Micro Data For Macro Models
Fall 2014
Lecture 1
(Prologue and
Consumption Inequality)
Course Pre-Amble
1998 – 2000 Cohort That Are Tenured at Top Schools
(with some omissions because my memory is bad)
Marianne Bertrand (Chicago)
Ananth Seshadri (Wisconsin)
Esther Duflo (MIT)
Amil Petrin (Minnesota)
Mike Greenstone (MIT)
Muhamet Yildiz (MIT)
Emmanuel Saez (Berkeley)
Marco Battaglini (Princeton)
Jonathan Levin (Stanford)
Xavier Gabaix (NYU)
Sendhil Mullainathan (Harvard)
Monika Piazzesi (Stanford)
Chang-Tai Hseih (Chicago)
Ricardo Reis (Columbia)
Erik Hurst (Chicago)
Enrico Moretti (Berkely)
Dirk Krueger (Penn)
Martin Schneider (Stanford)
Luigi Pistaferri (Stanford)
Annette Vissing-Jorgensen (Berkeley)
David Autor (MIT)
Mark Duggan (Wharton)
Mark Aguiar (Princeton)
Fabrizio Perri (Minnesota)
Marc Melitz (Harvard)
Ed Vytlacil (NYU)
Victor Chevnozhakov (MIT)
Wouter Dessein (Columbia GSB)
Ted Miguel (Berkeley)
~ 900 people got a Ph.D. from top 15
Markus Bruennermeier (Princeton)
David Lee (Princeton)
departments during this time period
~ 40- 50 (~5%) of people got tenured at top
15 departments
1998 – 2000 Cohort That Are Tenured at Top Schools
(with some omissions because my memory is bad)
Marianne Bertrand (Chicago)
Ananth Seshadri (Wisconsin)
Esther Duflo (MIT)
Amil Petrin (Minnesota)
Mike Greenstone (MIT)
Muhamet Yildiz (MIT)
Emmanuel Saez (Berkeley)
Marco Battaglini (Princeton)
Jonathan Levin (Stanford)
Xavier Gabaix (NYU)
Sendhil Mullainathan (Harvard)
Monika Piazzesi (Stanford)
Chang-Tai Hseih (Chicago)
Ricardo Reis (Columbia)
Erik Hurst (Chicago)
Enrico Moretti (Berkely)
Dirk Krueger (Penn)
Martin Schneider (Stanford)
Luigi Pistaferri (Stanford)
Annette Vissing-Jorgensen (Berkeley Haas)
David Autor (MIT)
Mark Duggan (Stanford)
Mark Aguiar (Princeton)
Fabrizio Perri (Minnesota)
Marc Melitz (Harvard)
Ed Vytlacil (NYU)
Victor Chevnozhakov (MIT)
Wouter Dessein (Columbia)
Ted Miguel (Berkeley)
~ 900 people got a Ph.D. from top 15
Markus Bruennermeier (Princeton)
David Lee (Princeton)
departments during this time period
~ 40- 50 (~5%) of people got tenured at top
15 departments
Publishing?
•
The median Ph.D. from a top 20 department never publishes anything
in a peer reviewed journal
•
The median peer reviewed article has less than 15 citations.
•
See Dan Hamermesh’s web site for:
“Young Economist’s Guide to Professional Etiquette”
https://webspace.utexas.edu/hamermes/www/JEP92.pdf
The Good News
•
The creation of research is a skill just like inverting a matrix, solving
DSGE models, computing standard errors, etc.
•
The more you work on it, the better you will become.
•
Read the early work of those recently tenured at top schools. Every
single one of you could have written the same papers.
It is not only our technical prowess that distinguishes us throughout our
careers, it is our ability to innovate and/or to come up with good questions.
Those who have impact on the profession do so because of their ideas.
What Skill Are Ph.D. Students Most Deficient?
•
Having the ability to identify interesting research questions
•
The confusion of theoretical or empirical fire power as being an “end” as
opposed to a “means”.
•
Not having the ability to explain why anyone would care about their
research.
Goal of This Class
•
Get you to start thinking about writing your dissertation
•
Familiarize you with many data sets that are used by macro economists
(and others) to be used as part of your dissertation.
•
Expose you to literatures within macroeconomics that have strong
empirical components.
•
Help you turn good research ideas into good research papers.
•
Teach you how to communicate your ideas to others.
Some Housekeeping….
•
T.A.:
•
Lots of work – hopefully all of it useful
o
o
o
Nick (with set up an email list for class participants
including auditors)
Reading
Homeworks
Virtual Paper
•
Slides/Course Info (on my faculty web page)
•
Co-Taught with Steve Davis: Timing (Steve’s slides/homework on
Chalk)
My Portion of the Course: Household Datasets
Topic 0:
Prolouge
Topic 1:
Consumption Inequality
Topic 2:
Lifecycle Consumption
Topic 3:
Home Production
Topic 4:
Occupational Choice
Topic 5:
Regional Economics (may make this Topic 4, depending on
timing)
Topic 6:
Understanding Small Businesses (if time allows)
Very Important
•
Prelim
1) A completed research paper
2) Need two faculty readers. The primary reader has to be a professor
who teaches in the empirical macro sequence (Steve, Erik, Rafael, or
Joe).
2) Could be the completion of the virtual paper (subject to Erik and
Steve’s approval).
3) You have to notify Erik by late April if you are planning to take the
prelim, what the paper topic is, and who your readers are. See the
syllabus for additional details. (Also need a B in all three courses)
Anatomy of Writing an Research Paper
1.
Identifying a research topic a broad level
o
o
o
2.
What broad area of research are you trying to speak to?
What existing research is done on that literature?
Is it interesting to contribute an answer to this broad research
question?
Identifying a research topic in the narrow
o
o
o
o
What is the specific question you are trying to address?
What existing research is done on this specific question?
Is the answer to this specific question interesting?
Do you have a way to answer this specific question?
Anatomy of Writing an Research Paper
3.
Executing the specific research question
o
o
o
o
4.
How do you tease out your mechanism from many other mechanisms?
What type of identification are you using to identify your mechanism
(for empirical papers).
What drives that identification (for empirical papers).
Good questions without a good research design is just philosophy
and/or killing trees.
Conveying the output of the research
o
o
o
o
Your ideas need to be conveyed to others in order to have impact.
Main way in which ideas are conveyed: written research paper.
Seminars are another way to convey the research idea.
Need to be clear in both writing and verbal presentations.
Example of the Research Process
1.
Question in the Broad
o
Why has U.S. Labor Force Participation Rate (and Employment
to Population Rate) fallen so sharply in recent years and remained
at such a low level.
a. Why is this question interesting?
b. Is the fact true?
Advice: It is always good to start your research process with interesting
background facts.
Unemployment Rate: 1970M1 – 2014M7
15
Employment to Population Rate: Men
16
Employment to Population Rate: Women
17
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Non-Employment Rate: Prime Age (21-55) Men
0.25
Solid Line: Data
Dashed Line: 3rd Order Polynomial
0.20
0.15
Δ(00-11)=0.070
0.10
0.05
0.00
18
Non-Employment Rate:
Prime Age Non-College and College Men
0.30
Solid Line: Data
Dashed Line: 3rd Order Polynomial
0.25
Less than College
Δ(00-11)=0.089
0.20
0.15
0.10
0.05
College and More
Δ(00-11)=0.032
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
0.00
19
Non-Employment Rate:
Prime Age Non-College and College Women
0.60
Solid Line: Data
Dashed Line: 3rd Order Polynomial
0.50
0.40
Less than College
Δ(00-11)=0.080
0.30
0.20
College and More
Δ(00-11)=0.027
0.10
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
0.00
20
Total Employment By Skill
(January 2000 Normalized to 1)
1.4
1.3
1.2
1.1
1.0
0.9
0.8
High School or Less
Some College
College or More
21
Summary of Facts
•
Non-employment rate for prime age workers started increasing in U.S.
around 2000. (Moffitt, BPEA 2012).
o
The fact is particularly pronounced for lower skilled men and women.
o
Some increase for high skilled men as well.
Summary of Facts
•
Non-employment rate for prime age workers started increasing in U.S.
around 2000. (Moffitt, BPEA 2012).
o
The fact is particularly pronounced for lower skilled men and women.
o
Some increase for high skilled men as well.
•
Research question in the broad:
•
Can we move from a “research question in the broad” to a “research
question in the narrow”?
Why has the employment to
population ration been falling?
Example of the Research Process
2.
Question in the Narrow
o
How much can the recent decline in manufacturing employment
explain the rise in non-employment of U.S. workers?
o
Why didn’t this effect show up sooner in aggregate statistics?
o
What is the mechanism by which a sectoral decline can lead to
increases in non-employment?
Hint:
Can some preliminary data lend potential plausibility to this
hypothesis?
Total Monthly U.S. Manufacturing Employment (in 1,000s):
1980M1-2014M5
22,000
~1 Million Jobs Lost
During 1980s and 1990s
20,000
18,000
16,000
14,000
12,000
10,000
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
8,000
Total Monthly U.S. Manufacturing Employment (in 1,000s):
1980M1-2014M5
22,000
~1 Million Jobs Lost
During 1980s and 1990s
20,000
18,000
16,000
14,000
12,000
~4 Million Jobs Lost
Between 2000-2007
(Housing Boom Years)
10,000
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
8,000
Total Monthly U.S. Manufacturing Employment (in 1,000s):
1980M1-2014M5
22,000
~1 Million Jobs Lost
During 1980s and 1990s
20,000
18,000
~1 Million Jobs
Lost
After 2007
16,000
14,000
12,000
~4 Million Jobs Lost
Between 2000-2007
(Housing Boom Years)
10,000
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
8,000
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
U.S. Employment Trends for Non-College Men (age 21-55)
0.45
0.40
Manufacturing + Construction
0.35
0.30
0.25
Manufacturing
0.20
0.15
0.10
Construction
0.05
0.00
0.02
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
U.S. Employment Trends for Non-College Men (age 21-55)
0.14
Manufacturing + Construction
0.12
0.10
Manufacturing
0.08
0.06
0.04
Construction
0.00
Example of the Research Process
3.
Going from research question to actual implementation…..
o
Can one construct a research design to test the specific research
question?
o
What type of variation can help to test the hypothesis?
o
What assumptions are necessary to make this variation useful to test
the hypothesis?
- “Wish” criteria (what do you “wish” drives the variation).
- “Plausibility” criteria (is your “wish” plausible).
Open Lecture Slides 1a
1.
The Lecture Slides “charles_noto_hurst_phd_lecture1.ppt” will go through
the research design of my paper:
“Manufacturing Decline, Housing Booms, and Nonemployment”
Goals of the Class Revisited
1.
Homeworks
o
The homeworks are designed to getting you thinking about the
components of the research process.
2.
Virtual Paper
3.
Lectures
o
Steve and I will try to bring in our perspective on the research process
through our discussions.
“Where Do Ideas Come From?”
•
Question that Ph.d. students ask most.
•
Where do ideas come from?
•
o
Reading literature (seeing holes in existing literature, being unsatisfied
with the consensus views on a topic, etc.)
o
Trying to understand world around us (“What drives employment
rates?”, “How would one measure policy uncertainty?”, etc.)
o
Exploring data sources (my work on time trends, etc.)
o
Talking with other graduate students!
Pick projects you are interested in. If you are not interested in the
answer to your question, no one else will be either!
Topic 1:
Consumption Inequality
Part A:
Background on Household Surveys
(Nick will Expand on this in TA Sessions)
Micro Expenditure Data: Household Surveys
•
Consumer Expenditure Survey (U.S. data)
Starts in 1980
Broad consumption measures
Some income and demographic data
Repeated cross-sections
•
Panel Study of Income Dynamics (U.S. data)
Starts in late 60s
Only food expenditure consistently
Housing/utilities (most of the time)
Broader measures (recently)
Very good income and demographics
Panel nature
Micro Expenditure Data: Household Surveys
•
British Household Panel (British Data)
o
Panel data including income and expenditure
•
Family Expenditure Survey (British Data)
•
Bank of Italy Survey of Household Income and Wealth (Italian Data)
o
Panel data including income and expenditure
•
There are others….many Scandinavian countries, Japan, Canada, etc.
•
Even some developing economies have detailed household surveys that
track some measures of consumption (e.g., Mexico, Taiwan, Thailand)
Micro Expenditure Data: Scanner Data
•
Nielsen Homescan Data
o
o
o
o
o
o
o
o
o
Large cross-section of households
Very detailed level transaction data (at the level of UPC code)
Some demographics
Some panel component
Matches quantities purchased with prices paid
Covers most of the large MSAs
Measurement error?
Selection?
Coverage of goods?
Micro Income/Employment Data: Household Surveys
•
Current Population Survey (CPS)
o
o
o
Usual data set used within U.S. to track labor supply and earnings.
Has panel component.
Can be found at www.ipums.org/cps/
•
PSID
•
Survey of Income and Program Participation (SIPP)
o
o
o
•
Can be found at http://psidonline.isr.umich.edu/
Four year rotating panel
Large sample sizes
Over samples poor
Census/American Community Survey
o
Can be found at www.ipums.org
Part B:
Trends in Consumption Inequality (Part 1)
Income and Consumption Inequality
• Large literature documenting the increase in income inequality within the
U.S. during the last 30 years (Katz and Autor, 1999; Autor, Katz, Kearney,
2008)
• Consumption is a better measure of well being than income (utility is U(C)
not U(Y)).
• Does income inequality imply consumption inequality?
Depends on whether income inequality is “permanent”
Depends on insurance mechanisms available to households
Depends on other margins of substitution (home production, female
labor supply, etc.).
41
Kevin Murphy’s Web Page
42
Kevin Murphy’s Web Page
43
Autor, Katz, Kearney (2008)
44
Why Do We Care About Consumption Inequality?
•
Why is it important?
o
Learn about well being over time (economic growth, standard of
livings, inequality, etc.).
o
Learn about insurance mechanisms available to households (public
insurance, private insurance, etc.)?
o
Learn about the nature of income processes (more on this in the
next set of lecture notes).
A Classic: Attanasio and Davis (1996)
A Classic: Attanasio and Davis (1996)
A Short Discussion:
The innovation of the Attanasio and Davis technique.
The creation of synthetic cohorts from cross sectional data.
Krueger and Perri (2006)
•
What they do:
o
Use data from the Consumer Expenditure Survey (CEX) to track
the evolution of consumption inequality.
o
CEX is includes a nationally representative sample of households.
-
o
Designed to compute consumption weights for CPI
Short panel dimension (4 quarters)
Mostly used as repeated cross sections.
Includes detailed spending measures on expenditures by
categories.
Use repeated cross sections to track consumption inequality.
Krueger and Perri (2006): What They Find
Krueger and Perri (2006): What They Find
Krueger and Perri (2006): What They Find
Krueger and Perri (2005): What They Conclude
•
Conclusions
o
Income inequality is much greater than consumption inequality
o
If some of the increase in income inequality is idiosyncratic,
households can self insure (or public sector can provide insurance)
making consumption inequality respond less than income inequality.
o
Write down a model where insurance is endogenously provided.
Increasing idiosyncratic shocks to income can increase demand for
insurance (leading to more insurance). Consistent with their model,
credit card access increased during this period.
o
Bottom line:
Use the consumption data to learn about the nature
of income processes and insurance mechanisms.
Part C:
A Caveat – Some Data Issues
A Data Problem: Average Real Consumption in CEX
9.8500
9.8000
9.7500
9.7000
9.6500
9.6000
9.5500
54
A Data Problem: Average Real Consumption in CEX
9.8500
9.8000
9.7500
9.7000
9.6500
9.6000
9.5500
55
Percent Change in Consumption in CEX (from 1981)
0
-0.02
-0.04
-0.06
-0.08
-0.1
-0.12
-0.14
-0.16
56
Trends in Real NIPA Aggregate Consumption
57
Part D:
Revisiting Trends in Consumption Inequality
Accounting for Measurement Error in Data
Can Measurement Error Alter Inequality Findings?
•
Yes
•
Depends on whether measurement error differs across the consumption
(income) distribution.
•
Suppose richer households have been underreporting their income to a
greater extent in recent periods (relative to the past).
•
The rich could be increasing their expenditure more (relative to other parts
of the distribution). However, the systematic measurement error could
also be increasing.
•
How to test for group specific differences in measurement error?
Aguair and Bils (2011)
•
Try to account for differential measurement error over different “incomedemographic” groups to get a sense of changing consumption inequality.
•
Some particulars:
Define xijt = average expenditure on good j, by group i, at time t
j goods = food at home, clothing, utilities, entertainment, etc.
i groups = cells based on income (5) and demographics (18)
Define Xit = average total expenditure for group i at time t.
Formally:
X it   j 1 xijt
J
The Essence of the Exercise
(From a Discussion by Jonathan Parker; NBER EFG 2011)
Log budget
share of good:
ln wi = ln (xi /X )
Observed Inferred
Estimated
2006
2006
Engel curve
for luxury
Observed
1980
Inferred
adjustment
to ln X90
Estimated
Engel curve for
normal good
ln X10
ln X10
Log total real
expenditure:
90 X = xLux+xNormal+xNec
ln X90 ln X90 ln X
Aguair and Bils (2011)
•
Assume measurement error in expenditure……
j
i
*  t t  vijt
ijt
xijt  x e
•
•
 tj
ti
represents a good specific error (common across all groups)
represents a group specific error (common across all goods)
Aguair and Bils (2011): Some Intuition
•
Difference-in-Difference Estimates (2 good case, 2 group case)
•
•
Goods = e (entertainment) and f(food)
Groups = high (rich) and low (poor)
xhigh ,e
xlow,e
xhigh , f
xlow, f


*
xhigh
,e
*
low ,e
x
*
xhigh
,f
*
xlow
,f
 high  low
e
 high  low
e
(difference out good specific error)
(difference out good specific error)
Aguair and Bils (2011): Some Intuition
•
Take differences across goods to eliminate group specific error
 xhigh , f
 xhigh ,e 
ln 
  ln 
 xlow,e 
 xlow, f
*
*


 xhigh

x
,e
high , f
  ln  *   ln  *
 xlow,e 

 xlow, f



•
Obtain an unbiased estimate of relative consumption inequality.
•
Need to map into units of total expenditure. Want to recover:
*
*
 ln X high


ln
X
,t
low,t
(1)
Aguair and Bils (2011): Some Intuition
Define:
 ijt*   ln xijt* ;  it*   ln X it* 
*
*
 high



, e ,t
e high ,t  .....
*
*
 low



, e ,t
e low ,t  .....
*
*
 high



, f ,t
f high ,t  .....
*
*
 low



, f ,t
f low ,t  .....
*
*
*
*
*
*
( high


)

(



)

(



)(



, e ,t
low ,e ,t
high , f ,t
low , f ,t
e
f
high ,t
low ,t )
Aguair and Bils (2011): Some Intuition
Using (1), we know that we can express:
*
*
*
*
*
*
( high


)

(



)

(



)(



, e ,t
low ,e ,t
high , f ,t
low , f ,t
e
f
high ,t
low ,t )
*
*
as: ( high,e,t   low,e,t )  ( high, f ,t   low, f ,t )  (e   f )( high


,t
low,t )
Aguair and Bils (2011)
•
Suppose for true expenditures, x* :
ln xijt*   *jt   j ln X ijt*   j Zi  ijt
•
Can estimate the following using actual data in some period 0 where
systematic measurement error is less of an issue:
ln xij 0   j 0   j ln X i 0   j Zi  uij 0
•
If there is no measurement error in the data, can uncover:
ˆ j   j
•
Assumes income elasticities are constant over time (and can be locally
estimated). Assume measurement error is zero in period 0.
Aguair and Bils (2011): Some Intuition
Using (1), we know that we can express:
*
*
*
*
*
*
( high


)

(



)

(



)(



, e ,t
low ,e ,t
high , f ,t
low , f ,t
e
f
high ,t
low ,t )
*
*
as: ( high,e,t   low,e,t )  ( high, f ,t   low, f ,t )  (e   f )( high


,t
low,t )
Substituting in the estimated β’s, we get:
*
*
( high,e,t   low,e,t )  ( high, f ,t   low, f ,t )  ( e   f )( high


,t
low,t )
Aguiar and Bils (2011) Findings
69
Aguiar and Bils (2011) Findings
70
Aguiar and Bils (2011) Findings
Relative Spending Differences Between High and Low Income Groups
71
Aguiar and Bils (2011) Findings
72
Aguiar and Bils (2011) Findings
Different Saving Rates From the CEX
73
Attanasio, Hurst, and Pistaferri (2012)
•
Also show that measurement error likely results in the underestimation of
changes in consumption inequality within the U.S.
•
Like Aguiar and Bils, find that consumption inequality and income
inequality have moved essentially one-for-one over the past thirty years.
•
Use other empirical approaches and data sets.
•
You can find a copy of the paper on my web page (under working papers).
Attanasio, Hurst, and Pistaferri (2012)
•
•
Use CE Diary Data (a separate survey) as opposed to CE Interview Data (which was
used by Krueger/Perri and Aguiar/Bils.
Diary data found to have less measurement error (better matches NIPA trends).
Attanasio, Hurst, and Pistaferri (2012)
•
•
Imputed PSID Consumption matches income inequality nearly identically.
Food PSID Consumption also matches income inequality trends (need to scale by
food income elasticity which is about 0.5).
Conclusions: Part 1 (A – D)
•
Measurement error is important in Consumer Expenditure Survey!
•
Even though there is measurement error, can still measure consumption
inequality.
•
Without controlling for measurement error, looks like small increases in
consumption inequality.
•
Much of that is due to the rich reporting less and less of their expenditures.
•
Controlling for the systematic recent underreporting of the rich increases
the estimated consumption inequality in the U.S. to levels that match the
changing income inequality.