EXAMINING MODELS TO DETERMINE THE COST OF A BACCALAUREATE
DEGREE AND BUILDING A DEGREE LEVEL COMPARATIVE COST
FRAMEWORK FOR THE CALIFORNIA STATE UNIVERSITY
Daren M. Otten
B.S., California State University, Chico, 1997
M.S., California State University, Chico, 2006
DISSERTATION
Submitted in partial fulfillment of
the requirement for the degree of
DOCTOR OF EDUCATION
in
EDUCATIONAL LEADERSHIP
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SPRING
2014
Copyright © 2014
Daren M. Otten
All rights reserved
ii
EXAMINING MODELS TO DETERMINE THE COST OF A BACCALAUREATE
DEGREE AND BUILDING A DEGREE LEVEL COMPARATIVE COST
FRAMEWORK FOR THE CALIFORNIA STATE UNIVERSITY
A Dissertation
by
Daren M. Otten
Approved by Dissertation Committee:
Andrea Venezia, Ph.D., Chair
Su Jin Jez, Ph.D.
Nancy Shulock, Ph.D.
SPRING 2014
iii
EXAMINING MODELS TO DETERMINE THE COST OF A BACCALAUREATE
DEGREE AND BUILDING A DEGREE LEVEL COMPARATIVE COST
FRAMEWORK FOR THE CALIFORNIA STATE UNIVERSITY
Student: Daren M. Otten
I certify that this student has met the requirements for format contained in the University
format manual, and that this dissertation is suitable for shelving in the library and credit is
to be awarded for the dissertation.
, Department Chair
Dr. Caroline Sotello Viernes Turner
Date
iv
ACKNOWLEDGMENTS
With any journey there are others who make it possible. Some of these people
have always been there, and others you meet along the way, but for me this entire
adventure would not have been considered, or even possible, without the support and
encouragement of the following people.
I must first and foremost thank my wife Rene, who is as deserving of this degree
as I, as she proofread every poorly written word I put on paper from the program
application to the doctoral dissertation.
I must also thank my daughter Carlene who has put up with my absence and
occasional grumpiness through this process.
I am unable to find the words to express my gratitude to Dr. Andrea Venezia. It
has been an honor to work with her and she has truly supported, challenged, and fostered
my thinking and writing through this process with amazing patience.
I am privileged and thankful to Dr. Su Jin Jez and Dr. Nancy Shulock for their
support. They are both amazing scholars and thinkers.
To my parents, Diane and Dale, who without question, have always supported me
on every successful and ill-fated journey I have undertaken.
I am indebted to many colleagues who have supported me but I must specifically
thank:
Dr. Mike Ward and Dr. Dirk Vanderloop who have truly shown and provided me
a path into higher education that I could have never imagined.
v
To Cohort V of the Ed.D. program at CSU, Sacramento, you are all amazing! I
have learned so much from each one of you and cannot wait to see the incredible impact
that each one of you will make to education in this state.
I must specifically thank Melissa Bramham, my cohort best friend who, through
the highlights and lowlights of this journey, has demonstrated a style and grace, that I can
only dream to duplicate, while proving to me that you are superwoman!
Thank you all and I cannot wait for the next chapter!
vi
CURRICULUM VITAE
EDUCATION
Master of Science, Manufacturing Engineering
California State University, Chico
Bachelor of Science, Industrial Technology
Option in Manufacturing Management
California State University, Chico
2006
1997
EXPERIENCE - Education
Vice-Chair, Department of Mechanical and Mechatronic Engineering and Sustainable
Manufacturing
Chico, CA
2013-Present
Responsibilities included administrative activities that include assessment reports,
accreditation preparation, student advising, course scheduling, faculty utilization reports,
enrollment and prerequisite issues, event operations, and general operations.
Sustainable Manufacturing Program Coordinator/Assistant Professor
California State University, Chico
Chico, CA
2004-Present
Instructor of Record:
SMFG 201 Manufacturing Graphics, SMFG 211 Material Science and Quality Testing,
SMFG 216 Introduction to Plastics, SMFG 218 Advanced Plastics Processing and Design,
SMFG 350 Industrial Supervision, SMFG 458 Project Management, SMFG 476 Injection
Mold Tooling, SMFG 490 Certified Manufacturing Engineer Exam Preparatory Course
EXPERIENCE –Industrial
CEO - Interim
Green Polymer Technologies, Inc.
President
Development Technologies, Inc.
Product/Tooling Engineer
Precision Mold, Inc.
Project Engineer/Manager
Sunbo/Lightwave Corp
Materials Manager/Supplier QA
Sel-Tech, Inc.
San Diego, CA
2011- 2012
Magalia, CA
2001- 2008
Kent, WA
1998- 2000
Taipei, Taiwan/Bellevue WA
Chico, CA
1997- 1998
1995-1997
CONSULTING
Expert Witness
Geometrics Inc. v. Solid Seismic LLC.
Parker County, TX
2013-14
Product Development/Design/Manufacturing Consultant
Rapid Ramen Inc.
Sacramento, CA
2012-2014
Consulting Executive/Product Development/Design/Manufacturing Consultant
EcoRacks Inc.
San Diego, CA
2013
Product Development/Design/Manufacturing Consultant
vii
Meraki Inc.
San Francisco, CA
2010
CERTIFICATES and TRAINING
2007-2013 CSU Sexual Harassment Training
2012 Ethics for Supervisors Training
2011 Lean Manufacturing 5S Training
2010 Defensive State Driver Training
2009 Grant Writing USA Training
2009 CSU Chico Environmental Health and Safety – Fire Prevention Plan
2009 CSU Chico Environmental Health and Safety – General Safety
HONORS and AWARDS
Innovate Northstate - Teacher of the Year
Bechtel Corporation Journal Fellow
Chico ER – Sustainable Manufacturing Feature Story
Upstate Business Journal – Feature Spotlight Story
Development Technologies, Inc. Product Design and Manufacturing
The Chico Project – Local Leader Award
Advantage Butte County Success Story Feature
Development Technologies, Inc. TCEDC and BCBIP Program
UNIVERSITY SERVICE
CSU Chico, Internal Leadership Academy “ACE Fellow Project”
College of Engineering Professional Learning Community
Founding Member
Department of Mechanical Engineering, Mechatronic Engineering,
and Manufacturing Technology Department Safety Committee
Faculty Lead – Sustainable Manufacturing Program Reaccreditation
CSU Chico Student Grievance Committee ECC Representative
CSU CFE, Judge Fall 2013 Business Competition
Faculty Lead CA MiHub Designation Application Liaison
MMEM Department Chair Search Committee Member
CSU CFE, Judge Fall 2012 Business Competition
College of Engineering Executive Council
Sustainable Manufacturing Program Scholarship Coordinator
COMMUNITY SERVICE
Butte College Business Education Program Review Visiting Team Member
Executive Board Member & Finance Committee, Work Training Center Inc.
Advisory Board Member, Butte College Business Department
Member, Board of Directors, Center for Economic Development
Member, Board of Directors, Innovate North State
Executive Committee, North Valley LEAN, Appointed February 2011
Board Member, Industrial Advisory Committee, Butte College and
Butte County Office of Education
ROP Welding, CAD/CAM, Manufacturing
viii
2013-2014
2012-2013
08/2010
06/2007
2005
02/2004
2013-Current
2013-Current
2007- Current
Spring 2014
2012-2013
Fall 2013
Spring 2013
Spring 2013
Fall 2012
2010-Current
2009-2012
March 2014
2013-Current
2013-Current
2012-Current
2012-Current
2011-Current
2007-Current
Member, Industrial Advisory Committee, Butte College Drafting
and Engineering Preparation
Paradise Charter Middle School – Faculty & Staff Compensation
Restructure Parent Representative Appointed
Advisor, Placer Jt. Union High School District
NSF Discovery Research K-12 Grant Project
2003-Current
2012-2013
2010-2012
PROFESSIONAL AFFILIATIONS
Member, Society of Manufacturing Engineers
Member, Society of Plastics Engineers
PUBLICATIONS, PAPERS, PRESENTATIONS, and PUBLIC QUOTATION
15 Minutes Column Feature Story, Chico and Sacramento News and Review, 10/24/13
“Using his Noodle”, by Vic Cantu
Feature Story Interview Action News Now KHSL/KNVN TV 10/15/2013 “Chico State
Professor and Students Create Rapid Ramen Cooker”
Speaker, North state Economic Stewardship Network Forum 5/22/2013, “The Future of
Manufacturing in Northern California”
Speaker, US Manufacturing Repatriation Summit 5/9/2013, “The Future of Manufacturing in
the US and Northern California”
Moderator and Speaker, 3/27/2013 Governor’s Office of Business and Economic
Development Advanced Manufacturing Summit
ix
Abstract
of
EXAMINING MODELS TO DETERMINE THE COST OF A BACCALAUREATE
DEGREE AND BUILDING A DEGREE LEVEL COMPARATIVE COST
FRAMEWORK FOR THE CALIFORNIA STATE UNIVERSITY
By
Daren M. Otten
The California State University is the largest bachelor’s degree granting level
education system in the United States. Higher education in California is expensive to
students, parents, and taxpayers. This research sought provide a standard methodology to
answer the question of what is the most accurate method for determining the cost of
producing a bachelor’s degree within the California State University system?
Educational cost is not a new topic, as the California Master Plan for Higher
Education (California Department of Education, 1960) dedicated an entire chapter to why
cost and cost management is critical for the sustainability of the educational systems.
The framers of the California higher education did not specify how costs would be
measured or suggest solutions for seeking efficiencies that could be scaled to each
campus. The CSU Chancellors office, through the CSU Synergy and the CSU
Graduation Initiatives, continue to seek effectiveness and efficiencies while balancing the
student learning and access mission on which the system is founded.
This work explored previously developed degree-costing methodologies and
ultimately proposed a revised costing model that can be used to determine degree cost.
x
The work is based both on actual and theoretical student course taking behavior, direct
and indirect educational cost accounting, the determination of educational cost drivers,
and the impact that student success and other factors have on the cost of a degree.
xi
TABLE OF CONTENTS
Page
Acknowledgments................................................................................................................v
Curriculum Vitae .............................................................................................................. vii
List of Tables .....................................................................................................................xv
List of Figures .................................................................................................................. xvi
Chapter
1. INTRODUCTION ...........................................................................................................1
Problem Statement ...................................................................................................4
Purpose Statement and Research Question ............................................................10
Implications............................................................................................................12
Assumptions and Limitations ................................................................................13
Definition of Terms................................................................................................15
2. REVIEW OF RELATED LITERATURE ....................................................................20
Educational Cost Analysis and Public Policy ........................................................22
Defining Educational Cost .....................................................................................24
Units of Measurement for Costing.........................................................................29
Literature-based Higher Education Costing Methods............................................32
Unaddressed Shortcomings of Existing Methods as Applied to the CSU .............47
Factors Influencing Higher Education Costs .........................................................49
Summary ................................................................................................................53
xii
3. METHODOLOGY ........................................................................................................54
Introduction ............................................................................................................54
Research Question .................................................................................................54
Research Design.....................................................................................................55
Role of the Researcher ...........................................................................................58
Data Collection and Instrumentation .....................................................................60
CSU Framework Plan “Contact Hour Plus Model” ...............................................64
Protection of Participants .......................................................................................66
4. MULTI-METHOD COST OUTPUTS AND FINDINGS ............................................67
Introduction ............................................................................................................67
General Campus Budget Data ................................................................................73
Delta Catalog .........................................................................................................80
Delta Transcript Cost .............................................................................................82
Delta Full Attribution Model .................................................................................86
NACUBO Model ...................................................................................................88
Contact Hour Costing/Contact Hour Plus Costing Methodology ..........................91
Contact Hour Plus Model.......................................................................................93
Contact Hour Plus Model Estimates for a Mechanical Engineering
Degree ..................................................................................................................100
Cost Drivers and System Scale Modeling for CSU 24 Using the
Contact Hour Plus Model.....................................................................................102
xiii
Conclusion ...........................................................................................................109
5. IMPLICATIONS, RECOMMENDATIONS, AND CONCLUSIONS ......................110
Introduction ..........................................................................................................110
Broad Implications of Degree-based Costing ......................................................112
Review of Previous Costing Models....................................................................113
Contact Hour Costing Model Plus (CHP) Applications ......................................120
CHP Implications and Opportunities ...................................................................125
Political Feasibility of Implementation ................................................................131
Suggestions for Future Research .........................................................................133
Recommendations and Conclusion ......................................................................136
References ........................................................................................................................139
xiv
LIST OF TABLES
Table
Page
1.
Costing Model Feature Comparison ........................................................................8
2.
Typical Cost Studies in Educational Policy Analysis ............................................23
3.
Review of Major Literature-based Costing Methods.............................................40
4.
Mechanical Engineering Major Academic Plan ....................................................72
5.
CSU Chico Campus Overall Expense Information ...............................................75
6.
Summary of Method Cost Outputs Delaware Costing Model ...............................77
7.
Delaware Costing Method .....................................................................................80
8.
Delta Catalog Costing Method...............................................................................82
9.
Delta Transcript Costing Method...........................................................................86
10.
Delta Full Attribution Costing Method ..................................................................88
11.
NACUBO Costing Method ....................................................................................91
12.
Estimated Leasehold Value of CSU Chico Facilities ............................................96
13.
Estimated Leasehold Value of CSU Chico Facilities ............................................98
14.
CHP Costing Model Outputs ...............................................................................101
15.
Impact of Increasing the Gradation Rate .............................................................128
xv
LIST OF FIGURES
Figure
Page
1.
Map of the California State University campuses .................................................10
2.
Flowchart of cost model development ...................................................................65
3.
MECH FTF graduates within six years .................................................................69
4.
Delaware cost model average and range cost reporting.........................................79
5.
Delta catalog cost model with comparisons to Delta Direct
Only and Delaware ................................................................................................81
6.
Direct and indirect per unit cost variability 2005-2006, campus level ..................83
7.
Per unit cost variability 2005-2006, department level, GE removed .....................84
8.
Delta Full Attribution Cost compared to other models ..........................................87
9.
NACUBO FTES Based Model compared to other models ...................................90
10.
Delta and Delaware Models – direct cost – contact hour v. unit ...........................93
11.
Contact Hour Plus Costing Model variants with suggested uses ...........................94
12.
Contact Hour Plus costing of a mechanical engineering degree
at CSU, Chico ......................................................................................................100
13.
Estimated direct educational unit cost location example .....................................104
14.
Contact Hour Plus Costing of a mechanical engineering degree
at theoretical CSU with 14% higher wages .........................................................105
15.
Annual average lease cost per square foot with various areas of
California (REIS) .................................................................................................107
xvi
16.
Contact hour plus costing of a mechanical engineering degree at
theoretical CSU with 14% higher wages and 108% higher facilities cost ...........108
17.
Results of all existing costing models evaluated (modified to report
degree cost in some cases) ...................................................................................114
18.
Delta versus NACUBO method comparisons......................................................116
19.
CHP Costing Model– CSU, Chico mechanical engineering degree
compared with CSU 24 ........................................................................................123
20.
High and low costs for a mechanical engineering degree ....................................126
21.
Impact of additional graduates on the cost of a degree ........................................127
xvii
1
Chapter 1
INTRODUCTION
The 1960 Master Plan for California Higher Education (California Department of
Education [CDE]) dedicates one chapter specifically to the costs of higher education
within the state. The framers of higher education within California truly believed that the
cost, cost management, and cost assessment is critical for educational planning and public
policy.
The Master Plan Survey Team considers a study of costs as basic to its study
outcomes. Formulation of educational policy involves weighing alternative
patterns or possibilities, and decisions thereon are influenced by the probable
costs. In particular, public higher education, supported by large legislative
appropriations, requires scrupulous policy planning to realize the maximum value
from the tax dollar. Thus, a careful assessment of cost factors is necessary to
provide an adequate basis for planning of the state’s higher education facilities.
(California Department of Education [CDE], 1960, p. 146)
The recognition of cost, cost analysis, and cost control was highlighted when the
Master Plan for California Higher Education was developed. Yet in 2014, over 50 years
later, the issue of cost remains at the forefront of questions surrounding higher education
within California. The purpose of this work provides a California State University (CSU)
specific framework that can be used to determine the cost of a bachelor’s degree. Such
information will provide critical data for campus and system educators and
administrators, and for policymakers, with which to make informed data-driven decisions
about postsecondary resources. This is especially important given California’s boom and
bust education finance cycles. The cyclical nature of California state finances has created
an environment in which education should be reactive rather than strategic. In a
2
economic down time, the CSU reaction almost always results in cuts often generalized to
the campus units at the same rate with the idea of equity and shared pain. Given a datadriven system for expenses that are reported at the programmatic level, administrators
would have a tool with which to identify costly programs and make strategic decisions
regarding resources and campus values and priorities. The tool will help provide (a) an
understanding of specific educational unit expenses, (b) a baseline formula to use to start
organizational efficiency discussions, and (c) a data-driven model for educational
administrators and lawmakers to make strategic decisions regarding effective resource
use and outcomes.
Funding levels for public higher education in California are volatile and generally
reflect economic conditions. These funding swings left the CSU in 2012 with lower state
funding than in 1998, while serving 35% more students (California State University
[CSU], Chancellors Office Budget Informational, 2012). Taxpayers reaffirmed their
support of education with the passage of Proposition 30 in 2013, which stabilized funding
at the time; however, Proposition 30 is scheduled to expire in two phases starting in 2016.
The Governor’s May 2013-2014 budget revise “prioritizes higher education by providing
new funds to begin reinvesting in the public universities, with the expectation that the
universities will improve the quality, performance and cost effectiveness of the
educational systems” (California Department of Finance [DOF], 2013, p. 23). While the
Governor backed off his original higher education 2013-2014 funding reform plan that
tied student outcomes to funding (Megerian, 2013), it could be reasonably expected that
3
questions on cost effectiveness and efficiencies regarding the resources allocated to
higher education within California will continue to be at the forefront of attention by
government, students, taxpayers, academic administrators, and policymakers. This is
especially true given that the Proposition 30 funds have essentially been designed as a
temporary fix and do not fix structural budget issues, cost control, or address efficiency
concerns.
The foundational issue when evaluating higher education cost effectiveness or
efficiency is that there is not a universally accepted costing method with which to begin
the discussion. Without an adopted CSU system-wide costing method, strategic
decisions and public policy operate in the dark. Additionally, existing costing methods
generally lack the ability to disaggregate to degree specific cost, which would provide a
baseline for cost effectiveness and efficiency comparisons. The California State
Legislative Analyst’s Office in the 2013-2104 Analysis of the Higher Education Budget
(DOF, 2013), stated that spending per degree is a measure of productivity that currently
cannot be easily systematically compared primarily due to site-level academic differences
regarding graduate and undergraduate curricular offerings and the lack of separate
expense data. Without consensus on how to measure cost, conversations about
production, effectiveness, or efficiency are impossible. Yet those are precisely the
conversations that should be occurring given the volatile nature of resources being
provided to postsecondary education by the state. This study evaluated commonly used
methods of determining cost as applied to the California State University (CSU) system
4
at a bachelor’s degree level. Using the results from the existing methods along with
reviewing the literature on costing, a CSU-specific framework was developed addressing
the unique needs of the largest public four-year postsecondary education system in the
country. Having a CSU-specific educational costing framework will provide a datadriven mechanism that can be used at the site and system levels to begin evaluating
resource effectiveness at all 23 campuses.
Chapter 1 reviews the reasons why that cost and the calculation of cost is as
important, if not more so, than before. The implications of this work, the assumptions,
limitations, and vocabulary are also introduced. Chapter 2 reviews literature regarding
the connection if educational cost to public policy and investigates previous costing work
and the shortcomings found in educational costing methods. The chapter concludes with
a discussion on cost drivers in higher education. The methods, research design, and data
are reviewed within Chapter 3. In Chapter 4, the findings from the data and research are
presented, and Chapter 5 reviews the implications of the findings.
Problem Statement
Through the literature about educational costs, authors have called for a widely
accepted methodology since the 1960s (Coordinating Council for Higher Education
[CCHE], 1972; Johnson, 2009), and yet in 2014, there is no universally accepted costing
system allowing for the determination of the educational cost of a specific bachelor’s
degree. The lack of a system-level method also does not allow for comparisons regarding
the cost of the same degree offered at multiple CSU campuses. Not only is the definition
5
of cost, as well as the calculation of cost, deceptively complex, but with no agreed-upon
model, campuses are able to define, calculate, and report as they deem fit. As such, the
ability to compare costs is limited, confusion is fostered, and an environment ripe for
misinformation is created.
This lack of consensus on a costing model, method, or system may be driven
partially by the fact that many of the current costing methods have focused on being
universally broad, and conformable to different sites, in order to fulfill the needs of all
types of higher educational systems and institutions (NACUBO, Delaware). Higher
education institutions and systems in the U.S. represent a diverse range of missions,
including foci on research, liberal arts programs, teaching, and technical programs.
Universal costing methods have attempted to cover all the variables in mission (research,
teaching, undergraduate education, graduate education, service, etc.) with one-size-fits-all
approaches. Economists refer to these production variables as outputs (Brinkman, 2000).
This concept of multiple outputs (research, teaching, service, graduates, etc.) has made
universal costing methods popular, since they can be adapted or tailored to a specific
campus; this flexibility limits comparability, however. Research-focused institutions
would naturally have a different cost structure than institutions such as the CSU, where
the focus and mission is on student teaching and learning. This difference is highlighted
through faculty release time, or lower teaching loads, often granted to professors at
research-intensive schools. When a faculty member is granted release time, or assigned a
lower teaching load, generally another instructor must be hired for the course load, which
6
increases educational expenses. The differences in teaching loads between research
universities and teaching institutions impact the labor cost and limits the ability for
degree-cost comparisons between higher educational systems. Due to the differences in
mission, and thus differences in costs, intersegmental comparisons (e.g., CSU to
University of California) should be limited, but within systems, cost comparisons
between majors can lead to the spread of best practices and to improved resource
effectiveness. This dissertation proposed that costing methods should be designed for the
CSU specific system of higher education, rather than a one-size-fits-all approach.
As previously mentioned with the models that attempt to create a “universal cost
framework,” there can be definition, terminology, and reporting differences that limit
comparability. To compare costs, there must be a common language and accepted units.
For example, the unit could be credit hours (the number of credits or units taught in a
program or institution) or contact hours (the actual number of hours faculty are
compensated to directly engage with students), but common definitions do not exist in
universal costing methods. Even the authors of universal costing methods acknowledge
that results from their methods should not be externally utilized by other institutions or
systems (National Association of College and University Business Officers [NACUBO],
2002), and comparisons should be limited to single site internally based year-over-year
comparisons.
The goal of this work was to create a costing methodology that accurately reports
programmatic or degree level educational costs for campuses within the CSU. The
7
developed system is firmly based in educational costing literature while addressing the
existing model shortcomings as currently applied to the CSU. To accomplish this goal,
cost data associated with the mechanical engineering program at CSU, Chico was applied
to the existing frameworks (see Table 1). Using the existing methods of degree costing
provided cost range information associated with the difference in calculated outputs.
This range or limit of estimated costs highlights method-induced variances that may
overestimate or underestimate the cost of a degree. CSU, Chico’s mechanical
engineering program was chosen as the sample program due to data access. Mechanical
engineering is also generally considered to be an expensive program to operate; it
typically has high laboratory contact with small course sections, has high student attrition
rates, and the degree is offered at multiple CSUs. The factors that have been found to
drive costs are supported using the mechanical engineering data to test and develop a
CSU-specific costing method that focuses on the idea of cost comparison.
Table 1
Costing Model Feature Comparison
Delaware
Delta Catalog
Delta Transcript
N/A - Not a Degree Level
Cost Tool
Cost per unit of instruction
is based on all students who
start the class
Not Included in Cost per
Degree
Not Included in Cost per
Degree
Reflected in cost per degree Reflected in cost per degree N/A - Not a Degree Level
Cost Tool
Reflected in cost per degree Reflected in cost per degree Reflected in annualized cost
per FTES
N/A - Not a Degree Level
Cost Tool
N/A - Not a Degree Level
Cost Tool
Not Included in Cost per
Degree
Not Included in Cost per
Degree
Reflected in cost per degree Reflected in cost per degree Reflected in annualized cost
per FTES
Not Included in Cost per
Reflected in cost per degree Reflected in annualized cost
Degree
per FTES
Unit of Cost Measure
Credit Hour
Credit Hour
Credit Hour
Credit Hour
FTES
Financial Information
Direct Educational Costs
Educational Asset Holdings
Indirect Cost Treatment
Facilities Costs Treatment
Included in Cost
Not Included in Cost
Not Included in Cost
Not Included in Cost
Included in Cost
Not Included in Cost
Included in Cost
Not Included in Cost
Included in Cost
Not Included in Cost
Included in Cost
Not Included in Cost
Included in Cost
Not Included in Cost
Included in Cost
Not Included in Cost
Included in Cost
Not Included in Cost
Included in Cost
Suggested that
Replacement Value is
included, "below the line"
Course Taken Information
Excess Unit Treatment/Accounting
Failed Course Treatment/Accounting
Change of Major Accounting
Dropout Accounting
Delta Full Cost
Method Cost Implications
Should typically report the Should typically report the Should typically report a
Should typically report a
lowest possible cost per
lowest possible cost of a
midrange possible cost of a higher if not the highest
credit
degree
degree
possible cost of a degree
Policy Implications
i.e. What is the model attempting to answer?
What is the educational
cost of a specific credit or
class for year over year or
similar institution
comparatives?
What is the lowest possible What is the cost of a degree
cost to produce a degree
that reflects the path that a
given current methods and student actually took?
curriculum?
Method Notes
Not truly designed for
degree level costing
Does not reflect actual
student course taking
activity
Method Output MECH Degree
$33,283
$98,235
NACUBO
Should typically report a
midrange cost per FTES
What is the cost of a degree What is the cost of
that reflects the path that a instruction for one year for
student actually took and an FTES?
accounts for expenses
associated with student
attrition?
Includes a specific students Is the delta transcript model Not truly designed for
path and reflects course
divided by the number of
degree level costing
failure, excess units, change degrees actually produced
of major, etc.
$116,857
$193,288
$109,710
8
9
By developing a CSU-specific educational costing method, issues associated with
definitions and data reporting discrepancies were minimized. The benefits of being able
to determine program-specific educational costs will allow campus-level financial
resource decisions and provide the CSU information on programmatic resource
efficiencies. The hope is the tool will provide significant value and add to the knowledge
base if scaled to the 23-campus CSU (see Figure 1). The CSU is the largest four-year
higher education system in the United States, the same degree is offered at many of the
campuses within the system, and little can be said about the cost of a specific degree at
each site. Without the ability to compare degree costs system wide, there is no method
on which to base a discussion of resource effectiveness or benchmark efficiency.
10
Source: California State University Chancellor’s Office (2012, slide 4)
Figure 1. Map of the California State University campuses.
Purpose Statement and Research Question
Analyzing cost is one of the largest jobs of management in almost all operations.
Cost must constantly be monitored to determine if resources are being appropriately
managed strategically where possible (Massy, 1996). Yet in education, cost appears
11
clouded in even the largest university system in the United States, the CSU, at a time
when fiscal transparency is being demanded by taxpayers and legislators within the state
(Bauldassare, Bonner, Petek, & Shrestha, 2013). Through this research, the following
question was answered:
What is the most accurate method for determining the cost of producing a
bachelor’s degree within the California State University system?
By answering this question, a CSU framework for program or degree level
educational costing that factors the variables found to influence cost within the system
was developed. At a specific site, CSU, Chico for example, the framework can be used
to compare year-to-year historical costs of the same program. At the CSU system level,
the framework can provide intercampus comparisons (CSU, Chico mechanical
engineering to CSU, Sacramento mechanical engineering) related to degree cost,
instructional cost, administration cost, and plant operations. For the purposes of this
research, educational cost consists of either the sum of or subtotal of direct educational
expense, indirect educational expense, and facilities. Through determining educational
cost as well as the variables driving cost, the framework will provide the CSU with a
strategic analysis tool for understanding expenses, thus providing a foundation for the
Governor’s mandate of improving educational efficiencies (California Department of
Finance, 2013).
12
Implications
The topic of higher educational cost has been addressed many times from many
perspectives (for example Delta, Delaware, NACUBO), yet it has been suggested that
educational cost analysis is an iterative process in which small discoveries are made
through each visitation of the topic (Reindl, 2000). Hence, this work joins, reviews,
compliments, and expands work on the topic. As this work is geared toward creating a
CSU-specific framework in a space that has seen almost 50 years’ worth of research, it is
important to step back and attempt to evaluate the possible impact of revisiting the base
concepts. Policy analysts will often attempt to provide perspective and estimate potential
impact of proposed work through the basis of the two-pronged tests.
As the first part of a two-pronged test, policy analysts often start the evaluative
process by reviewing what the problem is. In this case, the many years of national
costing work that has been done within higher education has attempted to be all things for
all institutions and systems of higher education. What, in fact, has happened is the issue,
terminologies, and definitions have become muddled, if not completely confused. The
confusion partially comes from how metrics and definitions of cost measurement within
education are done. Full-time equivalent students (FTES), for example, means nothing
outside an educational framework, and yet can be calculated in different ways within
education. Given the sheer size of the CSU, where over $4 billion will be spent for the
2013/2014 academic year (CSU, 2012), with the majority of the funding coming from the
California general fund and the balance being placed on students and their families,
13
investigating cost within the system with the goal of ultimately improving resource
effectiveness is critical. The current public reporting structure for the state does not
provide much detail about exactly what these dollars are supporting. To foster public
higher education transparency, what degrees, what graduation rates, what research, what
overhead, are all questions that should be easily answered when combined with, what
does it cost? Through the proposed framework, answering the micro-level cost questions
is possible, which then leads toward questions regarding appropriate usage, effectiveness,
and efficiency of the limited resources.
For the second part of the two-pronged test, analysts want to know who the
problem and the possible solution both impact. California taxpayers, lawmakers,
students, families, and higher educational administrators want information to ensure the
limited resources available for higher education are used effectively. Understanding the
costs of a specific degree will provide the first step in the evaluating what should be
supported. Further, cost, or the investment that the state makes into higher education, is
the first step in determining return on investment. The CSU must strategically use the
resources provided, and without degree level cost information, decisions are made that
rely on generally anecdotal evidence rather that data.
Assumptions and Limitations
It is important to include information about assumptions that drove this work
because within the context of where this work may be applied, assumptions may not
always be true and potentially induce error with broad generalized classifications.
14
Assumptions included, for example, that first-time freshmen who declare a major do
intend to ultimately earn a degree, at least when they start the process. Another
assumption was that all expenditures associated with university operations are ultimately
made with the intent of supporting the education of students within the CSU. Most
campuses run auxiliary operations to manage research, food services, athletics, housing,
etc., so these costs are generally not placed onto the student pursuing a degree and are
separate cost centers, even though assets (laboratory equipment for example) purchased
for research often end up supporting educational activities.
There were also several limitations of this research. First, current costs may not
reflect future costs. Institutional size and mission can impact cost. Students will take
courses at other institutions, arrive with transfer credit, and change majors. This model
currently only focuses on first-time freshman, but should provide an expandable
framework to use for transfer students once a standard for managing the cost of external
credit is developed. The challenge with external credit is the units may be earned from
institutions outside the CSU. The same can be said for students who change major paths.
To what degree path do costs get assigned if a student changes majors? Where these
costs are ultimately assigned could skew degree level cost calculations and are a concern.
The output information will also only be as good and as accurate as the data collected and
where it is assigned for the process.
15
Definition of Terms
This section defines various “costs” as defined within the literature. As many
definitions have been provided in many cases for essentially the same terminology,
organization has been provided by chronological resource, both for reference, but also to
show the confusion that has been created through the lack of common terminology.
Capital costs
Costs of property and/or equipment intended to last beyond a specific fiscal
period (California Postsecondary Education Commission [CPEC], 1980).
Carnegie unit
One hour per week for the semester equals one credit hour (Wellman, 2005).
Community cost classification
All costs of extracurricular cultural activities and facilities (NACUBO, 2002).
Cost center
Any entity, program, service, or activity to which cost can be allocated Jenny,
1996).
Contact hour
Instructor and student time contact resulting from course instruction.
Credit
A unit of education derived from a course, no longer directly related to
instructional time. Also see unit.
16
Departmental expense
Consisted of teaching expenses, plus department level research, plus department
administration (CDE, 1960).
Direct costs
(a) Costs directly attributed to a specific activity (CPEC, 1980) and (b) Costs
directly attributed to instruction, primarily personnel related, and distributed to
each credit hour (Johnson, 2009).
Facilities Capital cost
An estimate of the replacement cost of building/infrastructure/etc. multiplied by a
utilization rate estimate divided by the number of students (NACUBO, 2002).
Facilities Lease Rate
Rental or lease rate is the costs of the facilities and square footage needed to offer
education assuming the university was renting the space in a competitive market
(Winston, 2000).
Fixed costs
Costs that are stable with respect to a particular volume of activity (CPEC, 1980).
Full cost
(a) The sum of all variable and fixed resources used in producing a product or
rendering a service, including an appropriate allowance for physical assets’
depreciation and obsolescence, adjusted for any resale or salvage value (Jenny,
17
1996) and (b) The total cost of supporting an activity, generally the sum of direct
and indirect costs (CPEC, 1980).
Full-time Equivalent Student
(a) FTES is the total number of full-time students (definition on full-time is at the
institution’s discretion) added to the total number of credit hours taken in one
academic year by part-time students divided by 24, (b) A student enrolled in 12 or
more semester or quarter credits (NABUCO, 2002), and (c) CSU defined as a
student average of 15 units per semester.
Historical cost
Cost derived from past expenditure patterns (CPEC, 1980).
Indirect costs
(a) Costs assignable to an activity, which provides a support function for another
activity (CPEC, 1980) and (b) Costs primarily including student support,
academic administration, advising, university support, library services, financial
aid, and plant operations. These costs are distributed to each credit hour
(Johnson, 2009).
Institutional expense
Includes all departmental expenses, plus general administration, student services,
and libraries along with the operation and maintenance of the facilities (CDE,
1960).
18
Instruction and Student Services cost
Includes all core educational expenses related to faculty effort, student services,
and academic program administration. Also included is department research and
allocated costs of general administration, facilities operation, and depreciation
(NACUBO, 2002).
Macro-costing
Refers to the practice of determining aggregate costs of easily identifiable but
broad institution entities (Jenny, 1996).
Micro-costing
Determination of the full cost of the individual discrete academic or
administrative activities and processes (Jenny, 1996).
Operating costs
Costs incurred in the fiscal period in which the activity takes place (CPEC, 1980).
Production cost
The cost of delivering education to a single student (Harvey, Williams, Kirshstein,
O’Malley, & Wellman, 1998).
Projected costs
Future expenditures estimated on the basis of past experience (CPEC, 1980).
Semi-variable costs
Costs typically variable, but do not change in a linear relationship with changes in
volume (CPEC, 1980).
19
Target costs
Standardized levels of expenditure established as a spending objective (CPEC,
1980).
Teaching expense
Consists of the faculty wage portion of actual time dedicated to instruction added
to the cost of any clerical support and educationally related materials, supplies, or
equipment (CDE, 1960).
Unit
A unit of education derived from a course, no longer directly related to
instructional time.
Variable costs
Costs predictably dependent upon a volume of activity (CPEC, 1980).
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Chapter 2
REVIEW OF RELATED LITERATURE
Chapter 2 reviews the literature regarding the connection of educational cost to
public policy and investigates previous costing work and the shortcomings found in
educational costing methods. In-depth reviews of existing costing methods that include
the Delaware, Delta, and NACUBO methods are presented. Methods used for work with
these models ultimately have provided a guide for this research. This chapter concludes
by reviewing the factors that drive costs in higher education. The cost drivers are
considered when the methodology for cost determination within the CSU is developed.
As previously noted, the 1960 California Master Plan for California Higher
Education (CDE, 1960) dedicated a chapter to the costs of higher education within
California. The Master Plan for California Higher Education highlighted concerns about
cost, efficiency, redundancy, and scales of economy with operation; it did not, however,
define the metrics needed for cost accounting. The authors of the Master Plan for
California Higher Education also made significant disclaimers associated with making
campus or program cost comparisons, even while noting and suggesting the value
potential for such thinking. In 1998, the National Commission on the Cost of Higher
Education pointed out, “most academic institutions have permitted a veil of obscurity to
settle over their financial operations and many have yet to take seriously basic strategies
for reducing their costs” (Harvey et al., 1998, p. xiv). The CSU Northridge Provost
report for February 2013 highlighted those CSU efforts to analyze cost “have foundered
21
on technical questions that have ideological implications” (Hellenbrand, 2013, p. 2). The
report additionally states that if the goal is to compare costs within the system, the only
truly representative metric of higher education is the production of the bachelor’s degree
and further suggests that cost reporting must become part of the centralized Chancellor’s
office function (Hellenbrand, 2013). It is apparent that cost and CSU specific ideological
concerns regarding cost and the mission, vision, and values within the CSU warrant the
development of a CSU-specific costing model.
This chapter outlines the implications of cost on public policy and relevant cost
literature. The investigation of previous work that been done on higher educational
costing, credit-hour accounting, and educational cost drivers, provides context to the
discussion. The metrics associated with educational cost such as full-time equivalent
students (FTES), credit-hour, and graduates are also reviewed and defined. The various
methods that have been applied to determine educational cost are investigated. This
review also includes other studies that have evaluated cost specifically within educational
settings and reviews the possible strengths and shortcomings of the existing methods as
applied to the CSU. The last area in the literature review investigates factors influencing
or driving higher educational cost. The cost literature for this chapter was drawn from
government-supported documents, peer-reviewed journals, higher education think-tank
organizations, and national projects associated with the topic.
22
Educational Cost Analysis and Public Policy
According to Tsang (1997), many important policy issues within education are
concerned with resources invested into education. Such concerns often include whether
the level at which the state has invested in education is at an adequate or sustainable
level. Another example of the concern is whether there is effective and efficient use of
resources utilized to achieve the goals. There is a growing base of literature regarding
how cost analysis can improve policymaking and evaluation within education. Yet
educational administrators and decision makers are often limited in their understanding of
educational costs generally due to poor data (Tsang, 1997, 2002). Table 2 highlights the
concerns with educational cost analysis connected with the goals or policy objective. The
general connection of these policy concerns is to make actionable data-driven
recommendations and decisions. As the table highlights, existing limitations with much
of the cost data associated with higher education currently inhibit significant policy
action related to educational cost estimation, program feasibility, resource utilization,
economies of scale, and cost-benefit analysis, all of which are needed for strategic
management
23
Table 2
Typical Cost Studies in Educational Policy Analysis
Study Type
Cost Estimation
Policy Task/Objective
Determining the total
cost. For policy work this
is generally focused on
the cost burden and
equity concerns and
implications
Program
Feasibility/Sustainability
The evaluations of
resource feasibility during
program start,
consumption during
operations.
Resource Utilization
Economies of Scale
Cost Benefit Studies
Adapted from Tsang (2002)
Policy Concerns
Advances have been
made in costing, but the
lack of information
continues to be a
technical barrier. A
feeling that much of the
current educational cost
information may be
hiding real costs.
Poor existing data in
which to benchmark
from. Concerns about
consistent
methodologies in which
to base decisions and
comparisons.
Assessment of resource Generally poor data.
utilization as compared to Often an attempt to
norms to promote
ascertain localized
efficiency
conditions from national
norms that may not be
reflective of the
institution or system
being reviewed
Relationship of
Cross discipline
enrollment to output
educational subsidies
relates to cost
may hide inefficiencies
when not drilled to a
course level evaluation
Determining the rate of Conceptual and
return between
methodological
investment alternatives difficulties existing in
where efficiency is a
quantifying the value of
criteria
education.
24
Defining Educational Cost
Cost has many different definitions and many different subcategories within the
higher education literature. In this section, the various form of cost is reviewed from the
literature in an attempt to understand the breadth of cost within higher education. In its
most basic form, cost is the expense associated with delivering that education to a student
an educational institution or educational system incurs. Over the years, a multitude of
schemes and categorizations have been developed, along with educationally specific
definitions regarding cost. The challenge with categorizing higher educational expenses
has generally been associated with the multiple missions of different universities and
systems. These products can include graduates, research, public service, and so forth.
The scope of cost within the above multiple areas is subject to significant discretion, with
minimal definitions about what is included and what is not. As an example, faculty
members are generally expected to conduct research. Should these costs be included in
the cost of a degree? Defining educational cost is further complicated with measurement
or unit variations, also discussed later.
The California Master Plan for California Higher Education (CDE, 1960)
defined teaching expense, departmental expense, and institutional expense as three types
of unit costs. Teaching expense was faculty wage derived from the portion of their time
dedicated to instruction plus the cost of any clerical support and educationally related
materials, supplies, or equipment. Departmental expenses consisted of teaching
expenses, plus department-level research, plus department administration. Institutional
25
expenses included all instructional operations as noted before, plus general
administration, student services, libraries, and the operation and maintenance of the
educational facilities.
After the Master Plan for California Higher Education (CDE, 1960) was enacted,
the entity that created it, the Coordinating Council for Higher Education (CCHE),
determined that educational cost within the California’s higher education systems needed
to be investigated and further defined. The council called for a study of higher education
costs in California. The purpose of the cost study was to
make available quantitative data in general areas of instruction and research,
administration and general, physical plant operation and maintenance, and
physical plant utilization which would be useful in the development of a more
economical and educationally effective operation of California’s institutions of
higher education and which would facilitate the comparison. (CCHE, 1964, p. 10)
CCHE outlined distinct areas of cost, which was slightly modified and adopted by the
National Center for Higher Education Management Systems (NCHEMS) as a basis for
higher educational cost accounting (CCHE, 1968). The cost outline is as follows:
1. Instruction – Direct Educational Expense
2. Research – Sponsored and Unsponsored
3. Service – Campus Committee and Community Involvement
4. Academic Support – Records, Library Operations, etc.
5. Student Service – Financial Aid, Career Center, etc.
6. Institutional Support – Plant Operations and Administration
7. Independent Operations – Auxiliary Foundations
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In 1980, the California Postsecondary Education Commission (CPEC) (which
replaced CCHE in 1974 when defunded by Governor Brown) released the report
Determining the Cost of Instruction in California Public Higher Education in response to
a request from the state government’s Legislative Analyst’s Office (LAO). CPEC
suggested the NCHEMS’s scheme for identifying and calculating educational costs
would be used by all three segments of California higher education. The NCHEMS
universal program classification structure identified institutional activities and put them
into the following nine categories of cost, looking very similar to the CCHE structure
previously mentioned (Allen, 1980):
1. Instruction – Direct Educational Expense
2. Research – Sponsored and Unsponsored
3. Public Service – Campus Committee and Community Involvement
4. Academic Support – Records, Library Operations, and related activities.
5. Student Service – Student Health, Career Center, and related activities.
6. Institutional Administration – Executive Management
7. Physical and Plant Operation – Non-Capital Facilities Operations
8. Student Financial Aid – Aid and Aid Administration
9. Independent Operations – Auxiliary Foundations
CPEC and NCHEMS believed that by reporting costs within this universal structure, it
would provide a way to negate differences in organizational structure and local
terminology so comparisons of educational cost could ultimately be made.
27
In an attempt to clarify educational costing at a national level, and to simplify
their own 1996 Cost Accounting in Higher Education document, NACUBO (2002)
developed a universal costing method with the report Explaining College Costs. The
NACUBO method breaks down educational costs into the following categories:
1. Instruction and Student Services (includes departmental level research)
2. Institutional and Community Costs
3. Undergraduate Financial Aid Costs
4. Facilities Capital Cost
In NACUBO’s model, facilities and capital costs are reported, after all other
costs, essentially as a lump replacement cost; NACUBO, as well as all other costing
methods do not provide a method to distribute these costs the educational costs of a
student or degree (see Table 1). All cost organization methods, no matter how simple or
how complex, attempt to disaggregate operational expenditures for review and analysis.
The costs are distilled into like operations or organizational units such as facilities and
maintenance, instruction, and instructional support, but generally fail to truly drill to a
specific cost center such as a degree. California’s Master Plan for California Higher
Education (CDE, 1960) stated, “unit costs are a valuable tool for analyzing expenditure
data, but they are a hazardous device when used to compare the costs on instruction of
one institution with another” (p. 154). One reason to gather cost information is to
compare costs across campuses in order to seek efficiencies and best practices. Even
with the noted caveat, the report further states that the “institutional expense is the most
28
valid and valuable basis for comparisons between institutions with comparable programs”
and “represents the total instructional expense involved with the institution and therefore
serves as an index of the cost involved in educating students” (p. 155). In 1980, the
CPEC goal was to compare costs across institutions within the California higher
educational system to ultimately quantify costs and have the data to compare to economic
and educational effectiveness, the same goal outlined in the 1960s (CPEC, 1980). To
determine cost is to create comparatives, and creating agreed-upon terms is critical to
ensure “apples to apples” measurements. Currently, none of the existing costing work is
fully adopted by the CSU, nor are questions asked about cost at the degree level. The
Master Plan for California Higher Education (CDE), CCHE, and CPEC have seen the
significant value in evaluating cost using the total, or full-cost, model. Total, or fullcosting, where both direct and indirect educational expenses are included in cost,
contradicts some of the current cost determination and evaluation thinking, such as what
is found in the Delaware Cost Project (1996 on), because the Delaware Cost Project
(Midaugh, 2005) solely focuses on the instructional costs. Delaware has primarily
focused on direct educational costs as a way to benchmark adequate resources for similar
educational programs nationwide and because they have generally focused on the cost of
instruction within a subject matter, rather than on the cost of operating a university. Such
a method essentially negates the impact of non-instructional operations such as
maintenance and administration. Instructional costs are valuable and should be reported;
however, it only provides part of the information needed to make decisions regarding
29
effective resource use and may mask significant operational, administration, or other
costs not directly tied to the production of bachelor’s degree graduates. Finally, the term
“cost” can and is influenced from one’s particular viewpoint and reason for exploring
educational cost.
Units of Measurement for Costing
Cost is a summation of expense. However, cost alone needs context to support
understanding. For educational cost, this is also the case, yet it is an area that also causes
significant confusion. This section of the literature review explores measurement unit
terminology for expressing educational costs. It investigates whether cost should be
expressed in cost per student, per full-time equivalent student, per degree, per unit
earned, per unit attempted, per credit hour, per contact hour, etc. Not only is there
disagreement regarding which cost-ratio should be used, but there is also disagreement
and various metric definitions about what a credit hour or FTE or FTES is. FTE/FTES
has been used to attempt to standardize part-time status in students and employees often
for cost reporting and calculation purposes. The FTE is derived from the student credit
hour (SCH). In 1910, the Carnegie Foundation for the Advancement of Teaching (as
cited in Wellman, 2005) set the standard of one hour per week for the semester equals
one credit hour. The Carnegie Foundation also defined that a full-time student attends a
minimum of 12 hours of class per week, thus a minimum of 12 credits per semester are
earned (Wellman, 2005). The credit-hour became a normalizing measure adapted to
educational accounting while also providing a basis for transfer equivalent coursework
30
associated with curricular time. Wellman (2005) also pointed out that credit-hour
accounting fosters the status quo by creating credit hour problems arising from new
pedagogies or curriculum changes; it also does not reflect student learning or student
time-on-task activities.
Early educational accounting and costing methods within the CSU and University
of California (UC) adopted cost per student credit hour, and the California Community
Colleges (CCC) utilized cost per student contact hour (CDE, 1960; CPEC, 1980). The
difference between using credit-hour versus contact-hour costing is critical when costing
laboratory-based curricula. Using the Carnegie and Master Plan for California Higher
Education definition, four units would be earned through four hours per week of
classroom time. However, many laboratory courses that earn four units currently have
lecture and laboratory activities that are a minimum total of six hours per week.
According to the Carnegie (as cited in Wellman, 2005) and Master Plan for California
Higher Education (CDE, 1960) definition, six hours of contact per week should earn six
units upon completion of the semester, which creates inaccuracies between unit and
contact hour costing. As part of the CCC mission, there has always been vocational
education, often laboratory intensive; thus it is no wonder they chose to use the contact
hour for costing. Seeking a cost per course, the Master Plan for California Higher
Education (CDE, 1960) defined “the number of student credit hours as the sum of the
product of the credit hour value of each course times the number of students enrolled in
the course” (p. 154). Thirty students in a four-unit physics course at CSU, Chico, would
31
accumulate 120 credit hours; this number would then be divided by the portion of the
faculty member’s time used for teaching the course as well as a factor for overhead and
facilities. Undefined within the Master Plan for California Higher Education (CDE,
1960) is whether the credit hour values are derived from all students who enrolled, all
students who passed, or all students who were counted on the census date. It may have
been this concern that partially led to the statement “that one must exercise care in
judging institutional efficiency on the basis of comparative costs” (p. 154). The lack of
the definition or standards minimizes the comparable value associated with this method.
Adding to the confusion, during the 1960s, the CCHE stressed the need for the
development of standardized terminology in California for the definitions of “full-time
student,” “full-time equivalent student (FTES),” and “part-time student,” even though the
Master Plan for California Higher Education (CDE, 1960) had adopted the Carnegie
system (CPEC, 1980). CPEC (1980) ultimately defined the threshold of full-time versus
part-time enrollment at 12 credit-hours. A 12-unit full-time student does not equal one
FTES, however. FTES is the method in which student contact is counted. CPEC (1980)
defines one undergraduate FTE as the enrollment of 15 semester units for two
consecutive semesters. One FTES within California is defined as 525 class (contact)
hours of student instruction/activity. This can be credit or non-credit classes, but this is
the number used to determine state funding per FTES student. According to CPEC, the
number 525 is derived from the requirement of 175 days of academic instruction required
32
each year. A student, who attends three hours per day, or 15 hours per week, will attend
for 525 contact hours of instruction during an academic year.
Literature-based Higher Education Costing Methods
Over the past 60 years, costing methods have gone from focusing on defining
terms and providing calculations for very broad annual operational costing to focusing on
specific activity or cost centers. This section revisits how educational costing has come
to be, what is included, what is not, and the reasons for the evolving methods. Table 3
provides context regarding some of the major costing methods utilized within this work
and reviews the major existing methods of cost determination for higher education.
Within this table, similarities and differences are highlighted regarding both inputs and
outputs. The question each method attempts to answer is also highlighted.
In 1968, the concept of educational cost was revisited and the CCHE was
authorized by the California Legislature to investigate “expensive, specialized, limited
use academic programs” and under recommendation from the Legislative Analyst’s
Office, specifically sought to look at programs in engineering and the arts (CCHE, 1969,
p. 10). This CCHE report ultimately evaluated programs based on the number of credithours taken and degrees produced. As is the case today, questions regarding cost at the
degree level were raised. At the time, per credit, or per unit (unit or credit are
interchangeable) costs in the same discipline varied between campuses up to a 35:1 ratio;
however, concerns between methodological and functional differences for the reported
data plagued the report. Throughout the early 1970s, annual reports were commissioned
33
to continue to study cost concerns of higher education, ultimately concluding, in 1974,
that the reports had gone as far as possible because of the limits of available data.
Limited work on cost was done again until the mid-1990s, and many of the same data
functionality concerns were raised. Even today, the concerns regarding data and methods
exist (CCHE, 1971, 1972, 1973, 1974).
The CCHE work attempted to develop standard methods for gathering and
reporting California-specific costs associated with higher education. The CCHE report
identified many of the cost accounting challenges previously found and further cited the
lack of a state-level cost-accounting system, as well as inaccurate methods for cost
calculating at too high of a level of financial aggregation to look at activity or degree
level expenditures. For example, costs reported some campuses included staff salaries as
part of educational expenses, and others did not. This revelation renewed the call for a
state-level standardized accounting system. The same concerns impact educational
costing in California today, only with more data available to address the challenge, yet no
universal reporting structure or system has emerged.
CPEC did make the recommendation to adopt the NCHEMS costing methods
calling for both data management steps and functional calculation tools. Data
management called for all campus expenditure data to be translated from campus-based
accounting systems into a state-level, standardized accounting structure called the
Program Classification Structure (PCS). At the time, accounting of faculty activity was
limited and difficult to quantify. The CPEC recommended activity surveys would be
34
administered to all faculty, and then salaries, benefits, etc. would be prorated among
selected elements of the PCS (instruction, academic administration, etc.) based upon each
faculty member’s actual work pattern. Faculty activity today is managed through campus
central workload reports identifying release time not associated with classroom activities.
Campus overhead expenses (libraries, plant maintenance, executive management, etc.)
were recommended to be all prorated among instructional, research, and community
service activities applicable to the particular overhead category.
CPEC provided the following two methods for calculating educational cost based
off NCHEMS costing procedures:
1. To determine the cost per student credit/unit taught, the total cost of underwriting
a particular academic discipline is divided by the number of credit units taught in
the discipline.
2. To obtain the cost per credit/unit taken by student, the cost per credit unit taught
by each academic discipline is distributed to students based on actual student
enrollment records to obtain cost per credit taken by each student major.
The above two methods only account for instructional costs. To calculate full
costs, all other campus costs must be prorated across cost centers previously identified
and applied to an output metric cost. The report also noted that the methods suggested, as
with the work outlined in this paper, measures the average associated with cost-ofinstruction. From a policy standpoint, many decisions are more concerned with
35
comparative costs, rather than averages; thus caution should be exercised when making
marginal cost decisions that use these formulas.
In 1996 (Jenny), NACUBO released the Cost Accounting in Higher Education
manual. The manual outlined both macro and micro cost accounting techniques that had
been primarily adapted from the for-profit manufacturing sector and attempted to provide
metrics addressing the multiple products issue as well as facility utilization rate
distribution. The methodology primarily focused on activity-based costing in an attempt
to derive the highest level of precision associated with the calculated outputs. For the
purposes of this work, the most relevant is the information presented on micro-costing,
which refers to the full costs of the discrete academic and operational expenses incurred
to support the activity of a unit. In addition to budget data, micro-costing requires other
information regarding enrollment, staff size, square footage, etc. to fully realize the
associated cost data for an operation.
Realizing the complexity of the 1996 manual, NACUBO released its revised and
updated methodology in the 2002 report, Explaining College Costs, which essentially
simplified the methods outlined previously. The previous NACUBO work proved too
complicated to effectively use and lacked the buy-in from universities for
implementation. With the new report, NACUBO sought out significant assistance from
over 40 people (university presidents, business officers, cost accountants, and policy
analysts) who formed an ad hoc committee to develop the revised methodology. The
goal of the report was to create a simplified uniform system that any college or university
36
could use to explain costs both internally and externally while attempting to address
growing concerns about college affordability. The methodology was designed to rely on
basic averaging, focus on undergraduate education, use existing cost allocation methods,
and be simple enough for policymakers and administrators to use while providing
increased transparency and accountability regarding where the money in education goes.
What is critical to the current work and the building of a CSU-specific costing method is
the system developed must not be overly cumbersome to utilize or it will face the fate of
the 1996 NACUBO method (Jenny).
The simplified NACUBO method provides a tool that can be scaled for
institutions or programs, where the educational costs are to be grouped and divided by the
total FTE. This provides a cost per student. According to NACUBO, this method may
be drilled down to a college or school level by strictly providing the relevant data. Total
costs within the NACUBO framework are based on information colleges and universities
provide. Included within the data are enrollment, price, gross tuition and fees, which
were then itemized into expense categories. The numbers added together created what
NACUBO called total costs. NACUBO reported that many costs incurred happen while
simultaneously supporting several different products or activities. The activities often
include teaching, research, scholarship, community involvement, etc., but faculty wages
are not distributed or documented by these activities. The only quantifiable time is
generally associated with teaching. Finally, NACUBO provided a disclaimer that the
methods they employed were not designed for creating comparability benchmarks since
37
according to the report, “such efforts demand a far greater level of precision than the
methodology provides” (p. 21). The methods outlined limit institutional comparability,
as much discretion and freedom is left to the institution to determine its own weight for
FTEs, graduate students, and institutional costs.
Johnson (2009) evaluated accepted methods for calculating educational cost. The
approaches to degree cost he outlined (see Table 1) and that are relevant to this work
include the following:
1. Catalog cost
2. Transcript cost
3. Full-cost attribution
4. Regression-based cost estimation
Johnson’s work used data from the State University System of Florida (SUSF). The first
three methods used a credit-hour costing structure to determine the direct costs of
instruction, which are primarily personnel-related expenditures. Indirect costs included
student support services, administration, facilities, library services, and financial aid.
Johnson’s method of catalog costing was cost per credit hour multiplied by the catalog
requirements for a degree. This is essentially a theoretical cost, or in Johnson’s terms,
“sticker price” (p. 10) as students rarely would progress through the degree path as
outlined.
Catalog Cost = Cost per credit hour x Catalog requirements
Cost per Credit Hour = Instructional expenditures/Credit hours
38
The transcript costing method attempts to compensate for actual student behavior
and outcomes, where transcript cost is the total credit hours taken by a graduate
multiplied by the cost per credit hour divided by the number of degrees awarded.
Transcript costing does not apply the costs associated with students who do not succeed
in obtaining the degree.
Total credit hours taken by graduates
Transcript Cost
=
x cost per credit hour
Number of degrees awarded
Full-cost attribution attempts to disperse the costs associated with students who
do not graduate to the costs associated with students who do, directly amortizing student
failure onto student success. In other words, full-costing looks at the number of students
who start college compared to those who ultimately earn a degree. This is how one can
look at the full or actual cost of producing one graduate or degree.
All credits taken x
Full educational cost attribution =
three-year average credit cost
Three-year total of degrees awarded
The regression method attempts to address the higher educational issue of
multiple products based on normed data sets and is often applied when actual reports or
data are not available for a specific site. According to Johnson, the regression costing
method is a statistical exercise based on national educational spending data that provide
typical ratios of activity or operation for higher education. These ratios can be locally
applied to actual numbers to provide a rough estimate of cost associated with a specific
39
activity. National percentage of expenditure data does not necessarily reflect a specific
site and may not provide reflective data. Johnson (2009) concluded that this method
should only be applied in situations when data are not available and when estimates that
may have a large error factor are acceptable.
The large take away from all the previous work done regarding cost determination
within higher education is that there is not one method that adequately addresses all
potential accounting or transparency needs. The previous models examine cost from
generally different perspectives, which limit comparisons between methods. Further,
these methods also generally underestimate the cost of any laboratory-based curriculum
due to the differences between contact-hour and credit or unit-based costing. The
discussion regarding cost has been a major focus since California’s Master Plan for
California Higher Education was first adopted in 1960 (CDE). It is critical for academic
leaders and higher education policymakers to understand that, even in 2013, there are no
standardized practices or reporting procedures. Much of the early higher educational
costing work was designed for institutional accountants. The costing methods soon
shifted focus to addressing the management of cost (CPEC, 1980) and in 2014, the focus
on cost appears to come from a transparency and accountability concern (Johnson, 2009).
40
Table 3
Review of Major Literature-based Costing Methods
Costing Method
Delaware
Definition
Credit-hour costing calculated
from the mean sum of
instructionally related costs
per credit hours taught at the
discipline level
Delta Catalog
Delta Transcript Cost
Costing Formula
Direct instructional
expense per credit hour
taught = total direct
instructional
expenditures/ total
student credit hours
taught (Department or
Program Specific)
Credit/Contact Formula
Student Credit Hours =
Course Credit Value
(Typically 3-4 Units) X
Course Enrollment
Expenditures Included
Department Level Salaries and
Benefits, including all
associated faculty, clerical,
professional, supporting
instructional operations. Other
non-capital instructionally
related expenditures .
Expenditures Excluded
Central computing,
Administration, including
Deans, non-academically
related expenses including
plant operations, auxiliary
operations, externally funded
research, capital expenses
such as buildings and
equipment
Total sum of credit hour costs Catalog cost = cost per
to earn a specific degree per credit X catalog
the institutions catalog
requirements
Credit Hour Cost =
Instructional
Expenditures/Credit
Hours
Auxiliary operations,
externally funded research,
capital expenses such as
buildings and equipment
Total sum of the credit hour
Transcript Cost = (Total
costs that the average degree Credit Hours taken by
earner took
graduates X cost per
credit hour)/Number of
Degrees Awarded
Credit Hour Cost =
Instructional
Expenditures/Credit
Hours
Direct Costs: Costs directly
attributed to instruction, and
are primarily personnel related,
and are distributed to each
credit hour.
Indirect Costs: Costs that are
primarily student support,
academic administration,
advising, university support,
libraryCosts:
services,
financial
aid,
Direct
Costs
directly
Delta Full Attribution Cost
Total sum of the credit hour
costs at an institution, plus
indirect costs, divided by the
number of graduates
ultimately produced
Full Cost Attribution = (All
Credits Taken at an
institution X four-year
average credit hour
cost)/four years worth of
degrees awarded
Credit Hour Cost =
Instructional
Expenditures/Credit
Hours
NACUBO
Educational Cost = All
Educationally Related
Expenses / FTE Student
Enrollment
FTE = (Academic Year
Total Credit Hours for all
Full-Time Students + All
Part-time Student
Credits) / 24
Total sum of all educational
expenses within the
institution divided by the total
FTE (24 or more units per
academic year)students
attributed to instruction, and
are primarily personnel related,
and are distributed to each
credit hour.
Indirect Costs: Costs that are
primarily student support,
academic administration,
advising, university support,
library
services,
financial
aid,
Direct Costs:
Costs
directly
attributed to instruction, and
are primarily personnel related,
and are distributed to each
credit hour.
Indirect Costs: Costs that are
primarily student support,
academic administration,
advising, university support,
library
services,
financial
aid, it
Institution
decided,
however
is suggested that costs include
instruction, administration,
student services, building
depreciation, equipment
depreciation, operations and
maintenance.
Auxiliary operations,
externally funded research,
capital expenses such as
buildings and equipment
Auxiliary operations,
externally funded research,
capital expenses such as
buildings and equipment
Institution decided, however
it is suggested that costs do
not include auxiliary
operations and externally
funded research.
Applied Examples of Higher Education Costing Work
Site- and system-level researchers have investigated cost of their specific
environment using many of the techniques and methods that have been discussed for
determining cost (Ahumada, 1992; Glasper, 1995; Goodwin, Gleason, & Kontos, 1997;
Johnson, 2009). Prior application of the costing methods discussed in the previous
41
section has been done at some schools. The strengths and weaknesses of existing costing
methods were identified and are discussed in this section.
Ahumada (1992) was the first to apply US costing methods to institutions of
higher education within Latin America. Using much of the same costing literature that
drives this dissertation, Ahumada believed the justification for costing research is to
better provide data on which to base decisions, have information relevant to judging
operational efficiency, and provide a basis for strategic resource allocation or
reallocation. This aligns very well with the same reasons for pursuing this work within
the context of the CSU. Using the NCHEMS faculty utilization instrument and
NACUBO expense categories, Ahumada produced cost information reflecting the total
hours and applied salary, given a unit of instruction, to explain the cost differences within
multiple colleges at the University of Monterey in Mexico.
The average costing data Ahumada (1992) presented distinguished direct and
indirect costs while also providing the average cost for multiple instructional units. Costs
for student credit hours, FTE, and per class were presented within the seven colleges
represented. While the work did not drill down to the degree basis level as is the goal
with the CSU framework, the work provided a lens for information regarding discipline
and method cost variances between educational operation units. Direct instructional costs
were found to be lowest in humanities and highest in art. Cost drivers for the findings
were directly related to average faculty salary and class size. The variable with the
largest effect on instructional cost was the student-faculty ratio, which was consistent
42
with the work of other researchers (Brinkman & Leslie, 1986; McLaughlin, Montgomery,
Smith, Mahan, & Broomall, 1980).
The majority of costing methods, as well as the majority of applied educational
costing research, has generally been based on some derivative of a unit-based cost
estimation. Other methods to determine the direct cost of education that do not base cost
calculations from units exist. During the late 1990s, significant educational cost work
was conducted at many of the nation’s medical schools. The issues of multiple products
at teaching and research hospitals had confounded accountants and administrators about
what the cost was to educate medical students. In the case of medical schools, where
should cost be assigned for the doctor who is treating a patient while demonstrating how
a procedure is performed? Is this teaching or is this practicing medicine? Is this part of
the cost of education or a cost associated with health care services? The challenges were
outlined in three studies regarding cost of medical education at Virginia Commonwealth
University, University of Virginia, and the University of Texas-Houston (Franzini, Low,
& Proll, 1997; Goodwin et al., 1997; Rein et al., 1997). The applied medical school
costing work is relevant to this research because to account for the high laboratory
curriculum, the costing method used was based on contact hours rather than on units.
All three studies based the costs on faculty contact hours for classroom experience
and an instructional factor for clinical clerkships based on the required student hours.
The multiple product concerns were addressed through faculty work estimates, although a
format was created to conduct activity surveys if more precise data were needed.
43
Medical education is laboratory intensive and costing was determined to be more
accurate if cost was associated with contact hours rather than a derived credit. This may
be a consideration for the costing method ultimately applied to the CSU framework.
Similar to the approach being taken in developing a model specific to the CSU,
previous research has applied multiple costing methods to a specific educational system
or school. Johnson (2009) primarily looked at the differences in calculated outputs from
the Florida State University system, and Romano, Losinger, and Millard (2010) analyzed
Broom Community College (BCC) in New York utilizing Johnson’s methodology. The
application of Johnson’s work at BCC provided an applied basis for cost analysis using
existing methods, fostering the ability to compare and contrast. Neither of the above
works attempted to develop a system that drilled cost analysis to a specific degree.
As previously mentioned, Johnson (2009) evaluated a number of methods for
answering the question of educational cost. The approaches to degree cost that he
outlined and that are relevant to this work included catalog cost, transcript cost, full-cost
attribution, and regression-based cost estimation. To show the differences in costing
outputs based on the applied methods, Johnson primarily focused on the State University
System of Florida (SUSF). The first three methods use a credit-hour costing structure to
determine the primary costs of instruction, which are primarily personnel-related
expenditures. Indirect costs include student support services, administration, facilities,
library services, and financial aid. It was noted that determining the cost of a degree is
44
not as simple as it sounds primarily due to variance in accounting unit (FTE, credit hour,
etc.) rather than the completed degree.
The four methods of determining the cost of a degree yielded significant variance
for the cost outputs. Using the catalog method of costing for 120- to 128-unit bachelor’s
degree programs within SUSF output a range cost of $22,332 to $43,817. This method
used average cost per credit based on discipline and the path outlined within the catalog
for students to achieve their degrees. The transcript method answered the question about
exactly what the cost was for the path the student took, rather than the designed path as
with the catalog method. Johnson (2009) reported that the transcript costing method
provided a 25% higher cost than the catalog method and was reflective of students who
needed to repeat courses or took units above the degree requirements for whatever
reason.
The third method of full-cost attribution used the transcript method for all
students and then allocated all direct and indirect expenditures to only the number of
students who were successful in graduating. This method takes the cost of all students
and distributes them only to the graduates. Johnson stated the advantage of this approach
was it accounted for the costs of attrition, failed courses, and excess units. The costs
Johnson reported for this method were 53% higher than the catalog cost and 21% higher
than the transcript cost.
As the progress of and ultimately the earning of a degree takes place over time,
Johnson used constant 2006 dollars to manage inflationary concerns with the calculations
45
and comparisons. To manage inflationary concerns, regarding the value of the dollar,
constant 2005-2006 dollars were used for all Johnson’s calculations. Johnson reports a
range of $25,000 to $40,000 (2005-2006 dollars) of direct and indirect education cost per
undergraduate degree using the same data. The range was based on the method used and
what question of cost the applied method attempted to address. He further cautioned that
wide variations in cost calculations along with challenges that remain regarding
vocabulary, framework, and definition are all things policymakers and staff must be
sensitive to as they evaluate cost and productivity issues within higher education.
Romano et al. (2010) measured the cost of a community college degree at Broom
Community College (BCC) in upstate New York. With modifications tailored to the data
available, the work outlined catalog cost, transcript cost, and full cost, based on
Johnson’s (2009) methods. As community colleges offer associates degrees and
certificates and not bachelor’s degrees, the regression model was not employed, as it was
designed primarily for research-intensive schools with multiple product output concerns,
for which actual data did not exist. As a costing method for the CSU was designed, the
same argument was applicable regarding the regression methodology, as the CSU is
student learning and teaching centric, and has relatively good datasets, thus regression
estimations from national averages and norms were not required.
BCC, in accordance with the NACUBO standards, defined all operations aligned
with generating course credits as instructional costs, whereas all other costs, including
administration, secretarial, plant operations, etc., are considered indirect costs. In the
46
case in which a student was allowed to choose from a number of approved classes, such
as a group of courses that fulfill the same general education area, Romano et al. (2010)
decided they would use the average cost per credit of the courses that were approved
options. This occurs in many degree programs within the CSU, and it seems reasonable
this will be an acceptable solution when catalog costing is applied. Romano et al. also
highlighted what they felt was the lack of value to catalog costing, as it does not
accurately reflect the actual path students take.
Using Johnson’s (2009) methods, cost calculations per degree at BCC ranged
from a low of $11,826 to a high of $49,829 (2008-2009 dollars). The authors’ major
caveat was that while they believed the full-cost transcript method was the most accurate
way to measure what a degree costs, the number of degrees produced may not be the best
measure since many students may enter the community college without any intentions of
pursuing a degree. While the CSU allows students to be undeclared, it can be safely
assumed that all students who attend do so with the intent of ultimately earning a degree.
So while the number of degrees produced is a concern for all of higher education, at least
within the CSU, the concern associated with students not pursuing a degree is minimized
regarding the construction of a framework. This is a much greater concern for
community colleges where many students arrive with no intention of earning a degree,
but rather take a specific course, or series of courses, and return to the workforce.
47
Unaddressed Shortcomings of Existing Methods as Applied to the CSU
When using existing costing methods, analysts often struggle with similar
constraints when determining the cost of education. Winston (2000) described two
challenges that are relevant for almost all educational costing methods. First, the costs
related to building and land infrastructure are poorly reported and documented. Most
CSUs report acquisition costs for buildings rather than replacement costs. Due to this,
campus capital costs do not connect well to degree or educational costs. Second, the
higher education question regarding the production of multiple products or outcomes
remains. Resources are cross-pollinated to produce research, undergraduates, and
graduates, so direct accounting of costs to a specific activity can be difficult.
The question of capital facilities and land cost, according to Winston (2000), is
documented currently as if universities taught students in vacant lots; thus no facilities
costs are associated with the cost of instruction. The solution he proposed allows a cost
to be applied to education by using an applicable rental rate. The rental or lease rate is
the costs of the facilities and square footage needed to offer education assuming the
university was renting the space in a competitive market. This method converts all
difficulties encountered with acquisition, maintenance, and lifecycle costs, to an annual
expense that can be distributed to institutional activities.
Also, as noted, Winston (2000) struggled with the concept of multiple products.
He stated he did “not have much that is useful to say about the issue of multiple products,
cost allocation, or joint costs, not because it is unimportant but because its resolution is
48
either terribly simple or terribly complicated and quite institution specific” (p. 43).
However, in an attempt to address the multiple products issue, Winston’s solution was to
break the costs into three areas, regardless of the level of instruction based on the work of
To (1987). To’s work attempted to categorize educational costs directly related to
instruction, costs irrelevant of instruction, and joint costs. Winston felt that by using this
as a framework, it might be possible to drill down to degree specific costs. Within the
CSU, and as previously mentioned, at least faculty release time is reported in a way that
determines what time base is applicable to teaching and learning.
Winston (2000) also mentioned the FTE calculation concern. He defined FTE as
the conventional method to convert part-time students into FTE students, which assumes
that the part-time student takes one-third as many courses as a full-time student. He
cautioned that any school with a high proportion of part-time students, such as many
commuter schools found within the CSU, may require using credit hours or some other
more sensitive method of accounting and calculation.
As most of the cost methods rely on units or credit hours as the primary
accounting tool, the limitations and skewing nature of this metric need to be mentioned.
Johnson (2009) used the following example to highlight the limited value of credit cost
analysis:
System-wide [SUSF] costs for instruction range from $159 for an upper division
credit in family/consumer sciences to $509 for a credit in natural
resources/conservation. Discipline-level costs within institutions can range
widely, especially in small programs. At one mid-sized [SUSF] university, the
cost per credit for upper division instruction in mathematics and statistics was
$1,277 one semester and $754 the next, and amount attributable to the number of
49
credits increasing by the equivalent of one class of 32 students in a three-credit
course, from 249 to 356. The total amount expended decreased by $56,946,
equivalent to a single instructor’s salary and benefits. Examples such as this one
highlight the limited value of credit hour cost analysis – and by extension, degree
cost analysis that depends on credit hour costs – for comparison purposes at a
highly disaggregated level. (p. 8)
The limitations and concerns associated with costing methods had to be addressed as the
CSU framework was developed. Winston (2000) specifically provided a great idea on
how to account for facilities cost, which tends to be difficult to quantify on a
disaggregated basis.
Factors Influencing Higher Education Costs
As this dissertation sought to develop a CSU-specific degree-costing framework,
it is important to understand the drivers of educational cost. While there are many
similarities between educational institutions, the proposed model must be flexible enough
to capture the nuances of a specific degree at a specific university within the CSU.
Further, in order to test the proposed model, a mock dataset that could be reflective of the
differences found from campus to campus was created. The literature provides
information on variables impacting educational cost and provided a guide for the mock
dataset. Differences were found between campuses that drive expenses and that should
be evaluated from both the lens of what drives cost, but also the lens of comparison. If
this CSU costing tool is to be used as a basis for cost comparison, it is critical institutions
or programs can be compared by the similarities rather than the differences. Much of the
simplified costing work has negated cost differences from student level (NACUBO,
50
2002), and none has focused on the questions regarding major- or degree-specific cost
differentials.
Harvey et al. (1998) investigated why the costs of postsecondary education have
risen. It focused on determining and examining the factors that increase the cost of
college. It identified six categories of cost drivers for higher education. The categories
included financial aid, people (teachers, administrators, staff, etc.), facilities, technology,
regulations, and expectations. With the exception of financial aid expenses (which may
drive costs up as more people are eligible and thus demand education) and regulations
(all schools within the CSU have the same governing board and are subject to the same
laws) the other broad cost categories are catch-alls that may have regional differentials
within California. These possible differences in cost drivers for the model must be
accounted for when attempting to build a CSU-specific costing tool. The campus cost
differentials could be the difference between old and new infrastructure and the
associated maintenance factors. Institutions that focus on Science, Technology,
Engineering, and Math (STEM) may have higher technology costs than a campus that
does not. The cost of institutional employees can vary significantly within the CSU
depending on expertise, experience, and seniority. A newer institution, such as CSU
Channel Islands, established in 2003, may have fewer fully tenured faculty than an older
institution, and thus have lower personnel costs. The size of the student body matters,
especially when it comes to minimum administration costs. All campuses operate with a
President and Provost no matter how many students are being served. The final cost
51
identified within the report is one of expectations. Institutions will often have different
expenses based on the population they serve. More required parking spaces, childcare,
and student support services after-work hours may drive costs at a commuter school. A
residential school may have higher student housing expenses and student healthcare
requirements than the commuter counterpart. These differences are highlighted when
comparing operational costs between CSU institutions such as San Jose and Los Angeles,
which are urban based with a large commuter student population, to more rural
residential campuses as Chico or Cal Poly San Luis Obispo.
Student enrollment and faculty workload has also been determined to drive costs
(Brinkman 1981, 1989, 2000; Middaugh, 2005; Paulson, 1989) within higher education.
Changes in FTE enrollments either through changing classroom headcount or adjusting
teacher instructional loads dramatically impact higher education cost. The March 1971
report, Higher Cost Programs in California Public Higher Education (CDE, 1960),
specifically stated that small class size was the largest driver of high-unit teaching costs.
This would support the idea that large lecture classes cost less than small lab sections,
strictly due to headcount. This might also be found between lower and upper division
courses, or undergraduate versus graduate program offerings. It is drivers such as these
that make it irrelevant to compare the cost of a history degree to a physics degree.
Curriculum delivery that can support large lecture as opposed to small section
laboratories, such as found in many STEM programs, make these types of program
comparisons interesting but not actionable. The model developed for the CSU factors
52
these variables into the costing tool, but it does not provide a basis for comparing
programmatic costs that differ in scope or delivery.
Generally speaking, much of the literature also finds that one of the largest cost
drivers in higher education is average class size (Brinkman, 1981, 1989, 2000; Koshal &
Koshal, 1999; Sharp, 2007). Increasing the number of students to faculty or decreasing
the number of faculty to students will effectively reduce unit costs as faculty labor costs
are a large factor in instructional costs. However, this is not always the case. Curriculum
also presents itself as a cost driver. Studies by Dundar and Lewis (1998) found that arts
and social science curriculum decreased average cost with an increase in enrollment
while additional students in physical sciences and engineering find an increase in
marginal costs as student credit hours increased. While not specifically mentioned within
the work, one could wonder if small section laboratory activities were cost driving factors
for the STEM curriculum.
Additional factors determined to influence costs within higher education are the
number of full-time versus part-time faculty and part-time versus full-time students.
Brinkman and Leslie (1986) found that full-time students required 5-10 times the
resources of part-time students. This may be due to a full-time student’s proximity to
campus and thus, a higher utilization of campus resources occurs. Additionally, part-time
students may be enrolled in many more evening or weekend coursework, potentially
accessing less expensive part-time faculty. Finally, the largest growth area in educational
53
labor costs between 2000-2012 was pushed by a 28% growth in mid-level administrators
primarily in the student services arena (Desrochers et al. 2014).
As the CSU model is built, these cost drivers must remain at the forefront of
considerations to ensure the framework is reflective of the diverse nature of the CSU.
Allowing the model to be CSU system specific, while ensuring the model’s outputs are
reflective of each of the individual campuses, is critical to the acceptance and usefulness
of the framework. This is important, as an understanding of the various cost drivers will
possibly limit the value of full-cost comparisons; a commuter school or technical heavy
school will have significantly different cost drivers than a campus with a different
population or focus.
Summary
Previous works on the methods of calculating higher educational cost, from both
theoretical and applied perspectives, provide foundational information to inform the
development a CSU-specific model. Understanding higher education cost drivers, the
strengths and weaknesses of the existing cost models, along with the challenges
associated with distributing overhead costs and the need for simple and accurate
functionality will all factor into development of the model. The development of the CSU
24 mock data is driven by the literature as a way to represent the diversity in campuses
within the CSU and provide an application of cost comparison.
54
Chapter 3
METHODOLOGY
Introduction
Chapter 3 describes the datasets and the research design this study used. It also
includes the role of the researcher, the research question, setting and context of the
research, sample, instrumentation, data collection, and analysis techniques. The purpose
of this study was to create a framework that could be used at a site or system level to
determine the cost of a degree from the California State University system. Through this
research, a CSU-specific costing framework defining and calculating cost from a
common perspective using available data was created. Using the developed framework
will allow programmatic cost comparisons between campuses. The costs of instruction,
course sizes, wage differentials, student services, equipment, facilities, and utilization
rates were all investigated and appropriately applied to build a model that works at all 23
of the CSU campuses. Testing of the model was done with both actual degree-level data
as well as mock data that represent cost drivers variables found within the system.
Research Question
Using previously described methodologies; the following research question was
explored.
What is the most accurate method for determining the cost of producing a
bachelor’s degree within the California State University system?
55
Research Design
Given the question of the cost of a degree, the design method was quantitative in
nature and scalable to the CSU, ultimately for use by administrators and policymakers.
This study has three distinct phases. Phase 1, illustrated in Chapter 2, was based in the
review of existing literature and the examination of existing educational costing methods
found, at the time of the study, within higher education accounting practice, research
literature, government organizations, and educational think tanks. This process allowed
for the identification of best practices and existing model shortcomings. Existing
terminology and definitions are reviewed and utilized or adapted where appropriate to the
CSU.
Phase 2 utilized CSU programmatic data with a number of existing costing
models to determine the range of determined output costs and the functionality of each
individual model. The programmatic data used in this Phase is detailed in the Data
Collection and Instrumentation section and generally consists of faculty salary data,
campus financial information, degree-level catalog data, the transcripts of two first-time
freshman cohorts of students within a specific degree, campus facilities and space
information, staff and administration overhead expenses, and campus property reports
that highlight assets that support education. The existing models used for Phase 2 are
current and widely cited methods of calculating educational cost, but do not all report at
the degree level; most systems are designed to report at the FTES or credit hour level.
The models include the Delaware Cost Project, Delta Catalog, Delta Transcript Cost,
56
Delta Full Attribution, and the NACUBO methodologies. The existing models have
similarities, but will produce different numbers when addressing the question of cost.
The dataset used to test the five models (see Table 1) is drawn from first-time freshman
(FTF) who started between 2005 and 2006 in California State University, Chico’s
mechanical engineering degree program. For Phase 2, the outputs of the models were
compared to each other to create a method-induced overall range of cost per degree. The
output range data provided a baseline about the variance associated with costing for use
during the Phase 3 evaluation of the new model. This dataset was selected due to
researcher access as well as being reflective of a laboratory-intensive subject generally
considered expensive with high student attrition rates.
In Phase 3, a model specifically designed to meet the demands of educational
costing for the CSU was developed and tested. The model was tested using the same
mechanical engineering dataset along with creating a comparative mock dataset as CSU,
Chico may not accurately represent cost factors found at all other CSU campuses. This
could be due to the residential nature, primarily full-time student body, and minimal
graduate level offerings of the campus; however, the data provided a place to start the
CSU-specific framework. To build the CSU-specific costing model, a mock CSU (called
“CSU 24”) dataset was created to test literature-based cost drivers possibly found within
the system. CSU 24 is a customizable data set that represents the differences found at
other campuses possibly including being primarily a commuter school, having larger
part-time student enrollment, larger class sizes or having a greater number of graduate
57
programs available. CSU 24 provided context about how intersystem comparisons could
be done and allow for cost variables that could be found between campuses. Specifically,
costs driven by average course enrollment, regional differences in salaries, rental rates,
and student success based on graduation rates were all included as variables. While
comparisons were done with mock data to show the cost drivers of higher education and
to evaluate the strengths and weaknesses of the proposed model, the purpose of this work
was not to actually compare costs at this point, but rather to create a model that is
generalizable within the CSU. This method of simulation has been utilized in an attempt
to investigate lower costs per student (Bowen & Douglass, 1971; Gonyea, 1978; Massy
& Zemsky, 1994) and should prove applicable to simulate campus comparisons.
Phase 3 also worked to calibrate and validate the developed model. As this cost
estimation model was built using actual historical data, it is considered to be selfcalibrated according to the International Society of Parametric Analysts (2008).
Validation is more challenging. The goal of validation is to reliably report the costs
associated with a degree at any CSU. The International Society of Parametric Analysts
once again provided guidance. It was suggested that estimates could be made using
multiple methods and the outputs compared. Model validation occurred by comparing
the results found for a degree cost in Phase 2 using existing methods to results of the
developed model. Industrial engineering cost estimation techniques provide a better
understanding of accuracy of the calculated outputs by recognizing that variances of +/30% represent an order of magnitude difference, +/- 15% represent budget level
58
differences, and +/- 5% is considered to be definitive. A plus or minus 15% cost is
generally considered acceptable for budgetary purposes, where a variance of 30%
discounts the work. As the proposed model includes additional data detail on some costs
such as facilities cost, the model was validated at comparative points. These comparative
points are based around what has traditionally been considered direct and indirect
educational cost and provide a stepped validation of outputs. The stepped validation is
necessary, as the proposed model reports costs that do not have any historical data to
compare.
Role of the Researcher
As the researcher for this work, I was responsible for both gathering the
information as well as analyzing the data.
Data
All data utilized to develop the initial model are from CSU, Chico. It is a
residential campus with a college of engineering and a core focus on teaching and
learning. The population for this work is all first-time freshmen within the CSU who
declare mechanical engineering. The targeted sample for this work consists of
approximately 200 first-time freshmen who declared mechanical engineering at CSU,
Chico during the Fall of 2002, 2003, 2004, 2005, and 2006 whose transcript records were
provided from institutional records and enrollment management with all student
identifiers removed. The records included collegiate semester-by-semester records that
included all coursework attempted, coursework passed, transfer units, change-of-major,
59
and academic probation or disqualification. The students were chosen because six-year
graduation rates were available for them and transcript data were provided through the
subject institution’s Enrollment Reporting System Students (ERSS) and Enrollment
Reporting System Degrees (ERSD).
At CSU, Chico, the student population for fall 2012 consisted of 16,740
undergraduates and 1,183 graduate students. Fifty-three percent of the students were
female, 47% male, and the average first-time freshman had a 1037 mean SAT score and a
3.23 high school grade point average. The average age of an undergraduate was 23.
Ethnicity data included 0.7% American Indian, 5.5% Asian, 1.8% African American,
19.4% Hispanic/Latino, 0.2% Hawaiian/Pacific Islander, 55.4% White, 4.4% two or more
ethnicities, 8.7% declined to state, and 3.8% non-resident aliens (California State
University [CSU], Chico, 2012). The six-year graduation rate at CSU, Chico (CSU,
Chico) is about 60%, which is higher than the system average. Nine hundred ninetyseven faculty members taught at the institution in 2012. The total budget for CSU, Chico
from student fees and state support was reported at $145,764,000 for 2012-13.
The data from Chico were utilized with both the exiting costing models as well as
the model developed. Actual datasets highlighted limitations with current recordkeeping
and provided insight based on the cost estimates calculated using each method. To test
the developed costing model, a mock dataset, referred to as CSU 24, was created. The
mock data were for testing and model comparative purposes, are reflective of the CSU as
a whole, and can also be modified to reflect the extremes.
60
For the fall 2012 academic year, the CSU had a total headcount of over 426,000
or 378,794 FTES students across 23 campuses and three specialty programs run through
the Chancellors office. Fifty-seven percent of the students were female, 43% male. The
average age of an undergraduate is 23. System-wide ethnic distribution includes 0.4%
American Indians, 14.9% Asians, 4.8% African Americans, 23.6% Hispanics/Latinos,
0.5% Hawaiian/Pacific Islander, 31% Whites, 4.1% two or more ethnicities, 6.9%
declined to state, and 4.5% non-resident aliens (CSU, 2012).
In 2011-2012, 76,427 bachelors, 19,517 masters, and 208 doctoral degrees were
awarded system wide. According to the California Public Education Commission, CSU’s
average four-year graduation rate was 14.2%, five-year rate was 35.6%, and the six-year
rate was 46.4% for students. Total budget from student fees and state support was almost
$3.9 billion. System wide there were almost 22,300 faculty members of varying rank and
experience that taught during the 2011-2012 academic year. Mock data represented the
differences in the number of non-faculty employees found throughout the system and the
costs associated with them.
Data Collection and Instrumentation
Student demographic information and mechanical engineering transcript data are
provided through the subject institutions Enrollment Reporting System Students (ERSS)
and Enrollment Reporting System Degrees (ERSD). The cohort data included all firsttime freshmen who declared mechanical engineering and started fall 2005 and fall 2006
at CSU, Chico.
61
Students who declared double majors were eliminated from the dataset. It was
not possible to differentiate what substitutions or double counting was done in order to
fulfill which major requirements. Students earning minors had these costs included
within the cost reported for their degree. In many cases, it appears students earned
minors through the classes they took to fill out full semester loads as they pursued their
majors. While the minor was noted, the courses were accounted for the same as those for
students who took non-required coursework such as physical education or ballroom
dancing. Educational costs for students were only included while they were declared a
mechanical engineering major. In the case of a student who began as a mechanical
engineering major, and ultimately returned, all costs have been associated with the
degree.
Faculty time base data, which document any externally reimbursed release time,
were collected from workload reports documenting teaching activity from the internal
Insite database. All externally funded release time was not included as part of the direct
educational expense. All other time was assumed to support curricular activity. Faculty
compensation as well as campus budget data were collected from the office of budget and
finance for the campus.
Faculty costs were averaged by department or GE instructional section (i.e., Area
C Costs have been averaged by what students actually took). Department Chairs were
included within direct educational expenses, and staff and Dean expenses were assigned
to indirect educational costs. Faculty release time granted for participation within shared
62
governance activities, such as academic senate, also had their wages data adjusted to
reflect this as indirect educational expense. In the case of management personnel who
teach, a prorated portion of their salary was assigned to the direct educational costs.
Non-state supported sessions, such as summer or intersession educational expenses have
not been included to determine cost averages.
Faculty data were not provided from CSU, Chico. All salary information was
retrieved from a public record salary database hosted by the Chico Enterprise Record.
The salary information was reported by academic year for all years in question except
2009-10. To estimate 2009-10 wages, the average between 2008-09 and 2010-11 was
calculated and applied to the units taught by the instructors. A number of salaries were
not reported within the database and appear to be low compensated (less than $5,000)
per-year laboratory assistants. Any credits or hours associated with these instructors
were removed from the dataset for the purposes of wage calculations.
For determining instructional contact hours, the following rules were applied.
Fifty-minute courses were rounded to one hour for ease of account purposes. Courses
that met three times a week were accounted for as three hours. This was also done for
courses that met bi-weekly for 1 hour and 15 minutes. Most faculty members were
available both right before and right after class, and this justified the rounding. Weekly
contact hours were multiplied by 15 to determine the total contact hours for a given
semester.
63
Fixed asset costs such as computers, software, machinery, and test equipment are
determined from institutional property records. Property records for laboratory
equipment have been provided from CSUC’s property office, which has both acquisition
date and cost information about the assets needed for each department.
Campus facilities, plant operations, administrative, student services, and financial
aid tend to function as of economies of scale, and impact educational cost. Drawing from
the literature, campus facilities costs were included through a distributed rental rate,
where applicable. This allowed for the documentation of costs based around the amount
of teaching done within a facility during the academic year that costs were incurred. The
progress toward a degree happens over a significant timeframe. Final numbers, often
used for comparisons within this work were normalized to 2012 dollars using the
Consumer Price Index inflation calculator for the US Department of Labor.
As outlined, the research question was answered in three distinct phases during
the work. In developing the CSU-specific costing model, there were discrete phases, but
the work did not always happen in a linear fashion. During the process, information
gathered impacted and influenced changes and throughout the process.
Data used as the work was conducted included the following:
a.
2005-2012 CSU, Chico, Faculty Salary Public Record (Annual Wage Data
+ 35% benefits);
b.
2005-2012 CSU, Chico, Faculty Workload (Historical Teaching, Release,
and Workload Assignments);
64
c.
2005-2012 CSU, Chico, Academic Year Budgets;
d.
2005-2006 CSU, Chico, First-time freshman (FTF) mechanical
engineering students longitudinal transcripts;
e.
CSU, Chico Property Records – College Holdings; and
f.
CSU 24 Mock Data representing the spectrum of possibilities found within
the CSU and not represented by the mechanical engineering program at
CSU, Chico generally gathered from the extremes documented at
http://www.calstate.edu/as/stats.shtml
CSU Framework Plan “Contact Hour Plus Model”
The developed costing model is referred within this work as the Contact Hour
Plus model. It is a derivative of contact hour costing, drilled to a degree level, which also
attempts to quantify costs such as facilities capital expenses and operational equipment
assets. Figure 2 represents how this model was developed for use with this work.
65
Model Requirements
No
Does data support
model requirments?
Database Development
Model Development
Not OK
Calibration and
Validation
Document Results
Yes
Update Required?
Done
Figure 2. Flowchart of cost model development. (International Society of Parametric
Analysts [ISPA], 2008)
66
Protection of Participants
Student records data used for this research had all identifiers removed for the
protection of the individual. No identifiable information was collected or retained
associated with this work. Faculty information associated with transcript costing is
public record; however, due to the often sensitive nature of individual compensation,
specific faculty names have been removed from the costing data presented.
67
Chapter 4
MULTI-METHOD COST OUTPUTS AND FINDINGS
Introduction
Within this chapter, the findings from the analysis are presented. Chapter 4
reports the findings; Chapter 5 provides an interpretation of the findings and addresses
what the findings mean in the context of educational leadership and policy. This chapter
presents the results of each model reviewed (Delaware, Delta, NACUBO) using the data
from CSU, Chico regarding the two mechanical engineering cohorts that were tracked
with this work. The CHP model that was derived to better reflect the high contact hours
associated with applied education of the CSU is presented with the data from CSU,
Chico, and compared to results from the CSU 24 mock data. This comparison provides
context on how a CSU wide costing model could be used for management and strategic
planning.
As noted, to conduct this research, the bachelor’s degree program at CSU, Chico
in mechanical engineering was chosen to compare degree level costs using a number of
literature-based educational costing methods. The data in this section are reported from
CSU Chico’s mechanical engineering first-time freshman cohorts that began in the fall of
2005 and 2006. Mock data are also represented to exhibit how the proposed Contact
Hour Plus (CHP) costing model would work under different campus settings and
extremes. The developed CHP model is presented and attempts to overcome some of the
shortcomings of the existing models of educational costing. This chapter focuses on
68
reporting the findings from the research, while Chapter 5 focuses on analyzing what the
findings mean to the CSU.
During the fall 2005 and 2006 cohorts, 87 first-time freshmen who, by declaring
their major as mechanical engineering, began pursuing a bachelor’s degree as depicted in
Figure 3. By spring 2012, 22 (25%) of these students were successful in obtaining a
degree in mechanical engineering. Twenty-eight (32%) of the students in these two
cohorts finished a degree other than mechanical engineering at CSU, Chico, while 38
(44%) dropped out, were academically disqualified, or changed institutions. Twenty-five
percent of the students who began mechanical engineering in these two cohorts were
successful in obtaining the degree, which is similar to the 10-year average in mechanical
engineering. Fifty-seven percent of the cohort ultimately earned a mechanical
engineering or some other degree from CSU Chico, which is slightly lower than the 61%
six-year overall campus graduation rate.
69
100
90
87
80
70
60
50
40
30
22
20
10
0
Total FTF MECH Enrolled 05-06
Total MECH Graduates by S2012
Mechanical Engineering FTF to Grad
Figure 3. MECH FTF graduates within six years.
To facilitate costing method comparisons at the degree level, which occur over
the duration of time a student pursues a degree, all dollar figures were adjusted to 2012
dollars. To adjust to 2012 dollars, all numbers were normalized using the United States
Department of Labor Bureau of Labor Statistics inflation adjustment calculator. For a
matter of comparison, as well as justification for degree level costing, it is worth noting
the differences between total campus unit costing and department level unit costing.
Total campus unit costing does not capture programmatic nuances that are discipline
specific. These nuances could be factors such as higher faculty salaries in one area over
another, or low enrolled sections. These details can be lost when costing is done at the
campus or even the college level. Total campus costing does provide a campus level
70
baseline for cost comparison at the unit basis to understand if a program is more or less
expensive than the overall campus average.
Dividing CSU Chico’s total number of units taught (220,320) by the total of direct
instructionally related expenses ($47,866,383) provides a campus average cost of $217
per unit in 2012. When comparing a department level cost per unit (total units taught in a
department divided by department instructional labor expense) in 2012 to campus
averages, students on the mechanical engineering path actually find lower than campus
average unit costs in chemistry ($154/unit) and mechanical engineering ($142/unit)
courses. These department level differences are determined by dividing the department
instructional labor expense by the number of units instructed by a department. Higher
than campus average unit costs are found in physics ($249/unit) and civil engineering
($304/unit). These variations in cost at the department level are influenced by class size,
faculty workload, and instructional salaries. The question of workload is notable, as there
is considerable variance in workload between departments. There seems to be limited
standardization regarding assigned teaching workload. Departments that traditionally
assign fewer course teaching assignments to faculty have higher costs per unit, as all
unreimbursed salary (contract research is reimbursed and is not included in costs,
whereas non-instructional release time such as Department Chair is not) is assigned to the
instructional costs of students.
The mechanical engineering degree at CSU, Chico during these two cohorts had
catalog requirements that equaled 132 units. See Table 4 for the major academic plan
71
that outlines the required 132 units. The majority of bachelor’s degree programs within
the CSU are based on 120 units. Students from the two cohorts who ultimately
graduated with a mechanical engineering degree averaged 156 units during their tenure at
CSU, Chico, 18% more units than required. The range of the number of units taken also
proved to be interesting between these students earning the same degree. The lowest
number of units a student took at CSU, Chico to earn the degree was 140 units (6% more
units than required) and the highest was 209 (58% more units than required). All
graduates took more units than required for the degree with the average mechanical
engineering graduate taking 18% more units than required. The number of units a
student takes directly impacts the total cost of the student’s education.
72
Table 4
Mechanical Engineering Major Academic Plan
California State University, Chico MAJOR ACADEMIC PLAN (MAP)
(Consult 2005-07 University Catalog for official degree program)
Major: Mechanical Engineering Option:
First Semester
Major Units:108
Second Semester
Comments
CHEM 111 (GE Area B1)
4
MATH 121 [*1]
4
MATH 120 (GE Area A4) [*1]
4
MECH 100
2
MECH 140
3
MFGT 160
3
ENGL 130 (GE Area A2) [*1]
3
PHYS 204A [*1]
4
GE Area A1 [*1]
3
GE Area B2
3
TOTAL
17
16
CIVL 211
MATH 220
MECH 210
3
4
3
TOTAL
Fourth Semester
EECE 211
EECE 211L
MATH 260
PHYS 204B
4
MECH 200
2
GE Area C [*2]
3
PHYS 204C
4
GE Area D [*2]
3
TOTAL
17
Third Semester
17
TOTAL
Fifth Semester
3
1
4
Sixth Semester
NOTE: This is a high unit major
with modifications to GE which
are included in this plan.
[*1] C- or better is required for
this course.
Comments
[*2] The Cultural Diversity
(Ethnic and Non-Western)
requirement MUST be met with
GE.
Comments
CIVL 311
4
CIVL 302
3
CIVL 321
MECH 320
4
3
MECH 308 (SP)
MECH 338 (SP)
3
4
MECH 306 (FA)
4
MECH 340 (SP)
3
MECH 332
3
MECA 380 (SP)
3
TOTAL
18
TOTAL
16
Apply to graduate by May 15.
73
Table 4 continued
Seventh Semester
Eighth Semester
Comments
MECH 432 (FA)
4
CIVL 495
3
MECH 440A - WP (FA) [*3]
3
MECH 440B (SP)
2
MECA 482 (FA)
4
Technical Elective [*5]
3
GE UD Theme [*2] [*4]
3
GE UD theme [*2] [*4]
3
HIST 130
3
POLS 155
3
TOTAL
17
TOTAL
14
Units available for electives, minor, or certificate: 0
[*3] C- or better is required for
the WP course.
[*4] Only 2 UD Theme courses
are required. Please consult with
a department advisor.
[*5] See your major advisor for
approval.
132 units required for degree.
Approved
06/06/05
The major academic plan for mechanical engineering outlines the 132 units
required for the degree. Along with taking additional courses for enrichment or pleasure,
such as a number of kinesiology offerings (ballroom dance, weighs, or golf), students did
often have to repeat such courses. Thirteen (15%) of the mechanical engineering
students within these two cohorts had to take MATH 120 at least twice. Each repeated
course, and each unrequired course that was taken directly, impacted the cost of a
mechanical engineering degree.
General Campus Budget Data
As discussed previously, CSU, Chico is not representative of all the campuses
within the CSU. CSU, Chico has a primarily residential full-time student population,
with average age of 23. While not necessarily representative of the CSU as a whole, the
CSU, Chico campus and mechanical engineering data has been used only as a baseline to
provide real numbers in an effort to determine and develop a successful degree costing
method that can apply to the CSU as a whole. For the purposes of this work, actual
74
numbers from CSU, Chico, are compared to numbers derived from the mock CSU, 24
dataset that is representative of differences within the system. As noted in Chapter 2,
there are many drivers to educational cost, and the results of the mock data are
investigated later in this chapter and used to validate the new costing model.
This work relied on publically available salary data and campus financials that
have some limitations associated with reporting as well as a lack of line item detail.
These limitations and lack of detail in expense categories such as general operation
expenses and utilities costs cannot be drilled down to a degree level, as there is no
accounting of, for example, which degree programs might consume more electrical
power or require more consumable supplies in the course of instruction. In these types of
situations, costs are distributed equally to all the student body.
Individual course and department faculty salary information was used to drill
down on direct educational costs specific to a mechanical engineering degree. Unless
there was documentation within the individual average weighted teaching unit report,
reimbursed release time for a specific faculty member, all faculty salary costs were
assigned to the direct cost of education. Department chair and program coordinator
wages were all considered to be direct educational expenses, whereas Dean and staff
expenses were applied to indirect educational cost. The majority of tenure-track faculty
members have non-teaching employment requirements that include service and
scholarship. Current accounting records do not differentiate the percentage of costs
associated with teaching activities or other required work engaged in during the course of
75
employment, thus all faculty expenses were assumed to be applied to direct education,
unless otherwise noted with reimbursed release time. Even with the limitations, the
campus budget data provides a wealth of information regarding overall faculty costs,
management costs, and general operational expenses, which help derive direct and
indirect educational expenses.
Table 5
CSU Chico Campus Overall Expense Information
CSU, Chico Campus Expenditures 2005-2012 Academic Years
AY 05-06
AY 06-07
AY 07-08
AY 08-09
AY 09-10
Personnel Costs
Faculty
Non-Faculty & Management
Temp Help
Overtime
Shift Differentials
Compensation Increase
Personnel Costs (Non-Itemized)
Subtotal
Total with 35% Benefits
Operational Expenses
Operation Expenses (Non-Itemized)
General Operation Expense
Financial Aid/Grants/Student Loans
Item Specific Operation Expense
Utilities - Electric, Gas, Water, Sewage, Waste
Utilities - Energy Bins
Risk Pool Premiums
Space Rental/Lease
Arbitration Hearing Costs
SCO Personnel/Payroll Charges
HR Central Costs (SCO, benefit, Arb)
Benefits Admin
Auxiliary Audit HR Chargeback
DGS Charges
Credit Card Fee Discount Fees
Financial Services Central Costs (DGS, Credit)
CMS Software
CMS Software Maintenance
Health Services
Subtotal
Total Expense
$43,379,955 $48,211,687
AY 10-11
AY 11-12
$47,843,978 $49,858,713 $48,398,563 $43,885,710 $47,866,383
$41,831,162 $44,685,314 $43,321,356 $38,652,355 $41,288,114
$1,129,478 $1,129,478 $1,129,478 $1,129,478 $1,129,478
$434,637
$434,637
$349,637
$349,637
$349,637
$302,428
$302,428
$182,428
$182,428
$182,428
$0
$229,408
$0
$0
$0
$40,043,036 $44,503,095
$83,422,991 $92,714,782 $91,541,683 $96,639,978 $93,381,462 $84,199,608 $90,816,040
$112,621,038 $125,164,956 $123,581,272 $130,463,970 $126,064,974 $113,669,471 $122,601,654
$22,061,857 $19,843,244
$10,760,496 $10,760,496
$10,092,366 $10,663,450 $8,726,496 $8,107,144 $10,181,753
$10,682,846 $11,620,939 $12,541,573 $16,151,548 $17,144,348
$4,612,726
$770,950
$2,981,018
$449,953
$5,977
$4,500
$4,797,726
$770,950
$2,731,868
$449,953
$4,973,726
$770,950
$2,452,198
$449,953
$4,996,317
$770,950
$2,460,704
$349,953
$5,054,986
$770,950
$2,349,799
$301,953
$61,100
$61,100
$61,100
$61,100
-$16,500
-$59,258
-$59,258
-$59,258
$50,622
$10,489
$29,000
$3,000,000 $3,000,000
$35,822,353 $33,603,740
$39,489
$39,489
$39,489
$39,489
$2,387,212 $2,435,636 $2,338,275 $2,150,644 $1,920,463
-$468,000
-$469,000
-$468,000
-$468,000
-$468,000
$3,200,000 $3,200,000 $3,200,000 $3,500,000 $3,500,000
$34,809,659 $36,285,611 $35,026,502 $38,060,591 $40,797,583
$148,443,391 $158,768,696 $158,390,931 $166,749,581 $161,091,476 $151,730,062 $163,399,237
76
The overall campus financial picture provides insight into a difficult time in the
nation and California as campus budgets were reduced during the height of the recession.
This is especially evident during the 2010-2011 year. At CSU, Chico, the budget was
reduced by over $12 million, or a decrease of 7%, for 2010-11 over the previous year.
During the recession, the CSU system made almost $1 billion worth of cuts from 2007
through 2011, representing an 8% funding reduction over the duration (Campaign for
College Opportunity, 2014). Also notable is the very high percentage of personnel costs
when related to the overall budget. In 2012, 75% of the campus expenses directly
supported campus personnel, but approximately only 30% of the campus budget directly
supported instructional faculty. During the budget cuts of 2010-11, 12% of the total
campus personnel were cut, which crossed the lines of all labor units (staff,
administration, faculty) as the campus attempted to balance the budget. Understanding
the relationship of the cost of instructional faculty as a direct educational cost and the
balance of non-faculty campus personnel as indirect cost provides a foundation for
comprehending cost drivers in education. As visible in the previous table, the reporting
structure changed for the campus after the 2007 academic year, providing more detail in
the subsequent public financial statements in the areas of non-faculty wages, temporary
help, overtime, shift differentials, and compensation increases.
Further, it is worth noting that capital costs are missing within the expense
reports. Buildings, equipment, and non-consumable instructional assets are not generally
included within the direct or indirect educational costs. This appears to be standard
77
educational costing practice as highlighted in Chapter 2. Debt service on capital projects
are paid at the system level rather than the campus level, unless they are for an auxiliary
operation, in which case the debt service is paid through auxiliary operations, not campus
funds. In both cases, they are excluded from educational cost, although this research
does provide a method to estimate the possible expense of facilities.
The campus financials provide the data and drive the results to the question of
how much a degree costs. What becomes evident within this work are the differences in
how the data are used to determine the cost of a degree and the various answers to the
same question depend on the costing method employed. Table 6 shows the outputs of all
of the reviewed models to frame the differences found from each model. The following
sections detail the findings of degree cost using the different costing models used for this
work.
Table 6
Summary of Method Cost Outputs Delaware Costing Model
Method Induced Cost Output Variations of a Mechanical Engineering Degree at CSU, Chico
Method
Cost
Notes
Delaware Model Cost
$33,282
Direct Education Cost per Unit
Delta Catalog Direct Cost
$27,238
Direct Education Cost per Unit
Delta Transcript Cost
$116,857
Direct/Indirect Education Cost per Unit
Delta Full Attribution
$193,288
Direct/Indirect Education Cost per Unit for all MECH Students/MECH Graduates
NACUBO FTES
$109,710
Direct/Indirect Education Cost per FTES
CHP 1
$96,710
Direct Education Cost per Unit for all MECH Students/MECH Graduates
CHP 2
$253,951
Direct/Indirect Education Cost per Unit for all MECH Students/MECH Graduates
CHP 3
$294,511
Direct/Indirect Education Cost per Unit for all MECH Students/MECH Graduates
Plus Facilities Leaseholder Estimate
78
The Delaware Costing model bases cost estimates around instructionally related
costs per credit hour that are taught at the discipline level. These instructionally related
costs are faculty and administration at the department level, such as instructors and
department chairs. Dean level and staff costs are not included in instructional level direct
costs but are considered indirect expenses. Delaware suggests department support staff
expenses are also included within direct educational expenses. CSU, Chico does not
report support staff expenditures at the department level, so for the purpose of this work,
staff costs were considered indirect education expenses. Cost per credit hour divides
department level instructional expenses by the total units instructed.
For the 22 students who earned a mechanical engineering degree, the average cost
of the degree using the Delaware Costing Model was $33,283. Using the model, the
highest cost recorded for a mechanical engineering degree was $40,672 and the lowest
was $28,233. The range associated with the cost appears to be related to additional units
a student may have taken in order to build a semester course load. The lowest cost
degree student transcript also showed three transfer units, whereas the highest did not
have any transfer or test credits and repeated a number of courses during their degree
pathway due to low grade performance.
79
$45,000
$40,672
$40,000
$33,283
$35,000
$28,233
$30,000
$25,000
$20,000
$15,000
$10,000
$5,000
$0
High Cost Delaware Method
Average Cost Delaware
Method
Low Cost Delaware Method
Range of Cost Output Data CSU, Chico Delaware Costing Method
Figure 4. Delaware cost model average and range cost reporting.
The Delaware model has limitations in that it only looks at direct educational
expenses, is a unit-based costing method that may not reflect the costs of high contact
laboratory classes, and does not attempt to quantify all indirect educational costs. The
Delaware model does provide a method by which to determine the cost of instruction
associated with a degree that could be used as a comparison between programs. The
Delaware model provides an estimate of the direct educational costs of a degree. It does
not provide information regarding the efficiency in the production of degrees. The Delta
Catalog method, reviewed within the next section, provides a method to determine a
theoretical baseline cost of a degree if a student followed the designed pathway without
deviation.
80
Table 7
Delaware Costing Method
Average
Direct
Education
Degree
Cost
22
$33,282
Total Units Total Direct
MECH
taken by Education
Graduates
Graduates Cost
Delaware Costing Method
3425
$732,204
Unit cost varies by subject/department and year
Costs have been adjusted to 2012 dollars
Average units per graduate 156
Unit Range per graduate 140-209
Formula: (Sum of Direct Education Department Level Unit Cost X Department Level Units)/Cohort
Member Mechanical Engineering Graduates
Delta Catalog
The Delta Catalog method is similar to Delaware in calculating the cost of a
degree, except that it only looks at the theoretical cost based on the catalog course
requirements rather than on a student’s actual path. According to Delta, the method may
include direct and indirect cost as needed. If indirect costs are to be included, the method
suggests that costs such as library, administration, and plant operations be distributed to
each credit hour taught at the entire institution. Applying both direct and indirect costs to
the Delta Catalog model determines the cost of a mechanical engineering degree to be
$98,235.
To facilitate the comparability between methods, the same financial inputs for the
Delaware model also were used. Using only the direct educational costs, the Delta
81
Catalog method determines that the theoretical catalog cost of a mechanical engineering
degree at CSU Chico starting with these cohorts was $27,238. This represents 82% of
the average cost reported using the Delaware model. The Delta Catalog model provides a
baseline to investigate and compare costing drilled down to a degree level.
$120,000
$100,000
$98,235
$80,000
$60,000
$40,000
$27,238
$33,283
$20,000
$0
Delta Catalog Direct and
Indirect Cost
Delta Catalog Direct Cost
Delaware Average Model
Cost
Range of Cost Output Date CSU, Chico Delta and Delaware Costing Methods
Figure 5. Delta catalog cost model with comparisons to Delta Direct Only and Delaware.
The Delta Catalog model does capture cost differences between a 132-unit
program in engineering and a 120-unit program in another subject. Between institutions
engineering programs do vary in the total required units (typically 127-132 units), which
would be reflected using this method. The method does not, however, reflect actual
student course enrollment behavior as is found in most of the other methods such as the
previously reported Delaware model or the forthcoming Delta Transcript method.
82
Table 8
Delta Catalog Costing Method
Total Direct
Total Units Education
Cost
Delta Catalog Costing Method
132
$27,238
Unit cost varies by subject/department
Costs have been adjusted to 2012 dollars
Formula: (Sum of Direct Education Department Level Unit Cost X Degree Required Department Level Units)
Delta Transcript Cost
The Delta Transcript Cost is essentially the Delaware model, except that both
direct and indirect educational costs are included. The Delta Transcript model suggests
the direct educational costs are included along with the indirect cost of student support,
financial aid, academic administration, advising, university support, library services, and
plant operations. Essentially, the Delta Transcript Model includes all non-capital
expenses associated with a university distributed to the number of units taught. As an
example, CSU, Chico taught 218,100 units during the 2005-2006 academic year, and the
average campus-wide direct educational cost (direct educational expense/total units) per
unit was $235 and indirect cost allocated per unit was $568 (2012 Adjusted Figures)
making the combined total cost per unit to $803. Campus level costing provides an
average to understand if programs are comparatively high or low cost when department
level costing is evaluated.
83
$900
$800
$700
$600
$500
$568
$558
$516
$524
$555
$400
$501
$524
$300
$200
$100
$235
$243
$223
$224
$238
$204
$217
05-06
06-07
07-08
08-09
09-10
10-11
11-12
$0
Academic Year CSU, Chico Campus Average Educational Cost Per
Unit
Campus Average Indirect Educational Cost(2012 Dollars)
Campus Average Direct Educational Cost (2012 Dollars)
Figure 6. Direct and indirect per unit cost variability 2005-2006, campus level.
Indirect costs are applied equally to all units. Department/degree level direct
instructional costs vary throughout campus based on discipline and instructor. It is not
uncommon for faculty members of the same rank to have a tremendous difference in
salary depending on discipline and length of service. These factors impact the direct cost
of education. To illustrate the difference in per unit cost between disciplines that was
found through this process, one must only look at the mechanical engineering required
coursework and the average unit cost within each department that provides content
toward the degree. Not only did the average unit cost vary between departments, it
varied within the department on annual averages. Data collected and normalized to 2012
dollars at a department level between the academic years of 2005 and 2012 provided a
84
high unit direct cost of $587 per unit in civil engineering during the 2005-06 year and a
low of $79 per unit in general education (GE) Area A1 (Speech Communication) in the
2008-09 year. Figure 7 compares the unit cost variability in mechanical engineering
curriculum with the GE averages removed as students have a choice in which GE courses
they take; the options cross many departments. Department costs from mechanical
engineering (MMEM), physics (PHYS), mathematics (MATH), civil engineering
(CIVL), chemistry (CHEM), and electrical engineering (EECE) are all represented in
Figure 7, as mechanical engineering degree seekers are required to take courses from
each noted departments. It is worth noting that none of the departments offered master’s
degrees within the subject material, and while graduate students from other programs
could have possibly enrolled, the costs are reflective of undergraduate curriculum.
700
600
500
MMEM
400
PHYS
300
MATH
CIVL
200
CHEM
100
EECE
0
05-06
06-07
07-08
08-09
09-10
10-11
11-12
Academic Year Average Direcy Educational Cost per Unit by Department
Figure 7. Per unit cost variability 2005-2006, department level, GE removed.
85
As was noted in the beginning of this chapter, the total number of units a student
ultimately takes on the path to a degree varies widely and thus provides a very large
range impact on cost. For the two mechanical engineering cohorts reviewed within this
work, the average total educational cost (direct and indirect) was $116,857. Noting the
impact cost from the number of units, the range for total costs using the Delta Transcript
Model was $104,890 to $152,630.
The Delta transcript model is truly reflective of the path students take while
reporting both direct and indirect educational expenses. Including both direct and
indirect costs dramatically increases the cost of a degree over what the Delaware model
reports. This model provides insight not only into the instructional costs but also the
operational costs of an institution. Seeing that the indirect educational expenses often
operate at 2 times the cost of direct educational expenses per unit based on the averages
presented, it appears critical these figures are reported. The concept of applying all costs
to units instructed, fosters a tangible output to cost association. The Delta Full Cost
Attribution Model reported next takes measuring the cost of output value to the next level
through applying the incurred costs of all students who pursued the degree to only the
students who were successful in earning the degree.
86
Table 9
Delta Transcript Costing Method
Delta Transcipt Costing Method
Total
Average
Total Units Total Direct
Indirect
MECH
Direct
taken by Education
Education Graduates Education
Graduates Cost
Cost
Degree Cost
3425 $732,204 $1,838,650
22
$116,857
Unit cost varies by subject/department and year
Costs have been adjusted to 2012 dollars
Average units per graduate 156
Unit Range per graduate 140-209
Formula: (Sum of Direct Education Department Level Unit Cost For Graduates X Department Level
Units) + (Sum of Indirect Eduational Cost for Graduates per Unit)/Cohort Member Mechanical
Engineering Graduates
Delta Full Attribution Model
The Delta Full Attribution Model is designed to quantify the cost of a degree
when all costs of producing the degree are included. The model includes both direct and
indirect educational expenses and then includes the costs associated with all students who
began a journey toward a degree and were not successful. To functionalize the model at
a degree level, all costs associated with students who declared mechanical engineering
were included until they dropped out, changed majors, or ultimately earned the degree.
As previously noted, 87 first-time freshmen began the degree, and 22 were ultimately
successful. The total cost (direct and indirect) of all the units taken by students while
declared mechanical engineering was $4,252,338. Dividing this number by 22 (total
number of mechanical engineering degrees awarded) determines each degree produced
cost $193,288.
87
Graduation rates directly impact this number. Programs with higher graduation
rates, fewer dropouts, change of majors, etc. would see a reduction in per degree cost. In
Figure 8, the Delta Full Attribution Model is compared to the previous models discussed.
$250,000
$200,000
$193,288
$150,000
$116,857
$98,235
$100,000
$50,000
$27,238
$33,283
$0
Delta Full
Attribution
Delta
Delta Catalog Delta Catalog Delaware
Transcript Direct and Direct Cost
Average
Cost
Indirect Cost
Model Cost
Range of Cost Output Data CSU, Chico Delta and Delaware
Costing Methods
Figure 8. Delta Full Attribution Cost compared to other models.
The Delta Full Attribution Cost model quantifies student success while including
both direct and indirect educational expenses. As such, the cost of a degree when nongraduates are included reflects a dramatically higher cost than other methods. All the
methods reported to this point that include indirect costs, distribute the costs, at least
initially on a headcount basis (The Delta Full Cost, ultimately distributes all indirect and
direct costs only to graduates). This assumes that indirect costs for both part-time and
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full-time students are the same. The NACUBO costing model is the only method that
attempts to distribute costs by FTES rather than headcount or total enrollment.
Table 10
Delta Full Attribution Costing Method
Total
Total Units Total Direct
Indirect
taken by Education
Education
Majors*
Cost
Cost
Delta Full Attribution Costing
Method
6466 $1,409,133 $2,843,205
Average
MECH
Direct
Graduates Education
Degree Cost
22
$193,288
* All units taken by students within the two cohorts are included until they change majors, leave
the institution, or graduate
Unit cost varies by subject/department and year
Costs have been adjusted to 2012 dollars
Average units per graduate 156
Unit Range per graduate 140-209
Formula: (Sum of Direct Education Department Level Unit Cost For Cohort X Department Level Units)
+ (Sum of Indirect Eduational Cost for Cohort per Unit)/Cohort Member Mechanical Engineering
Graduates
NACUBO Model
The NACUBO model differs from the previous models in that it reports costs per
FTES rather than unit. FTES attempts to account for cost variable driven from part-time
versus full-time student status. FTES allocation is really a method in which education is
funded rather than a relation to cost. Within the CSU an FTES is defined as 15 units per
semester or 30 units per academic year, which equals one full-time equivalent student.
FTES can be confusing when presented at the degree level. The number of enrolled
students and the number of units the course earns impact the number of FTES for a given
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course. If a course has 30 students enrolled in a three-unit class, 90 units are taught
(3X30=90). Ninety units divided by 15 is six FTES.
Using real numbers from the mechanical engineering data to determine the FTESbased cost for a course, one chemistry FTE in 2011-2012 had a direct educational cost of
$2,308. The average enrollment of a chemistry section in 2012 was 20. The required
chemistry course for engineering students earned four units. The cost of this course
equals 20 students multiplied by four units each, divided by 15 with a result of 5.33
FTES. This translates to an educational cost of $12,309 for a chemistry section. As this
method remains unit-based, the direct educational cost per student remains the same as
the other unit-based costing formulas.
As noted, the NACUBO method and the use of FTES as the basis for
measurement are rooted in the ability to address part-time versus full-time student status.
Direct educational costs essentially remain unchanged from other unit-based costing
methodologies. The only difference is how indirect costs are distributed to the student
body. The NACUBO method is not really designed to determine the cost of a degree. In
attempting to adapt the model for degree costing, the average number of units a student
took for the mechanical engineering degree (156) was divided by 15. This provides an
estimate of 10.4 FTES worth of direct and indirect cost. Multiplying 10.4 FTES by the
average 2005-2012 academic year indirect FTES of $10,549, provides a direct and
indirect degree cost estimate of $109,710.
90
$250,000
$193,288
$200,000
$150,000
$100,000
$50,000
$109,710
$116,857
$98,235
$27,238
$33,283
$0
NACUBO Delta Full
Delta
Delta
Delta
Delaware
FTES Attribution Transcript Catalog
Catalog Average
Cost
Direct and Direct Cost Model
Indirect
Cost
Cost
CSU, Chico Mechanical Engineering Degree Cost Outputs per
Method
Figure 9. NACUBO FTES Based Model compared to other models.
Using FTES to distribute overhead makes sense in some situations; however,
generalized FTES calculations do not always reflect the unit-taking averages of all
students. Average unit loads for students vary from semester by semester. Over the last
five academic years, all students at CSU Chico have averaged 13.9 units per semester.
Full-time students have averaged 14.6 units per semester. The current calculation method
assumes a full-time student takes 15 units, so inherently there is error induced in cost
distribution with this method as well. All the previous methods generally fail to account
for costs associated with high contact hour courses.
91
Table 11
NACUBO Costing Method
NACUBO Costing Method
Average
Average
Average
Graduate Direct and Direct
Units
FTES
Indirect
Education
taken by
Conversion Cost per
Degree
Graduates
FTES
Cost
156
10.4
$10,549
$109,710
Unit cost varies by subject/department and year
Costs have been adjusted to 2012 dollars
Average units per graduate 156
Unit Range per graduate 140-209
Formula: (Average Units Taken by Graduates)/15 = FTES
FTES X 2005-2012 Average Normalized Direct and Indirect 2012 Cost per FTES
Contact Hour Costing/Contact Hour Plus Costing Methodology
The final method not widely used in educational costing, outside of medical
schools, is based on contact hours. This differs from the other methods and is the basis
for the CSU-specific method proposed within this research. Contact hour costing
essentially determines the cost associated with actual instructional time at the course
level. This model is different since all other models are based off of units, and course
contact hours do not directly relate to the number of units earned within a course. Within
the mechanical engineering curriculum, there are three unit courses that meet for 45 hours
per semester, as well as 60 and 75 hours. Cost per student is based on the direct
educational expense applied to the actual students enrolled within the course or degree.
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By summing the educational cost of the enrolled student/contact hours, cost can be
broken down by student or degree. The CSU currently does not report contact hours for
faculty, so contact hours were determined through historical instructor schedules. Like
unit-based costing, this method also does discount all non-classroom time a faculty
member spends on scholarship and service. The CSU does not provide any current
activity reports that document faculty time to this level. Contact hour costing does not
easily address non-direct educational expenses, making reliance for these expenses to
remain either unit- or FTES-based.
Contact-hour costing modifications were applied to direct educational costs in the
place of units for both the Delaware and Delta Catalog models to foster comparisons.
Direct educational cost using the Delta Catalog method modified with contact hours is
$38,427. Using the Delaware model modified from a unit basis to a contact-hour basis
provides an average cost of $51,581. Figure 10 provides a visual comparison that shows
in both cases unit-based costing underestimates the direct cost of education when
compared to the contact hour-based costing.
93
$60,000
$52,581
$50,000
$40,000
$38,427
$33,283
$27,238
$30,000
$20,000
$10,000
$0
Delta Catalog Contact Hour
Delta Catalog Unit
Delaware Model - Delaware Model Contact Hour
Unit
Delta and Delaware Unit and Contact Hour Methods
Figure 10. Delta and Delaware Models – direct cost – contact hour v. unit.
The premise of this work has been that unit-based costing underestimates costs
when compared to contact hour costing. Within laboratory-heavy curricular
environments, where there is little connection between contact hours and units, it is very
critical to understand the shortcomings of unit-based costing. Figure 10 shows a 57%
higher average direct educational cost for a mechanical engineering degree at CSU,
Chico using the Delaware model when modified for contact hours.
Contact Hour Plus Model
The Contact Hour Plus (CHP) model, which has been developed as a result of this
research, takes the best of the existing methods and includes additional data that help
define what a degree costs within the CSU. The model reflects direct educational costing
94
based on contact hours, rather than credits, as credits do not well represent the resources
needed for high-contact curriculum. The model, however, retains credits when
distributing all indirect and space costs associated with the education. While the model
was designed to answer the higher-level question of what a degree costs, it is understood
there is often value in the evaluation of student or class level expense. The model is
designed to be flexible based on what the user would like to know and allows for
comparisons that could be internal or external to an institution depending again on what
needs to be answered.
Contact Hour Plus Costing Model Variants
INPUTS
OUTPUTS
USES
1
Direct Educational
Contact Hour Costs
Direct Educational
Cost Per Class/Student or
Total Cost / Number of
Degrees Produced
Compa ring i nstructional costs
between programs or
i ns titutions . Ma y or ma y not
fa ctor degree production a s
needed.
2
Indirect Educational Costs
Distributed to Units
Direct/Indirect Educational
Cost Per Class/Student or
Total Cost / Number of
Degrees Produced
Compa ring total educational
cos ts between institutions .
Ma y or ma y not fa ctor degree
production as needed.
3
Regional Based Square
Footage Leaseholder
Estimate/Unit
Direct/Indirect + Capital
Estimate
Total Cost / Number of Degrees
Produced
Compa ring total educational
cos ts between institutions
fa ctori ng student success a nd
fa ci lities
Figure 11. Contact Hour Plus Costing Model variants with suggested uses.
As previously noted and shown in the Figure 11, the CHP model allows for
costing that can reflect only direct educational cost at a student, class, or degree level
(variant 1). It also allows for indirect and direct costs at the student, class, or degree level
(variant 2). Finally, the model is the only costing method that attempts to value the space
95
used for instruction and embraces the Delta Full-Cost Attribution concept by dividing the
total costs only by the number of degrees that are produced (variant 3).
University Infrastructure and Buildings
As pointed out previously, the costs associated with capital projects such as
buildings or the acquisition of laboratory equipment are generally not included in the
indirect cost of education. As suggested within the literature review, an appropriate lease
rate should be determined to estimate the cost of the space utilized for education. This
lease or rental rate is applied to the occupied space of owned facilities to estimate an
annual space expense. Many factors influence regional lease rates provided in datasets
from Real Estate Information Services (REIS), a provider of commercial real estate
market and research data.
CSU, Chico occupies 1,835,239 square feet of state-supported and state-owned
space used for offices, laboratories, classrooms, support operations, libraries, and
healthcare facilities. There is an additional 1.1 million square feet operated by the
University Foundation or Associated Students which include dormitories, parking
structures, and student recreation facilities, which are not factored into the lease rate
calculations as these are technically separate entities from CSU, Chico. Within the
University, operating units are assigned space for administrative operations, dedicated
laboratory space, and classroom activities. Classrooms are generally considered flexible
and are centrally assigned for use across colleges and departments. After investigating
methods to see if campus space utilization could be drilled down to a department or
96
degree level, it became evident that similar to the assigning of utilization rates to the
library at the degree level, campus records do not support this level of detail.
At CSU, Chico, 66.6% of the campus space is centrally managed for flexible
learning space, administration, and operations. Six and nine-tenths percent of the space is
assigned to College of Agriculture, 2.9% to Behavioral and Social Sciences, 1.4% to
Business, 7.1% to Communication and Education, 3.1% to Engineering, 6.7% to
Humanities and Fine Arts, and 5.3% to Natural Sciences. Within the greater Chico area
the median annual lease rate for mixed-use commercial property is $15.65 per square
foot. This translates to a total annual estimated expense of $28,721,490 for the 1,835,239
square feet of space the university occupies.
Table 12
Estimated Leasehold Value of CSU Chico Facilities
Total Campus Space
2012 Median Annual Chico Area Cost/Square Foot
Percentage of Assigned Space
General Centrally Managed Space
College of Agriculture
College of Behavioral and Social Science
College of Business
College of Communication and Education
College of Engineering
College of Humanities and Fine Arts
College of Natural Science
Total Campus Space
1,835,239
$15.65
66.6%
6.9%
2.9%
1.4%
7.1%
3.1%
6.7%
5.3%
100.0%
Annual Expense
$19,128,513
$1,981,783
$832,923
$402,101
$2,039,226
$890,366
$1,924,340
$1,522,239
$28,721,490
Using the leaseholder finance mechanism to assign a cost to the operation of a
university, the following information provides us a baseline for truly identifying what
97
that total cost of education might be. Leaseholder expense estimations have been
provided in Table 13 on an annualized enrollment, FTES, and unit basis for comparison
purposes only. Unfortunately, due to the inability to determine where students spent time
on the campus, only broad based estimates can be added to the indirect portion of
education cost.
For the purposes of the CHP, the leasehold costs are distributed per unit. This
allows for a differentiation of part-time and full-time students, yet gives an accurate cost
of overhead distribution. By providing the leasehold cost estimate, a better understanding
of the resources needed to support higher education operations are realized. It is an
imperfect science, but brings to the discussion costs generally overlooked within
education.
98
Table 13
Estimated Leasehold Value of CSU Chico Facilities
Total Campus Space
2012 Median Annual Chico Area Cost/Square
Foot
Total Annual Estimated Leasehold Cost
2005-2006 Annualized Headcount
2006-2007 Annualized Headcount
2007-2008 Annualized Headcount
2008-2009 Annualized Headcount
2009-2010 Annualized Headcount
2010-2011 Annualized Headcount
2011-20112 Annualized Headcount
2005-2006 Annualized FTES
2006-2007 Annualized FTES
2007-2008 Annualized FTES
2008-2009 Annualized FTES
2009-2010 Annualized FTES
2010-2011 Annualized FTES
2011-2012 Annualized FTES
2005-2006 Units Taught
2006-2007 Units Taught
2007-2008 Units Taught
2008-2009 Units Taught
2009-2010 Units Taught
2010-2011 Units Taught
2011-2012 Units Taught
1,835,239
$15.65
$28,721,490
16249
16601
17442
17381
17021
15647
15669
Est. Expense Per
Student
$1,768
$1,730
$1,647
$1,652
$1,687
$1,836
$1,833
14540
15048
15855
15898
15492
14640
14688
Est. Expense Per
FTES
$1,975
$1,909
$1,812
$1,807
$1,854
$1,962
$1,955
218,100
225,720
237,825
238,470
217,380
219,600
220,320
Est. Expense Per
Unit
$132
$127
$121
$120
$132
$131
$130
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Specialized Equipment and Holdings
The Contact Hour Plus model also suggests institutional assets are reported, but
these expenses are not suggested to be distributed to the cost of a degree. The costs are
suggested to be reported to provide estimates of asset needs and requirements for startup
or expansion of institutions, colleges, or programs. Also, as pointed out previously, the
costs associated with specialized or dedicated laboratory equipment are generally not
included in the indirect cost of education. To support the mechanical engineering
program at CSU, Chico, capital assets with an acquisition cost of slightly over $1.25
million are used to support the educational operation. Further determining the percentage
of usage of a beaker in a chemistry lab by a mechanical engineering student is not
feasible. It is difficult to report an acceptable one-size-fits-all cost ratio for electronic
equipment that may have a short life span when compared to a manual mill or lathe that
could reasonably be expected to function over 20 years.
Campus wide, inventory assets of over 23,000 property identification numbers
support educational operations. The acquisition cost of all assets currently reported on
CSU, Chico’s inventory report is $43,155,565. In reviewing the literature there was no
mention of the management of traditionally depreciated assets that have exceeded their
lifespan but are still in use or a method or mechanism for the allocation of a value within
an educational institution. CSU, Chico asset acquisition dates span over 50 years, which
presents a challenge to distribute these assets to the cost of a degree. For the purpose of
understanding the capital equipment requirements for offering a degree, to facilitate
100
comparison, and to have information for making strategic decisions about programmatic
expansion, it is suggested the value of the inventory report is included in any documents
as a below-the-line expense.
Contact Hour Plus Model Estimates for a Mechanical Engineering Degree
Using the Contact Hour Plus model for estimating the cost of a mechanical
engineering degree at CSU, Chico provides the following estimates. All dollars have
been normalized to 2012 dollars and costs have been averaged from 2005–2012 to
estimate the indirect educational and space costs using the leaseholder method attributed
to 22 students who were successful in earning a mechanical engineering degree. Figure
12 represents $96,710 of direct educational cost, $157,241 in indirect educational cost,
and $40,560 in space requirements, for a grand total of $294,511 for a mechanical
engineering degree from the two cohorts at CSU, Chico that were evaluated.
MECH
Leaseholder
Space Cost
14%
MECH Direct
Educational
Cost
33%
MECH Indirect
Educational
Cost
53%
Figure 12. Contact Hour Plus costing of a mechanical engineering degree at CSU, Chico.
101
It takes $294,511 to produce one mechanical engineering degree at CSU, Chico
using the Contact Hour Plus variant three, which distributes costs only to the production
of degrees. Is this higher or lower than at another CSU? Are there factors that can
reduce the production cost of a degree? Using mock data modified to reflect literaturebased cost drivers and the CHP model, estimates can be made about the impact certain
types of expenses can have on the cost of a degree.
Table 14
CHP Costing Model Outputs
Total Direct
Total
Total Units Education
Indirect
taken by Cost Education
Majors*
Contact
Cost
Hour
CHP #1
CHP #2
CHP #3
Total
Leaseholder MECH
Space
Graduates
Requirement
6466 $2,127,620 N/A
N/A
6466 $2,127,620 $3,459,302 N/A
6466 $2,127,620 $3,459,302
$892,320
Average
Degree Cost
22
22
22
$96,710
$253,951
$294,511
* All units taken by students within the two cohorts are included until they change majors, leave
the institution, or graduate
Unit cost varies by subject/department and year
Costs have been adjusted to 2012 dollars
Average units per graduate 156
Unit Range per graduate 140-209
Formula(s):
Course Contact Hour Cost Per Student = (Average Hourly Department Level Wage and Consumable X Required
Course Contact Hours)/Aver age Course Enrollment
Total Direct Educational Cost - Contact Hour = Sum of all Cohort Members Course Contact Hour per Student while a
Mechanical Engineering Major
Total Indirect Educational Cost = Sum of Indirect Educational Cost for Cohort Members per Unit while a Mechanical
Engineering Major
Total Leaseholder Space Requirements = Sum of Leaseholder Facilities Cost for Cohort Members per Unit
CHP #1 Degree Cost = Sum Total Direct Educational Cost/Graduates
CHP #2 Degree Cost = Sum Total Direct Educational Cost + Total Indirect Educational Cost/Graduates
CHP #3 Degree Cost = Sum Total Direct Educational Cost + Total Indirect Educational Cost+Total Leaseholder Space
Requirements/Graduates
102
Cost Drivers and System Scale Modeling for CSU 24 Using the Contact Hour Plus
Model
To test the developed Contact Hour Plus model, factors from the literature
reviewed that were deemed to influence educational expense were included within a
mock data set. The mock data set, referred to as CSU 24, allowed for changes to
variables from the CSU, Chico data to compare the impact of cost that could be found at
other possible campuses within the CSU. The mock data could be adjusted to represent
higher or lower student average course enrollment, higher or lower average indirect and
direct educational expenses, and include the regional variables associated with estimating
real estate costs for the use of buildings and facilities. This was done to show a way in
which the Contact Hour Plus model might be used for strategic planning with data
commonly available within the CSU as well as a functional verification of the method.
Course Size
The developed costing model generally depends on class contact hours for
determining expense just like the models that rely on units for costing; a student’s parttime or full-time status is moot. Per much of the literature, average student course
enrollment is the largest driver of direct educational cost. Generally speaking, course
costs go down with more enrollments, as the contact hour cost is divided across more
students within a lecture-only course format. At CSU, Chico, the average course
enrollment mechanical engineering students took was 29. The range of course
enrollment the students took was 1 to 170 students.
103
The CSU 24 data were manipulated to estimate educational costs per unit at a
theoretical campus. To foster comparison, the data set was modified first to reflect higher
average course enrollment and the higher enrollments’ ultimate impact on cost. From the
baseline average course mechanical engineering students took at CSU, Chico with an
enrollment of 29, the data were manipulated to reflect a 10% or an average of addition of
three students per section. The 10% increase in course enrollment was driven by average
student enrollment to course enrollment capacity. Ten percent takes the utilization rate to
95%. Looking only at direct educational costs, adding three students to each class would
reduce the direct educational cost of a mechanical engineering degree from $52,581 to
$47,775 using variant 1 of the CHP costing method.
It should be noted that, generally, for all courses, only a maximum number of
students can enroll within a section. At CSU, Chico the current enrollment rate is 85% of
capacity. Laboratory courses generally have smaller sections than lecture-only courses.
In reviewing alternative campuses, laboratory course sections in the sciences and
engineering are typically found in the 15-20 student range, which is represented by the
data from CSU, Chico. Adding additional students to a course with a laboratory does not
obtain the scale of economy found in a lecture-only environment, as more laboratory
sections must be added to accommodate a larger lecture component. Using the CHP
model does, however, reflect the higher costs overlooked with unit-based costing
methods associated with laboratory time.
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Cost of Living/Comparable Wages
For direct educational expenses, again whether unit- or contact hour-based, labor
wages drive cost. The CSU does maintain a standardized pay scale within the system
essentially designed for pay equity; however, there is variability and range within the
scale between campuses and within campuses. The low range of the scale for an assistant
professor within the CSU pay scale was $62,064 and the high for a full professor was
$174,544. Administration expenses could also see regional pay scale influence as well.
In reviewing California cost of living calculators, the Chico area from where the cost data
were obtained is on the lower end of the scale for the state. A CSU 24 campus located in
a southern California urban area in 2012 maintained an approximately 14% higher cost of
living than the Chico area (see Figure 13).
$300
$250
CSU 24 S. California Urban
Campus COLA Difference
(2012 Dollars) Per Unit
$200
$150
$100
Campus Average Direct
Educational Cost (2012
Dollars) Chico Area Per
Unit
$50
$0
05-06 06-07 07-08 08-09 09-10 10-11 11-12
Academic Year Cost Differences
Figure 13. Estimated direct educational unit cost location example.
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As discussed, faculty wages drive much of the direct educational costs. Using
Chico, California as the baseline, the cost of living index reported a 33.2% higher cost in
San Jose, California; 14.7% higher cost of living for Los Angeles, California; and a 7.6%
lower cost in Fresno, California (U.S. Department of State, n.d.). These cities all have
CSU campuses. Faculty within the CSU are all paid on the same wage scale; however,
the scale allows for significant flexibility to provide appropriate compensation based on
skill sets, experience, demand, and regional wage factors (CSU, 2010). Using the second
variant of the CHP costing model, and adjusting wages 14% higher on both direct and
indirect educational expenses, to reflect a theoretical southern California CSU provides
insight on what wages do to educational cost. All other data points besides wages were
held constant with the actual CSU Chico data to provide comparison.
MECH
Leaseholder
Space Cost
12%
MECH Direct
Educational
Cost
33%
MECH Indirect
Educational
Cost
55%
Figure 14. Contact Hour Plus Costing of a mechanical engineering degree at theoretical
CSU with 14% higher wages.
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As previously reported using the third CHP model, a mechanical engineering
degree at CSU, Chico cost $294,511. Using the mock data that reflect an area with 14%
higher wages, while keeping all other variables the same, show the same degree costing
$333,148. Higher wages reflect higher costs of living based often on real estate costs.
The next section reviews the impact that factors the cost of the facilities needed for
education.
Location
A large cost driver within education often overlooked is the building
infrastructure used to house campus classrooms, laboratories, administration, and
operations. The larger the institution, on an enrollment basis, the more space is generally
required for all operations. Generally speaking, the larger higher-enrolled CSUs tend to
be in more urban areas, with generally higher estimated square footage costs than lowerenrolled schools. With the exception of CSU, Fresno, the schools with over 20,000
students enrolled include CSU, Fullerton, Los Angeles, Pomona, Sacramento, San Diego,
San Francisco, and San Jose (CSU, 2013a). It should be noted that in more urban areas,
there is often a greater part-time student enrollment that often seeks night and weekend
courses, which can provide better space utilization than is found in residential campuses
(CSU, 2013b). This can be due to part-time or working students who seek to enroll in
courses during traditionally off hours that include nights and weekends. As a result, an
institution with double the student body, and enough off-hour critical mass, does not
always require double the square footage. As Figure 15 depicts, the cost of square
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footage varies dramatically throughout the state. As has been discussed, none of the
current costing models address the concern associated with assigning a cost to the
buildings in which courses are taught.
45
$41.91
40
35
$32.53
30
25
$20.01
20
$15.65
15
10
5
0
Chico
Fresno
San Francisco
CSU Location
Los Angeles
Figure 15. Annual average lease cost per square foot with various areas of California
(REIS).
108
MECH
Leaseholder
Space Cost
22%
MECH Direct
Educational
Cost
30%
MECH Indirect
Educational
Cost
48%
Note: (Representative of an urban Southern California CSU campus)
Figure 16. Contact hour plus costing of a mechanical engineering degree at theoretical
CSU with 14% higher wages and 108% higher facilities cost.
This final mock data manipulation shows that all things being equal to CSU,
Chico as far as student enrollment, required square footage, etc., but adjusting for a
theoretical CSU that has 14% higher wages for faculty, staff, and administration and
leaseholder cost estimates 108% higher than Chico, the cost of a degree is much higher
using the CHP model three. As previously reported using the third CHP model, a
mechanical engineering degree at CSU, Chico cost $294,511. With these data
manipulations, the same degree would cost $376,953.
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Conclusion
Within this chapter, the cost of a mechanical engineering degree has been
reported using existing costing models, as well as the Contact Hour Plus model, which
has been specifically derived for the CSU. The CSU 24 mock data set was then
manipulated to estimate the cost of a degree if classes were larger by 10%, if wages were
higher reflecting a different region within the state, and if facilities costs were included at
regionalized rental rates. In Chapter 5, these findings are analyzed and the implications
of the results and methods are reviewed.
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Chapter 5
IMPLICATIONS, RECOMMENDATIONS, AND CONCLUSIONS
Introduction
The previous chapter presented the data derived from the different costing
methods, along with an introduction to the CHP methodology. The CHP methodology
was presented with both the CSU, Chico mechanical engineering and the CSU, 24 data,
in an effort to provide context on how the tool might be used if implemented for
comparison and strategic planning purposes. This work sought to standardize the answer,
at least within the CSU, to the question of a degree at what cost? Determining the cost of
a degree is not about weighing the value of one degree over another. Determining cost is
about identifying best practices, finding efficiencies, and establishing what factors are
actually driving expenses in educational operations and degree production.
The differences to the cost of a degree vary across the methods used.
Understanding what each method is attempting to determine, and the appropriate
application of each method, is critical for answering the questions regarding the multiple
ways (direct, indirect, and attributed) the cost of a degree can be framed. It is for this
reason this work was undertaken. As was shown from the Master Plan for California
Higher Education (CDE, 1960), cost, cost management, and the goals of efficiency and
continuous improvement set the foundation without defining how these activities should
be conducted. With financial efficiency, strategic management, and improvement being
the goal for the CSU Chancellor’s office, this work sought to further the understanding
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and provide context for the cost of a Bachelor’s degree within the California State
University. To be useful, a costing method should provide valid and reliable information
that includes as many cost-influencing factors as possible on which to base data-driven
decisions. Such decisions associated with cost can include, for example, program
resource needs or requirements, student success intervention strategies, and strategic
growth or contraction planning.
Attempting to answer the question of what a degree costs is an outcomes-based
frame in which to view cost. A number of the methods reviewed for this dissertation
(Delaware, NACUBO) were designed to answer the question of what it costs to educate a
student for a year, rather than over the duration of time needed to earn a degree. Further,
with the exception of the Delta Full Attribution method, costs incurred by students who
do not earn the degree are negated in costing models. Within California, there is an
increased focus on outcomes along with a degree completion agenda that is being pushed
from CSU Chancellor’s office. The concept of applying all degree-seeker costs only to
those who complete a degree is logical considering this perspective. Generally speaking,
the more graduates, the more degrees in which to distribute costs to, effectively reducing
the total cost of the individual degree. This perspective frames the implications from the
CHP-based methods developed for this dissertation.
Twenty-three campuses exist in the largest bachelor’s degree granting system
within the country. Through geography, size, and liberal arts focus, duplications and
redundancies exist within the system, and probably always will. If, as a system,
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educators and administrators could evaluate cost comparably, it may be possible to find
constructive efficiencies that could be scaled to other campuses. Finding efficiencies can
free up resources for other areas of educational need. This chapter explores the data
presented in Chapter 4 and discusses the implications of degree-based costing, reviews
and analyzes previous models cost outputs, further explains the reasoning for the CHP
model, reviews the opportunities related to, and feasibility of, CHP model
implementation, suggests areas for additional future research, and makes final
recommendations. Finally, it discusses the following question: What is the most accurate
method for determining the cost of producing a bachelor’s degree within the California
State University system?
Broad Implications of Degree-based Costing
With any costing methods (Delta Full Attribution, CHP) that distribute costs only
to those students who successfully graduate, there is valid concern for a student accessdriven system such as the CSU that focusing only on the cost of the production of degrees
may have unintended consequences. The CSU currently accepts the top one-third of all
California high school graduates. By distributing the cost of all students to only those
who graduate, the cost of a degree appears much higher than methods that do not
approach degree cost this way (CHP or Delta Full Attribution degree costs are much as
compared to the other methods). The concern regarding this is that institutions might
seek to limit access for students with the goal of admitting students who are less
expensive to graduate. Universities may also limit their offerings of majors or degrees
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that have higher costs or lower graduation rates, as is often found in engineering and
sciences. Perhaps with a focus on degree production and cost, institutions might
potentially reduce the quality and rigor of a program in order to increase the number of
graduates.
These are all valid concerns for the students, taxpayers, educators, policymakers
and legislators of California. With a growing focus on student success as well as
efficiency at both the CSU system and site levels, it does appear it is time for such
information and such a perspective when reviewing degree costs. Within California,
educational budgets have currently stabilized and are even increasing slightly. However,
given California’s cyclical budgetary situation, it is only a matter of time before another
downturn spurs new cuts. Only through robust degree level cost analysis can educators
focus strategically on student success with programs that are efficient, given the
resources provided. Only with an understanding of cost can leaders then focus on
allocation for excellence.
Review of Previous Costing Models
The costing models evaluated have been used in education but are not
standardized, nor are they widely adopted as methods to determine degree cost. The
models were designed to all answer slightly different questions associated with cost. Due
to these designed differences, method output differences should not be taken as
inaccuracies. The inaccuracies occur when using methods that underestimate cost due to
method-induced error, such as unit-based costing for high contact hour courses, where
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unit-based costing often underestimates the cost. While the proposed CHP model
addresses the shortcomings of other models by determining cost using contact hours,
factoring degree production, and estimating capital facilities costs, issues with the value
of laboratory assets and utilization rates remain. Through reviewing existing costing
models, applying data makes clear both the strengths and weaknesses of each existing
model. Figure 17 compares the cost estimates for a Bachelor’s degree in mechanical
engineering at CSU, Chico for each of the models.
$350,000
$300,000
$250,000
$200,000
$150,000
$100,000
$50,000
$0
$294,511
$253,951
$193,288
$116,857
$109,710 $96,710
$33,282 $27,238
Costing Method Output Calculation Differences
Figure 17. Results of all existing costing models evaluated (modified to report degree
cost in some cases).
Delaware and Delta Catalog Costing Models
In reviewing the findings, the only two models that focus solely on direct
educational expense are the Delaware Model and the Delta Catalog direct cost model.
These two models negate any costs not incurred within the classroom. The Delta Catalog
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method provides a very important baseline for cost and provides an idea about the
theoretical lowest cost possible associated with a degree. The Catalog model baseline
can also be used to determine a factor that can be used for efficiency metric. For
example, the CSU, Chico mechanical engineering program is based on 132 units. The
average mechanical engineering graduate at CSU, Chico took 156 units, or 18% more
units than needed. It should be noted that program variations within the system influence
cost and builds in degree cost variance from campus to campus. There are engineering
programs within the CSU based on a range of 120-132 required units. Through
comparing this percentage of units over the CSU, Chico required units with other
mechanical engineering programs within the CSU, a measure and benchmark of cost
effectiveness could be made. From a cost perspective, looking at the Delaware ($33,282)
and Delta Catalog ($27,238) direct cost models for degree costing reflect a difference in
calculated outputs of just over $6,000, or 22%. If the Mechanical Engineering program
at CSU, Chico was using the theoretical Delta Catalog model for estimating cost, this
$6,000 variance associated with 22 graduates would underestimate degree cost by over
$132,000. Without creating system level benchmarks, it is unknown if this is efficient or
not.
NACUBO and Delta Transcript Costing Models
The NACUBO method, and the use of FTES as the basis for measurement, is
rooted in the ability to address part-time versus full-time student status with respect to
state funding formulas that refer to FTES as the driving allocation factor. As FTESs are
116
unit based, direct educational costs essentially remain unchanged from other unit-based
costing methodologies. The only difference is how indirect costs are distributed to the
student body.
$118,000
$116,857
$116,000
$114,000
$112,000
$109,710
$110,000
$108,000
$106,000
Delta Transcript Cost
NACUBO
Method Output for Direct and Indirect Degree Costing
Figure 18. Delta versus NACUBO method comparisons.
The NACUBO method is not designed to determine the cost of a degree, rather it
is designed to determine the cost of education per FTES at a specific institution over the
duration of one academic year. FTES is typically a funding methodology, rather than a
costing metric. In attempting to adapt the model for degree costing, the average number
of units a student took for the mechanical engineering degree (156) was divided by 15
(15 is the number of units estimated to be taken by a full-time student within the CSU).
This provides an estimate of 10.4 FTES worth of educational expense. Multiplying 10.4
FTES by the average 2005-2012 academic year cost per FTES of $10,549 provides a
degree cost estimate of $109,710.
117
Generally speaking, the Delta Transcript model will provide a better cost estimate
than NACUBO, as it distributes indirect costs to the actual unit level rather than broad
sweeping FTES generalizations. The NACUBO method, however, requires much less
data with which to estimate cost when compared to the Delta Transcript method. The
need for less data with a method that is often quicker, and in some cases easier, to
calculate than other methods, does support the use of the NACUBO method when
precision is not needed or a rough estimate is appropriate. In the case of the CSU, Chico
data, NACUBO gets the cost estimate within 6% of the transcript model. This may be an
acceptable tolerance or variance, and with further research it may be possible to get
results closer than within 6% if an appropriate ratio or scale factor could be developed.
Underestimating cost is essentially over promising resource effectiveness, which for any
leader can be problematic, since it can call into question credibility and competence.
With proper context, the theoretical cost associated with the Delta Catalog method is
good, as it provides the lowest possible cost target.
Delta Full Attribution Costing Model
The Delta Full Attribution model is the only model that begins to evaluate the
program’s ability to produce graduates while factoring the resources used to educate all
students who started on the path to a specific degree. It is an imperfect science when
applied to the CSU, as currently campuses do not track students who leave the institution.
These leavers have all the costs they incur toward a degree distributed to the cost of the
degree for those students who are successful. These students may leave and ultimately
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finish a degree elsewhere, yet their costs are not tracked to where they earn the degree.
Students who eventually graduate would currently not be counted as successful due to a
lack of a longitudinal statewide student set. There are challenges for accounting with
students who pursue double (or more) majors as well as multiple minors or change
majors along the way. With the significant double counting often done in most multiple
major cases, assigning costs to a particular degree is all but impossible. Challenges with
this model also exist for students who start a degree and then change majors. Should all
the costs be assigned to the degree in which the student started or to the degree a student
earned? In the case of departure, which unit costs should be applied to what degree path
especially if the student changed majors multiple times? As was noted earlier, for the
purpose of this work, all costs incurred while being declared a mechanical engineering
major (freshman at CSU, Chico must declare a major) would be applied to the attributed
cost of the mechanical engineering graduates. Any costing model that bases cost on
production or outputs will face challenges along these lines.
The Delta Full Attribution model begins to evaluate a number of items valuable to
educational leaders, policymakers, taxpayers, and lawmakers. First, it is the only model
that looks at outcomes from the perspective of the number of students successful in
completing their degrees. With the exception of the CHP model developed, the other
previous costing methods do not factor the costs associated with students who do not
complete the degree. Distributing the costs of all students to only those who graduate
provides context regarding retention, persistence, and student success, as they relate to
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conversion rates of student to graduate. Investigating and arguing for the resources that
would be needed to improve graduation conversion rates for students can be supported
using costing models that factor degree completion while providing a perspective that
reflects the totality of the investment needed to produce a specific degree. The CHP
model borrows this framework of cost from the Delta Full Attribution method.
Shortcomings and Challenges Related to Previous Costing Models
All the existing models fall short when it comes to representing the actual costs of
courses and degrees, as well as estimating the cost of space required for universities to
operate, and the assets needed for instruction and campus operation. These shortcomings
are related to the difference in contact hour versus, credit costing, the general lack of
accounting for the costs incurred by unsuccessful students, and the lack of inclusion of
capital infrastructure needs for instruction.
Negating the identified shortcomings of the previous degree costing methods, it is
reasonable to still ask why none have been fully implemented within the CSU. This is a
politically charged issue, since evaluating costs without accounting for access, the liberal
arts focus of the CSU and other such factors could create a situation in which degree
programs that are at the heart of a university’s mission would be cut. This concern can
drive opposition from those whose professional lives depend, to a certain extent, on
academic freedom and a liberal arts core. The faculty-driven governance model within
the CSU, as opposed to legislative or gubernatorial-controlled higher education systems,
can perhaps run counter to outcomes-based discussions occurring nationally. In addition,
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given the number of different methods, the different questions they each answer, and lack
of consensus about findings and about costs to be included or not, evaluating costs is
confusing.
Through design, the CHP model derived from this research focused on the
addressing the concerns with the function and implementation of the prior models. The
CHP model provides different levels of cost calculation through variants that will allow
the users to compare data at various points (direct, direct, and indirect, etc.) while
standardizing methods and terminology at a time when the cost seems to be at the
forefront of educational discussions.
Contact Hour Costing Model Plus (CHP) Applications
The Contact Hour Plus (CHP) model was developed because of the shortcomings
of the previous degree costing models. The CHP model provides multi-point
comparisons that can be managed by the user to provide information that could be needed
by educational administrators when asked for the cost of a degree, without the need to be
familiar with all the previously reviewed methods. As such, the flexibility of the three
CHP variants allows the user to answer questions across the board and foster the
comparisons that may include direct and indirect educational costs, and the expense
associated with facilities and actual degree production often overlooked or deemed too
difficult to quantify.
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CHP Variant One
Three variants of the CHP model were developed specifically for benchmarking
and strategic planning needs. By using CHP 1, the contact hour direct educational cost
variant, campuses can use the outputted degree cost in a number of ways. CHP 1
provides the cost of instruction for a degree and allows quick calculations to create
projections of what scenarios impact this cost. Direct educational costs are generally
driven by faculty wages, course enrollment size and, to some extent, consumables needed
for instruction. The CHP 1 model can be used for strategic planning in relation to wage
changes over time, estimating the impact of generally lower priced lecturers to tenure
track faculty, and class size. Externally, comparing the direct educational cost between
campuses may reveal best practices or benchmarks of efficiency that can be scaled to
similar programs within the system.
CHP Variant Two
The second variant not only allows for the discussions included in CHP 1 but
starts to look at operational costs of institutions. CHP 2 will most likely be utilized as an
external comparison metric, as an acceptable ratio of direct to indirect cost can be
established. At CSU, Chico, direct educational cost is only 62% of incurred indirect cost
for a mechanical engineering degree. With further work in this area, using the CHP
model to compare mechanical engineering programs within the CSU, it could be
determined if this is an acceptable ratio or not. To highlight this in application, 14%
higher wages for both indirect and direct labor costs were applied to the CSU, Chico data
122
to provide an estimate of the same degree path located in a higher cost of living area. In
this case, direct educational costs are only 61% of the total indirect cost for the same
degree. This shows how regardless of regionalized cost factors that comparison still can
be made.
CHP Variant Three
CHP 3, the third variant, attempts to quantify the expense associated with the
space needed for both direct and indirect educational operations. Additional data would
need to be gathered for each site within the CSU reflecting regional space costs. For the
CSU, recognizing the localized cost drivers could be of value for strategic growth plans.
As has been discussed, bond payment for facilities is centrally managed and truly
disconnects these costs from site-specific education. As the actual and compared CSU 24
mock data results provided, locality alone could result in facilities cost estimates being
reflective of one quarter of the cost of a degree (see Figure 19).
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$400,000
$350,000
$300,000
$250,000
$200,000
$150,000
$100,000
$50,000
$0
$84,365
$40,560
$40,560
$157,241
$96,710
$181,341
$181,341
$111,247
$111,247
CSU, Chico MECH
14% COLA MECH
Degree Total $294,511 Degree Total $333,148
MECH Direct Educational Cost
14% COLA/So Cal
Leaseholder MECH
Degree Total $376,953
MECH Indirect Educational Cost
MECH Leaseholder Space Cost
Figure 19. CHP Costing Model– CSU, Chico mechanical engineering degree compared
with CSU 24.
In evaluating the outcomes of CHP variant 3, the difference between the cost of a
degree between CSU, Chico and an urban location in southern California is 22%,
assuming the FTF-to-graduate conversion rate and course enrollment averages are the
same. The total cost for the production of 22 mechanical engineering graduates at CSU,
Chico is $6,479,242, as compared to $8,292,966 when adjustments were made to the
mock data to represent the regional cost differences. Recognizing the same degree costs
22% more in one locality as compared to another must be considered as the CSU seeks to
balance access, cost, and growth. As has been highlighted, none of the prior cost
methods attempt to include space as part of degree cost.
124
CHP Model Limitations
Even with the CHP model, there are still shortcomings of cost reporting. The
CHP model suggests that assets required for operation and instruction are reported below
the line. Below the line asset reporting does not allow for the allocation of cost to a
timeframe or degree. This is similar to how NACUBO traditionally recommended
managing capital building costs. As previously discussed, the variant three CHP method
provides a solution to facilities costs through the rental or leaseholder rate. Determining
a rental rate for regional leased space is a relatively simple solution, but unfortunately the
same cannot be said about determining a rate for capital assets needed for operation and
instruction. While a shortcoming of all costing models related to physical operational
and instructional assets, reporting the acquisition costs does provide an idea about the
startup needs of an institution or program, which can be strategically considered if
expanding a degree program.
Currently, the CHP model focuses only on first-time freshmen and does not
specify how to determine the cost of degrees for students who are double majors, transfer
students, or leave the institution where they started, and yet ultimately earn a degree.
While it is expected that with further refinement the CHP model can be expanded to
include these student groups, at this time this represents a large functional concern. This
concern is primarily fostered by CSU database limitations.
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CHP Implications and Opportunities
With the increased scrutiny that public higher education is likely to continue to
see, given the current focus on completion, the CHP costing method provides
opportunities for strategic management. Users must be aware, however, of both the
positive and negative implications that may arise from its use. The following discusses
the opportunity to standardize costing within the CSU, the impact of graduation rate on
the CHP model, and the possibility of unintended consequences that could arise from the
use CHP. Further discussions review possible ways the model could be used by
educational administrators or policymakers.
Cost Standardization
This work has outlined the existing methods that could be used to estimate costs
within the CSU and has shown that the calculated cost of a degree can vary by as much as
10 times. Even if CHP variant three, which estimates the facilities cost, were discounted,
the CHP variant two model still represents a 9 times factor over the Delaware model cost
estimate (see Figure 20). The opportunity regarding this is one of standardization.
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$350,000
$294,511
$300,000
$250,000
$200,000
$150,000
$100,000
$50,000
$33,282
$0
Delaware Model Cost
CHP 3
Low to High Cost Comparison
Figure 20. High and low costs for a mechanical engineering degree.
Student Success and Graduation Rates
The CHP method provides a starting point to discuss the cost and benefit of
student success strategies that might increase the number of graduates. Increasing the
number of graduates provides a larger pool across which to spread the cost of all the
students, effectively lowering the cost of a degree. This is especially true with students
who are close completers. Close completers have taken many units, have incurred high
costs, and are within one semester or 15 credits or units from earning a degree. In
evaluating the transcripts of students in this cohort, two students exceeded the number of
units required for degree but were one or two classes short of graduation. If to foster
student success and a higher graduation rate, these two students were encouraged and
worked with to take the needed classes to finish the degree, this action would reduce the
cost of a mechanical engineering degree by almost 10% associated with the cohorts that
127
were studied (see Figure 21). Implementing strategies to turn those students who are
close to graduation into graduates would be a priority if costs were viewed from an
output-based calculation and aligned with the current CSU Graduation Initiative, which
seeks to increase the number of successful students. Institutions could also seek to
evaluate the intervention cost of converting students who obtained junior status (90 units
or more) into graduates. In the case of CSU, Chico, there were also two additional
students who met this threshold within the two cohorts evaluated. Ninety plus units
without completing a degree adds significantly more incurred cost than a student who
leaves after their first year.
$350,000
$294,511
$270,236
$253,951
$250,766
$233,056
$216,446
$300,000
$250,000
$200,000
$150,000
$100,000
$96,710
$88,651
$81,832
$50,000
$0
CHP #1
CHP #2
CHP #3
Cost of a Mechanical Engineering Degree - Impact of Graduates
22 Graduates
24 Graduates
26 Graduates
Figure 21. Impact of additional graduates on the cost of a degree.
Increasing the graduation rate of students, especially those who are close to a
degree, is a strategic method by which to reduce the cost of degree. In the case of CSU,
Chico, by increasing the graduates within the mechanical engineering sample by 9%, an
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almost 10% reduction in degrees cost would be realized. With an 18% increase of
graduates (22 to 26 graduates), the cost per degree would be reduced by 15%, assuming
the focus was on the close completers and high unit completers (90+) and that they did
not need to take any courses more than once. Table 15 provides information on the
additional units needed for these students to graduate and the resultant costs from each
method for the cost of a degree at CSU, Chico as found from the data and as estimated
with two and four additional graduates from the cohorts studied showing the impact of
graduation rate on the cost of a degree.
Table 15
Impact of Increasing the Gradation Rate
Total Direct
Total
Total Units Education
Indirect
taken by Cost Education
Majors*
Contact
Cost
Hour
CHP #1 MECH Degree CSU, Chico
CHP #2 MECH Degree CSU, Chico
CHP #3 MECH Degree CSU, Chico
6466 $2,127,620 N/A
N/A
6466 $2,127,620 $3,459,302 N/A
6466 $2,127,620 $3,459,302
$892,320
Total Direct
Total
Total Units Education
Indirect
taken by Cost Education
Majors*
Contact
Cost
Hour
CHP #1 (+2 Graduates)
CHP #2 (+2 Graduates)
CHP #3 (+2 Graduates)
Average
Degree Cost
22
22
22
Total
Leaseholder MECH
Space
Graduates
Requirement
6478 $2,127,620 N/A
N/A
6478 $2,127,620 $3,465,730 N/A
6478 $2,127,620 $3,465,730
$892,320
Total Direct
Total
Total Units Education
Indirect
taken by Cost Education
Majors*
Contact
Cost
Hour
CHP #1 (+4 Graduates)
CHP #2 (+4 Graduates)
CHP #3 (+4 Graduates)
Total
Leaseholder MECH
Space
Graduates
Requirement
Average
Degree Cost
24
24
24
Total
Leaseholder MECH
Space
Graduates
Requirement
6542 $2,127,620 N/A
N/A
6542 $2,127,620 $3,499,970 N/A
6542 $2,127,620 $3,499,970
$892,320
$96,710
$253,951
$294,511
$88,651
$233,056
$270,236
Average
Degree Cost
26
26
26
$81,832
$216,446
$250,766
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Educational Cost Drivers
Also evident from the mock data is that when the CHP model is used for costing,
the results do not represent what the literature finds as the largest driver of educational
cost. The majority of the literature suggests that high educational costs, at least where
researchers focused on direct educational costs only, are directly related to the number of
students enrolled in a section (Brinkman 1981, 1989, 2000; Middaugh 2005; Paulson,
1989). Modifying the CSU 24 mock data to reflect a 10% increase in student course
enrollment within the mechanical engineering degree path did not relate to a 10%
reduction in the direct cost of a degree. Looking only at direct educational costs, adding
three students to each class (increasing the average course size from 29 students to 32)
would reduce the direct educational cost of a mechanical engineering degree from
$52,581 to $47,775 using variant 1 of the CHP costing method.
With the CHP model, direct educational costs, or those costs that could be directly
impacted by the number of students enrolled within a course, only accounts for 30-33%
of the cost of a degree when using both CHP variants 2 and 3, with both actual numbers
and in CHP 24 mock data situations. This suggests educational leaders and policymakers
might look elsewhere when seeking efficiencies in education before immediately
increasing class size in the name of cost savings.
The CHP model identifies that indirect educational costs are the largest driver in
the cost of a degree. Understanding that these indirect costs exceed the direct costs of
education should foster questions about the size of an institution and the number of non-
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instructional personnel employed at an institution. Indirect costs include administration,
staff, and all non-capital operational expenses. Using the CHP model, 67% ($197,801) of
the cost of a mechanical engineering degree was associated with indirect and leaseholder
costs. With the CSU 24 data, higher wages and lease rates increase the non-direct portion
of the degree to 70% or $265,705. Indirect costs are part of education. If scaled to the
CSU, the CHP model could be used to benchmark appropriate indirect costs per degree.
The CHP model alone does not make recommendations on appropriate levels of indirect
cost; it only provides data that can be compared between campuses. It may be that
indirect costs go up with increased student services that increase graduation rate, which
effectively reduces the cost per degree.
Possible Unintended Consequences
One potential unintended consequence of a degree costing method is that it
distributes the costs of the unsuccessful students to those who earn their degree relates to
access. It may be tempting to tighten access requirements to enroll students who are
more likely to be successful. Raising the entrance requirements for the CSU could
potentially increase the number of graduates; however, the unintended consequences of
attempting to reduce degree cost in this way would be limiting access for the very
students the CSU was designed to support. This same concern would exist if course
requirements were tightened, essentially creating a gatekeeper early in the degree process
to effectively “weed out” students who might not be successful later in the program.
However, the earlier this action occurs, the lower the cost associated with students who
131
may not complete the degree. The CSU was designed for access, and while it may be
financially prudent to attempt to reduce costs through these types of efforts, the CHP
model was designed for effectiveness and efficiency comparisons, not to limit
opportunity.
The model will also continue to provide context to the discussion on what degrees
the CSU should be offering. There will be questions about high cost, low output degree
programs, and if the CSU should be offering such degrees. Limiting the offering of
certain degrees within the CSU may also create regional access concerns even though
students are much more mobile than when the CSU originated. For example, while an
inconvenience, students who grew up in Sonoma potentially have the ability to attend
school in Los Angeles if they are interested in a major not offered locally. Engineering
programs within the CSU have already been generally consolidated.
Political Feasibility of Implementation
Some political challenges may come from adoption any standardized method of
degree costing within the CSU. These challenges may also be part of the reason previous
costing methods may have had limited implementation. For the CHP model, as it frames
cost through the lens of the production of graduates and seeks to increase awareness of
traditionally under reported educational operational costs, it is likely to be received with
some level of possible skepticism.
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Faculty Concerns and Perspective Regarding Degree Cost
Faculty unions will express concern with any costing method such as the CHP
method that looks at outcomes or degree production. These concerns could include that
degree production is not currently part of the retention, tenure, and promotion (RTP)
process for faculty. Instructional faculty are held to effective teaching standards for RTP,
but not the production of degrees. Faculty are disconnected from degree production, as
they must focus on student content competency. If faculty focused on degree production
rather than discrete subject student learning, they are put in a difficult position of
competing values between content mastery and graduate enabler. Additionally, using the
CHP model to compare degree costs between campuses and discounting what was found
with this research could point to higher cost efficiencies that might be found with larger
class sizes. Workload concerns and issues are almost always contentious. Finally, from
the faculty union perspective, any costing work that may ultimately change resource
allocation formulas is likely to be met with resistance.
CSU Administration Concerns and Perspective Regarding Degree Cost
The faculty will not be the only group with concerns should a CSU-wide costing
method be implemented. While there is nothing but hypothetical data to compare the
cost with, a cost of $294,511 to produce one mechanical engineering graduate at CSU,
Chico seems high. The CSU Chancellor’s office has publically sought improved
efficiencies and cost reduction, but has primarily focused on university operations rather
than education delivery or content. The efficiency focus has generally been on
133
operations, as management has attempted to minimize potential conflict with academic
freedom, unionized faculty, and entrenched academic culture. Including student success
into degree costing does cross into the education portion of the equation. There may be
concern that this mode highlights all the costs incurred for students who do not graduate
are spent for naught and brings to the forefront concerns that range from educational
effectiveness to perceived top-heavy management and administration.
Suggestions for Future Research
By determining the cost of a degree within the CSU using the same method at all
23 campuses, many questions, concerns, and shortcomings arise that have fostered many
thoughts on areas that would be worth investigating. Expanding on the basis for degree
costing opens many possibilities for information that can ultimately help the CSU and
California seek better financial management while supporting and expanding student
success.
Suggested Research Area One
Recognizing it may take significant time before the data management systems for
the CSU can adequately provide instructional contact hours for all students, the first area
of research would be to sample a number of programs at a number of campuses using the
CHP method outlined within this research. As the system wide data do not currently
report contact hours, it is possible a ratio or scale factor could be determined that could
quickly estimate CHP-like costs off existing unit-based data sets. It is suggested that
both high and low laboratory content degrees are chosen to determine a factor for
134
different types of majors. This would be an interim step taken to represent contact hour
costs until a database with faculty contact hours could be created.
Suggested Research Area Two
The second area of suggested research is to further investigate the student
behavior-based cost drivers associated with course taking. Student course taking
behavior directly impacts the cost of a degree. A student who took personally valuable,
but not degree-required courses, would increase the overall cost of a degree. What is the
relationship of the number of units required in a major (120-132 units) to contact hours,
which is reflective of actual student behavior system wide? As the CSU looks to trim
unit numbers to a goal of 120 for all programs, are contact hours remaining the same
while unit count is being manipulated, thus not actually reducing costs? As some majors
reduce units within a degree path, are students still taking the same amount of time to
earn the degree, and filling out their semester schedules with unrequired courses, thus not
actually reducing cost? Further, can a relationship be determined between unit
requirements and the number of courses a student repeats or takes? As the CHP model
distributes costs to the number of degrees produced, intervention strategies need to be
developed that allow a student to explore a degree path, but provide them alternatives
before significant cost is incurred if they are likely to not be successful. Determining
these conversion points will go a significant way to determining cost efficiencies as
related to student success, while keeping student access in mind.
135
With any costing method that uses degree production as a metric of cost, the
actual behavior of students must be reflected in the method. Further research is needed to
develop an accounting and distribution mechanism of costs associated with students who
are double majors, change majors multiple times, and yet ultimately graduate. There may
be a method in which costs of unapplied courses are distributed to the original major, and
applied courses are applied to the granted degree. For double majors, it may be possible
to assign 50% of the costs of each double counted course to the particular earned degree.
However, this is an area where further work is needed.
Suggested Research Area Three
Determining a method by which to distribute asset cost to a degree requires
further research. There must also be a method that could be adapted to estimate a longterm asset and the distribution of cost for library books, laboratory equipment, etc. and
account for these costs in a way they could be applied to a degree. Similar to the high or
low laboratory curriculum question, this is an important area, especially with programs
that require significant technology or limited-use equipment in order to offer the degree.
Using the CHP model, which distributes costs across only successful students,
brings to the forefront questions regarding what can be done to increase graduation rates.
The costs and benefits of intervention programs must be investigated. A longitudinal
study of student success related to student support services does need to be conducted.
Increasing graduation rates are likely to have significant cost, so determining what works
and scaling those solutions to the CSU is critical. Determining where the maximum
136
value or return can be made to support the largest number of students to graduation is
needed to foster student success.
Suggested Research Area Four
Finally, this work provides a method by which to determine base costs or
expenses with the production of a degree. This provides the foundation to start a return
on investment (ROI) discussion. Further work would be needed to determine the average
cost of a degree from the CSU in every program within the system. From there, ROI
could be investigated from both the state and student perspectives. Investigating ROI
would also start to look at degree alignment and workforce needs of the state along with
the rate of return each degree option would provide. However, the ROI discussion cannot
start until a uniform costing method is adopted on which to base all other calculations, no
matter where within the CSU the degree was granted.
Recommendations and Conclusion
Standardized Methodology
First, the CSU should consider adopting a standard costing methodology that can
be utilized at all campuses, while meeting the need to compare costs at various points
(Direct, Direct and Indirect, etc.). To be useful, educational costing methods must have
the flexibility to answer the needs of the specific question being asked. As the CHP
model has shown, the methods should also be as inclusive as possible as to not mask any
possible costs, such as buildings or administrative overhead, from being considered as
part of the cost of a degree. Further, any costing method must be outcome-based or
137
focused. As has been shown, it is useful to quantify the cost of students who enroll
within a program who are unsuccessful, as it provides an idea of the totality of
educational investment. It is worth mentioning that institutions should not focus solely
on degrees as the only quantifiable outcome for fear of diluting the rigor in the name of
perceived high achievement, but it is recommended that any costing method does include
student success. Based on the model construction and related analyses, this dissertation
offers the recommendation that the CSU adopt the CHP model.
Data, Benchmarking, and Comparison
Fostering comparisons between institutions is highly valuable in order to seek
benchmarks and best practices. The comparisons of costs, through all variants within
three CHP models and the data presented, provides context and application for how the
model can be utilized for comparing costs. The models can work at the class level and
answer questions of ideal enrollment or the influence that regional wage or expertise
factors have on the cost of a degree. The model also works to review both direct and
indirect costs, should provide information on typical situations, and suggests acceptable
ratios of indirect versus direct cost of a degree. By including student success and
distributing costs only to a completed degree will firmly answer the question of what a
degree costs to produce.
To encourage best practices, it is recommended a centralized CSU-wide database
should be made available to administrators with which to compare site-to-site costs per
degree. This database, which is suggested to also include contact hours for all courses
138
and faculty within the CSU, should provide insight into system wide workload concerns,
optimum campus size, and effective resource utilization. Many of the current CHP
method limitations are data needs to be addressed.
Conclusion
This work sought to further define and answer the question of what does a degree
in the CSU cost. As understood when the California Master Plan was written, and
definitely in the era in which education operates in 2014, understanding cost, production,
and seeking efficiency should be concerns of all involved. Providing the information on
which to base data-driven decisions can help CSU institutions and the system effectively
and strategically utilize the limited resources available for their mission.
139
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