GDP that comes from the
Exports of Goods and Services. There is no clear trend in the
region.
Graph 6 shows the Percentage of GDP that comes from
Manufacturing. There tends to be a trend of increasing
Manufacturing percentage as time goes on with a recent
decrease.
EMPIRICAL RESULTS
Table 2 is a correlation matrix for PPP converted GDP per
capita. A correlation matrix shows the relationship between
two variables. The closer a number is to positive 1, the more
highly correlated the two variables are.
Proceedings of the 2012 Pennsylvania Economic Association Conference
118
Life Expectancy and FDI are both highly correlated with PPP
converted GDP per capita. Also, Manufacturing is expected
to have a negative relationship with GDP per capita, PPP.
Model 7 and Model 8 shows mostly the same pattern in
results as Model 5 and Model 6.The only difference is that
Volume of Exports of Goods and Services affects the Natural
Log of GDP negatively. However, while possibly
economically significant, this is not statistically significant.
Regression Models
CONCLUSIONS/POLICY RECCOMENDATIONS
Table 3 shows the fixed effect results from the panel
regressions. Each model has the coefficient with
corresponding p-value below. In Model 1 and Model 2, the
dependent variable is economic growth measured in PPP
GDP per capita and Gross Capital Formation is at 2000
constant rates. In Model 3 and 4, the dependent variable is
the natural log of PPP GDP per capita and Gross Capital
Formation is measured with the natural log of 2000 constant
rates.
The Fixed Effects results showed interesting results.
However, due to the results of the Hausman test, the Random
Effects models better explain the development of the given
region.
Table 4 shows the Random Effects results from the panel
regressions. Each model has the coefficient with
corresponding p-value below. In Model 5 and Model 6, the
dependent variable is economic growth measured in PPP
GDP per capita and Gross Capital Formation is at 2000
constant rates. In Model 7 and 8, the dependent variable is
the natural log of PPP GDP per capita and Gross Capital
Formation is measured with the natural log of 2000 constant
rates.
In regression analysis, coefficients show how much of an
effect that a given variable has on the dependent variable.
The coefficient tells how much the dependent variable will
increase with each increase in the independent variable, or
conversely shows how much the dependent variable will
decrease with each increase in the independent variable.
These are denoted by positive coefficients and negative
coefficients respectively.
Also important in regression analysis is p-values. A p-value
shows the probability that a correlation between two
variables is indeed important instead of being random. If pvalues are low, the more probability that the independent
variable has an effect on the dependent variable.
Model 5 shows that Manufacturing affects the standard of
living negatively. Also, the volume of exports of goods and
services does now have an effect on the development.
Therefore, Model 6 shows the results without the Exports of
Goods and Services. An increase in Manufacturing still
results in a decrease in the standard of living. All of the other
variables are statistically significant and affect the standard of
living positively.
This study produced interesting results. First, the Volume of
exports of goods and services was completely statistically
insignificant. This goes against previous literature as well as
theories supporting export expansion as a way of increasing
growth. Also, each increase in the percent of GDP that comes
from Manufacturing actually decreases the standard of living
in the region. Not surprisingly, larger net inflows of FDI,
Gross Capital Formation and Life Expectancy all affect the
standard of living positively.
A major caveat for this study would be the data. Limited data
for several of the countries, to the point where they could not
be included, was further exacerbated by the lack of reporting
in the remaining countries. There was also a lack of
Educational data for the region. Furthermore, there was
heteroskedasticity that could not be corrected for.
According to the results, if countries in this region want
growth, policies toward increasing inflows of FDI, Gross
Capital Formation, and Life Expectancy are all pertinent. By
not focusing on export expansion in the region and instead
concentrating on these proven growth enhancers will result in
economic growth. Unfortunately, export expansion is
currently believed to be one of the main growth enhancing
strategies that is promoted. This was proven unsuccessful in
the region and therefore will be limited in the future.
QUALITATIVE FORECAST
A lack of historical data made it impossible to do a
quantitative forecast. However, a qualitative forecast is
plausible.
Due to the very nature of Life Expectancy, increasing Life
Expectancy will result in growth. Life Expectancy is not ever
expected to decrease. Therefore, to assume that it will
decrease in the future (and decrease growth simultaneously)
is unfounded. In fact, the only decrease that happened in the
historic data can be attributed to a major natural disaster in
Cambodia. This is shown in Graph 4. Furthermore, the
coefficients for Life Expectancy in the regression model
outputs are largely positive implying that this will increase
growth as time goes on. Growth will come from taking
measures to increase Life Expectancy.
Foreign Direct Investment inflows present great potential for
the region. Singapore, the most developed country in the
region, almost has yearly the largest percentage of their GDP
Proceedings of the 2012 Pennsylvania Economic Association Conference
119
coming from FDI over every other country from 1970 on.
This is shown graphically in Graph 2. FDI will be attracted to
this region due mainly to the success that Singapore has
already had.
The Exports of Goods and Services do nothing for the region.
Exports are shown in Graph 5. They fluctuate from year to
year and have no pattern. Due to export expansion
promotion, they will continue to try to increase their
exporting. This has been proven to not help with growth and
should not continue to take place.
TABLES AND GRAPHS
Table 1: Variable Description
Variables (Y and
Xs)
Economic
Growth
(PCRGDP)
FDI
(FDI)
Savings
(REALK)
Health
(LIFE)
Exports
(VEXP)
Manufacturing
(MFG)
Measure
Source
Years
PPP GDP per capita(constant
2005 international $)
World Bank
1980-2010
Foreign Direct Investment, net
inflows (% of GDP)
Gross capital formation
(constant 2000 US$)
Life Expectancy at Birth in
years
Volume of Exports of Goods
and Services
Manufacturing as a
Percentage of GDP
World Bank
1980-2010
World Bank
1980-2010
World Bank
1980-2010
Gapminder
1980-2010
World Bank
1980-2010
Table 2: Correlation Matrix
PCRGDP
FDI
REALK
LIFE
VEXP
MFG
YEAR
PCRGDP
1.00000
0.73534
0.17118
0.72475
-0.025344
0.24163
0.12257
FDI
REALK
LIFE
VEXP
MFG
YEAR
1.00000
-0.11194
0.55627
0.13654
-0.017440
0.15483
1.00000
0.32569
-0.16012
0.63156
0.23271
1.00000
-0.077926
0.52272
0.22081
1.00000
-0.17698
-0.061674
1.00000
0.10964
1.00000
Table 3: Fixed and Random Effects
Variable
FDI
REALK
LIFE
VEXP
MFG
R-squared
Model 1
294.519
.093
.811520E-07
.031
1099.81
.000
2.32371
.814
-330.055
.004
.911734
Model 2
296.071
.092
.817391E-07
.028
1096.06
.000
-330.801
.004
.911723
Model 3
.542660E-02
.042
.214970
.000
.067133
.000
-.144041E-03
.691
,015458
.000
.992051
Model 4
.531747E-02
.045
.215991
.000
.067103
.000
.015478
.000
.992047
Proceedings of the 2012 Pennsylvania Economic Association Conference
120
Table 4: Fixed and Random Effects 2
Variable
FDI
Model 5
325.276
.001
.758159E-07
.037
1116.80
.000
2.28777
.880
-325.031
.000
-64798.7
.000
.625298
REALK
LIFE
VEXP
MFG
C
R-squared
Model 6
326.473
.001
.763696E-07
.034
1113.34
.000
-325.841
.000
-64536.9
.000
.625657
Model 7
.57437E-02
.042
.209041
.000
.068366
.000
-.157203E-03
.713
.015419
.000
-1.61027
.000
.626445
Model 8
.56168E-02
.045
.215991
.000
.068318
.000
.015441
.000
-1.63557
.000
.625410
Graph 1: Economic Growth
Economic Growth
Cambodia
50000
Indonesia
40000
Lao PDR
30000
Malaysia
Philippines
20000
Singapore
10000
Thailand
Vietnam
0
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
GDP per capita, PPP (constant)
60000
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121
Graph 2: Foreign Direct Investment
Foreign Direct Investment, net
inflows
25
Cambodia
Percent of GDP
20
Indonesia
15
Lao PDR
10
Malaysia
Philippines
5
Singapore
2009
2006
2003
2000
1997
1994
1991
1988
1985
1982
1979
1976
1973
-5
1970
0
Thailand
Graph 3: Savings
8E+10
7E+10
6E+10
5E+10
4E+10
3E+10
2E+10
1E+10
0
Cambodia
Indonesia
Lao PDR
Malaysia
Philippines
Singapore
Thailand
1960
1964
1968
1972
1976
1980
1984
1988
1992
1996
2000
2004
2008
Volume (constant)
Gross Capital Formation
Vietnam
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Graph 4: Health
90
Cambodia
80
Indonesia
70
Laos
60
Malaysia
50
40
Philippines
30
Singapore
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
Live Expectancy (Number of
Years)
Health
Thailand
Graph 5: Volume of Exports of Goods and Services
Volume of Exports of Goods and
Services
Percent of GDP
200
Cambodia
150
Indonesia
100
Laos
50
Malaysia
0
-50 1980
1987
1994
2001
2008
Philippines
Singapore
-100
Graph 6: Manufacturing, Value Added
Manufacturing, Value Added
35
Cambodia
30
Indonesia
25
Lao PDR
20
Malaysia
15
Philippines
10
Singapore
5
Thailand
2008
2004
2000
1996
1992
1988
1984
1980
1976
1972
1968
1964
0
1960
Percent of GDP
40
Vietnam
Proceedings of the 2012 Pennsylvania Economic Association Conference
123
REFERENCES
Booth, A. (2004). Linking, de-linking and re-linking: Southeast Asia in the global economy in the Twentieth Century.
Australian Economic History Review, 44(1), 35-51.
de Mello, L. r., & Fukasaku, K. (2000). Trade and foreign direct investment in Latin America and Southeast Asia: Temporal
Causality Analysis. Journal Of International Development, 12(7), 903-924.
Ghirmay, T., Grabowski, R., & Sharma, S. C. (2001). Exports, investment, efficiency and economic growth in LDC: An
empirical investigation. Applied Economics, 33(6), 689-700.
Greene, William H. (2000). Econometric analysis, 4th ed. Prentice Hall Inc. Upper Saddle River, NJ.
Jongwanich, J. (2010). Determinants of export performance in East and Southeast Asia. World Economy, 33(1), 20-41.
Rasiah, R., & Hing, A. (2009). Industrializing Southeast Asia. Journal Of The Asia Pacific Economy, 14(2), 107-115.
Rostow, W. W. (1960). The stages of economic growth: A non-communist manifesto. Cambridge: Cambridge University Press.
Hsiao, F. T., & Hsiao, M. W. (2006). FDI, exports, and GDP in East and Southeast Asia--Panel data versus time-series causality
analyses. Journal Of Asian Economics, 17(6), 1082-1106. doi:http://dx.doi.org/10.1016/j.asieco.2006.09.011
Thomsen, S. (1999). Southeast Asia: The role of foreign direct investment policies in development.
Trindade, V. (2005). The big push, industrialization and international trade: The role of exports. Journal Of Development
Economics, 78(1), 22-48. doi:http://dx.doi.org/10.1016/j.jdeveco.2004.08.006
Proceedings of the 2012 Pennsylvania Economic Association Conference
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The Dodd-Frank “Wall Street Reform” Act of 2010:
Is our Financial System More Stable Now?
Adora D. Holstein
Department of Economics and Legal Studies
Robert Morris University
Moon Township, PA 15108
ABSTRACT
This study maps key provisions of the Dodd-Frank Act against regulatory gaps and systemic vulnerabilities revealed by the
recent financial crisis. Issues of regulatory capital arbitrage have been addressed, but limits on the use of short-term funding
for long-term assets, reducing runs on money market funds, and privatizing Fannie Mae and Freddie Mac still need to be
worked out. A systemic oversight council now closely monitors, regulates, and limits the growth of large, systemically
important banks and nonbanks. The ‘living will’ required has the potential of making complex institutions more transparent,
and easier for the FDIC to liquidate. However, no periodic fee is assessed on nonbanks to build up a fund that will preclude the
use of taxpayer’s money. Moreover, the Volcker Rule provision falls short of restoring the Glass-Steagal separation of banking
and investment banking. Ultimately, enforcement of new rules will depend on the regulatory philosophy of the oversight council
members, and adequate funding of regulatory agencies so they can keep up with financial innovations.
INTRODUCTION
The subprime crisis drastically changed the financial
landscape in the United States. Of the five largest
investment banks, only two survived (Goldman Sachs and
Morgan Stanley), but along with large consumer finance
companies (American Express and CIT) they obtained Fed
authorization to become bank holding companies (BHCs).1
The federal government took substantial ownership stake
in the world’s largest insurance company (AIG), and took
two government sponsored enterprises (Fannie Mae and
Freddie Mac) into conservatorship. There is a general
understanding that the root cause of the financial crisis was
the bursting of a housing bubble fueled by lax underwriting
and securitization of subprime mortgages. Yet asset
bubbles (most recently the dot.com bubble) have burst in
the past but did not push the U.S. and the global economy
at the brink of another depression. The financial crisis of
2008 revealed how vulnerable the U.S. financial system
was to systemic risk. With the Fed’s aggressive use of new
emergency lending facilities and Congressional
authorization of the Troubled Assets Relief program, fewer
banks failed during this crisis than during the S&L crises
of the 1980s and the Great Depression.
In July 2010, the Dodd–Frank Wall Street Reform and
Consumer Protection Act (DFA) was signed into law by
President Obama. It has been hailed as the most
comprehensive reform of financial regulation in the U.S. It
was designed to (1) strengthen safeguards for consumers
and investors, (2) provide better tools for limiting risk in
major financial institutions and markets, and (3) help create
a stronger, more resilient financial system that is less
vulnerable to crisis, fraud and abuse and more efficient in
allocating financial resources (Senate Banking Committee
2010). As expected, there is an ongoing debate about
whether DFA, known to many as “Wall Street Reform,” is
excessive and doubts about how well it can prevent another
financial crisis abound.
The objectives of this paper are: (1) to review the views
of academic researchers as well as financial regulators on
what critical regulatory flaws and vulnerabilities of the
financial system led to the housing bubble, spread the
negative repercussions throughout the financial system,
and ultimately imposed costs on the real sector of the
economy; (2) to map the provisions of DFA against these
critical regulatory flaws and financial system
vulnerabilities; and (3) to determine what flaws and
vulnerabilities, if any, are not addressed adequately or at all
in the DFA. To start, I will review the literature on the
market failures that justify government intervention in the
financial sector of the economy and the difference in
regulation of banks and nonbank financial institutions.
Against this backdrop, I will then review the academic
literature as well as reports of evaluations done by
regulatory agencies on what caused the housing bubble and
the financial crisis that followed once it burst. This is
followed by a mapping of the provisions of DFA against
these critical regulatory flaws and financial system
vulnerabilities. Following an evaluation of DFA’s potential
for making the financial system more stable, the paper
closes with some concluding comments.
LITERATURE REVIEW
Despite the reliance of the U.S. on the market system to
determine the prices of most goods and services, financial
institutions are subject to varying degrees of government
Proceedings of the 2012 Pennsylvania Economic Association Conference
125
regulation. The need for government intervention in the
finance sector of the economy and the extent of such
intervention has many rationales in the economics
literature. Among these are negative externalities (Pigou
1932), efficient markets hypothesis (Fama 1970), and
asymmetric information (Ackerloff 1970), which brings
about the problems of adverse selection and moral hazard.
Prior to the establishment of the Federal Deposit Insurance
Corporation (FDIC) in 1934, the failure of a bank created
uncertainty about which banks will fail next and resulted in
bank runs. Depositors panic and rush to close their
accounts when they are unable to distinguish the good
banks from the bad banks (a problem of adverse selection).
This problem arises from the fact that bank insiders have
better information about the soundness of the bank’s
financial position than its depositors (a problem of
asymmetric information).
Banks perform a vital function of transferring funds from
savers to borrowers who can use it for productive
investments. When there is uncertainty about the financial
soundness of banks, the resulting drop in prices of their
stocks easily gets transmitted across the stock market. A
banking crisis ultimately affects the whole economy
adversely, because a drop in equity capital among banks
will mean a reduction in loans that banks can extend to
consumers and businesses, imposing a negative externality
to people who lose their jobs or whose net worth shrink.
The importance of infusing liquidity into the banking
system during a financial crisis was recognized by Pigou
(1932) eight decades ago when he said that “…other things
being equal, the actual occurrence of business failures will
be more or less widespread, according [to whether]
bankers' loans, in the face of crisis of demands, are less or
more readily obtainable.” Hence, regulation of banks in
the U.S. has been relatively more intrusive than the
regulation of nonbank financial institutions like brokerdealers and mutual funds. Instead of exercising due
diligence in determining what risks their banks are taking
and moving their deposit accounts elsewhere if these are
above acceptable levels, most depositors rely on the
protective cover not only of FDIC insurance, but also of
various federal regulators who ensure that banks maintain a
minimum level of liquidity, adequate capital commensurate
with the market risk of their loan and investment portfolio,
and orderly liquidation if they fail.
In contrast, regulation of the securities industry has leaned
more towards ensuring disclosure of information so that
investors can effectively exercise market discipline should
they perceive mispricing: selling securities that are
overpriced and buying securities that are underpriced. This
regulatory stance is predicated on the efficient markets
hypothesis (EMH), which states that asset prices are
always and everywhere correct as all available information
is processed by rational market actors (Fama 1970). Thus,
investors and stockholders of investment banks, insurance
companies, mutual funds, and other nonbank financial
institutions exercise their own due diligence or pay for
expert advice to analyze bond ratings by credit rating
agencies, and independently audited, financial statements
and other information that the Securities and Exchange
Commission (SEC) requires them to disclose to the public.
Sarbanes-Oxley (2002) has added another assurance by
requiring that financial statements be attested to by the
Chief Executive Officer of public corporations. There is
also an assumption that these public entities are subject to
oversight by a Board of Directors.
Economists would caution that certain forms of
government intervention in the finance industry have
unintended consequences. For example, the safety net
created by banks’ access to the Fed’s discount window and
the FDIC’s insurance fund creates a moral hazard that
depositors will no longer have the incentive to monitor the
risks that their banks take. In addition, as the FDIC have
been observed to use the purchase and assumption
approach to resolving a bank that is too big to fail, instead
of paying off its depositors up to the insured limit, yet
another moral hazard problem arises. The assurance that
its liability to all depositors can be transferred to the FDIC
and a potential buyer increases management’s incentive to
take risks. As Bullard et al. (2009) point out, systemic risk
can become more of a problem for the economy when
firms who should fail are not allowed to fail.
The causes of the financial crisis have been the subject of
many studies in academia as well as the private sector.
Acharya and Richardson (2009) estimated that losses from
underlying mortgages accounted for less than half of the
total asset write-downs that financial institutions made
since the third quarter of 2007, the rest resulted from
amplifiers such as:
1) Excessive leverage. Investment banks loaded up on
debt to increase returns on equity when asset prices
were rising. They also increased their exposure to
product leverage through complex derivatives which
needed only a slight deterioration in the value of
underlying assets for losses to escalate rapidly. Lastly,
they overindulged in liquidity leverage, using
structured investment vehicles (SIVs) or relying too
much on wholesale markets to exploit the difference
between borrowing cheap short term money and
investing in higher-yielding long-term assets. The
combined effect was that falls in asset values cut
deeply into equity and triggered margin calls from
lenders. The drying up of liquidity had an immediate
impact because debt was being rolled over so
frequently;
2) Derivatives like collateralized debt obligations
(CDOs), credit default swaps and interest rate swaps,
Proceedings of the 2012 Pennsylvania Economic Association Conference
126
which created ‘synthetic’ exposures to subprime
mortgages without investors actually having to own
them, and asset backed-commercial paper (ABCP),
which was the major source of short-term funds for
SIVs;
3) Concentration of counterparty risk. The decision by
the Fed to facilitate JP Morgan’s acquisition of Bear
Sterns had less to do with the size of Bear’s assets than
with its central role in markets for credit-default and
interest rate swaps; and
4) Fair-value accounting rules which require financial
institutions to mark down the value of mortgagebacked securities (MBS) and other derivative
securities to current market prices. Downward price
movements trigger the need to unwind investments,
further depressing prices.
Other studies echo the above assessments. Hellwig (2009)
claims that a faulty financial infrastructure that allowed
mortgage-backed instruments to allocate risk in the real
estate market, along with structured investment vehicles
and a lack of capital at financial institutions combined to
create a downward spiral of problems. Morgan (2009)
adds to this by saying that the crisis was due to the failure
by market participants to process information, reliance on
flawed risk management models, and inadequate
interaction between central banks and other regulators.
Rajan (2009) puts the blame on unsustainably low interest
rates set by monetary policymakers and financial
innovations that led to the housing boom and subsequent
defaults Shin (2011) focused on the role of U.S. branches
and subsidiaries of foreign (mainly European) banks in
spreading the crisis outside the U.S. by borrowing or
raising wholesale short-term funding through U.S. money
market funds, then lending it back as they used these funds
to buy long-term private-label mortgage-backed securities
and structured products.
Reports issued by U.S. and international regulatory
authorities after their own investigations also mirror those
in the academic literature. In its March 2008 report, the
President’s Working Group on Financial Markets 2 (PWG
2008) identified instances when existing regulatory
policies failed to mitigate risk-taking among financial
institutions:
1) Supervisory authorities did not insist on appropriate
disclosures of potential exposure to off-balance sheet
vehicles;
2) Existing capital requirements encouraged the
securitization of assets through facilities with very low
capital requirements; and
3) Existing regulation failed to provide adequate
incentives to maintain capital and liquidity buffers
sufficient to absorb extreme system-wide shocks
without taking actions that tended to amplify shocks.
The Bank for International Settlement’s Committee on the
Global Financial System (BIS 2009) issued a report in
April 2009 noting that what set the stage for the crisis was
a surge in leverage at banks and investment banks in the
U.S. and Europe, but in in less visible ways such as:
1) risk embedded in structured credit products, which
made traditional measures of balance sheet leverage
less meaningful;
2) assets held in highly leveraged off-balance sheet
vehicles; and
3) funding by off-balance sheet vehicles and by some
large financial institutions of a growing amount of
long-term assets with short-term liabilities in
wholesale markets, a mismatch in maturity that
increased their exposure to liquidity risk.
Similarly, the Government Accountability Office (GAO
2009) reported the following findings from their
investigation:
1) the traditional measure of leverage, total assets to
equity on the balance sheet, did not fully capture risk
exposure arising from the use of derivatives;
2) deleveraging by selling these derivatives caused prices
to spiral downward during times of market stress and
exacerbated the financial crisis;
3) the SEC relaxed the net capital rule on the five largest
broker-dealers in 2004 and did not do an independent
assessment of their risk-management models; and
4) multiple regulators were responsible for individual
markets or institutions, but no one has clear
responsibility to assess the potential effects of the
buildup of system-wide leverage or the collective
effect of institutions’ deleveraging activities.
The Securities and Exchange Commission (SEC)
Chairman Mary Schapiro testified before the Financial
Crisis Inquiry Commission in January 2010 that the crisis
resulted from many interconnected and mutually
reinforcing causes, including:
1) The rise of mortgage securitization (a process
originally viewed as a risk reduction mechanism) and
its unintended facilitation of weaker underwriting
standards by originators and excessive reliance on
credit ratings by investors;
2) A wide-spread view that markets were almost always
self-correcting and an inadequate appreciation of the
risks of deregulation that, in some areas, resulted in
weaker standards and regulatory gaps;
3) The proliferation of complex financial products,
including derivatives, with illiquidity and other risk
characteristics that were not fully transparent or
understood;
4) Perverse incentives and asymmetric compensation
arrangements that encouraged significant risk-taking;
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5) Insufficient risk management and risk oversight by
companies involved in marketing and purchasing
complex financial products; and
6) A siloed financial regulatory framework that lacked
the ability to monitor and reduce risks flowing across
regulated entities and markets.
The assessment of the Basel Committee on Banking
Supervision 3 (2010) is that the crisis was caused by:
1) excess liquidity, resulting in too much credit and weak
underwriting standards;
2) excess leverage;
3) inadequate liquidity buffers;
4) too little capital of insufficient quality;
5) major shortcomings around risk management,
corporate governance, market transparency, and the
quality of supervision;
6) procyclical deleveraging process; and
7) interconnectedness of systemically important, too-bigto fail financial institutions.
A study by the Financial Stability Oversight Council
(2010) created by the Dodd-Frank Act, concluded that the
bursting of the housing bubble exposed certain
vulnerabilities in the U.S. financial infrastructure, namely:
(1) interconnectedness of large, complex financial
institutions due to exposure to a common counterparty in
subprime MBS-related derivatives transactions; (2) risk
concentration in subprime MBS; and (3) lack of
transparency about which institutions were interconnected
and the extent of their exposure to subprime MBS risk
caused the markets to seize up.
Most recently, Fed Chairman Bernanke gave a lecture on
“The Fed and the Financial Crisis” at George Washington
University in March 2012, in which, looking back, he
noted that the decline in house prices and the associated
mortgage losses were key triggers of the crisis, but that the
effects of those triggers were amplified by the following
vulnerabilities in the financial system (Bernanke 2012):
1) borrowers and lenders took on too much debt or
leverage;
2) banks and other financial institutions failed to
adequately monitor and manage their risk exposures to
subprime mortgages;
3) financial institutions relied excessively on short-term
funding, such as commercial paper;
4) the increased use of exotic financial instruments
concentrated risk;
5) gaps in the regulatory structure left important firms
without strong supervision; and
6) failures of regulation and supervision, including
consumer protection, and insufficient attention paid to
the stability of the financial system as a whole.
In the next two sections, I will discuss the provisions of
DFA according to two categories of regulatory flaws and
financial system vulnerabilities revealed by the financial
crisis of 2008. The first section will discuss the DFA
provisions that address problems that contributed to
excessive leverage that fueled a housing bubble. This is
followed by a discussion of the DFA provisions that
address problems that spread the risk throughout the
financial system.
DFA PROVISIONS PERTAINING TO PROBLEMS
THAT LED TO THE HOUSING BUBBLE
Provisions of the Dodd-Frank Act (DFA) are mapped to
the regulatory flaws and financial system vulnerabilities
revealed by the financial crisis of 2008 in Table 1. A more
detailed discussion of each problem that led to the housing
bubble which burst in the Spring of 2006 is outlined below
followed by a discussion of relevant provisions of DFA
that address such problem. This discussion owes much to a
book edited by Acharya and Richardson (2009) and an
article written by Crotty (2009), both of which provide a
detailed description of the roles played by credit rating
agencies , unregulated ‘shadow banks’ (e.g. mortgage
brokers, finance companies, off-balance conduits,
investment banks, insurance companies, hedge funds, and
money market funds), lightly regulated derivatives (e.g.
collateralized debt obligations and credit default swaps) ,
as well as compensation schemes that were skewed
towards excessive risk-taking.
No credit risk retention for securitized loans
By selling mortgage loans to investment banks and
government-sponsored enterprises (Fannie Mae and
Freddie Mac) for securitization into mortgage backed
securities (MBS), banks were able to augment deposit
funds with capital market funds to extend a larger amount
of mortgage loans. Securitization, however, enabled banks
to pass on 100% of the credit risk, thus removing their
incentive to observe strict underwriting standards.
Moreover, with the growing demand for high-yielding
subprime mortgage-backed securities, banks were able to
increase the volume of loans originated through their
mortgage brokers or finance company affiliates. Because
these affiliates were not regulated at the federal level, and
state regulation was less stringent, and unevenly enforced
compared to federal regulation, lax underwriting standards
and fraudulent lending practices gave rise to high-risk
creative loan contracts (e.g. no income documentation or
Alt-A loans, interest only payments, negative
amortization).
Title X of DFA created a new Bureau of Consumer
Financial Protection (BCFB) within the Federal Reserve
to regulate
mortgage brokers and finance companies at the federal
level for the first time. It also consolidated the
responsibilities previously handled by various federal
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agencies pertaining to consumer complaints against
mortgage-related businesses. The BCFB is empowered to
act fast without waiting for Congress to pass a law
prohibiting bad lending practices that harm consumers. In
addition, Section 1141 of DFA amended the Truth in
Lending Act to prohibit creditors from making mortgage
loans without regard to a consumer’s repayment ability.
To ensure that banks and bank holding companies
(hereafter referred to as banking organizations) would
have “skin in the game” or have something to lose if they
do not observe strict underwriting standards, DFA
requires that capital be reserved against no less than five
percent (5%) of mortgage loans sold for securitization.
Securitizers are also required to disclose information that
would enable investors to assess the quality of underlying
loans.
Regulatory arbitrage through off-balance sheet conduits
As part of the securitization process, a bank sets up and
administers an off-balance sheet conduit, a shell company
that issues asset-backed commercial paper (ABCP) and
sells it to investors, among the biggest of which are money
market mutual funds. This short-term funding is then used
by the conduit to purchase long-term mortgage-backed
securities (MBS) other collateralized debt obligations
(CDOs) as assets. Banks often became the servicers of
subprime mortgage loans that they have sold, earning fees
for collecting the monthly payments from homebuyers and
passing on a proportionate share of these collections to
ABCP investors. Securitizing subprime mortgage loans and
moving the MBS into off-balance sheet conduits enable the
banks to reduce the capital required to meet capital
adequacy requirements, thus increasing its leverage. As
administrator of the conduit, the bank provides it with:
a) liquidity enhancement (LE): the bank insures the
conduit against liquidity risk, making a commitment to
provide a backup line of credit by repurchasing the
conduit’s non-defaulted assets in case it cannot rollover
maturing commercial paper. This saves capital charge
because the capital requirement on mortgage loans held
on balance sheet is at least three times that required for
on-balance sheet liquidity enhancement facilities; and
b) credit enhancement (CE): the bank insures the conduit
against credit losses from the MBS and other assets
held. The capital requirement for credit enhancements
is larger than for liquidity enhancements, but the sum of
capital requirements on these two enhancements is still
lower than that for on-balance sheet mortgage loans.
According to Acharya and Schnabel (2009), a bank’s risk
exposure to the conduit varies across three types of
conduits:
a) Fully supported conduit: LE covers 100% of ABCP
outstanding and CE covers 100% of all assets held, i.e.
with full recourse to the bank’s balance sheet;
b) Partially supported conduit: LE covers 100% of
ABCP outstanding but CE only covers an average of
7~10% of total assets;
c) Structured investment vehicle (SIV): LE and CE cover
an average of 25% of assets.
Although only the backup liquidity line and credit
enhancement were on the balance sheet and subject to a
capital charge, the bank is exposed to the risk of default
and potential illiquidity of the MBS collateral. During the
financial crisis, the limitations on LE and CE were largely
ineffective as large financial institutions like Citibank were
forced to take back assets or extend more CE, thus
effectively providing full recourse to its balance sheet.
DFA authorized the revision of capital adequacy
requirements on banking organizations consistent with the
Basel III international capital standards, which were
established to incorporate lessons learned from the
financial crisis (see Table 2 for increases in risk-weights
assigned to certain assets). Federal bank regulators have
proposed that effective January 1, 2015, all banks and bank
holding companies comply, on a consolidated basis, with
the following minimum capital ratios: (a) baseline total
capital to risk-weighted assets ratio of 8% (unchanged); (b)
tier 1 capital to risk-weighted assets ratio of 6% (increased
from 4%); (c) leverage ratio or tier 1 capital to average
consolidated assets (not risk-weighted) of 4% (unchanged
but with stricter definition of tier 1 capital); (d) new
common equity tier 1 capital ratio of 4.5%; and (e) new
capital conservation buffer or common equity to riskweighted assets of no less than 2.5% (see Table 3). The
last two requirements in the list will mean that banks will
have to keep common equity of at least 7% of riskweighted assets. The common equity tier 1 capital ratio is a
new minimum requirement designed to ensure that banking
organizations hold high-quality regulatory capital that is
available to absorb losses in times of market stress. In
addition, a bank that has a capital buffer below 2.5% will
be subject to restrictions that no more than 60% of retained
earnings (or less depending on how low the buffer is) can
be paid out as capital distributions and discretionary bonus
payments.
To remove the incentive to reduce capital charges through
off-balance sheet activities, DFA also requires a banking
organization to hold capital, with 100% risk weight,
against off-balance sheet guarantees, repurchase
agreements and other repo-style transactions, financial
standby letters of credit, forward agreements, and other
derivative contracts. In addition, effective January 1, 2018,
large, complex banks (with over $50 billion in assets)
designated as systemically important will be required to
maintain supplemental tier 1 capital of no less than 3% of
its total leverage exposure, including unconditionally
cancellable commitments such as liquidity and credit
enhancements (exposure is calculated at10% of the
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129
notional amount), and all other off-balance sheet exposures
(exposure is calculated at 100% of the notional amount).
This is intended to discourage the acquisition of excess
leverage and to act as a backstop to the risk-based capital
requirements. Finally, DFA attempts to remove future
regulatory arbitrage opportunity by prohibiting a bank from
converting its charter in order to get out from under an
enforcement action, unless both the old regulator and new
regulator approve of such conversion).
Loose oversight of derivatives and hedge funds
During the financial crisis, lack of transparency about the
extent of exposure to subprime MBS by financial
institutions caused the markets to seize up. The structuring
of CDOs backed by subprime mortgages became so
opaque, and the valuation of these products became too
complex for many investors to exercise due diligence, and
for directors and stockholders of financial institutions to
exercise market discipline. Opaqueness of off-balance
sheet conduits also made it difficult for banks to assess
which banks would be hit by losses and how much if
confidence in the ABCP market made it difficult for
conduits to issue new commercial paper, the so-called
‘rollover risk’. This caused the ABCP market to freeze up
in the Fall of 2008.
Derivatives like CDOs, ABCP, and credit default swaps, as
well as the hedge funds and private equity funds that
invested in these derivatives were not subject to disclosure
requirements by the SEC, and were instead loosely
regulated by the CFTC. This regulatory stance was
predicated on the fact that investors in derivatives were
sophisticated investors who can do their own due diligence
or are able to pay for management fees charged by hedge
funds and private equity firms. Credit default swaps, which
in effect, insured investors in derivative products backed
by subprime mortgages, enhanced the marketability of
these products not only in the U.S. but in other countries as
well, thus fueling the housing bubble.
DFA requires the standardization of credit default swaps
and trading of many over-the counter derivatives in central
clearinghouses or exchanges. It empowers the SEC and
CFTC to (a) impose capital and margin requirements on
swap dealers and major swap participants, and (b) collect
and publish data to increase transparency of derivative
products, their trading volumes, and counterparties. To
incentivize centralized clearing, derivative and repo-style
transactions cleared through central counterparties, instead
of over-the-counter through bilateral transactions, will have
reduced capital requirements. Large foreign and domestic
banking organizations operating in the U.S. that have been
designated as systemically important will be required to
maintain a supplementary tier 1 capital of no less than 3%
of its potential exposure for each derivative contract to
which it is a counterparty, and to disclose the exposure
amount and other counterparties to such derivative
contract.
Over-reliance on credit rating agencies
Investors in the U.S. and abroad relied on credit rating
agencies (CRAs) in assessing the investment risk of
complex CDOs and ABCP, rather than doing their own due
diligence. The financial crisis revealed that CRAs (a) gave
certain mortgage-related securities higher ratings than were
deserved, and were too slow in issuing downgrades once
the credit markets slumped, (b) did not adequately
differentiate the risk of MBS from a corporate bond of
similar rating, (c) engaged in conflict of interest when
investment banks hired them as consultants and earned fees
for providing advice to arrangers of structured financial
products on how CDO tranches can obtain desired credit
ratings, and (d) did not make their track record and
methodologies used in arriving at credit ratings publicly
available. Despite the fact that CDOs were new financial
products, models used by CRAs relied on historical default
rates that proved to be too low.
The inaccuracy of ratings on structured financial products
contributed significantly to the mismanagement of risks by
financial institutions and investors. For example, banks are
restricted to invest only in investment grade fixed-income
securities, but as long as a CDO tranche backed by
subprime mortgage loans got AAA ratings banks did not
violate any regulation by investing in it. Holding a AAA
CDO tranche as an investment involved a lower capital
charge than holding mortgage loans on their balance
sheets. The consequence of this is not trivial. While
securitization was hailed as a way to transfer risk outside
of the banking system, about half of AAA-rated MBS
remained within the banking system, 30% held by banks as
on-balance sheet investments, and another 20% by offbalance sheet ABCP conduits and SIVs (Acharya et al.
2009). Likewise, money market mutual funds which are
known to guarantee a stable net asset value became the
largest holders of AAA-rated asset-backed commercial
paper. On August 9, 2007, the ABCP market froze when
BNP Paribas suspended calculation of the NAV of two of
its money market mutual funds that had invested in ABCP.
This led banks to take a large number of ABCP conduits
and SIVs back on their balance sheets, causing interbank
lending rate to rise sharply. This happened again the days
immediately following Lehman’s bankruptcy.
Moreover, federal bank regulators based capital
requirements against securities held by banks and brokerdealers on ratings by credit rating agencies. By basing
capital requirements on risk-weighted assets, regulators did
not catch the build-up of excessive leverage made possible
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130
by faulty credit ratings and regulatory arbitrage through the
use of off-balance sheet conduits. According to an IMF
(2008) data, total bank assets almost doubled from about
$8 trillion in 2004 to about $15.5 trillion in mid-2007, but
risk-weighted assets only grew from $4 trillion to $5.5
trillion.
DFA created a new Office of Credit Ratings within the
SEC to: (a) increase transparency by requiring CRAs to
publicly disclose their rating methodologies and ratings
track record, and (b) to remove conflicts of interest issues
by requiring the use of third-party sources of data, not just
the institution being rated, prohibiting compliance officers
from working on ratings, methodologies , or sales, and
requiring a one-year look back review when a rating
agency employee goes to work for an obligor or
underwriter of a security or money market instrument
subject to the agency’s rating. The SEC has been
empowered to register an agency for providing bad ratings
over time. By nullifying SEC Rule 436 (g) which
exempted credit ratings provided by CRAs from being
considered part of the registration statement for securities,
investors will be able to sue CRAs for reckless failure to
conduct a reasonable investigation of the facts or to obtain
an analysis from an independent source.
The capital adequacy requirements have also been
modified so that if a banking organization sells mortgage
loans for securitization but later invests or holds every
tranche of the securitization, its overall capital charge
would be greater than if the bank held the underlying
mortgage loans. This is intended to discourage regulatory
capital arbitrage through securitization, and ensure that
securitization really transfers the risk outside of the
banking system. To deal with the problem of regulators
relying on security ratings by credit rating agencies, section
939A of DFA requires federal agencies to remove
references to credit ratings from regulations and replace
credit ratings with appropriate alternatives. The
implementation rulings issued since July 2010 include
alternative measures of creditworthiness for determining
required risk-based capital for residential mortgages,
securitization exposures, and counterparty credit risk.
To assess a bank’s risk exposure to mortgage loans on
single-family homes valued up to $1 million that are not
government guaranteed, bank regulators have proposed
that starting in 2015, the loan-to-value (LTV) ratio and
loan terms be used. Under this proposal, the property value
in the LTV ratio will be based on an appraisal that
conforms to appraisal regulations. Loans are to be
classified into two categories: Category 1 would be for
loans that do not have loan terms associated with higher
credit risk as the subprime foreclosure crisis revealed (e.g.
undocumented income, balloon payment, negative
amortization, over 30-year maturities, adjustable-rate
mortgages (ARM) with an annual interest rate cap above
2% and life of loan cap above 6%). In addition, Category 1
loans can only include those that are first lien loans, not 90
days or more past due, and in the case of an ARM loan, the
borrower qualified accounting for interest rates going up
by the maximum 6% life-of-loan cap, real estate taxes, and
mortgage insurance. Loans that do not satisfy these criteria
will be classified in Category 2. Category 1 loans will have
lower risk weights, and within each category, the higher
the LTV ratio, the higher is the risk weight (see Table 4).
For securitization exposures, banking organizations are
required to satisfy specific due diligence requirements
instead of relying exclusively on credit ratings. This
includes conducting and documenting an analysis of the
risk characteristics of the underlying assets (property types,
occupancy rate, average LTV ratio, industry and
geographic diversification) prior to acquisition, and at least
every quarter thereafter, relevant information regarding the
performance of the underlying credit exposures (e.g.
default rates, prepayment rates, loans in foreclosure). If the
banking organization is not able to demonstrate a
comprehensive understanding of a securitization exposure
to the satisfaction of its primary federal supervisor, it will
be required to assign a risk weight of 1,250% to the
exposure. For a minimum required capital ratio of 8%, this
would mean putting up capital equal to the exposure
amount.
In June 2012, bank regulators proposed alternative
approaches for determining the risk exposure to
securitization. A banking organization that chooses to
apply another approach will be required to assign a 1,250%
risk weight to all securitization exposures. There is a 20%
floor on the risk weight for securitization exposures, and
varies up to 100% depending on the weighted average risk
of the underlying mortgage loans or other assets.
Irrational (herding) behavior of market participants
Herding behavior is said to occur when investors buy or
sell to imitate what other market participants are doing
rather than on the basis of their own assessment of the
asset’s fundamentals. Some argue that even if more
information were available about subprime MBS, CDOs,
and ACBP, such herding behavior would still have caused
some market investors to underestimate the risk of these
financial products or overestimate the duration of the
housing boom. While no regulation can ever be designed
to prevent irrational or excessive risk-taking behavior
among homebuyers and investors, many of the DFA
provisions already discussed are designed to increase
transparency in the securitization and derivatives markets.
Hopefully, the previous experience of market loss will
motivate market participants to use enhanced information
to analyze the fundamentals of investments. As for
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131
nonbank financial institutions that take on too much risk,
DFA has restricted the Fed to extending access to their
liquidity facilities, but not bailing out failing institutions.
Instead, as will be discussed in the next section, DFA
subjects large, complex financial institutions to stricter
prudential standards and an orderly liquidation process in
case of bankruptcy.
Compensation schemes rewarded excessive risk-taking
Executives, traders, loan managers, and brokers were
rewarded with bonuses proportional to the trading volumes
or returns they have generated in a specific year, without
imposing any loss on them when risky activities that were
initially profitable, later led to losses.
DFA has a ‘clawback’ provision which requires a publiclytraded company that restates its earnings because of serious
accounting errors to seek repayment of compensation from
any past or present executive officer in excess of what
would have been paid under the restatement. Also if a large
financial company is put into receivership, the FDIC can
take back the last two years of compensation from any
senior executive or director, past or present, who is
substantially responsible for the failure of the firm. In
addition, a ‘Say on Pay’ provision gives stockholders of
financial institutions a right to a non-binding vote on
executive pay and golden parachutes, thus giving them a
chance to disapprove where they see misguided incentive
that increase risk-taking beyond what they find acceptable.
To promote transparency, DFA directs the SEC to clarify
disclosures relating to compensation, including requiring
public corporations to provide charts that compare their
executive compensation with stock performance over a
five-year period.
Last, but not the least, systemically important financial
institutions (the designation criteria for which will be
discussed later) will be subject to restrictions on the
amount of bonuses they can pay out of retained earnings if
they are not able to maintain a required capital
conservation buffer. This will be further explained below.
DFA PROVISIONS ON SYSTEMIC RISK
The financial crisis revealed the interconnectedness of
large financial institutions on one hand, and the financial
and real sectors of the economy, on the other. The phases
of the run on shadow banks and traditional banks in 2007
and 2008 following the surge in foreclosures on homes
financed by subprime mortgage loans are outlined in
Acharya et al. (2009). In early 2007, Countrywide
Financial and hundreds of nonbank mortgage lenders
specialized in subprime and Alt-A mortgages (loans with
no income documentation) collapsed when their source of
wholesale financing dried up. Some were merged into large
banking institutions to which they were affiliated under a
Financial Holding Company.
This was followed by the collapse of an entire system of
structured investment vehicles and asset- backed
commercial paper conduits when investors realized the
assets were illiquid and ‘toxic’. Large banks took back
assets in off-balance sheet conduits back to their balance
sheets, and started to mark down their holdings of CDOs
and subprime mortgages that they could no longer
securitize. The next phase was the collapse of hedge funds
hedge funds such as those managed by Bear Sterns in June
2007, and independent broker-dealers starting with Bear
Sterns in March 2008, when the repurchase agreement or
‘repo’ market, which was their main source of short-term
funding for purchases of subprime mortgage-backed
securities, unraveled. A run on hedge funds, can easily
happen because investors could redeem their investments
after a short ‘lock-up’ period. Although private equity
funds have longer-maturity financing, a refinancing crisis
also became possible once primary brokers failed or cut
back financing. Thus, the Fed’s most radical postDepression action was to set up a Primary Dealer Credit
Facility to provide liquidity to primary dealers after the
collapse of two hedge funds at Bear Sterns.
Next came the run on the three trillion dollar money
market mutual fund industry, which invested funds into
commercial paper backed by AAA tranches of CDOs. The
Fed had to extend temporary guarantees to money market
mutual fund investors to unfreeze the market for
commercial paper, which is a main source of short-term
funding for corporations. The drying up of liquidity also
led to AIG being unable to post significant additional
collateral when its credit default swap obligations
ballooned. Likewise, many monoline bond insurers lost
their AAA ratings and eventually failed. After Lehman’s
bankruptcy in September 2008 and acquisition of Merrill
Lynch by Bank of America, there was a run on the three
other big investment banks, and interbank lending froze as
banks started to hoard cash. Thus, the runs on nonbank
financial institutions ultimately threatened depository
institutions that are the main source of funding to
consumers and businesses in the real sector of the
economy. The flaws in the financial infrastructure of the
U.S. financial system are discussed below with the
corresponding provisions of DFA that are intended to
correct these.
No one regulator to monitor and regulate systemic risk
Banks lobbied for many years to repeal the Glass-Steagal
Act, which was passed after the Great Depression, to
separate the business of banking from that of investment
banking. Banks argued that they could not compete with
Proceedings of the 2012 Pennsylvania Economic Association Conference
132
large foreign banks, which could engage in securities
underwriting, insurance, and in the case of Japan and
Germany, also could own equity interest in nonfinancial
corporations under so-called “universal banking” system.
The Gramm-Leach- Bliley Act (GLBA) enacted in
November 1999 allowed the creation of financial holding
companies under which umbrella, a bank could engage in
securities underwriting, insurance, and real estate
operations. This led to consolidation in the financial
industry which gave rise to large, complex financial
institutions. However, GLBA provided for regulation by
function, subjecting such institutions to multiple regulators
(see Figure 1). There was no one regulator to monitor,
identify, and act on early warning signs of risk to the
stability of the financial system.
Moreover, regulation by function led some financial
institutions to shop for the least restrictive regulators. Some
examples are discussed below:
a) GLBA allowed banks to be affiliated with mortgage
brokers and finance companies under a financial
holding company umbrella. The lax underwriting
practices of these affiliates were not subject to
oversight by the bank regulators. Most of the subprime
loans that eventually got securitized and held by banks
in their books as investments or in off-balance sheet
conduits were originated by these affiliates;
b) Faced by the possibility of regulation in European
countries in 2004, the five largest U.S. investment
banks with foreign subsidiaries (the ‘Big Five’)
requested an exemption from the SEC’s net capital
rule, which limited their debt –to-equity ratio to 8:1.
The SEC Commissioners yielded to this request, in
exchange for Fed oversight of the financial holding
company each belonged to. An investigation by the
Government Accountability Office (GAO 2009)
requested by Congress found that this resulted in the
Big Five’s debt to equity ratio increasing three to four
times just before the Fall of 2008, without sending
warning signals to any of the functional regulators. In
addition, the GAO report revealed that the SEC relied
on the internal risk-management models of the holding
companies without doing its own independent review;
and
c) AIG, the largest insurance company in the world,
marketed credit default swaps as derivatives, when in
fact these were insurance products that obligated AIG
to pay CDO investors in the event of default by
subprime borrowers. Derivatives were loosely
regulated by the CFTC, while insurance products were
strictly regulated by the New York State Insurance
Commission. A Frontline (2009) investigation reports
that in 2004, CFTC Chairman Brooksley Born
recommended increased reporting requirements for
derivatives but the Fed Chairman, Treasury Secretary,
and the Economic Adviser to then President Bush
strongly opposed her.
This highlighted the need for one regulator to provide a
backstop if one regulator did not effectively do its job and
to monitor emerging threats to the whole financial system.
The Council also noted that the Basil II capital
requirements failed to provide adequate incentives for
financial institutions to maintain capital and liquidity
buffers sufficient to absorb extreme system-wide shocks
without taking actions that tended to amplify shocks.
Upon enactment in July 2010, Section 622 of DFA created
a new Financial Stability Oversight Council (hereafter, the
Council) within the U.S. Treasury, with ten voting
members: Treasury Secretary (as Chair) along with the
heads of Fed, FDIC, SEC, CFTC, FHFA, NCUA, OCC,
the new BCFP, and an insurance industry expert. A
minimum of 2/3 or 7 votes are required for any Council
decision. As systemic regulator, the Council is empowered
to designate Systemically Important Financial Institutions
(SIFI) for orderly liquidation by the FDIC in case of
failure, but otherwise, for higher prudential standards by
the Fed (stricter examination, higher capital, liquidity, and
reporting requirements, limits on growth, activities, and
credit exposures). DFA gave the Council broad authority to
require information from SIFIs for the purpose of
comprehensive monitoring to: (a) identify systemic risks
that could arise from the material financial distress or
failure, or ongoing activities of such companies, or that
could arise outside the financial services marketplace; (b)
promote market discipline, by eliminating expectations on
the part of shareholders, creditors, and counterparties of
such companies that the U.S. government will shield them
from losses in the event of failure; and (c) respond to
emerging threats.
A financial institution that received TARP assistance and
subsequently de-banks is automatically designated as a
SIFI under the so-called “Hotel California” provision of
DFA. Other candidates are banks, bank holding companies
(BHCs), and nonbank financial institutions with $50 billion
or more in assets, including a foreign bank or broker-dealer
operating in the U.S. if the home country has not adopted,
or made progress towards adopting, appropriate regulation
to mitigate systemic risk. The Council may also request
information from any financial market utility or financial
institution for purposes of determining whether the utility
or financial institution’s payment, clearing, and settlement
activities are systemically significant. The Paperwork
Reduction Act analysis portion of the Notice of Proposed
Ruling estimated that about 124 companies (37 FDICinsured banks, 26 U.S. BHCs, and 98 subsidiaries of
foreign-owned banks) could be designated as SIFIs. The
Council uses a three-stage process and metrics to designate
a SIFI:
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133
Stage 1. A pool is selected from U.S. and foreign
nonbanks with consolidated assets of $50B that
also exceeds a threshold test on one quantitative
metric pertaining to short-term debt and leverage
ratios, loans and bonds outstanding , and amount
of exposure to credit default swaps and other
derivatives;
Stage 2. Further evaluation of the potential threat posed by
the Stage 1 pool to financial system stability;
Stage 3. Council determines whether the company’s
material financial distress would pose a threat to
U.S. financial stability based on (a) transmission
channels through which the company’s distress
would affect the broader economy (e.g.
interconnectedness, liquidity risk and maturity
mismatch, substitutability, size, extent of
regulatory scrutiny), and (b) whether resolution of
this nonbank would pose a threat to U.S. financial
stability (Polk and Wardell 2011).
While keeping insurance regulation at the state level, DFA
also created a new Federal Insurance Office (FIO) within
the Treasury that is authorized to collect data to monitor
insurance companies and recommend an insurer, including
its affiliate/s, for designation by the Council as a SIFI. A
financial market utility (FMU) can also be designated as
systemically important if the Council determines that its
failure or a disruption to its operations could threaten the
stability of the U.S. financial system by way of spreading
significant liquidity or credit problems among financial
institutions or markets. DFA defines an FMU as “any
person that manages or operates a multilateral system for
the purposes of transferring, clearing, or settling payments,
securities, or other financial transactions among financial
institutions or between financial institutions and the
person. An FMU designated by the Council as
systemically important would become subject to additional
reporting, examination and enforcement action by its
primary federal regulator, as well as compliance with risk
management standards governing its ability to complete
timely clearing and settlement of financial transactions, its
policies and procedures relating to participant or
counterparty default, capital and financial resource
requirements, and margin and collateral requirements.
In July 2012, the Council designated eight FMUs as
systemically important: The Clearing House Payments
Com LLC on the basis of its role as operator of the
Clearing House Interbank Payments System, the
Depository Trust Co., the Chicago Mercantile Exchange,
Inc., CLS Bank International, Fixed Income Clearing
Corp., ICE Clear Credit, LLC, National Securities Clearing
Corp., and The Options Clearing Corp. An Office of
Financial Research (OF was created, within the Treasury,
to operate a Data Center and a Research and Analysis
Center to support the functions of the Council. The heads
of the OFR and FIO serve as advisors or non-voting
members of the Council, along one state insurance
commissioner, one state banking supervisor, and one state
securities commissioner, who are appointed to two-year
terms by state regulators.
Interconnectedness and “Too Big To Fail”
The financial crisis revealed weaknesses in risk
management that made the largest financial institutions so
interconnected due to exposure to a common counterparty
in subprime mortgage-related derivatives transactions. The
decision by the Fed to facilitate JP Morgan’s acquisition of
Bear Sterns in March 2007 had less to do with the size of
Bear’s assets than with its central role in markets for credit
default and interest rate swaps. Banks and broker-dealers
experienced difficulties in assessing counterparty risk,
aggregating exposures across business lines, valuing
instruments when markets became illiquid, managing
contingent liquidity facilities, and managing liquidity risk,
especially with respect to holdings of asset-backed
commercial paper (ABCP) to fund off-balance sheet SIVs
(Burns 2008). Moreover, while banks were subject to
orderly liquidation by the FDIC, there was no orderly
liquidation process for nonbank financial institutions.
Values of subprime mortgages and the derivatives based on
these fell too fast during the financial crisis and could not
be easily valued once the market froze. This was
incompatible with the long period needed to unwind a
business under the Bankruptcy Code. To unfreeze financial
markets, the Fed was left with no choice but to extend
emergency loans to nonbanks, and Congress eventually
authorized the use of taxpayer’s money to bailout financial
institutions.
To enhance transparency in how SIFIs are interconnected,
the OFR will create publicly accessible databases of
financial companies and financial instruments, including
financial transaction and position data, which identifies
counterparties, subject to confidentiality restrictions. To
enhance transparency in the derivatives market, in
particular, DFA incentivizes the clearing of derivative
contracts through a centralized counterparty by reducing
the capital requirement compared to those traded over the
counter. Central clearinghouses are subject to disclosure,
capital and margin requirements.
To promote financial stability and address the perception
that large financial institutions are “too big to fail,” Section
165 of DFA establishes limits on growth of financial
institutions, which in effect, also limits industry
concentration. These are (a) prohibition of a merger or
consolidation or acquisition that would result in a
company’s consolidated liabilities exceeding10 percent of
the aggregate consolidated liabilities of all financial
companies, and (b) Fed approval of a SIFI’s acquisition of
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134
a financial company with $10 billion or more in
consolidated assets. Management and director interlocks
between unaffiliated SIFIs are prohibited, except if
temporary in case of a merger, acquisition or consolidation.
A SIFI will be subject to an annual stress test by the Fed,
and is also required to do its own stress test at least once a
year. The results of these stress tests are to be made public
to increase transparency. In addition, SIFIs are required to
maintain a countercyclical capital buffer to build up an
extra layer of capital or reduce leverage in times of
excessive aggregate credit growth that, in the judgment of
bank regulators, elevates system-wide risk.
DFA empowers the Fed & FDIC to set standards for risk
management and crisis avoidance. The FDIC can conduct a
special examination of a SIFI deemed not to be in a
financially sound condition or recommend enforcement
action against a BHC to the appropriate Federal banking
agency if its conduct poses a risk to the Deposit Insurance
Fund. DFA gives the FDIC back-up authority if such
agency does not act. Every year, each SIFI is required to
submit a Resolution Plan or ‘living will’ specifying
remediation efforts for going concerns under conditions of
severe financial distress, or in case of failure or insolvency,
how it will be reorganized or liquidated under the
Bankruptcy Code within a reasonable period of time and in
a manner that substantially mitigates serious adverse
effects on the financial system. In the case of foreignowned banks, the required resolution plan is limited to its
U.S. operations.
The Council prescribes a minimum content for this
Resolution Plan, among which are: (a) mapping of the
interconnectedness of the domestic and foreign core
business lines and critical operations that, if disrupted,
would materially affect the funding or operations of the
firm; (b) unconsolidated balance sheet and a consolidating
schedule for all entities that are subject to consolidation,
detailing liabilities, collateral management, of-balance
sheet exposures, trading and derivatives activities, material
hedges and hedging strategies mapped to a legal entity,
major counterparties of the company and the likelihood
that their failures would result in the firm’s material
financial distress or failure, and trading, payment, and
settlement systems; (c) strategic plan for rapid and orderly
resolution in the event of material financial distress or
failure, including funding, liquidity, capital needs and
available resources for each core business line and critical
operation, strategies to maintain operations and funding of
its material entities and ensure that any bank subsidiary
will be adequately protected from the activities of any
nonbank subsidiary.
The first set of Resolution Plans were submitted in July
2012 by the largest eight banking organizations with
$250B or more in assets , namely: Bank of America Corp,
Barclays PLC, Citigroup Inc., Credit Suisse Group AG,
Deutsche Bank AG, Goldman Sachs Group Inc., JP
Morgan Chase & Co., Morgan Stanley & Co LLC, and
UBS AG. The middle-tier nonbanks (with $100~$250
billion in assets) are required to submit theirs in July 2013,
while the lowest –tier nonbanks (with $50~$100 billion in
assets) have up to the end of 2013. In the event of change
in conditions or circumstances that render the resolution
plan ineffective until revisions are made, the FDIC and the
Fed can require an interim update.
Once received, both the Fed and the FDIC will review the
plan for credibility, i.e. the resolution strategies are based
on information and data that are verifiable and employ
reasonable projections assuming (a) financial distress at a
time when the entire financial system would also be under
stress, and (b) no extraordinary support by the U.S. or any
other government entity would be available to prevent the
company’s failure. If the Fed and FDIC jointly determine
that the plan is not credible or is deficient, the SIFI will be
required to fix the deficiencies and resubmit a plan within
90 days. If the deficiencies are not remedied, the FDIC and
the Fed will impose more stringent capital, leverage or
liquidity requirements. If within two years following the
imposition of such requirements, the revised resolution
plan is still deficient, the Fed and FDIC can either restrict
growth, activities, and operations or mandate divestitures.
Triggers for remediation such as capital levels, stress test
results, and risk-management weaknesses will be used by
the FDIC and the Fed to identify a SIFI that will subject to
early remediation requirements (similar to the FDIC’s
Prompt Corrective Action authority). Remedial actions will
include restrictions on growth, capital distributions,
executive compensation, and sale of assets and common
stocks.
Extension of FDIC and Fed safety net to nonbanks
The runs on shadow banks (mortgage brokers, finance
companies, money market mutual funds, hedge funds,
broker-dealers, and investment banks) eventually led to a
deleveraging process that caused banks to mark down their
holdings of subprime mortgage-related assets, and liquidity
to dry up as large depositors withdrew cash while interbank
lending froze. The FDIC had to increase the insurance limit
to $250,000, and eventually, Congress agreed to pass the
Trouble Assets Relief Program (TARP), authorizing the
use of taxpayer funds to augment the FDIC insurance fund
and to lend cash to banks throughout the country. While
over 85% of TARP funds have been repaid, there was
strong public outcry to prevent future use of taxpayer’s
money to extend the Fed and FDIC safety net for banks to
nonbank financial institutions.
Section 619 of DFA, also known as the Volcker Rule, was
designed to protects taxpayers and depositors by
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135
prohibiting insured banks from using deposits and the
bank’s capital to a) engage in proprietary trading (trading
to profit from near-term price movements), and (b) lend or
extend guarantees, and invest more than 3% of tier 1capital
on hedge funds and private equity funds. The proposed rule
to implement the Volcker Rule lists the following as
permitted or non-proprietary trading activities:
(1) market making-related activity or holding an inventory
of securities to meet reasonably expected near term
demands of clients, customers or counterparties; (2) riskmitigating hedging on a portfolio basis (i.e. conducted at a
level above that of a specific trading desk) in recognition
of a bank’s need to reduce the risk they face from
exposures to interest rate, exchange rate, and other risk
factors; (3) underwriting that is essential for facilitating
equity and debt issuance for raising capital; (4) transactions
on behalf of customers such as purchase, sale, acquisition,
or disposition of securities and other instruments; (5)
transactions in government securities; (5) investments in
small business investment companies, public welfare
investments and certain qualified rehabilitation
expenditures under federal or state tax laws; and (7) certain
offshore activities and insurance activity.
In contrast, banks are prohibited from so-called ‘bright
line’ proprietary trading, which involves the use of the
bank’s capital to benefit from future price movements, and
in addition, has one or more of the following additional
characteristics: (1) the trading activity is organized and
conducted for the sole purpose of generating profits from
trading strategies; (2) the level of trading is not
commensurate with formal market making responsibilities
or customer exposure; (3) there is physical and/or
operational separation from market making and other
operations having customer contact; (4) trades with or use
the services of sell side analysts or brokers/dealers; (5)
receives or utilizes research or soft dollar credits provided
by other broker-dealers; and (6) the compensation structure
is similar to those of hedge fund managers and other
managers of private pools of capital.
As for hedge funds and private equity funds, a bank is
permitted to organize and offer such a fund only if: (1) it is
part of the bank’s trust, fiduciary, or investment advisory
services to customers; (2) the bank’s ownership interest
one year after a fund is established or the aggregated
investments of the bank in such funds does not exceed 3%
of the bank’s Tier 1 capital; (3) the bank does not
guarantee, assume, or insure the obligations or
performance of the fund; (4) no bank director or employee
has an ownership interest in the fund unless he or she is
directly engaged in providing services to the fund; and (5)
the bank does not share the same name, or variation of the
same name, with the fund.
If a bank engages in any activity or investment that
functions as an evasion, if not a violation, of the Volcker
Rule restrictions, regulatory agencies are empowered to
ask the bank to terminate such activity or subject it to
additional capital requirements and quantitative limits on
activities. Full compliance by banks is scheduled for July
21, 2014 with possible three one-year extensions up to or
July 21, 2017, to give banks up to five years to divest
inventories of illiquid mortgage-backed securities affected
by the implementation of the Volcker Rule prohibitions. So
far, the writing of the final rule to implement the Volcker
Rule has faced repeated delays as regulators grappled with
the complexity of the proposal and widespread discontent
from the financial industry, reflected in thousands of
comment letters from banks and lobbyists concerned about
loss of profits, jobs, international competitiveness, and
market liquidity. Issuance of the final rule was pushed
from September 2011 to July 2012.
For permitted trading activities, a bank holding company or
bank that has aggregated trading assets and trading
liabilities equal to 10% or more of its tier 1 capital and has
quarter-end total assets of $1 billion or more will be
required to put up a so-called market risk capital to be
considered adequately capitalized. No small bank holding
companies or banks would be subject to this rule. The
minimum required ratio of total qualifying capital (the sum
of tier 1 capital and tier 2 capital, net of all deductions) to
the sum of credit risk-weighted assets and market riskequivalent assets is 8%. The market risk-equivalent assets
is calculated as the new market risk measure multiplied by
a factor of 12.5, where the new market risk measure is
expanded to include three new market risk components,
namely: stressed value-at-risk, incremental risk, and
comprehensive risk. There is also a quarterly requirement
to publicly disclose the aggregate amount of on-balance
sheet and off- balance sheet securitization positions by
exposure type and the aggregate amount of correlation
trading positions. In addition, for each material portfolio
covered by the market risk capital rule, a bank is required
to disclose the high, low, and mean value at risk (VaR)based measures over the reporting period, a comparison of
VaR-based measures with actual results and an analysis of
important outliers, incremental and comprehensive risk
capital requirements, the bank's valuation policies,
procedures, and methodologies for covered positions
(including, for securitization positions, the methods and
key assumptions used for valuing such positions),
characteristics of its internal models, the approach used by
the bank to determine liquidity horizons, and a description
of the stress tests applied to each market risk category.
ANALYSIS
In the previous two sections, I presented provisions of
DFA that address the failures of regulation and flaws in the
financial system that weakened market participants’
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136
exercise of market discipline. Many of the provisions of
DFA are steps in the right direction and have the potential
to make the financial system more stable. In the discussion
below, I will focus on remedies that I find weak or lacking.
No credit risk retention for securitized loans
Taking oversight of mortgage brokers and finance
companies out of state regulators and into uniform
regulation at the federal level by the newly created Bureau
of Consumer Financial Protection has the potential of
preventing the lax underwriting and fraudulent lending that
led to the massive growth of subprime mortgage loans.
Although many critics blame the Community
Reinvestment Act for encouraging banks to lend to lower
income Americans, this legislation did not authorize banks
to give up sound underwriting practices, nor allow their
affiliate mortgage brokers and finance companies to do the
same. As in any regulatory agency, the effectiveness of the
BCFB depends on the regulatory philosophy of its
Chairman, or the President that appoints him/her to that
position. If the leadership takes on the position that
markets are self-correcting and industry self-regulation is
sufficient, the authority to act on bad lending practices
given to it may not be exercised. Likewise, a Congress that
believes in the same regulatory philosophy could simply
starve the agency of funds, hence qualified staff, so that it
could not implement its rules.
In addition, the requirement that banks and securitizers
disclose as much information about the underlying
mortgages of a structured product is a good way to increase
transparency for investors, but it does not prevent some
investors from relying on apparent market evaluation by a
‘herd’ of investors instead of a fundamental analysis of the
investment based on information that has been made
available for their use. Finally, I find the required
minimum capital reserve set at 5% of the credit risk or
future loss from defaults of mortgage loans sold to be too
low of a potential loss from bad underwriting practices to
be an effective disincentive.
Regulatory arbitrage and off-balance sheet conduits
The revisions of capital adequacy requirements do have the
potential to discourage the use of off-balance sheet
conduits to reduce capital charge. Given that the baseline
capital requirement is only 8%, or 11% on banks with over
$50 billion in assets, the question remains: why allow such
off balance sheet commitments or transactions at all? For
as long as some transactions can be reported off the books,
the temptation to hide risky transactions remains.
Moreover, making the required capital on bank investment
in investment-grade tranches of CDOs greater than that on
mortgage and other loans banks originate, could discourage
or reduce such investment, but not completely eliminate it.
If the benefit of securitization is really to allow banks to
transfer the risk outside of the banking system, it would
seem that prohibiting such an investment altogether makes
more sense. It will also simplify regulatory oversight of
banks in that bank examiners will not have to verify that
the investment-grade classification of such investment is
appropriate.
To distinguish prime from subprime mortgage loans, the
use of loan-to-value ratios and loan characteristics, instead
of credit ratings, seem easy to implement and appropriate.
If, hypothetically, a subprime borrower with an outstanding
Category 2 loan of $200,00 finds the appraisal value of the
house to be lower, say, $140,000 (i.e. the borrower is
‘under water’), the LTV ratio is 143%. The risk-weight is
200%, which would mean the capital charge will be
calculated on twice the outstanding balance ($400,000). A
10.5% capital charge (85 plus the capital buffer of 2.5%)
means that if the borrower who is now under water and has
no incentive to keep the house were to walk away, bank
stockholders would absorb the first $6,300 of the $60,000
loss from a foreclosure sale of this house at the appraised
value. Provided the probability that a 30% housing market
correction is only 10.5% (about once in 10 years), the
FDIC insurance fund and bank creditors are protected from
this loss. Perhaps, the risk-weights ought to also account
for the difference in real estate market volatility, assigning
a higher weight to the ten largest metropolitan areas
tracked by the Case-Shiller Home Price Index.
Loose oversight of derivatives and hedge derivatives
Transparency will indeed be enhanced by to finally
subjecting derivatives to disclosure, capital and margin
requirements, and hedge funds and private equity fund to
registration requirements. The requirement and incentives
provided to trade credit default swaps and other derivatives
that can be standardized through centralized clearinghouses
will also increase transparency as far as counterparty
exposures and also reduce counterparty risk. Establishing a
separate clearinghouse for each type of derivative may,
however, increase counterparty risk as well if there are not
enough participants in that particular market. Duffle and
Zhu (2011) recommend that credit default swaps, for
example, be traded with interest rate swaps and other
derivatives that are already traded in a centralized
clearinghouse, instead of a clearinghouse dedicated to
credit default swaps only.
Over-reliance on credit rating agencies
While capital adequacy requirements on baking
organizations are no longer dependent on credit ratings,
market investors are still dependent on them. DFA imposes
new disclosure requirements that will enhance
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137
transparency of CRAs rating methodologies and track
record. Yet, DFA does not provide market investors with
increased choice. Some argue that there is a need to relax
entry barriers to increase the number of competing credit
rating agencies, or have a public rating option since credit
rating has an element of a public good in it, or subject a
credit rating agency to a penalty if future default on a
security rated turns out to be more than what was implied
by its rating (Hunt 2011).
Irrational (herding) behavior of market participants
Here is one where losses borne by market participants and
longer experience in the market can be far better deterrents
than government regulation, except, if there is fraud
involved or publicly disclosed information is not consistent
with generally accepted accounting principles.
Compensation schemes rewarded excessive risk-taking
Provisions of DFA intended to correct this problem is
probably the most criticized in Wall Street (see Earle,
2011; Gold et al. 2011), but a closer examination of the
provisions show that these are quite lame. For example, the
stockholder’s ‘say on pay’ is embodied in a non-binding
vote. Moreover, the ‘clawback’ provision is limited to
taking back executive compensation that was based on
inaccurate financial statements, but does not provide
incentives for financial companies to design compensation
schemes that would not reward traders for short-term gains
from excessively risky trades that later result in significant
losses. Also, for a failed institution that is under FDIC
receivership, DFA caps what the FDIC can take back to the
last two years’ compensation from past or present senior
executive or director, who is substantially responsible for
the failure of the firm, instead of giving the FDIC the
discretion to determine that amount in proportion to the
loss borne by stakeholders of the institution and the
Deposit Insurance Fund.
No one regulator to monitor and regulate systemic risk
In composition, the Financial Stability Oversight Council
resembles the Clinton era President’s Working Group,
which was supposed to be a forum for heads of various
federal financial regulators to coordinate oversight and
consult with each other. This group was in existence
throughout the build-up of the housing bubble and chose
not to regulate the derivatives market. It failed to detect or
act on early warning signs mainly because the more
influential members believed in less regulation. So, the
two-year old Council is susceptible to the same thinking.
What makes the Council potentially more effective is that
it is supported by an Office of Financial Research which
will support it in terms of collecting and analyzing
measures of systemic risk, and DFA has taken many
institutions that used to be in the shadow banking system
under federal regulation and subject to disclosure
requirements. It is also empowered to designated large,
complex financial institutions as systemically important so
that they can be subject to stricter regulation and
monitoring by the Fed and FDIC.
The fact that SIFIs are subject to higher prudential
standards, restrictions on growth, and closer scrutiny
should be an incentive to divest certain operations in order
to fall below the $50 billion asset cut-off. The fact that no
major divestiture in the finance industry has occurred since
DFA was enacted suggests that SIFI designation is not
enough of a disincentive. As I said earlier, how well early
warning signs can be detected, and how well data,
including the annual resolution plans, can be analyzed
largely depends on the quality of OFR analysts hired, and
ultimately how competitive the salaries that the
government can pay them are compared to what the private
sector pays. A bleak outlook on the Council’s ability to
obtain early warning signs in the future is cast by the fact
that JP Morgan Chase’s trading loss made public in May,
escaped risk management oversight not only of its top
management and the Fed staff embedded in the company’s
headquarters. It seems more cost-effective to break-up the
large systemically important financial institutions by (a)
ending the bank holding company status of Goldman Sachs
and Morgan Stanley, and (b) repealing the Gramm-LeachBliley Act which allowed the comingling of banking,
investment banking, and insurance activities in one
financial holding company or banking organization.
Interconnectedness and “Too big to fail”
The Resolution Plans do function as some kind of a ‘living
will’ in that it requires systemically important financial
institutions to map the interconnectedness of their lines of
business and operations, identify counterparties to
significant transactions, and their transactions, as well as
financial institutions that might possibly acquire them in
times of financial distress. Although there are now limits
on the growth of financial institutions designated as
systemically important, at their existing size, they are still
“too big to fail”. If financial markets should ever freeze up
again as happened in 2007~2008, no one can convince me,
and I am sure, the executives of systemically important
financial institutions, that the Fed or the Treasury, through
Congress, would not bail them out. The moral hazard will
always be there for as long as that perception exists. Aside
from the break-up of large financial institutions mentioned
earlier, reducing the interconnectedness of these large
institutions will reduce the urgency of a bailout. There is
nothing in DFA that credibly reduces current size because
the two surviving large investment banks are now bank
holding companies within the Fed’s safety net.
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Moreover, the Volker Rule not only stops short of going
back to the Glass-Steagal separation of banking from
investment banking and insurance businesses, but will
engage federal bank regulators in a ‘cat-and-mouse’ game
with banks in discriminating between permissible and
prohibited trading activities. A study by the Financial
Stability Oversight Council completed in January 2011
identified difficulties in implementing the Volcker Rule,
some of which are listed below:
a) Banking entities can engage in prohibited proprietary
trading through inconsistent or incomplete hedging in
the context of their market making activities;
b) A combination of permitted activities might be used to
circumvent the proprietary trading restrictions;
c) Discerning the source of a market maker’s profit is
challenging, especially for less-liquid financial
instruments;
d) Measuring the revenue that is attributable to the bidask spread is difficult and not consistently observable
especially in illiquid markets. Moreover, to the extent
that market makers need to assume some risk to
facilitate customer transactions, a portion of trading
revenues will always derive from underlying market
movements;
e) Market makers often deal with other intermediaries in
order to manage their overall risk exposure and
maintain market continuity. Distinguishing appropriate
volumes of inter-dealer trading for market making
versus proprietary trading may be a challenge; and
f) Measures of “near term” trading accounts and “shortterm” price movements are dependent on market
instrument characteristics. What constitutes trading in
the near term, therefore, may depend on the
characteristics and the trading volume of the particular
market. As a result, a trading account would not
preclude illiquid financial instruments, such as swaps.
g)
It would no doubt be difficult and costly to enforce the
Volcker Rule, and violations would be costly to adjudicate.
A lot depends on the regulatory agency’s budget,
independence, integrity, and ability to get ahead of
financial innovation and regulatory arbitrage. A second
best solution to breaking up SIFIs or going back to the
Glass-Steagal separation of banking, investment banking,
and insurance businesses, is to establish an FDIC-like
insurance fund that can be tapped to bailout a nonbank in
distress. The institution of deposit insurance in 1934
significantly reduced episodes of banking crises in the U.S.
to two: the S & L crisis of the 1980s and the financial
crisis of 2008. Acharya et al. (2011) proposed periodic
assessment on nonbanks to build up a fund that can be used
to bail out or liquidate a failing nonbank institution. It will
serve as some kind of a Pigouvian tax so that the negative
externality that the potential failure of a nonbank imposes
on the economy can be internalized. I believe such an
assessment was proposed when the shape of Wall Street
Reform was being deliberated in Congress, but a few
Republicans whose votes were needed to pass DFA asked
that it be struck down. Another idea that has been
presented is to require financial institutions to maintain a
certain amount of contingent capital like bonds that would
convert to preferred stock or common stock in times of
market stress (Coffey 2011).
CONCLUSION AND AREAS FOR FURTHER
REGULATORY ACTION
The Dodd-Frank Act is indeed a good faith effort on the
part of Congress and the Obama administration to establish
centralized monitoring of systemic risk, close previous
gaps in regulation, and increase transparency of complex
financial products to enable market participants to exercise
market discipline. It addresses many of the key flaws and
vulnerabilities that led to the subprime crisis. However, its
effectiveness in preventing another financial crisis depends
on how adequately the regulatory agencies involved will be
funded, the regulatory philosophy of the top leadership of
these agencies, which will determine how strictly the
provisions will be enforced, and how well the regulated
institutions can innovate their way around the new rules.
Moreover, the work of financial reform is not complete.
While bank regulators have proposed new capital
requirements on banks that address credit and market risks,
they have not proposed liquidity thresholds. Likewise,
work still needs to be done on putting limits so that
reliance by financial institutions on short-term funding of
longer term assets can be reduced. Lastly, privatization of
Fannie Mae and Freddie Mac, which were put under
federal conservatorship in 2008, still has to be worked out.
It is important that the moral hazard inherent in the
perception of government guarantee be removed. At the
very least, once privatized, these institutions have to be
designated as SIFIs and subjected to the orderly liquidation
authority of the FDIC.
In its second annual report to Congress, the Financial
Stability Oversight Council identified financial structure
vulnerabilities that have not been addressed in DFA. One is
the risk of runs on money market funds (MMF) due to two
core characteristics: (1) lack of mechanism to absorb a
sudden loss in the value of a portfolio security without
threatening the stable $1.00 NAV; (2) a “first mover”
advantage, which gives investors the incentive to redeem
as soon as a threat to the value or liquidity of the MMF is
perceived. The Council expressed support for either of two
recommendation made by SEC Chairman Shapiro: (a)
mandatory floating NAV to reflect the true market value of
underlying assets; and (b) a capital buffer to absorb losses,
possible combined with a restriction to reduce the incentive
to exit the fund. A restriction to reduce the incentive to exit
MMFs would make these funds less attractive compared to
bank deposits. Rather, than impose such a restriction,
Proceedings of the 2012 Pennsylvania Economic Association Conference
139
adopting the floating NAV to make these funds more
transparent and (not or) requiring a capital buffer would
limit risk-taking by fund managers and promote market
discipline among investors.
repo market, in which MMFs are lenders. The Council also
recommended that the CFTC and SEC keep pace with
competitive and technological developments in financial
markets, such as high speed, automated trading activities
and cyber security threats.
Another related area where the Council recommends action
is the elimination of intraday credit usage in the tri-party
Figure 1: Regulatory Structure: November 1999~July 2010
Financial
Holding Company
National
bank
State
member
bank
Commercial
paper
funding
corporation
State
nonmember
bank
National
thrift
Non-U.S.
commercial
bank
Special
financing
entity
State
thrift
Bank
holding
company
Asset
management
company
National
bank
U.S. federal regulators
Broker/
dealer
Non-U.S.
investment
bank
Other
CFTC
OTS
State regulator
FDIC
SEC
Non-U.S. regulator
Federal Reserve
OCC
Futures
commission
merchant
Futures
commission
merchant
National
bank
Life insurance
company
(broker/
agent/
underwriter)
U.S.
securities
broker/
dealer /
underwrite
r
Mortgage
Broker/
Finance
Company
Non- U.S.
securities
broker/
dealer /
underwrite
r
Unregulated
SRO
Source: Government Accountability Office (GAO 2009)
Note: The Dodd-Frank Act would add the Financial Stability Oversight Council on top of the Financial Holding Company in
this chart, and subject the mortgage brokers and finance companies to federal regulation by the new Consumer Financial
Protection Bureau. It also abolished the OTS and transferred its regulatory authority to the FDIC and the OCC (see Legend
below).
Proceedings of the 2012 Pennsylvania Economic Association Conference
140
Legend:
CFTC Commodity Futures Trading Commission
FDIC Federal Deposit Insurance Corporation
Federal Reserve Board of Governors
OCC Office of the Comptroller of the Currency
OTS Office of Thrift Supervision
SEC Securities and Exchange Commission
SRO
Self-regulatory organization (financial industry
associations)
Table 1: Dodd-Frank Act Provisions and Problems that Led to the Housing Bubble and Systemic Risk
Problem
DFA Provision
Securitization
• Banks are required to put up capital against no less than 5% of
the subprime mortgage loans sold so that they would have “skin
Banks had no incentive to observe strict underwriting
in the game”;
standards because banks could originate subprime
• Securitizers are required to disclose information that would
mortgage loans, then pass on 100% of the credit risk of
enable assessment by purchasers of mortgage backed securities
such loans by sale to investment banks and governmentof the quality of underlying loans.
sponsored enterprises (Fannie Mae and Freddie Mac).
New Bureau of Consumer Protection to:
Shadow Banking: mortgage brokers & finance
companies
• consolidate the responsibilities previously handled by OCC,
No Federal regulator of mortgage brokers & finance
OTS, FDIC, the Fed, NCUA, HUD, FTC pertaining to consumer
companies; regulation by States were uneven and lax.
complaints against mortgage-related businesses;
• act fast without waiting for Congress to pass a law prohibiting
Loose prosecution of mortgage lending fraud.
bad lending practices harming consumers.
Shadow Banking: off-balance sheet SIVs
Banks moved MBS off-balance sheet into SIVs. Only the
backup liquidity line was on the balance sheet and
subject to a capital charge, although 100% the bank was
exposed to the risk of default and potentially illiquidity
of the MBS collateral.
Credit Rating Agencies
• Loose oversight of credit rating agencies who engaged
in a conflict of interest by earned consulting fees to
advise investment banks on the optimal way to
securitize MBS to get desired credit ratings;
• Lack of transparency about credit rating methodologies
and track record.
Derivatives
Loose Oversight of Complex or Opaque Derivatives
like credit default swaps, asset-backed commercial paper,
and collateralized debt obligations led to lack of
transparency as to risk of the financial product and
counterparty risk exposure.
No single regulator of systemic risk
Banks are now required to put up capital of no less than 5% of the
risk exposure to SIVs.
New Office of Credit Ratings within SEC to:
• prohibit compliance officers from working on ratings;
• require a 1-year look back review when an employee is hired by
an underwriter of a previously-rated security;
• require public disclosure of rating methodologies and ratings
track record;
• require use of 3rd party sources not just the institution being
rated.
• Trading of standardized derivative products in central
clearinghouse or exchanges will be required;
• SEC and CFTC to collect and publish data to increase
transparency of different derivative products, volumes, and
counterparties.
• SEC and CFTC to impose capital & margin requirements on
swap dealers and major swap participants.
New Financial Stability Oversight Council (Council) within the
U.S. Treasury
• Treasury Secretary as chairman with 9 other voting members:
Heads of FED, FDIC, SEC, CFTC, FHFA, NCUA, OCC,
BCFP, Insurance Expert
• has broad authority to monitor & designate Systemically
Important Financial Institutions for stricter regulation by the
Federal Reserve Board, and in case of failure, orderly
Proceedings of the 2012 Pennsylvania Economic Association Conference
141
Transfer of FDIC & Fed Safety Net for Banks
to Nonbanks
Failure of nontraditional banking operations during the
Financial Crisis was a drain to the FDIC Insurance Fund,
the Fed’s Emergency Lending Facilities, and Treasury.
Moral Hazard & Externality Problems of “Too Big
To Fail”
• No orderly liquidation process for SIFIs
• Financial asset values fall too fast or cannot be easily
valued in times of market stress; incompatible with the
long period needed to unwind a business under the
Bankruptcy Code ;
Reliance on emergency loans from the Fed or taxpayer
bailout loans from the Treasury.
liquidation by the FDIC.
Volcker Rule
• Protects taxpayers and consumers by limiting banks’ ability to
gamble with customer deposits.
• Prohibits Insured Banks from the following activities:
• Proprietary trading - trading activity in which it acts as a
principal in order to profit from near-term price movements
[speculative trading];
• Investing, making loans, purchasing assets, extending
guarantees to hedge funds and private equity funds, and
affiliate/s for which the banking entity acts as investment
manager or advisor or sponsor.
Annual Living Will & Orderly Liquidation
• Fed & FDIC to:
set standards for risk management and crisis avoidance;
• require the annual submission by each SIFI of a 0Resolution
Plan specifying remediation efforts for going concerns under
conditions of severe financial distress or orderly liquidation by
the FDIC in case of insolvency or failure.
Table 2 Changes in Asset Risk-weight Determination
Credit Exposures to:
Foreign sovereigns, banks,
public sector entities
Changes in Asset Risk-weight Determination
More risk-sensitive treatment using the Country Risk Classification measure produced
by the Organization for Economic Co-operation and Development.
Risk weight is assigned according to loan terms and loan-to-value ratio instead of credit ratings.
Risk weight changed from one risk weight of 35% to weights of 35%~100% (see Table 4).
Increased risk-weight from 100% for all commercial real estate exposure to 150% risk weight for certain credit facilities
Highly volatile commercial
that finance the acquisition, development or construction of real property.
real estate
Replaces the current external credit ratings-based approach with a formula-based approach for determining a
Securitization
securitization exposure’s risk weight based on the weighted average risk of underlying assets and exposure’s relative
position in the securitization’s structure.
Raises the credit conversion factor for most short-term commitments (extension of credit or purchase assets, contingent
liabilities, repo-style transactions, standby letters of credit, forward agreements) that cannot be cancelled
Off-balance Sheet items
unconditionally: Maturity of 1 year or less: raised from 0 to 20%; Maturity over 1 year: 50%
Credit enhancement or warranty on assets sold/transferred to 3rd parties: 100%
Revises the measure of the counterparty credit risk of repo-style transactions.
Removes the cap of 50% risk weight for derivative contracts.
Derivative Contracts
Transactions cleared through Provides preferential capital requirements for derivatives and repo-style cleared with central counterparties that meet
specified standards. Also requires that a clearing member of a central counterparty calculate a capital requirement for its
a central clearinghouse
default fund contributions to that central counterparty.
Residential mortgage
Table 3 Proposed Capital Ratios to be Applied to Risk-weighted Assets
Aspect of Proposed Requirements
Proposed Minimum Capital Ratios
Baseline total capital ratio
Unchanged at 8 %
Tier 1 capital ratio
Increased from 4% to 6 %
New common equity tier 1 capital ratio
4.5%
Unchanged at 3% banks with CAMEL rating of 1; 4% otherwise,
but modified by more stringent definition of tier 1 capital.
New supplementary leverage ratio required of SIFIs.
New common equity tier 1 capital of at least 2.5% must be maintained above the 8% minimum risk-based
capital requirement to avoid restrictions on capital distributions and certain discretionary bonus payments.
Leverage ratio (Equity to total assets)
Capital Conservation Buffer
Proceedings of the 2012 Pennsylvania Economic Association Conference
142
Table 4 – Proposed Risk Weights (percentages of 8% baseline capital requirement for Residential Mortgage Exposures
Loan-to-value ratio
Category 1
Risk-weight
Category 2
Risk-weight
60% or less
60 ~ 80%
80 ~ 90%
90% or more
35%
50%
75%
100%
100%
100%
150%
200%
3
ENDNOTES
1
Bear Sterns was acquired by Goldman Sachs, Lehman
Brothers filed for bankruptcy and Merrill Lynch was
purchased by Bank of America in 2008.
2
The President’s Working Group includes the Treasury
Secretary, and the chairmen of the Fed, SEC, and
Commodities Futures Trading Commission (CFTC). It was
created by President Clinton in 1998 in the aftermath of the
1987 stock market crash, and has since then served as an
inter-agency coordinator for financial market regulation
and policy issues.
The BCBS is a committee of banking supervisory
authorities, which was established by the central bank
governors of the G–10 countries in 1975. It currently
consists of senior representatives of bank supervisory
authorities and central banks from the United States, and
25 other countries (Argentina, Australia, Belgium, Brazil,
Canada, China, France, Germany, Hong Kong, India,
Indonesia, Italy, Japan, Korea, Luxembourg, Mexico, the
Netherlands, Russia, Saudi Arabia, Singapore, South
Africa, Sweden, Switzerland, Turkey, and the United
Kingdom).
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Acharya, Viral and M. Richardson. ed. 2009. Restoring Financial Stability: How to Repair a Failed Financial System. NJ: John
Wiley & Sons.
_____, T. Philippon, M. Richardson, and N. Roubini. 2009. The Financial Crisis of 2007-2009: Causes and Remedies. A Bird’s
Eye View, in V. Acharya and M. Richardson. ed. Restoring Financial Stability.
_____ and Phillip Schnabel.2009. How Banks Played the Leverage Game, in V. Acharya and M. Richardson. ed. Restoring
Financial Stability.
_____, T. Cooley, M. Richardson, R. Sylla, and I. Walter. 2011. The Dodd-Frank Wall Street Reform and Consumer Protection
Act: Accomplishments and Limitations. Journal of Applied Corporate Finance, 23(1), 43-56.
Akerlof, George. 1970. “The Market for "Lemons": Quality Uncertainty and the Market Mechanism,” Quarterly Journal of
Economics, 84 (3): 488-500.
Basel Committee on Banking Supervision. 2010. Basel III and Financial Stability. Speech by Stefan Walter, Secretary General,
at the 5th Biennial Conference on Risk Management and Supervision, Financial Stability Institute, Bank for International
Settlements, Basel, November 3~4, 2010.
Bank for International Settlements. 2009. The Role of Valuation and Leverage in Procyclicality. Committee on the Global
Financial System. Publication No. 34, Bank for International Settlements, April 2009. http://www.bis.org/pub/cgfs34.pdf
Bernanke, Ben. 2012. The Federal Reserve and the Financial Crisis. Lecture delivered at George Washington University, March
2012.
Bullard, James; Christopher Neely, and David Wheelock. 2009. Systemic Risk and the Financial Crisis: A Primer. Federal
Reserve Bank of St. Louis Review, September-October 2009, 91(5): 403-17.
Coffee Jr., John. 2011. Systemic Risk After Dodd-Frank: Contingent Capital and the Need for Regulatory Strategies Beyond
Oversight. Columbia Law Review, 111(4):795-847.
Crotty, James. 2009. Structural causes of the global financial crisis: a critical assessment of the ‘new financial architecture’.
Cambridge Journal of Economics, 33(4):563-580.
Davis Polk and Wardwell, LLP. 2011. Council Releases Proposed Rules on Designation of Systemically Important Nonbank
Financial Companies. Client Memorandum. October 17, 2011.
Dodd–Frank Wall Street Reform and Consumer Protection Act .2010. Public Law 111-203. July 21, 2010.
Duffle, Darrell and Haoxiang Zhu. 2011. Does a Central Clearing Counterparty Reduce Counterparty Risk? Review of Asset
Pricing Studies, April 2011.
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Earle, J. E. (2011). The Dodd-Frank Act: Immediate and Longer-Term Impacts on Executive Compensation. Benefits Law
Journal, 24(1): 58-69.
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Minimum Regulatory Capital Ratios, Capital Adequacy, Prompt Corrective Action, and Transition Provisions.” Notice
of Proposed Ruling, June 7, 2012.
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Proceedings of the 2012 Pennsylvania Economic Association Conference
144
GOVERNMENT POLICY AND RESULTANT EFFECT ON NICHE INDUSTRIES:
THE CASE OF USPS “EVERY DOOR DIRECT MAIL”
Brenda Ponsford, Ph.D., J.D.
Clarion University
William R. Hawkins, M.A., M.A.
U.S. House of Representatives
Committee on Foreign Affairs
Subcommittee for Oversight and Investigations
ABSTRACT
The USPS has introduced a new service for direct mail
advertisers called “Every Door Direct Mail”. The concept is
that the advertiser identifies the target neighborhoods and the
USPS carrier simply puts a piece in every mail drop on his
route. Yet, for something so easy to conceive and implement,
the impact is harmful to the private mailing service industry
that was created to deal with past USPS bureaucracy and
inefficiency. This paper examines the interaction of
inefficiency and economic principles, entrepreneurial
response to burdensome regulations, and the impact on
private firms when a state enterprise suddenly become
entrepreneurial itself.
The United States Postal Service (USPS) has introduced a
new service for direct mail advertisers called “Every Door
Direct Mail” (EDDM). The concept is extremely simple. The
advertiser identifies the neighborhoods it wants to target, and
its printed material is delivered with the day's mail to every
address for less than 14.5 cents apiece. The advertiser doesn't
need to know names or street addresses; meaning no labeling
is needed. The USPS carrier simply puts a piece in every
mail drop on his route. Yet, for something so easy to
conceive and implement, the impact is revolutionary on the
private mailing service industry that was created to deal with
past USPS bureaucracy and inefficiency.
Direct mail is the most heavily used direct marketing
medium, and its popularity continues to grow despite postage
increases. The mail is the third largest advertising medium
after newspapers and television because it can be directed to
a specific target audience and sent cheaply using bulk
mailing rates. The largest single expense of direct mailing is
postage, and can account for one-third of the total cost of a
direct mail campaign. Other costs include designing, writing,
printing and packing the mailing --- and buying mail lists. A
four-color postcard is the simplest and most common
medium and is also the least expensive to mail. Other forms
of direct marketing such as brochures, newsletters, catalogs,
packets or offers with return envelops can raise costs
considerably.
For the USPS, direct mail advertising, called “standard mail”
by the USPS but also called “junk mail” by many outside the
industry, is the second largest source of revenue behind First
Class mail. It generates 26 percent of revenue compared to 51
percent from First Class (USPS 2011). Advertising, both
First Class and Standard combined, generated $20 billion of
the USPS $67 billion of total revenues in 2010 (30 percent).
Paul Vogel, president and chief marketing officer for the
USPS has stated, "We believe it could be a billion-dollar
product for the Postal Service by 2016 (Liberto 2012.) The
USPS estimates the new direct mail “saturation” program
will bring the struggling agency some $750 to $800 million
in 2012. Its simplicity is attractive to advertisers, especially
small business; but it also provides the USPS the opportunity
to wipe out a class of private middle men who have made
direct mail profitable by working to overcome past USPS
bureaucratic practices that made using direct mail more
difficult and expensive than it needed to be.
Until the advent of EDDM, the USPS refused to delivery any
mail that did not have a correct mailing address. As the USPS
(2012a) has stated in its promotion of EDDM, “
In the past, if a company mailed books or
fliers to a city route, specific addresses had
to be imprinted on the pieces. However, for
rural routes that wasn’t necessary. All that
had to be printed was “Postal Customer.”
Now, under EDDM, all mailings can be
distributed by Postal carriers without
specific addresses because both city and
rural routes are treated the same. Letter
carriers deliver supplements along with the
day’s mail to every door that businesses
want to reach.
Proceedings of the 2012 Pennsylvania Economic Association Conference
145
The typical small business that wanted to do direct mail
advertising found it difficult to compile an address list over
any substantial area. This created a market for specialty firms
to go to the expense of compiling address lists, and keeping
them up to date. Such firms would then sell printed address
labels for designated zip codes to advertisers. This was a
practical example of the principle of economy of scale. The
larger mailing service firms would not just compile and sell
lists in their own communities, but would have data bases for
entire regions or even the entire country.
It also became an example of the principle of barriers to entry
because of the expensive process using obsolete methods by
which the USPS “helped” the mailing services keep their lists
up-to-date. If a mailing service managed to collect 90 percent
of the addresses on a carrier route, the USPS would provide
the missing 10 percent to complete the list. The process for
doing this was cumbersome and primitive. The mailer’s list
would be sent to the local post office in the form of cards,
one for each address. The cards would be the old “don’t fold,
spindle or mutilate” punch cards that could be read by a
computer. These obsolete cards were convenient, however,
because the list was handled at the local level by each
individual letter carrier. The carrier would first put the cards
in the order that he walked his route, then add cards for the
missing addresses.
The cards for the missing addresses would be filled out by
hand by the carrier. The deck of cards would be shipped back
to the mailer where staff would make new punch cards for
the new addresses and run the complete deck of punch cards
through the computer to generate an undated, carrier routed
list from which labels could be printed for sale. Shipping a
box of cards to every carrier in the covered area several times
a year was an expensive and time consuming process,
especially for the larger, national mailers. There was also a
need to maintain secure warehouse space to store the decks
of cards. To qualify for discounted commercial postage rates,
address lists need to be updated as frequently as ever 90 days.
There is no USPS charge for the manual ALSS other than the
cost of shipping the cards to the proper post offices.
Placing decks into the hands of carriers was necessary
because until fairly recently there was no central list of
addresses in the USPS at any level above the carrier. Only
the carrier knows who was on his route and how he chose to
walk it. The Address List Sequencing Service has not
changed in decades. It is currently described on the USPS
(2012b) website as follows:
Address List Sequencing is a service
provided to mailers who have mailing lists
containing at least 90 percent—but not
more than 110 percent— of the total
possible deliveries in a 5-digit ZIP Code™
area. The Postal Service receives cards
from the mailer containing mailing
addresses and sequences the addresses in
delivery order. The mailer can then place
mail in sequence and present it to the Postal
Service as carrier-ready mail.
The Postal Service does not compile
mailing lists: Address List Sequencing is
designed primarily for use in high-density
or saturation mailings to a particular area
for mailers who already maintain a list.
The USPS also offers a service that can check the accuracy of
mailer software in properly coding address lists (CASS).
Mailers must pass with a minimum score of 98.5 percent to
be certified by the USPS and thus be eligible for discounted
postal rates.
Established mailing service firms have opposed any
modernization of the USPS process than would lower costs
and thus make it easier for new firms to enter the industry or
for advertisers to compile mailing lists on their own. There
has been a fear among mailers for two decades that the USPS
would compile and digitize in some central manner all the
address information for a given zip code. In other words,
create accurate lists that could compete with the lists
compiled by mailers. The USPS could then sell this
information itself to advertisers, cutting out the middle men.
What would be logical than for the USPS to know to whom it
was delivering and to provide this information as part of its
service? But the USPS is prevented by law from providing
any address lists to the public (Title 39 of the U.S. Code). It
can only due updates of listed that are 90 percent compiled
by a mailer.
The USPS can now do Electronic Address Sequencing. Lists
are sent to the National Customer Support Center (NCSC) in
Memphis, TN. Mailers must still compile the initial list
themselves. It is expected that the mailer will have already
used the ALSS process in putting their list together and will
have certified it via the CASS process before submitting it to
EAS. Files are limited to 500,000 records per file. Customers
are limited to submitting three files per day. First time
customers using the EAS process may submit only one ZIP
Code on their first submission to ensure their file meets
processing requirements. There is a fee charged by the USPS
for the EAS service.
To protect their lists, mailers sell one-time use labels to
customers. To prevent the labels from being copied and
resold, mailers “seed” their lists with fake addresses unique
to their business. Material received at a seeded address will
be reported to the mailer who will then know that its list is
being used by someone else without authorization. The USPS
has aided the security of mailers by refusing to update lists
found to have seeded addresses from list owners other than
Proceedings of the 2012 Pennsylvania Economic Association Conference
146
the firm that has submitted the list for updating. As the USPS
(2012c) states:
If a seed address is identified in the
qualification process, the CDS List Owner
and the mailer will be notified. All ZIP
Codes and/or address groups containing
seed addresses will be disqualified.
Customers will not receive any address
information from the NCSC for any ZIP
Codes and/or address groups disqualified
due to the presence of seed addresses.
By dropping the need for address information to use the new
EDDM service, the mail service industry is effectively out of
the mass direct mailing business. For over forty years
entrepreneurs built an industry that only existed because of
bureaucratic behavior by the USPS, reinforced by lobbying
by the private mailing service industry against
modernization. But under the pressure of being made a quasiprivate business itself and facing massive deficits as its legal
monopoly in First Class mail has been undercut by email and
online bill paying and its package service was forced to
compete with private hyper-efficient firms like FedEx, the
USPS has had to become more entrepreneurial itself. It has
circumvented the legal ban on selling address lists in
competition with private mailing service by devising an
EDDM that does not use addresses at all. Rather than marvel
at the innovation of the USPS, the better question is why
were addresses needed at all for advertisers who wanted their
material to be delivered to every stop? The resources of an
entire industry were wasted on doing something that really
never needed to be done.
REFERENCES
Liberto, Jennifer 2012. “Postal Service: We Need More Junk
Mail” CNN Money, March 21,
http://money.cnn.com/2012/03/20/smallbusiness/postalservice-junk-mail/index.htm
USPS 2011. Postal Service Revenue: Structure, Facts, and
Future Possibilities, U.S. Postal Service Office of Inspector
General, Report Number: RARC-WP-12-002, October, p. 2.
http://www.uspsoig.gov/foia_files/RARC-WP-12-002.pdf
USPS 2012a. “Hit the Target with Every Door Direct Mail,”
USPS website,
http://www.delivermagazine.com/2012/01/hit-the-targetwith-every-door-direct-mail/
USPS 2012b. Address Quality Services, United States Postal
https://www.usps.com/business/address-qualityService,
services.htm
USPS 2012c. Electronic Address Sequencing (EAS) User
Guide, USPS, January 2012.
https://ribbs.usps.gov/eas/documents/tech_guides/EAS_USE
R_GUIDE.PDF
Proceedings of the 2012 Pennsylvania Economic Association Conference
147
FOREIGN AID EFFECTS ON GROWTH IN LATIN AMERICA
Tai McNaughton
Clarion University of Pennsylvania
Clarion, PA 16214
ABSTRACT
Although Latin America has had much potential for
growth, the countries still remain burdened by low growth
and high corruption rates. To combat the low growth rates,
foreign aid was given to the region. To find the true effect
of aid on the region, several regressions were estimated
where the dependent variable is income. Due to the
controversial nature of aid, the initial hypothesis was that
foreign aid could either incite growth or hinder it in the
Latin American region. The empirical evidence brought
forth from this panel study suggests that foreign aid
negatively effects growth in the region of Latin America.
This paper expands on what previous literature has done
by not only adding new important variables to the
empirical pool of evidence, but also by accounting for
country effects to capture factors that are unique to each
Latin American country. Once random effects are
accounted for, foreign aid injections are shown to
negatively impact growth.
Latin America has had stagnating growth throughout the
past century. The countries in this region have been
receiving Foreign Aid to try and help reinvigorate the
growth process. It has been generally accepted that giving
a country Foreign Aid helps boost growth. But several
economists, including Easterly (2003), have not taken this
statement as a given truth. This study attempts to see the
true effects of Foreign Aid on growth in Latin America.
More years, countries, and control variables are added to
this panel data study than previous ones. Furthermore,
Latin America has typically been studied from the
inequality and corruption perspective. Few studies of the
region have shown the effects of aid. Throughout the
literature, it was found that Aid is affected by corruption
and inequality. For these reasons, the initial assumption
was that Foreign Aid could either incite or hinder growth it
in the Latin American region. For this study, Foreign Aid
was regressed on a growth measure while controlling for
other important regional indicators of growth. The random
effect results suggested that Easterly (2003) was correct
when he stated that foreign aid negatively impacts growth.
measure of economic success. However, Angeriz also
states the importance of using a per capita measure of
income for measuring economic success, as it can be a
better indicator of a standard of living for the individuals of
a country. Crespi (2012) also uses per capita income as a
measure of growth. Andres’ (2011) study used the natural
logarithm of GDP per capita as their proxy for standard of
living. In a study by Deutsch (2011), the standard of living
in the countries of Latin America was approximated, using
a durable goods index instead of a traditional variable
measure used for standard of living. Deutsch concluded
that “the standard of living increases with the size of the
city, the education level of the individual and his/her age”
(584).
Foreign Aid
Foreign Aid effects are very controversial. Easterly (2003)
disagrees with the widely promoted statement that aid acts
as a catalyst for growth. Mosley (1987) suggests that there
is a micro-macro paradox, where aid is effective at the
microeconomic level but not the macroeconomic level.
Furthermore, the effectiveness of aid at the microeconomic
level has been proven to positively affect growth (World
Bank, 2008). However, Bornschier (1995) found that aid
has a short term effect on increasing the rate of economic
growth. Recently, Arndt (2010) finds that foreign aid, over
the long run, has a positive effect on growth in developing
countries.
Contrastingly, Rajan and Subramanian (2008) concluded
that there was no systematic effect of aid on growth. Most
often the explanations for this are for political reasons as
“aid inflows can weaken governance, for example, by
increasing the returns to corruption and/or increasing rent
seeking activities.” (Arndt, 2010, p. 3) Corruption is a huge
problem facing the Latin American region (Andres, 2011).
Corruption has been measured by the ICRG and the
Freedom from Corruption Index. Furthermore, the study
by Rajan (2008) confirms this as it was shown that some of
aid is directed at consumption and activities that are not
necessarily for growth.
LITERATURE REVIEW
Investment
Standard of Living
According to Angeriz (2011), Gross Domestic Product
(GDP) is widely used, and is a popular indicator as a
Investment in education and infrastructure are both needed
for development (Astorga, 2011). As a proxy for
education, Deutsch (2011) used the number of years of
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schooling. Investment in education can also be used to
measure education. As a proxy for education, Astorga
(2011) used the investment share of GDP as well as literacy
rates. According to Puryear (2011), education investment is
“widely agreed to be one of the most powerful tools for
reducing inequality” (p. 124). Furthermore, stagnating
taxes caused a lack of funds for education, as well as for
new infrastructure, which furthers inequality in the region
(Astorga, 2011).
According to Astorga (2011), low levels of investment in
infrastructure and human capital are at the root of the low
productivity performance in Latin America. Crespi (2012)
agrees as it is stated that productivity is needed for
developing countries to grow. According to Astorga (2011),
The most successful period in the Latin American countries
economies were led by “public provision of economic
infrastructure and human capital” (p. 220). Unfortunately,
a according to Crespi (2012), low productivity growth is
said to be the biggest challenge facing Latin America.
On the positive side, Latin America has recently started
increasing its spending in the education sector (Puryear,
2011). Even with the increase in spending, it is still ranked
below the average for educational spending in the world.
However, spending does not account for quality. The
governments of Latin America have been focusing their
efforts on increasing enrollment while failing to address the
quality of the education. Sadly, even though access to
education has increased, the low quality of education is
said to be failing to significantly contribute to increasing
human capital.
Inequality
According to a study by Goni (2011), social equity should
be at the top of this region’s development agenda.
Furthermore, he states that high inequality is absolutely
detrimental to development and prosperity as income
inequality often translates to higher poverty levels. It is
said that income inequality leading to poverty is said to be
an underdevelopment trap. A proxy for income inequality
is measured most often by the Gini Coeffcient (Andres,
2011).
Ethnic minorities form the majority of disadvantaged
groups in these societies (Deutsch, 2011). Family
background is highly associated with opportunity
deprivation. According to Goni (2011), high inequality
leads to conflict, political instability, and increased social
tensions which all hinder growth.
According to Goni (2011), the fiscal system in developing
countries is said to be of little to no help in combating
inequality. In Latin America there is weak fiscal
redistribution as cash transfers are used and there are low
levels of tax collection which are said to be below the
international norm. However, transfers are said to help
more with fiscal redistribution, and the role of tax is
relatively minor. Tax collection volumes in Latin America
have been increasing steadily for years, but still are not
where they should be due mainly to inefficiencies
including evasion and concessions. The study by Goni
concluded that Latin Americas failing fiscal redistribution
can be explained by a low volume of resources being
collected and transferred, tax collection being too
regressive, and transfers are poorly targeted. Possibly
more importantly, a study by Andres (2011) concluded that
tax corruption leads to the stunting of the education and
health programs whose funding is being taken away for
personal gains.
Macro-Economic Variables
According to Andres (2011), like most models of Latin
America, variables that are also included are ones such as
openness of the economy measured in trade, FDI, and
inflation. While most economists agree that FDI boosts
growth, Bornschier (1995) suggests that FDI over the long
run decreases the rate of growth. Perez insinuates that
exchange rate appreciation will help the country develop
faster. He goes further to state that exchange rate policies
are needed to promote expansion (Perez, 2010). Most
economist agree that openness to international trade will
result in greater productivity but Astorga (2011) points out
that trade integration is negatively correlated with growth
in this region. Also, different sectors were used for growth
during different times in history. However, not one type of
export sector is dominant across the region. Separate
regions of Latin America produce different exports, some
export of labor while other regions export commodities
(Perez, 2010).
DATA AND METHODS
Both pooled least squares and country effects were
estimated with EVIEWS software with the purpose to see
if Economic Growth is dependent on these eleven
variables: Foreign Aid, Health, Population, Exchange
Rates, Trade, Savings, FDI, Education, Inflation,
Inequality, and Corruption. The natural log was taken of
the measures for Economic Growth, Foreign Aid,
Population, Exchange Rates, and Inflation to make the data
more comparable across the sample of countries. The
education variable was prorated for all of the countries.
Measures for the quality of education, infrastructure, as
well as the measure of taxes were sporadic at best,
completely missing at worst. Therefore, these variables
were not included in this study.
There are twenty countries included in this study of Latin
America. The countries are Argentina, Belize, Brazil,
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149
Chile, Columbia, Costa Rica, Dominican Republic,
Ecuador, El Salvador, Guatemala, Guyana, Haiti,
Honduras, Jamaica, Mexico, Nicaragua, Paraguay, Peru,
Uruguay, and Venezuela. The following is the regression
model used:
k
LNGDPit = β + β LNOFFDEVit + ∑ β i xkit + ε it
0
k =3
1
(1)
LNGDPit : The natural log of GDP PPP per
capita for each cross section ( i ) over time ( t )
β : Intercept
0
β LNOFFDEVit : Slope ( β
) of the natural
EMPIRICAL RESULTS
1
1
log of Official Development Assistance for each
cross section ( i ) over time ( t )
k
∑ β i xkit : Specific explanatory variable ( k ) with
k =3
slope ( β i ) from a specific cross section ( i ) over
time(t) with independent variables denoted as ( x )
ε it
Savings: With a higher percentage of gross savings there
should be a higher economic growth rate.
FDI: With a higher level of FDI there should be a higher
economic growth rate.
Education: With more years completed there should be a
higher economic growth rate.
Inflation: With a lower inflation rate there should be a
higher economic growth rate.
Inequality: With a lower Gini coefficient there should be a
higher economic growth rate.
Corruption: With a higher level of Freedom from
Corruption there should be a higher economic growth rate.
: Random error term( ε ) for each country( i )
over time( t )
Where:
Economic Growth (Y) = f(Foreign Aid, Health,
Population, Exchange rates, Trade, Gross Savings,
FDI, Primary Education, Inflation rates, GINI
coefficients, and Freedom from Corruption)
Table 1 lists the variable, the variable measure, its source,
and whether the natural log of the measure was taken for
the study. After adjustments, all the variables begin at the
year 1993.
Table 2 shows the mean, standard deviation, minimum,
maximum and number of observations for each variable.
Table 3 shows the correlation matrix. A correlation matrix
shows the relationship between two variables.
Hypotheses
Foreign aid: With an increase in foreign aid there should
be a higher or lower economic growth rate.
Health: With an increase in life expectancy there should be
a higher economic growth rate.
Population: With a larger population there may be a
higher or lower economic growth rate.
Exchange Rates: With an increase in exchange rate there
should be a higher economic growth rate as a country’s
currency is appreciating.
Trade: With an increase in trade there should be a higher
economic growth rate.
Table 4 shows the results from pooled least square, fixed
effect, and random effect estimations where the dependent
variable is economic growth measured by the natural log of
PPP GDP per capita. These regressions show the
coefficients for each variable followed by the p-value
directly underneath. Also reported are the number of
countries included, number of observations, the adjusted
R2, and the Durbin Watson Statistic.
The pooled least squares estimations suggest that official
development assistance does not contribute to growth.
Also, the coefficients for trade, the natural log of inflation
and the Gini coefficient were negative. Life expectancy, the
natural log of the exchange rate, gross savings, primary
education, and freedom from corruption were all positive.
These were all expected results. Furthermore, the
significance of the natural log of official development
assistance, life expectancy, the natural log of population,
the natural log of the exchange rate, trade, primary
education, the natural log of inflation, and freedom from
corruption were all statistically significant at the .15 level.
Surprisingly FDI and the Gini coefficient were not
statistically significant. However, their economic
significance is confirmed.
Pooled least square approaches show the overall results of
the region. However, when using time series data across
countries in a panel study, it must be determined whether
differences across countries can be captured by differences
in the constant term β0 (fixed effects approach) or whether
the constant terms for each country are randomly
distributed (random effects approach). This approach to
panel estimation is outlined in Greene (2000).
According to the country fixed effect approach, the natural
log of official development assistance, life expectancy, the
natural log of population, the natural log of exchange rates,
trade, FDI, and the natural log of the inflation rate effect
growth positively while only life expectancy, the natural
log of the exchange rate, trade and the natural log of the
inflation rate are statistically significant. Savings, primary
Proceedings of the 2012 Pennsylvania Economic Association Conference
150
education, the GINI coefficient, and freedom from
corruption all effect growth negatively. However, primary
education and freedom from corruption are statistically
insignificant. Furthermore, due to the results of the
Hausman test, the random effect approach best explains the
growth of the region.
The random effects results suggest that official
development assistance, savings, FDI, the natural log of
inflation, the GINI coefficient and freedom from corruption
effect the growth negatively in the region. None of these
variables are statistically significant. Furthermore, life
expectancy, the natural log of population, the natural log of
the exchange rate, trade, and primary education effect
growth positively. Trade and primary education are also
statistically insignificant.
education, the natural log of inflation and inequality were
all consistent with previous literature. On the contrary,
savings, FDI, and freedom from corruption conflicts with
the literature. Gross savings effecting growth negatively
could mean that this economy needs to consume more at
this point for growth to occur. Furthermore, at this point in
time, FDI does not seem to help with growth. A possible
explanation is that an MNE is financing in the host country
therefore driving up the interest rate and crowding out
domestic savings. The negative coefficient on freedom
from corruption implies that more corruption results in
more growth due to the way that freedom from corruption
is measured. This could have implications that corruption
actually helps growth in the region or that there is
curvature in the data.
CONCLUSIONS/POLICY RECCOMENDATIONS
Table 5 shows the expected coefficient sign results with the
actual sign results for pooled least squares (Pooled), the
fixed effect approach (Fixed), and the random effect
approach (Random). Positive signs are denoted as (+),
negative signs are denoted as (-), and variables that could
have either sign in the region are denoted as (+/-).
Due to the controversial nature of Official Development
Assistance and Population’s effect on growth, either sign
could have been expected. Also, variables that have the
same sign as the expected sign are said to have economic
significance. An example would be for the resulting sign of
life expectancy and savings coefficients in the pooled least
squares column.
Robustly, several coefficient signs were consistent across
all three models. First, the natural log of population and the
natural log of exchange rates were consistently positive
across all models. Population increases having a positive
effect on growth in this region backs development theories
for increases in population. In line with previous literature,
exchange rate appreciation had a positive effect on growth.
Increases in exchange rates relates to increases in
purchasing power. Increasing purchasing power allows for
more items to be bought with less money therefore inciting
people to purchase and consume more. Also consistent
across the models was that inequality effects growth
negatively. This also backs theories for decreasing income
inequality.
Because the random effects model better explains this set
of data, the other models are no longer concentrated on in
this study.
According to the random effects approach, the Latin
America region has negative effects of foreign aid on
growth. This is consistent with theories that oppose aid.
Furthermore, life expectancy, the natural log of population,
the natural log of the exchange rate, trade, primary
Like most studies of developing countries, this one was
limited due to insufficient data for several variables and
countries. There was also a lack of data reporting for the
countries in the Latin American region. Furthermore, the
data was also shown to have first order autocorrelation for
which it was not corrected.
According to the random effect results, foreign aid,
measured in official development assistance, was shown to
effect growth negatively in the Latin American region.
Therefore giving aid with the preconceived notion that it
will help the region grow is off base. This backs up
theories by Easterly (2003) as he states that aid agencies
have been failing as they have been expecting to give
money to countries in exchange for sustained growth. Aid
has been empirically shown to fail at stimulating growth in
the Latin American region. Aid is given under the
assumption that it will be used to reinvest in the nation
therefore inducing growth. As suggested by Easterly
(2007), if the end results of aid were changed to not only
be growth enhancing, these large lump sums of money
could be used to help the members of a country in other
ways. This includes improving the human securities of
each and every individual of the country. Reform is needed
on the policies of aid to actually help the poor in every
country.
Other variables that positively affect growth should be
increased through policy as well. Life Expectancy can be
increased through advancements in medicine. Requiring
the spending of money to invest in life saving equipment
and techniques would benefit the region. Also, children’s
health should be increased. Healthier children can go to
school longer and live longer. This could have implications
on increasing the population which was also shown to
affect growth positively. Education needs to be expanded
further. The region is doing an excellent job of increasing
Proceedings of the 2012 Pennsylvania Economic Association Conference
151
primary education enrollment. It cannot regress if growth
is wanted. Incentives toward enrollment and completion
should be expanded.
FURTHER RESEARCH
For future studies, I would like to get a proxy for the
quality of education, infrastructure, and taxes whether this
is through doing the research or finding a measure. More
data could mean better results. It could also be important to
see if secondary schooling and secondary education quality
would have a greater impact on growth. Also, I would
correct for autocorrelation and possible heteroskadasticity.
Furthermore, a reordering of my variables for inequality
and freedom from corruption could give different results.
Increasing trade and the exchange rate will result in more
growth. Generally with a steadier inflation rate, more
stable government and economic system, and better terms
of trade, the result is exchange rate appreciation.
Discentives toward savings, FDI, and inflation could help
with growth in Latin America as well. Also, decreasing
inequality could have a lasting impact by increasing
growth. Restructuring to get rid of such wide disparities in
income should be considered.
TABLES
Table 1. Variable Description
Variable
Measure
Source
Growth
World Bank
Y
World Bank
Y
Gapminder
World Bank
Unctadstats
N
Y
Y
World Bank
World Bank
Unctad
N
N
N
Barro&Lee
IMF
World Bank
Heritage
N
Y
N
N
Foreign Aid
Health
Population
Exchange Rates
Trade
Savings
FDI
Education
Inflation
Inequality
Corruption
GDP per capita, PPP (constant 2005
international $)
Net official development assistance received
(constant 2009 US$)-WB
Life Expectancy at Birth(years)
Population, Total
Exchange Rate crossrates between the United
States and Individual Countries
Trade (% of GDP)
Gross savings(% of GDP)
Inward and Outward FDI stock annual (% of
GDP)
Primary Education Completed
Inflation, average consumer prices
GINI Index
Freedom from Corruption Index
Table 2. Descriptive Statistics
LNPGDP LNOFFDEV? LIFE?
8.562323 18.97561 69.2634
Mean
Standard 0.572299 1.100375 5.408678
50.906
Minimum
6.90407 13.28788
Maximum 9.572384 21.20087
79.188
620
Count
609
593
LNPOP?
15.93171
1.498078
11.89136
19.08824
620
LNEXCH? TRADE?
-1.11307 64.5953
5.237882 38.72677
-8.76785 11.54567
24.67823 280.361
620
595
GRSAV?
16.20325
7.581806
-24.0038
40.6815
589
Natural Log (Y/N)
FDI?
PREDU?
20.63873 22.76774
20.30434 11.95348
0.065406
2.9
60.5
165.3109
611
620
LNINFL? GINI?
FRCO?
4.423169 51.88986 34.43125
4.489624 5.442963 14.68867
10
-6.90776
34.42
26.57375
70.81
79
599
278
320
Table 3. Correlation Matrix
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152
LNPGDP?NOFFDEV
LNPGDP?
1
1
LNOFFDE -0.34108
LIFE?
0.709122 -0.36828
LNPOP? 0.470559 0.344469
LNEXCH? 0.073015 0.121607
TRADE? -0.37574 -0.21454
GRSAV? 0.423355 -0.07988
FDI?
0.082025 -0.16464
PREDU? 0.201109 -0.41609
LNINFL? 0.211682 0.037989
GINI?
-0.19988 0.405963
FRCO?
0.546838 -0.38323
LIFE?
1
0.083543
-0.29835
0.013314
0.329464
0.385739
0.414463
0.196784
-0.41089
0.554528
LNPOP? LNEXCH? TRADE? GRSAV?
1
0.211349
-0.69334
0.300639
-0.18881
-0.37933
0.3841
0.370313
-0.0749
1
-0.3406
-0.01629
-0.2956
-0.06036
-0.00398
0.077676
-0.06484
1
-0.13415
0.404718
0.30193
-0.14323
-0.09689
-0.06444
1
0.045229
0.060186
0.203784
0.079114
-0.07743
FDI?
PREDU? LNINFL?
GINI?
1
0.095219
1
0.134612 0.086443
1
0.036342 -0.19483 0.31907
1
0.173973 0.182202 -0.02023 -0.20691
FRCO?
1
Table 4. Pooled, Fixed, and Random Results-Dependent Variable: LNGDP
Pooled
Fixed
Random
C
4.425475
2.335439
1.238492
0
0.3878
0.0266
-0.130692
0.009262
-0.007915
0
0.3464
0.3888
0.064815
0.047262
0
0
0
0.141731
0.189705
0.136844
0
0.3113
0
0.01327
0.09483
0.038852
0.0227
0
0.0003
0.001452
-0.002536
-0.001779
0.5599
0.1262
0.2027
-0.003922
0.001757
0.000325
0
0.011
0.5414
0.000667
0.000273
-0.000211
0.3859
0.4933
0.5681
0.002214
-0.001091
0.001627
0.1383
0.7229
0.4304
-0.005564
0.063822
-0.002586
0.0934
0.0178
0.711
-0.002386
-0.007908
-0.002635
0.5238
0.0048
0.2637
0.004631
-0.000649
-8.49E-05
0.0002
0.3714
0.9027
19
19
19
Observations
188
188
188
Adjusted R2
0.841022
0.976953
0.944908
D-W stat
0.419615
0.479277
0.265063
LNOFFDEV?
LIFE?
LNPOP?
LNEXCH?
GRSAV?
TRADE?
FDI?
PREDU?
LNINFL?
GINI?
FRCO?
Countries
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Table 5. Sign of Coefficient
Variable
Expected Sign
Resulting SignPooled
-
Resulting SignFixed
+
Resulting SignRandom
+
LN of Official
Development
Life Expectancy
+/+
+
+
-
LN of Population
+/-
+
+
+
LN of Exchange Rates
+
+
+
+
Savings
+
+
-
-
Trade
+
-
+
+
FDI
+
+
+
-
Primary Education
+
+
-
+
LN of Inflation
-
-
+
-
Inequality
-
-
-
-
Corruption
+
+
-
-
Proceedings of the 2012 Pennsylvania Economic Association Conference
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REFERENCES
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Proceedings of the 2012 Pennsylvania Economic Association Conference
155
OFFSETS IN THE DEFENSE TRADE:
COUNTERING COMPARATIVE ADVANTAGE INERNATIONAL BUSINESS
Brenda Ponsford, Ph.D., J.D.
Clarion University
William R. Hawkins, M.A., M.A.
U.S. House of Representatives
Committee on Foreign Affairs
Subcommittee for Oversight and Investigations
ABSTRACT
The United States has a comparative trade advantage in
military equipment. Its foreign customers, however,
demand offsets against the cost of imports that prevent
the American economy from fully realizing its gains
from trade. The Commerce Department has reported
that from 2000 to 2010, the value of offset agreements
equaled 75.7 percent of the $70 billion in reported
military sales to foreign governments. Requirements
that American producers buy imports, transfer
technology, share work or invest in foreign projects
raise a variety of issues for American defense firms and
the U.S. government to maximize benefits to the
national industrial base.
Nobel laureate Paul Samuelson (1964) was challenged
by the mathematician Stanislaw Ulam to "name me one
proposition in all of the social sciences which is both
true and non-trivial." It was several years later that he
thought of the correct response: “comparative
advantage”. 10 Every student of business and economics
knows the famous wine and cloth example of this
theory presented by David Ricardo in his 1817 work On
the Principles of Political Economy and Taxation. A
more concise statement come from the Financial Times
(2012) “The idea that a country or region should
specialise in making and exporting goods and services
that it can produce most efficiently. In turn, the country
should import goods and services that it has a
comparative disadvantage producing. This should in
theory lead to an increase in overall trade.” 11
Americans are told that under the theory of comparative
advantage they must accept a loss in competitiveness
and employment in a number of consumer goods
categories, from electronics to clothing to automobiles,
and should concentrate their efforts in other areas of
comparative strength. As Samuelson noted in his reply
to Ulam, “the thousands of important and intelligent
men who have never been able to grasp the doctrine for
themselves or to believe it after it was explained to
them.” This is particularly true when the United States
has been running massive trade deficits for over a
decade. The shift of capital and labor between
industries to bring the economy back into balance has
not been very evident.
One of the few bright spots in the U.S. trade accounts
has been aerospace, both civilian and military. In 2011,
the U.S. ran an overall trade deficit in goods of $726.3
billion, but a surplus in aerospace trade of $42 billion.
U.S. arms exports for 2011 again lead the world at $10
billion. 12 For the period 2006-2011, U.S. arms exports
totaled $46.5 billion. Yet, when the United States has a
clearly demonstrated comparative advantage in a major
sector, such as armaments and military equipment, its
customers demand offsets against the cost of imported
U.S. defense systems that prevent the American
economy from fully realizing its gains from trade.
Offsets in defense trade encompass a range of industrial
compensation arrangements required by foreign
governments as a condition for the purchase of defense
articles and services from American suppliers. This
mandatory compensation can be directly related to the
purchased defense article or service or it can involve
activities or goods unrelated to the defense sale
(indirect offset). In either case, the intent of the offset is
to force U.S. exporters to give back a portion of their
proceeds from the sale. According to the Commerce
Department, during 1993-2010, 52 U.S. firms reported
entering into 763 offset-related defense export sales
contracts worth $111.59 billion with 47 countries. The
associated offset agreements were valued at $78.08
billion. This amounted to a 70 percent give back on the
value of the contracts. 13
Direct offsets can involve co-production, licensed
production or subcontractor production of all or part of
Proceedings of the 2012 Pennsylvania Economic Association Conference
156
the U.S. origin defense product being exported, moving
manufacturing from the U.S. to an overseas site.
Investment arising from the offset agreement can
establish or expand a subsidiary or joint venture in the
foreign country. It can be either a direct or indirect
offset.
Technology transfers occur from moving
production overseas or in providing assistance along
with capital in creating subsidiaries or joint ventures.
Indirect offsets often involve various types of
commercial counter trade arrangements including
barter, that specifies the exchange of selected goods or
services, and counter-purchase - an agreement by the
exporter to buy (or to find a buyer for) a specific value
of goods from the importer.
considers offsets to be economically inefficient and
trade distorting, and prohibits any agency of the U.S.
Government from encouraging, entering directly into,
or committing U.S. firms to any offset arrangement in
connection with the sale of defense goods or services to
foreign governments. U.S. prime contractors generally
see offsets as a reality of the marketplace for companies
competing for international defense sales. Several U.S.
prime contractors have informed BIS that offsets are
usually necessary in order to make a defense sale.” 15
The hands off policy of the U.S. government leaves
American firms to face the demands of foreign
governments on their own.
In the U.S. executive branch, responsibility over
defense industrial policy is shared between several
departments. The Office of Management and Budget is
the interagency coordinator for preparing the annual
report for Congress on offset policy. Other agencies
involved in the process include the Departments of
Commerce, Defense, Labor, State, and the Treasury,
and the Office of the U.S. Trade Representative. In
1984, the Congress amended the Defense Production
Act, adding Section 309 addressing offsets in defense
trade. It required the President to submit an annual
report assessing the impact of offsets on the U.S.
defense industrial base to Congress. When the DPA
was amended in 1992, the Secretary of Commerce was
authorized to develop and administer the regulations
necessary to collect offset data from U.S. defense
exporters. The Secretary of Commerce delegated this
authority to the Bureau of Industry and Security (BIS).
The 2007 BIS report attempts to counter critics who
allege offsets cost American jobs 16. “BIS has developed
an estimate of employment impact related to offsets by
using U.S. aerospace-related employment and value
added data collected by the U.S. Department of
Commerce, Bureau of the Census. Based on BIS’
calculations, it appears that 2002-2005 defense export
sales had a net positive effect on employment in the
defense sector during the four-year period (an annual
average of 16,085 work years), says the report. 17 But
BIS notes that “These calculations are based on the
supposition that this value represents 100 percent U.S.
content in all exports, which is not necessarily an
accurate assumption.” The calculation also does not
take into account the negative impact on U.S. jobs from
indirect offsets that by definition affect non-defense
sectors.
The DPA was last reauthorized in 2003. The amended
DPA required the President to designate a chairman of
an Interagency Team to consult with foreign nations
and domestic firms on the adverse effects of offsets.
The Interagency Team was formally established in
August, 2004, chaired by the Defense Department, with
members representing the departments of State,
Commerce, Labor and the USTR.
In 2009, the BIS amended its offset reporting regulation
to require that companies assign the appropriate North
American Industry Classification System (NAICS)
code(s) to each offset-related defense export sales
contract and offset transaction reported. Prior to 2009,
BIS only required classification by broad industry
descriptions. The change to NAICS classification
reporting has allowed BIS to gather more accurate
information to better assess the economic impact of
offsets on the U.S. industrial base.
The BIS 2007 annual report to Congress acknowledged,
“Some legislators in the U.S. Congress have raised
concerns about the effects of offsets on the U.S.
industrial base, because most offset arrangements
involve purchasing, subcontracting, and co-production
opportunities for U.S. competitors, as well as
transferring technology and know-how.” 14 The BIS
dropped this statement in its 2012 report, even though
Congressional concern remains. The BIS stated in both
2007 and 2012, “The official U.S. Government policy
on offsets in defense trade states that the Government
The top four industry sectors reported by industry
during 2009-2010 were aircraft manufacturing (NAICS
336411); other guided missile and space vehicle parts
and auxiliary equipment manufacturing (NAICS
336419); radio and television broadcasting and wireless
communications equipment manufacturing (NAICS
334220); and military armored vehicle, tank, and tank
component manufacturing (NAICS 336992). These four
categories represented 58.0 percent of all defense
export sales contracts reported during 2009-2010 based
Proceedings of the 2012 Pennsylvania Economic Association Conference
157
on quantity and 70.3 percent of the defense export sales
contracts based on value, totaling $9.8 billion for the
four sectors
Using NAICS data for both exports and offsets, the BIS
found that exports by prime defense contractors during
the 2009-2010 period created 45, 576 jobs in American
manufacturing; but offsets cost the domestic
manufacturing base 23,022, over half the number of
jobs created by exports. This is a case of the glass being
half empty or half full. If offsets help prime contractors
land export deals, the net effect on the U.S. economy is
positive. But if offsets are seen as an unfair trade policy
by foreign governments meant to lessen the benefit to
America from its comparative advantage in defense
goods, then the BIS data confirms this effect as well. 18
In the American industries of aircraft parts and aircraft
engine parts, total employment is reduced by offsets,
the result of the transfer of work from American
factories to foreign factories as part of direct offset
deals. Many foreign governments demand that some of
the work generated by the production of the equipment
being purchased be “shared” with the importing
country. Shifting parts manufacturing to the overseas
client is an easy way to fulfill this demand.
Consider the F-35 Joint Strike Fighter (JSF) program.
The U.S. planned a total of 2,456 aircraft for the Air
Force, Marine Corps, and Navy. The F-35 Lightning II
is a fifth-generation fighter-bomber with stealth
characteristics, making it the world’s most advanced
tactical fighter. The export market for this warplane is
expected to be around 1,000 aircraft.
Eight countries (Australia, Canada, Denmark, Italy,
Netherlands, Norway, Turkey and the United Kingdom)
are participating in the JSF development program to
facilitate sales while avoiding messy offset agreements.
Israel and Singapore have also joined as “security
cooperation participants.” But this approach has not
been as smooth as hoped. Foreign partners have
disagreed over work shares and the transfer of
proprietary
technology.
Denmark,
Italy,
the
Netherlands, Norway, and Turkey expressed
dissatisfaction in 2003-2004 with the type and quantity
of the work their companies had been awarded on the
F-35. These countries threatened to reduce their
participation in the program, or to purchase European
fighters instead of the F-35. 19
Italy and the UK have lobbied for F-35 assembly
facilities to be established in their countries. The British
are the lead foreign partner to the U.S. in the
development program with a number of UK firms, such
as BAE and Rolls-Royce participating. The F-35 tail
section will be built in the UK. Yet, the UK has also
announced that it will greatly reduce purchases of the
aircraft, perhaps only buying 40.
In July 2010, Lockheed and the Italian firm Alenia
Aeronautica reached an agreement to establish an F-35
final assembly and checkout facility at Cameri Air base,
Italy, beginning in 2014. It was also reported that South
Korean companies could bid for work on the F-35 if
South Korea purchases the aircraft. In November 2009,
it was reported that the Confederation of Danish
Industries had demanded that the Danish government
secure subcontract guarantees with Lockheed regarding
Danish work on the F-35 program before the Danish
government decides to buy the plane. Some foreign
partners in the F-35 program have argued that the
United States has been too cautious regarding the
transfer of JSF technologies. 20
The motive for direct offsets is to build the capabilities
of domestic defense industries and the technological
base of the importing country. Murad Bayar,
Undersecretary of National Defense for Turkey made
the standard case at an offsets conference held in
Ankara in 2009. “Local design and development” and
co-production are favored, with an emphasis on
manufacturing final products rather than parts or
components. Turkey wants goods it can export as well
as provide on a long-term basis into the supply chains
of foreign prime contractors. Turkey is looking to
increase industrial output, not cash savings. Local
production will be more costly, but worth it to improve
local capabilities and create jobs. According to Bayar,
the offset agreement counts for 40 percent of the factors
that will decide which foreign systems to buy. 21
Countries of all sizes and levels of development
demand direct offsets and local production. In the UK,
the Ministry of Defense will ask prime contractors to
outline what they will do to increase small business
participation in their supply chains for government
contracts. 22
India’s offset policy, which applies to procurements
valued at $65.12 million or higher, requires foreign
contractors to invest 50 percent of the deal's value in
the Indian defense industry. 23 Even Azerbaijan, whose
defense industry was only founded in 2005, has an
informal policy that requires many basic defense
purchases to include domestic industrial participation. 24
Not all countries have a defense sector robust enough to
Proceedings of the 2012 Pennsylvania Economic Association Conference
158
make full use of offsets. And even in larger countries,
the need to provide benefits to the civilian sector for
political reasons has lead to the increasing use indirect
offsets to leverage defense purchases into commercial
sectors. Developing countries with less industrialized
economies generally pursue indirect offsets to help
create profitable businesses and to build their domestic
infrastructure.
According to the most recent published BIS data, in
2010, direct offsets accounted for 33.10 percent of the
actual value of reported offset transactions. Indirect
offsets accounted for 63.11 percent of the actual value
of reported offset transactions. During 1993-2010,
direct offsets accounted for 40.22 percent of the actual
value of the reported offset transactions, with indirect
offsets accounting for 59.04 percent. 25 In order of
value, offsets imposed in 2010 amounted to the
following: Indirect purchases of goods, $1.2 billion;
Technology transfer, $874.8 million; Subcontracting,
$605.6 million; licensed production, $380.3 million;
overseas investment, $185.3 million; and Coproduction, $148.3 million. 26
In the commercial sector, the use of offsets is
considered an illegitimate distortion of normal trade
practices. This view is embodied in the Agreement on
Government Procurement (GPA) which was signed in
Marrakesh on 15 April 1994 — at the same time as the
Agreement Establishing the World Trade Organization.
The use of offsets, defined as measures to encourage
local development or improve the balance-of-payments
accounts by means of domestic content, licensing of
technology, investment requirements, counter-trade or
similar requirements, is explicitly prohibited in the
GPA. However, national security is exempt from the
GPA, as it should be because it is too important to be
left in a “free trade” environment or subject only to
commercial considerations. 27
Only 37 countries have joined the GPA, 27 of who are
members of the European Union. None of the so-called
developing nations have done so.
Of the 28 countries on which detailed information was
compiled by the Interagency Team, 16 used offsets
from military procurement to aid civilian sectors. And
India and Taiwan were thought to be trending towards
the use of offsets for civilian purposes. All used both
direct and indirect offsets. 28 So the legitimate concern
over national security is being used as leverage to extort
unfair advantages in the commercial sector which
would be illegal if attempted openly.
The U.S. attempt to improve the “transparency” of
government procurement during the Doha Round of
global trade talks failed even before the overall
negotiations broke down last year. The WTO General
Council Decision of August 1, 2004 dropped this issue
from the Doha agenda. According to the USTR, “a
number of WTO Members remained concerned that a
transparency agreement could lead to market access
commitments.” 29 The aversion to expanding even
slightly the GPA came even though the agreement has
plenty of loopholes. Article V allows states to take into
account “the development, financial and trade needs so
that they can continue to restrict procurement to
“promote the establishment or development of domestic
industries....[and] support industrial units so long as
they are wholly or substantially dependent on
government procurement.” They are also free to
negotiate “mutually acceptable exclusions from the
rules on national treatment.”
Governments have long used procurement to support
domestic industry, particularly public infrastructure,
national defense and other strategic sectors. It is both
good politics and sound economics for money taken out
of the economy through taxes to be plowed back into
the economy via procurement. Government spending is
also a vital stabilizer during the business cycle, as the
United States is again demonstrating with its rescue
packages aimed at the financial system and the auto
industry, and the large number of proposals for new
stimulus packages and infrastructure programs.
The drawn out recovery from the deep world recession
will only intensify the competitive pressures that finally
sank the troubled Doha Round over issues of market
access and support for strategic agricultural and
industrial sectors. Among the findings of the InterAgency Team from its dialogues with European
governments (France, Germany, Italy, Spain, Sweden
and the United Kingdom) in 2007, “There is no
common national view. There are differences in views
between national defense sectors and government
departments/agencies” 30
In many countries, the acquisition of the best available
military capabilities, the priority of defense ministries,
has taken second place behind the desire to develop
broader industrial policies favored by non-defense
ministries to benefit the national economy or influential
special interests. To the extent that offsets are the “deal
maker” in bids for weapons systems, especially indirect
offsets, then governments are indicating a willingness
to sacrifice defense capabilities for other economic
benefits This division of interests can become
Proceedings of the 2012 Pennsylvania Economic Association Conference
159
particularly marked in regions where the threat of major
war is thought to be remote. Compare, for example,
offset policies in Europe to those used in regions such
as the Middle East and Africa were imminent conflict is
a more serious concern. Sacrificing military capability
in a war zone would not be rational, so choosing the
best weapons to purchase has top priority.
Despite some annual fluctuations, the average offset
percentage demanded by the 25 European countries
involved in offset activity during the 14-year (19932006) reporting period was 97.7 percent of the export
contract values. These percentages reached a peak of
153.3 percent in 2003. In 2006, the European average
offset percentage increased from 83.7 percent in 2005
to 85.5 percent. Many European governments require a
minimum of 100 percent offsets on purchases of foreign
defense systems. Of the 313 offset agreements with
Europe during the 14-year period, 206 (65.8 percent)
had offset percentages of 100 percent. Another 27
agreements specified offset percentages of more than
100 percent, including two for which the offset
percentage was at least 200 percent. In sum, 74.4
percent (by number) of offset agreements with Europe
featured offset percentages of 100 percent or more
during 1993-2006. Unfortunately, the BIS stopped
reporting data by country or even geographical region
after 2006, releasing information only in terms of
global totals.
The 18 non-European countries surveyed by the BIS for
the same 1993-2006 period accounted for 34.2 percent
of offset agreements (by value), but more than half
(52.1 percent) of the value of reported U.S. defense
export contracts. The non-European countries’ average
offset requirement for the 14-year reporting period was
46.7 percent of contract value. Offset demands on U.S.
defense firms by the Middle East and Africa region
ranged between 35.3 percent and 55.3 percent, with an
average percentage during the 14-year period of 43.7
percent of the export contract values. The Middle
Eastern and African average offset percentage increased
from 43.2 percent in 2005 to 50.3 percent in 2006.
From an American perspective, it is clear that the main
objective of foreign offset demands is to transfer
production capacity and technology from the United
States to industrial centers overseas. Co-production
transactions have involved the construction of major
facilities overseas for the assembly of entire defense
systems, such as aircraft, missiles, or ground systems.
Co-production arrangements of this kind generally
impose a high cost on the foreign government,
including up-front construction and tooling costs and
increased unit costs for limited production runs. Some
countries negotiate with prime contractors for
production or assembly contracts related to future sales
to third countries of the defense systems or system
components in order to justify these investments.
U.S. prime contractors sometimes develop long-term
supplier relationships with overseas subcontractors
based on short-term offset requirements. These new
relationships, combined with mandatory offset
requirements and obligations, can endanger future
business opportunities for U.S. subcontractors and
suppliers, with possible negative consequences for the
domestic industrial base. Other kinds of offsets can
increase research and development spending and capital
investment in foreign countries for defense or nondefense industries. Such offsets can also help create or
enhance current and future competitors for U.S.
subcontractors and suppliers, and in some cases prime
contractors. 31
The final report issued in 2006 by the Interagency
Team concluded that there were several adverse effects
from the use of offsets. The first was obvious, direct
offsets reduce the near-term benefits of the sale by
reducing the amount of domestic work supported in the
United States. Offsets are not free; estimates indicate
that they increase the price of defense equipment by as
much as 15 to 30 percent. Certain types of offsets
distort the ability of the provider to fulfill the offset
requirement in accordance with best business practices,
especially those demanded for political reasons or as a
source of foreign aid or economic assistance. Indirect
offsets may create business incentives for prime
contractors to place future defense work in foreign
countries that would otherwise be performed by U.S.
domestic subcontractors. 32
Other major military equipment exporters have found
similar problems. Patrick Colas des Francs, chief
executive of the French firm Coges, recently said,
“Offsets are a threat to small and medium-sized
companies. This is a real problem.” About 10 large
companies dominate the French land arms industry, but
about 4,000 small and medium-sized companies depend
on them for sub-contracting work. Demands by
countries such as Brazil and India for the transfer of
technology to set up local assembly of the Rafale may
be acceptable to large companies such as Dassault,
which builds the fighter jet, Colas des Francs argued.
“Offset deals not only take production work away from
French subcontractors and suppliers, but also provide
accelerated access to knowledge and skills that allows
companies in the client country to compete in world
Proceedings of the 2012 Pennsylvania Economic Association Conference
160
markets,” he said. 33
The 2011 BIS report takes an even-handed approach to
the question of the impact on the U.S. industrial base of
offsets, "Defense export sales can be an important
component of U.S. defense contractors’ revenues and
further U.S. foreign policy and economic interests.
Exports of major defense systems can also lower
overhead and unit costs for the Department of Defense
(DOD); and help sustain production facilities,
workforce expertise, and the supplier base to support
current and future U.S. defense requirements. Exports
also promote interoperability of defense systems
between the United States and friends and allies and
contribute positively to U.S. international trade account
balances. However, offset agreements and associated
offset transactions can negate some of the potential
economic and industrial base benefits accrued through
defense exports if the offset activity displaces work that
otherwise would have been conducted in the United
States and/or if competitors are established in foreign
countries” 34
A paper on offsets was published in April, 2008 by the
Economic Policy Institute, a think tank that is very
influential in the Democratic Party. It was written by
Owen E. Herrnstadt, director of the Trade and
Globalization Department of the International
Association of Machinists and Aerospace Workers. 35
The paper argues that offsets have reached such a high
level that they need active oversight, including a review
process that can limit or block such transactions.
Herrnstadt suggests an expanded role for the U.S.
Export-Import Bank, an agency created to assist in
financing the export of U.S. goods and services. The
Ex-Im Bank has as its objective “to contribute to
maintaining or increasing employment of United States
workers,” and it has Congressional authority to conduct
economic impact analysis and reviews of “exports of
capital goods and services (e.g., manufacturing
equipment, licensing agreements) that will result in the
foreign production of exportable goods.” Since many
offset agreements result in the substitution of foreign
production for American export goods, the Ex-Im Bank
already has the basic authority that can be expanded to
change U.S. policy from benign neglect to proactive
supervision and control. The Ex-Im Bank is due for
reauthorization by Congress this year.
Another approach would be to build on the Committee
on Foreign Investment in the U.S. (CFIUS) model.
CFIUS is a multi-agency committee, co-chaired by the
Treasury and Homeland Security. It was created in
1988 to analyze foreign acquisitions of privately-owned
American entities. It has the authority to modify or
block any deal that is deemed to pose a risk to national
security and the definition of what constituted such a
danger was expanded by the Foreign Investment and
National Security Act of 2007. Recreating and
expanding the role of the Interagency Team, led by the
Defense Department and including the State and
Commerce Departments plus the office of the USTR,
into an oversight committee with the power to modify
or block offset agreements could be done fairly quickly.
It would provide a flexible organization that could deal
with issues on a case by case basis as an alternative to a
more restrictive legislation.
Proceedings of the 2012 Pennsylvania Economic Association Conference
161
ENDNOTES
1
Paul .A. Samuelson , 1964. "The Way of an Economist," in P.A. Samuelson, ed., International Economic
Relations: Proceedings of the Third Congress of the International Economic Association, Macmillan: London, 1969
pp. 1-11.
2
Financial Times Lexicon, 2012. http://lexicon.ft.com/Term?term=comparative-advantage.
3
Paul Holtom, Mark Bromley, Pieter D. Wezeman and Siemon T. Wezeman, Trends in International Arms
Transfers 2011, Stockholm International Peace Research Institute, March, 2012. The world’s second largest arms
exporter was Russia at $7.9 billion in 2011 and $35.7 billion over the 2006-2011 period. France was the third largest
exporter in 2011 at $2.4 billion, but Germany was third for the 2006-2011 period at $14.4 billion.
http://armstrade.sipri.org/armstrade/html/export_toplist.php
4
U.S. Department of Commerce, Bureau of Industry and Security, Office of Strategic Industries and Economic
Security, Offsets in Defense Trade: Sixteenth Study, January 2012, p. 3.
5
U.S. Department of Commerce, Bureau of Industry and Security. Office of Strategic Industries and Economic
Security, Offsets in Defense Trade: Twelfth Study, December, 2007, p. iii. See also Defense Production Act
Amendments of 1992 (Pub. L. 102-558, Title I, Part C, §123).
http://www.bis.doc.gov/defenseindustrialbaseprograms/osies/offsets/default.htm.
6
Offsets in Defense Trade: Sixteenth Study, 2012. p. 1.
7
William Matthews 2007. “Dodd Seeks Law Overhaul to Protect High-Tech Base” Defense News, January 29, p.
14. Sen. Christopher Dodd (D-CT) had claimed that offsets cost the defense industry 10,000 jobs annually, with
even more jobs lost in the manufacturing supply chain. Connecticut is home to several major defense contractors,
including jet engine maker Pratt & Whitney, helicopter manufacturer Sikorsky, submarine builder Electric Boat and
aircraft producer Northrop Grumman,
8
Offsets in Defense Trade: Twelfth Study, 2007. p. v.
9
Offsets in Defense Trade: Sixteenth Study 2012. p. 13.
10
“Norway Signs Industrial Partnership with Eurofighter Consortium,” Defense Daily, January 29, 2003. Joris
Janssen Lok, “Frustration Mounts Among JSF Partners,” Jane’s Defence Weekly, March 24, 2004; Thomas Dodd,
“Danish Companies Consider Quitting JSF Programme,” Jane’s Defence Weekly, January 9, 2004. Tom Kingston,
“Unsatisfied Italy May Cut JSF Participation,” Defense News, May 10, 2004. Lale Sariibrahimoglu, “Turkey may
withdraw from JSF program,” Jane’s Defence Weekly, November 10, 2004.
11
Jeremiah Gertler 2012. F-35 Joint Strike Fighter (JSF) Program, Congressional Research Service, Feb. 16, p. 17.
12
From notes taken by William Hawkins at the SMi Group conference Offsets 2009, Ankara, Turkey, January 27,
2009.
13
“Britain’s new industrial participation policy protects SMEs and sets $1.6m threshold” Counter-Trade & Offsets,
Feb. 13, 2012.
14
Vivek Raghuvanshi 2010. “Indian State Seeks Special Zone for Defense Firms,” Defense News, April 22, India's
central state of Gujarat is seeking approval to set up a special economic zone in which foreign firms could operate to
fulfill offset requirements.
15
“Azerbaijan goes global in search for industrial participation partners” Counter-Trade & Offsets, Dec. 12, 2011.
Proceedings of the 2012 Pennsylvania Economic Association Conference
162
16
17
Offsets in Defense Trade: Sixteenth Study, 2012. p. 5.
Ibid, pp. 23-24
18
. The GPA allows, “the protection of essential security interests relating to the procurement of arms, ammunition
or war materials, or to procurement indispensable for national security or for national defense purposes.” The more
general security exemption which places “the traffic in arms, ammunition and implements of war and to such traffic
in other goods and materials as is carried on directly or indirectly for the purpose of supplying a military
establishment” outside the regular rules of international commerce is Article XXI of the 1994 General Agreement on
Tariffs and Trade.
19
Ibid, Appendix F.
20
2005 Trade Policy Agenda and 2004 Annual Report, Office of the U.S. Trade Representative, Washington, DC,
March 2005, p. 45.
21
Report of the Interagency Team on Consultation with Foreign Nations on Limiting the Adverse Effects of Offsets
in Defense Procurement, December 17, 2007, p. 8.
22
Offsets in Defense Trade: Sixteenth Study, p. 6. See also General Accountability Office (GAO) report on offset
activities, “Defense Trade: U.S. Contractors Employ Diverse Activities to Meet Offset Obligations,” December
1998 (GAO/NSIAD-99-35), pp. 4-5.
23
U.S. Department of Commerce, Bureau of Industry and Security (BIS), Offsets In Defense Trade, Eleventh Study,
Washington, D.C., February, 2007, Appendix H: Interagency Team Final Report on Consultation with Foreign
Nations on Limiting the Adverse Effects of Offsets in Defense Procurement, p. 5-2.
24
Pierre Tran, “Smaller French Firms Feel Offset Heat,” Defense News, Feb. 19, 2012
25
Offsets In Defense Trade, Eleventh Study, 2007. p. 6.
26
Owen E. Herrnstadt, Offsets and the lack of a comprehensive U.S. policy: What do other countries know that we
don't? Economic Policy Institute, Briefing Paper #201, April 17, 2008.
REFERENCES
Are embedded in the endnotes due to the unusual nature of many of the secondary sources.
Proceedings of the 2012 Pennsylvania Economic Association Conference
163
ACADEMIC PERFORMANCE IN GRADUATE MANAGERIAL ECONOMICS
Rod D. Raehsler
Department of Economics
Clarion University
Clarion, PA 16214
ABSTRACT
Using an ordered probit model, final grades for
graduate managerial economics are analyzed. It is
found that the most important factors influencing
final grades for students are scores on the GMAT as
well as grades earned in undergraduate sections of
business statistics and principles of microeconomics.
For the GMAT, it is found that scores achieved on
the quantitative portion are more relevant than scores
measuring verbal ability.
INTRODUCTION
How often do we, as economics faculty, hear students
describe their trepidation (and sometimes outright
fear) of enrolling in an economics course? If I have
learned anything during my many years of teaching
experience it is that most students simply do not like
economics courses. This, of course, is especially true
among students who find critical thinking and
quantitative analysis to be particularly challenging.
For that reason, I have enjoyed teaching the
managerial economics course in the MBA program at
Clarion University. Through self-selection, students
in that course are well-prepared and come into the
course with a much more positive attitude on learning
important economic concepts that will help them
make sound business decisions in the future. That
was, at least, the case until just the past two years.
Over the past two years I have casually noticed a
decline in attentiveness and attitude among graduate
students taking the graduate managerial economics
course (ECON 510) in either a traditional or online
environment. My colleagues have noticed a similar
trend. This paper represents an initial attempt to
develop an explanation for this attitudinal shift.
While the determination of student perception of the
course will not be accomplished in this analysis, I
will take a look at factors influencing academic
performance in the course.
All educators are interested in determining or
verifying factors that influence student academic
performance. The identification of new factors that
positively influence this performance can often signal
a need for changes in an academic curriculum
whereas the empirical establishment of linkages
between an important sequence of courses and
disciplines that are historically expected can support
claims that are often made to students and the
maintenance of existing curricula. A great deal more
attention has been paid to the empirical analysis of
possible determinants of academic performance since
the work of Spector and Mazzeo (1980). In their
seminal work, they employed a logit model to
examine the performance of students in intermediate
macroeconomics.
The subsequent literature is
extensive, including work on principles of economics
courses by Becker (1983), Borg et al (1989), Park
(1990), Watts and Bosshardt (1991), Becker and
Watts (1996, 1999, and 2001).
Papers on
intermediate economics or econometrics are
relatively scarce but include Specter and Mazzeo
(1980), Ramonda et al (1990), Becker and Greene
(2001), and Yang and Raehsler (2005).
While the literature in accounting is mostly limited to
gender-related study (Mutchler et al, 1987; Lipe,
1989; Tyson, 1989; Ravenscroft and Buckless, 1992),
other studies do exist that focus on income tax
courses, CPA exams or other related accounting
topics (Murphy and Stanga, 1994; Grave et al, 1993;
Brahmasrene and Whitten, 2001). In the literature of
finance, Berry and Farragher (1987), among others,
were the first to survey introductory financial
management courses. Since then there have been
papers on introductory finance courses (Ely and
Hittle, 1990; Paulsen and Gentry, 1995; Chan et al,
1996; Cooley and Heck, 1996; Sen et al, 1997; Chan
et al, 1997; Nofsinger and Petry, 1999; VanNess et
al, 1999) but no use of more advanced specifications
modeling academic performance in any of the
existing educational literature. For example, VanNess
et al, (1999) employed ordinary least squares and
ordered probit models to discover that part-time
instructors typically assigned higher grades,
cumulative grade point average was a significant
explanatory variable for the final grade, and students
majoring in either economics or finance had higher
average academic performance in the class. In a
similar fashion, Sen el al.(1997) identified grade
point average, gender, and performance in
prerequisite courses as important indicators of final
grades in introductory finance. In their work, females
Proceedings of the 2012 Pennsylvania Economic Association Conference
164
tended to outperform males in the sample while grade
point average and prerequisite course grades had a
positive influence on performance in the finance
course. Interestingly, Didia and Hasnat (1998)
discovered that female students did not display a
grade disadvantage and transfer students may not fare
worse than traditional students. A few studies on
higher level or graduate finance courses were
completed by Rubash (1994), Mark (1998), and Trine
and Schellenger, (1999). Trine and Schellenger,
(1995) find identify six factors as significant at either
ten percent or two percent levels with a coefficient of
determination measure of 0.15 in a stepwise multiple
regression analysis. Additional work to identify
factors in greater detail and with greater precision has
not been accomplished in the financial education
literature.
This paper concentrates on the academic performance
in the graduate managerial economics course (ECON
510), a required course for all students in the MBA
program at Clarion University. The organization of
this paper is as follows: Section II introduces an
ordered probit model; Section III presents estimated
results of the ordered probit model; and Section IV
examines marginal probabilities from changes in
GMAT scores and the statistically important dummy
variables. The conclusion is presented in Section V.
DATA DESCRIPTION AND THE ORDERED
PROBIT MODEL
In this study, students enrolled in ECON 510from the
fall semester of 2006 through the spring semester of
2012. As discussed above, ECON 510 is a required
core course for all MBA students in the college and
prerequisites consist of two principles of economics
courses (macroeconomics and microeconomics)
along with the two business statistics courses (ECON
221 and ECON 222) as well as two beginning
accounting courses. Academic performance in the
principles of microeconomics course is considered
here as that course is particularly relevant to material
taught in ECON 510 (some material is actually
identical). The ECON 222 is the only business
statistics course included in this analysis since that
course is the first that presents regression analysis
critical for success in the ECON 510 course. The
lengthy time period chosen for this analysis
encompasses a total of 185 graduate MBA students in
the potential sample. Students who have not yet taken
ECON 510 or who received GMAT waivers are not
included in this analysis thereby reducing the useable
sample to 104 students. When quantitative and verbal
scores are used from the GMAT, the sample size
shrinks to 100. This is still a sufficient sample size to
produce a relevant statistical result. Variation in
grading was minimal throughout this sample as the
same two instructors taught the course with identical
grading methods and material used during each term.
This and the large sample size helps in providing
robust empirical results using the ordered probit
model in this study.
It is well known in the econometric literature that
when dealing with qualitative measures such as
grades or success and failure, the standard ordinary
least squares (OLS) regression technique can produce
spurious probability estimates (probabilities that are
negative or exceed unity) and negative variances
(Greene, 2003). To overcome these shortcomings, a
binary probit or logit model, which produces
consistent probability estimates, provides a better
explanation for two outcomes. When more than two
outcomes exist, a model of multiple choices such as a
multinomial logit or a similar probit model is often
used. However, a multinomial logit or probit model
suffers from the “independence from irrelevant
alternatives” problem. In other words, odds ratios
between outcomes i and j are to be independent of all
other alternatives; an extremely restrictive condition
placed on the model for most types of data analysis.
As a result, we opt for the ordered probit model
which takes into account the ordinal nature in the
dependent variable (Zavoina and McElvey, 1975).
The use of the ordered probit model is justified in the
multiple-category case (final letter grades in this
study), which is considered ordinal in nature. This
phenomenon is particularly true in a state-supported
university where “curving” the final letter grade and
examinations is more common. As a consequence,
the distance between various letter grades (the
dependent variable Y) is not a fixed interval. In other
words, the difference between an A and a B is not the
same as that between a B and a C (and so forth). Very
often, a greater degree of grading leniency is given to
students performing at the lower end of the grading
scale and educators find it more difficult to fail
students for a wide variety of reasons (budget
conditions at academic institutions, student
complaints, and even the human nature of
instructors). This greater leniency given to low
performers compared to high performers in class
makes the distribution of different letter grades
generally ordinal in nature. Stated more succinctly, a
final grade of A is better than a B, which is better
than a C, and so on, however, the measured
difference between an A and B and a B and C are not
necessarily the same.
Proceedings of the 2012 Pennsylvania Economic Association Conference
165
This being the case consider a latent regression
equation in matrix form:
rather than the composite GMAT scores (variable
names are QU and VE respectively).
y* = x′β + ε ..............................................................(1)
Y = βo+ β1ENRi + β2YEXPi + β3UGPAi + β4QUi +
β5VEi + β6M1i + β7M2i + β8S1i + β9S2i + εi ………(6)
Or in linear form for the first model:
EMPIRICAL RESULTS
Y = βo + β1 ENRi + β2EXPi + β3UGPAi +
β4GMATi + β5M1i + β6 M2i + β7S1i + β8S2i + εi .....(2)
Where y* = unobserved latent variable of letter grades
y = 0 = C or below if y* ≤ 0……………………......(3)
y = 1 = B if 0 < y* ≤ µ …………………………….(4)
y = 2 = A if y* ≥ µ …………………………...……(5)
and where µ is the threshold value by which
expected letter grades in ECON 510 are determined.
Individual variable definitions are as follows:
UGPA = undergraduate grade point average on a 4.0
scale.
YEXP = years of previous work experience.
ENR = ‘1’ for taking the course online and ‘0’ for
taking the course in a traditional delivery mode (live).
GMAT = Score on the GMAT examination.
M1 = ‘1’ denotes a grade of C in principles of
microeconomics (minimum to take ECON 510). A
‘0’ implies an A or B.
M2 = ‘1’ denotes a student received a B in principles
of microeconomics; M2 = 0 implies he or she
received a letter grade of other a C or A. It is to be
pointed out that the letter grade A is used as the
reference group.
S1 = ‘1’ denotes a student received a C in the second
business statistics course (ECON 222), ‘0’ for other
letter grades (A or B since a minimum of C is
required).
S2 = ‘1’ denotes a student received a B in ECON
222, ‘0’ for other letter grades. A grade of A is the
reference group.
ε i = Normally distributed residual.
A second model used in this analysis considers the
use of quantitative and verbal scores on the GMAT
Before looking at empirical estimates of the structural
models, it is worthwhile to look at whether university
MBA programs in the region require managerial
economics to see if results developed here have a
broader impact. Table 1 presents a summary of this
type of information for programs across eight states
in this region.
There is considerable variation in the percentage of
MBA programs requiring managerial economics
across states ranging from 80 percent in Maryland to
only 29.73 percent in New York. Pennsylvania,
interestingly, has the second lowest percentage of
MBA programs requiring managerial economics at
40 percent. It is worth noting that the cumulative
numbers show that less than half of the MBA
programs across eight states in this region require
students to take managerial economics. These
percentages fall (to an overall value of 42.22 percent)
when observing AACSB accredited programs in the
same region. This counters a popular claim that
managerial economics is necessary for graduate
programs to retain or secure accreditation. Most
schools either have managerial economics as an
elective course or have a required economics course
that concentrates on macroeconomic issues and
forecasting macroeconomic variables rather than the
microeconomic bent found in the managerial course.
Table 2 below provides mean values for important
variables used in the analysis. The GMAT sample
refers to that part of the analysis focused on using
composite GMAT scores as an explanatory variable
while the VE/QU sample refers to the sample using
the verbal and quantitative scores from the GMAT in
the model.
Information from Table 2 shows that the average
grade in graduate managerial economics over the
sample period is slightly above a B (an A is scored as
‘2’ and a B scored as ‘1’ with C or lower assigned a
score of ‘0’). The undergraduate grade point average
is above 3.33 (as would be expected for a graduate
program) while the average number of years of
experience in the workplace a little below 1.5 years.
The enrollment variable (ENR) shows that only
around 23 percent of students in the sample took the
course online (assigned the ‘1’ for the dummy
Proceedings of the 2012 Pennsylvania Economic Association Conference
166
variable). The principles of microeconomics
foundation grade dummies (M1 and M2) show that
approximately 23 percent of students received a C, 34
percent received a B, and 43 percent earned an A in
that course. Likewise, the business statistics dummy
variables (S1 and S2) show that about 19 percent of
students in the sample earned a C, 40 percent earned
a B, and the remaining 41 percent earned an A in the
second undergraduate statistics course. The average
GMAT score turned out to be 484.808 with average
verbal and quantitative scores at 26.75 and 29.95
respectively. These are at the lower end of the
historic GMAT score distributions (national averages
for the GMAT, verbal examination, and quantitative
section are 540, 29, and 37 respectively), something
to be expected of a smaller MBA program at a public
institution. This will be an important point when
viewing the empirical results of the ordered probit
model estimation. It may be that important variables
predicting academic performance in graduate
managerial economics might differ across various
types of MBA programs. Table 1 already eluded to
the wide variation across MBA programs with
respect to requiring this particular course.
Estimates of the ordered probit models needed to
calculate expected final grades and the marginal
impact of important explanatory variables on
expected grades in ECON 510 are derived using TSP
version 4.5 (2002). The initial estimates of the
GMAT sample using equation (2) are shown in
Table 3.
After eliminating the statistically insignificant
explanatory variables using a stepwise approach, the
optimal ordered-probit model to be used in the
comparative statics analysis can be determined. This
model is presented in Table 4.
Note that the undergraduate grade point average, type
of enrollment (online or traditional), years of work
experience, and undergraduate grades in principles of
microeconomics do not significantly help explain the
distribution of final grades in ECON 510. Given that
this course utilizes both microeconomics and applied
business statistics, it is somewhat disconcerting that
final grades in principles of microeconomics are not
significant. Fortunately, this result changes when
GMAT scores are replaced with verbal and
quantitative test results. Variables that matter the
most in helping to forecast grades in ECON 510
using the results from Table 4 are the composite
GMAT scores and final grades in the undergraduate
business statistics course that focused on regression
analysis. It is also important to point out that the
threshold variable (µ) is statistically significant (p-
value is 0.000). This result verifies that the ordered
probit model is appropriate to use with this data set.
Rather than relying solely on the composite GMAT
score as a predictive variable, I also ran the ordered
probit model by incorporating more specific
measures of verbal and quantitative (or
mathematical) performance on the GMAT. Table 5
shows the estimated values for the full model
described by Equation (6).
Performing a stepwise procedure on the original
ordered probit mode, I found that two similar models
were very close in terms of predictability and
variable significance. One model included dummy
variables identifying grades in undergraduate
business statistics while the second model
incorporated grades in the principles of
microeconomics course. When dummy variables for
both were included, the predictive ability of the
model declined significantly. This implies that there
is a significant correlation between the undergraduate
grades in business statistics and the beginning
microeconomics course. Table 6 below shows that
the quantitative score along with grades in business
statistics are important in predicting final grades in
ECON 510. Table 7 shows that the quantitative score
along with final grades in principles of
microeconomics are important explanatory variables.
Even though the results for the two models are
similar, both are retained in the analysis because the
first supports the previously established importance
of undergraduate business statistics while the second
identifies the principles of microeconomics as an
important course. In both cases, the threshold
variable is statistically significant thereby identifying
the ordered probit model as appropriate for this
analysis.
APPLICATIONS OF THE ORDERED PROBIT
MODEL
One of the primary advantages in employing an
ordered probit analysis to final grades is that it allows
for the development of expected probabilities and a
more complete discussion of the marginal effects of
important explanatory variables on final grades for a
course. The use of a cumulative normal distribution
in developing these probabilities represents a distinct
advantage over a linear estimation model. The value
of probabilities for an average student to receive a
particular letter grade in ECON 510 can be found
using the relevant average values of explanatory
variables provided in Table 2 for each of the
equations below:
Proceedings of the 2012 Pennsylvania Economic Association Conference
167
Prob [y = 0 or C] = φ (–β′x) …………….….……..(7)
Prob [y = 1 or B] = φ [µ – β′x] – φ (–β′x) ……...…(8)
Prob [y = 2 or A] = 1 – φ(µ – β′x) ………………..(9)
∂ Prob [Y = 1 or B] / ∂ GMAT =
[ø(–β′x) –ø(µ –β′x)]*(𝛽̂ 2) ………………..(11)
= (0.0885 – 0.6179) * 0.004156
= -0.002200
For example, with the GMAT sample the value for
β’x is found by plugging the average values into the
optimal version of Equation (2) which can be found
in Table 3. When this is done, the value of β’x turns
out to be 1.35 with the threshold value of µ equal to
φ is the cumulative normal
1.65. In each formula
density function. For instance, the probability for a
typical student to receive a C (or less) or B or A
using the GMAT-focused data can be calculated as
p (y = 0, C or below) = φ (-1.35) = 0.0885
p (y = 1 or B) = φ (1.65 – 1.35) – φ (-1.35)
= 0.6179 – 0.0885 = 0.5294
p (y = 2 or A) = 1 – φ (1.65 – 1.35)
= 1 – 0.6179 = 0.3821
This means that a typical student has an 8.85 percent
chance of earning a C, a 52.94 percent chance of a B,
and a 38.21 percent chance of earning an A in ECON
510. An identical procedure can be employed on the
second data set that focuses on verbal and
quantitative scores using the optimal results for
equation (6) provided in Table 6 and Table 7. Results
of all calculations are presented in Table 8.
Since scores from the GMAT play an important role
in helping determine final grades in ECON 510, the
marginal probabilities as described in Greene (2003)
are calculated using the first data set and results:
∂ Prob [Y = 0 or C] / ∂ GMAT = – ø(–β′x)*(𝛽̂ 2) ..(10)
= –ø(-1.35)*(0.004156)
= -0.0885*0.004156
= -0.000368
∂ Prob [Y = 2 or A] / ∂ GMAT =
ø (µ – β′x)]*(β2) …………………………..(12)
= 0.6179 * 0.004156
= 0.002568
Where β2 is the slope on GMAT for the optimal
model. Equations (7) through (12) can also be run on
the second model using the quantitative scores (QU)
in place of the GMAT composite scores. Results of
the calculations above show that a one point increase
in the GMAT composite score results in a 0.0368
percent decrease in the probability of a student
receiving a C in ECON 510. Likewise, that same
increase leads to a probability decrease of 0.2200
percent and a probability increase of 0.2568 percent
of receiving a B and A respectively in ECON 510.
Therefore, a student scoring fifty points higher than
average (for the sample) on the GMAT (a score
around 534) will have a 12.84 percent higher chance
of earning an A as a final grade in ECON 510. A
summary of these marginal probabilities is provided
in Table 9.
As described earlier, results from Table 8 show that
expected grades in ECON 510 are relatively high.
Regardless of which sample is used, most students
earn B’s while relatively few students receive C’s or
less (less than nine percent for each). Expectations
for A’s are very high for the typical student ranging
from 38.21 percent to 38.59 percent.
Table 9 indicates that higher GMAT scores and, in
particular, higher scores on the quantitative section
lead to significant increases in grade expectations.
For example, only a one point increase in the
quantitative portion score will increase the
probability of earning an A in ECON 510 by between
2.7 and 2.8 percent.
Using the dummy variables S1, S2, M1, and M2
along with equations (7), (8), and (9) it is possible to
calculate the marginal impact of earning higher (or
lower) letter grades in undergraduate business
statistics and principles of microeconomics on
expected grades in ECON 510. Table 10 below, for
example, shows that using the GMAT model a
student earning an A in business statistics increases
their probability of getting an A in ECON 510 by a
Proceedings of the 2012 Pennsylvania Economic Association Conference
168
significant margin (28.14 percent). Obtaining a C in
business statistics, however, will decrease the
probability of that student earning an A in ECON 510
by 25.23 percent.
C in principles of microeconomics should focus on
earning a B or C rather than expect an A in ECON
510.
CONCLUSION
The same analysis can be done using the data set that
incorporated quantitative scores in place of the
composite GMAT score. Results of this analysis
using this data along with the same dummy variables
for grades in business statistics are presented in Table
11. It is easy to see that the results are very similar.
For variety in the discussion, suppose a student earns
a B in business statistics. One can see that the
probabilities of earning a B or C in ECON 510 for
this student increase (10.46 percent and 8.52 percent
respectively) while the probability of earning an A
falls by 18.98 percent. Clearly, students earning an A
in business statistics have a distinct advantage when
it comes to competing for the top grade in ECON
510.
The final part of this analysis uses dummy variable
for grades earned in the principles of microeconomics
course rather than business statistics. Using the
results shown in Table 12 it is easy to see that earning
an A in principles of microeconomics is just as
important as earning an A in business statistics. The
fact that the optimal model does not include all
dummy variables simultaneously indicates that the
majority of students earning an A in one course also
earned an A in the other undergraduate foundation
course. A student earning an A in principles of
microeconomics has a 24.36 percent higher chance of
earning an A in ECON 510 while a student earning a
Even though this represents a very preliminary
inquiry into factors determining final grades in
ECON 510, a few important results can be
emphasized. First, GMAT scores and, in particular,
scores on the quantitative portion of the GMAT
appear to matter greatly in determining student
expectations on final grades in graduate managerial
economics. This might be an important consideration
if programs begin to look at other entrance criteria or
providing waivers of the GMAT for entrance into the
MBA program. Results also show that grades in two
undergraduate foundation courses, business statistics
and principles, significantly affect grade expectations
in ECON 510. In fact, earning an A in both courses
dramatically increases the probability of a student
earning an A in the graduate managerial economics
course. It is interesting that the undergraduate grade
point average does not factor in, however, this might
be due to the fact that the undergraduate major of the
student was not included as a variable (a more labor
intensive data collection activity). This needs to be
done as high grades in one field might not be
equivalent in effect as higher grades in a more
challenging field (such as economics and finance).
Dummy variables for academic major and
gender do need to be included in future
work.
Proceedings of the 2012 Pennsylvania Economic Association Conference
169
Table 1: Managerial Economics Requirements Across MBA Programs
__________________________________________________________________________________________
All Schools
AACSB Accredited Schools
Number
Percent
Number
Percent
Number
Requiring
Requiring
Number
Requiring
Requiring
of
Managerial
Managerial
of
Managerial
Managerial
State
Schools
Economics
Economics
Schools
Economics
Economics
__________________________________________________________________________________________
MD
10
8
80.00
5
4
80.00
WV
6
4
66.67
2
2
100.00
DE
5
3
60.00
2
1
50.00
VA
20
11
55.00
11
6
54.55
NJ
17
9
52.94
9
5
55.56
OH
27
14
51.86
15
6
40.00
PA
40
16
40.00
21
8
38.10
NY
37
11
29.73
25
6
24.00
All
162
76
46.91
90
38
42.22
__________________________________________________________________________________________
Table 2: Mean Values of Variables
_________________________________________________________________
GMAT
VE/QU
Variable
Sample
Sample
Y (ECON 510 Grade)
1.279
1.290
UGPA
3.332
3.333
YEXP
1.471
1.520
ENR
0.221
0.230
GMAT
484.808
-------VE
-------26.750
QU
-------29.950
M1
0.231
0.230
M2
0.337
0.340
S1
0.192
0.200
S2
0.404
0.390
n
104
100
_________________________________________________________________
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Table 3: Estimates of Ordered Probit Model (Model 1)
________________________________________________________________
Variable
Coefficient
Standard Error
t ratio
p value
Constant
-0.772684
1.543
-0.501
0.617
ENR
0.047591
0.297
0.160
0.873
UGPA
0.197797
0.380
0.521
0.603
YEXP
0.023949
0.042
0.564
0.573
GMAT
0.004007
0.002
2.418**
0.016
M1
-0.407402
0.321
-1.269
0.204
M2
-0.429951
0.290
-1.481
0.139
S1
-0.507629
0.357
-1.420
0.156
S2
-0.399440
0.282
-1.418
0.156
µ
1.68721
0.202
8.337*
0.000
_________________________________________________________________
Dependent variable = letter grade for graduate managerial economics
Number of observations = 104
Likelihood ratio = 21.7253 (p value = 0.005)
Log likelihood function = -89.5556
Scaled R-squared = 0.1984
* = significant at 1%
** = significant at 5%
*** = significant at 10%
Table 4: Optimal Model Estimation of Ordered Probit Model (Model 1)
_______________________________________________________________
Variable
Coefficient
Standard Error
t ratio
p value
Constant
-0.318299
0.822
-0.387
0.699
GMAT
0.004156
0.002
2.584**
0.010
S1
-0.743270
0.323
-2.299**
0.022
S2
-0.514727
0.268
-1.918***
0.055
µ
1.65309
0.199
8.310*
0.000
_________________________________________________________________
Dependent variable = letter grade for graduate managerial economics
Number of observations = 104
Likelihood ratio = 17.4640 (p value = 0.001)
Log likelihood function = -91.6862
Scaled R-squared = 0.1611
* = significant at 1%
** = significant at 5%
*** = significant at 10%
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Table 5: Estimates of Ordered Probit Model (Model 2)
_________________________________________________________________
Variable
Coefficient
Standard Error
t ratio
p value
Constant
-0.974964
1.532
-0.636
0.617
ENR
-0.044372
0.304
-0.146
0.873
UGPA
0.525767
0.405
1.299
0.603
YEXP
0.030606
0.042
0.723
0.573
VE
-0.010437
0.021
-0.487
0.016
QU
0.045893
0.016
2.908*
0.016
M1
-0.374835
0.333
-1.127
0.204
M2
-0.472975
0.299
-1.582
0.139
S1
-0.467198
0.362
-1.291
0.156
S2
-0.331975
0.290
-1.146
0.156
µ
1.73684
0.213
8.147*
0.000
_________________________________________________________________
Dependent variable = letter grade for graduate managerial economics
Number of observations = 100
Likelihood ratio = 24.1941 (p value = 0.004)
Log likelihood function = -83.7887
Scaled R-squared = 0.2279
* = significant at 1%
** = significant at 5%
*** = significant at 10%
Table 6: Optimal Model Estimation 1 of Ordered Probit Model (Model 2)
_________________________________________________________________
Variable
Coefficient
Standard Error
t ratio
p value
Constant
0.420441
0.507
0.830
0.407
QU
0.044018
0.015
2.875*
0.004
S1
-0.809875
0.323
-2.509**
0.012
S2
-0.503465
0.268
-1.879***
0.060
µ
1.66952
0.205
8.157*
0.000
_________________________________________________________________
Dependent variable = letter grade for graduate managerial economics
Number of observations = 100
Likelihood ratio = 17.4782 (p value = 0.001)
Log likelihood function = -87.1467
Scaled R-squared = 0.1675
* = significant at 1%
** = significant at 5%
*** = significant at 10%
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172
Table 7: Optimal Model Estimation 2 of Ordered Probit Model (Model 2)
_________________________________________________________________
Variable
Coefficient
Standard Error
t ratio
p value
Constant
0.404717
0.489
0.827
0.408
QU
0.045934
0.015
3.015*
0.003
M1
-0.644362
0.304
-2.119**
0.034
M2
-0.719322
0.273
-2.630*
0.009
µ
1.67537
0.205
8.182*
0.000
_________________________________________________________________
Dependent variable = letter grade for graduate managerial economics
Number of observations = 100
Likelihood ratio = 18.5771 (p value = 0.000)
Log likelihood function = -86.5973
Scaled R-squared = 0.1775
* = significant at 1%
** = significant at 5%
*** = significant at 10%
Table 8: Probabilities of Grade Values in Graduate Managerial Economics
_________________________________________________________________
Grade
Model 1
Model 2
Model 3
C
0.0885
0.0838
0.0823
B
0.5294
0.5303
0.5318
A
0.3821
0.3859
0.3859
_________________________________________________________________
Model 1: GMAT model with business statistics dummies
Model 2: QU model with business statistics dummies
Model 3: QU model with principles of microeconomics dummies
Table 9: Marginal Effect of GMAT Score or Quantitative Score on the
Probabilities of Final Grades in Graduate Managerial Economics
_________________________________________________________________
QU
QU
Grade
GMAT
Model 1
Model 2
A
+0.002568
+0.027031
+0.028208
B
-0.002200
-0.023343
-0.024428
C
-0.000368
-0.003689
-0.003780
_________________________________________________________________
Model 1: QU model with business statistics dummies
Model 2: QU model with principles of microeconomics dummies
Proceedings of the 2012 Pennsylvania Economic Association Conference
173
Table 10:
Marginal Effect of Grades in Business Statistics on the
Probabilities of Final Grades in Graduate Managerial Economics
(GMAT Model)
_________________________________________________________________
Grade in ECON 222:
ECON 510
Grade
A
B
C
A
+0.2814
-0.1893
-0.2523
B
-0.1712
+0.1007
+0.0938
C
-0.1102
+0.0886
+0.1585
_________________________________________________________________
Table 11: Marginal Effect of Grades in Business Statistics on the
Probabilities of Final Grades in Graduate Managerial Economics
(QU Model)
_________________________________________________________________
Grade in ECON 222:
ECON 510
Grade
A
B
C
A
+0.3077
-0.1898
-0.2747
B
-0.1922
+0.1046
+0.1038
C
-0.1155
+0.0852
+0.1709
_________________________________________________________________
Table 12: Marginal Effect of Grades in Principles of Microeconomics on the
Probabilities of Final Grades in Graduate Managerial Economics
(QU Model)
_________________________________________________________________
Grade in Microeconomics:
ECON 510
Grade
A
B
C
A
+0.2436
-0.0595
-0.2295
B
-0.1502
-0.0703
+0.1046
C
-0.0934
+0.1298
+0.1249
_________________________________________________________________
Proceedings of the 2012 Pennsylvania Economic Association Conference
174
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Proceedings of the 2012 Pennsylvania Economic Association Conference
176
DECOMPOSING RECENT MONEY SUPPLY CHANGES
WITH IMPLICATIONS FOR CURRENT FED POLICY
Richard Robinson and Marwan El Nasser
School of Business, E336 Thompson Hall
SUNY at Fredonia, Fredonia, NY, 14063
ABSTRACT
In response to the financial crisis of 2008, the Federal
Reserve radically increased the monetary base. Banks
responded by increasing excess reserves rather than
increasing bank loans, and the public responded with a
substantial flight to liquidity in the form of currency and
demand deposits. As a result, the money-supply multipliers
substantially decreased so that the actual money supply
measures grew more moderately than the base. This paper
presents decompositions of the money-multiplier changes,
as they have occurred since the onset of the financial crisis,
into changes in the currency-to-deposit ratios, and change
in the reserve-to-deposit ratio. By doing so, possible nearterm increases in the multipliers are simulated so that the
possibility of full, or partial restoration to their pre crisis
levels are assessed. In conjunction with the equation of
exchange, Fed policy possibilities for controlling inflation
over various horizons are simulated. This analysis further
illustrates the Fed’s exit dilemma from its financial-crisis
policy.
INTRODUCTION
One conventional view of Federal Reserve policy during
the post 2008 financial crisis is that its open-market
operations were merely “pushing on the string of a
liquidity trap.” In fact, the Fed began lowering its Federal
Funds rate target in September 2007. Over the following
year, it lowered this target rate from 5.25 percent to 2
percent. Post the financial panic of mid-September, 2008
(the collapse of Lehman Brothers), it further dropped the
target rate to approximately .25 percent. As indicated by
Blinder (2010), it became apparent at this time that
lowering “liquidity premiums,” and “risk premiums”
became the essentially needed task of monetary policy.1
Since the riskless overnight rate became essentially zero,
the Fed needed to more directly flatten both the risk-free
yield curve and the risk-structure of yields.
The policies the Fed utilized to accomplish this narrowing
of spreads has been termed quantitative easing, i.e. QEI
was initiated in late 2008, QEII in late 2009, and operation
twist in 2011. Essentially this latter policy involved
purchasing very large amounts of targeted securities: i.e.
mortgage-backed securities, agency debt securities (which
allowed agencies to further support the mortgage market),
commercial paper, money-market mutual fund securities,
and medium and longer-term Treasury bonds. Essentially
this quantitative easing involved the Fed directly
purchasing one of the risky or less-liquid assets from
financial markets, and paying for these purchases by either
liquidating its portfolio of T-bills or increasing the
monetary base. The former involved no net increase in the
Fed’s assets, but the latter resulted in an increase. Total
Fed assets skyrocketed from approximately $.91 trillion on
September 3, 2008, to approximately $2.2 trillion on
November 12, 2008. Almost all of this increase was
absorbed by the banking system as excess reserves which
increased from approximately $2 billion in August, 2008,
to $.77 trillion in December, 2008.
The early stages of the quantitative easing policy was “ad
hoc, reactive and institution based” (Blinder, 2010, p. 469).
Initially, in late 2008, the Fed developed its policy “on the
fly,” often on short-term notice, by acquiring assets as a
result of rescue operations, e.g. the Maiden Lane MBS
portfolio from AIG. The problem of this early financialcrisis period, however, was that the resulting increase in
the monetary base was not loaned by the banking system.
The increase remained as excess reserves, as shown in
Table 1a. As a result, the money multiplier collapsed, also
shown in Table 1a. Because of radical increase in the
monetary base, however, on net the money supply did
increase at relatively modest rates during this period and up
to the present time (March of 2012). These rates are also
shown in Table 1b. As bank intermediaries ceased their
loan function, the economy sank into the Great Recession.
The problems posed for Fed policy now concern what
might happen when banks resume their loan activity,
especially with this very large existing monetary base.
In addition to the problem with excess reserves, the public
responded to the financial crisis with a substantial flight to
liquidity in the form of currency and demand deposits. As
a result, the money-supply multipliers substantially
decreased.2 The monetary policy dilemma the Fed must
now confront is that if the money multipliers rapidly
increase towards their pre-crisis levels then a radical
increase in the money supply and consequent significant
inflation will surely result. This is a problematic fact that
looms over Fed policy, a problem examined here.
Proceedings of the 2012 Pennsylvania Economic Association Conference
177
This paper presents decompositions of the growth in M1
and the changes in its multiplier. This decomposition
allows insights into the future course and effects of
monetary policy. For example, any increase in the M1
multiplier that results from a switch from excess reserves
to bank loans will have a different effect on the economy
than an increase in the multiplier caused by a reduction in
currency held by the public. Consequently we decompose
changes in the money multiplier, as they have occurred
since the onset of the financial crisis, into various factors
such as the changes in the currency-to-deposit ratio,
changes in the other-checkable-deposit ratio, and changes
in the total-reserve-to-deposit ratio. In light of this
decomposition, possible near-term increases in the
multiplier are simulated so that the possibility of full or
partial restoration of the multiplier to its pre-crisis level is
assessed. This examination includes explorations of the
options the Fed has for handling the excess reserves, and
simulations of the money supply expansion process, which
together with simulations of the equation-of-exchange,
allow explorations of possible scenarios for inflation. This
simulation analysis further illustrates the Fed’s exit
dilemma from its financial-crisis policy.
OPTIONS FOR THE FED’S EXIT STRATEGY
The Fed currently (3/2012) holds $.950 trillion in MBS and
Agency Debt Securities, and $1.66 trillion in US Treasury
securities.3 The problem is the liquidation of its holdings of
securities while also reducing excess bank reserves, which
are approximately $1.6 trillion. These reserves are eligible
for loans by banks, and if they are loaned quickly, then
inflation will surely result. The simulations provided in the
next section indicate the problems the Fed must face in
controlling inflation as a result of its current portfolio.
Bernanke (2010) stated the options the Fed has for a
planned “exit strategy.”
1.
Redeeming or selling securities in conventional
(traditional) open-market operations.
2. Passively redeeming MBS, agency debt, and
Treasuries as they mature, and not purchasing
replacement securities.
3. Selling reverse repurchase agreements so as to
maintain the prices of these securities.
4. Increasing the interest rates it pays on reserve
deposits so that banks will not loan these reserves
out.
5. “Offer to depository institutions term-deposits
which … could not be counted as reserves.” (p.8)
With respect to the first option, i.e., the selling of securities
in conventional open-market operations, we note that
because the Fed’s current portfolio of securities is largely
longer-term Treasuries, and mortgage-backed securities,
both of which it acquired to support different segments of
capital markets, it must be reluctant to sell these securities
at least until after these particular market segments have
experienced robust and sustained restoration. Over the
previous two years, the Fed has not used open market
operations to reduce the monetary base, but if markets
warrant, this is certainly the most viable policy option. The
Fed did sell the Maiden Lane MBS portfolio during 2011
although it sterilized this sale by purchase of long-term
Treasuries.
With respect to the second Fed option, i.e., the passive
allowance of redemption of securities without repurchasing
similar securities, we note that as of September, 2011, the
Fed has done the opposite by purchasing similar
replacements to those that matured and were redeemed.
This action maintained the monetary base rather than
allowing it to contract. Still, this policy option has potential
for decreasing the base if and when credit conditions
warrant.
With respect to the third option, the selling of reverserepurchase agreements, note that if this option is utilized, it
would allow the Fed to maintain the prices of securities
involved in that if market conditions for the securities the
Fed sells on a repo basis deteriorate, repurchase could
restore the price level. Of course, since the monetary base
has been stable over 2010 to early 2012, this option
remains to be utilized to any substantial amount.
With respect to the fourth option, i.e. the payment of
interest on reserve deposits, this can only be an effective
option when credit conditions are poor, and the interest
rates the Fed must pay are low, such as its current .25
percent. (Blinder, 2010, examines this difficulty.) It must
be kept in mind that the interest paid on these reserves
becomes an addition to the monetary base, so that as an
instrument for controlling the base, this could only work
when competitive rates are low. If credit conditions heat up
and interest rates rise so that banks could earn perhaps 6
percent or higher on loans, then for the Fed to pay higher
than this to keep banks from loaning out their excess
reserves would mean substantial increases in the very base
its controls are aimed at. This only delays and exacerbates
the inevitable problem.
The fifth option, the conversion of excess reserves into
time deposits with appropriate interest paid, has the same
problem as the fourth option. The interest is added to the
same reserves that are to be controlled, so that the problem
of liquidating excess reserves is exacerbated and merely
delayed.
It is interesting to note that Bernanke did not mention
increasing reserve requirements, which certainly appears to
be a viable option under the current circumstances of
extremely high excess reserves. It appears that increasing
Proceedings of the 2012 Pennsylvania Economic Association Conference
178
the required-reserve-to-deposit ratio would absorb at least a
portion of the excess, and as a consequence, ameliorate a
portion of this problem. The possible reasons why this
might not be considered as a current option include:
• The Fed might fear that the announcement effects of
changes in reserve requirements could mistakenly
indicate an over tightening of credit when conditions
do not warrant this reaction.
• An increase in reserve requirements might have
differential impacts on banks where those that have
already begun to loan, something the Fed surely wants,
are hurt relative to those who have preferred to sit on
their current position.
• Note that Chairman Bernanke is a scholar of the Great
Depression of the 1930s (see Bernanke, 1983, and
2005), when reserve requirements were doubled over a
six month period in 1936, and credit markets collapsed
so as to cause a second massive decline in real output
during the Great Depression (the first being between
1930 and 1933). Surely the Fed is likely to be reluctant
to repeat this historical experience.4
After considering these three reasons, only moderate
increases in the required reserve ratio are acceptable, an
increase which we do consider in the simulations presented
below.
If the loan markets do heat up, then the money multiplier
will rise rapidly towards its pre-crisis level, and the Fed
will have to reverse its quantitative easing policy, and
reduce the monetary base. To not do so would result in
substantial increases in the money supply, and consequent
inflation. In order to indicate the extent of the problem, and
the tight path the Fed must follow to manage and avoid this
inflation, below we provide simulations of changes in the
money-multiplier, the base, money velocity and real
economic activity, as measured over various time horizons,
and then the consequent inflation rates that follow as an
endogenous result.
MULTIPLIER DECOMPOSITION
c=
currency in the hands of the public
o=
other checkable deposits
demand deposits
demand deposits
In equation (2), L is a simplified money multiplier in that
the variable “reserves” includes both required and excess
reserves, and M1 includes currency in the hands of the
public, plus demand deposits, plus other checkable
deposits.6 Equation (3) gives the total differential of (2),
and (4) gives this differential in the form of instantaneous
percentage changes, i.e. dln(L)/dt where t is time.
dL = (dc + do)[
dL/dt
Lt
=
dc
dt
[
1
L
1+c+o
1+c+o
-
-
dr
dt
] – (dr + dc)[
1
r+c
[
] -
1
r+c
]+
do
dt
[
1
L
r+c
1+c+o
]
]
(3)
(4)
Equation (4) suggests the time-series model (5) where ∆L,
∆r, ∆c and ∆o are all changes through time, and ut is a
random error term that reflects any imperfections in the
simplified multiplier measurements.
∆L
Lt
= β1∆ct + β2∆rt + β3∆ot + ut
where β1 =
β2 = β3 =
1
r+c
1
1+c+o
1
1+c+o
<0
(5)
-
1
r+c
= β2 + β3
>0
Model equation (5) can be estimated from time-series data.
This allows the decomposition of the money multiplier into
changes in the currency ratio (∆c), changes in the reserve
ratio (∆r), and changes in the other-checkable-deposits
ratio (∆o).
EMPIRICAL ESTIMATION
The money supply process for M1 is described by equation
(1) where L is the money multiplier as given by (2), and
Base consists of the reserves of the banking system plus
currency in the hands of the public.5
M1 = L•Base
(1)
1+c+o
(2)
L=
r+c
where
r=
reserves
demand deposits
Data Analyzed
To estimate equation (5), monthly data for M1, the
Monetary Base, and Total Reserves were gathered from
series H.3, www.federal reserve.gov, 12/2006 to 3/2012,
seventy-five monthly observations. All data was Not
Seasonally Adjusted, and the reserve data was also not
adjusted for required reserves. Similar monthly data for
Currency in the Hands of the Public, and Demand
Deposits, were gathered from series H.6, Not Seasonally
Adjusted for the same months. Table 2a presents the basic
statistics for the data analyzed. Table 2b presents the
Proceedings of the 2012 Pennsylvania Economic Association Conference
179
means and standard deviations for the relevant variables as
measured over the time period 8/2008 to 3/2012.
The statistics show that although there was a flight to
currency during the period 8/2008 to 3/2012, the coincident
increase in demand deposits was greater so that the
currency-to-deposit ratio actually declined. The reservesto-deposit ratio, however, increased by approximately
1,345 percent, so that this more than counteracted the
effects on the multiplier of a decrease in c, and as a result,
the net effect decreased the multiplier. Equation (5)
indicates that the effects of ∆c on the multiplier must be
less than ∆r, and the large positive magnitude of ∆r must
account for the almost 50 percent decline in the multiplier
over the 2008 – 2012 period.
Table 2a also shows that the Base increased substantially
(213 percent) over the 8/2008 to 3/2012 period. This Base
increase offset the Multiplier decline to result in an
increase in M1 by 60 percent over this period. Equation (6)
presents the decomposition of this decline in M1.
%∆M1= %∆Mult.+ %∆Base + (%∆Mult.)(%∆Base)
(6)
Table 3 presents the OLS estimate of equation (5), the
percentage change in the multiplier, with the constantintercept term suppressed in this regression.
Since the OLS estimation of equation (5) indicates that
both β1 and β3 are insignificantly different from zero, then
equation (7) was also estimated. The results are reported in
Table 4. The F-ratio for inclusion of ∆c is F = .67. As a
result, the OLS analysis that the hypothesis that ∆c and ∆o
contribute explanatory power to the model is rejected at
99% significance level, i.e. the joint hypothesis that
β1 = β3 = 0 cannot be rejected.
Lt
= β2∆rt + ut
We conclude, from the combination of the mean and OLS
analysis that increases in the total-reserve ratio and
increases in the other-checkable-deposits ratio have effects
of similar, but opposite in direction, magnitudes on the
money multiplier. Changes in the currency ratio have a
smaller, but occasionally significant magnitude, effect.
Implications for Monetary Policy
.5983 = -.4898 + 2.1329 - (.4898)(2.1329)
∆L
The effects of changes in the total reserve ratio (∆r) clearly
had an impact of substantial size. The mean of the
calculations of β2 shows the largest absolute magnitude
among the coefficients; ∆r clearly had a substantial inverse
effect on the multiplier. The impact of ∆o on the multiplier
is clearly positive; an increase in “other-checkabledeposits” as a ratio of demand deposits increases the
multiplier. In addition, the t-statistics shown in Table 5
indicates that the means statistics for β1, β2, and β3 are
significantly different from 0 for each. Also, the ranges for
β2 and β3 are entirely positive, although the range for β1
includes both negative and positive values. Also, at the
means for these coefficients we do have β1 = β2 + β3 as
established by (5).
(7)
Calculated Effects of c, o and r on the Multiplier
The OLS empirical analysis indicates that changes in the
currency ratio ∆c, and the other-checkable-deposits ratio
∆o, have little to no effects on the money multiplier, i.e.
β1 ≈ β3 ≈ 0. Table 5, however, presents descriptive statistics
of β1, β2, and β3 as based upon monthly calculations using
the formulae of equation (5) and monthly calculations of c,
r and o. The mean estimates align closely with the OLS
estimates, but the range for each coefficient is fairly wide.
For example, the minimum value for β1 is -.18 which
indicates that ∆c might have had a significant inverse
impact on the multiplier over some sub-period, although in
total over the entire period investigated, its effects often
have little to no impact.
The estimate of equation (5) has interesting implications
for the future of monetary policy in that it allows an
estimation of the change in the money-multiplier given a
return to normal levels with respect to excess reserves.
Table 6 presents the percentage changes in the multiplier
(L) given returns of r, o and c to their former levels on
8/2008. Table 2a shows their levels on the selected dates of
8/2008 and 3/2012, and the time difference values of
∆r = +1.94, ∆c = -1.20, and ∆o = -.44 as calculated. These
reversals are presented in Table 6. Using the mean values
for β1, β2, and β3 presented on Table 5, the percentage
changes in L are computed and presented. As shown, if all
three variables (r, c and o) return to their pre-financial
crisis levels, the multiplier will increase by 65.31 percent.
The flight to liquidity involving the increase in currency
ratio (c) and other-checkable-deposits ratio (o), add only
slightly more than 5% to the multiplier, but the reduction in
the total reserves-to-deposit ratio to its pre-crisis level
increases the multiplier slightly more than 60%.
During 2006, and before the onset of the financial crisis,
the reserve-to-deposit ratio hovered about its approximate
long-term average of .14. Post the crisis, and because of the
radical increase in the excess reserves, this reserve-todeposit ratio has hovered above 2.0. A return to
“normality” of a ratio of .14, i.e. if the banking system
loans the excess reserves as is typically the case during
economic growth periods, then the money supply increases
by approximately 60 percent assuming no change in the
monetary base. It should be obvious that a 60 percent
Proceedings of the 2012 Pennsylvania Economic Association Conference
180
increase in the money supply M1 over a period of even 5
years or less is unacceptable for the course of Federal
Reserve monetary policy. A 60 percent increase over a
period of even a decade would likely be unacceptably large
because of consequent inflation.
Simulated Inflation Results under Various Fed Options
The purpose of quantitative easing is to purchase private
sector assets to such a substantial amount so as to reduce
term- and risk-premiums. Anderson, et al. (2010) examined
these monetary policies across international central banks.
They point out that quantitative easing policies have been
effective only when the central banks are credible with
respect to unwinding their balance-sheet adjustments so as
to prevent consequent inflation. Bullard (2010) reinforces
the importance of this credibility for Fed policy with
respect to the current need for unwinding excess reserves.
Note that not all economic analysts find the Fed’s policy as
credible. (See Meltzer, 2010, as an example.) Its credibility
depends upon two characteristics:
• The central bank’s political authority (perhaps
independence from other governmental influence) and
willingness to handle the inflation problem.
• The central bank’s power to resolve the problem.
The latter depends upon fundamental economic timesequence characteristics, i.e., is the increase in the
monetary base that results from quantitative easing capable
of being unwound, and can the money multiplier be
controlled? To examine these issues, we utilized timesequence simulations to illustrate the extent of the Fed’s
problem, and possible resolutions of these difficulties.
The monthly measures of the multiplier for M1 indicate it
was stable at 1.66 for the months of October, 2007 through
August, 2008, the year prior to the financial crisis. Starting
in September, 2008, the multiplier began to collapse to its
low level of .82 in January of 2012, a decline of slightly
over 50 percent.7 Table 7 shows the required annualized
geometric growth rate if the multiplier restores to 1.66 over
three different time-horizons: a 3 year period, a 5 year
period, and an 8 year period. Given these calculations, and
given scenarios for changes in the monetary base presented
in Table 8, various changes in M1 can be simulated as
presented in Table 9.
To complete the simulations, consider the equation of
exchange presented by equation (8), and its associated
percentage change presented by (9a), and (9b) for small
percentage changes. Emerging from a recession is usually
associated with an increase in the velocity of money, i.e.,
%ΔV > 0. Assume for the sake of this simple analysis that
%ΔV = 0, a rather favorable scenario for Fed policy in
controlling inflation. In a similar fashion, emergence from
a recession generally means that the slack economy allows
real GDP to grow at a 3-4.5 percent rate, at least for the
first year or two of recovery. Over the years simulated by
Tables 7, 8 and 9, allow a 3 percent annual growth rate in
real GDP, and allow it to extend throughout the entire time
horizon simulated. This is a most generous scenario for
controlling inflation, which is consequently simulated by
the figures presented in Table 10.
M1•V = P•Q
(8)
where V is the velocity of circulation,
P is the average price index,
Q is real GDP
%ΔM1 + %ΔV + (%ΔM1)(%ΔV) = %ΔP + %ΔQ +
+ (%ΔP)( %ΔQ)
%ΔM1 + %ΔV ≈ %ΔP + %ΔQ
(9a)
(9b)
We can consider the inflation rates presented by Table 10
as the minimum rates we can expect over the various
horizons assuming the multiplier for M1 is allowed to
restore to its pre-crisis level of 1.66. Changes in the
velocity of circulation are likely to be positive, however,
rather than no change. The average growth rate in real
GDP is not likely to equal 3 percent on average since it is
currently below that figure. In addition, for reasons
indicated below, the growth rate in the monetary base may
actually be positive, perhaps significantly so. Since
inflation rates in excess of 6 percent per year are certainly
not desirable, it appears that Federal Reserve policy must
be aimed at controlling the multiplier, preventing it from
restoring to its pre-crisis level.
The simulations indicate that actual reductions in the base
by amounts greater than 3 percent per year, and over
horizons of at least 8 years, are likely to be the only option
the Fed has for controlling inflation. In fact, price stability
occurs if the base is reduced by 50 percent over any of the
horizons analyzed, provided velocity does not increase.
Moderate inflation (3.26%) results from reductions on onequarter of the monetary base spread over ten years, but this
again assumes a stable velocity and a real GDP growth rate
of three percent. Increases in velocity, however, will
require greater reductions in the base for the purpose of
controlling inflation.
To what extent might an increase in the required reserve
ratio be useful for controlling the money multiplier? If the
reserve to deposit ratio decreases from its current value of
2.08 down to .28, rather than to its pre-September, 2008,
level of .14 (a doubling of the current required reserve
ratio), then for the simulations presented in Table 6,
∆r = -1.80. For the purpose of the Table 6 calculations, this
Proceedings of the 2012 Pennsylvania Economic Association Conference
181
moderate increase in the reserve-to-deposit ratio still
results in the money multiplier increasing by
approximately fifty-six percent rather than sixty percent.
Increasing the required reserve ratio, therefore, has only a
potential of moderately controlling the restoration of the
money multiplier and consequent inflation. This is at first
review a perplexing result, but this moderation of the Fed’s
traditional tools for controlling the money supply is the
natural consequence of the enormous flight to liquidity of
the 2008 financial crisis.
highly likely that substantial inflation will result. This
occurs because a mobilization of excess reserves into bank
loans will increase the money multiplier by upwards of 50
percent. This translates into a similar increase in the money
supply. Given this, simulations indicate that a reduction of
¼ of the monetary base over a ten-year horizon results in a
3.26 percent annual inflation rate provided velocity of
money does not increase (the demand for money does not
drop). This latter contingency is unlikely during a robust
recovery. If velocity increases by whatever percentage per
year, then this percentage increase will be an addition to
the inflation rate. If we have no change in the monetary
base over the decade that follows the initial stages of loan
restoration, i.e. if the Fed decides to continue its support of
the Treasury bond and MBS markets for these long-term
securities, then avoiding inflation rates of more than 5
percent per year is unlikely. The conclusion is
unmistakable.
CONCLUSION
The money multiplier and money supply simulations
presented above show some startling evidence concerning
future monetary policy, i.e. if the Fed does not liquidate at
least some of its holdings of Treasuries and MBS, and
provided that bank loans do return to normal levels, it is
Table 1a: Money Supply Components (in $billions except for Mult. 1)1
3/2006
3/2008
3/2010
3/2012
M1
$1,383.4
$1,387.7
$1,712.3
$2,220.7
Mult. 1
1.734
1.683
0.825
0.838
Monetary
$798.0
$824.4
$2,074.6
$2,650.4
$43.9
$45.0
$1,186.9
$1,608.0
$1.5
$2.6
$1,120.3
$1,509.7
$42.4
$42.3
$65.6
$98.3
Base
2
Total
Reserves
Excess
Reserves
Required
Reserves
1
2
See www.federalreserve.gov for sources of money supply data, “Series H.3.”
The “Monetary Base” consists of bank reserves plus currency in the hands of the public.
Table 1b: Annualized Growth Rates in M1, 2008 to 2011
Year
2008
2009
2010
2011
Annualized M1 Growth
Rates
14.12%
7.67%
10.52%
18.09%
Proceedings of the 2012 Pennsylvania Economic Association Conference
182
Table 2a: Basic Statistics for M1 and Its Multiplier
Month
M11
Base1
3/2012
8/2008
%∆ 5
2,239.2
1,400.9
+59.83
2,654.54
847.30
+213.29
Total
Reserves1
1,606.48
44.13
3,540.34
Demand
Deposits1
Currency2
Other
Check.3
771.8
305.6
+152.55
1,033.1
774.8
+33.23
430.2
305.7
40.73
Mult.4
R
c
o
.8435
1.6745
-48.98
2.08
0.14
1,344
1.34
2.54
-47.24
0.56
1.00
-44.28
1
Denoted in $billions.
“Currency in the Hands of the Public” in $billions.
3
“Other Checkable Deposits” in $billions.
4
Multiplier = M1/Base = L in equation (2).
5
%∆ is the percentage change over the period 8/2008 through 3/2012.
2
Table 2b: Mean Averages and Standard Deviations Over Period 8/2008 to 3/2012
M1
Base
Demand
Deposits
$426.6
Currency
Multiplier
r
c
o
$1,526.4
Total
Reserves
$674.7
Means
$1,617.9
$836.9
1.2343
1.314
2.0877
.8642
Std. Dev.
265.8
694.9
618.3
142.3
88.4
0.4066
1.080
0.3945
.1568
Table 3: OLS Estimates of Model Equation (5)1
Coefficient
OLS
S.E. of Est.
Estimate
β1
-.0453
.0737
β2
-.2492
.0252
β3
+.2701
.2077
R2 = .6066
F=
p = .000
36.50
1
The constant-intercept term was suppressed in the OLS estimation.
t-statistic
pvalue
.46
.000
.198
.000
-0.62
-9.90
+1.30
Table 4: OLS Estimates of Model Equation (7)1
Coefficient
OLS
S.E. of
Estimate
Est.
β2
-.2551
.0247
R2
.5969
1
The constant-intercept term was suppressed in the OLS estimation.
t-statistic
pvalue
.000
-10.32
Table 5: Descriptive Statistics Using Monthly Observations from Equation (5),
1/2006 to 3/2012
Coefficient
Mean
SE Mean
t-statistic
Minimum
Maximum
Range
β1
-.0515
.0116
-4.44
-.1845
+.0793
.2638
β2
-.3099
.0082
-37.79
-.4212
-.2078
.2134
β3
+.2584
.0046
+56.17
+.2090
+.3552
.1461
Proceedings of the 2012 Pennsylvania Economic Association Conference
183
Table 6: Impacts on %∆L of Restoration of r, c and o to 8/2008 Levels
∆L/L
Variable
%∆L
Change
∆r = -
∆r(Mean β2) = (-1.94)(-.3099) =
1.94
+.6012
∆c =
∆c(Mean β1) = (+1.20)(-.0515) =
+1.20
-.0618
∆o =
∆o(Mean β3) = (+.44)(+.2584) =
+.44
+.1137
60.12%
-6.18%
11.37%
Total = +.6531
Total =
65.31%
Table 7: Annualized Growth Rates for Restoration
of the M1 Multiplier from .82 to 1.661
1
N Year Horizon
Growth Rate Per Year
3 Year
26.50%
5 Year
15.15%
8 Year
9.22%
10 Year
7.31%
The multiplier was .82 in January, 2012, and 1.66 in July, 2008.
Annualized growth rates were calculated by g = (1.66/.82)1/N - 1.
Table 8: Annualized Growth Rates of Monetary Base with
Overall Reductions as Stated Over Various Horizons
Horizon
3 Years
Base Reduced by ½
Over Horizon
-20.63%
Base Reduced by ¼
Over Horizon
-9.14%
5 Years
-12.94%
-5.59%
8 Years
-8.30%
-3.53%
10 Years
-6.70%
-2.84%
Proceedings of the 2012 Pennsylvania Economic Association Conference
184
Table 9: Annualized Growth Rates for M1 Given Assumptions
for Growth Rates in the Multiplier as Presented in Table 7.1
Horizon
3 Years
No Change in
Base
26.50%
5 Years
15.15%
8.72%
.0025%
8 Years
9.22%
5.36%
.0015%
10 Years
7.31%
4.26%
.0013%
1
Base Reduced
by 1/4
14.94%
Base Reduced
by ½
.4000%
%ΔM1 = %ΔMult + %ΔBase + (%ΔMult)( %ΔBase)
Table 10: Inflation rates Given the Simulated Data of Tables 7
8, and 9, Stable Velocity, and 3% Growth Rates in Real GDP.1
Horizon
1
3 Years
No Change in
Base
23.50%
Base Reduced
by ¼
13.94%
Base Reduced
by ½
-.6000%
5 Years
12.15%
7.72%
-.0075%
8 Years
6.22%
4.36%
-.0085%
10 Years
4.31%
3.26%
-.0087%
Using (3b), Inflation Rate = %ΔM1 - .03 since %ΔV = 0.
REFERENCES
Anderson, Richard G., Charles S. Gascon, and Yang Liu.
2010, “Doubling Your Monetary Base and Surviving:
Some International Experience,” Federal Reserve Bank of
St. Louis Review, November/December, p. 481-505.
______________ 2010b, “Federal Reserve’s Exit
Strategy,” Testimony before the Committee on Financial
Services, US House of Representatives, Washington, DC,
March 25, 2010.
www.federalreserve.gov/newsevents/testimony/bernanke2
0100325a.htm.
Bernanke, Ben S. 2009, “The Crisis and Policy Response,”
The Stamp Lecture at the London School of Economics,
January 13, London, England. See
www.federalreserve.gov/newsevents/speech/bernanke2009
0113a.htm.
Blinder, Alan S. 2010, “Quantitative Easing: Entrance and
Exit Strategies,” Federal Reserve Bank of St. Louis Review,
November/December, p. 465-479.
______________ .1983, “Nonmonetary Effects of the
Financial Crisis in the Propagation of the Great
Depression,” American Economic Review, (73 (3): p. 257276.
______________. 2005, Essays on the Great Depression.
Princeton University Press, Princeton, NJ.
______________ 2010a, “Federal Reserve’s Exit
Strategy,” Testimony before the Committee on Financial
Services, US House of Representatives, Washington, DC,
February 10, 2010.
www.federalreserve.gov/newsevents/testimony/bernanke2
0100210a.htm.
Brown, E.C. 1956, “Fiscal Policy in the Thirties: A
Reappraisal,” American Economic Review, (December).
Bullard, James. 2010, “Three Lessons for Monetary Policy
from the Panic of 2008,” Federal Reserve Bank of St. Louis
Review, May/June, p. 155-163.
Curdia, Vasco and Michael Woodford. 2010, “The Central
Bank Balance Sheet as an Instrument of Monetary Policy,”
presented at the 75th Carnegie-Rochester Conference on
Public Policy, “The Future of Central Banking,” April 1617, 2010, www.carnegie-rochester.edu/april10pdfs/Curdia%20Woodford.pdf.
Proceedings of the 2012 Pennsylvania Economic Association Conference
185
Federal Reserve. 2009, Detailed Information about the
Federal Reserve’s Balance Sheet and Monetary Policy can
be found at “Release H.4.1”
http://www.federalreserve.gov/monetarypolicy/bst.htm
Friedman, Milton and Anna Schwartz. 1963, A Monetary
History of the United States, 1860-1960, Princeton
University Press, Princeton, New Jersey.
Hanson, A.H. 1941, Fiscal Policy and Business Cycles,
W.W. Norton & Co., New York
Hession, Charles H. and Hyman Sardy. 1969, Ascent to
Affluence: A History of American Economic Development,
Allyn and Bacon, Inc. Boston, Mass.
Meltzer, Allan. 2010, “The Fed’s Anti-Inflation Exit
Strategy Will fail,” Wall Street Journal, January 27, 2010.
Mishkin, Frederic. 2010, The Economics of Money,
Banking & Financial Markets, 9th edition, Addison
Wesley, Imprint of Pearson, Boston, Mass.
Robinson, Richard and Marwan ElNasser. 2010, “Escaping
the Trap: Prospects for Federal Reserve Policy During the
Recovery,” Financial Decisions, Volume 22, Number 1.
Taylor, John B. 2010, “An Exit Rule for Monetary Policy,”
Working Paper, Stanford University, February 10, 2010,
www.stanford.edu/~johntay/House%20FSC%2010%20201
0.pdf.
ENDNOTES
1
The history presented here is largely taken from Blinder
(2010), Anderson, et al. (2010), Bullard (2010), Taylor
(2010), and Curdia and Woodford (2010).
2
This decrease in the money multiplier did not result in a
decrease in the money supply since the Fed radically
increased the monetary base.
3
As of 3/21/2012. See www.federalreserve.gov, “Release
H.4.1.”
4
See Robinson and El Nasser (2009), and also Friedman
and Schwartz (1963), for a review of this monetary policy
explanation of the 1936-37 experience. Also see Hession
and Sardy (1969) for the economic history of this 1936-37
experience. For various fiscal policy interpretations of this
downturn see Hanson (1941), and Brown (1956).
5
See Table H.6, at www.federalreserve.gov for definitions
of M1 and M2.
6
Since L =
M1
=
Base
currency+demand deposits+other checkable deposits
currency+reserves
then dividing both numerator and denominator by demand
deposits gives (2).
7
The M1 multiplier reached .90 in May of 2009, and has
continued a slight declining path since then.
Proceedings of the 2012 Pennsylvania Economic Association Conference
186
WORLD OIL PRICES: ECONOMIC IMPACT AND ECONOMETRIC FORECAST
Carrie R. Williams
Department of Economics Undergraduate
Clarion University of Pennsylvania
Clarion, PA 16214
ABSTRACT
Early in any principles of economics course students
examine how oil shocks and changes in the price of
crude oil can impact markets and the macroeconomy.
A rise in the price of oil will cause the supply of most
manufactured goods to decline leading to a price
increase in those markets. Together this can lead to
an overall increase in aggregate prices and
unemployment known to economists as stagflation.
Minimal empirical research has been done to assess
the precise relationship between changes in oil prices
and important macroeconomic variables.
This
research aims to explore this relationship by
evaluating oil price movements in the world market
and important linkages to macroeconomic variables
in the United States beginning with data in 1919.
Analysis will be qualitative and quantitatively based
through the use of simple correlation analysis,
multivariate regression analysis, and within sample
forecasting models. This will help identify the
models with the lowest root mean squared error
(RMSE) to operate out-of-sample forecasts and
produce rational predictions of oil prices for the
months of March 2012 to February 2013. By
analyzing results of these models, this study is able to
elaborate on the relevance macroeconomic variables
have on the accuracy of various forecasting models
when examining a highly dependent variable such as
oil prices.
INTRODUCTION
With the shock of West Texas Spot Price of Oil
reaching record highs in April of 2011 at a rate of
$110.040 a barrel, economists, politicians, and
citizens alike are anxious about the coming months.
After an almost $15 spike in oil prices from February
to March of 2011, concerns were reconciled with a
slow decline of prices returning to the $80 a barrel
range in the fall of 2011. Concurrently, growth rates
were reported at 2.3 percent at the time of the oil
price shock and dropped to below 0.5 percent for the
two consecutive quarters of the declining oil prices.
Oil prices and growth rates began to climb at the start
of the July to September quarter.
Although the economy has shown respectable
improvement in the past quarters, the nation is
continually affected by the increase in the price of
oil. Companies in attempts to maintain profits with
increasing production costs are struggling to avoid
cutting wages or worse, worker layoffs. Citizens
whom are making better wages are being forced to
spend their income on high gasoline prices and
increased prices of goods and services such as fuel
efficient vehicles. For example consumer spending,
which is primarily focused on automobile sales, rose
at a rate of 2.1 percent in the fourth quarter according
to an article published by Associated Press (2012).
When it comes to economic growth, the numbers for
the last quarter of 2011 reflected the highest growth
rates since the spring of 2010 at 3 percent.
Unfortunately, growth rates for the start of 2012 are
expected to slow once again. Will this mean oil
prices will remain above $100 a barrel in the next
year or will prices fall once again as they did in
2011?
This study looks to examine through various
forecasting models which of these outcomes will
most likely take place. According to patterns in the
data,
forecasting
models
that
incorporate
macroeconomic variables will provide the best
estimate of the next year’s oil prices. Models being
evaluated include linear trend, quadratic trend, two
Auto-regressive
Integrated
Moving
Average
(ARIMA) models, two Ordinary Least Squares
(OLS) models, and two Vector Autoregressive
(VAR) models. Previous literature primarily focuses
on VAR models; therefore, this study will examine
the six models; the ones with the lowest RMSE when
performing within sample forecasts will be used to
make the best fitting out-of-sample predictions of oil
prices for the next twelve months.
LITERATURE REVIEW
When it comes to research in the field of
macroeconomic variables and oil prices, many
researchers have focused on predicting GDP growth
rather than oil prices. Researchers such as Hamilton
(2008, 2009) have shown that predicting GDP growth
in correlation to oil prices is difficult due to the
volatility of oil prices. Decreases in oil prices have
Proceedings of the 2012 Pennsylvania Economic Association Conference
187
been found to have significantly less of an impact on
GDP growth than those of increasing periods.
Studies have shown that 10 out of 11 recessionary
periods in United States history have been preceded
closely by sharp increases in oil prices. (Hamilton
2005) According to Kilian, in order to determine this
relationship a nonlinear and linear approach to
forecasting should be taken to avoid biased results.
Research that has been done on forecasting oil prices
and that have provided linkages to macroeconomic
variables have focused solely on the VAR models.
They provide effects of high oil prices as being
consumer spending increases, reduced economic
output in the short term resulting in worker layoffs or
wage cuts, a negative effect on the purchasing power
of the U.S. dollar. (EIA 2012) Aligning with previous
studies decreases in oil prices have not been
successfully linked to the same responses in the
economy. This study looks to further examine these
effects through incorporation of a variety of
forecasting models and comparing their results to
historical patterns as well as their correlation to
prominent macroeconomic variables.
Wu and McCallum (2012) make the clear distinction
between oil futures prices and oil spot prices; yet,
include both price categories in their forecast model.
This study does not investigate this difference in
prices, but instead utilizes spot prices in order to
better capture interest rates such as Hotelling’s
(1929) research proved effective. This is further
supported by research done by Cologni and Manera
(2005) where oil price shocks were found to have an
unexpected impact on interest rates. As a result, this
study looks to further support the relationship oil
prices have with the macroeconomy.
VARIABLE INPUTS
West Texas Spot Price of Oil: This variable is
measured in dollars per barrel.
West Texas
Intermediate (WTI) also called as Texas light sweet
is defined as a type of crude oil used as a benchmark
in oil pricing and is the underlying commodity of
New York Mercantile Exchange's oil futures
contracts. WTI is light crude, lighter than Brent
crude. It contains about 0.24% sulfur and is rated as
sweet crude, again sweeter than Brent. WTI is refined
mostly in the Midwest and Gulf Coast regions in the
U.S., since it is high quality fuel and is produced
within the country. (Oil and Gas IQ 2012)
Real Gross Domestic Product: Real GDP is gross
domestic product in billions of constant dollars. In
other words, it is a nation's total output of goods and
services, adjusted for price changes. (Investor
Glossary 2012)
Housing Starts: The number of residential buildings
(single-family and multi-family) construction units in
thousands begun during a given time period (usually
one month) based on the number of building permits
issued. Housing starts are sensitive to interest rate
changes and reflect the household sectors willingness
to invest in new construction. It is a key indicator of
business-cycle activity. More specifically, it is one of
the 12 leading economic indicators tracked by the
Bureau of Economic Analysis. (Economic Glossary
2012)
Consumer Price Index: The Consumer Price Index
(CPI) is a measure of the average change over time in
the prices paid by urban consumers for a market
basket of consumer goods and services with 1982-84
as the base year. (BLS 2011)
Index of Industrial Production: An industrial
production index (IP) is an index covering production
in mining, manufacturing and public utilities
(electricity, gas and water), but excluding
construction with 2007 as the base year. (OECD
2001)
To see graphical representations of the above
variables for the past fifteen years, refer to Figures 14. Graphically, the data shows similar time series
structure to oil prices in the GDP, CPI and IP
datasets, but differing trends in housing starts
compared to oil prices. This will be essential in the
conclusions of the various forecast models.
STRUCTURE OF FORECASTING MODELS
Each model is initially evaluated as a within sample
forecast. To elaborate, within sample forecasts use
the model to predict oil prices for March 2011 to
February 2012, values of which are known prior to
running the forecast. Examining the root mean
squared error, RMSE of each within sample forecast
demonstrates how accurate the forecast model was in
predicting the actual values of oil prices. The
following formula is used to determine the RMSE,
with the difference between the forecasted value and
the actual value represented by Fi minus Ai.
∑𝑖(𝐹𝑖 −𝐴𝑖 )2
�
𝑛
(1)
As one would expect, the models with the smallest
difference between the forecasted and actual values
will have the lowest RMSE. The models with the
Proceedings of the 2012 Pennsylvania Economic Association Conference
188
lowest RMSE will presumably output the closest
prediction of oil prices for the unknown or out-ofsample forecast of March 2012 to February 2013.
Trend Models: When using trend models to forecast
future oil prices, economic variables are not included
either directly or indirectly into the analysis. Future
prices are reliant upon the linear or quadratic trend
resulting from past prices, not past prices themselves.
As the graphical representation shows in the
Appendix, oil prices do not follow a strictly linear or
quadratic trend; therefore, the RMSE is expected to
be insignificant for both linear and quadratic trend
within sample forecasts.
Linear Trend Model: A linear equation (2) is used
to forecast the oil prices over time, Pt as a function of
a constant, C and time, t with εt being the normally
distributed error term with zero mean and constant
variance.
Pt = C + βt + εt
(2)
Quadratic Trend Model: Here, a quadratic equation
with oil prices over time is forecast using the trend
model with an additional t2 term, which allows for
concavity in the trend to occur.
Pt = C + βt + βt2 + εt
(3)
linkage is further evaluated and considered when
making conclusions on the resulting predictions.
ARIMA Model (1,2,0)
ARIMA Model (1,2,2)
Structural Ordinary Least Squares (OLS)
Approach: After running simple correlation matrices
(see Tables 1-3) with the macroeconomic variables
described in the input variable section, the strength of
the relationship to oil prices is determined on a scale.
Strong correlation exists when variables report a
value between +/- 0.70 and 1, otherwise weak to no
correlation applies. Oil prices are shown to be
strongly correlated with consumer price index (CPI),
industrial production (IP), and real gross domestic
product (GDP). This can be further described
visually when comparing the graphs of the individual
variables in Figures 1-4. Through lagging those
correlated macroeconomic variables by 12 months,
this approach provides a forecast of oil prices with a
direct linkage to the variables in question. To further
explain, the association can be made on how
influential last year’s CPI, IP, and GDP were on
today’s oil prices. The two models chosen for this
study are as follows with a constant, C and time, t:
OLS1 (lagCPI, lag IP, lag GDP):
Pt = C + β1CPIt-12 + β2IPt-12 +β3GDPt-12
(5)
Univariate Auto-regressive Integrated Moving
Average (ARIMA) Models: This type of forecast is
formulated to be correlated with past oil prices as
well as past shocks or errors; where the autoregressive, AR portion is a function of past oil prices
and the moving average, MA indentifies errors and
inconsistencies.
Pt = β0 +β1Pt-1+…+ βpPt-p + θ1εt-1 + θ2εt-2
+…+ θqεt-q + εt
(4)
The differing factor, I represented by d in ARIMA
(p,d,q) completes the model by removing the nonstationarity of the generalized ARMA model. Using
the Box-Jenkins procedure, possible combinations of
p, d, and q are identified through comparisons in the
output diagrams of the Autocorrelation function and
the Partial Autocorrelation function.
Once
combinations are categorized, the model with the
lowest RMSE is used for further analysis. The two
models listed below were chosen as the optimal cases
for study.
Economists believe macroeconomic variables can be
indirectly linked to this model through interest rates
projected through past oil prices. This indirect
OLS2 (OLS1 + time):
Pt = C + β1CPIt-12 + β2IPt-12 +β3GDPt-12 + t
(6)
Vector Autoregressive (VAR) Models: The final
model type assesses the forecast with the same
macroeconomic variables as the OLS approach, but
the variables are not calculated and examined in the
same format. Here, each variable is dependent upon
the number of determined lags of each of the other
variables along with itself. For example:
Pt = C + β1Pt-1 +α1CPIt-1 + θ1IPt-1 + λ1GDPt-1 + t +t2 +εt
(7)
VAR lag 1 and VAR lag 9 were determined as being
the best suited for this study through within sample
forecasts to identify the lowest RMSE. A lag of 1
represents one month and without fail a lag of 9
represents 9 months or ¾ of a year. Being that the
macroeconomic variables are not only lagged, but
also reflective of the interdependence of these
variables in the economy, expectations for significant
results are high.
Proceedings of the 2012 Pennsylvania Economic Association Conference
189
EMPIRICAL RESULTS
The following table illustrates each model and its
RMSE found through within sample forecasts. The
results show the ARIMA and VAR models provided
the lowest RMSE, while both trend models were
significantly greater, as expected. In addition to high
RMSE results, exempting the trend models from
further analysis was confirmed by the lack of
correlation to the macroeconomic variables of this
study.
However, the remaining models with
relatively lower RMSE results, either directly or
indirectly incorporated the macroeconomic variables
and therefore out-of-sample forecasts were computed
using ARIMA (1,2,0), ARIMA (1,2,2), OLS1, OLS2,
VAR lag 1 and VAR lag 9.
TABLE 4: RMSE RESULTS
Model
RMSE
Trend
49.5413
Quadratic Trend
33.321
ARIMA (1,2,0)
8.7986
ARIMA (1,2,2)
8.8007
OLS1 (lagCPI, lagIP, lagGDP)
26.2274
OLS2 (OLS1 + time)
26.436
VAR lag 1
9.0285
VAR lag 9
9.8585
The results of the out-of-sample forecasts illustrated
sweeping differences across the six models. The oil
prices predicted by all six models are detailed in the
table as well as graphically in Table 5 and Figures 510. The results show similarities in the increasing
trend of oil prices in the ARIMA (1,2,0), ARIMA
(1,2,2) and VAR lag 1 models. Yet, the difference in
the rate at which they increase and the final price at
which the year will conclude is inconsistent.
Alternative conclusions can be seen when examining
the remaining models, where decreases in the prices
of oil occur.
CONCLUSION
As discussed above, this study found the ARIMA
(1,2,0), ARIMA (1,2,2) and VAR lag 1 to be similar;
nonetheless, the VAR lag 1 model has the most
potential of the three. Both ARIMA models have
significant increases in oil prices over the next year
with about a 20% increase in ARIMA (1,2,0) and an
overwhelming increase of about 130% in ARIMA
(1,2,2). With past trends in oil prices and the state of
the current economy, these models do not incorporate
the macroeconomic variables to the extent necessary.
With the support of past research, increases in oil
prices have been shown to coincide with increases in
economic growth rate. Using the ARIMA (1,2,2)
model, oil price increases such as this would coincide
with above average increases in the economic growth
rate, an unsustainable prospect for a developed
economy such as the United States. According to
research in the field of economic development, a
developed country will not grow at rates greater than
about 5% annually. Consequently, oil prices and the
economic growth rate of the United States would
stabilize or decrease at some undisclosed period of
time. With the current slowing of the economic
growth predicted to occur at the end of the first
quarter of 2012, we might expect to see prices of oil
to decreases or remain stable rather than increase.
Therefore, the ARIMA models are not chosen as the
most accurate models for predictions of oil prices in
the next year based primarily of poor macroeconomic
integration and unrealistic results for an economy
such as the United States.
The models with the strongest predictions of oil
prices are the VAR lag 1 and VAR lag 9, since both
models incorporate the interdependence of the
macroeconomic variables and qualified as the second
model type in terms of accuracy. These two models
are similar in structure, but a lag of 1 is a one month
analysis compared to a lag 9 that is three quarters of
the entire year. The difference in the lag length
yields notably different predictions for the coming
year.
Beginning with the VAR lag 1 model, an increase of
about 0.62 percent over the next year is a more
honest prediction than that of either ARIMA models.
Being that this model allows for the next months
prices and values for other economic variables to be
predicted through the previous months values, this
model should accurately forecast the relationship oil
prices have with the entire economy. Since CPI and
IP drive more than 70% of economic growth, these
macroeconomic variables are vital to the continued
growth of the economy (Zhang 2012). Increases in
oil prices typically increase the costs of goods and
services and increase production costs for producers,
especially in oil related industries, which can easily
decrease the rate at which the economy grows. With
increases as gradual as this model predicts, oil prices
would not have that dynamic of an effect on economy
growth. Even though the economy experienced an
unexpected 3% growth rate last quarter, growth rates
are predicted to fall to around the 2.5% range for the
next quarter, according to Zhang (2012).
If the
growth rates continue to stay at or around 2 percent,
Proceedings of the 2012 Pennsylvania Economic Association Conference
190
we should expect this model to be the best forecast of
where oil prices will be within the next year.
FIGURE 2: CONSUMER PRICE INDEX
150
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
U.S. Dollars
1982-84 Base Year
200
Year
FIGURE
3:
INDEX
PRODUCTION
OF
INDUSTRIAL
Index of Industrial
Production
110
60
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Overall, the oil price forecasts depict various
possibilities for the future, but only one prediction
will be accurate. As the results have shown, models
with the ability to portray the macroeconomic
variables and their linkage to oil prices will have the
highest probability of being accurate. Whether
studies focus on the ARIMA model or the VAR
models, oil prices rising are inevitable.
Consumer Price Index
2007 Base Year
Finally, the VAR lag 9 model offers a hopeful future
in the eyes of the consumers, but not necessarily for
the economy in terms of growth initially. Being able
to incorporate oil price shocks and errors throughout
a longer period of time allows for a more accurate
statement of the economy. Here, we notice the prices
of oil decline instantly and continue to decline until
October 2012, where minor increases occur to meet
the final price of $103.40 per barrel in February
2013. A decrease followed by an increase in oil
prices parallels the historical pattern (Figures 1-4) as
well as incorporates the possible recovery of the
economy in the near future. This model incorporates
historical trends in both oil prices and the
macroeconomic variables, has shown accuracy in
predicting within sample oil prices and corresponds
well with the reality of the current economy;
therefore, the VAR lag 9 is the optimal model in
which to predict oil prices for March 2012 to
February 2013.
Year
FIGURE 4: HOUSING STARTS
FIGURE 1: WEST TEXAS SPOT OIL PRICES
3000
2000
1000
0
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
In thousands
Housing Starts
Year
Proceedings of the 2012 Pennsylvania Economic Association Conference
191
TABLES 1-3: CORRELATION MATRICES
FOR OLS MODELS
Table 1:
P
LagCPI
P
LagIP
1
LagCPI
0.80879
1
LagIP
0.77598
0.96905
1
Table 2:
P
LagCPI
LagIP
LagHS
P
LagCPI
LagIP
1
0.77627
0.75039
-0.2486
1
0.97049
1
LagHS
1
Table 3:
P
P
LagCPI
LagIP
LagRGDP
LagCPI
LagIP
LagRGDP
1
0.80758
1
0.7763
0.969
1
0.81512
0.9872
0.99212
1
FIGURE 5: ARIMA (1,2,0)
TABLE 5: FORECAST RESULTS
12-Mar
12-Apr
12-May
12-Jun
12-Jul
12-Aug
12-Sep
12-Oct
12-Nov
12-Dec
13-Jan
13-Feb
ARIMA (1,2,0)
104.13619
106.07203
107.99265
109.92229
111.8519
113.78487
115.71992
117.65753
119.59753
121.53998
123.48486
125.43216
Out-of-Sample Forecast Results
ARIMA (1,2,2) VAR lag 1 VAR lag 9
110.63516
102.64344
102.75974
121.37798
103.03889
101.35284
133.3922
103.43644
99.50581
146.09218
103.83614
98.78431
159.16216
104.23808
98.04998
172.43188
104.64233
98.33978
185.8096
105.04893
98.93201
199.2458
105.45798
100.10676
212.71381
105.86952
101.08132
226.19923
106.28362
102.17473
239.69432
106.70035
102.92626
253.19489
107.11975
103.40427
OLS1
74.07549
74.68449
73.78339
74.71326
72.0032
71.16884
72.40003
71.41613
71.33563
72.13512
71.12897
70.7487
Proceedings of the 2012 Pennsylvania Economic Association Conference
OLS2
74.17126
7479378
73.88547
74.81686
72.07515
71.23466
72.46633
71.4733
71.39623
72.17596
71.16102
70.78098
192
130
110
90
Month
West Texas Spot Oil Price (dollars per barrel)
West Texas Spot Oil Price (dollars per barrel)
ARIMA (1,2,0)
FIGURE 8: VAR LAG 9
VAR lag 9
102
97
Month
ARIMA (1,2,2)
190
90
Month
FIGURE 9: OLS 1
West Texas Spot Oil Price (dollars per barrel)
West Texas Spot Oil Price (dollars per barrel)
FIGURE 6: ARIMA (1,2,2)
OLS 1
74
69
Month
VAR lag 1
105
100
Month
FIGURE 10: OLS 2
West Texas Spot Oil Price (dollars per barrel)
West Texas Spot Oil Price (dollars per barrel)
FIGURE 7: VAR LAG 1
OLS 2
80
75
70
65
Month
Proceedings of the 2012 Pennsylvania Economic Association Conference
193
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Proceedings of the 2012 Pennsylvania Economic Association Conference
195
ADVANTAGEOUS SELECTION IN HEALTH INSURANCE
Paul Sangrey, Grove City College, 1025 Bells Hill Road, Landisburg, PA 17040
ABSTRACT
Advantageous selection refers to people with higher levels of risk buying lower levels of insurance because of the
correlation between having a high level of risk and the willingness to live with a high level of risk. It interacts with adverse
selection to create an ambiguous correlation between health status and purchasing insurance. Advantageous selection can exist
because people who have a lower desired level of risk will take actions that reduce their overall risk and have some control over
their own level of risk. Advantageous selection has been demonstrated empirically by Wang, Huang, and Tzeng; by Eldridge,
Koc, Onur, & Velamuri; and by Fang, Keane, and Silverman.
Starting with Akerlof’s seminal article (1970), information
asymmetries and their resulting effects have become a major
focus of economic analysis. Health Economics is particularly
notable in this area because of the large number of
information asymmetries and the importance of their effects.
Perhaps the most important of these is adverse selection in
the insurance market – people who have a higher level of risk
than the public information would predict taking advantage
of the asymmetric information and purchasing more
insurance because they face a relatively lower price. In recent
years, researchers have begun to investigate the possibility of
advantageous selection. Advantageous selection refers to
people with higher levels of risk buying lower levels of
insurance because of the correlation between having a high
level of risk and the willingness to live with a high level of
risk (de Meza & Webb, 2001). It has been most conclusively
demonstrated in certain non-health insurance markets.
However, advantageous selection is present in the health
insurance market and has a significant dampening effect on
adverse selection as evidenced by the ambiguous correlation
between health status and insurance ownership.
When deciding how much to charge for insurance, the
insurance company looks at the average risk and calculates
its cost accordingly. If the insured group did not differ
materially from the general population, then the average risk
for the insured population would be the same as the
population as a whole, and so the actuarially fair premium
would be the same. However, the adverse selection literature
has demonstrated that people who choose to buy insurance
are not a random sample drawn from the population
(Rothschild & Stiglitz, 1976).
The adverse selection model is built on four assumptions.
First, insurance purchases are voluntary. Second, risks are
heterogeneous. Third, heterogonous risks are lumped
together and charged the same price. Fourth, prospective
purchasers know more about their own risk than the
insurance company does (Hemenway, 1990, p. 1065).
risk in the insurance population is higher than the population
as a whole forcing the insurer to charge a higher price and,
thereby, leading to a large number of relatively low risks
dropping out of the market. This analysis is relatively noncontroversial, and highly influential. For example, this is the
reason behind the mandate in the Patient Protection and
Affordable Care Act of 2010 (Marcus, 2012). In doing so, the
bill would force everyone to become part of the insurance
pool, thereby making the average risk of an insured person
equal to the average risk of the general population.
However, this is not the only way in which the insured
population differs from the general population. People who
choose health insurance do so because they are willing to
give up a certain level of current wealth in order to reduce
their risk. In other words, they reduce their expected income
but increase their expected utility (Folland, Goodman, &
Stano, 2001).
However, the level at which people optimize their total utility
by substituting certain income for expected income differs
from person to person. As a result, the preferred level of
insurance coverage will differ between people simply
because of tastes and preferences and be entirely unrelated to
their expected loss.
Insurance minimizes the difference between the risk being
faced by the insuree and the insuree’s utility-maximizing
level of risk by decreasing the expected loss, and thereby, the
level of risk born by the actor. If the utility-maximizing level
of risk is equal to or above the expected risk the person in
question will not purchase insurance.
In adverse selection, for people with a higher expected loss
the expected risk is higher, and so the desired level of
insurance is higher. However, if people with a higher
expected loss, and thereby a higher expected level of risk,
also have a higher utility-maximizing level of risk, they may
not choose to purchase insurance anyway, and so the adverse
selection problem would be smaller or even nonexistent.
This leads people who have higher risks to buy more
insurance than people with lower risks. Therefore, average
Proceedings of the 2012 Pennsylvania Economic Association Conference
196
As in Figure 1, a person with a desired risk of DR1, in other
words, a utility-maximizing level of risk of DR1, and an
expected loss of EL1 would purchase insurance. However, if
the desired risk rose to DR2, the person would no longer be
interested in buying insurance, unless the expected loss also
rose to at least EL2.
This tendency would not have any effect, however, if
expected loss and desired risk were independent of each
other, and would actually lead to greater levels of adverse
selection if they were negatively correlated. If they were
positively correlated, especially significantly so, it would
have a significant dampening effect on the level of adverse
selection in the market.
Postulating such a correlation rests upon two assumptions.
First, people who have a lower desired level of risk will take
actions that reduce their overall risk. Second, people have
some control over their own level of risk. If these two
conditions hold, people who buy more insurance will take
actions to reduce their expected loss in areas where they have
some control over that risk, thereby reducing the expected
loss seen by the insurance company, and the actuarially fair
premium. Moral hazard and adverse selection effects will
also exist contemporaneously, and so the net effect could be
positive or negative, but that does not deny the existence of
an advantageous selection effect.
The first of these two statements is not that shocking at first
glance. People who prefer a lower level of risk will
presumably both buy insurance to lower their level of risk
and take actions to reduce the actual risk of a loss. However,
as Ehrlich and Becker (1972) demonstrated, insurance and
self-protection can be both complements and substitutes.
Donder and Hindriks (2006) argue that the only equilibrium
that will exist in equilibrium is a separating equilibrium,
where insurance companies offer only a few contracts. In
order to get the risk avoidant people in one group, they
actually offer them either more insurance than they would
prefer or less than they would prefer, in such a way that the
people choose a greater quantity than they would if they
could determine the quantity. Essentially, insurance
companies limit the possibilities of insurance that the
potential insurees have, and use their decision in that
situation to discover information about them. The insurance
companies then use that information to optimize their
decisions. This forces people to buy insurance to reduce their
risk to below the optimal level given the cost of the
insurance. Using the Ehrlich and Becker’s result, Donder and
Hindricks argue this can go to the point where people who
were originally more risk avoidant may actually engage in a
lower level of self-protection than people who were
originally more risk avoidant because they have a higher
level of insurance, and this reduces their risk below the
optimal level.
However, even if insurance companies offer heavily risk
avoidant people more insurance than they would want in
order to separate people by their risk level, in any insurance
market it is not possible for the person to place all of the risk
onto the insurance company. This is especially true in the
health insurance market. People almost always have to pay
the time costs associated with the medical expense. They also
have to deal with the pain of being sick. Furthermore, a
person rarely insures against the entire monetary loss.
Therefore, the insuree still faces a certain level of risk no
matter what level of insurance they have purchased, and a
heavily risk avoidant person would attempt to minimize this
risk. In doing so, they would necessarily limit the risk faced
by the insurance company. There may be a few individuals
who were originally somewhat risk avoidant and the level of
insurance they are forced to take to create the separating
equilibrium as described by Donder and Hindricks (2006)
reduces their level of risk to such a great extent that they
would substantially reduce their efforts expended on selfprotection. However, this would only apply to the people
who marginally belong to the heavily risk avoidant group.
Therefore, the majority of heavily risk avoidant people would
engage in more self-protection than people who are not very
risk avoidant, which would make them better risks, thereby
reducing the actuarially fair premium to insure them.
The second proposition, that people have some control over
their own level of risk seems harder to prove, and for some
kinds of healthcare costs, such as genetic diseases, may not
apply. However, this is not the case at all for most forms of
health care. The most obvious examples are areas such as
HIV/AIDS. If someone is highly risk avoidant, they will be
less likely to engage in risky sexual behavior or to share
needles. Likewise, people who are risk avoidant are less
likely to smoke, use illegal drugs, or binge drink, thereby
reducing their risk and the risk of the insurance company.
More importantly, someone who is risk avoidant will invest
in a greater level of health capital through exercise, good
nutrition, and preventative care. This reduces their risk of a
loss as well. In fact, this even applies in areas such as genetic
diseases where the patient has no control over their risk of
contracting the disease but still has a substantial degree of
control over the strength of their immune system and,
therefore, the associated expense of fighting it.
This last argument is a little less strong for investing in
human capital is inherently risky, as evidenced by
psychological studies that have demonstrated that higher
educated people have lower levels of risk aversion (Shaw,
1996). This would have a mitigating effect on people
investing in health capital to reduce their expected loss. They
would be substituting one form of risk for another. However,
many ways of investing in health capital, such as good
nutrition and good exercise, protect the investor from a
variety of diseases. In a sense, a person is able to diversify
Proceedings of the 2012 Pennsylvania Economic Association Conference
197
his investment across a number of different diseases, thereby
reducing the risk of investing in health capital.
Furthermore, certain risky health behaviors such as smoking,
drug use, and certain sexual behaviors have significant nonhealth losses associated with them as well. As a result,
someone who was risk-avoidant would engage in a lower
amount of these behaviors than would be caused by just
looking at their desire to avoid the health loss. This leads to
an even greater level of advantageous selection, and an even
further decreased level of risk seen by the health insurance
company.
However, even though advantageous selection is theoretically
possible, its existence as a statistically significant
phenomenon is an empirical question, not a theoretical one.
Some strong evidence exists for it in certain insurance
markets. Most importantly, Wang, Huang, and Tzeng (2009)
performed one of the better studies testing for advantageous
selection. They focused their research on the commercial fire
insurance market in Taiwan, not in health insurance.
However, the theoretical model is general and applies to
almost, if not all, forms of insurance, and so if it is
demonstrated to be valid empirically in one insurance market,
it is more likely to be so in other insurance markets, such as
health insurance.
Wang et al. conducted research on a certain type of company
in the two largest cities in Taiwan – class-A was the technical
term used. They used data on the actual incidence of fires,
not just insurance claims, in order to count all of the fires, not
just those that were reported to the insurance companies.
They also conducted information on protection activities that
was not observable by the insurance companies. They
measured this using data on whether the fire safety
equipment worked. The insurance companies knew about the
existence of the equipment but did not have information
regarding whether it actually worked. They also collected
data on informal fire defense organizations within the firm,
which were relatively common and normally undergo twiceyearly training and have responsibilities that reduce the risk
of a fire (Wang et al., 2009).
They found that, “(1) firms that purchase market insurance
have a greater tendency to make an effort to engage in selfprotection; (2) firms that engage in self-protection are less
likely to suffer a fire accident; and (3) firms with commercial
fire insurance have a lower chance of suffering a fire accident
than those without such insurance,” (Wang et al., 2009, p.
18). The first conclusion applies directly to the health
insurance market. If someone who purchases fire insurance
also engages in self-protection, then by analogy someone
who purchases health insurance would engage in activities
that reduce one’s risk of getting sick. The second conclusion
also seems reasonable for the reasons described previously:
people have some control over their risk of getting sick by
limiting certain risky activities and investing in health capital.
The third conclusion, however, may not be valid in the health
insurance market. If the risk of realizing a loss because of a
fire has a lower correlation with information the insuree has
than the risk of realizing a loss because of a health
emergency has, then the information asymmetry will be
larger in the health insurance market. This will lead to greater
adverse selection effects, which could very well offset any
advantageous selection effects.
To further substantiate the statistically significant existence
of advantageous selection in the health insurance market, one
can look at some studies that found a negative correlation
between buying health insurance and risk in the health
insurance market. Fang, Keene, and Silverman conducted
research in the Medigap insurance market. The Medigap
insurance market is ideal for testing for advantageous
selection for two reasons. First, the market is heavily
regulated. Specifically, in 47 states, there are only ten
standardized Medigap plans, and in a designated enrollment
period the insurer cannot deny coverage, place conditions on
a policy, or charge more for pre-existing conditions (Fang,
Keane, & Silverman, 2006, p. 305). This essentially
strengthens the information asymmetry because the insurance
company is not allowed to use some of the information
available to it, and so one would expect to cause a great deal
of adverse selection. Secondly, the Medigap program is
closely linked to the Medicare program. As a result, the
researchers had access to the Medicare Current Beneficiary
Survey (MCBS), which combines survey data with Medicare
administrative records, and the Health and Retirement Study
(HRS), which is a longitudinal study covering large sample
of the people eligible for Medigap insurance. Between the
two sources they had access to data on health expenditures,
diagnoses, treatments, and risk aversion (Fang et al., 2006, p.
305).
They found that medical expenditures for senior citizens with
Medigap coverage were about $4,000.00 less than those
without. Furthermore, once they adjusted for health, which
the insurance companies could not use in pricing the plans,
people with Medigap spent about $2,000.00 more than those
without. In other words, the reason that people with Medigap
plans spent $4,000.00 less on average, rather than $2,000.00
more on average was because they were healthier (Fang et
al., 2006, p. 306). Because the insurance companies could not
use the insured’s health status in pricing, essentially the
insurance companies did not know the health status of the
patients, which, as mentioned above, would be expected to
lead to substantial adverse selection. However, the likelihood
of moral hazard combined with the observed advantageous
selection points to a substantial advantageous selection
effect.
This test was for correlation, and not causation, and so it does
not directly argue that the observed correlation was because
Proceedings of the 2012 Pennsylvania Economic Association Conference
198
of people taking actions to mitigate their risk, as the fire
insurance study did. However, the correlation has to be
explained, and this requirement does substantially support the
theoretical model because the theoretical model predicts what
the empirical study has found.
Evidence for advantageous selection has also been found in
the insurance market in Australia. Australia, like many
European countries, has both a universal public health system
funded through taxation and a privately-funded health system
(Eldridge, Koc, Onur, & Velamuri, 2011, p. 3). The private
hospitals specialize in elective procedures, and are primarily
fed by private insurance companies. The public hospitals tend
to deal with a large majority of all emergency
services(Eldridge et al., 2011, p. 5). As a result, large,
unpredictable expenses are usually covered through the
public health system. So the people who purchase private
health insurance are not insuring against those kinds of risks.
However, the private health market allows for certain
procedures, which would be difficult or impossible to obtain
the public insurance market, and so private health insurance
still reduces the level of risk seen by the insuree.
As in all insurance markets, people with a greater level of
risk avoidance relative to the cost will participate in the
private insurance market; while those will a lower level of
risk avoidance relative to the cost will not. Several different
studies have found evidence of advantageous selection
including those conducted by Ellis and Savage using 2001
data from the National Health Survey (NHS) and conducted
by Buchmueller, Fiebig, Jones & Savage using 2005 NHS
data (Eldridge et al., 2011, p. 8). Eldridge et al. (2011)used
2005 NHS data in their study and said that “Those who report
being in good health are more likely to be insured relative to
those in poor health – by 17% and 18% in the two
specifications respectively.”
Again, this is a correlational study, not a causal one, and only
demonstrates that it is empirically possible for people to be
healthier if they have insurance. As mentioned above, private
health insurance companies specialize in elective procedures.
Unless that specialization results in people participating in
the private insurance market receiving significantly more
useful preventative care, the people choosing the health
insurance would have had to be healthier when they chose
the health insurance. In other words, there would have to be
some level of advantageous selection.
However, not all empirical studies of health insurance have
found evidence of advantageous selection. At best, the results
have been ambiguous. On the other end of the spectrum,
when Harvard started to subsidize its health care plans with a
fixed payment, as opposed to a proportional payment of some
kind, adverse selection set into place a death-spiral that
resulted in the eventual closing of the most generous plan
(Cutler & Zeckhauser, 1998). In this market, there was strong
evidence for a positive correlation between risk and
purchasing insurance.
However, most of the studies have found only a minimal
amount of adverse selection, less than would be expected
based on an educated guess using a model like the one
developed by Rothschild and Stiglitz’s (1976) seminal
article. For example, Cardon and Hendel (2001) used data
from the 1987 National Medical expenditure survey, which
provided relatively accurate consumption data and
information on all policies offered to each employee. They
found that the link between health insurance choice and
health care consumption to be mostly explained by
observables. Neither Finkelstein, McGarry and Sufi (2005) in
their analysis of the long-term care market nor Chiappori and
Salanie (2000) in their study on the French automobile
insurance market found strong evidence for a statistically
significant positive correlation between purchasing insurance
and having a higher level of risk. Essentially, as Cohen and
Siegelman (2010, p. 77) argue, “The studies yield different
findings about the existence of the coverage-risk correlation
and…there is good basis for expecting the existence of…a
coverage-risk correlation to vary across markets, and, indeed,
even across segments of the same market.”
This heterogeneity in the market is evidence of the
phenomenon of advantageous selection. For adverse selection
only causes a positive correlation, and the moral hazard
phenomenon also can only cause a positive correlation
between consumption of healthcare and buying insurance
(Cohen & Siegelman, 2010, p. 39). Since neither can explain
a negative correlation, one would expect a relatively large
positive correlation. However, this is not the case. Instead,
the correlation is rather varied, which points to the
correlation actually being a net effect with the variance being
caused by the heterogeneity of the two effects. This other
effect is advantageous selection. There are likely other
determinants involved as well, but that does not negate the
existence of advantageous selection.
One would expect the advantageous selection to vary across
markets, as mentioned above. The control people have over
their level of sickness depends on the type of sickness being
faced by the person. For example, there is a large difference
between the control someone has over genetic factors and
nutrition factors. As a result, one would expect a greater level
of adverse selection in areas where the person has less
control over their level of sickness, and less of a level in
areas where they do not.
Essentially, the insured population is not a random sample
drawn from the population, and it does not differ just because
of adverse selection caused by asymmetric information.
Rather, it is more risk avoidant than the general population at
a statistically significant level. As a result, it takes actions to
Proceedings of the 2012 Pennsylvania Economic Association Conference
199
avoid the risk they face, thereby, reducing the risk faced by
the insurance company.
This conclusion can have significant policy implications. For
if adverse selection is not a particularly significant problem,
then trying to solve it using governmental mandates of some
kind may not be necessary, in fact, in some cases, they
actually may make the actuarially fair premium more
expensive. In Donder and Hindrick’s model (2006, p. 9),
under certain conditions establishing a social insurance plan
in which participation if required was actually a Pareto
inferior action, in other words, it makes everyone worse-off
utility-wise.
However, this field of advantageous selection is a relatively
new field. Not much research has been done, and most of the
research that has been done has been theoretical. As a result,
more empirical studies attempting to directly demonstrate
advantageous selection would be useful. A study such as the
Wang et al. study would be particularly useful because it
does a better job at establishing causality than strictly
correlational studies such those conducted by Eldridge et al.
and Fang et al. This would better establish that it is actual
advantageous selection, and not something such as
unobservables simply being unimportant, that causes the
level of the correlation to be small.
In the years since Akerlof’s (1970) article, the research in
information, especially as it pertains to health economics has
come a long way, but it has a long way to go. However,
information asymmetry is not the only thing going on in the
health insurance market. There is also a psychological
phenomenon where people who prefer a lower level of
financial risk also prefer a lower level of other kinds of risk,
such as risk of pain and risk of lost-time. As a result, people
in the insurance market would be lower risks, if this was the
only phenomenon occurring. However, adverse selection and
moral hazard also occur. As a result, the a priori net effect is
ambiguous. The empirical evidence regarding the correlation
between risk and insurance level has also been ambiguous.
Certain studies have found evidence of a positive correlation,
some of essentially no correlation, and some of a negative
correlation.
The existence of a positive or ambiguous correlation has
broad policy implications because it raises strong concerns
about the wisdom of engaging in Herculean efforts to try to
bring everyone into the insured pool. Premiums will likely
not have to be much higher if people have choice regarding
their participation in the pool. Furthermore, it explains why
insurance companies have been able to engage in community
rating and other policies, which would be expected to cause
significant adverse selection effects.
FIGURE 1: DESIRED INSURANCE
Proceedings of the 2012 Pennsylvania Economic Association Conference
200
REFERENCES
Akerlof, G. A. 1970. The Market for "Lemons": Quality
Uncertainty and the Market Mechanism. The
Quarterly Journal of Economics, 84, 488-500.
Cardon, J. H., & Hendel, I. 2001. Asymmetric Information in
Health Insurance: Evidence from the National
Medical Expenditure Survey. The RAND Journal of
Economics, 32, 408-427.
Chiappori, P. A., & Salanie, B. 2000. Testing for
Asymmetric Information in Insurance Markets.
Journal of Political Economy, 108, 56-78.
Cohen, A., & Siegelman, P. 2010. Testing for Adverse
Selection in Insurance Markets. Journal of Risk and
Insurance, 77, 39-84.
Cutler, D. M., & Zeckhauser, R. J. 1998. Adverse Selection
in Health Insurance. Forum for Health Economics &
Policy, 1(2), 1-31.
de Meza, D., & Webb, D. C. 2001. Advantageous Selection
in Insurance Markets. The RAND Journal of
Economics, 32, 249-262.
Hemenway, D. 1990. Propitious Selection. The Quarterly
Journal of Economics, 105(4), 1063-1069.
Marcus, R. 2012, March 20). 116 Billion Reasons to Be for
the Individual Mandate Retrieved April 6, 2012,
from http://www.washingtonpost.com/opinions/116billion-reasons-to-be-for-the-individualmandate/2012/03/20/gIQAFt1LQS_story.html
Rothschild, M., & Stiglitz, J. 1976. Equilibrium in
Competitive Insurance Markets: An Essay on the
Economics of Imperfect Information. The Quarterly
Journal of Economics, 90, 629-649.
Shaw, K. L. 1996. An Empirical Analysis of Risk Aversion and
Income Growth. Journal of Labor Economics, 14,
626-653.
Wang, K. C., Huang, R. J., & Tzeng, L. Y. 2009. Empirical
Evidence for Advantageous Selection in the
Comercial Fire Insurance Market. The Geneva Risk
and Insurance Review, 34, 1-19.
Donder, P. D., & Hindriks, J. 2006. Does Propitious
Selection Explain why Riskier People Buy Less
Insurance. Louvain-la-Neuve, Belgium: Department
of Economic Sciences of the Catholic University of
Louvain: Institute of Economic and Social Research.
Ehrlich, I., & Becker, G. S. 1972. Market Insurance, SelfInsurance, and Self-Protection. Journal of Political
Economy, 80, 623-648.
Eldridge, D., Koc, C., Onur, I., & Velamuri, M. (2011). The
Impact of Private Hospital Insurance on Utilization
of Hospital Care in Australia: Evidence from the
National Health Survey Working Paper Series:
Victoria University of Wellington, School of
Economics and Finance.
Fang, H., Keane, M. P., & Silverman, D. 2006. Sources of
Advantageous Selection: Evidence from the
Medigap Insurance Market Working Paper Series:
National Bureau of Economic Research.
Finkelstein, A., McGarry, K., & Sufi, A. 2005. Dynamic
Inefficiencies in Insurance Markets: Evidence from
Long-Term Care Insurance. The American
Economic Review, 95, 224-228.
Folland, S., Goodman, A. C., & Stano, M. 2001. The
Economics of Health and Health Care (6th ed.).
Boston: Prentice Hall.
Proceedings of the 2012 Pennsylvania Economic Association Conference
201
THE EFFECT OF RELIGION AND EMPOWERMENT OF WOMEN ON FERTILITY
Denae A. Heath
Cameron D. McConnell
Clarion University of PA
840 Wood Street
Clarion, PA 16214
ABSTRACT
This study uses regression analysis to determine the
associations between women empowerment, religions, and
the fertility rates in developed and developing countries. The
authors test the hypothesis that factors of women’s
empowerment and the health of the woman affect fertility
rates in a country, more so in the underdeveloped countries.
The countries looked at in this study include a variety of
religions and are: Afghanistan, Argentina, Bolivia, Chad,
India, Mongolia, Switzerland, United States, and Yemen.
This study provides empirical evidence that is representative
of women's empowerment and indicators that are used for
determining the development of a country for the period
1960-2010.
INTRODUCTION
Throughout the world, there are many differences between
each country in regard to the levels of fertility, the
empowerment of women, and the religious beliefs (including
the extent to which the religion is practiced). Previous
research has shown a correlation of religion on fertility and
how fertility rates are impacted by the degree to which
women are empowered, but all of the variables that affect
fertility have been presented separately. This research was
conducted with the idea that, as underdevelopment is linked
to a lack in women’s empowerment, it is also linked to high
fertility rates. Religion fits right in with this idea as well;
based on the fundamentals of the religion, some discourage
women from obtaining a higher status and others have very
strict rules about products that may reduce pregnancies, such
as contraceptives. Other factors to be considered are the
availability of advanced healthcare systems and access/
affordability to surgical procedures that prevent further
reproduction. The religion’s stance on abortion is yet another
aspect that affects a country’s overall fertility rate.
There have been many papers written about all of these
factors, but none tying them all together and few
encompassing the array of countries that are focused on in
this paper. The hypothesis that is tested here is that factors of
women’s empowerment and the health of the woman affect
fertility rates in a country, more so in the underdeveloped
countries. Also, the religion that the majority practices in a
given country has an effect on fertility rates and an indirect
effect on the development stage of the country.
LITERATURE REVIEW
Religion’s Effect on a Country
One researcher that agrees about religion’s effect on
economic growth is McQuillan (2004), who claims that an
important variable to be considered concerning human
fertility is one’s religion. Some economies are more affected
by religion than others, for instance in countries where
Catholicism and Middle Eastern religions are prominent. He
continues, stating that religion affects fertility rates the
greatest when the church has a strong influence on its
members. Many religions have already revised their rules
regarding abortion and contraceptive use, causing some of
the outlooks of the church not to have as great of an impact
on its members. With the societies of today’s world, many
members of churches aren’t as avid about the rules of their
religion compared to yesteryears. In addition, Wonsub (2011)
also found that through observed tests, it is shown that
explanatory power of both religious division and separation
is substantial enough to argue that they strongly influence the
economic growth of a society. On the other hand, through
McCleary’s (2003) results for given religious beliefs, the
overall effect from greater church attendance is to reduce
economic growth. These effects include: resources used up
by the religious sector, the social-capital aspect, and the
influence of organized religion on laws and regulations.
The Economics of Population and Development
Population is an important key in determining how
economies grow in less developed countries. Gobin and
Simon (1992) state that the higher the population density, the
faster economic growth will be in those types of countries.
Furthermore, they claim that there is no correlation between
the population growth rate and economic growth, as well as
total population size and economic growth. Simon (1997), in
a separate study years later, disagrees, claiming that, a high
population growth rate in less developed countries with high
fertility decreases per capita income, decreasing economic
growth in the short run. This eventually negatively affects
output per worker, thus decreasing economic growth overall.
Simon also finds that there is a correlation between total
population size and economic growth, specifically that if
population decreases, the economy will decline. They both
concur that a lower population growth rate helps economic
Proceedings of the 2012 Pennsylvania Economic Association Conference
202
performance in the long run (Simon, 1997) and has a positive
effect on per capita income (Gobin, 1992).
Fertility’s Relation to a Country’s Status
When it comes to fertility and its impact on economic
growth, there are differences that can be seen when looking
at developed countries versus less developed countries.
Aarssen (1995) believes wealthier countries ought to have
lower fertility rates countries than countries with less wealth.
Moreover, Aarssen (1995) argues that there is more of a
focus on the time and money needed to raise a child along
with the presence of sterility due to Western lifestyles. In
addition to Aarssen’s (1995) look at changes in society and
how they’ve affected fertility, Moe (1998) studies about
which societies give more importance to family planning.
Moe (1998) concludes that as economies begin to progress
and become more developed, the expenditures necessary for
raising a child are deemed more important than the utility of
a child until the parents are economically stable.
Women’s Empowerment and Fertility
Singh (1994) agrees with Aarssen’s (1995) outlook on high
fertility rates and brings forth that the lack of empowerment
of women is a consequence of a country being less
developed. He also incorporates women’s education, labor
force participation, and family planning services available to
societies with a decrease in fertility rates, mostly attributable
to economies that are more developed. Singh’s (1994)
thoughts directly relate to the purpose of the research
conducted.
DATA AND METHODS
The independent variables used in this research include
female population, the female labor participation rate, female
health, female education, the income of the country (per
capita), and the human development index (HDI) of the
country. Presented in Table I is the data collected, all of
which were extracted from the World Bank, except for the
maternal mortality rate and the HDI- which were extracted
from the International Human Development Indicators,
Online. For the maternal mortality ratio, the natural log was
used in the panel regression and proration was necessary
throughout some of the variables. The dependent variable for
this research is the fertility rate, total births per woman
(FERT) for the years ranging from 1960- 2010; which was
extracted from the World Bank as well.
The countries that were used in this research were chosen
based on their level of gender inequality, development level,
and the majority religion that was practiced. Though the first
two considerations are expressed through our data, the third
factor, religion, is not due to a lack of accessible data. In
Table II, you will find a chart expressing each of our chosen
countries and the religion that the majority practices within
them. By using this pie-chart, the intention is to show a well
rounded set of data that will hopefully prove our hypothesis
and incorporate religions of the world.
In Table III, the descriptive statistics for the data are shown.
It is clear that the data used was of a large variety and the
means are quite reflective of the range of data encompassed.
Presented in Table IV, in table form, is a correlation matrix.
As one can see, there is a strong negative correlation between
fertility and female life expectancy. The same can be said for
fertility with HDI and there is a strong positive correlation
between maternal mortality and fertility. In terms of female
life expectancy, there is a strong negative correlation between
it and maternal mortality. There is a strong positive
correlation between female life expectancy and GDP per
capita PPP as well as with female life expectancy and HDI.
EMPIRICAL RESULTS
Displayed in Table V is the linear relationship between
fertility rates and variables that portray women empowerment
and economic development/ growth. As predicted, female
population is positively associated to fertility throughout all
models. Similar to previous research, female life expectancy
(Models 1-5), female secondary education (Models 2-5), and
female labor participation (Models 3-6) are all negatively
associated with fertility. This was expected and assists to
prove the hypothesis here. Maternal mortality was positively
associated with fertility in two out of its three included
models. GDP per capita PPP was, as anticipated, positively
associated to the fertility rates and HDI was negatively
associated with fertility. Both of these variables were only
used in separate single models. Given the information
previously presented, most of the results from the data is
economically significant and proves that there is a solid
relationship between our variables and fertility rates.
When looking at female population, female life expectancy,
female labor participation, GDP per capita PPP, and HDI all
compared to fertility, they are all very statistically significant,
with p-values of 0.0000. As for female secondary education,
Models 3 and 4 are 0.0000, making them statistically
significant, whereas Model 2 is at 0.0626 and Model 4 has a
p-value of 0.0051; all of these values have a great statistical
significance, but aren’t as significant compared to the other
variables. Maternal mortality in Models 5 and 6 have
statistically significant p-values (0.0000), but Model 4 has a
p-value of 0.9641, making it statistically insignificant.
Overall, all of the models can be considered statistically
significant, but Model 4 does raise questions based on the pvalue of maternal mortality.
Proceedings of the 2012 Pennsylvania Economic Association Conference
203
CONCLUSION
At a confidence level of 95% it can be concluded that all of
the variables from all models, with an exception of two, have
a significant effect on fertility. This is due to their calculated
values of t being greater than their respected t-value
compared to the number of observations. One of the
exceptions found in this research is female secondary
education in Model 2. The t-value is relatively close to being
seen as significant but persists to show no relationship. The
second exception, maternal mortality, doesn’t have a t-value
in the proximity of the necessary value to be considered
significant, proving it to have no relationship with fertility,
but only in Model 4. This finding was odd, seeing that the
relationship and statistical significance can be proven for this
variable in the other models. The results show proof to
support our thesis that the negatively associated variables, in
relation to fertility, prove that fertility decreases while female
population, female life expectancy, and female labor
participation all increase. It can be said when looking at these
variables that as women are empowered, fertility rates will
decline. This leads to a negative relationship between fertility
and GDP as well as fertility and HDI, allowing for a
conclusion to be drawn that as women are empowered,
economic development and growth occur. This can be said
for all researched countries, so it’s prevalent for all religions.
Some refinements that should be included are that the data
availability for the selected countries was limited. In terms of
religion, not many existed, making it difficult to incorporate
that into the panel regression. Access to family planning
sources and use of contraceptives were also factors that
should have been considered, but as stated before, the data
was unrecorded in different countries/ years. In the case of
India, one thing to be separately considered is the preference
for a son by most families. This causes an increase in their
fertility rate (with families trying longer to obtain this goal)
and female infanticide in India. Selective abortions also
occur, as do child trafficking/ kidnapping. An inequality
index was also difficult to obtain for this research purpose.
Statistically, autocorrelation was present due to the DurbinWatson test. Ceteris paribus, the results are still considered
significant due to the correlations and the levels of p-values
(as with the t-value tests). Though the variables were
considered significant in this research, many variables still
exist that would further prove the effects on fertility and
assist in implementing better policies to support growth in
each country.
POLICY RECOMMENDATION
In terms of policies, the lower developed countries are
encouraged to take action in promoting women and their
empowerment. Things of this nature include the access to
contraceptives and necessary procedures, the ability to enter
the labor force, a reduction in inequality, and an equal
distribution of income. More advanced healthcare and
education would provide for development to progress and for
an overall better economy (not just in terms of financial
factors, but with factors that determine development as well).
Something that policy cannot affect is one’s religion and the
religious actions taken due to beliefs. Depending on the faith
one has in his/ her religion, it will affect the degree to which
a policy will affect a country. With high faiths, many policies
may not work, but as one digresses from his/ her belief in the
religion, policies will have a much greater effect.
Proceedings of the 2012 Pennsylvania Economic Association Conference
204
TABLES
Table I:
Variable
Female Population
Female Participation
Rate
Female Education
Female Health
Pregnant Women’s
Health
Income
Development
Measure
Population, female (%
of total)
Labor participation
rate, female
Percentage of female
students, total
secondary
Life expectancy at
birth, female
Maternal mortality
ratio
GDP per capita PPP
Human development
index (HDI)
Abbreviation
Source
Years
POPF
World Bank
1960-2010
LABPARF
World Bank
1960-2010
FEMSTUSEC
World Bank
1960-2010
LEFEM
World Bank
1960-2010
MM
International HDI
Indicators
World Bank
International HDI
Indicators
1960-2010
GDPPCPPP
HDI
1960-2010
1960-2010
Table II:
Mongolia
Afghanistan, Chad,
Yemen
Argentina, Bolivia,
Switzerland, USA
India
Proceedings of the 2012 Pennsylvania Economic Association Conference
Buddhism
Christianity
Hinduism
Islamic
205
Table III:
Std.
Error
Mean
Median
Std. Dev.
Skew
0.048
0.965
0.583
0.779
0.071
0.367
FERT
4.814
0.109
4.937
2.319
POPF
50.093
0.054
50.589
1.149
LABPARF
47.900
0.983
55.800
16.158
FEMSTUSEC
39.559
0.749
46.308
13.210
LEFEM
60.634
0.662
60.045
14.041
MM
5.072
0.135
5.500
1.761
11094.979
880.966
3140.408
13789.292
0.589
0.014
0.577
0.217
GDPPCPPP
HDI
1.127
0.086
Min.
Max.
Count
1.380
9.223
450
47.854
51.561
459
15.900
67.800
270
7.598
57.345
311
30.813
84.400
450
1.946
7.496
171
568.552
43710.279
245
0.198
0.908
239
Table IV:
FERT
POPF
LABPARF
FEMSTUSEC
LEFEM
MM
GDPPCPPP
FERT
1
POPF
-0.3782
1
LABPARF
-0.4736
0.5811
1
FEMSTUSEC
-0.7581
0.4916
0.2914
1
LEFEM
-0.8957
0.5687
0.3268
0.7810
1
0.8665
-0.6856
-0.3762
-0.7430
-0.9574
1
GDPPCPPP
-0.7007
0.5342
0.2535
0.4038
0.8182
-0.8715
1
HDI
-0.9017
0.7967
0.5504
0.7839
0.9607
-0.9407
0.8728
MM
HDI
1
Table V:
Variables
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Fertility (Y)
-4.66515
(-2.04618)
0.0413
0.390323
(8.123735)
0.0000
-0.16615
(-42.1478)
0.0000
-1.39998
(-0.70786)
0.4796
0.326968
(7.694058)
0.0000
-0.1628
(-30.7498)
0.0000
-0.00865
(-1.86909)
0.0626
-24.0761
(-9.95835)
0.0000
0.858058
(15.85043)
0.0000
-0.18354
(-37.8345)
0.0000
-0.01765
(-4.39036)
0.0000
-0.04358
(-12.9503)
-26.2727
(-9.50902)
0.0000
0.903599
(14.98068)
0.0000
-0.16907
(-15.1059)
0.0000
-0.0358
(-7.03295)
0.0000
-0.0481
(-13.3438)
-37.4928
(-16.959)
0.0000
1.019908
(22.19574)
0.0000
-0.1441
(-14.7503)
0.0000
-0.01491
(-2.84615)
0.0051
-0.05121
(-17.0361)
-28.7122
(-5.25879)
0.0000
0.683518
(6.392881)
0.0000
Female
Population
Female Life
Expectancy
Female
Secondary
Education
Female Labor
Participation
Proceedings of the 2012 Pennsylvania Economic Association Conference
-0.03708
(-6.30985)
206
0.0000
Maternal
Mortality
0.0000
-0.00288
(-0.04507)
0.9641
GDP per capita
PPP
0.0000
0.512605
(8.188415)
0.0000
0.000037
(7.856456)
0.0000
HDI
0.0000
0.636408
(5.449248)
0.0000
-5.03059
(-5.38343)
0.0000
9
Countries
9
9
9
9
9
Included
Observations
50
40
30
19
19
19
450
307
250
162
150
156
0.962469
0.036084
0.974993
0.046088
0.847937
0.030161
Total Pool
(Balanced)
Observations
0.826943
0.901789
0.949987
Adjusted R2
0.010635
0.011832
0.027212
D-W Stat
Listed first is the coefficient, then the (t-statistic), then the p-value
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McQuillan, Kevin. "When Does Religion Influence Fertility?." Population And Development Review 30.1 (2004): 2556.EconLit. Web. 22 Feb. 2012.
Moe, Karine S. "Fertility, Time Use, And Economic Development." Review Of Economic Dynamics 1.3 (1998): 699718.EconLit. Web. 22 Feb. 2012.
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of population: Key modern writings. Volume 1. 176-204. Elgar Reference Collection. International Library of Critical
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Simon, Julian L., and Roy Gobin. "The Relationship Between Population And Economic Growth In Ldcs." Population and
development in poor countries: Selected essays. 180-198. Princeton:, 1992. EconLit. Web. 22 Feb. 2012.
Singh, Ram D. "Fertility-Mortality Variations Across Ldcs: Women's Education, Labor Force Participation, And ContraceptiveUse." Kyklos 47.2 (1994): 209-229. EconLit. Web. 22 Feb. 2012.
Wonsub, Eum. "Religion and Economic Development- A Study on Religious Variables Influencing GDP Growth over
Countries." University of California, Berkeley (2011): 30. 22 Feb. 2012.
World Bank, World Development Indicators Online, 2010.
Proceedings of the 2012 Pennsylvania Economic Association Conference
207
THE EFFECT OF HUMAN DEVELOPMENT LEVEL ON THE RELATIONSHIP BETWEEN ENERGY
CONSUMPTION AND ECONOMIC GROWTH: A CHAOS THEORY APPROACH
Dr. Ezatollah Abbasian
Department of Economics
Bu-Ali Sina University
Hamedan, Iran
Email: abbasian@basu.ac.ir
Phone No: (+98)9125025902, Fax: (+98)8118272084
Maysam Nasrindoost
Department of Economics
Bu-Ali Sina University
Hamedan, Iran
Email: m.nasrindoost@gmail.com
ABSTRACT
This paper studies the effect of human development level on
the complexity of the relationship between energy
consumption and economic growth. Energy intensity is a key
variable that could be used as an indicator of the change on
socio-economic structures, monetary structures or
environment-economy relations. It could be used as an
indicator of the appearance of new structures too. Standard
economic analysis takes a continuous, deterministic and
predictive approach, which encourages the search for
predictive policy to correct environmental problems. This is
actually what happens with the relationship between
economic growth and energy consumption.
The point is that nonlinear interdependent relationships can
be described via chaos methods, which help us understand
patterns and structures of behavior. Using the energy
intensity and human development indices in 139 world
countries, the paper's results show that low human
development is correlated with most likely chaotic energy
intensity time series. Economic instability and inappropriate
government policies are recognized as the main causes of
results. Furthermore, some implications of chaotic energy
intensity series are pointed out.
INTRODUCTION
Recently, the issue of the dematerialization of developed
economies (the reduction of material as well as energy
intensities over time) has gained popularity in the field of
ecological economics. The so-called hypothesis of “intensity
of use” was first put forward by Malenbaum (1978) and
states that income is the main factor that explains the
consumption of materials. That is, during the process of
economic development, countries would tend to increase
consumption of energy and materials at the same rate as
growth in income, until one defined level of income is
reached. Beyond that level, however, we have to expect a delinking between economic growth and the consumption of
materials.
The study of relationship between energy consumption and
economic growth dates back to the late 1970s, especially
after two world energy crises, from literature's perspective.
This group of studies emerged by the work of Kraft and Kraft
(1978) who investigated the relationship between GNP and
energy consumption. Currently, there are four hypotheses
about energy consumption and economic growth relationship
(Payne; 2008).
The first one states that energy consumption affects the
economic growth as a complementary factor for the labor and
capital inputs. Therefore, “growth hypothesis” states a
unidirectional causal relation from energy consumption to
economic growth.
According to the second hyp
othesis, there is a unidirectional causal relation from
economic growth to energy consumption, “thrift hypothesis”.
It may be true because economic growth is affected by
energy consumption for some reasons (Ramos-Martin and
Ortega-Cerda; 2003) such as: a structural change in the
economy (for example, shifting from high energy intensity
sectors to lower intensity ones); an improvement in energy
efficiency (for example, technological improvements) and,
changes in consumption patterns (for example, a change in
consumer’s culture).
According to the third hypothesis which is “feedback
hypothesis”, there is an interaction between energy
consumption and economic growth.
Finally, in the “neutral hypothesis”, there is no relation
between energy consumption and economic growth. One of
Proceedings of the 2012 Pennsylvania Economic Association Conference
208
the most popular reasons for this neutrality, is because of
little energy consumption share of GDP 36.
In here, feedback hypothesis is the most disputable among
others and more relevant to our objectives: because feedback
is one of the fundamental properties of chaotic series. While
energy consumption and economic growth relationship is
unidirectional (or there is not any relationship), the economic
policies will take place only in one step, but existence of
feedback causes more complexity in economic policies
decision making. “Rebound effect” is an example of such
complexity 37.
Because of requirements to consider the feedback effect in
the relationship between energy consumption and economic
growth, the question which demands to answer in this paper
is: how the existence of such a relationship is related to the
performance of nations in social and economic aspects? To
answer this question we classify the challenges in this respect
into three categories:
1.
2.
3.
As it is recently argued, policy makers are confused
about the use of studies about energy consumption
and economic growth (see Chontanawat et Al.;
2008). This may occur due to various results of the
studies, even in one country (see Lee and Chang;
2007). Fatai et Al. (2004) showed that energy
consumption has a significant positive effect on
economic growth in India and Indonesia, but Masih
and Masih (1996), reached a completely different
results for the same countries. One of the most
probable reasons for this might be methodological
differences in definitions, specification of variables
and econometric models.
More of done studies in this area didn't consider
nonlinearity and dynamic properties of the energy
consumption and economic growth relationship. Lee
and Chang (2007), express that nonlinearity
assumption has shed more light on the study of such
relationships. Ramos-Martinand and Ortega-Serda
(2003), believe that nonlinear processes include both
external shocks and internal causes within the
system. Adrangi et Al (2001), found some evidences
of nonlinear behavior in the energy price (which
probably transmits to energy consumption). Seifritz
and Hodgkin (1991) showed strong evidences of
nonlinear properties of energy consumption, by
analysis of overall world energy information
collected over last 150 years. We can see similar
ideas in the other papers such as: Homilton (1996),
Mork et Al. (1994) and Balk et Al. (1999). Unruh
and Moomaw (1998), states that all of historical
events and shocks in the energy price are visible in
dynamic studies.
Some economists believe that the relationship
between energy consumption and economic growth
is weak, so that without more efforts, we can agree
with neutral hypothesis. One of the most important
reasons for this is assumed as fossil fuels become
scarcer, their price will rise, which in turn will
trigger technological changes and substitutions that
improve energy efficiency. Then, the role of energy
in economic activities will decline (see Dias et Al.;
2006).
Therefore, in order to answer the paper's question, we need to
use an appropriate method which take cares about three
above mentioned concerns in it. Such method must be able to
investigate the existence of feedback in the energy
consumption and economic growth relationship, as:
1.
Study the relations without any need to determine
econometric specification.
2. Consider nonlinear and dynamic aspects of energy
consumption and economic growth relationship.
3. Focus on the quality of energy consumption as well
as quantity of it in correlation with economic
growth 38.
Thus, in this paper we do three mentioned tasks as follows:
• To tackle the challenges 1 and 2:
The simultaneous use of chaos theory and energy intensity
index satisfies such properties. Energy intensity is one of the
most important energy efficiency indices. It yields quantity of
energy used (usually in British Thermal Units, BTU) for 1$
gross domestic production. So, the quality of energy
consumption and economic growth relation are determinates
of energy intensity index. On the other hand, chaos theory is
based on theory of nonlinear growth with feedback. Adrangi
et Al (2001) states that chaotic dynamic is necessarily
nonlinear and may be able to explain a richer array of time
series behavior. Also, Ramos-Martinand and Ortega-Serda
(2003) believe that evaluation of energy intensity under the
complex (or chaotic) system framework will be helpful to a
better understanding of the relationships. Therefore, when the
behavior of energy intensity series is chaotic, energy
consumption and economic growth have an interaction with
each other 39.
• To tackle the challenge 3:
For investigating the relationship between energy
consumption and economic growth, we take Human
Development Index (HDI) into account. On the one hand,
HDI was introduced by economists such as Mahbub ul Haq
Haq and Amartya Sen in 1990s to aggregate two new
dimensions to the comparison of the performance of nations.
In the other hand, energy use and social, political and
economic development of nations represent a phenomenon
with a relationship of dependence that is the influence of
each one on the other (Dias et Al.,2006). There are some
studies which consider the role of HDI in quality energyeconomic growth relationship. For example, Martinez and
Ebenhack (2008) found a strong relationship between HDI
Proceedings of the 2012 Pennsylvania Economic Association Conference
209
values and energy consumption in the majority of the world
countries. Also, Dias et Al.(2006) found that when energy
use was associated with HDI, it was possible to find
opportunities to put into practice the energy conservation
concepts, by looking for new conditions to achieve
development at a less intensive energy use.
More important of others in this respect, Chontanawat et Al.
(2008) studied “growth hypothesis” in a sample countries
which includes more than 100 countries by considering their
human development index (HDI). They found that growth
hypothesis is valid for 69 percent of high, 42 percent of
medium and 35 percent of low HDI countries.
So, we can conclude that simultaneous attention to non-liner
and dynamic aspects of the relationship between energy
consumption and economic growth as well as considering to
the social effects of human behavior on such relation is a new
incentive to study in this paper.
In the next section of this paper, the Kolmogorov Entropy
test as preferable test for investigation of chaotic behaviors is
introduced. The empirical results are presented in the third
section and finally discussions and conclusions will be made
in section four.
KOLMOGOROV ENTROPY TEST
There are numerous tests for investigating the chaotic
behavior. All of them are based on distinguishing between
one or more properties of chaotic behavior. The most popular
tests for chaos are: BDS, Lyapunov Exponent, Correlation
Dimension and Kolmogorov Entropy tests. BDS tests the
existence of nonlinear structure in residuals of a nonlinear
model (such as GARCH). In other words, it tests chaos
indirectly. For this reason, BDS test is not sufficient to
recognize the existence of chaotic behavior. Lyapunov
Exponent and Kolmogorov Entropy tests are based on
“sensitive dependence on initial conditions” property (or
“butterfly effect”). While Lyapunov Exponent calculates the
average degree of divergence of nearby orbits in phase space,
Kolmogorov Entropy measures whether nearby trajectories
separate as required by chaotic structure. Finally, Correlation
Dimension is based on “strange attractor” property of chaotic
series.
In this paper, we consider Kolmogorov Entropy test for some
reasons: firstly, as Adrangi et Al (2001) argues, among the
chaos tests, Kolmogorov Entropy test probably represents the
most direct test for chaos. Secondly, this test has a high
similarity with Lyapunov Exponent test. So, Kolmogorov
Entropy test is preferred to all other tests for our purpose.
Consider X. and X. + ∆X. as two points in the state space.
Both of them make an orbit in the state space by use of
system equations. These orbits are functions of time because
system variables are so. If one of them is assumed to be the
reference orbit, the difference between these two orbits (∆X.)
is a function of time, too. Also, due to “sensitive dependence
on initial conditions”, ∆X. is a function of initial values. So,
we have: ∆X = ∆X (X., t). This is illustrated in Fig (1). As we
can see, difference between orbit 1 and orbit 2 is not equal in
any pair of time points (for example t0 and t1), necessarily.
Also, at the same time (for instance t2), the difference
between orbits 1 and 2 is not the same as it between orbits 3
and 4, necessarily. It is true because initial values are
different. Then, time and initial value are two factors that
affect the difference between orbits.
∆X = ∆X (X., t) decreases asymptotically along with time in
a system containing attracting fixed point. It is true because
orbits converge to a special point in such systems. But, ∆X =
∆X (X., t) will be erratic in a chaotic system. In other words,
divergence in the initial conditions makes orbits different
such that their differences after “t” time steps are our criteria
to distinguish chaotic series from others. Grassberger and
Procaccia (1983) calculate the average divergence rate by use
of correlation integral concept as:
K2 = Limε→∞ Limm→∞ Lim N→∞ Ln ( Cm,N(ε) / Cm+1,N(ε) )
(1)
Cm,N(ε) is correlation integral in “m” dimension, with N
observation and ε usually is 0.5, 1, 1.5 or 2 standard errors of
data. Due to “sensitive dependence on initial conditions”,
orbits in a chaotic series change rapidly and then K2 must be
positive.
Our methodology in this paper is calculating the K2 for
energy intensity data. We calculate it with different
assumptions about parameter m, (m=2,...,14) and in 139
world countries. In the other word, we run the Kolmogorov
test 14*139=1946 times and classify these results by HDI
ranking of countries. Details will be illustrated in next
section.
DATA AND EMPIRICAL RESULTS
The level of human development is calculated annually in
177 countries in three groups (high, medium and low human
development countries) by World Bank (WB). HDI is based
on three other indices: 1) A long and healthy life 40, 2) Access
to knowledge 41 and 3) A decent standard of living 42. These
three dimensions are standardized to values between 0 and 1,
so that the geometric mean of these values is equivalent to
HDI value in the range 0 to 1. According to World Bank
report in year 2008, about 39 percent of world countries have
high, 48 percent medium and 13 percent low HDI.
On the other hand, energy intensity time series are available
from Energy Information Administration (EIA) for the 1980-
Proceedings of the 2012 Pennsylvania Economic Association Conference
210
2005 periods. EIA publishes official world's overall energy
estimates and sometimes analyses and forecasts them. The
unit of energy intensity is BTU per 1$ GDP.
Energy intensity data does not exist for all of 177 HDI
ranking countries, so we test the chaotic behavior in energy
intensity time series in 139 world countries. Among our
sample countries, there are 38 percent of the sample countries
with high HDI, 47 percent with medium HDI and 15 percent
with low HDI. It is concluded that there is no serious error
associated with the selection of countries, when we compare
the composition of countries in our sample with one in the
mentioned WB ranking.
By calculating the Kolmogorov Entropy test statistic, for
example, for Iranian energy intensity time series is equal to
0.139 when dimension and epsilon, (ε), are assumed 5 and
0.5*S.E. respectively. Therefore, under such assumptions,
Iranian energy intensity time series is chaotic. We can repeat
above process for all of 139 sample countries. The result is
showed in Fig (2) by HDI ranking. As Fig (2) shows, based
on the study assumption, energy intensity is chaotic in the
most of low HDI countries. Also, this process can be repeat
for different dimensions (m=1, 2, ..., 14), while epsilon, (ε),
is the same as before. We summarized these results in table
(1) and Fig (3) by HDI ranking. As Fig (3) shows, with all of
the assumed dimensions, the highest percentage of the
chaotic energy intensity is correlated to low HDI countries
and vice versa. In other words, there is a negative correlation
between human development level and chaos in energy
intensity variable.
Finally, there are some chaotic energy intensity implications:
•
Complexity in the relationship between energy
consumption and economic growth is more in low HDI
countries.
•
Feedback in relationship between energy consumption
and economic growth is stronger in low HDI countries.
•
In the low HDI countries, slight errors in study of energy
intensity leads to an increased difference in results
(butterfly effect).
•
The relationship between energy consumption and
economic growth most probably must be nonlinear in
low HDI countries.
•
Chaos in the energy intensity time series of countries is
mostly of the low-dimension type.
According
to
these
consequents,
some
policy
recommendations are emerged for low HDI countries:
1.
2.
DISCUSSION AND CONCLUSION
Using the energy intensity and human development indices in
139 world countries and chaos theory approach, this study
demonstrates that low human development is correlated with
more chaotic energy intensity time series. Details of such a
result can be debated from two aspects: causes and
consequents.
From cause's aspect of view, one of the most important
reasons for chaos in the economic variables in these countries
is the low socio-economic development level. The economic
instability sheds some light on this claim. Considering energy
as a strategic commodity makes its price instable. In this
case, in the next step, it would causes instability in whole of
the economy. Since energy intensity is deeply correlated to
quality of energy consumption and economic growth
relationship, chaos in energy intensity series is expected.
Therefore, chaos in energy intensity series is less probable in
the developed countries (which are more economically
stable).
Furthermore, government policies might also be
inappropriate from time and functions viewpoints in the low
HDI countries as well. Once more, it causes chaos in energy
intensity series. Fig (4) summarizes these factors.
3.
4.
5.
Policy makers should pay attention to the quality of
energy use in the policies designs which are related
to energy. For example, longevity and educational
level of people in these countries are more important
factors in policy design and should be taken into
account.
The relation between energy consumption and
economic growth is more probably non-liner and
dynamical in these countries. Then, it is better to
determine policies which emerge from non-liner and
dynamic models.
Measurement errors in these countries led to more
divergence of true policy. Then we need to more
precise data to determine energy policies in these
countries.
Energy policies making in these countries should
pay attention to instability in economic conditions.
In these countries, energy policies must be
strategically and multi-step. It is true because of
existence of feedback in energy-economic growth.
Proceedings of the 2012 Pennsylvania Economic Association Conference
211
FIGURES AND TABLES
m
Table (1): Chaotic Behavior Percentage in the Countries’ Energy Intensity for Different Dimensions
2
3
4
5
6
7
8
9
10
11
12
13
14
HDI
Low
100
100
90.48
71.43
66.67
61.9
47.62
28.57
28.57
14.29
14.29
9.52
9.52
Medium
100
100
90.77
67.69
47.69
38.46
23.08
15.38
9.23
3.08
1.54
1.54
1.54
High
100
98.11
83.02
64.15
35.85
24.53
18.87
13.21
9.43
5.66
5.66
3.77
1.89
Source: Author's observations
Fig (1): Factors that effect on the difference between the orbits
Source: Author's observations
Series1, Low,
71.43
Percent
Series1,
Medium, 67.69
Series1, High,
64.15
HDI ranking
Fig (2): Chaotic Behavior Percentage in the Countries’ Energy Intensity (m=5)
Source: Author's observations
Proceedings of the 2012 Pennsylvania Economic Association Conference
212
Percent
Low dimensions
Medium
High
Fig (3), Chaotic Behavior Percentage in the Countries’ Energy Intensity for Different Dimensions
Source: Author's observations
Fig (4): Factors of chaos in energy intensity series
Source: Author's observations
Proceedings of the 2012 Pennsylvania Economic Association Conference
213
ENDNOTES
1
- See Payne(2008) and Chontanawat et Al.(2006) for comprehensive list of studies in this subject.
2
- As said by Schipper and Grubb (2000): “claims surface that improving energy efficiency improvements do not in
fact reduce demand nearly as much as expected (a weak rebound) or that improved efficiency leads to effects that
erase most of the expected savings (a strong rebound) or indeed stimulates greater energy use than if no
improvements had taken place at all (a backfire) by lowering the cost of energy services and by stimulating
economic activity”.
3
Some other economists argue that energy consumption and economic growth relationship is a strong one. They
said it is true not due to quantity of energy used but for energy quality. For example it is said that “Energy quality is
important to account for in the assessment of E/GDP ratios”. (See Stern; 1993 and Kaufmann; 1992). Also,
econometric analysis confirms a strong connection between energy use and GDP when energy quality is accounted
for (see Stern; 1993 and Cleveland et Al.; 2000).
4
The inverse of this phenomenon is not necessarily true.
5
As measured by life expectancy at birth.
6
Measured by two indicators: the adult literacy rate and the combined gross enrolment ratio (GER) in primary,
secondary and tertiary education).
7
As measured by the GDP per capita expressed in purchasing power parity [PPP] US dollars
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markets". Energy Economics (23), 405-25.
Balke, NS; Brown, SPA and Yucel, M., 1999."Oil price shocks and the US economy: where dose the
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Proceedings of the 2012 Pennsylvania Economic Association Conference
215
LIVING IN KEYNES’S LONG RUN: THE EFFECTS OF THE OVERUSE OF ECONOMIC STIMULUS
David Nugent
3235 Wainbell Avenue
Pittsburgh PA 15216
ABSTRACT
This paper is a theoretical paper that addresses the issue of
the role of short run economic stimulus in long run economic
growth. Topics addressed include a general model of
economic output and cycles of economic growth and
recession, the strategy of employing Keynesian policies of
economic stimulus to increase demand during periods of idle
capacity and the determination of the appropriate stage in an
economic cycle to curtail economic stimulus. Topics also
include unintended consequences of economic stimulus.
INTRODUCTION
In “A Tract on Monetary Reform”, John Maynard Keynes
(1923) stated that “In the long run, we are all dead”. In more
recent years, the comment has been made that Keynes is
dead, and we are living in his long run. Although Keynes
might have been joking, and the statement was made in
reference to monetary policy at the time rather than the fiscal
stimulus that came to be associated with Keynes in later
years, the 90 year old statement “In the long run, we are all
dead” seems relevant at a time when years of deficit spending
has resulted in levels of government debt and government
spending that may be difficult to sustain.
This paper begins with a general description of economic
growth and economic cycles that entail expansion followed
by recession. The paper then addresses government action in
the form of economic stimulus that may increase aggregate
demand and if successful may shorten the duration and
severity of a recession. The paper will also address adverse
consequences of economic stimulus and the effects of
continued economic stimulus after a recession has ended.
labor and overall increases in total output of goods and
services.
Consistent, strong growth tends to be temporary. At some
point during an economic cycle, output reaches a peak, and
expansion turns into recession. Aggregate demand falls,
resulting in falling output, idle factory capacity, office
vacancies, less employment of labor and other consequences
of economic contraction.
As an economic cycle continues, market adjustments take
place that eventually result in economic activity reaching a
trough, followed by renewed expansion. Reliance on market
forces may result in lengthy periods of recession. An
alternative may be government intervention in the form of
economic stimulus, which entails monetary and fiscal
policies.
MONETARY POLICY
Economics textbooks (Samuelson, 1970; Parkin, 1998)
describe monetary policy in terms of actions taken by the
Federal Reserve to maintain economic growth and economic
stability. Actions include changes in the money supply and
setting the discount rate, which is the rate charged by the
Federal Reserve for borrowing by banks.
If the Federal Reserve were to increase the money supply, the
potential result could be economic stimulus that would result
if more money were available to people, as well as the
avoidance of deflation that might result from an inadequate
money supply. If economic growth results in real increases in
the production of goods and services, growth in the money
supply that roughly matches economic growth is appropriate.
ECONOMIC GROWTH AND ECONOMIC CYCLES
Consider how economic output occurs in a period of growth.
Economic textbooks (Samuelson, 1970; Schiller, 2006)
describe factors of production as capital, labor and raw
materials that combine to produce valuable goods and
services. When an economy is growing, demand for goods
and services is typically sufficient for producers to sell their
output. If business owners perceive that demand will grow,
they will invest in new capital assets to increase productive
capacity. Producers of raw materials will similarly increase
their output. Increased productive capacity and increased raw
material production will result in increased employment of
Decreases in the Federal Reserve’s discount rate can serve to
stimulate economic growth. If interest rates charged by banks
to investors and consumers were to also decrease, the result
could be greater investment and greater consumer demand.
Conversely, increases in the Federal Reserve’s discount rate
can serve to counteract inflationary pressures.
ADVERSE EFFECTS OF MONETARY POLICY
In general, if the money supply increases faster than the
growth in output of goods and services without a
corresponding decrease in the velocity, or turnover, of
money, the result can be inflation. In “Money Mischief”,
Proceedings of the 2012 Pennsylvania Economic Association Conference
216
Milton Friedman stated that “substantial inflation is always
and everywhere a monetary phenomenon” (Friedman, 1992,
page 193).
Examples of substantial inflation include the German
hyperinflation of the early 1920s, and the Chinese
hyperinflation of the 1930s and 1940s. Milton Friedman
suggested that economic turmoil caused by the Chinese
hyperinflation contributed to the communist victory over the
Chinese Nationalist government. (Friedman, 1992)
Schiller (2006) describes hyperinflation that occurred in the
United States during the Revolutionary War. The Continental
Congress issued Continental dollars in great quantities,
giving rise to hyperinflation. “The expression “not worth a
continental” became a popular reference to things of little
value”. (Schiller, 2006, page 316)
Although the United States has not experienced
hyperinflation in recent decades, inflation has occurred,
tending to be slight to moderate, including several years of
double digit inflation in the late 1970s and early 1980s. In
1980, the inflation rate was 13.58%. Between April 1945 and
April 2012, the Consumer Price Index rose from 17.8 to
230.1 (Historical Inflation, inflationdata.com).
Regarding adverse effects of the Federal Reserve’s policy of
reducing the discount rate, it might be argued that reductions
in interest rates contributed to the housing bubble that
resulted in economic disaster from which the economy has
not yet fully recovered.
To illustrate how interest rate changes might have
contributed to a housing bubble, suppose that a hypothetical
family has annual income of $40,000. Further suppose that
the family can comfortably spend 25% of their income on
interest. That family could spend $10,000 per year on interest
(40,000 X .25 = 10,000). If the mortgage interest rate were
10%, the family could borrow $100,000. (10,000 / .10 =
100,000; 100,000 X .10 = 10,000). Suppose that the
mortgage interest rate were to fall to 5%, the family could
borrow $200,000 (10,000 / .05 = 200,000; 200,000 X .05 =
10,000). Suppose that the mortgage interest rate were to fall
further, to 2.5%. The family could borrow $400,000 (10,000 /
.025 = 400,000; 400,000 X .025 = 10,000).
It seems plausible that large numbers of families, with access
to greater and greater mortgage loans, would bid up the
prices of houses so that a house that sold for $100,000 when
mortgage rates were 10% might rise to $400,000 as mortgage
rates fall to 2.5%. It also seems plausible that at some point,
home buyers would realize that a house that just a few years
earlier sold for $100,000 is not really worth $400,000, and
the market values would plummet. The hypothetical family
with annual income of $40,000 might find itself with
$400,000 of debt and a house worth $100,000.
Alternately, consider a similar hypothetical family with
annual income of $40,000 that purchased a house for
$100,000 when mortgage rates were 10%. Suppose that as
interest rates fell from 10% to 5% and then to 2.5%, they
learned that the increased market value of their home enabled
them to refinance their home for higher loan balances without
increasing their interest payments. Suppose that family then
spent the money. During the period of rising home values,
the family spent tens of thousands of dollars beyond their
income. That spending could provide economic stimulus that
could have led to substantial economic growth, until the
housing bubble burst. Then that family would find itself in
the same position as the other hypothetical family, with
$40,000 of annual income, $400,000 of debt and a house
worth $100,000.
FISCAL STIMULUS
Much of the economic stimulus employed in recent decades
has been in the form of fiscal stimulus that can be traced to
theories suggested by John Maynard Keynes in “The General
Theory of Employment, Interest and Money”, (Keynes,
1936). Fiscal stimulus typically entails the incurrence of
deficit spending by governments. If a government increases
spending without increasing tax revenue, or if a government
reduces tax revenue without reducing spending, the result
may be the stimulus of demand.
Economic stimulus through fiscal policy is theorized to arise
from a multiplier effect (Parkin, 1998; Samuelson, 1970;
Schiller, 2006). A multiplier effect entails increases in
spending from a series of transactions arising from an initial
expenditure. Consider an illustration that an economics
professor might provide to a class. Image that the
government hires somebody to cut grass in a park. Suppose
that person receives $20 from the government. That person
might then go to a farmer and use the $20 to buy food. The
farmer might use the $20 to go to a barber and get a haircut.
The barber might use the $20 to hire somebody to paint the
door of the barbershop. The painter might then spend the $20
to buy something, and the seller will use the $20 to buy
something else, and so forth. Although there are additional
factors, such as taxes and the tendency for a person’s
marginal propensity to consume to be less than 100%, that
would limit the multiplier effect, the point is that the initial
expenditure of $20 would result in total expenditures and an
increase in demand for goods and services that would exceed
the initial $20.
Along similar lines, a tax cut can theoretically have a
multiplier effect. If a tax cut were to result in greater after-tax
income, the spending of the greater income could have a
multiplier effect in a manner similar to the preceding.
Proceedings of the 2012 Pennsylvania Economic Association Conference
217
EVIDENCE FROM WORLD WAR TWO
It is often suggested that economic performance during
World War Two is evidence of the validity of fiscal stimulus.
Data available in reference and economics books
(Encyclopedia Britannica Almanac, 2005; Schiller, 2006)
seem to be consistent with theories of economic stimulus.
United States federal spending increased from $13.7 billion
in 1941 to $92.7 billion in 1945. The federal deficit increased
from $4.9 billion in 1941, peaked at $54.6 billion in 1943 and
decreased to $47.6 billion in 1945. Gross Domestic Product
increased from $125 billion in 1941 to $213 billion in 1945.
The increase in Gross Domestic Product during the same
period of time that spending and deficits also increased seems
to be evidence of the validity of fiscal stimulus.
In the years since World War Two, fiscal policy has been
used in attempts to stimulate the economy, with the result
that deficits have been incurred in many of those years.
ADVERSE EFFECTS OF FISCAL STIMULUS
Potential adverse effects of fiscal stimulus include crowding
out effects and effects of increasing debt burden.
Economics textbooks (Parkin, 1998; Schiller, 2006) suggest
that if the government finances deficits through borrowing,
the government borrowing could crowd out private
borrowing, resulting in less private investment and slower
economic growth. Although current low levels of interest
rates suggest that a significant crowding out effect has not yet
occurred, conditions could change as the economy improves.
If the government were to run a $1.5 trillion deficit during a
period of economic expansion and rising interest rates, it
seems plausible that a crowding out effect could be
significant.
The effects of increasing debt burden include the increasing
debt service that arises as ongoing deficit spending increases
Federal debt. In terms of government budgets, debt service
can be defined as “the interest required to be paid each year
on outstanding debt” (Schiller, 2006, page 258). If deficits
were to occur for a number of years, at some point the debt
service might become so substantial that it could place a
great burden on the economy.
In implementing fiscal policy, excessive debt burden might
be avoided if deficits were limited to periods of recession. An
inference that can be made regarding theories of fiscal
stimulus is that deficit spending is meant to be temporary.
The multiplier effect is most effective if there is excess
capacity in the economy such that increased aggregate
demand will reduce idle capacity and increase employment
of labor. However, once labor is fully employed, excess
inventory is sold, factories increase output to full capacity,
and the economy is growing, stimulus can be curtailed.
Further stimulus through deficit spending has the potential to
crowd out private investment, unnecessarily increase debt
burden, and cause other adverse effects.
If fiscal policy were to follow a pattern of incurring budget
deficits during recessions, and incurring budget surpluses
during periods of economic growth, the reduction in debt
during economic expansion would allow for deficits during
recessions without creating overwhelming debt burden.
The reduction in deficit spending following a crisis can be
illustrated by the years following World War Two. Between
1940 and 1945, the national debt increased from $43 billion
to $259 billion. During the same period United States gross
domestic product (GDP) increased from $100 billion in 1940
to $213 in 1945. (Schiller, 2006) As a percentage of GDP,
the national debt in 1945 was 121.6% (259 / 213 = 1.216)
In the years following World War Two, federal spending fell.
In 1945, federal spending was $92.7 billion and the federal
budget deficit was $47.6 billion. By 1948, federal spending
fell to $29.8 billion and there was a federal budget surplus of
$11.8 billion (Britannica Almanac, 2004).
Although federal spending has increased steadily since 1948,
and there have been only a few years of surplus, for several
decades budget deficits were relatively small. In general,
GDP grew faster than the national debt. As a result, the
national debt as a percentage of GDP declined. In 1980, the
national debt was $914.3 billion, and the GDP was $2.795
trillion (Schiller, 2006). National debt as a percentage of
GDP was 32.7% (914.3 / 2795 = 0.327).
In the years since 1980, federal deficits have generally been
substantial (with the exception of surpluses from 1998 to
2001). Particularly in the last few years, national debt has
grown faster than GDP. As a result, national debt as a
percentage of GDP has grown.
In 2011, the national debt was $14.764 trillion and GDP was
$15.094
trillion
(Government
Debt
Chart,
usgovernmentspending.com). National debt as a percentage
of GDP was 97.8% (14.764 / 15.094 = 0.978).
The substantial increase in national debt gives rise to the
question of whether the debt burden is becoming too great.
An increase in national debt from $914.3 billion in 1980 to
$14.764 trillion in 2011 seems to be tremendous. However,
the national debt as a percentage of GDP of 97.8% in 2011 is
still less than the 121.6% figure in 1945. The post World War
Two prosperity that allowed for the reduction of the relative
magnitude of national debt as a percentage of GDP suggests
that prosperity combined with small deficits may be possible.
Proceedings of the 2012 Pennsylvania Economic Association Conference
218
Differences in circumstances and political atmosphere may
make recreation of the post World War Two scenario
difficult. World War Two deficits began at a time when the
national debt as a percentage of GDP was substantially lower
than the current level. In 1940, the national debt was $43
billion and the GDP was $100 billion (Schiller 2006).
National debt as a percentage of GDP was 43% (43 / 100 =
0.43). It could be argued that the 43% figure in 1940 meant
that the national debt had room to grow that it may not have
today.
The current debate regarding fiscal stimulus includes the
suggestion by some economists such as Paul Krugman
(2012) that the problem with the economic stimulus is that
the magnitude of the stimulus is inadequate, and that deficits
comparable to World War Two levels may be warranted. The
relative magnitudes of current deficits are smaller than during
World War Two. For example, in 1943, the federal budget
deficit was $54.6 billion and GDP was $192 billion
(Encyclopedia Britannica Almanac, 2005, Schiller, 2006).
The federal deficit as a percentage of GDP was 28.4% (54.6 /
192 = 0.284). In contrast, in 2011, the federal deficit was
$1.30 trillion and GDP was 15.09 trillion (Historical Federal
Receipt and Outlay Summary, taxpolicycenter.org;
Government Debt Chart, usgovernmentspending.com). The
federal deficit as a percentage of GDP was 8.6% in 2011
(1.30 / 15.09 = 0.086).
The apparent stimulus effect of deficits during World War
Two suggests that larger deficits today might provide greater
economic stimulus. However, the expected effects of such
deficits on the debt burden might make such deficits
politically impractical. If the federal deficit were increased to
28.4% of the GDP for 2011, the result would be a deficit of
$4.286 trillion (15.09 X .284 = 4.286). With national debt as
a percentage of GDP close to 100% as of 2011 and likely to
exceed 100% by the end of fiscal year 2012, an acceleration
of the growth of debt would be difficult to implement.
If a fiscal policy had been employed that limited deficit
spending to periods of recession, and if the result had been a
substantially lower ratio of national debt to GDP, the
government would have been in a better position to increase
borrowing. Years of deficit spending during periods of
expansion may effectively limit the ability to increase
borrowing at a time when deficit spending may be warranted
to stimulate the economy.
A concern that might arise if World War Two levels of
deficit spending were proposed is that the spending increases
meant to be temporary might become permanent. A fiscal
policy that would call for reduction in spending when the
economy improves may be inconsistent with actual spending
patterns. In contrast to the period following World War Two
when spending reductions largely entailed cuts in defense
spending from World War levels, current spending appears to
be very difficult to cut because it entails social spending that
many voters do not want to see cut, as well as necessary
ongoing levels of defense spending.
ADDRESSING ISSUES OF DEBT BURDEN
Concerns about the increasing level of national debt as a
percentage of GDP raises the question of what level of debt is
too much. The issue regarding debt burden is not necessarily
that the federal government will be unable to pay its debts,
but rather whether the debt service can be handled
comfortably. Currently, interest rates are so low that interest
payments are relatively modest.
In 2011, interest on the national debt was approximately
$250 billion (United States Federal Budget, wikipedia.org)
and the national debt was $14.764 trillion (Government Debt
Chart, usgovernmentspending.com). This suggests an
average interest rate of 1.7% (250 / 14,764 = 0.017). This
level of interest does not seem to be an excessive burden.
Perhaps the concern should be what could happen if ongoing
deficits were combined with the level of interest rates that
occurred in the late 1970s and early 1980s. For example, in
March 1980, the interest rate on 3 month Treasury Bills was
15.526% (Treasury Securities, hsh.com).
To illustrate a debt burden that could result in a difficult level
of debt service, suppose that over a period of years, the
national debt were to rise to 200% of GDP. Suppose that over
the same period, expansion of the money supply were to give
rise to substantial inflation, and that the Federal Reserve were
no longer able to suppress interest rates, which were to rise to
an average rate of 15% on Federal debt. The result would be
interest payments of 30% of GDP (15% X 200% of GDP).
Such a level of debt service could be a tremendous hindrance
to the economy.
POSSIBLE ACTIONS TO AVERT ADVERSE
EFFECTS
To avoid a debt burden that may hinder economic growth and
may make increased deficit spending difficult at a time when
fiscal stimulus is genuinely warranted, policy makers may
want to consider seeking a fiscal policy comparable to the
years following World War Two. Between 1945 and 1980,
the national debt as a percentage of GDP fell not because of
debt repayment, but rather because the economy grew faster
than the national debt.
If fiscal policy were adopted that would result in growth of
the national debt that were less than the economic growth
rate, the national debt as a percentage of GDP would decline.
To illustrate, consider a deficit that would maintain the status
quo. Suppose that the national debt and GDP were both $15
trillion, resulting in a ratio of national debt to GDP of 100%.
Proceedings of the 2012 Pennsylvania Economic Association Conference
219
Further suppose that the economy were to grow at 4% per
year. The GDP would rise by $600 billion (15 trillion X .04 =
600 billion). If the budget deficit were 4% of the national
debt, the resulting deficit of $600 billion (15 trillion X .04 =
600 billion) would maintain the ratio of 100% (15.6 trillion /
15.6 trillion = 1.00).
Alternately, suppose that the national debt and GDP were
both $15 trillion, and the economy were to grow at 4% ($600
billion increase in GDP). However, suppose the budget
deficit were 1% of the national debt. The resulting deficit
would be $150 billion (15 trillion X .01 = 150 billion).
Because the rate of growth of the national debt is less than
economic growth, the national debt as a percentage of GDP
would fall. The national debt would increase to $15.15
trillion (15 billion + 150 billion = 15.15 trillion). GDP would
increase to $15.60 trillion (15 trillion + 600 billion = 15.60
trillion). National debt as a percentage of GDP would
decrease to 97.1% (15.16 / 15.60 = 0.971).
If such a fiscal policy were followed during periods of
economic growth, over a period of years, reduction in the
ratio of national debt to GDP would make increased budget
deficits during recessions more palatable.
CONCLUSIONS
Economic activity tends to occur in cycles. Periods of growth
are followed by periods of recession. During periods of
recession, governments (and government agencies) attempt to
reduce the severity of a recession through monetary and
fiscal policies.
Monetary policy entails actions by the Federal Reserve to
maintain economic growth and economic stability. Decreases
in the Federal Reserve’s discount rate may encourage
investment. Expansion of the money supply may provide
money needed for transactions as the economy grows.
However, excessive expansion of the money supply can
result in inflation.
Fiscal stimulus entails attempts to stimulate economic growth
through budget deficits. If government spending were
increased while tax revenue remains the same, the result
could be a multiplier effect that could increase aggregate
demand during a recession. Similarly, if taxes were reduced
while government spending remained the same, the resulting
increase in after-tax income could result in increased
aggregate demand. During a recession, increased aggregate
demand can result in increased employment of labor and idle
productive capacity. However, when full employment of
labor and productive capacity is reached, continued fiscal
stimulus can have adverse effects.
out effect may not be an issue at current depressed levels of
economic output, but it may become an issue if trillion dollar
deficits occur during economic expansion.
Debt burden in the United States is not so much a current
problem, but rather is a potential problem. Although the
national debt has grown substantially in recent years, as a
percentage of GDP, the national debt is, as of 2012, is still
less than the level at the end of World War Two. As a result
of relatively low interest rates, the debt service (interest
expense) on the national debt is currently manageable.
Potential problems with debt burden may arise if the national
debt as a percentage of GDP continues to grow. Reduction of
debt burden does not necessarily mean that the government
must have a budget surplus. If the relative size of the budget
deficit were reduced such that the growth rate in the national
debt were less than the growth rate of the economy, the
national debt as a percentage of GDP would decline.
Although high levels of spending and debt may not
necessarily mean economic ruin, high levels of spending may
have adverse implications for economic growth. Spending
that begins as part of a fiscal stimulus package tends to
become permanent, making prospects for reducing
government spending dim.
In 2009, Newsweek Magazine published a cover article titled
“We Are All Socialists Now” (Meacham and Thomas, 2009)
which stated that in the United States, total government
spending was expected to be 39.9% of GDP in 2010,
compared with 47.1% in the typical European country. The
implication seems to be that government control of an
economy does not necessarily mean that the government
owns the means of production. Rather, it seems to suggest
that if government has a claim on a large portion of output,
that government has substantial control over the economy. As
government spending in the United States approaches
European levels, the United States may experience European
style slow economic growth.
If high levels of government spending hinder economic
growth, continued expansion of government spending as a
percentage of GDP may further hinder economic growth.
Perhaps diminished economic prospects are the long run
effect of decades of employing Keynesian theories of fiscal
stimulus as a rationale for budget deficits and government
expansion. Slow economic growth may be the price of fiscal
stimulus.
Adverse effects of fiscal stimulus may include crowding out
effects and effects of increasing debt burden. The crowding
Proceedings of the 2012 Pennsylvania Economic Association Conference
220
REFERENCES
Encyclopedia Britannica 2005, Chicago: Encyclopedia
Britannica, Inc.
Friedman, Milton, 1992, Money Mischief: Episodes in
Monetary History, New York: Harcourt Brace Jovanovich.
Government
Debt
Chart,
www.usgovernmentspending.com/spending_chart_1997_201
7usb_hof, retrieved 5/21/2012.
Historical
Inflation,
www.inflationdata.com/inflation/inflation_rate/historicalinfla
tion.aspx, retrieved 6/16/2012.
Keynes, John Maynard, 1923, A Tract on Monetary Reform,
London: Macmillan.
Keynes, John Maynard, 1936, The General Theory of
Employment, Interest and Money, London: Macmillan.
Meacham, Jon, and Evan Thomas, 2009, “We Are All
Socialists Now”, Newsweek, February 16, 2009 Issue, from
www.thedailybeast.com/newsweek/2009/02/06/we-are-allsocialists-now.html, retrieved 5/24/2012.
Parkin, Michael, 1998, Macroeconomics, New York:
Addison-Wesley.
Samuelson, Paul A., Economics, New York: McGraw-Hill.
Schiller, Bradley R., The Macro Economy Today, New York:
McGraw-Hill Irwin.
Treasury
Securities
1980-1989,
www.hsh.com/indices/tsec80s.html, retrieved 5/19/2012.
United
States
Federal
Budget,
http://en.wikipedia.org/wiki/2011_United_States_federal_bu
dget, retrieved 5/22/2012.
Krugman, Paul, 2012, End This depression Now!, New York:
W. W. Norton & Company.
Proceedings of the 2012 Pennsylvania Economic Association Conference
221
DISCUSSANT COMMENT ON LIVING IN KEYNES'S LONG RUN:
THE EFFECTS OF THE OVERUSE OF ECONOMIC STIMULUS
Michael J. Hannan, Ph.D.
Office of the Provost/VPAA
Edinboro University of Pennsylvania
Edinboro, PA 16444
In general the paper sought to examine the role of short run economic stimuli on long run economic growth. However, the
paper primarily presented basic macroeconomic concepts such as the definition and role of fiscal and monetary policy and
potential economic implications of such policy. Much was presented at a level expected in a principles of macroeconomics
course and nearly no relevant or recent literature was cited.
Throughout the paper, I attempted to keep in mind the main premise stated in the abstract, which focused on how short run
stimuli would impact long run economic growth - this is a good topic. Instead, the paper discussed two major concepts.
The first concept was the crowding out effect, resulting from an ever-rising debt burden leading to higher interest rates that
would crowd out private investment. While this is a basic concept, there was no literature cited or empirical work performed to
show that such an effect was expected. The paper seemed to ignore the existence of world money markets and the fact that
crowding out would not necessarily occur provided that other global lenders were willing to invest in U.S. federal obligations.
The second concept focused on how high government spending as a percent of GDP would lead to greater government control
of the economy and hinder economic growth. This was rationalized from data showing the figures of 39.9% for the U.S. and
47.1% in Europe. Since European growth was characterized as slow, it was reasoned that as U.S. government spending
approached this level, the country would also suffer slow economic growth in the future. No literature was cited regarding this
hypothesis and the author unfortunately ignored other economic, historical, political and demographic differences between the
U.S. and Europe in making this assertion. Slow economic growth in Europe is due to many factors beyond government
spending as a share of GDP.
There were a number of other issues within the paper that will not be discussed here. Instead, I offer a few suggestions for more
targeted research.
First, as mentioned earlier, this is an interesting topic and if one has an interest in the crowding out effect, I would recommend
developing a thorough review of the literature on this topic. With historically large U.S. budget deficits and the European debt
crisis, it is a good time to be looking anew at crowding out possibilities and there should be recent literature in this area. While
interest rates are at very low historical levels, there is concern about the amount and type of assets held on the Federal Reserve
balance sheet and whether this may eventually lead to higher inflation and higher real interest rates.
Second, in a number of places in the paper the author attempts to draw parallels between current deficit and debt conditions
with those that existed during WWII. If this is an area of interest, the author should consider a thorough and focused
comparison between conditions in the two periods recognizing differences in the sizes of the economies, Federal Reserve
policies, industrial composition of the economy, globalization of financial and goods markets, among related variables.
Proceedings of the 2012 Pennsylvania Economic Association Conference
222
KANTIAN MARKETS, BOYCOTTS, AND EFFICIENCY
Richard Robinson
School of Business, E336 Thompson Hall
SUNY at Fredonia, Fredonia, NY, 14063
ABSTRACT
The welfare efficiency of competitive markets certainly
requires that participants conform to moral duties. These
include negative duties such as those against fraud, deception
and coercion, and also positive duties such as those that favor
beneficence. Mainstream Western philosophical notions of
these duties are examined here where product, capital and
internal labor markets are shown to be capable of pricing
conformance to these duties through both formally- and
informally-organized boycotts. A definition of Kantian
markets is provided here which properly categorizes and
explores those duties shown to be necessary for any claim of
market efficiency. Through this exploration, the so called
“Adam Smith” problem concerning the morality of the
invisible hand is resolved. JEL: D63, D64. Keywords:
Boycotts, Ethics
INTRODUCTION
Market philosophers have long argued about “the Adam
Smith problem,” i.e. the seemingly apparent contradiction
between Adam Smith’s ethical philosophy as expressed in
The Theory of Moral Sentiments (1759), and the egotistical
agent of the invisible-hand as expressed in Wealth of Nations
(1776). In attempting to resolve this apparent conflict,
Samuel Fleischacker (2004, p. 91) argued that “human beings
can pursue even their individual interests together, that even
society without benevolence need not be a hostile society,
that economic exchange, even among entirely self-interested
people is not a zero-sum game.” More recently, Mark White
(2008) argued that in the Wealth of Nations, Smith did not
make an argument in favor of egoism, although he showed
that pursuit of self-interest is sufficient for a hypothetically
flourishing society. Smith was only arguing that because
agents knew their own self-interests, the economic
philosophy of mercantilism was inefficient by its nature, i.e.,
that markets can operate based on self interest, not that they
should operate in this way. The moral behavior of
participants still determines whether markets are hostile to
society’s interests, and the extent of this hostility.
Chambers and Lacey (1996) point out that markets can even
price society’s sense of ethics in that through product- and
capital-market boycotts, society forces its ethical will on
producers. We have a considerable history of product- and
capital-market boycotts of those producers who violate
society’s sense of propriety. Generally these boycotts have
been effective in changing business practices in areas such as
child labor, negative environmental impacts, consumer fraud,
and the like. A few of these boycotts are reviewed below.
In general, economists would argue that pricing away
practices that are generally perceived as unethical is an
effective method for society to reach an efficient allocation of
resources; that if boycotts of this sort are effective, then in
general, public welfare must be enhanced. Society wishes to
discourage immoral practices, at least what it considers as
immoral, and the market can certainly play a role in
expressing society’s outrage. Through this mechanism,
society must believe it is better off or it would not have
supported the boycott to begin with. This is the invisible hand
argument supporting the ethical workings of competitive
markets. If society is not outraged by some practice that
philosophers, or other moralists, consider to be unethical
(assuming the public is made aware of this practice), then the
democratic discourse of the marketplace has considered this
practice and judged it as meeting ethical constraints. Many
boycotts do fail.
Perhaps we live in a highly ethical society; perhaps we live in
an amoral society. Whatever society’s ethical sense, it will be
reflected through its markets. In this light, it is worthy to
examine:
1. a hypothetical society where markets meet certain
generally accepted philosophical standards which
are expressed here as Kantian standards of
objective ethical criteria,
2. examine to what extent our Kantian criteria helps to
understand the market forces that price ethics
through different types of boycotts, and
3. judge whether such markets are necessary for
welfare efficiency.
Shleifer (2004) argues that competition weakens the ethical
behavior of firms. He argues that ethics evolved from the
needs of social cooperation; that some ethical standards are
antiquated, or develop as political indoctrination, or are
subject to cultural relativism, and capable of economic versus
morality tradeoffs. As an example, Shleifer argues that
demands for cheaper shoes overcome society’s objections to
asian child-labor. This is a particularly simplistic and dark
view of the relation between morality and markets, a view we
attempt to refute here. I show that the evidence supports the
argument that through boycotts, markets force changes in
some business practices society judges as unethical. I
organize this empirical evidence around certain boycott
Proceedings of the 2012 Pennsylvania Economic Association Conference
223
characteristics, a categorization that I hope allows some
understanding of the forces that lead to success.
Kant (1785, p. 402-404) argued that the CI merely formalized
the ethical decision-making of the common person.3
Any organization of society’s view of ethics must be based
upon more substantive philosophy than cultural relativism or
political indoctrination. For this reason I organize our
analysis around the widely accepted Kantian notions of
duties, both perfect (full obligations), and imperfect (partial
obligations with practical limits). This view of duty lays the
foundation for our analysis of the market pricing of ethics.
As already stated, Kant presents three versions of the
categorical imperative.4 Kant envisioned these versions,
presented below as three “formulas,” as entirely consistent
with each other, and in fact he envisioned each as logically
necessitated by the others. It is important to keep in mind that
these formulas are axiomatic guides for the derivation of
applicable and practical maxims:
We also show that the Kantian criteria, and the associated
duties required of market-participants, have strong
implications for welfare efficiency. I argue that the pricing of
society’s sense of ethics is essentially the pricing of certain
types of Kantian duties, all of which conceivably can be
fairly priced by the market. Since these duties are expressed
by the practical moral maxims derived from, and in
conformance with, Kant’s categorical imperative (CI), we
begin with a brief examination.
Formula 1 – the formula of autonomy or of universal law:
“I ought never to act in such a way that I could not also
will that my maxim should be a universal law.” (1785, p.
402)
MORAL MARKETS
Kant’s Categorical Imperative, Markets and Practical
Moral Maxims
Kant argued that the very notion of morality requires free
will, and this requires individuals who are autonomous in
their actions. Unlike the Plato-Socratic approach, or that of
Aristotle (1953), or of religious ethics (all perfectionist),
Kant did not attempt to objectively establish notions of the
good in human characteristics, actions or results. He argued
that fundamental moral principles as they apply to society
must be adopted by a rational, open and democratic
discourse, but this discourse should only be engaged with
associated rational reflection.1 Kant argued that from rational
free will, a “supreme practical principle,” called the
categorical imperative (CI), follows, namely that the moral
maxims we personally decide to live by must be those we
will that all live by. As a result, through democratic
discourse, our moral maxims become universal; they must
apply to all.
Kant actually presented three interdependent versions of this
categorical imperative (CI), as reviewed below. These
versions do not stem from abstract laws of nature. They
would not exist without the imposition of the reasoning man.
They do not transcend our experience as in Socratic
philosophy. They are person-centered and proceed from the
reasonable (rational) free will.2 These versions of the CI
appear to us as obviously true. They are therefore axiomatic,
so we can derive practical maxims from them. They need no
prior argument to establish them. They stand alone in
forming the premises for our supporting arguments in favor
of our derived practical maxims. Furthermore, they should
not be viewed as unrealistic, or overly formulaic. In fact,
Formula 2 – the formula for the respect for the dignity of
persons: “Act so that you treat humanity, whether in your
own person or in that of any other, always as an end and
never as a means only.” (1785, p. 429)
Formula 3 – the formula of legislation for a moral
community: “All maxims that proceed from our own
making of law ought to harmonize with a possible kingdom
of ends.” (1785, p. 436)
Kant’s first formula, the imperative of universal law,
prohibits us from behaving by personal maxims that are
applicable only to us, and that are designed only for our
convenience. For example, if our business temporarily
suffered from financial distress, and we decided that it would
be acceptable to commit some fraudulent act to ameliorate
our problem, we would violate the imperative of universal
law. We could never will this temporarily fraudulent
behavior to be universal. That would be equivalent to willing
that the foundation of trust upon which our business relations
are built be universally destroyed. A maxim of “commit fraud
only when we temporarily suffer from financial distress” is
unacceptable in Kant’s ethic.
In a similar way, Kant’s formula for the respect for the
dignity of persons would also be violated by the maxim of
fraud described in the above paragraph. Fraud is essentially a
lie. It deceives others into serving our own ends, while not
allowing others to pursue their personal ends. This example
illustrates the consistency of the first two formulas. Indeed,
Kant argued that one formula logically follows from, and is
necessitated by the other.
The motivation for pursuit of the first two formulas lies in the
third, the formula of legislation for a moral community. This
motivation is rooted in Greek philosophy as developed in the
Socratic dialogues, such as Gorgias (see Plato, 1989). In that
dialogue, Socrates develops two principles:
• “No man does evil voluntarily.”
• “It is better to suffer evil than to commit evil.”
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With respect to the first of these propositions, Socrates states
that to know what is good, and to choose what is evil, is an
absurdity. No person knowingly and willingly selects evil;
they select evil only in spite of the fact that it is evil, not
knowingly and voluntarily because it is evil. Evil is merely
the necessary result of ignorance. This is true because
willingly committing evil is self destructive of the reasoning
individual, and no one rationally selects self destruction, at
least according to Plato and Socrates. (Of course, this
assumes that we are all reasoning individuals.) Note that this
preserves the notion of free will in that people could still
select to perform evil actions, but they would do so only out
of ignorance.
Reasoned free will is what gives meaning to life, according to
Socrates, Plato, and certainly also Kant. By selecting evil, a
person therefore destroys that which gives meaning to life.
Kant, however, wants to extend this Greek philosophical
argument. Kant argues that we seek a kingdom of ends, what
in Greek philosophy is termed the good. By kingdom, Kant
means “the union of different rational beings in a system by
common laws” or maxims. (1785, p. 4.433)5 Through the
second formula, duties follow, but they are motivated by the
pursuit of this kingdom of ends. These duties are actually
derived from the practical ethical maxims formed from the
categorical imperative. By harmony, Kant means that these
rational beings pursue consistent and coordinated duties
aimed ultimately at pursuing this kingdom of ends. Moral
actions are therefore those that are motivated by the pursuit
of this ultimate good. They cannot be those that serve only
the self at the expense of others in this “union of rational
beings.” (1785, p. 4.430) Indeed, Kant argued that
examination of motivation is the only basis for judging the
morality of some action, and Formula 3 provides the only
justifiable motivation. Other possible motivations are
egotistical, that is they are inherently selfish.
Kant’s kingdom of ends refers, of course, to an harmonious
overall society, one where reasoning people pursue maxims
which they form democratically and therefore find
acceptable, and which are derived from the other two
formulas of the categorical imperative. Can this kingdom of
ends motivation ever be applied to the neoclassical
economic-market participant who we generally describe as
self interested? This is the apparent contradiction examined
here.
Certainly one can argue that pursuit of this kingdom of ends
is not practical, especially within the neoclassical firm. What
else could motivate managers: greed, gluttony, sexual
addiction, … ? These pursuits are also not practical in that
they can never be ultimately satisfying, but only transitory in
satisfaction, if even that. It can be argued that pursuit of
harmony, however, has the potential of leading to success in
business or other market endeavors, even if perfection in that
pursuit is not ultimately achievable. Pursuit of profits may be
the goal of the firm, but the motive for market participants
behaving ethically should be the pursuit of the kingdom of
ends. The practical maxims derived from the CI provide the
moral constraints on market behavior.
Kantian Markets
The notion of a Kantian market is necessarily abstract, but
given the review of the categorical imperative presented
above, my definition is nonetheless straightforward. It
requires two conditions:
1.
Kantian markets are ruled by a complete set of
practical moral maxims that are consistent with the
categorical imperative.
2.
The market participants in Kantian markets are all
motivated by the pursuit of a moral community.
Concerning the first condition, by a complete set of moral
maxims we mean a set that is sufficient to operationalize the
first two formulas of the categorical imperative. For example,
this set of maxims would include those aimed at allowing no
personal exceptions to their conformance, i.e., all must
comply because all must be allowed to pursue their own ends
provided they do not impinge on the freedom of others to
pursue their own ends.6 This certainly requires that deception,
outright fraud, and coercion are declared in violation, and this
would apply to all contracts whether explicit or implicit, i.e.,
those contracts reached by both formal (legal) and informal
agreement.7
The second condition is clearly the most abstract. It is the
formula for the pursuit of the kingdom of ends that is the
most difficult to apply to markets. We cannot envision
legally requiring proper motivation, but we still must specify
this requirement to describe our abstract notion of Kantian
markets. In this abstract idealistic model, all our market
interactions must be constrained by this motivation. Of
course, the above two conditions are not independent. The
second condition requires the first in that the pursuit of a
moral community implies that all agents approve the maxims
referred to in the first condition. It must be explained how the
kingdom of ends motivation could operate in a market system
of largely self-interested transactions. I state “largely self
interested” since there are market transactions among more
intimately connected agents such as those within families,
within firms, and even within friendly communities. We can
all envision the motivation of pursuing a moral community to
be accepted and active in these latter groups, but we ponder
how such motivation could flourish in markets with more
disinterested agents and transactions.
The answer is explained by Sullivan (1989, p. 214-16). In
Kantian analysis, there are two levels of moral community to
be pursued:
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1.
2.
a civic moral-community of justice in the sense of
pursuing all the moral maxims needed for judicially
meeting the second formula, i.e., an impersonal
community of judicial duty,
a community that meets the required judicial duty
conditions, but that also fully pursues the kingdomof-ends motivation, i.e., a community of full moral
duty.
We can envision sub-communities within an overall
community of judicial duty that also meet the conditions for
being a community of full moral duty. An overall market
economy should certainly meet the conditions for a
community of judicial duty, and perhaps markets such as the
labor market that is internal-to-the-firm could be envisioned
as meeting the conditions for a community of full moral duty.
It is shown below that the efficiency of an overall economy’s
markets requires that the judicial level of duties be a
necessary, but not sufficient condition. Other markets, such
as the internal-labor-markets within firms, must meet certain
necessary additional full moral duties.
Notions of Duties and Market Efficiency
In Kantian analysis, there are two types of duties: perfect and
imperfect duties, which are frequently termed negative and
positive duties. (See Sullivan, 1994, Chapter 8, for a review,
and in the original see Kant, 1785, p. 4:421 – 431.) Duties
stem from the moral law, or in our analysis, they stem from
the moral maxims specified as necessary for a community of
full moral duty. Perfect duties, often termed negative duties,
are absolute prohibitions against actions that violate the
second formula, actions involving deception, fraud, coercion
and the like. Imperfect duties, often termed positive duties,
stem from obligatory actions that have practical limitations,
actions such as beneficence. For these duties, we are obliged
to perform some actions such as various forms of charity, but
for practical survivability reasons, we cannot give all to the
poor. Although we are obliged to be charitable, there must be
practical limitations to this charity; we are not obliged to
make ourselves poor because of these activities.
Judicial duties are negative (or perfect) duties; they prohibit
actions of fraud and the like. A community of full moral duty
clearly requires that all are committed to the second formula
(respect for the dignity of others), and its associated moral
maxims. The markets that compose our overall economy
must be motivated to exhibit these duties or our markets
would be, in a broad sense, inefficient and probably
considerably underperforming. A community of full moral
duty, however, requires that both negative and positive duties
be fully obligatory. We could hope that the latter
classification of duties should be performed within certain
markets where agents are more closely connected, such as the
internal labor markets of firms, rather than in the broader
more impersonal markets where agents are distant from each
other.
In Kantian markets, the self worth of agents motivates them
to “pursue their own morally permissible welfare and
happiness, but also to promote those of others.” (Sullivan,
1994, p. 156.) Markets are an expression of the mutual
dependence of their participants, who we assume aim at
fulfilling their own needs. Mutual respect requires that these
agents treat each other not merely as the means to their own
ends, but also by allowing others to pursue their ends, i.e.
conditions specified under the second formula. For freely
competitive markets, we assume that both sides of a
transaction pursue their own ends, but they are also interested
in enabling others to achieve their ends, and the closer the
market participants, the greater the interest. Positive duties
are clearly necessary for promoting the interests of all, but in
Kantian analysis, the closer the relationship between agents,
the greater the expectation of duties of a positive sort. Market
transactions between agents who are closer, rather than more
distant, should exhibit these positive duties with practical
limitations that would themselves strengthen the closer the
relationship is. The obligation for beneficence is stronger the
closer the agents, and this closeness is largely determined by
the nature of the particular market in question.
For example, consider the duty of charity as briefly reviewed
above, especially in the context of the competitive firm, in
particular the competitive corporation with publicly traded
shares. These firms do have charitable expenses that are
usually aimed at community relations, or the seeking of
positive publicity. In either case, these contributions have
bottom line impacts that justify their expenditures. There are
other charitable contributions, however, those explained as
being required of corporate social responsibility (CSR), that
are unrelated to the bottom line, but are said to be required
from a social-duty obligation. These CSR expenditures are,
however, expected to be impersonal in that the corporation is
distant from the intended beneficiaries. The counter argument
of Chambers and Lacey (1996) is that distribution of these
funds back to the corporate owners (in the form of dividends)
allows those closer to intended beneficiaries to make the
charitable decisions. Corporate shareholders legally own
these revenues, and their charitable expenditures by
management pose agency problems unless these expenditures
positively affect future revenue. The duty of beneficence is
greater among those closer to the beneficiary. We would
expect, therefore, a greater extent and degree of effectiveness
in charitable giving if these revenues were expended by
owners rather than management.
Complete market efficiency clearly requires a community of
judicial duty. Markets based on fraud and deception could
not possibly be termed efficient since these markets would
implode in that they would be abandoned by participants.
The more participants perceive the probability of
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encountering outright fraud or even partial deception
concerning the product, service, or payment, the more
participants would leave the market. Furthermore, without
the moral motivation of the pursuit of the kingdom of ends,
we could not expect agents to fully conform to these negative
duties, although fear of retribution, or ostracism if caught,
might motivate a considerable degree of conformance. For
this reason, the motivation of the third formula is also
necessary for many market transactions, but perhaps not all.
THE MARKET PRICING OF ETHICS
Motivation for Boycotts
As reviewed above, Chambers and Lacey (1996) pointed out
that markets are capable of pricing ethics through boycotts of
both product and capital markets. Boycotts are expressions of
moral disapproval and outrage by multiple agents who refuse
to either purchase a product, or participate in its production.
(A strike is a sort of boycott.) With widespread participation,
boycotts are expressions of society’s moral disapproval.
We must also point out that labor mobility, and certainly
participation in internal labor markets, also are capable of
pricing ethical behavior. People change firms if they believe
that the internal responsibility assignments, evaluation and
reward practices of their firms are unethical, or that abusive
or humiliating behavior is practiced. Certainly a firm’s
employment reputation influences its ability to engage
talented employees, and therefore the firm’s performance.
All of these market forces are essentially transfer
mechanisms for imposing society’s sense of ethics onto the
firm. With respect to these market forces, the relevant
question is “What do we mean by society’s sense of ethics?”
Our notion of Kantian markets assists in answering this
question.
To the extent of deviations from the two levels of Kantian
markets reviewed above, we might expect boycotts of all
three of the markets mentioned: product, capital, and internal
labor. In particular, violations of obligations of a community
of judicial duty (prohibitions against fraud, deception and the
like) should solicit either formally or informally organized
boycotts to the extent that society is outraged by the
violations, i.e., the greater the contempt for society’s sense of
judicial-duty obligation, the greater society’s outrage.
Formally organized boycotts have leaders; they manifest
reasoned argumentative-communication from these leaders to
possible participants (propaganda); and public expressions
about the moral violations perceived. Informal boycotts do
not have leaders, but do have a variety of communications
(letters to the editor, blogs, etc.) that express outrage.
Informal boycotts are often so quickly successful that formal
organization does not have time to be arranged. The
perceived immoral behavior is altered quickly before the
boycott can be further organized.
Given the closeness of internal employee arrangements, we
expect that positive duty violations would solicit reactions
from participating employees, perhaps even boycotts.
Beneficence is expected among close agents within firms.
This is a positive duty, and as such, it has practical
limitations. The extent of its practice is actually subject to a
marginalist solution, but certainly there is the potential for
beneficence to impact the firm’s returns. Hence the motive of
pursuit of a community of full-moral duty provides positive
duties that are also subject to market forces if violated, and
perhaps boycotts.
With respect to our duties, both positive and negative, we can
conclude that markets are capable of exerting pricing forces.
Our notion of Kantian markets, with their associated perfect
and imperfect duties, are not unrealistic unless the
operational specifications of the duties themselves are overly
extreme, and it is unlikely that the public would form
unrealistic-impractical notions of duty. Markets would likely
only react to violations of duties society judges as ethically
necessary and realistically formed.
Chambers and Lacey (1996) point out the democratic nature
of the firm’s pursuit of profit. As envisioned by Smith
(1776), the marketplace is essentially a voting place where
the invisible hand moves participants to respond to meet
society’s needs. Under conditions of sufficient knowledge
about the workings of the firm (especially about any
externalities generated), the product market will
democratically voice the concerns of the populace about
perceived ethical lapses. Firms that pursue maximization of
profit as a goal must respond to society’s sense of moral
rightness, and this sense forms the constraints on the profit
goal. As evidence of this, we examine and classify several
successful boycotts in a section below. By “successful” we
mean that they ultimately lead to changes in firm behavior,
and/or social policy.
Judging the Morality of Markets
White (2008) points out that a market in which agents follow
only negative (perfect) duties would be the impersonal
marketplace of Smith’s Wealth of Nations; it would be
minimally ethical. In Smith’s The Theory of Moral
Sentiments, it is the sympathy for the suffering of others that
motivates moral behavior. Indifference to the suffering of
others essentially violates Kant’s second formula of respect
for the dignity of others, of treating others as an “ends.”
Markets are, however, mostly impersonal, and feeling
sympathy for the suffering of another agent during an
impersonal market transaction is not generally expected.
Kant’s analysis of respectful beneficence in such transactions
is relevant for these impersonal market transactions: “Since
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227
our duty is to behave as if our help is either merely what is
due him, or but a slight service of love, to spare him
humiliation and maintain his respect for himself.” (1798,
4:448-9) Clearly, even impersonal market transactions
require more than mere performance of judicial duties if
market transactions can be said to be ethical, i.e., we need
more than a minimally ethical marketplace.
It is argued here that ethical markets require motivation from
both Smith’s sympathy for the suffering of others, and Kant’s
pursuit of the moral community. The latter motivation is
capable of stirring emotions of outrage over violations of
established negative duties, and perhaps even violations of
established positive duties. In essence, the boycott
mechanism needs both motivations: Smithian sympathy, and
Kantian outrage. The former is likely to lead to new
developments of, or variations in required duties, and the
latter is likely to lead to enforcement of the already
established duties. When we witness the suffering of others
due to some market practice, a suffering that might be
addressed by some new norm, we form a hypothetical
alleviation for the problem. If others wish to alleviate this
suffering by similar means, a boycott of that market practice
might be organized. It is the suffering of others that
ultimately motivates the boycott and possible new norm. In
other situations that involve the flaunting of existing norms, a
Kantian outrage over this flaunting might also lead to a
boycott.
When active in markets, the boycott mechanism transfers
these emotions of sympathy and outrage into imposing
society’s sense of ethics onto firm behavior. We must realize
that boycotts are expressions of reasoned reflection about
acceptable and enforceable moral duties. They are formed
through public-democratic discourse, and hence they meet
the Kantian requirements for forming or enforcing moral
maxims.
Gauthier (1986, Chapter VIII, p. 84-5) argues that markets
are the ideal model for an ethical society. He argues that “in
understanding the perfect market as a morally free zone we
shall be led back to its underlying, antecedent morality” of
mutually agreed upon constraints on behavior. This is a
peculiar use of the term “morally free zone.” It appears to
perpetuate the notion that perfect markets are essentially
amoral, and that only violations of the neoclassical perfectmarket definitional-conditions should be interpreted as
unethical. Given our boycott analysis, however, a preferable
term in this context would be fully-moral zone, where
society’s sense of ethics is fully operationalized through the
pricing mechanism of competitive markets. The less
competitive the marketplace is, the less the boycott effect
could impose society’s ethical sense on the firm. A perfect
monopoly would have a decreased motive to respond to
society’s outrage.
Boycott Classifications and Effectiveness
Today’s social media particularly enables the organization of
boycotts. For example, Facebook offers a boycott
communication page. As a result of instant electronic
communication, attempts to organize boycotts are numerous
and with lower transactions costs than in the pre-social-media
era. Complaints against business practices are easily masscommunicated, and similar complainers are more easily
found. A casual review clearly indicates that most of these
boycott attempts fail in having any effect on the business in
question. In this section, however, we wish to review several
of the more classically successful boycotts for the purpose of
a possible bifurcation- classification system which might
enable a judgment of conditions that are more likely to lead
to boycott success. We also review some less than successful
boycotts for comparison purposes.
The successful product-market boycotts examined here
include the California grape boycott in support of the Migrant
Farm Workers’ Union during the 1960s and 1970s; the tuna
boycott of Bumblebee, Starkist, and Chicken of the Sea in the
late 1980s; and the Calvin Klein clothing boycott in 1995. A
successful capital-market boycott is also examined, i.e. the
South African boycott of the 1960s. The ongoing Swiss
company Nestle Foods boycott (due to problems associated
with its powdered baby-formula exports to Africa), and the
boycott of the French company Danone in 2001 (due to its
employee relations) are two less-than-successful attempts
reviewed here for comparison purposes.
As reviewed above, there are two categories of market
interactions: (1) the impersonal transactions of strangers, and
(2) the more intimate contacts of those familiar with each
other during ongoing transactions. The example I reviewed of
the former category is the supermarket purchase, and the
example I presented for the latter category is the internallabor market. In addition, I suggested that although both
categories require a complete judicial conformance of
negative duties, and both require some degree of positive
duty, it is the latter category that is more likely to require a
higher degree of positive duty given the greater personal
contact. There are more extensive personal obligations of
beneficence in the internal-labor market.
I also suggested that there are two motives for organizing or
joining boycotts: (1) Smithian sympathy, and (2) Kantian
outrage. The former motivates individuals to attempt to
organize new social norms, and this is motivated by
perceived sympathy for the suffering of others, a suffering
that current social norms do not alleviate. The latter motive
(Kantian outrage) stems from concern for the flaunting of the
perceived current social norms or laws, the more blatant the
flaunting, the greater the outrage, and the more likely a
boycott will be organized and successful.
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228
Table 1 presents a two-by-two matrix that categorizes
boycotts into whether the perceived violation is between
close or distant relations, and also whether the boycotts
appear to be motivated by Smithian sympathy (a desire to
develop new norms), or Kantian outrage (a desire to see
current norms enforced whether these norms are actual law or
generally accepted, but not legal, social standards). We
classify six boycotts according to this matrix, four classic and
successful boycotts, and two more recent, but unsuccessful
boycotts. Note that in Table 1, it is questionable whether
boycotts involving distant relations are purely due to
perceived violations of negative duties. They might involve
some degree of flaunting of a lower-level of positive duty. In
a similar way, violations involving those with close relations
could involve violations of negative duties as well as positive
duties as indicated.
Table 1: Boycott Classifications
Smithian
Sympathy
Kantian
Outrage
Distant Relations
Close Relations
Tuna
South African
Nestle
Calvin Klein
Danone
California Grape
The Calvin Klein boycott, the tuna boycott, the South African
disinvestment boycott, and the Nestle Foods boycott all
involved the more distant relations of impersonal market
transactions. Although not formerly an organized boycott, a
rapid reduction in demand occurred for Calvin Klein’s
products in response to the public’s negative outrage
concerning the company’s use of pubescent children in
sexually provocative advertisements during 1995. These
billboard and magazine advertisements were withdrawn
quickly. This can be categorized as an informal boycott. The
company’s policies changed too rapidly for any organized
boycott
to
be
formed.
(See
www.icmrindia.org/casestudies/catalogue/Marketing/MKTG
084.html for a complete story of these events.)
The Nestle boycott concerns its marketing of baby formula in
less-than-developed countries, especially Africa. The baby
formula, as compared to breast feeding, is criticized for
preventing a full development of the infants’ immune system.
Although the boycott was formally organized in 1978,
publicity concerning the problem began in 1973. Various
agreements with the company were reached over the years,
but these were judged as quickly violated, and the boycott
reorganized. It is currently ongoing. These agreements
clearly were attempts at establishing new norms for
marketing baby formula, and as such, they clearly involved
Smithian sympathy to develop new socially acceptable
norms. (See the Wikipedia story on the Nestle boycott.)
Both the French company Danone Foods and the California
Grape Boycotts involved labor relations. As such, both
involved potential violations of close relations involving
employees. The Danone boycott came in response to an
announcement that the company would layoff approximately
200 employees in March, of 2001. The announcement came
as a surprise to those separated from the company. Some
local mayors ordered their hospitals and schools to boycott
purchases from Danone, and organized a more general public
boycott. The impact on Danone’s revenues and security
prices was modest, and the boycott was eventually
abandoned, but since Danone previously had very good
employee relations, and these relations deteriorated, perhaps
the
boycott
had
some
effect.
(See
www.paristempo.com/danone1.html for a complete story
about these events.)
The tuna boycott involved the methods for catching tuna,
which are frequently found with dolphin. Netting methods
catch both, and in the past, both have been canned as one
fish. In 1986 the International Marine Mammal Project and
the Earth Island Institute began the boycott. In 1990,
Starkist, Chicken of the Sea, and Bumblebee signed
agreements to stop purchasing, processing or selling tuna
caught in the offending way, and the U.S. Legal Standard for
the Dolphin Safe Tuna label was established. This boycott
clearly exhibited Smithian sympathy for development of new
norms, and also clearly involved offenses of negative duties
(perceived broad environmental degradation) of distant
transactions. (See the Wikipedia story on the history of the
tuna boycott.)
The California Grape Boycott began in 1966 with the
organization of the United Farm Workers as led by Cesar
Chavez. The boycott was aimed at consumers of table grapes
with the objective being the establishment of collectivebargaining agreements with the grape growers. The boycott
was nationwide, and by 1970, a majority of growers had
signed collective agreements. The boycott was generally
judged as successful. (See the Wikipedia report titled “Delano
Grape Boycott.”)
Racial-segregation policies formally began in South Africa in
1948. Boycotts of those companies doing business in South
Africa began in the 1960s, along with boycotts of sports
teams, university exchanges, and export products. Capital
market boycotts also voice society’s demand for moral
reform of business behavior, and these were initiated in the
late 1960s by university and other foundations. In 1994,
multi-racial elections were held and the apartheid policies
were entirely ended. These boycotts were clearly initiated
from Smithian sympathy, and involved distant relations. (See
the Wikipedia story concerning the history of the South
African boycotts.)
Proceedings of the 2012 Pennsylvania Economic Association Conference
229
CONCLUSION
Two of the six boycotts reviewed above, Nestle and Danone,
were motivated by Smithian sympathy, but they failed in that
the clearness of the norms to be established was murky. It
was not entirely clear that the layoffs at Danone (small in
magnitude as compared to the size of the firm) should have
been prevented, nor that the baby formula problem at Nestle
was sufficiently widespread to warrant prohibition. Two of
the successful boycotts (the tuna and South African boycotts)
were also motivated by Smithian sympathy to establish new
norms: new standards for fishing and canning in the former
boycott, and new integration norms for the latter. Both
established clear and well-defined new norms that were
ultimately formed.
The California grape and Calvin Klein boycotts were
motivated by Kantian outrage over violation of clear norms.
The former involved the grape growers refusal to recognize
the migrants’ union, a violation of clear expectations for
employee relations. The latter involved a company’s
exploitation of children in violation of social-sexual norms.
In both cases, the norms violated were clear and generally
accepted by society.
Boycotts of the Smithian sympathy sort are clearly the least
likely to succeed. New norms are difficult to form, and
success would require a clear need for a well-defined new
norm. Boycotts involving Kantian outrage over flaunting a
clearly established norm are the most likely to succeed.
It is most important to recognize that it is the profit motive
that allows boycotts to be particularly effective. In essence,
profit seeking allows the market-voting mechanism to
function.
I argued that formal and informal boycotts are an efficient
mechanism for transferring society’s sense of ethics through
the marketplace and onto the competitive firm. For this
mechanism to work, society needs clarity concerning the
duties demanded of market participants, a clarity provided by
the Western ethical tradition of Kantian analysis of perfect
and imperfect duties. These duties are derived from the
practical moral maxims that stem from reflective-reasoned
democratic discourse. When we envision the marketplace
(product markets, capital markets, and internal labor markets)
as essentially a mechanism for voting, the expectation of
ethical duty demanded of the participating agents are viewed
as voiced.
The above definition of Kantian markets voices two separate
levels of what is meant by moral markets: the community of
judicial duty, and the community of full moral duty. The
former notion is applicable for the more impersonal product
and capital markets. The latter notion is applicable for
markets exhibiting closer relations such as the internal labor
market. Boycotts work to enforce ethical notions of both
types of communities depending upon the type of market.
The motivations for these boycotts are Smithian sympathy
and Kantian outrage, the former leads to the development of
new market duties, and the latter leads to enforcement of
existing norms.
This Kantian analysis more properly describes the
competitive market’s ability to pursue welfare efficiency than
the traditional neoclassical model that restricts the ethical
content of markets to violations of the perfect competitive
conditions. In this sense, it properly compliments this
neoclassical analysis. It properly moves economic notions of
competitive markets from the amoral (morally neutral) vision
to an ethical vision.
REFERENCES
Aristotle, 1953, The Ethics, Book II, Penguin Classics, London, UK.
Chambers, Donald and Nelson Lacey, 1996, "Corporate Ethics and Shareholder Wealth Maximization," Financial Management,
Spring/Summer, p. 93-95.
Fleischacker, Samuel, 2004, “On Adam Smith’s Wealth of Nations,” Princeton University Press, Princeton, NJ.
Gauthier, David, 1989, Morals by Agreement, Oxford University Press, Oxford, UK.
Kant, Immanuel, 1785, Fundamental Principles of the Metaphysics of Morals, in Basic Writings of Kant, edited by Allen W.
Wood, The Modern Library Classics, The Modern Library, New York.
Proceedings of the 2012 Pennsylvania Economic Association Conference
230
_____________, 1798, The Metaphysics of Morals, edited by Mary Gregor, Cambridge University Press, Cambridge, UK.
Plato, 1989, The Collected Dialogue Including the Letters, edited by Edith Hamilton and Huntington Cairns, Bollingen Series
LXXI, Princeton University Press.
Rawls, John, 2001, Justice as Fairness: A Restatement, Belknap Press of Harvard University Press, Cambridge, MA.
Shleifer, Andrei, 2004, “Does Competition Destroy Ethical Behavior?” American Economic Review, Vol. 94, No.2, (May), p.
414 - 418.
Smith, Adam, 1776, An Inquiry into the Nature and Causes of the Wealth of Nations, repr inted by Clarendon Press, 1996.
_____________, 1759, The Theory of Moral Sentiments, edited by D.D. Raphael and A.L. Macfie, Liberty Fund, Indianapolis
(1982).
Sullivan, Roger, 1989, Immanuel Kant’s Moral Theory, Cambridge University Press, Cambridge, UK.
_____________, 1994, 1997, An Introduction to Kant’s Ethics, Cambridge University Press.
White, Mark D. 2008, “Adam Smith and Immanuel Kant: On Markets, Duties, and Moral Sentiments,” http://
ssrn.com/abstract=1318605.
_____________ 2011, Kantian Ethics and Economics, Standford University Press, Standford, CA.
ENDNOTES
1
Kant (1724 – 1804) wrote at the dawn of the democratic age so his political philosophy was not entirely developed. John
Rawls is Kant’s primary modern extender. Rawls (2001) is a more complete presentation of this democratic discourse.
2
By “rational” Kant meant logical, i.e., not self contradictory, but with logical deductions after proper reflection.
3
See White (2011, p.23) for a review of this “common person” analysis.
4
Below, we use Sullivan’s (1994, p. 29) interpretations from the original German language of these imperatives
5
This method of reference to Kant is standard and generally used in the philosophy literature.
6
In Kant’s analysis, this non-impingement of the freedom of others to pursue their own ends is termed the universal principle
of justice.
7
Coercion is defined as an application of some type of force that prevents someone from making a decision or acting upon
some decision.
Proceedings of the 2012 Pennsylvania Economic Association Conference
231
THE ECONOMIC IMPACT OF ALVERNIA UNIVERSITY
Tufan Tiglioglu
Ph.D. Program
Alvernia University
Reading, PA, 19611
Lisa Cooper, Bari Dzomba, Rachel Gifford, and Joseph Hess
Doctoral Students in Leadership
Alvernia University
Reading, PA, 19611
ABSTRACT
Research has shown that economic impact of colleges and universities on local, regional, and state economies are greater than
their direct spending. This paper examines the direct, indirect, and induced economic impact of Alvernia University on the
local and regional economies. The university’s total spending on goods, services, payroll expenditures, capital investment,
ancillary spending by students, visitors, and employees are analyzed using the IMPLAN input output model quantifying the
direct, indirect, and induced economic impact on Berks County, Pennsylvania. Qualitative measures such as employee and
student volunteer hours, institution sponsored sporting, and community events are analyzed. This paper supports Alvernia
University’s impact on the local and regional economic sustainability and growth.
INTRODUCTION
Economic impact analysis has been used to analyze the
direct, indirect, and induced economic impact that
universities and colleges have on the local economy where
they are located. Direct effects include transactions directly
attributable to the university, such as employee salaries.
Indirect and induced effects are those that result from
associated university spending, for example, the university
purchasing food at a local restaurant and that restaurant
hiring additional staff to accommodate purchasing demand.
In addition to these quantitative measurements, qualitative
measures such as employee and student volunteer hours, as
well as institution sponsored events including lectures,
concerts, and sporting events should also be considered for a
more comprehensive analysis.
In a report released in 2009 by the Association of
Independent Colleges and Universities of Pennsylvania, it
was estimated that the total state wide economic impact of
independent higher education institutes was $16,133 million.
Of this, $8,259 million was estimated to have come from
institutional expenditures, $955 million from construction,
$1,814 million from students spending, $5,022 million from
faculty and staff spending, and an additional $83 million
from visitor spending (Association, 2009).
In 2011, the economic impact of the University of Scranton
on the city of Scranton and Northeastern Pennsylvania was
determined to be $382 million overall, with over $182 direct
expenditures coming from the university alone (The
University of Scranton, 2011). In another study, Bucknell
University was found to have impacted the surrounding six
county region of Union, Columbia, Lycoming, Montour,
Northumberland, and Snyder by $206 million and the state of
Pennsylvania by $263 million during the 2009-2010
academic year (Rousu, 2011). The university spent over
$164 million in wages, construction costs, and operating
expenses. An economic impact analysis conducted in 2005
indicates that the University of Pennsylvania, which is
comprised of 12 graduate and professional schools, provided
a $9.6 billion direct and indirect impact on the state of
Pennsylvania, as well as a $9.8 billion impact on the 11
county region, and $6.5 billion for the city of Philadelphia
(Econsult Corporation, 2006). Similarly, an economic
analysis conducted for Penn State University’s 24 campuses
on each of Pennsylvania’s counties was conducted in 2008,
which reports a total direct and indirect impact of $8.46
billion (Tripp Umbach, 2009). The total economic impact of
Dickinson College for Cumberland County in Pennsylvania
equals an estimated $150,431,937 (Bellinger at al., 2010).
Lastly, Hope College contributed a total impact of $213
million on the city of Holland, Michigan, as reported in the
2011 economic impact study by Stokes (2011).
The purpose of this paper is to analyze the direct, indirect,
and induced economic impact of Alvernia University on
Berks County, Pennsylvania. According to the 2010 U.S.
Census, the largest city located in Berks County is Reading,
which is also the poorest city in the U.S., with the median
household income at $28,197 annually. In 2010, the
population of Berks County was 411,442 people, with 88,082
individuals residing in Reading (QuickFacts, 2012).
Additional demographic and economic information on Berks
County, obtained from the IMPLAN modeling software for
the model year 2009, is provided in the Appendix in Table 1.
Proceedings of the 2012 Pennsylvania Economic Association Conference
232
In an effort to improve economic competiveness, eight
principal stakeholder organizations involved in the promotion
of the economic prosperity of the Greater Reading Area and
Berks County, analyzed past economic development
strategies and recommendations and identified five key issues
needing attention: entrepreneurship and innovation,
workforce/talent development, industry clustering, sites and
infrastructure, and quality of place. By developing these
areas, it is hoped that residents would be more prosperous
and happier to live in the area, and local businesses would
have more opportunity, innovation, and would grow faster
(Ride to Prosperity, 2010). Local colleges and universities
have the opportunity to support and improve upon these key
areas, and Alvernia University is committed to doing so
through integrated, community-based, inclusive, and ethical
learning.
This paper is organized as follows. The first section provides
a review of the literature of economic impact analysis
methods. The second section provides a description of the
methodologies utilized for determining the direct, indirect,
and induced impact of Alvernia University on Berks County,
Pennsylvania. The third section provides the results of the
analysis. The fourth section provides a discussion of the
analysis results as well as conclusions.
LITERATURE REVIEW
Communities possess a solid sense of place and a university's
presence within an area serves as a central hub for providing
quality of place for the citizens within the communities it
serves. From arts and cultural events, to sporting events and
lecture series, a university's identity enhances the well-being
of the community. The most trusted way for universities to
determine the impact of their presence on the local
community is to conduct an economic impact analysis.
These studies are usually designed as unique research
projects to meet local and regional needs (Caffrey & Isaacs
1979). Economic impact analyses are a critical tool for
universities to demonstrate the value of their presence in a
particular geographic area on local businesses and policies.
Since the introduction of university economic impact studies
by Caffrey and Isaacs in 1971, a number of studies have been
conducted using various methods to quantify the economic
benefits of a university in a particular area. The most
common methods employed in impact studies include, the
American Counsel of Education (ACE) method, the Ryan
Short-Cut method, the Input Output method or IMPLAN
model, and the skills based approach.
The ACE method, developed by Caffrey and Isaacs (1971) is
the oldest method of calculating economic impact and is still
used extensively by universities today including Bucknell
(Rousu, 2011) and Penn State (Tripp Umbach, 2009). The
ACE method consists of a series of models created by a
review of economic impact previously conducted by several
American colleges and universities. The primary objective of
their study was to derive models for which data could be
obtained from the normal records kept by universities, local
governments, and businesses; from statistical publications of
the federal government. The ACE method tracks the flow of
money from the university through a designated area or
community to show the ways in which funds derived from
the university benefit the local community (Tripp Umbach,
2009).
These models consisted of business models,
government models, and individual models (Caffrey &
Isaacs, 1971). Specifically, the ACE method examines a
university’s impact on local businesses, local individuals, and
local government. A university’s impact on local business is
measured by the amount of money a university spends on
supplies and services. A university’s impact on individuals is
assessed by how many individuals are employed by the
university, and how those individuals affect the local
economy by producing goods and services. The impact on
local government is measured by examining how much the
university contributes to local government in the form of
various taxes and revenue derived from the university
(Garrido-Yserte & Gallo-Rivera, 2010).
The ACE
methodology gathers specific cash flow data from the
university’s financial documentation, which is needed to
assess the impact on each of the three areas outlined above.
Financial documents generally used include tax and income
statements, as well as specific documentation related to
purchases within the specified geographic area. The ACE
method also employs surveys to measure the spending of
university employees and students within the local
community. Once the data has been gathered a regional
economic multiplier is applied to the spending data in order
to determine the total economic impact (Tripp Umbach,
2009). The models are limited to use in estimation of shortterm economic impact, i.e. over a given period of time, such
as an academic school year. They are not able to assess the
ultimate economic impact of the university upon the
community and they do not embody considerations such as
what a community might have been like without the college
(Caffrey & Iassacs, 1979). It is necessary to note that the
ACE method provides a built-in understatement: the actual
economic impacts are probably greater than the models
suggest. Caffrey and Isaacs (1979) reasoned that it is better
to err on the side of too little than too much, particularly
when a public relations function is being served and it is
impractical to account for all the real expenditures of every
individual and group associated with the university.
The Ryan Short-Cut method, developed in 1981 by J.G.
Ryan, is a simplified version of the ACE method. According
to Ryan, there are three major problems with the ACE
method when employing it for smaller universities and
community colleges. First, the economic estimates suggested
by the ACE method are inappropriate for small schools.
Second, the development and administration of surveys to
measure student and employee spending are too time
Proceedings of the 2012 Pennsylvania Economic Association Conference
233
consuming and expensive for small colleges and the response
rate is often too low to obtain accurate results. Finally, the
retail gravity method used by the ACE model, which
measures individual non-housing expenditures, has been
found to be too mathematically complex. Additionally, it is
difficult for small universities and colleges to operationalize
a retail sales area and obtain appropriate retail sales
information. The Ryan Short-Cut method remedies these
problems by replacing survey information with readily
available, nationally produced datasets identified for
substitution of the retail gravity model. The Ryan Short-Cut
method also uses a more conservative multiplier which is
more appropriate for smaller institutions (Ryan & Malgieri,
1992).
The Input Output method or IMPLAN model is similar to the
ACE and Ryan Short-Cut methods, in that it measures the
impact of university spending on a number of different
industries. The major difference in the IMPLAN model is
that it measures the forward and backward linkages in an
economy rather than linear cash flow.
Forward and
backward linkages are examined through inter and intra
industry transactions by looking at the direct, indirect, and
induced effects of university spending (Caroll & Smith,
2006). The model measures the total annual economic
activity that results from inter and intra industry transactions.
As mentioned previously, direct effects measure university
spending on items such as payroll. Indirect effects measure
university spending on supplies and contracts with local
vendors, and induced effects measure individual household
spending as a result of university payroll. The IMPLAN
model uses extensive documentation from the university in
the form of financial statements and questionnaires given to
students and staff to measure the direct provision of the
university by providing jobs and purchasing goods and
services for production. The model also quantifies indirect
impacts of the university in the form of employee spending
as a result of the income they earn from the university. Once
accurate financial information is compiled, it is analyzed
through the use of IMPLAN commercial software that
utilizes input output data for over 500 industries to create
industry specific multipliers for states and communities. The
data used by IMPLAN comes from federal government
sources and is used to create appropriate multipliers to
estimate economic impact (Morgan, 2010). IMPLAN has
been widely used as a method for economic impact
assessment by universities such as the University of
Pennsylvania (Econsult Corporation, 2006), Bowling Green
State University (Carroll, 2006), the University of Phoenix
(CBRE Consulting, 2009), and the University of North
Carolina (Walden, 2009). The popularity of the IMPLAN
model is due to its ease of use and accuracy in creating
multipliers compared to other economic impact analysis
methods. Other economic impact methods, such as ACE,
involve complex calculations to determine multipliers which
may result in multipliers that are too large, thus
overestimating the impact of a university on a particular area.
The benchmarked economic models and multipliers used by
the IMPLAN software allow for greater accuracy in
calculating impact and reduce the chance of error (Morgan,
2010). The outputs could result in multipliers that are too
large or small thus over or underestimating the economic
impact.
Though the IMPLAN model has been found to be one of the
most accurate models in assessing quantitative economic
impact data, it lacks the capability to assess the qualitative
impacts universities often have on their communities.
Scoble, Dickson, Hanney, and Rodgers (2010) suggest that
the inclusion of qualitative measures, such as volunteer hours
and social programs can be a valuable addition to any
economic impact report. The use of both qualitative and
quantitative data provides a thorough understanding of all of
the ways a university impacts a community.
The skills based approach developed by Elliott, Levin, and
Meisel (1988), was created to measure some of the
qualitative impacts of a university in order to compliment the
quantitative data utilized by the methods previously
described. The skills based approach assumes that, in
addition to providing employment and increased industry
spending to an area, universities are also providing a
community with a more educated population. It is reasoned
that the highly educated students they produce have greater
skill sets than non-educated community members and will
therefore earn more substantial incomes, thus contributing
more taxes to the local government. In addition to spending
more in taxes, university alumni are also expected to spend
more money in the local economy through the purchase of
goods and services which provides further stimulation to
other local businesses (Brown & Heaney, 1997).
The weakness of the skills based approach, which has led to
much criticism, is that it often overestimates the impact of
alumni on the local community. There is often little data
available to provide an accurate estimate of the true impact as
a result of alumni. Additionally, research suggests that the
attainment of a college degree increases the chance of
migration of college students, making them less likely to stay
in the area where they gained their education. Researchers
are not able to accurately predict the rate of migration,
resulting in the skills based approach being viewed as a
highly subjective method of calculating impact (Brown &
Heaney, 1997).
The skills based approach is not the only method that has
received criticism relating to the reliably and truth of
statistics. Siegfried, Sanderson, and McHenry (2008), claim
that almost all methods of calculating economic impact are in
some way inaccurate since they fail to account for
counterfactual information in their analysis. The authors
define the counterfactuals as the businesses, industries, or
Proceedings of the 2012 Pennsylvania Economic Association Conference
234
people which may have been present in a community if the
university never existed.
By determining what the
community’s economy would look like if a university did not
exist in the defined area, researchers are able to more
accurately isolate the specific economic benefits that only the
university can and does provide to the community (Siegfried,
Sanderson, & McHenry, 2008).
Based on the information provided in the literature on the
various methods of determining local economic impact by
universities, the Input Output method was determined to be
most appropriate to assess Alvernia University’s impact on
Berks County. Alvernia University resides inside the city
boundaries of Reading, Pennsylvania, an economic area
which is recovering at a pace faster than the rest of the
Commonwealth of Pennsylvania and nation. While Reading
lacks employment diversity and high-value-added service
positions, it is an attractive area to focus on with strengths
such as below-average cost of living, lower than average
foreclosure rates, and affordable housing and business costs.
Prior to the recession, the unemployment rate in the Reading
area was under 4%.
As of December 2011, the
unemployment rate in the Reading area was 7.9%, well under
the national average of 8.5% (Moody's, 2012). Loss of
manufacturing jobs are the root cause for the decline in
employment rates in the area, thus showing that a local
university within Reading that is well established and
focused on the constituents within the community could
provide economic and quality of life opportunities for the
residents of Berks County, Pennsylvania.
METHODOLOGY
The most widely used and accepted methodology for
measuring the economic impacts of a university on a local
economy is the Input Output method, which is used to
describe economic transactions between various sectors in a
defined economy within a given time period (Cooperatives,
2012). The IMPLAN model was utilized for this analysis of
Alvernia University’s economic impact on Berks County,
Pennsylvania during the 2010-2011 academic year.
IMPLAN economic modeling software utilizes multiplier
models to estimate the direct, indirect, and induced effects of
the university on the local area. Direct effects measured by
the model include transactions directly attributable to the
university, such as employee salaries. Indirect effects
assessed include transactions made to the local area by the
university on items such as supplies, services, and labor.
Induced effects assessed are the re-spending that occurs as a
result of the indirect spending.
The average of total liabilities and net assets reported in
Alvernia University’s publically available 2009 Form 990,
approximately $90 million, was inferred as 2010-2011
academic year total operating costs.
Additionally,
approximately $11 million in construction costs was reported
in 2009 and was also inferred for 2010. These data were
analyzed by the IMPLAN software, using the ShannonWeaver Index of 0.7992 and Industry 392, private junior
colleges, colleges, universities, and professional schools, to
determine total direct, indirect, and induced effects, as well
as direct, indirect, and induced effects without construction
costs. These effects were also analyzed for construction
costs alone.
In 2011, 2450 students attended Alvernia University’s main
campus, with 839 students living on campus. We assume,
therefore, that 1,611 students lived off campus, of which
approximately 75%, or 1208 students, commute to the
university approximately 150 days of the year. We estimate
that each of these commuting students spend $11 each day
they commute; $2 for beverages, $1 for candy, $3 for gas,
and $5 for food. Therefore, $2 million was used to assess the
direct, indirect, and induced effects of student spending.
Finally, the impact of student volunteer hours during the
2010-2011 academic year was quantified by multiplying the
number of hours volunteered by the 2010 Pennsylvania
minimum wage rate of $7.25 per hour.
RESULTS
Total operating expenses of Alvernia University for the
2010-2011 was approximately $90 million. This total
amount includes $23 million spent by the university on
employee salaries and wages, of which only 80% was likely
retained in Berks County after accounting for taxes and other
deductions, as well as the small percentage of employees
located at satellite offices outside of Berks County.
Therefore, the resulting salary/wage estimate is $16 million.
The total benefits paid during the 2010-2011 academic year
were approximately $6 million. Though Alvernia provides
several types of benefits, key benefits included current
employee and retirement medical benefits. In addition to
paying significant amounts of money toward salaries and
benefits which were used by employees throughout the
region, the university also had a significant impact on the
community in the form of local taxes, amounting to $158
thousand during the 2010-2011 academic year.
Additionally, the 2010-2011 total expenditures includes $4
million spent on Aladdin, the food service provider for the
campus, as well as other key areas of spending including
repairs and maintenance, consulting/professional fees,
insurance, library books and materials, equipment and
technology, travel and entertainment, utilities, and postage
and shipping. Specifically, a total of $549 thousand was
spent by Alvernia University on repairs and maintenance. To
account for purchase of materials outside of Berks County,
only 90% of that total was used. Alvernia University spent
$421 thousand in consulting and professional fees. We
assume that half (50%) of these services were fulfilled
locally. The university also paid $232 thousand in insurance
Proceedings of the 2012 Pennsylvania Economic Association Conference
235
during the 2010-2011 academic year, of which only 10% is
assumed to have been retained in Berks County.
Approximately $106 thousand was spent on library books
and materials and $277 thousand was spent on equipment and
technology. Only 10% of each is assumed to have been
retained in Berks County. During the 2010-2011 academic
year, $334 thousand was spent on travel and entertainment
expenses of which 35% is assumed to have been spent at
local businesses such as hotels and restaurants. Over $1.7
million was spent on utilities of which 20% is assumed to
have been retained in Berks County. Finally, approximately
$252 thousand was paid by Alvernia University in postage
and shipping, 80% of which was likely retained in Berks
County. These key areas of spending are outlined in the
Appendix, Table 2.
million from indirect effects, and $4.5 million from induced
effects (see Table 7). The top ten industries impacted by
employment are presented in Table 8, and include
construction of new nonresidential commercial and health
care structures (83 employees), food services and drinking
places (5 employees), architectural, engineering, and related
services (5 employees), as well as an additional 16
employees from other industries. Tables 8 also illustrates the
top ten industries impacted financially by construction.
Financially impacted industries include the construction of
new nonresidential commercial and health care structures
($11 million), food services and drinking places ($280
thousand), architectural, engineering, and related services
($714 thousand), as well as approximately $1.6 million from
other industries.
Federal and state grants obtained for local spending, equaling
approximately $1 million, is also included in the total
expenditures.
Alvernia University also provided $83
thousand in local donations. Finally, over the last few years,
Alvernia University has been updating existing structures, as
well as rapidly expanding its campus. The 2009 Form 990
lists construction costs at $11 million.
The impact of student spending in Berks County was also
assessed, with the total effect $2.9 million. Direct effects
seen were $1.9 million, indirect effects were $603 thousand,
and induced effects were $430 thousand. These results are
provided in the Appendix, Table 9. As presented in Table
10, the top ten industries impacted financially by student
spending included food services and drinking places ($932
thousand), petroleum refineries ($541 thousand), soft drink
and ice manufacturing ($226 thousand), and an additional
$575 thousand from other industries. Table 10 also lists the
top ten industries impacted by employment by student
spending, including food services and drinking places (17
employees), wholesale trade business (0.5 employees), real
estate establishments (0.4 employees), with an additional 1.6
employees from other industries.
For this study, the impact of annual operating expenditures
was examined using a total operating budget of $90 million
as described above. The resulting total effect was $127
million, with $72 million resulting from direct effects, $25.5
million from indirect effects, and $29 million from induced
effects (see Table 3). The top ten industries impacted
financially by the university are outlined in Table 4, and
includes private junior colleges, colleges, universities, and
professional schools ($72 million), real estate establishments
($5.5 million), imputed rental activity for own-occupied
dwellings ($4 million), and other industries (total $15
million).
Approximately $11,000,000 of the total operating budget was
spent on construction. It is important to differentiate
operations and construction in economic analysis because
operations spending is an annual reoccurrence while
construction spending is only made for the duration of the
project. Therefore, the impact of university expenditures
excluding construction spending (capital investment) was
analyzed and the total effect was approximately $111 million
(Table 5). The top ten industries impacted financially by the
university are outlined in Table 6, and includes private junior
colleges, colleges, universities, and professional schools ($63
million), real estate establishments ($4.8 million), other state
and local government enterprises ($3.1 million), wholesale
trade business ($2.1 million), private hospitals ($1.9 million),
food services and drinking places ($1.8 million), and other
industries (total $34 million).
The total effect of construction was approximately $18
million, with $11 million resulting from direct effects, $2.6
Finally, during the 2010-2011 academic year, over 1200
students contributed 20,623 hours of service to the local
community, much of which was through the Holleran Center
for Community Engagement at Alvernia University. In order
to provide a quantified metric for impact, the volunteer hours
are multiplied by the minimum wage rate in 2010, $7.25 per
hour. Therefore, Alvernia University’s students provided
approximately $150 thousand in free labor to the local
community during the 2010-2011 academic year.
CONCLUSION
This study indicates that Alvernia University has a major
economic impact on the local economy of Berks
County. The study focused the impact of total operating
expenditures, operating expenditures without construction
costs, construction costs alone, student spending, and student
volunteer hours. The full extent of Alvernia University’s
economic impact cannot be realized by analyzing only these
areas. However, they do offer insight into role Alvernia
University plays in ensuring continued and future economic
success of Reading and Berks County through existing and
new opportunities for purchasing and employment.
Proceedings of the 2012 Pennsylvania Economic Association Conference
236
Alvernia University’s impact on Reading and Berks County
is significant, with a total effect of $130 million (effects from
total operating expenditures and student spending). This is
approximately half of that seen from similar, small
universities such as Bucknell ($206 million) and Scranton
University ($382 million). It is unclear why such a large
difference is seen, however, it is likely that differences in
assumptions as well as validity of data sources may play a
role. Future studies analyzing Alvernia University’s impact
should include other variables such as employee and visitor
spending.
Few studies include an analysis of the impact of volunteer
hours, yet the impact from this alone is significant to a
community. Quantifying volunteer hours by multiplying
hours by minimum wage only touches on the true impact that
such generosity generates. It is even more difficult to
quantify the emotional, educational, and spiritual returns such
volunteers may provide. Alvernia University was named in
the President’s Higher Education Community Service Honor
Roll, with Distinction, by the Corporation for National and
Community Service (CNCS) and the U.S. Department of
Education.
The honor recognizes the university’s
commitment to the local community through community
service.
Additionally, Alvernia University sponsored
numerous cultural, sporting, and educational events open to
the community.
university also draws hundreds of students and their families
to the area on a regular basis. Each day these students spend
money on food, gas, and other items generating over $2
million dollars to the local economy. This indirect effect
spending no doubt has created induced effects to the
economy in the form of more jobs to produce the food and
other services that are demanded by commuting students.
Alvernia University is recognized as one of the leading
Catholic colleges in the South East region of Pennsylvania.
In addition, the university is recognized for their commitment
to service learning and community engagement through the
Holleran Center for Community Engagement. This study has
added to the available literature stating that colleges and
universities provide a significant impact on the local
economies where they are located, and Alvernia University
provides similar benefit.
While Alvernia University’s impact is great, to more
accurately portray its impact, counterfactual should also be
considered. Although it is impossible to know what may
have existed on the land Alvernia University currently
occupies, it is likely that the land may have been used for
housing or other small businesses, similar to what exists on
the property surrounding the university. Any alternative
options short of a large corporation would be unlikely to
produce the magnitude of economic impact that Alvernia
University provides to the community. As an institute of
higher education, Alvernia University offers a variety of
unique benefits to the community. Specifically, it is
increasing entrepreneurship and innovation, workforce/talent
development, infrastructure, and quality of place, by not only
providing well-paying jobs to an otherwise economically
depressed area, but also by preparing the next generation for
the workforce.
In addition to Alvernia University’s ability to provide quality
employment to area residents as well as workforce
development, the university is able to draw hundreds of
people to the area that may not have been attracted otherwise.
The university has brought numerous highly skilled
professors from around the country and around the world to
Berks County, who regularly use their talents and skills to
benefit the community. These talented professors often
conduct valuable research for community organizations
securing grants and other funding to improve the area. The
Proceedings of the 2012 Pennsylvania Economic Association Conference
237
APPENDIX
Table 1
Berks County Economic Information for 2009 Model Year
GRP
$15,521,300,942
Total Personal
$14,457,4300,000
Income
Total Employment
207,069
Number of Industries
300
Land Area (Sq.
859
Miles)
Area Count
55
Population
407,125
Total Households
162,503
Average Household
$88,967
Income
Description
Primary battery manufacturing
Iron and steel mills and ferroalloy
manufacturing
Imputed rental activity for owner-occupied
dwellings
Wholesale trade businesses
Private hospitals
Monetary authorities and depository credit
intermediation activities
Management of companies and enterprises
Natural gas distribution
Offices of physicians, dentists, and other
health practitioners
* Employment and payroll only (state &
local govt, education)
Employee Compensation
Proprietor Income
$8,683,467,143
$961,653,961
Other Property Type Income
Indirect Business Taxes
$4,802,555,156
$1,073,624,682
Final Demand
State/Local Government
Federal Government
$2,208,570,383
$253,421,577
Capital
Exports
Imports
Institutional Sales
Top 10 Industries
Employment
4,067
1,822
7,034
7,206
3,024
3,555
525
5,902
11,496
$1,852,405,232
$14,565,006,983
-$14,095,477,693
-$1,229,421,850
Labor Income
$248,671,200
Output
$2,359,493,000
$167,960,600
$2,030,181,000
$0
$483,771,300
$446,926,800
$1,393,623,000
$1,267,336,000
$952,643,600
$205,105,900
$432,738,200
$55,859,720
$807,553,900
$796,593,900
$768,677,600
$444,181,100
$748,020,100
$607,348,500
$689,956,600
Table 2
Other Key Areas of Spending During the 2010-2011 Academic Year
Expense
Total Amount
Repairs and maintenance
Consulting/professional fees
Insurance
Library books and materials
Equipment and technology
Travel and entertainment
Utilities
Postage and shipping
Total
$548,871
$421,407
$232,190
$106,290
$276,761
$334,211
$1,770,449
$252,245
$3,942,424
Amount Spent in
Berks County
$493,984
$210,704
$23,219
$10,629
$27,676
$116,974
$354,090
$20,180
$1,257,456
Proceedings of the 2012 Pennsylvania Economic Association Conference
238
Table 3
Direct, Indirect and Induced Effects of Total Operating Costs
Impact Summary
Employment
Direct Effect
928.2
Indirect Effect
161.4
Induced Effect
265.4
Total Effect
1,355
Labor Income
$31,915,374
$6,713,608
$10,014,634
$48,643,616
Effect
$72,000,000
$25,526,114
$29,365,425
$126,891,540
Table 4
Top 10 Industries Impacted Financially by Total Operating Costs
Industry
Employment
Labor Income
Private junior colleges, colleges,
931.8
$32,032,366
universities, and professional schools
Real estate establishments
46.5
$555,193
Imputed rental activity for owner0
$0
occupied dwellings
Other state and local government
14
$844,005
enterprises
Natural gas distribution
2.3
$244,208
Wholesale trade business
13.8
$969,041
Private hospitals
16
$1,010,482
Offices of physicians, dentists, and
15.8
$1,213,906
other health practitioners
Food services and drinking places
39.7
$711,032
Animal (except poultry) slaughtering,
3.2
$167,792
rendering, and processing
Total Effect
$72,263,932
$5,558,677
$4,325,609
$3,607,720
$3,409,117
$2,448,590
$2,179,931
$2,076,665
$2,073,726
$1,474,173
Table 5
Direct, Indirect and Induced Effects of Total Operating Costs without Construction Spending
Impact Summary
Employment
Labor Income
Direct Effect
878.9
$28,014,606
Indirect Effect
141.7
$5,893,056
Induced Effect
233.0
$8,790,623
Total Effect
1,253.5
$42,698,285
Effect
$63,200,001
$22,406,255
$25,776,317
$111,382,573
Proceedings of the 2012 Pennsylvania Economic Association Conference
239
Table 6
Top 10 Industries Impacted by Total Operating Costs without Construction Spending
Industry
Employment
Labor Income
Total Effect
Private junior colleges, colleges,
universities, and professional schools
882.1
$28,117,299
$63,431,674
Real estate establishments
40.9
$487,336
$4,879,283
Food services and drinking places
34.8
$624,128
$1,819,393
Private hospitals
Offices of physicians, dentists, and other
health practitioners
Other state and local government enterprises
Wholesale trade businesses
Retail Nonstores - Direct and electronic
sales
Retail Stores - General merchandise
Retail Stores - Food and beverage
14.0
$886,978
$1,913,495
13.9
$1,065,539
$1,822,850
12.3
12.1
$740,849
$850,603
$3,166,776
$2,149,318
9.9
$92,877
$324,915
9.0
8.6
$235,094
$256,379
$444,885
$489,830
Table 7
Direct, Indirect and Induced Effects of Construction
Impact Summary
Employment
Direct Effect
83.2
Indirect Effect
17.9
Induced Effect
41.5
Total Effect
142.6
Labor Income
$4,935,133
$1,053,497
$1,563,211
$7,551,841
Effect
$11,000,000
$2,623,863
$4,587,999
$18,211,862
Table 8
Top 10 Industries Impacted by Employment by Construction
Industry
Employment
Construction of new nonresidential commercial
83.2
and health care structures
Food services and drinking places
5.4
Architectural, engineering, and related services
5.0
Wholesale trade businesses
2.7
Private hospitals
2.5
Offices of physicians, dentists, and other health
2.5
practitioners
Real estate establishments
1.9
Retail Nonstores - Direct and electronic sales
1.9
Retail Stores - General merchandise
1.8
Retail Stores - Food and beverage
1.7
Labor Income
$4,935,133
Total Effect
$11,000,000
$96,000
$422,800
$192,848
$156,589
$188,503
$279,849
$713,941
$487,292
$337,812
$322,477
$23,236
$17,375
$46,882
$49,632
$232,640
$60,783
$88,718
$94,825
Proceedings of the 2012 Pennsylvania Economic Association Conference
240
Table 9
Direct, Indirect and Induced Effects of Student Spending
Impact Summary
Employment
Direct Effect
17.3
Indirect Effect
3.6
Induced Effect
3.8
Total Effect
24.6
Labor Income
$384,514
$184,755
$147,612
$716,881
Effect
$1,937,551
$603,883
$430,557
$2,971,991
Table 10
Top 10 Industries Impacted by Student Spending
Industry
Employment
Food services and drinking places
17.0
Petroleum refineries
0.1
Soft drink and ice manufacturing
0.3
Chocolate and confectionery
0.3
manufacturing from cacao beans
Wholesale trade businesses
0.5
Fruit and vegetable canning, pickling,
0.2
and drying
Fluid milk and butter manufacturing
0.1
Imputed rental activity for owner0.0
occupied dwellings
Management of companies and
0.2
enterprises
Real estate establishments
0.4
Labor Income
$317,900
$9,898
$32,448
Total Effect
$932,002
$540,892
$226,140
$19,651
$179,391
$37,251
$87,570
$12,176
$84,959
$5,781
$65,473
$0
$58,783
$27,990
$53,782
$4,558
$46,774
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Proceedings of the 2012 Pennsylvania Economic Association Conference
242
A STUDY OF FACTORS AFFECTING THE ECONOMIC FEASIBILITY OF THE IMPLEMENTATION OF
TORREFACTION TECHNOLOGY BY THE PENNSYLVANIA WOOD PELLET INDUSTRY
Robert F. Brooker
Dahlkemper School of Business Administration
109 University Square
Gannon University
Erie, PA 16541
Harry R. Diz
School of Engineering and Computer Science
109 University Square
Gannon University
Erie, PA 16541
ABSTRACT
Firms in the Pennsylvania wood pellet industry process wood
byproducts into pellets that are burned for home heating
purposes. Studies indicate that pellet production exhibits
economies of scale, but scale is constrained by the logistics
of wood byproduct availability. In this paper, an economic
model is used to describe the effects of this constraint.
Torrefaction technology, by allowing firms to use alternative
feedstocks such as animal and food production byproducts,
could allow firms to increase scale with consequent lower
average pellet production costs.
THE WOOD PELLET INDUSTRY AND
TORREFACTION
Between 2003 and 2009, the demand for wood pellets for
home heating in North America increased at an annual
compounded rate of 22%. During the same period, wood
pellet production capacity increased at an annual
compounded rate of 33%. The growth of wood pellet
production capacity in excess of North American demand is
attributed to the growth in exports to European countries.
Total pellet production in North America in 2008 was
approximately 3.5 million tons while production capacity
was approximately 4.5 million tons. Spelter and Toth (2009).
Annual pellet production capacity in Pennsylvania in 2009
was estimated to be one-half million tons, implying that the
industry has the ability to process approximately one million
tons of wood residues annually, assuming an average
feedstock moisture content of 50%. Pellet production
capacity in Pennsylvania is increasing rapidly, and “new
plant announcements in the region are made almost weekly.”
Ciolkosz and Ray (2009).
The feedstock used to produce wood pellets is wood
byproducts, primarily sawdust and shavings that are
generated by the lumber and furniture industries. Firms that
produce wood pellets are called pellet mills. The production
process involves three parts: (1) drying, to reduce moisture
content of the feedstock to a specified level, (2) milling, to
homogenize feedstock particle size, and (3) pelletization. The
last step compresses, or densifies, the processed wood
feedstock into pellets of uniform size and density. Wood
pellets are used in the U.S. primarily for home heating
purposes, although other uses such as electric generation are
growing in importance, particularly in European nations.
Pellet mills employ a technology that exhibits increasing
returns to scale, which implies that firms with a larger
capacity that produce a larger quantity of pellets per year will
have a lower average cost of production. Unfortunately,
feedstock supply logistics (transportation and storage costs)
constrain supply. The number and scale of sawmills that are
within a cost-effective distance from a pellet mill limit
available feedstock. Mani, et al (2006) and Pirraglia, et al
(2010).
Because of its reliance for feedstock on the production of
wood byproducts, the wood pellet industry is a captive of
sawmills that process timber for the lumber and furniture
industries. The pellet mill is like a remora attached to a shark.
If the shark doesn’t eat, neither does the remora. If sawmills
doesn’t process timber, then the supply of wood byproducts
is curtailed and pellet mills go hungry. Torrefaction can
ameliorate this problem.
Torrefaction adds an additional step to the pelletization
process in which dried and milled feedstock is subjected to
heat in a low-oxygen environment before being pelletized.
Torrefaction chars the processed feedstock, adding value to
the pellets that are produced from it by increasing energy
density, reducing transportation costs, eliminating biological
activity, and enhancing water resistance. In addition, and of
key interest here, is that torrefaction increases the variety of
potential organic feedstocks for pelletization, reducing the
extent to which a pellet mill is in thrall to lumber mill
production rates.
An economic model of production by the lumber industry
that illustrates this point is developed below. It details the
Proceedings of the 2012 Pennsylvania Economic Association Conference
243
economic linkages that determine the supply of feedstock to
pellet mills. The model is then generalized and used to
identify industries with a similar economic structure that
could supply suitable feedstock if torrefaction was
implemented.
The solution values are Q = 150, PQ = 2.975, L = 135,
PL = 3.25, W = 15, and PW = 0.5.
PL = 10 – 0.05L
(1’)
PW = 2 – 0.1W
(2’)
MC = 2.225 + 0.005Q
(6’)
ECONOMIC MODEL OF FEEDSTOCK SUPPLY
The feedstock used to produce wood pellets, primarily
sawdust and shavings, is a byproduct of the production of
lumber from timber by saw mills. The economic model that
describes the supply of feedstock is that of joint production
where the output of wood byproducts is proportional to the
rate of lumber production. The model, which assumes perfect
competition, is defined as follows.
The demand for lumber is (1), where L is tons per year and
PL is price per ton. The demand for wood residue is (2),
where W is tons per year and PW is price per ton. Total
sawmill output (Q) is (3). The proportion of Q that is lumber
(L/Q) is denoted α so (4) and (5) define the output of L and
W in terms of Q. Market supply of Q by sawmills, which is
the horizontal summation of individual firms’ marginal costs,
is (6). Total revenue per unit of Q is (7), where PQ is the
implicit price of Q, and (4) and (5) are used to eliminate W
and L. Marginal revenue (PQ) is defined by (8) as the
weighted average of the prices of lumber and wood residue
based on the proportion that each constitutes in total output.
Substituting (4) into (1) and (5) into (2) and then substituting
the resulting relationships into (8) yields (9), which defines
marginal revenue in terms of Q. Finally, the optimal value of
Q is found by setting PQ = MC and solving.
PL = PL(L)
(1)
PW = PW(W)
(2)
Q=L+W
(3)
L = αQ
(4)
W = (1 – α)Q
(5)
MC = MC(Q)
(6)
QPQ = LPL + WPW = αQPL + (1 – α)QPW
(7)
PQ = αPL(αQ) + (1 – α)PW((1 – α)Q)
(9)
PQ = αPL + (1 – α)PW
(8)
As a way to provide a baseline for comparative statics
analysis, the following numerical example will be used. It
assumes that that the proportion L/Q = ∝ = 0.9, demand for L
and W are (1’) and (2’), and MC is (6’). Marginal revenue
(PQ) in (9’) is derived from (1’) and (2’) as described above.
PQ = 0.9(10 – (0.05)(0.9Q)) + (0.1)(2 – (0.1)(0.1Q)) (9’)
PQ = 9.2 - 0.0415Q
Figure 1 represents the solution graphically. Panel a
displays the supply of output by saw mills and the implied
demand for output, which is derived from the demand for
lumber (panel b) and the demand for wood byproducts
(panel c). Notice that, because Q is determined by the
intersection of supply and the weighted average of the
demand for L and for W, supply in panels b and c is
perfectly inelastic.
Figure 2 represents the effect of an increase in the
demand for lumber (1’’). The solution values are Q = 247,
PQ = 3.459, L = 222, PL = 3.895, W = 24.7, and
PW = - 0.47. Notice that PW is negative. This is a refutation
of the Mae West hypothesis: “Too much of a good thing is
wonderful.” A negative price for wood byproducts implies
that the quantity produced is such that the value of the
marginal product of the quantity produced to buyers is
negative. In other words, the sawmill must pay to dispose
of the excess.
PL = 15 – 0.05L
(1’’)
Figure 3 represents the effect of a decrease in the demand
for lumber (1’’’). The 2008 recession, and the major
decline in U.S. construction had this effect. The solution
values are Q = 53, PQ = 2.491, L = 47.9, PL = 2.60, W =
5.32, and PW = 1.47. Notice that the effect on PW is far
greater than the effect on PL, even though production of
both decline by the same proportion.
PL = 5 – 0.05L
(1’’’)
Figure 4 represents the effect of an increase in the
demand for wood byproducts (2’’). The solution values
are Q = 154, PQ = 3, L = 138.9, PL = 3.06, W = 15.43, and
PW = 2.46. Notice that the primary effect of this demand
change is on PW, while the change has little effect on PL.
PW = 2 – 0.1W
(2’’)
The implication of this model is that, because the supply of
feedstock to the wood pellet industry is determined primarily
Proceedings of the 2012 Pennsylvania Economic Association Conference
244
by the demand for lumber and other wood products, the wood
pellet industry is subject to wide fluctuations in feedstock
supply and price. Further, changes in demand for feedstock
elicit negligible changes in quantity supplied and relatively
large changes in price. Several researchers have noted in this
regard that feedstock cost is a major determinant of the
feasibility of pellet production. Mani, et al (2006), Nolan, et
al (2010), and Uasaf and Becker (2011).
ECONOMICS OF TORREFACTION
CONCLUSION
The wood pellet industry is growing rapidly. The supply of
feedstock is a limiting factor which prevents pellet mills from
taking advantage of economies of scale in production. The
implementation of torrefaction technology by pellet mills
would make it possible to draw on alternative feedstocks,
resulting in better opportunities for scale economies. In
addition, torrefaction produces pellets that are qualitatively
superior to plain wood pellets, increasing their commercial
value.
The model above describes why the production of wood
byproducts is largely unresponsive to changes in the demand
for wood pellets and why opportunities to take advantage of
economies of scale are constrained by the logistics of
feedstock supply. Torrefaction makes it possible to process a
variety of non-wood feedstocks into pellets. It does this by
charring the feedstock heat to eliminate biological activity
and other negative characteristics of the feedstock. Research
at Gannon University has identified and analyzed torrifiable
feedstocks that are available in quantity in the Northwest
Pennsylvania region. These include grape pomace, which is
the residue from crushing grapes, horse manure, and waste
from the production of dog food. Collectively, these sources
could provide approximately 8,000 tons (dry weight) of pellet
feedstock annually. Diz, et al in progress. Other research has
demonstrated the feasibility of using torrefaction for
processing other types of animal manure. Ro, et al (2009).
The economic model presented above describes a situation in
which an industry jointly produces a primary product
(lumber) which is its primary source of revenue, and a
byproduct that is relatively low in value. The firm maximizes
profit by minimizing the quantity of byproduct produced per
unit of its primary product. The byproduct may be valuable in
some applications (composite wood products, pulp, wood
pellets) but excess supply can drive the price below zero,
implying that the firm must pay to dispose of the byproduct.
The situation described by this economic model applies to
industries other than the lumber industry. For example,
experiments conducted at Gannon University have
demonstrated that byproducts from other industries can be
used in combination with torrefaction to produce pellets that
have superior characteristics. Specifically, the research
demonstrated that feedstocks could include byproducts from
the equine industry (manure), industrial food processing
(grape pomace), and animal food production (dog food).
These are byproducts that are of value in small quantities (for
example, as fertilizer) but require costly disposal in large
quantities. Torrefaction would add economic value to these
byproducts while avoiding the economic costs of disposal.
Further, the increased supply of feedstock would allow pellet
mills to operate at a larger scale, yielding cost savings.
Proceedings of the 2012 Pennsylvania Economic Association Conference
245
Figure 1a
16
14
12
10
PQ 8
6
4
2
0
0
50
100
150
200
Q
250
300
350
400
Figure 1b
16
14
12
10
PL 8
6
4
2
0
0
50
100
150
200
250
300
350
L
Figure 1c
2.5
2
1.5
PW
1
0.5
0
0
5
10
15
W
20
25
30
Proceedings of the 2012 Pennsylvania Economic Association Conference
246
Figure 2a
16
14
12
10
PQ 8
6
4
2
0
0
50
100
150
200
Q
250
300
350
400
Figure 2b
16
14
12
10
PL 8
6
4
2
0
0
50
100
150
200
250
300
350
L
Figure 2c
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Figure 4a
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REFERENCES
Ciolkosz, Daniel, and Charles Ray. 2009. Status and Needs of the Wood Pellet Industry in Pennsylvania 2009. The
Pennsylvania State University. University Par, PA. 16802.
Diz, et al. In progress. Torrefaction of Organic Wastes for the Production of Solid Fuel Pellets.
Mani, S., S. Sokhansanj, X. Bi, A. Turhollow. 2006. Economics of Producing Fuel Pellets from Biomass. American
Society of Agricultural and Biological Engineers. Vol 22(3): 421-426. ISSN 0883-8542.
Nolan, Anthony, Kevin McDonnell, Ger J. Devlin, John P. Carroll and John Finnan. 2010. Economic Analysis of
Manufacturing Costs of Pellet Production in the Republic of Ireland Using Non-Woody Biomass. The Open
Renewable Energy Journal, 2010, 3, 1-11.
Pirraglia, Adrian, R. Gonzalez, and D. Saloni. 2010. Techno-economical analysis of wood pellets production for
U.S. manufacturers. BioResources 5(4), 2374-2390.
Ro, K.S., K.BG. Cantrell, P.G. Hunt, T.F. Ducey, M.B.Vanotti, A.A. Szogi. 2009. Thermochemical conversion of
livestock wastes: Carbonization of swine solids. Bioresource Technology, Volume 100 (2009) 5466-5471.
Spelter, Henry and Daniel Toth. 2009. North America’s Wood Pellet Sector. United States Department of
Agriculture (2009) Forest Service Forest Products Laboratory Research Paper. FPL–RP–656.
Uasuf, Augusto and Gero Becker. (2011) Wood pellets production costs and energy consumption under different
framework conditions in Northeast Argentina. Biomass and Bioenergy, Volume 35, Issue 3, March 2011, Pages
1357-1366.
Proceedings of the 2012 Pennsylvania Economic Association Conference
250
THE COLLEGE EDUCATED AND THE PUBLIC/PRIVATE SALARY DIFFERENTIAL: CAN
OCCUPATION EXPLAIN DIFFERENCES?
Mary Ellen Benedict
Department of Economics
Bowling Green State University
Bowling Green, OH 43403
Michael Bajic
Department of Economics
Bowling Green State University
Bowling Green, OH 43403
David McClough
College of Business Administration
Ohio Northern University
Ada, OH 45810
ABSTRACT
Using the National Survey of College Graduates, this
study examines the public/private salary differential.
We find that when employing OLS regressions,
public sector workers receive a lower average salary
than their private sector counterparts.
This
differential remains when we divide the public sector
into federal and state/local workers; however, federal
workers face a relatively small differential. In
addition, we find that public sector workers trade
salary for job attributes, including benefits, job
security, and social responsibility.
INTRODUCTION
Declining tax revenue resulting from the economic
downturn of 2007-2009 exacerbated budget
challenges facing all levels of government and
rekindled interest in the well-established wage
disparity between public and private sector workers.
In well publicized instances, newly elected governors
in Wisconsin and Ohio faced with large budget
deficits pursued legislation requiring public sector
employees to pay larger proportions of health and
pension benefits. In addition, legislation sought to
eliminate collective bargaining rights of public sector
workers. Among the most adamant opponents to the
legislation, teacher unions mounted effective
campaigns to force a recall election (June 5, 2012) of
the governor in Wisconsin and to win a ballot
initiative in Ohio overturning legislation that secured
passage in the Ohio State Assembly.
A consequence of the contentiousness of the debate is
the tendency of analysts to offer normative
prescriptions informed by empirical research that
appears to be, in many instances, motivated by
ideology. In some sense even academic research
proposing neutrality in the debate is vulnerable to
charges of ideological bias based on the choice of
analytical method, which very much influences the
empirical results and consequently informs the policy
prescriptions.
The present paper employs an empirical analysis that
includes the usual human capital variables while
controlling for fourteen occupational categories. This
research deviates from the conventional analysis by
employing data that includes variables reporting the
extent to which workers value certain attributes of
their job and the satisfaction associated with the work
attribute. Using these data, we are able to identify
trade-offs associated with the wage premium
regardless of whether the premium accrues to
workers in the public or private sector. Lastly, this
research limits the scope of the analysis to only the
most educated workers, those who have at least
completed a college degree. The data delineate
college graduates and accommodate examination of
the public/private wage disparity across a range of
college and post-baccalaureate graduates.
The paper will proceed with a review of the
relevant literature, description of the data,
specification of the model, discussion of the results,
and concluding comments.
LITERATURE REVIEW
The observed wage disparity between private and
public sector workers is well established. Early
studies were motivated by the rapid growth of
government employment in the second half of the
Proceedings of the 2012 Pennsylvania Economic Association Conference
251
20th century (Smith, 1977; Gyourko and Tracy,
1988).
Smith (1977) specified separate wage
functions to determine if the underlying wage
structure differed in the public sector, in part, due to
the lack of sensitivity to traditional market forces.
She found that public sector wages exceeded the
private sector although the differential varied by the
level of government and gender. Subsequent studies
found that federal workers enjoyed a ten to twenty
percent premium compared to the private sector
(Ehrenberg and Schwarz, 1986; Gregory and
Borland, 1999). In addition to employment growth
in the public sector, an increase in the rate of
unionization of public workers motivated research
examining the effect of unions on wage disparity
(Gyourko and Tracy, 1988; Edwards, 2010; Mohanty,
1994).
Two distinct methodological approaches have
emerged to examine the observed wage disparity
between private and public sector workers. Early
empirical research compared private and public
sector occupations. After controlling for occupation,
Hartman (1983) found that federal workers receive
10 percent less than workers in similar occupations in
the private sector. Referring to data from the Bureau
of Labor Statistics (BLS), Cauchon (2010) reports
that the federal government workers receive higher
average salaries compared to the private sector in
eight out of ten occupations. Of the 216 occupations
reviewed, 180 occupations paid higher salaries to
federal government workers compared to 36
occupations that paid higher salaries in the private
sector.
Although federal workers received, on
average, higher salaries, the range of salaries was
narrower than the private sector. Cauchon reports
that state and local government workers receive 5
percent less pay compared to the private sector;
however, when using total compensation, salary and
benefits, rather than salary alone as the dependent
variable, state and local workers, on average, receive
more than private sector workers.
A second empirical approach arose from challenges
that many government occupations have no private
sector equivalent. Keefe (2010) observes that ideally
workers performing similar work would be compared
but too many public sector occupations have no
comparable private sector equivalent. BLS data
reveal 124 federal government occupations for which
there are no direct equivalents (e.g. air traffic
controllers) in the private sector (Cauchon, 2010).
To address this challenge, research has compared
human capital of private and public sector workers.
The perceived benefit of the human capital approach
is the ability to compare individuals across
occupations (Smith, 1977).
Illustrative of this
approach using the Current Population Survey (CPS),
Keefe (2010) does not control for occupation but
does control for traditional human capital variables,
organizational size, and personal characteristic to find
that state and local government workers are slightly
underpaid compared to similar private sector
workers.
A challenge facing the human capital approach is the
observed differences in the distribution of human
capital across the public and private sectors.
According to a recent report from the Congressional
Budget Office (2012), 52 percent of federal workers
have earned at least a bachelor’s degree compared to
32 percent in the private sector. The disparity is not
unique to the United States. Snowden (2012)
illustrates a similar education discrepancy using data
from the UK Office for National Statistics (ONS) that
reports that 40 percent of government workers earned
degrees compared to 25 percent in the private sector.
These data suggest either that government work
requires more skill or that government hires over
educated workers. Snowden (2012) offers potential
insight into this question when he reports that that the
ONS study identifies 59 percent of public sector
employees as high skill or upper-middle skill
compared to 49 percent in the private sector. It
appears therefore that public sector workers require
more skill but that the government may nonetheless
hire over educated workers given that the ratio of
college educated workers to non-college educated
workers exceeds the ratio of high and middle skill
workers in the public and private sector.
Unobserved worker heterogeneity motivated a large
body of research. Gyourko and Tracy (1988) find
evidence of unobserved worker heterogeneity across
both the public and private sector with a negative
self-selection effect in the unionized public sector. In
contrast, they find a smaller positive selection effect
in the non-union private sector. Lee (2004) asserts
that previous research ignores worker heterogeneity
and self-selection across sectors.
Specifying
econometric models to account for heterogeneity and
selection bias, Lee finds that earlier OLS estimates of
federal government wage differentials were biased
upward for men and downward for women. Krueger
(1988) employs longitudinal data to find that the
premium received by federal workers is smaller than
estimates derived from cross-sectional data.
Gittleman and Pierce (2011) use multiple data sets
both showing public sector workers to be more
skilled as evidenced by higher average educational
attainment.
Using CPS data for individual
Proceedings of the 2012 Pennsylvania Economic Association Conference
252
characteristics, they then estimate a compensation
mark-up from BLS employer cost data. Gittleman
and Pierce find that state employees cost 3-9 percent
more and local government workers incur
compensation cost 10-19 percent more than similarly
skilled private sector workers.
EMPIRICAL ANALYSIS
Overview of the National Survey of College
Graduates
The National Survey of College Graduates (NSCG),
sponsored by the National Science Foundation, is a
“once in a decade opportunity” to examine the
educational and career characteristics of United
States college graduates. The latest version of the
data, the 2003 NSCG, surveyed a random sample of
individuals living in the United States, under the age
of 76, who had received a bachelor’s degree or higher
prior to the new millennium. The public-use sample
includes data on 100,042 individuals.
The NSCG includes a rich set of information on
individual and job-related characteristics.
Our
analysis includes full-time employed individuals and
eliminates those who are K-12 teachers, working in
higher education and nonprofits, and those
respondents who do not identify their race or if the
race category included too few observations. We
eliminated K-12 teachers because there were so few
observations in the private sector. Respondents from
academia and the nonprofit sectors are eliminated due
to the unique job duties specific to the organizations
in which they work. Additional deletions due to
missing data reduced the sample to 50,966
observations, of which 42,137 are individuals in the
private sector and 8,829 are in the public sector.
Consistent with the literature on public/private
differentials and salary regressions more generally,
the final dataset includes information on individual
characteristics (age, gender, race, US citizenship) and
family characteristics (marital status, number of
children). Regional controls are also included but not
listed in the final tables. The human capital effect is
captured with several binary variables representing
the type of highest degree (a bachelor’s degree is the
omitted category in the base regression) and the
number of years since the individual obtained the
highest degree, used as a proxy for experience.
Current job tenure and employer size, defined by a
set of binary variables representing the number of
employees in the firm, capture the current job’s
human capital development and the possibility that
larger employers have the ability to pay more (Brown
and Medoff, 1989). Occupation has been aggregated
into fourteen occupation categories. The omitted
category represents a grouping of non-science
occupations, including communications, journalism,
criminal justice, leisure and fitness, public affairs,
and other fields not listed in the NSCG survey.
Research indicates varying differentials between
federal and private and state/local and private
workers so we control for public sector workers, in
general, as well as distinguish between federal and
state/local workers. Due to the narrow focus on
college-educated workers of the present research
paper, we do not consider the unionization issue
because unions tend to disproportionately represent
less educated workers receiving lower salaries.
Appendix 1 lists means and standard deviations for
the variables used in the analysis for the total sample
and delineated by employment sector. Note that even
in this dataset, the public sector is on average more
educated than the private sector. Although the
percentage of individuals holding professional
degrees is similar across sectors, 29 percent of public
sector workers hold a Master’s degree as their highest
degree compared to 25 percent in the private sector,
and a higher percentage of federal sector workers
have Ph.Ds. making the public sector sample more
educated overall. The distribution of occupations is
not dramatically different between the two sectors.
Public/Private Differentials under Different
Methods and Controls
We first examine the private/public differential using
what Gittleman and Pierce (2011) call the “people
approach,” whereby OLS regressions are used to
examine the salary differential having controlled for
the major factors that affect salary. Table 1 provides
the salary differential under different functional form
assumptions for earnings and various control factors.
Row 2 presents the unadjusted raw differential
between the private sector and the two public sectors.
The unadjusted differential between private and
federal workers is 19.57 percent; the differential is
48.6 percent between private and state/local workers.
The results are consistent with the literature on
federal workers, where Falk (2012) found a raw
differential of 23 percent for college-educated federal
and private workers;
however, the unadjusted
differential for state and local workers is much larger
than the 22 percent found with weekly wages from
the CPS (Gittleman and Pierce, 2011). Because the
norm is to employ log-linear regression functions and
because salary decomposition is based on that
functional form, we also estimate the unadjusted
differential using natural logs of salary.
The
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253
differential falls to 1.9 percent for federal workers
and 45.5 percent for state and local workers (Row 3).
When we employ a log-linear regression to control
for the usual demographics (Row 4), the estimated
differential is 2.25 percent and 25.35 percent for
federal and state/local workers, respectively. Thus,
even though the analysis is limited to collegeeducated, the controls for race and gender, postbaccalaureate schooling, and post-education human
capital development (via general and firm-specific
experience) explain a good part of the differential,
but the federal/private differential rises, perhaps due
to larger returns to the private sector associated with
some independent variables. For example, a set of
regressions by employment sector indicates that
private sector workers see larger returns to Ph.D.
degrees compared to either federal or state workers
(See Appendix 2).
The next row in Table 1 controls for firm size using
five binary variables representing categories of
increasing number of employees. Brown and Medoff
(1989) found empirical support for the notion that
larger employers hire better-quality workers,
although the wage-firm size premium is still largely
unexplained. We include the controls for employer
size to account for this possibility. Gittleman and
Pierce (2011) suggest that including size controls
confounds any public sector effect, but they also
indicate that they do not include size controls because
their particular dataset does not include the
information. Using the CPS 2009, Keefe (2010)
controls for size in a comparison between state and
local to private sector workers and finds a 15.57
percent and a 9.46 percent differential for state and
local workers, respectively.
Our employer-size
controls increase the public/private salary gap to 13.9
percent for federal workers and 28.19 percent for
state/local workers. The expected value of earnings
for private sector workers with at least a college
degree in Keefe’s sample was only $59,911.66 in
2009 dollars, much less than the nearly $84,000
average salary earned by private sector workers in the
2003 NSCG ($97,696 in 2009 constant dollars). Our
larger differentials compared to Keefe may be due to
the reduced private sector pay as a result of the most
recent recession.
The last row in Table 1 includes controls for fourteen
occupations (See Appendix 1 for a list of the
occupations). These controls reduce the salary
differential for federal workers to 6.5 percent and
18.1 percent for state/local workers. While our
results do not switch the sign of the differential as
occupational controls do in Gittleman and Pierce
(2011), we do see that controlling for occupations
reduces the differentials by 53 percent for federal
workers and over 35 percent for state workers. Thus,
occupational choice may affect the differential
because returns to particular occupations vary across
sectors.
For many years, labor economists have analyzed
wage and salary differentials using a decomposition
method that explains the differential in terms of
differences in mean levels of the independent
variables, the advantage one group has over another
group, and the disadvantage one group has due to
external factors, such as discrimination (Oaxaca,
1973). Cotton (1988) provides the basis for the
following decomposition:
𝑙𝑛𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠 𝑃𝑅 − 𝑙𝑛𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠 𝑃𝑈 = ∑ 𝛽𝑗∗ (𝑋�𝑗𝑃𝑅 −
𝑋�𝑗𝑃𝑈 ) + ∑ 𝑋�𝑗𝑃𝑅 �𝛽𝑗𝑃𝑅 − 𝛽𝑗∗ � + ∑ 𝑋�𝑗𝑃𝑈 �𝛽𝑗𝑃𝑈 − 𝛽𝑗∗ � (1)
The first term on the right-hand side of the equation
captures the effect of the differences in the mean
values of the independent variables between private
(PR) and the public (PU) sectors. So, for example, if
private sector workers have higher levels of firm
experience compared to their public sector
counterparts, this first term captures that difference.
The second term estimates the benefit of being in the
private sector, while the third term represents the
disadvantage of being in the public sector. The last
two components present the degree to which earnings
are overestimated in the private sector or
underestimated in the public sector. 𝛽* is a
proportional average of the coefficients, based on
each group’s sample size (i.e. β*= βPR *nprivate/n + βPU
*npublic/n, where n is the total number of individuals
in the sample). β* is considered the average return
for a particular control factor in the regression. Table
1 presents the decomposition under the natural log
differential.
The estimated federal/private differential is only 1.9
percent using log salaries. The small differential
occurs because private sector workers have relatively
higher proportions in key independent variables, such
as the percentage who are doctors, in computer
science or math, or the life sciences, where the
returns in the private sector are relatively higher. The
differences in independent variables and the returns
to the private sector explain 70 percent and 30
percent, respectively, of the differential. There is no
estimated disadvantage to the federal sector.
On the other hand, the state and local/private salary
differential is quite large. There is little advantage to
Proceedings of the 2012 Pennsylvania Economic Association Conference
254
working in the private sector (explains about 9
percent of the differential), and the difference
between state/local and private workers is largely due
to differences in the average values for the
independent variables (23 percent) and for the
disadvantage facing state and local workers in returns
(67 percent). The former is mainly due to employer
size (where private sector workers have 21 percent of
workers in firms with at least 25,000 employees,
while state and local workers only have 13 percent)
and with the occupational distribution, where there
are more private sector individuals in occupations
such as engineering, computer science and
mathematics, and health practitioners (doctors). The
disadvantage to state and local workers is due to the
relatively lower returns state and local workers
receive for the independent variables. For example,
lawyers in the private sector have an average salary
that is 77 percent greater than those individuals in the
omitted occupation category (the non-science jobs
listed earlier), while state and local lawyers receive
only a 25 percent return. Likewise, doctors and
computer scientists and mathematicians in the private
sector receive a higher return to their respective
occupations compared to their state and local
counterparts (See Appendix 2 for the related
regression coefficients). Thus, it appears that we can
explain most of the differential for each public sector.
Two final methods are used to examine the
differential more closely. The first method examines
the pay differential by deciles and/or quintiles, to see
whether the differential changes when average pay is
relatively low or high. In other words, can we
explain the overall differential because public
workers are paid less than their private sector
counterparts within a particular part of the income
distribution? Table 2 presents the average salary and
related OLS coefficients for several different
percentiles of the salary distribution (using the
regression with all independent variables).
The results in Table 2 indicate that the federal/private
differential is large for the lowest percentile, but
federal workers have relatively higher salaries in the
25th percentile. The federal advantage disappears in
higher percentiles, but the private sector advantage is
very small. On the other hand, the state and
local/private differential favors state and local
workers in the lowest percentile, indicating that state
workers actually have a salary premium in those jobs
with relatively low pay. The differential becomes
negative but is never more than 2.8 percent (75th
percentile).
These results indicate that withindistribution salary differences are small, but given the
results in Table 1, are large overall. Thus, public
sector workers are more likely to be in the lower
percentiles relative to the private sector workers,
creating a wide differential across the income
distribution.
A second method is used when the researcher
assumes that individuals select into their respective
sectors. Here, the model is assumed as follows
(Greene 2012):
𝑦𝑖 = 𝛽 ′ 𝑥𝑖 + 𝛿𝐸𝑖 + 𝜀𝑖
(2)
𝐿𝑒𝑡 𝐸𝑖∗ = 𝛾′𝑍 + 𝑢𝑖
(3)
𝐸[𝑦𝑖 |𝐸𝑖 = 1] = 𝛽 ′ 𝑋 + 𝛿 + 𝜌𝜎𝜀 𝜆(−𝛾’Z)
(4)
Where log salary is a function of 𝑥 independent
variables and 𝐸 is a binary variable indicating
whether the observation is from the private or public
sector. However, we may find that the binary
variable does not capture choices made regarding
sector, therefore:
where 𝐸𝑖∗ is the probability of choosing to work either
in the public or private sector (a standard normal
distribution is assumed for the probability). We do
not observe the probability, only the outcome, 𝐸𝑖 . If
one employs OLS regression with 𝐸 on the righthand-side of the salary equation, then δ in equation
(2) will be biased and inconsistent, overestimating
the impact of the differential. In other words, the
coefficients estimated in Table 1 are too negative
because they do not account for the choice one makes
in terms of employment sector (which is represented
by the correlation between the error term in (2) with
the error term in the salary regression). The related
salary regression is therefore:
We now must include the last term on the right-handside to account for the sector choice, represented by
the inverse Mills ratio and estimate the relevant
coefficient, λ.
Why might we consider one choosing an employment
sector? There are two obvious reasons. First, a
negative coefficient associated with a public sector
binary variable becomes less negative or switches
signs when the regression moves from salary to total
compensation (Gittleman and Pierce, 2011). Thus, it
seems that public sector workers tradeoff salary for
other work benefits (pensions, vacations, leave).
Second, we might consider a variety of job attributes,
where public sector workers have different attitudes
about the attribute compared to private sector
workers. In our dataset, we can examine whether this
is the case with a set of survey questions that inquire
Proceedings of the 2012 Pennsylvania Economic Association Conference
255
about the respondent’s level of interest in several job
attributes, as listed in Table 3. In particular, public
sector workers are more likely to report that benefits,
job security, and social responsibility are very
important or somewhat important compared to
private sector workers. This result is consistent with
the finding that business economists trade salary for
socially responsible work (Benedict et al., 2006).
Because the choice is between the public versus the
private sector and the differences above are small
between the federal and state/local sectors, we
employ a probit model to estimate the probability that
an individual selects the public sector.
The
independent variables include binary variables
representing whether an individual ranked job
characteristics as important or not important.
Controls for race and gender are included because
minorities and women may move toward the public
sector if they feel less discrimination (further, more
minorities and women are situated in the public
sector). The salary regression is as before, only with
a binary variable representing the public sector,
rather than two variables for the sub-sectors. The
OLS regression in Appendix 3 indicates that the
average differential between the public and private
sector is -0.14 (i.e. public workers earn 14 percent
less on average than their private sector
counterparts). However, when we assume that the
individual chooses the employment sector, we find
that the differential is reduced to 8.1percent, which
suggests that public sector workers tradeoff salary for
job attributes by going into the public sector. The
coefficient on the inverse Mills ratio is statistically
significant and negative, indicating that sector choice
is indeed important to the model. These results
indicate that a compensating differential exists for
public sector workers, in that they are willing to
receive relatively lower salaries for job
characteristics that are important to them. For
example, in the probit regression, caring about
benefits, job security, and social responsibility are
associated with positive coefficients that are
statistically significant. These are the three main job
characteristics that seem to be traded off for salaries
in the second-stage salary regression.
selection model we include individual attitudes
regarding job characteristics. We find that public
sector workers trade salary for fringe benefits, job
security, and social responsibility.
The other addition we make to the literature is due to
the unique qualities of the NSCG data. Because we
can focus only on the college-educated, our results
are not confounded by the fact that low-skilled, loweducated public sector workers have a wage
premium, while educated workers do not. Earlier
studies are unable to delineate among different postbaccalaureate degrees. We find that individuals with
a Master’s degree receive a higher return in the
federal government sector compared to their private
sector counterparts.
Further, by distinguishing
between federal workers and state and local workers,
we find that the differential varies by government
sector. State and local workers have lower average
salaries compared to their federal and private sector
counterparts in all of the estimated regressions. We
also find that much of the disadvantage to state/local
workers is due to the lower returns they receive
compared to private sector workers. This result may
reinforce the notion of a compensatory differential,
where public sector workers realize higher salaries
exist in private sector occupations such as doctors
and lawyers, but they choose the public sector
because they care about different job attributes.
CONCLUSION
This paper examines the public/private salary
differential for the college-educated. Our findings
confirm previous research that controls for human
capital, employer size, and occupation reduce the
unadjusted differential. In addition, our self-selection
model supports earlier findings that the differential is
reduced after controlling for selection bias. In our
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256
TABLE 1 – PUBLIC/PRIVATE PAY DIFFERENTIALS
Private
Federal
State/Local
$83,771.29
(65,056.23)
$75,434.11
(30,131.30)
-19.57%
$56,383.94
(28,548.04)
-48.6%
1.9%
-45.5%
70%
23%
% Advantage for Private Sector
30%
9%
%Disadvantage for Public Sector
0%
67%
4. Demographic/Regional controls
-2.25%***
-25.35%***
5. Employer Size controls
-13.90%***
-28.19%***
6. Occupation controls
-6.50%****
-18.10%***
1. Average Salary
2. Unadjusted Differential
3. Unadjusted LnSalprivate – LnSalpublic
Explained by:
% Differences in Averages
Data Source: 2003 National Survey of College Graduates. Regression coefficients are reported
in this table. Appendix 2 contains the regression estimation. *** indicates statistical significance
at the 1 percent level of significance. Rows 5 and 6 are adding additional controls to the previous
row.
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TABLE 2 – ESTIMATED SALARY DIFFERENTIAL BY PERCENTILE
Table 2. Estimated Salary differentials by Percentiles
Percentile
10
25
50
75
90
Average Salary
$20,015.33
(9095.64)
$38,963.97
(4159.55)
$56,448.75
(5920.67)
$78,986.64
(7481.93)
$107,819.45
(10621.54)
Federal
-0.218*
State
0.117**
0.032***
-0.010***
-0.009**
-0.017***
-0.013***
-0.028***
-0.013***
-0.019***
TABLE 3 – ATTITUDES ABOUT JOB CHARACTERISTICS
Respondent cares about:
Table 3. Attitudes about Job Characteristics
Very important Somewhat
Somewhat
important
unimportant
Not important at All
Advancement
Private
46.57%
44.18%
7.14%
2.11%
Federal
48.09
45.18
5.35
1.37
State/Local
43.91
48.15
6.65
1.29
Benefits
Private
63.95
32.71
2.36
0.99
Federal
69.75
28.85
1.07
0.33
State/Local
74.68
24.32
0.87
0.13
Challenge
Private
63.29
33.95
2.26
0.49
Federal
64.07
33.38
2.22
0.33
State/Local
62.40
35.23
2.04
0.33
Independence
Private
61.78
35.12
2.78
0.32
Federal
60.36
36.12
3.21
0.30
State/Local
61.84
36.06
1.89
0.21
Responsibility
Private
46.67
46.74
5.73
0.86
Federal
45.13
48.37
5.71
0.80
State/Local
46.12
48.71
4.63
0.54
Salary
Private
62.07
36.52
1.09
0.32
Federal
57.97
40.52
1.21
0.30
State/Local
58.20
40.53
1.10
0.17
Job Security
Private
61.51
33.85
3.60
1.04
Federal
70.79
27.26
1.67
0.27
State/Local
73.18
24.86
1.68
0.29
Social Responsibility
Private
36.96
48.50
12.03
2.52
Federal
52.35
40.98
5.74
0.93
State/Local
57.52
37.00
4.51
0.96
Data Source: 2003 National Survey of College Graduates. Probabilities are conditional on the employment sector.
Proceedings of the 2012 Pennsylvania Economic Association Conference
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Proceedings of the 2012 Pennsylvania Economic Association Conference
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APPENDIX 1 – DESCRIPTIVE STATISTICS
Variable
Salary
LnSalary
Female
Married
Number of Children
US Citizen
Asian
Black
Hispanic
Highest Degree-MA
Highest Degree-PHD
Highest DegreeProfessional
Years Since Highest
Degree
Years Squared
Current Job Tenure
Current Job Tenure
Squared
Employer Size:100-499
Employer Size: 500-999
Employer Size: 1K-5K
Employer Size: 5K-25K
Employer Size: 25K+
Computer Science or
Math
Life Science
Chemistry
Physical Sciences
Total Sample
N=50,966
80,388.58
(60965.27)
11.084
(0.742)
0.300
(0.458)
0.784
(0.412)
0.913
(1.146)
0.923
(0.266)
0.142
(0.349)
State/Local
N=5,186
56,383.94
(28548.04)
10.821
(0.559)
0.445
(0.497)
0.731
(0.444)
0.801
(1.086)
0.970
(0.169)
0.099
(0.298)
Federal
N=3,643
75,434.11
(30131.30)
11.145
(0.488)
0.314
(0.464)
0.771
(0.420)
0.849
(1.126)
0.985
(0.120)
0.103
(0.304)
Private
N=42,137
83,771.29
(65056.27)
11.112
(0.773)
0.281
(0.449)
0.791
(0.407)
0.932
(1.154)
0.912
(0.284)
0.151
(0.358)
0.070
(0.255)
0.073
(0.261)
0.261
(0.439)
0.054
(0.226)
0.081
(0.272)
17.815
(10.248)
422.407
(433.502)
8.095
(8.274)
133.983
(246.630)
0.123
(0.329)
0.055
(0.228)
0.124
(0.330)
0.132
(0.338)
0.261
(0.439)
0.156
(0.363)
0.023
(0.149)
0.013
(0.114)
0.011
(0.106)
0.161
(0.367)
0.107
(0.309)
0.285
(0.452)
0.031
(0.173)
0.078
(0.269)
18.266
(9.987)
433.355
(406.891)
9.001
(8.028)
145.450
(220.818)
0.186
(0.389)
0.116
(0.321)
0.258
(0.437)
0.195
(0.396)
0.132
(0.338)
0.079
(0.270)
0.038
(0.190)
0.009
(0.096)
0.018
(0.133)
0.121
(0.327)
0.084
(0.278)
0.300
(0.458)
0.106
(0.308)
0.066
(0.249)
18.277
(10.300)
440.120
(425.929)
10.545
(9.219)
196.150
(277.582)
n/a
0.054
(0.226)
0.068
(0.252)
0.254
(0.435)
0.052
(0.223)
0.082
(0.275)
17.720
(10.273)
419.529
(437.261)
7.772
(8.173)
127.197
(245.999)
0.126
(0.322)
0.052
(0.223)
0.118
(0.323)
0.135
(0.342)
0.213
(0.409)
0.169
(0.375)
0.015
(0.123)
0.014
(0.116)
0.008
(0.090)
n/a
n/a
n/a
n/a
0.115
(0.319)
0.084
(0.278)
0.014
(0.118)
0.040
(0.196)
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Social Sciences
0.017
0.030
(0.129)
(0.170)
Engineering
0.171
0.125
(0.376)
(0.331)
Health Practitioners
0.044
0.014
(0.204)
(0.116)
Other Health-Related
0.041
0.062
(0.198)
(0.241)
Science or Engineering
0.040
0.032
Technology
(0.197)
(0.176)
Management
0.189
0.182
(0.392)
(0.386)
Social-related
0.020
0.129
(0.141)
(0.335)
Sales or Marketing
0.097
0.006
(0.296)
(0.077)
Art & Music
0.020
0.008
(0.141)
(0.092)
Law
0.032
0.056
(0.176)
(0.230)
Data Source: National Survey of College Graduates.
0.031
(0.174)
0.182
(0.386)
0.022
(0.147)
0.046
(0.209)
0.024
(0.152)
0.200
(0.400)
0.010
(0.098)
0.005
(0.074)
0.009
(0.095)
0.029
(0.169)
Proceedings of the 2012 Pennsylvania Economic Association Conference
0.014
(0.118)
0.175
(0.380)
0.049
(0.216)
0.038
(0.191)
0.043
(0.202)
0.189
(0.392)
0.008
(0.087)
0.117
(0.321)
0.023
(0.149)
0.029
(0.169)
261
APPENDIX 2 – REGRESSION OUTPUT
Variable
CONSTANT
Female
Married
Number of Children
US Citizen
Asian
Black
Hispanic
Federal
State/Local
Highest Degree-MA
Highest Degree-PHD
Highest DegreeProfessional
Years Since Highest
Degree
Years Squared
Current Job Tenure
Current Job Tenure
Squared
Employer Size:100-499
Employer Size: 500-999
Employer Size: 1K-5K
Employer Size: 5K-25K
Employer Size: 25K+
Computer Science or Math
Life Science
Chemistry
Physical Sciences
Total Sample
10.194***
(0.021)
-0.207***
(0.007)
0.087***
(0.008)
0.033 ***
(0.003)
0.041 ***
(0.014)
-0.068***
(0.011)
-0.102***
(0.013)
-0.141***
(0.013)
-0.065 ***
(0.014)
-0.181***
(0.011)
0.162***
(0.008)
0.333 ***
(0.017)
0.278 ***
(0.023)
0.021 ***
(0.001)
-0.0004 ***
(0.00003)
0.014***
(0.001)
-0.0002 ***
(0.00004)
0.174 ***
(0.010)
0.196 ***
(0.014)
0.217***
(0.011)
0.227 ***
(0.010)
0.273 ***
(0.009)
0.498***
(0.012)
0.275***
(0.026)
0.328***
(0.033)
0.345***
(0.035)
State/Local
10.326***
(0.050)
-0.125***
(0.015)
0.049***
(0.016)
0.007
(0.007)
0.077*
(0.046)
-0.068**
(0.027)
-0.047**
(0.020)
-0.142***
(0.025)
n/a
Federal
10.401***
(0.079)
-0.056***
(0.017)
0.026
(0.019)
0.035***
(0.007)
0.062
(0.069)
-0.091***
(0.028)
-0.072**
(0.024)
-0.039
(0.029)
n/a
Private
10.124***
(0.024)
-0.227***
(0.008)
0.097***
(0.009)
0.035 ***
(0.003)
0.039 ***
(0.015)
-0.067 ***
(0.012)
-0.130***
(0.0176)
-0.149***
(0.015)
n/a
n/a
n/a
n/a
0.165***
(0.016)
0.236***
(0.047)
0.300***
(0.045)
0.014***
(0.003)
-0.0003***
(0.0001)
0.017***
(0.003)
-0.0003***
(0.0001)
0.105***
(0.025)
0.173***
(0.028)
0.169***
(0.024)
0.194***
(0.025)
0.169***
(0.028)
0.240***
(0.031)
-0.01
(0.043)
0.101
(0.088)
0.096
(0.060)
0.229***
(0.018)
0.322***
(0.032)
0.292***
(0.050)
0.021***
(0.003)
-0.0004***
(0.0001)
0.010***
(0.003)
-0.0001
(0.0001)
n/a
0.153***
(0.009)
0.337 ***
(0.020)
0.271 ***
(0.027)
0.022 ***
(0.001)
-0.0005 ***
(0.00003)
0.013 ***
(0.001)
-0.0002 ***
(0.00004)
0.180***
(0.012)
0.195***
(0.017)
0.222***
(0.012)
0.227***
(0.012)
0.281***
(0.010)
0.564 ***
(0.014)
0.417 ***
(0.035)
0.393 ***
(0.038)
0.451***
(0.047)
n/a
n/a
n/a
n/a
0.028***
(0.029)
0.031
(0.035)
0.203***
(0.029)
0.179***
(0.048)
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Social Sciences
0.396***
0.130***
0.177***
0.492 ***
(0.028)
(0.046)
(0.050)
(0.035)
Engineering
0.479***
0.316***
0.314***
0.541 ***
(0.012)
(0.027)
(0.026)
(0.014)
Health Practitioners
0.918***
0.602***
0.303***
1.007 ***
(0.027)
(0.074)
(0.070)
(0.031)
Other Health-Related
0.285***
0.066**
0.125***
0.357***
(0.016)
(0.030)
(0.036)
(0.020)
Science or Engineering
0.357***
0.097**
0.122**
0.431 ***
Technology
(0.018)
(0.043)
(0.052)
(0.021)
Management
0.561***
0.284***
0.310***
0.640 ***
(0.010)
(0.020)
(0.022)
(0.012)
Social-Related
0.073***
-0.077***
-0.037
0.016
(0.023)
(0.024)
(0.074)
(0.041)
Sales or Marketing
0.294***
0.094
-0.094
0.355 ***
(0.012)
(0.084)
(0.092)
(0.013)
Art
0.156***
0.101
0.142*
0.213***
(0.020)
(0.069)
(0.076)
(0.022)
Law
0.648***
0.251***
0.333***
0.770***
(0.027)
(0.050)
(0.076)
(0.032)
Adjusted R2
0.226
0.233
0.241
0.224
F-Statistic
345.28***
39.41***
33.11***
297.38***
Data source: 2003 National Survey of College Graduates. Statistical significance:
***=0.01, **=0.05, *=0.10. Note that employer size binary variables are not included in the
regression for federal workers, because the employer size is always greater than 25,000.
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APPENDIX 3 – SELF-SELECTION MODEL OUTPUT
Variable
Constant
Public
Female
Married
Number of Children
US Citizen
Asian
Black
Hispanic
Highest Degree-MA
Highest Degree-PHD
Highest DegreeProfessional
Years Since Highest
Degree
Years Squared
Current Job Tenure
Current Job Tenure
Squared
Employer Size:100-499
Employer Size: 500-999
Employer Size: 1K-5K
Employer Size: 5K-25K
Employer Size: 25K+
Computer Science or Math
Life Science
Chemistry
Physical Sciences
Social Sciences
OLS
10.190***
(0.021)
-0.140***
(0.009)
-0.208***
(0.007)
0.087***
(0.008)
0.033***
(0.003)
0.043***
(0.014)
-0.068***
(0.011)
-0.105***
(0.013)
-0.141***
(0.013)
0.162***
(0.008)
0.337***
(0.017)
0.279***
(0.023)
0.021***
(0.001)
-0.0004***
(0.00003)
0.014***
(0.001)
-0.0002***
(0.00003)
0.170***
(0.010)
0.189***
(0.014)
0.210***
(0.011)
0.223***
(0.010)
0.292***
(0.009)
0.498***
(0.012)
0.282***
(0.026)
0.328***
(0.033)
0.352***
(0.035)
0.396***
Self-Selection
10.226***
(0.022)
-0.081***
(0.020)
-0.190***
(0.007)
0.073***
(0.008)
0.031***
(0.003)
0.034***
(0.012)
-0.053***
(0.009)
-0.116***
(0.013)
-0.128***
(0.011)
0.155***
(0.007)
0.332***
(0.012)
0.287***
(0.027)
0.020***
(0.001)
-0.0004***
(0.00003)
0.012***
(0.001)
-0.0002***
(0.0004)
0.155***
(0.010)
0.168***
(0.013)
0.201***
(0.010)
0.216***
(0.010)
0.285***
(0.009)
0.492***
(0.012)
0.286***
(0.017)
0.316***
(0.018)
0.348***
(0.023)
0.385***
Probit Estimates
-1.704***
(0.091)
0.224***
(0.014)
-0.205***
(0.021)
0.564***
(0.023)
0.211***
(0.024)
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Engineering
Health Practitioners
Other Health-Related
Science or Engineering
Technology
Management
Social-related
Sales or Marketing
Art & Music
Law
(0.028)
0.479***
(0.012)
0.920***
(0.027)
0.286***
(0.016)
0.357***
(0.018)
0.562***
(0.010)
0.052
(0.023)
0.294***
(0.012)
0.158***
(0.020)
0.646***
(0.027)
(0.024)
0.469***
(0.012)
0.909***
(0.033)
0.268***
(0.018)
0.357***
(0.015)
0.565***
(0.012)
0.046***
(0.023)
0.272***
(0.016)
0.128***
(0.030)
0.623***
(0.032)
Advancement
0.017
(0.028)
0.530***
(0.054)
-0.169***
(0.047)
-0.109**
(0.043)
-0.068**
(0.032)
-0.338***
(0.065)
0.331***
(0.043)
0.515***
(0.025)
Benefits
Challenge
Independence
Responsibility
Salary
Security
Social Responsibility
Adj. R2
F-Statistic
λ
0.224
352.13***
-0.043***
(0.010)
Χ2
156.26***
Data source: 2003 National Survey of College Graduates. Statistical significance:
***=0.01, **=0.05, *=0.10.
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Proceedings of the 2012 Pennsylvania Economic Association Conference
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AUTHOR INDEX
Ezatollah Abbasian
Thomas O. Armstrong
Michael Bajic
Nic Banting
William Bellinger
Mary Ellen Benedict
Robert F. Brooker
Jay Bryson
Henry F. Check, Jr.
Lisa Cooper
James Dehnert
Harry R. Diz
Bari Dzomba
Marwan El Nasser
Rachel Gifford
William R. Hawkins
Michael J. Hannan
Denae A. Heath
Joseph Hess
Adora D. Holstein
David McClough
page 208
page 101
page 251
page 101
page 55
page 251
page 243
page 48
page 90
page 232
page 101
page 243
page 232
page 177
page 232
page 145,156
page 222
page 202
page 232
page 125
page 251
Cameron D. McConnell
Orhan Kara
Tai McNaughton
Daniel Meuser
Tracy C. Miller
Maysam Nasrindoost
David Nugent
Cristian Pardo
Brenda Ponsford
Tim Quinlan
Rod D. Raehsler
Karen L. Randall
Richard Robinson
Paul Sangrey
Joe Seydl
Yaya Sissoko
Niloufer Sohrabji
Tufan Tiglioglu,
John S. Walker
Carrie R. Williams
page 202
page 37
page 117, 148
page 101
page 85
page 208
page 216
page 16
page 145,156
page 48
page 164
page 90
page 177, 223
page 196
page 48
page 66
page 66
page 232
page 90
page 187
Mary Ellen Benedict, Michael Bajic, and David McClough
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