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Public policy in higher education for economic progress: a qualitative study of quantitative instrumentation on California and New York

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Public policy in higher education for economic progress: a qualitative study of quantitative instrumentation on California and New York

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Content Running head: PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 1

Public Policy in Higher Education for Economic Progress: A Qualitative Study of Quantitative
Instrumentation on California and New York
Sharif S. Islam

A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERISTY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION

December 2019

Copyright 2019 Sharif Islam
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 2

Acknowledgments
I give my endless appreciation and love to my mother Dr. Sondos Mohammed-Saleh
Islam. It was her inspiration that first started me down this path, and she is the role-model to
which I am eternally driven, and shall never arrive. I would like to express my love and give
special thanks for the support and care of my father Sadad Ibrahim Islam, without which I would
not have been able to take on this great endeavor.
Writing this dissertation has been an intensive and life-changing process. I would like to
thank and acknowledge the infinite wisdom and patience of my Chair Dr. Patricia Elaine Tobey.
She has remained an inspiration and a guiding light that kept me moving even during the darkest
nights. I would also like to display deep gratitude and appreciation for my committee members
Dr. Robert Filback and Dr. Steve Michael.
Thank you.

PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 3

Table of Contents
Abstract ......................................................................................................................................... 10
Chapter 1: Introduction ................................................................................................................ 11
Overview and Background ........................................................................................................ 11
Statement of the Problem ...................................................................................................... 13
Purpose of the Study .............................................................................................................. 15
Significance of the Study ....................................................................................................... 16
Limitations ............................................................................................................................. 17
Delimitations ......................................................................................................................... 18
Definition of Terms ............................................................................................................... 19
Organization of the Study ...................................................................................................... 22
Chapter 2: Literature Review ....................................................................................................... 23
Historical Trends in American Governmental Investments ...................................................... 24
Railroads and Agriculture ...................................................................................................... 24
The G.I. Bill and Beyond....................................................................................................... 27
Public Policy and Higher Education ......................................................................................... 29
The Development of Endogenous Growth Theory ................................................................... 37
The Solow Growth Model ..................................................................................................... 37
Human Capital and Endogenous Growth .............................................................................. 40
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 4

Human Capital Theory. ...................................................................................................... 40
Endogenous Growth Theory. ............................................................................................. 42
Romer. ................................................................................................................................ 43
Technology and human capital. ......................................................................................... 45
Measuring human capital. .................................................................................................. 46
Trends in Finance and Governance of the American Higher Education System ...................... 49
Governance ............................................................................................................................ 50
Marketization. .................................................................................................................... 52
Financial Aid ......................................................................................................................... 55
Higher Education in Transition ............................................................................................. 57
Summary ................................................................................................................................... 58
Chapter 3: Methodology .............................................................................................................. 60
Rationale.................................................................................................................................... 60
Sample ....................................................................................................................................... 62
Indicators and Data Collection .................................................................................................. 63
Policy ..................................................................................................................................... 63
Autonomy. ......................................................................................................................... 63
Funding. ............................................................................................................................. 65
Funding student’s education........................................................................................... 65
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 5

Funding for equity. ......................................................................................................... 66
Incentives in higher education funding. ......................................................................... 67
Performance ........................................................................................................................... 68
Graduate employment and graduation. .............................................................................. 68
Graduate employment. ................................................................................................... 69
The throughput ............................................................................................................... 69
Size of the student body. .................................................................................................... 70
Student body and population. ......................................................................................... 70
Transition students. ........................................................................................................ 70
International students. .................................................................................................... 71
Research productivity and attractiveness. .......................................................................... 72
Scientific publications. ................................................................................................... 72
International visibility and attractiveness. ...................................................................... 73
Fellowships and grants. .................................................................................................. 73
Connectivity. .................................................................................................................. 73
Economic Output ................................................................................................................... 74
Knowledge intensive activities. ......................................................................................... 75
Labor productivity. ............................................................................................................ 75
Quantitative Analysis ................................................................................................................ 76
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 6

Factorizing Variables ............................................................................................................. 77
Factor Weights ....................................................................................................................... 77
Assessing Policy Impact ........................................................................................................ 78
Limitations ............................................................................................................................. 78
Analysis and Discussion............................................................................................................ 79
Chapter 4: Results and Discussion ............................................................................................... 80
Results ....................................................................................................................................... 81
Policy ..................................................................................................................................... 81
Organizational, policy, and financial autonomy. ............................................................... 82
Limitations of autonomy scale. ...................................................................................... 82
Funding........................................................................................................................... 83
Expenditure per student relative to GDP per capita. ...................................................... 83
Expenditure on financial aid as a percentage of total public expenditure on education at
the tertiary level. ............................................................................................................. 84
Role of formulas and contracts in funding mechanism X. ............................................. 84
Performance ........................................................................................................................... 86
Research productivity and attractiveness. ...................................................................... 87
Scientific publications within the 10% most cited scientific publication worldwide as a
percentage of total scientific publications. ..................................................................... 87
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 7

Number of universities in the top 500 ARWU ranking per million inhabitants per state.
........................................................................................................................................ 88
Number of incoming National Science Foundation grants per million inhabitants. ...... 88
Number of incoming National Science Foundation Graduate Research Fellowship
Program awardees. ......................................................................................................... 89
Number of public-private co-publications by state in proportion to population. ........... 90
Graduate employment and graduation. .............................................................................. 92
Employment rate by degree attainment of bachelor’s degree and higher in proportion to
the civilian labor force.................................................................................................... 92
Graduates in proportion to the total enrolled students per state. .................................... 92
Size of the student body. .................................................................................................... 94
Full time and part time transfer students. ....................................................................... 94
Undergraduate and graduate students aged 20-24 in proportion to state population of the
same age. ........................................................................................................................ 94
Inward mobile students as a percentage of the student population in the host state. ..... 95
Economic Output ................................................................................................................... 96
Innovation. ......................................................................................................................... 97
Percentage of employees in knowledge intensive fields related to total employment. .. 97
Real GDP per capita. ...................................................................................................... 98
Discussion ................................................................................................................................. 99
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 8

Chapter 5: Conclusion................................................................................................................. 101
Summary of Findings .............................................................................................................. 102
Implications for Practice ......................................................................................................... 105
Recommendations for Research .............................................................................................. 107
Conclusion ............................................................................................................................... 108
References ................................................................................................................................... 110
Appendix ..................................................................................................................................... 125

PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 9

List of Tables
Table 1. Autonomy Factor Indicators ........................................................................................... 83
Table 2. Funding Factor Indicators ............................................................................................... 86
Table 3. Research Productivity and Attractiveness Factor Indicators .......................................... 91
Table 4. Graduate Employment and Graduation Factor Indicators .............................................. 93
Table 5. Size of the Student Body Factor Indicators .................................................................... 96
Table 6. Innovation Factor Indicators ........................................................................................... 99
List of Figures
Figure 1. The Solow-Swan Model of Economic Growth. .......................................................... 39
Figure 2. Human Capital Theory ................................................................................................. 41
Figure 3. Endogenous Growth Theory ......................................................................................... 43
Figure 4. Process model.. ............................................................................................................. 61
Figure 5. Linear Relationship ....................................................................................................... 76

PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 10

Abstract
The dissertation sought to pilot a European quantitative instrument on two American states in
order to highlight indicator shortcomings. The purpose being to identify those aspects of the
instrument that would require adjustment before appropriate application to all states. The
theoretical framework of the instruments consists of Human Capital Theory and Endogenous
Growth Theory, and quantitatively explores the impacts of public policy on the innovation
economy through higher education performance. Results display those indicators in need of
adjustment, how data collection practices could be improved, and how the instrument could be
made more suitable for American states. This dissertation represents a qualitative first step in the
development of quantitative instrument focused public policy, higher education, and economic
growth.
Keywords: higher education, public policy, economic development, instrumentation,
replication

PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 11

Chapter 1: Introduction
Overview and Background
The roles of the government, the higher education system, and society are ones that are
both intimate and in flux. The state provides resources and funding to higher education systems
under the paradigm that such investments positively impact social development as well as their
own political agenda. Among the services provided by the higher education system is the
development and diffusion of new knowledge under a principal of community service through
the training of graduates (Romer, 1990; Becker & Toutkoushian, 2014; Becker & Andrews,
2004). This public higher education system is, however, largely dependent on funds from state
and federal sources in order to act in the best interest of society (Heller D. E., 2014). Society, by
funding the higher education system indirectly through tax payments, is then expected to reap the
benefits of higher education (Becker & Toutkoushian, 2014; McMahon, 2009; Deming, Goldin,
& Katz, 2014). These benefits largely take the form of socioeconomic development, as a portion
of the growing economic earnings of these individuals will inevitably end up with the state
through taxation.
While the system of relationships is theoretically satisfying, obstacles arise in practice.
Both governmental actors and taxpayers require evidence that public funds invested in higher
education are not only being appropriately applied, but that individuals are reaping the benefits
of those investments (Deming, Goldin, & Katz, 2014). The funding of higher education becomes
an issue of accountability as higher education institutions are increasingly obligated to provide
evidence of their performance (Ehrenberg, 2014).
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 12

As calls for greater accountability are made, higher education institutions are under
increasing pressure to provide measures of their performance or face cuts to funding (Heller D.
E., 2014). For example, the graduation rate of an institution may point to a certain level of
performance, but it does not connect with the economic impact on communities or as a function
of the public funds invested. As demand for nuanced evidence rises, there is a need to develop
an instrument that will capture the performance and economic impact of the triumvirate
relationship between government investments, higher education output, and economic growth.
Pragmatism then calls for measurable indicators to justify public funds invested in higher
education, to ensure those funds are being used in an optimal manner, and that communities reap
socioeconomic benefits.
This dissertation sought to apply a European instrument to American states in order to
garner quantitative insights on certain dynamics of these relationships. More specifically, the
researcher identified those areas, of a European quantitative instrument, that would need to be
adjusted in order to make the instrument appropriate to the new setting of American states. The
original authors of the instrument created a set of indicators that quantitatively explore the
relationship between public policy and higher education, and higher education with economic
growth. While the instrument by no means provides answers to all concerns regarding these
relationships, it does provide insight to and evidence of how these relationships impact
performance. To achieve similar insights in regard to American states, the instrument must first
be calibrated. In order to appropriately calibrate the instrument, it is necessary to identify those
areas that fall short so that they can be tended to while maintaining the conceptual framework of
the original.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 13

Statement of the Problem
May a set of indicators developed to analyze the relationships between public policy,
higher education performance, and economic growth in European Union countries be applied to
American states? More specifically, which individual measures would require adapting in order
for such an instrument to be validly and reliably applied to all American states? The assumption
being that because this instrument was designed specifically for European Union countries, there
are some aspects that would not be suitable for American states, so that an unchanged instrument
would not be valid or reliable. The problem under focus in this dissertation is related to the areas
of the instrument that would require translating before the instrument could be applied to the new
setting.
The impact of governmental investments is important to investigate because government
entities will often provide an influx of funds into higher education in order to boost a specific
socioeconomic sector. For example, the Servicemen’s Readjustment Act of 1944, also known as
the G.I. Bill, was passed to educate and reintegrate veterans returning from World War II
(Executive Order 13607, 2012). A significant portion of this bill provided tuition payments and
living expenses for veterans wishing to return to school. Although there do exist criticisms of
the bill, it is also often credited as one of the sources for post-war economic growth and for
contributing to the American supply of human capital. Other examples of government
investments include Pell Grants, which were created as a subsidy to assist financially challenged
individuals in attaining a higher education degree (U.S. Department of Education, 2016). More
recently, The Office of the President released a federal five-year strategic plan to encourage the
growth of students enrolling in science, technology, engineering, and math fields (Committee on
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 14

STEM Education, 2013). Again, such investments are made with the implicit understanding that
these graduates will contribute to human capital and the socioeconomic growth of their
communities (Becker & Toutkoushian, 2014). Success in this regard, however, is not always a
given, which is why instruments are required to gauge performance. For example, one common
measure for gauging the value of education in general is by assessing the lifetime earnings of
individuals by level of educational attainment (Tamborini, Kim, & Sakamoto, 2015).
The underlying assumption (Hoareau, Ritzen, & Marconi, 2012) is that an increase in
benefits for one actor should improve outcomes for the other actors, with an implicit time delay.
An increase in funding to the higher education system should have a positive impact on
socioeconomic development of the communities served. It does take several years, however,
before a student studying at a college or university graduates and begins contributing
economically. While seemingly simplistic in theoretical terms, the assumed positive benefits of
this relationship have proven difficult to measure in a meaningful manner where the
socioeconomic benefits are clear to the entities that provide resources (Hoareau, Ritzen, &
Marconi, 2012; Ehrenberg, 2014). It is relatively simple to state the graduation rate of a cohort,
or that an economy has grown by a certain percentage, but how much of that economic growth
can be attributed to higher education outputs? In such a scenario, the government may gauge
higher education performance not just by the number of graduates, but by the ability of those
graduates to positively impact economic growth through their contributions to the working stock
of human capital (Deming, Goldin, & Katz, 2014).
The call for greater accountability is a problem because public higher education systems
are largely dependent on the state to fund their activities (Dynarski & Scott-Clayton, 2014). If
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 15

higher education is to have a significant impact, it must also reach out to those on the lower end
of the socioeconomic spectrum and whom often do not have the funds to pay high tuitions
(Heller D. E., 2014). In order to continually reach that segment of the population, higher
education institutions depend on the state for subsidies and resources so they can provide tuitions
prices at less than cost of the services provided (Becker & Toutkoushian, 2014). The state,
however, is increasingly demanding of evidence of the positive outcomes of their funding, an
issue that becomes especially acute when the economy is performing poorly and resources
become scarce (Deming, Goldin, & Katz, 2014; Ehrenberg, 2014). Existing benchmarks, such as
the graduation rate for example, become increasingly inadequate measures of higher education
performance as demands for evidence of student learning outcomes rise (Ehrenberg, 2014). The
state demands further specific evidence of a positive socioeconomic impact with the possibility
of restricting resources should no appropriate evidence arise. The ability to display positive
socioeconomic impact in terms of higher education performance is therefore critical to the
livelihood of higher education. There is a need for more instruments that can provide
quantitative evidence of performance to decision makers and constituents. This instrument has
the potential to at least partially fulfill that demand, but only if it can be applied to American
states.
Purpose of the Study
This dissertation addressed the problem by piloting the European instrument (Hoareau,
Ritzen, & Marconi, 2012a, 2012b; Hoareau, Ritzen, & Marconi, 2013), in its original form, on a
small number of American states. The tool measures the impact of public policies on higher
education performance, and then the impact of higher education performance on the innovation
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 16

economy. The purpose of these indicators is to determine whether certain policies towards
higher education are positively contributing to the creation of an innovative economy under the
assumption that a more innovative economy will grow faster and be more competitive (Hoareau,
Ritzen, & Marconi, 2013). By piloting the instrument in a small number of American states, the
dissertation determined those specific areas in need of calibration and in what manner. Which
indicators require adjustment to make the instrument as a whole appropriate for American states?
Why does the indicator not work in its current form, and how would it need to be adjusted?
Significance of the Study
This dissertation is significant because it has initiated the development of an instrument
that would give decision makers, higher education practitioners, and the public greater insight
and knowledge, based on quantifiable measures, of the impact of public investments in higher
education on the economy. Any measure, however, is only as good as its level of validity and
reliability (Creswell, 2014). Although a direct application of the instrument may be more
satisfying, a pilot is necessary in order to determine those aspects of the instrument that must be
adjusted, and how.
A fully calibrated instrument would provide lawmakers with better information about
their relationship with the state higher education system, as well as provide evidence to
constituents as to the impact of these decisions. Higher education institutions and systems may
gain greater insight into how to improve performance to positively impact socioeconomic
development, at least in the form of innovative economies. Institutions would be able to provide
additional evidence of their long-term economic impact on communities. Finally, citizens may
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 17

have greater confidence that their tax dollars are being spent wisely, and that their communities
benefit from the subsidized activities of higher education (Hoareau, Ritzen, & Marconi, 2012).
While the actual development of a fully calibrated instrument was beyond the scope of
this dissertation, it does represent a necessary first step in order to develop an instrument that is
both valid and reliable in measuring aspects of these relationships. Such an instrument would
expand the scholarly literature by providing an additional measure of the relationships and
impacts these entities have on the other, as opposed to a measure in isolation. This dissertation is
significant because it provides the roadmap to developing such an instrument in the future.
Limitations
The major limitation has to do with the shift in the premise of the original instrument.
The set of indicators was designed and applied to the countries of the European Union (Hoareau,
Ritzen, & Marconi, 2012). This means that the indicators were designed to deal with national
level higher education systems, and how these systems interact with their accompanying nations
and impact their communities. As this study seeks to take those indicators and apply them in an
American state setting, there are some implicit limitations that must be kept in mind.
Although there is overlap, clearly American states are different entities than European
countries. It cannot be assumed that because these indicators were successful in providing
insight in a European nation context, that they will be able to provide those same sorts of insights
if applied to American states. Because this specific limitation is actually quite significant, this
study intended to test the viability of these indicators on American states. The limitation of the
new setting was also the main reason only two American states were sampled.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 18

The other major limitation has to do with the indicators themselves. Because the
instrument was designed for a European setting, a certain amount of translation was necessary to
apply it to an American setting. While some indicators remained unchanged, for example the
size of the student body, others required some form of translation in order to be applicable to an
American setting. For example, an indicator including the number of incoming Marie Curie
fellows, a European fellowship program, would not be applicable in an American setting. In
such circumstances, the study sought an equivalent indicator that was available to the new
context and maintained the intent and purpose of the original. How these indicators were
translated and applied is discussed further in Chapters 3 and 4.
After conducting the study, both these limitations remained obstacles to the successful
development of the instrument for a new environment. For example, some data measures that
were available for European Union countries were not available for American states. The search
for other indicators that could adequately replace the original, although possible at times, was
also not possible at other times when the substitute indicator did not adequately satisfy the intent
and purpose of the original. While presenting a challenge, these limitations have provided value
in identifying and highlighting those areas of the instrument that most need tending before future
application to American states.
Delimitations
This dissertation was focused on the degree to which the instrument could be applied to
American states as opposed to European countries. Specifically, the researcher sought to
highlight those aspects of the instrument that did not apply to the new setting. The intent was
that future research may address those shortcomings and design an instrument that would be
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 19

valid, reliable, and provide utility for public policy in higher education. This is the reason only
two American states were sampled, because the dissertation was focused on the validity of the
instrument rather than actually applying the instrument, in which case all American states would
be needed. While this dissertation has laid down the foundation to appropriately translate the
instrument, it represents a qualitative first step in the process.
The researcher limited the scope of the study to the instrument and its shortcomings.
While brief discussions of possible fixes or changes are had, there was no proposal made for a
fully readjusted instrument that is valid to American states, as such a feat would require a
separate dissertation and greater resources.
Definition of Terms
The following is a list of terms. Each term shall be accompanied by a definition and how
the term will be used throughout the study. These will also include words or terms that are
interchangeably used with the following terms:
• Economic Output: Economic output was understood, for the purposes of this
dissertation, in terms of economic innovation and technology.
• Endogenous Growth Theory: Theoretical umbrella of economic thought emphasizing
that “economic growth is an endogenous outcome of an economic system, not the
result of forces that impinge from outside,” (Romer, 1994, p. 3).
• Human Capital: “The stock of skills that the labor force possesses,” (Goldin, 2014, p.
1)
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 20

• Indicators: “An indicator provides evidence that a certain condition exists, or certain
results have or have not been achieved,” (Brizius & Campbell, 1991, pp. A-15).
Indicators are required in an accountability system so that decision makers may have
access to short and long-term data to make the most appropriate decisions (Horsch,
1997). In the case of this study, the entire set of indicators may be referred to as an
“instrument” or “tool”. Each individual indicator is named after what it is measuring
and has a numerical representation in order to gauge performance.
• Innovation: Any technological change that improves labor productivity.
• Narrative Research: Humanities-based form of research in which lives of individuals
are explored, and the final product is often a descriptive narrative of both the subject
and researcher (Creswell, 2014). For the case of this dissertation, narrative research
is focused on the experiences of the researcher adapting a quantitative instrument to a
new setting.
• Performance: In terms of this study and higher education, performance referred to the
activity outputs of higher education institutions (Hoareau, Ritzen, & Marconi, 2012).
• Postpositivism: A deterministic worldview where causes generate outcomes and
objective studies of measurable variables are needed. Also known as the scientific
method (Creswell, 2014).
• Pragmatism: A worldview that “arises out of actions, situations, and consequences
rather than antecedent conditions (as in Postpositivism). There is a concern with
applications… and solutions to problems,” (Creswell, 2014, p. 10; Patton, 1990).
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 21

• Qualitative Research: “The process of research involves emerging questions and
procedures, data typically collected in the participant’s setting, data analysis
inductively building from particulars to general themes, and the researcher making
interpretations of the meaning of the data,” (Creswell, 2014, p. 4).
• Quantitative Research: “An approach for testing objective theories by examining the
relationship among variables. These variables, in turn, can be measured, typically on
instruments, so that numbered data can be analyzed using statistical procedures,”
(Creswell, 2014, p. 4).
• State: The term “state” was used in several ways. In theoretically abstract
discussions, such as during the literature review, the state was used interchangeably
with government. Although a state may also refer to a country, the term was not used
in that manner during this study. In those cases, the specific terms of “Nation” or
“Country” were used. In context of methodology and the dissertation itself, states
refer to American states, such as California or New York, and state governments were
referred to directly as state governments versus federal government.
• STEM: An abbreviation for science, technology, engineering, and math (U.S.
Department of Education, 2015).
• Technological Change: “Improvement in the instructions for mixing together raw
materials,” (Romer, 1990).
• Title IV: Refers to Title IV of the Higher Education Act of 1965, which authorizes
student aid programs that provide direct federal funding to students pursuing
postsecondary education (Hegji, 2018).
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 22

• Translate: Used in this study to refer to the import to America, from the European
Union, a set of indicators specifically designed for a European setting. The word was
used in this study to refer to the process of applying small changes to specific
indicators in order to make them viable in the new environment while maintain the
original measure. The original instrument is in English, and as such no translation of
language was necessary.
Organization of the Study
In order to gauge the appropriateness of the set of indicators in an American setting, the
states of California and New York have been selected as a test sample. The assumption was that
should the set of indicators prove appropriate for California and New York, then they would also
be applicable to the remaining American states specifically in regard to publicly available data
measures. Both the reasons for choosing these states and the impact on research design will be
discussed further in Chapter 3. The remainder of the paper is broken down accordingly. Chapter
2 consists of a review of the literature with a brief history of American governmental
investments, a discussion of public policy and higher education, an analysis of Endogenous
Growth Theory and the development and measurement of human capital, and end with a
discussion on the finance and governance of higher education in America. Chapter 3 covers
methodology by discussing how the original study was replicated and translated from a European
setting to an American state context. Chapter 4 displays and discusses the results of the research
and the implications. Finally, Chapter 5 concludes the study and makes suggestions for future
research.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 23

Chapter 2: Literature Review
The great burden set upon higher education is to prove its socioeconomic contributions in
order to justify continued public investments. This presents a challenge to higher education
organizations because it tasks them with quantifying what is essentially a highly variable
qualitative experience. The issue is compounded further when considering that higher education,
at best, may only contribute to economic development indirectly through the development of
human capital.
In discussing how institutions of higher education contribute to greater national or
regional development, it is often more helpful to apply a macroeconomic perspective. By doing
so, higher education systems take place within a greater, and more complex, ecosystem; rather
than focusing on higher education in isolation. While this may certainly present a challenge,
academia and history provide foundational work upon which one may be able to further clarify
the role of higher education in economic development.
The literature review will begin with a brief historical discussion of American
governmental investments in industry towards economic growth. This is important to cover so
that one may comprehend the role of government in developing modern America. This will then
be followed by a discussion on autonomy and higher education. The discussion takes place from
a more global perspective to cover the differing forms of relationships depending on the context.
The conversation then moves on to Endogenous Growth Theory (EGT). EGT lays down the
theoretical framework upon which the study is based. EGT, although not exclusively concerned
with education, is focused on those internal, or endogenous, factors that contribute to sustained
national or regional economic growth (Romer, 1994; Lucas Jr., 1988). Doing so will provide
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 24

nuance as to the indirect relationship between higher education and economic growth. This shall
be accomplished by focusing on the evolving theoretical perspectives on the human contribution
to long-term economic growth, as well as how that contribution is measured. Finally, the review
will end in a more pragmatic space with a discussion on trends in finance and governance of the
American higher education system to give the reader a practical sense of the ecosystem.
Historical Trends in American Governmental Investments
A review of the patterns of investments in infrastructure and, indirectly, the economy is
necessary in order to comprehend the current context within the historical spectrum. It is
important to highlight the motivation behind these investments, and to question whether the main
beneficiary was intended to be the recipient of the funds or another party altogether. The goal of
this section is to achieve a greater understanding of the role of government investment in the
economic development of the United States of America.
Railroads and Agriculture
During the turmoil of the American Civil War, federal legislation was passed with the
intention of uniting and developing the country (Brown J. , 2012). The year 1862 seems to be
particularly significant when discussing legislation, as two major acts were passed. Although
they would both receive modifications to certain provisions throughout the following years, it is
significant to note both these bills passing, not only in the same year, but also in the midst of a
brutal civil war. These were the Pacific Railroad Act of 1862 (Haney, 1968) (shall herein be
referred to as The Pacific Railroad Act) and the Morrill Land-Grant Act of 1862 (Brown,
Pendleton-Julian, & Adler, 2010) (shall herein be referred to as the Morrill Land-Grant Act)
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 25

The Pacific Railroad Act was a government effort that sought to serve a dual purpose.
On the one hand, the states needed to be united due to the conflicts and anxiety of the Civil War.
Secondly, it was seen as an effort towards the settlement of the public domain through the
harnessing of nature (Kammer, 2017). While legislators sought to invest in the infrastructure in
order to promote traffic, the majority of the capitalists at the time preferred to wait for rising
traffic before taking on such a massive investment (Greever, 1951). The Pacific Railroad Act
tended to capitalist anxieties by supplying them with land they could use to finance the endeavor.
Between 1850 and 1871, roughly 130,000,000 acres were handed to railroad tycoons (Kammer,
2017).
The Morrill Land-Grant Act adopted a similar strategy, but to a different client. In this
case, the recipients were states instead of capitalists. Each state was allotted 30,000 acres of land
to act as an endowment towards public higher education and specifically the “agricultural and
mechanical arts” (Pearson & Atucha, 2015). This act would eventually be expanded with the
Hatch Act of 1887, which allotted federal funds to state land-grant colleges in order to develop
agricultural experiment stations and disseminate new information from their findings (Seals,
1991). The Morrill Land-Grant Act would then be expanded in 1890 to include annual federal
financial support for those colleges. The Smith-Lever Act of 1914 would then build upon the
Hatch Act of 1887 in the interest of disseminating new knowledge to farmers, but to do so from a
more instructional, rather than academic, perspective (Seals, 1991).
These descriptions are not to imply that the legislations were perfect. They did
experience shortcomings and criticisms. In the case of the Pacific Railroad Act, the greed of the
railroad tycoons became a serious impediment to the growth of many communities (Fleisig,
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 26

1973). For example, the Northern Pacific alone had over 3000 formal land contests, and
accusation of corruption were not uncommon (Kammer, 2017). In the case of the Morrill Land-
Grant Act, elitism may have been more of an issue than greed. Although these endowments
were intended to fund agricultural research to the benefit of farmers, many farmers found
themselves completely disconnected from the research conducted at public colleges. Legislators
would eventually pass the various expansions of the Morrill Land-Grant Act to address these
shortcomings (Brown, Pendleton-Julian, & Adler, 2010). The Smith-Lever Act attempted to
remedy the problem by shifting the dissemination of new findings towards home instruction and
a more vocational rather than academic character (Seals, 1991).
In both cases of the Pacific Railroad Act and the Morrill Land-Grant Act, government
investments were intended to lay the groundwork towards future stability and growth. In the
case of the railroad, improved transportation methods would facilitate growth in commerce and
efficiency (Brown J. , 2012). In the case of the state colleges, the intention was to improve
agricultural practices towards sustainable sustenance (Pearson & Atucha, 2015). The importance
of both these endeavors becomes especially significant when considering the economic and
agricultural damage that had occurred during the Civil War. In both cases, the government used
industry as mediator to benefit communities and/or the citizenry.
These legislations did not take place in isolation, but they are important to highlight
because they are historical examples of American government investment in industry to boost
development and growth. In both cases, the recipients of these large investments were not the
intended ends in themselves, but the means towards the end of improving the lives of the
citizenry. The second part of this historical discussion will skip past a number of decades
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 27

towards the post-World War II era and beyond. The purpose is to display that while the method
or intended target of government investments may shift, there remained an effort to boost growth
through investments in industry.
The G.I. Bill and Beyond
Due largely to the turmoil of World War II (WWII), the federal government passed the
Servicemen’s Readjustment Act of 1944 (Also referred to as the G.I. Bill). The intention of this
bill was to provide benefits to veterans of WWII to reintegrate them into civil society (U.S.
Department of Veterans Affairs, 2017). These benefits were various and included tuition
payments for schooling and a living expense. It also granted veterans access to low interest
loans and mortgages, and provided them with unemployment compensation (Altschuler &
Blumin, 2009). Sending veterans back to school was a large part of retraining them towards
professional development so they could achieve employment and become active members of
society. Although the bill certainly has critics and was not perfect, many historians consider the
bill a political and economic success (Spaulding, 2000). More specifically, by including tuition
payments in the bill, the investment was able to build and develop the stock of human capital that
would contribute to American economic growth post-WWII (O'Donnell, 2001).
The Federal Aid Highway Act of 1956 (also known as the National Interstate and
Defense Highways Act) represented a paradigm shift of the federal role in the highway system.
Before the passing of this legislation, road development was relegated to the state and local level,
with federal funding accounting for about 10% of those costs (Netzer, 1957). The Act shifted the
payment model so that the federal government would cover 90% of the costs while states paid
for the remaining 10% (Weingroff, 1996). The Federal Aid Highway Act of 1956 was passed at
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 28

the same time as another bill which addressed the financial mechanism to cover federal costs.
The Highway Revenue Act of 1956 increased the gas tax and imposed other user fees for
highway users (Weingroff, 1996).
From a national defense perspective, the interstate highway system would allow for the
efficient transportation of military units should the need ever rise. Another impact of the
investment had to do with community development and the focus on automobiles as the main
form of transportation (Weingroff, 2017). Whichever perspective one may favor, the impact of
the federal investment on the lives of citizens, and the development of communities, was
significant.
The Higher Education Act of 1965 was legislation passed as part of President Lyndon B.
Johnson’s Great Society domestic agenda (Dynarski & Scott-Clayton, 2014). The purpose of the
act was to provide resource support to colleges and universities while also providing financial
assistance to the students they serve (Rose, 2016). It is worth noting that this legislation was part
of a greater Great Society domestic agenda, the intention of which was to alleviate poverty and
social injustice (Heller D. E., 2014). Such an agenda makes clear the motivation behind this
investment in higher education, and that these investments represent a means to an end.
This review of legislation is far from complete or comprehensive, but it is intended to
display a pattern of purpose in government investments. Government investments are often used
to boost growth or activity in industries that may not have enough momentum to achieve the
same results on their own accord. In the case of infrastructure, one may refer to the investment
in a national railroad system and then, further into the future, a national interstate system, so as to
boost transportation and the movement of people. The initial federal investment in higher
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 29

education institutions was motivated by a desire to develop agricultural technologies, and those
investments continued as the demand for agricultural knowledge shifted. The investment in
returning veterans after WWII also resulted from a need to pivot away from a war economy,
higher education institutions at this point being very different from the agriculturally focused
institutions of the past. It would be understandable if the success of the G.I. Bill, especially the
educational aspects, was some of the motivation behind the Higher Education Act of 1965
(Heller D. E., 2014), except with a different target audience. While they may certainly have their
critics, government investments pay out their dividends in the form of improved socio-economic
developments amongst varying communities. A significant portion of the modern world and its
accompanying systems, from roads to higher education, can be credited to early governmental
investments.
Public Policy and Higher Education
In considering the impact of public policy on higher education, one is essentially focusing
on issues of autonomy and to what degree an organization or entity is free to act upon its will to
achieve goals. The research on autonomy and higher education expanded on the concept by
defining and clarifying categories into essentially: financial autonomy, personnel autonomy, and
academic autonomy (Levy, 1979).
In discussing the relationship between higher education institutions and state and federal
governmental entities, it is helpful to establish a baseline for the debate. This can be found in
Levy’s 1979 literature review studying the relationship between higher education, politics, and
the public interest. In analyzing the debate at the time, Levy manages to capture themes that
remain salient to this day. With growing demand from the government that higher education
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 30

institutions serve the public interest, one must then ask to what degree, or within which bounds, a
university is authorized to act to pursue those goals (Levy, 1979).
Because of public expenditures on higher education, some sort of accountability is
required (Levy, 1979), but accountability to whom? Levy identifies the government as the
obvious answer, but also highlights how the university is accountable to itself, to the governing
board, to peer review, and to the markets (Levy, 1979). Although, Levy notes, accountability to
the market may conflict with accountability to the public interest, even in cases where both
public and private exist at the same time. In discussing accountability to the government, Levy
does note that the literature was much more in favor of accountability to state governments as
opposed to the federal government (Levy, 1979).
Levy (1979) described the general belief in a limited government role in higher education
as a "distinct U.S. bias" (p. 24), but also notes a clear trend in the literature addressing the
desirability of state involvement. The author concludes, despite the need for further study on the
topic, that what was most significant in his review was the idea that government interests and the
public interest do not necessarily overlap (Levy, 1979), and that further study was needed to
clarify the relationship. If, however, the functional assumption for increased accountability is
that higher education institutions may put their own interests ahead of the public interest, then it
seems entirely possible that the government may do the same (Levy, 1979).
While Levy (1979) was successful at capturing the essence of the debate and at
recommending areas for future study, he was not able to provide much statistical information to
inform future research and activity. For such data, one may turn to the studies conducted by
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 31

James Fredericks Volkwein and his colleagues during the 1980s and 1990s (Volkwein, 1986;
Volkwein, 1986; Volkwein & Malik, 1997).
Volkwein was able to consolidate financial and personnel autonomy into a single factor
of financial/personnel autonomy (Volkwein & Malik, 1997) (Volkwein, 1986). By doing so,
Volkwein was able to focus his studies on issues of academic and financial/personnel autonomy.
His studies were largely conducted on the state level, focusing on the interplay between state
characteristics, campus characteristics, and state regulation and management flexibility
(Volkwein & Malik, 1997). The reason behind this strategy is that state behavior, within the
United States of America, often varies widely depending on the history, culture, and other
characteristics of the state. In addition, doing so allowed the author to divide each category into
a specific number of characteristics or indicators. State attributes were gauged using 37
indicators under the 3 broad categories of economic, social/demographic, and
political/bureaucratic. Campus characteristics were subdivided into four broad categories of
organizational size, financial support, mission/complexity/diversity, and quality/selectivity
(Volkwein & Malik, 1997). State regulation was measured through campus responses of survey
items and scales addressing 47 types of flexibility and control (Volkwein & Malik, 1997).
It is interesting to note that the instrumentation used by Volkwein over the decades
improved over time. This is to say that the instruments of the 1990s built upon those of the
1980s, and were able to provide the authors with more nuance. Volkwein used data from a
variety of sources including IPEDS, NCES, Census data, and telephone interviews. As these
instruments became more nuanced over time, the authors were able to improve their
understanding of the role of autonomy in higher education (Volkwein & Malik, 1997).
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 32

In spite of much of the rhetoric in favor of higher education autonomy and deregulation,
multiple studies by Volkwein found very little to no significant evidence that higher levels of
state regulation negatively impacted higher education quality or productivity, and that many
colleges and/or universities have actually experienced increased flexibility since 1983. Instead,
the studies found that the size of the institution and access to resources had a more significant
impact on higher education quality. One gap in the studies, which Volkwein does address, is the
impact of increased regulation on managerial satisfaction, and the impact this can have on
productivity (1997).
What is interesting to note of the Volkwein studies is the differing nature of the
relationship between the state and higher education depending on the state in focus. Volkwein
was able to empirically isolate and highlight the concepts of academic autonomy and
administrative autonomy in a higher education setting (Volkwein & Malik, 1997), and then show
how these dimensions differed on a state level, placing them in either a low, medium, or high
position on a flexibility continuum (Volkwein & Malik, 1997). While Volkwein was able to
offer an instrument of increasing sophistication to measure these concepts, he was unable to offer
a practical prescription for best practices. What became more apparent was the idea that higher
education autonomy, whether academic or administrative, is much more dependent on the
characteristics of the state rather than the other way around. The idea of a standard application
for higher education autonomy in the United States, much to the chagrin of those who promote
complete autonomy from government actors, remains impractical.
After looking at the relationship between the government and higher education from an
American perspective, it is worth noting how other regions of the world handle these concepts.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 33

The study by Ren and Li (2013) provides a great deal of insight in that regard. The authors, both
Chinese academics, analyze the works of two other scholars in order to understand the
relationship between higher education, the government, and the public interest in both the United
States and Europe (Ren & Li, 2013). In their study, the authors conduct a critical review of
works by Karran (Karran, 2007; Karran, 2009a; Karran, 2009b) on the European side, and
Thorens (2006) on the American side.
In reviewing these articles, Ren and Li sought to understand the contribution made by the
Higher Education Policy journal towards the internalization of academic freedom and autonomy,
and how these findings may contribute to a working definition in order to promote best practices.
From the perspective of the authors, they seek to apply such a framework to a Chinese higher
education setting by understanding how these concepts are actualized in Europe and the United
States. In addition, the methodology included a critical literature review, discourse analysis, and
an interview over emails with Karran, as well as the personal experiences of the two authors
(Ren & Li, 2013).
Ren and Li pursued this line of research in recognition of the increasing significance of
higher education in China's development, and the important role played by academic freedom
and autonomy in higher education. The authors first address the more American perspective of
Thorens (2006). Thorens’ status as honorary president of the International Association of
Universities, Ren and Li claim, makes him the perfect source for reflective analysis due to his
experience, expertise, and authority (Ren & Li, 2013).
Thorens argues in favor of academic freedom and autonomy, and how even government
funded research may act as a hindrance to the core pursuits of higher education (Thorens, 2006).
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 34

In covering the historical origins of the University and university autonomy, Thorens is
effectively able to describe the development of higher education from the European Middle Ages
up to how it is understood today in the United States. It is significant to note that despite his
endorsement of complete academic freedom and autonomy, he underscores the importance of
academic duty towards the pursuit of truth, which Thorens believes to be the core responsibility
of higher education institutions. Thorens also argues that implementing such a paradigm is
impractical as the government always acts as the overarching force looming over higher
education. In pursuing the truth, however, the university serves the public interest, albeit
indirectly and over the long-term. The catch, then, is that the university is most able to pursue
the truth when it is unhindered by political and/or social demands, and is thus dependent on the
wisdom of politicians and the populace to keep academia free. While Thorens does argue in
favor of a standard definition for concepts of academic freedom and autonomy in higher
education, he also notes that these definitions must not be stringent as the practical application
can vary so widely depending on the context.
Ren and Li then move on to the works of Karran. Karran’s approach to academic
freedom was more empirical and less abstract than Thorens’. Karran sought to move the debate
towards a working definition of academic freedom for higher education institutions within the
European Union (Karran, 2009). He argues that this endeavor is important because the lack of
definition is what allows external forces to crack away at academic freedom (Karran, 2009). In
essence, to define academic freedom is to defend academic freedom, and its lack of definition is
a vulnerability to higher education institutions.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 35

In order to provide a framework for such an endeavor, Karran conducted a study of 23
European countries to comparatively measure constitutional protections and legislation covering
freedom of speech, academic freedom, institutional governance, the appointment of the Rector,
and tenure (Karran, 2007). These parameters were selected from the “Recommendations
Concerning the Status of Higher-Education Teaching Personnel (United Nations Educational,
Scientific, and Cultural Organization, 1997) in addition to a review of the literature, to place
European countries on a spectrum of low, medium, or high for protections of academic freedom.
Although these measures may not be able to capture the entire real effect of these parameters,
they do provide a framework on how academic freedom may be gauged, and in doing so assist in
defining a working definition, as well as rallying behind increased protections (Karran, 2009). It
should be highlighted, however, that Karran, although continuing to develop a working
definition based on the call by Thorens, does so solely and specifically in the European context.
Finally, Ren and Li move on to a discussion of how these principles may be applied in
the Chinese context. The authors note the significant role of the government in higher education
system in China. They discuss how Confucian epistemology has guided Chinese higher
education development, and how these differ from the experiences of the United States and
Europe (Ren & Li, 2013). Two schools of thought grew out of the two most influential Chinese
Universities between the 1910s and 1930s, Peking and Fudan. One supported what may be
related to academic freedom in support of the pursuit of true knowledge and more in line with
the European paradigm. The other was more concerned with what may be related to intellectual
freedom, believing in the direct contribution to improving communities, and more in line with
the American paradigm. The authors argue that the current character of higher education in
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 36

China seems more in line with service to the community and people, protected by law. The close
relationships between higher education and the Chinese government, however, is markedly
different from the American and European traditions. Ren and Li choose the term self-mastery
as opposed to autonomy, as autonomy implies a certain amount of separation from the
government that is just not appropriate for the Chinese higher education system. While higher
education and the government in China do work much more closely together, the higher
education institutions are also responsible for their own survival.
This is significant in how just the definition of autonomy and the relationship to academia
varies so widely depending on the context. The study by Ren and Li, despite the lack of any
major results, is still relevant to the discussion. Through qualitative analysis of academic
freedom and autonomy in three drastically varied settings, the authors help clarify the definition.
For example, independence from the government as an admitted aspect of autonomy in higher
education, according to Ren and Li, disqualifies Chinese higher education from using the term
autonomy, proposing self-mastery as more appropriate for the Chinese context.
Despite the search for a single working definition of these concepts of academic freedom
and autonomy, there is instead movement towards the discussion of these concepts on a regional
level. As the discussion clarifies the mutual understanding of any specific higher education
context, and how they should best be applied. There is still demand for researchers to determine
a general working definition of academic freedom that is neither too specific nor too rigid, but a
guideline for critical aspects of the discussion to achieve the most beneficial mutual
understanding of the relationship between higher education, the public interest, and public policy
in a specific context.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 37

These discussions have highlighted the complexity of academic freedom and the
difficulty of developing a single working definition. Instead, scholars have highlighted the
debates that take place between different entities in different settings. In doing so, it becomes
clear that the driving force should be laid behind having appropriate discussions with specific
points to address various issues of academic freedom in order to determine the optimal form in
that place and time.
The Development of Endogenous Growth Theory
The Solow Growth Model
It is important to discuss the Solow Growth model (also referred to as the Solow model or
the Solow-Swan model) because it is representative of the prevailing economic models before
Endogenous Growth Theory (EGT) came into existence. More importantly, the shortcomings of
this model will be discussed in order to understand how EGT was needed to fill a gap in the
theoretical literature. The Solow model is an exogenous growth theory in that it largely
attributes changes in economic growth to factors outside the model. What separated Solow from
previous economic models was the inclusion of labor and technological progress as factors to
economic growth (Acemoglu, 2007). Solow was able to mathematically build upon the
equations of his predecessors through the inclusion of these factors. While still based in
exogenous growth theory, the model dips a toe in the EGT pond through the introduction of
population and, more importantly, technology as driving factors to economic growth.
The focus of the Solow-Swan model is spread over three areas: capital, labor, and
technology. By doing so, the model makes several assumptions. Perhaps the most important
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 38

assumption is that, within the closed nature of the model, capital is subject to diminishing returns
(Solow, 1956; Acemoglu, 2007). This essentially means that the output value of a machine
(capital) will depreciate over time, but the model also includes the replacement of older
machines with newer machines.
A limitation of the model worth pointing out is the consideration of human activity,
which is noticeably a separate category from capital in the Solow model. Human activity is
largely assigned to factors of consumption and population growth, which itself is related to
issues of labor (Solow, 1956; Acemoglu, 2007). Although the model does place a great deal of
emphasis on the role of changing technology to economic growth, it is largely separate from
human activity, and therefor consistent with the assumption of diminishing returns.
An assumption of the model also worth noting has to do with the nature of technology, in
that it is considered both non-excludable and non-rival as a public good (Solow, 1956). Solow
assumes technology as a public good in that anyone can use it, and one person’s use does not
preclude it to another. This is important to note because the model, as well as subsequent
theories, emphasize the impact of technologies on improving efficiency and outcomes. Later
models that will be discussed will further develop the role of human activity, as well as the
justification of their categorization as capital, based on their ability to develop and adopt new
technologies (Romer, 1986). This allows the model to disregard individual motivation in the
development of new technologies, and instead focuses on how those new technologies are
adopted. As later models shall point out, this disregards major factors that have an impact on
economic growth.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 39

The reason this brief overview of the Solow Growth model is necessary is because of the
introduction of technology as a factor of growth. Technology turned out to be a necessary
component to address diminishing returns on capital and improving efficiency. Although the
model does still attribute changes in growth to factors exogenous to the model, the inclusion of
technology acts as a spark for further study into the role of human activity in economic
development. As technology feeds into capital to address diminishing returns, future scholars
would come to consider the role of humans in developing technology and continue to build upon
the work of the Solow Growth model. This represents a move towards endogenous growth
theories, where the drivers of economic change are internal, as opposed to external, to the model.
Figure 1 below visually represents the theory’s mathematical descriptions of the relationship of
capital and labor to output.

Figure 1. The Solow-Swan Model of
Economic Growth. Model links economic
output (Y) to production inputs of capital
(K) and labor (L). (Chand, 2019)

PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 40

Human Capital and Endogenous Growth
Human Capital Theory. Before delving into the topic of Endogenous Growth Theory,
it is helpful to start with a brief discussion of Human Capital Theory (HCT). Human capital is
important to discuss because it is the concept around which these theories are built. The roots of
HCT can be traced back to Adam Smith and his fourth definition of capital as investments in
humans towards the acquisition of talents or skills through various forms of education, and that
the real costs of acquiring those skills make them capital investments in the individual (Smith,
1776; Aspromourgos, 2013; Goldin, 2014). The use of the term human capital and the
development of HCT can be traced back to Theodore Schultz (Schultz, 1960; Schultz, 1961),
Gary Becker (1993), and Jacob Mincer (1958).
The significance of HCT, in reference to this dissertation, is the concept that the
acquisition of education, skills, talents, and knowledge are investments in individuals, and the
consequences of those investments are forms of capital (Schultz, 1961). Implicit in the idea of
human capital is that policies or programs encouraging education could be used to facilitate
national goals (Holden & Biddle, 2016). In order to facilitate such investments in the stock of
human capital, however, governments are dependent upon the existence of enabling institutions;
these institutions are the organizations and practices that facilitate access to higher education
(Goldin, 2014). For example, the rule of law can be considered an institution that allows
individuals to function under certain standards of conduct to focus on their own development
rather than their physical safety. In regard to the transfer, development, and preservation of
knowledge, however, there is a need for institutions of higher education, or schools (Goldin,
2014). Finally, the great power of HCT was that it was able to demonstrate the high rate of
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 41

return on investments towards education, thereby providing quantitative proof of the benefits of
investments on human capital (Holden & Biddle, 2016).
With such ideas and evidence in tow, it is understandable why efforts to publicly support
education became such a prominent policy during the 1960s and beyond (Heller D. E., 2014;
Holden & Biddle, 2016). Figure 2 is a visual representation of Human Capital Theory and the
differences in lifetime incomes based on investments in post-secondary education. The
prominent reintroduction of the human capital idea during the 1960s laid the groundwork for the
development of Endogenous Growth Theory as well as methods for measuring human capital,
both of which are discussed further below.

Figure 2. Human Capital Theory. Lifetime income paths based on
18 year old decision to attend post-secondary education or not.
(Usher, 2019)
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 42

Endogenous Growth Theory. At its core, EGT is a model of long-term economic
growth that argues that economic growth is a result of endogenous, or internal, factors (Romer,
1994; Lucas Jr., 1988). Although the Solow model touched on this, it remained an exogenously
based theory. Still, the emphasis on new technology in the Solow model may be seen as the
spark for future scholars to investigate those internal dynamics further. The EGT movement
began after World War II during the 1950s, but did not really gain momentum until the 1980s,
after the experiences of the external economic shocks of the 1970s (Acemoglu, 2007). By
shifting the focus to those endogenous factors, EGT models may take diverse new variables into
consideration such as ideas, technology, education, and innovation. Because EGT is concerned
with aspects of sustainable growth, it is focused on the ability of human capital to reduce
diminishing returns through technology and innovation (Romer, 1986; Lucas Jr., 1988). Figure 3
below is a basic visual representation of the impact of human capital on technology and labor
productivity.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 43

Romer. Paul Romer (1986) is generally credited for building the bridge between the
Solow model and EGT. Romer includes the technological variable, as Solow did, but rather than
leaving the source of technological change as a factor exogenous to the model, Romer provided
an endogenous explanation of technological change through the inclusion of human capital as a
distinct variable from labor and capital (Romer, 1986). Romer further develops the role of
technology in long-term economic growth. Romer (1990) describes the nature of technology as
a nonrival and partially excludable good. This essentially means that once a new technology is
developed, the use of that technology by one agent does not prevent the use of that technology by
another agent. Although classically the use of a new technology could not be hindered by
agents, copyright and intellectual property issues can make new technology partially excludable,
Figure 3. Endogenous Growth Theory. Improvements in technology
increases capital per worker and labor productivity (Campbell, 2014)

PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 44

meaning there are cases when an agent can be prevented from using or adopting the new
technology.
The Romer (1986) model accordingly carries with it some profound assumptions. The
first is that once the cost has been incurred to develop a new technology or set of instructions,
then those may be reused at no extra cost, thereby making the price of continual technological
development fixed (Romer, 1990). Romer then goes on to say that these fixed costs and
continual technological development impacts the rate of growth of markets, which will then have
an influence on welfare, salaries, and rate of economic growth (Romer, 1990). Larger markets
then, accordingly, invest more in research and development. The Romer model is one that seeks
a natural state of equilibrium as technology and the market attempt to keep up with each other.
Two critical assumptions that Romer makes in his model has to do with increasing
returns on outputs, through the accumulation of knowledge capital, by increasing production per
hour worked; The second assumption has to do with decreasing returns on new knowledge
production (Romer, 1986). The first assumption had major implications, as output from capital
had assumed decreasing returns over time in previous models. With increasing returns, however,
long-term economic growth becomes more feasible and sustainable. The second assumption has
to do with the stock of human capital and how the needs of the market are met. Because of this
assumption, Romer pays more attention to the stock of human capital, within an economy, as
opposed to the size of the labor force or the population. Romer states that this assumption is
important to ensure both “consumption and utility” do not grow too fast (Romer, 1986, p. 1004).
The differentiating factor of the Romer model is not the inclusion of innovation, but the
introduction of human capital as the driver of technological change and, therefore, economic
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 45

growth. Romer defines technological change, also known as innovation, as any “improvement in
the instructions for mixing together raw materials,” (Romer, 1990, p. S72). Romer places a great
deal of emphasis on the stock of human capital as it is the source of technological change and
innovation. As EGT models continued to develop, the issue of how human capital should best be
measured becomes a focal point, and will be discussed later in the chapter. Romer concludes
that much of the economic growth of nations, or lack thereof, may be attributed to the stock of
human capital and the accumulation of knowledge (Romer, 1990; Romer, 1986).
Technology and human capital. Other scholars would further develop the work
initiated by Romer. These studies would tend to focus on aspects of either technology or human
capital, the two being closely related within EGT. Technology and capital (human) can be
thought of as two streams of research within EGT. The technology stream tends to be concerned
with issues of innovation and the relationship between technology, education, and labor
production (Romer, 1990). Human capital is more concerned with how human capital is
developed (Lucas Jr., 1988).
Benhabib and Spiegel (1994) conducted a cross-country empirical analysis to determine
the impact on national economic growth of human capital, in terms of labor, versus the stock of
human capital and its ability to innovate and adopt new technologies. Using estimates of
physical and human capital stocks, the authors applied the two different models across 42
countries in order to determine which had a greater contribution to economic growth. While
both models contain human capital, they differ in how that human capital is measured. The size
of the labor force showed to have little impact on economic growth. Applying a different model,
where the stock of human capital impacted both the ability to innovate and adopt new
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 46

technologies, provided the scholars with more positive results as to the impact on growth of per
capita income (Benhabib & Spiegel, 1994).
Human capital serves a dual role in regard to innovation. On the one side, there is the
actual development of new technologies, or research and development. On the other side there is
the adoption and implementation of those new technologies (Hanushek & Kimko, 2000). The
previous discussion highlighted how viewing human activity in the form of capital, as opposed to
labor, allowed for a greater attribution to economic growth. While there is little doubt of the
importance of human capital, the issue becomes complex when considering the optimal manner
to undertake measurement. This challenge also highlights the attraction of viewing human
activity as labor, in that it is much simpler to measure. As the purposes of this dissertation are
inclined towards higher education and the development of human capital, the discussion shall
now focus on how studies go about measuring human capital.
Measuring human capital. Although it may be simple to recognize the value of human
capital, debates begin to fire up when considering the optimal form of measurement. It is
conceivable that the complexity of endogenous factors was the exact reason neoclassical theories
of economic growth left them out. If, however, one is to accept that human activity, under the
correct circumstances, is the one form of capital which may not lead to diminishing returns, then
it is necessary to discuss how that capital will be gauged.
The primary and simplest method of measuring human capital tended to involve
quantifying outputs of higher education, for example the number of degrees issued, number of
years of education, and the number of intellectual property and patents filed (Mankiw, Romer, &
Weil, 1992 ). While this information does provide part of a picture, that picture is quite limited,
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 47

lacking nuance in description of quality of human capital. Discussing the quality of human
capital, under the given circumstances, involves the ability of graduates entering the work force
to either develop or adopt new technologies that improve efficiency of labor output (Benhabib &
Spiegel, 1994).
Barro (2001) conducted a study in which he analyzed the contributions to economic
growth from quantity of schooling versus quality of schooling. An empirical comparison of 100
countries, with data gathered from 1965 to 1995, was conducted within the internationally
comparable assessment in science, mathematics, and reading. The more traditional method was
to use the numbers of years of attainment of education. While both factors impacted economic
growth, the results indicated that a standard deviation in years of attainment positively impacted
the growth rate by 0.2 percent annually (Barro, 2001). Quality of education factors, and
specifically science scores, found that an increase of one standard deviation would positively
impact the growth rate by 1.0 percent annually (Barro, 2001).
It is worth noting that the focus on human capital is not the direct end of itself. The
interest in human capital has to do with the primary concern of improving efficiency in labor
productivity. The production of new technologies in of itself, is not the desired goal either, but
the regular adoption of that new technology in order to improve efficiency and output in labor
productivity (Romer, 1986). In which case, the number of copyrights and intellectual properties
becomes less impactful, and one must instead ask how quickly and readily those new
technologies are adopted and implemented, which is more a factor of quality of human capital as
opposed to quantity. The discussion, then, becomes about how higher education institutions may
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 48

produce individuals in pursuit of greater labor productivity efficiency, and, perhaps more
importantly, how the outputs of those institutions are measured.
Although the initial method involved quantifying higher education outputs, this method
proved to be lacking when concerns over the quality of human capital trump those over quantity.
Eric Hanushek and his colleagues (Hanushek & Kimko, 2000; Hanushek & Woessmann, 2012)
have conducted a number of studies in order to provide greater nuance to the understanding of
how quality of human capital impacts economic growth. Hanushek & Kimko (2000) sought a
more accurate explanation of cross-country differences in economic growth by including the use
of quality measures of human capital. Using quantity of formal schooling years as an indicator
of quality of human capital did not satisfactorily explain the variance of economic growth rates
between nations. Instead, Hanushek and Kimko (2000) proposed that more appropriate variables
could be found in the assessment of critical math and science skills. By building on the ideas of
Romer (1986; 1990), Hanushek and Kimko (2000) used assessments in math and science
specifically because of those subjects’ relationship to research, development, and adoption of
new technologies, which then impacts economic growth (Romer, 1986; Romer, 1990; Bishop,
1992). Hanushek and Kimko (2000) used six internationally comparable assessment instruments
of math and science that were administered over a period of about 30 years. The authors
required countries that both participated in the international assessments as well as having the
required economic data to measure performance over time, leaving them with a sample of 31
nations. The authors sought to compare alternative endogenous growth models in order to assess
the impact of including quality control measures. The quality of human capital, as measured in
the study, had shown to be significantly impactful, consistent, and stable over time. Although
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 49

the study was not able to specifically identify the magnitude of the impact, they were able to
conclude that quality has a causal relationship with economic growth, and accounts for a
significant portion of the variance in economic growth between nations to differences in the
quality of schooling.
Hanushek and Woessmann (2012) further develop the human capital measures of
Hanushek and Kimko (2000). Beyond refining and expanding the international test measures,
the authors also include further endogenous factors, within a country, to provide more nuance.
The international assessments utilized still consisted of math and science, but was then expanded
to include reading. Consistent measures allowed the authors to compare a demographic over
time, such as the math activity of a 15-year-old in 1975 versus a 15-year-old in 1995. Consistent
with Hanushek and Kimko (2000), the study found that cognitive skills provided a better
explanation for the variance between countries than years of school attainment.
Trends in Finance and Governance of the American Higher Education System
This section is devoted specifically to the character of the American higher education
system, and the trends that have shaped its current form. The intention is to give the reader an
idea of the overall arena of higher education in the United States. In many ways, the trends that
apply to the U.S. are global in nature. There are, however, ways in which the American system
is distinct in character. It is worth learning about these distinctions so that, when the dissertation
attempts to apply the instrument to American states, one can better comprehend those areas that
were suitable versus those that fell short. In addition, an understanding of the American higher
education system would assist in tending to any shortcomings that may need adjustment in order
to make the instrument suitable to the new setting.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 50

Governance
Autonomy refers to the freedom or ability of higher education institutions to pursue their
mission without interference from the government. As mentioned previously, the definition or
role of institutional autonomy often varies depending on the regional context and history of a
given location (Levy, 1979; Ren & Li, 2013). The United States differs from other national
systems in that the federal government levies control over higher education indirectly through
regional accrediting bodies (Deming, Goldin, & Katz, 2014; U.S. Department of Education,
2018). These bodies influence higher education institutions by granting accreditation to those
who follow their minimum guidelines and standards. Institutions must have accreditation if they
wish to be eligible for federally funded student loan programs (U.S. Department of Education,
2011a), and thereby greatly incentivizes universities to meet these standards. By setting these
guidelines and tying them to funding streams, the federal government may indirectly guide the
activities of higher education institutions, while still giving them the freedom to fulfill their
mission.
At the state level, theoretical discussions of higher education autonomy often take the
form of centralizing versus decentralizing trends (McLendon, Deaton, & Hearn, 2007;
MacTaggart, 1998 ). On the one hand, there are forces that seek to centralize power and decision
making to some form of government, whether that be federal, state, or local. On the other hand,
there is a counter force to move power and decision making to the control of the institutions
themselves and thereby decentralize authority. These movements influence decision making in
the areas related to higher education that may include issues such as enrollment, tuition, faculty
hiring, or degree programs to name a few (McLendon M. K., 2003).
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It was during the post-war boom years of the 1950s and 1960s that the current trends in
higher education governance arose, and a visible shift in authority from the institution to the state
took place (McLendon, Deaton, & Hearn, 2007). The reasons behind this movement are
multiple, and include the increased investment in higher education by the federal government
(Heller D. E., 2014), the overall rise of college enrollments, the improved regulatory capacity of
states (McLendon M. K., 2003), as well as the general movement of social, political, and
economic discussions (Holden & Biddle, 2016).
Although there are multiple modes that the centralization of authority to the state took in
practice, depending on the state, there are also common trends. It is important to include a
discussion of how researchers measured these overall movements, whether centralizing or
decentralizing, because it reinforces understanding of how these trends are gauged. State
authority over higher education often took the form of governing and coordinating boards
(McLendon M. K., 2003). Governing boards were granted authority over many day-to-day
decisions over designated institutions (McGuinness, 1997), while coordinating boards acted
more as intermediaries to ensure integration and coordination amongst institutions across the
state, but with no direct control over individual institutions (McLendon, Deaton, & Hearn, 2007).
While critics of the movement to centralize authority over higher education to the state
certainly existed, the movement towards decentralization began to really take hold during the
1980s and 1990s (McLendon, Deaton, & Hearn, 2007). This movement is often characterized in
the literature by the number of measures introduced and reforms made to change the governance
system under calls for greater accountability, efficiency, cost controls, or innovation to name a
few (McGuinness, 1997; McLendon, Deaton, & Hearn, 2007).
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The form and degree of these trends can vary widely, with some states choosing to
decentralize authority to the institutions and other states choosing to maintain authority. There is
also a great deal of variance in the form of the actual policies implemented. The nebulous nature
of the structures of authority explain why much of the literature on higher education autonomy is
focused on case studies of individual or select groups of states (Taira, 2004; McCombs, 2003;
Mills, 2007; McLendon, Deaton, & Hearn, 2007). Although variance in socio-political trends
amongst states gives each a distinct character, there are also commonalities that bind them, as is
stated:
Each of the 50 states is distinctive in its own right, but collectively the states share much
in common, and it is the constrained variance of these institutional contexts that makes
the American states such an ideal setting for testing social-science theories concerning
policy formulation, design, and implementation. (McLendon, Deaton, & Hearn, 2007, p.
667)
Overall, however, the trends in recent history have been towards the general decentralization of
authority. On the more extreme end of this spectrum can be seen the rise for calls of
marketization and of private for-profit institutions in general, which is discussed further in the
following section.
Marketization. Governments, both state and federal, have encouraged their citizenry to
pursue postsecondary education with the implicit understanding that benefits accrue to both the
individual and their external communities as a whole (McMahon, 2009). In addition, there is the
macro-level belief that education acts as a driver of economic growth, which then allows a state,
region, or nation to be more competitive with other actors (Dill, 1997). Given these beliefs, it is
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 53

understandable why many higher education systems start with a more centralized authority
regarding the management of higher education. Recently, however, the trend for higher
education, and especially true for the United States, has been to move towards a decentralized
form of management (McLendon M. K., 2003). Practically speaking, this equates with the
freedom of students to choose their location and area of study, as well as the institutions
themselves competing over students and resources. Proponents of this movement argue that
these shifts lead to greater efficiency in higher education products and, therefore, a higher
standard of living (Jongbloed, 2003). Critics of this movement of marketization in higher
education are wary of the costs to academia as institutions may turn to emphasizing degree
production (Gibbs, 2001), extramural funding, or partnerships with industry and deemphasize
critical thinking and skills associated with more traditional academic inquiry (Slaughter &
Rhoades, 2009; Bok, 2003).
This general movement towards marketization can also be seen in the rise of big business
through the for-profit sector of higher education. The for-profit sector generally arose from the
vocational schools of the past that were answering the calls of industries looking for specific
skills such as accounting, managerial, secretarial, business, and others (Deming, Goldin, & Katz,
2014). Being private for-profits, these institutions have been much more agile in responding to
market demands than traditional universities and colleges, and this is evidenced by the dramatic
rise of online education and enrollments in for-profits, which has “increased from 0.2 percent to
9.1 percent of total enrollment in degree-granting schools from 1970 to 2009,” (Deming, Goldin,
& Katz, 2014, p. 45).
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The positive aspect of the rise of for-profits has been their ability to reach nontraditional
students and respond to market demands. They tend to serve a larger percentage of minority,
disadvantaged, and older students that may often be excluded from traditional higher education
institutions for various reasons. In addition, they have been effective at producing workers for
specific industries in demand, including information technology and health care. These abilities
are largely reflective of the big business character of for-profits (Bennett, Lucchesi, & Vedder,
2010). On the other hand, the majority of for-profits rely on governmental student aid, their
students have a higher default rate on loans than those from other institutions, and there has been
evidence of aggressive recruitment techniques and even fraud (U.S. Government Accountability
Office, 2010). As the for-profits continue to play a significant role, it is important to consider
both their costs and benefits. While they have been able to reach an audience that may be
disenfranchised by traditional postsecondary education, it is also important to note what
sacrifices may be made in the interest of profit-maximization that is largely paid for with tax
dollars. Beyond meeting the criteria for Title IV funding, the section of the Higher Education
Act that houses the federal student aid programs (Dynarski & Scott-Clayton, 2014), private for-
profit schools are also required to provide training for gainful employment, gauged through the
use of at least one of three metrics (U.S. Department of Education, 2011b), or a program of study
leading to a baccalaureate degree (U.S. Department of Education, 2011a).
As the industry moves towards a more decentralized model, it is important to look at the
activities of private for-profit institutions because they represent the more extreme end of the
marketization of higher education spectrum. Both the positive and negative aspects of the for-
profit sector should be considered as there is a general move towards decentralization. Which
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dangers, such as an emphasis on degree production or aggressive recruitment techniques, may
require oversight to ensure product quality, versus the more positive aspects of private institution
agility that allows them to respond more efficiently to market demands and reach more minority
and nontraditional students. Increased government scrutiny and investigations have led to a
slowdown in enrollments in the for-profits (Fain, 2011) in recent years and increased awareness
of potentially hazardous activities. The trick for government regulation, then, is the degree they
can restrain the negative activities of for-profits while nurturing those more positive aspects of
private industry efficiency.
Financial Aid
The Higher Education Act of 1965, signed into law by President Lyndon Johnson,
successfully expanded postsecondary enrollments and tuition support by making the federal
government the primary provider of financial assistance for higher education (Dynarski & Scott-
Clayton, 2014). Those federal programs, developed under the act, were precursors to what
would eventually become Pell Grants, Stafford Loans, and federal work-study (Dynarski &
Scott-Clayton, 2014). During those days, The U.S. Department of Education provided aid
mostly focused on what were considered traditional college students, which is to say recent high
school graduates attending college full-time, with low incomes. Since that time, however, the
sources, forms, and audience for financial aid has significantly expanded.
Today, financial aid is provided not just by the U.S. Department of Education, but also by
the U.S. Department of Treasury and individual state governments. Forms of governmental aid
include grants (federal, state, and local), federal loans, work-study, and tax benefits for education
(Dynarski & Scott-Clayton, 2014). Those eligible for aid has also expanded beyond lower-
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 56

income students to those from middle- and even upper-incomes, and has come to include
nontraditional individuals such as older students, part-time students, or those without a high
school diploma.
Although the initial focus of financial aid was on the low-income segment of the
population, the trends over time have led to an expansion towards non-needs-based forms of aid.
This trend can be seen through initiatives aimed at middle-income families such as an emphasis
on loans over grants by making them entitlements. Generally, loans are more accessible to
middleclass families than they are to poorer families (Heller D. E., 2014), and the use of tax
credits has also tended to benefit middle- and upper-income families more so than poorer
families that may not pay taxes and not receive tax benefits (Heller D. E., 2014). These
initiatives expand the potential audience of aid while also limiting access for the financially
needy.
As part of its movement to encourage postsecondary access, the federal government also
instituted the State Student Incentive Grant program as part of the 1972 reauthorization of the
Higher Education Act (Heller D. E., 2014). The program provided matching grants from the
federal government to any state that developed their own needs-based grant programs. While
successful at initiating state investment in this area, the states would eventually go their own
way. This is evidenced by the rise of merit-based state grant programs that are dependent on
academic performance and not needs-based at all (Heller & Marin, 2004; Dougherty, Natow,
Bork, Jones, & Vega, 2014).
Although it is a complex field, the two trends at play in regard to financial aid is between
the focus on the financially needy versus non-needs-based forms of aid. This conflict is
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 57

important to highlight because as governments seek to restrain spending, these programs are
vulnerable if they cannot provide evidence of their performance, and an overall preference for
non-needs-based aid programs may result in financially needy segments of the population being
cut off from access (Dynarski & Scott-Clayton, 2014).
Higher Education in Transition
The discussion on finance and American higher education displays the complexity of this
field. The distinct relationship between the government and higher education creates a unique
market with characteristics that change over time depending on the role government wants to
play. Currently, economic pressures are one of the major drivers of change to how higher
education is managed as subsidies and appropriations decline (Bound, Braga, Khanna, & Turner,
2019). For example, the percentage of full-time faculty has decreased, and non-tenure track
positions have increased. Cuts in state appropriations have led to increases in tuition, which still
cannot offset the financial loss (Ehrenberg, 2014). Academic leaders have decided to focus on
technology as an avenue to improve learning while reducing the cost per student to deliver
instruction (Stripling, 2011). These examples show how higher education institutions must find
ways to adapt to decisions made by the government in order to both survive and pursue their
missions.
What is clear, however, is that each state will react to pressures in their own individual
way, as that is implicit to the more decentralized system in the United States. State grants are
their own province, and “decisions regarding their structure, targeting, and funding levels are
made by state legislatures and governors. Each of the states is a laboratory for innovation, with
postsecondary policies subject to the local political culture,” (Heller D. E., 2014, p. 158;
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 58

McLendon, Heller, & Young, 2005). The devolution of authority has even seemingly continued
from the state to the individual institutions or systems (MacTaggart, 1998 ; McLendon M. K.,
2003), perhaps making the entire field even more complex.
Summary
The previous review of the literature began with a historical discussion of American
government investment in diverse sectors and industries. The intent was to relay to the reader
the sense that much of American socioeconomic growth came as the result of government
investments. In areas where capitalists and entrepreneurs are wary to invest their funds due to
risk, the government often takes an active role to spur investment in areas it deems relevant to
the socioeconomic growth of states and the nation as a whole.
The literature review then shifted to a more global discussion on the ideas of autonomy
and higher education. The purpose of this section was to display to the reader a sense that the
relationship between government and higher education varies depending on sociocultural norms
and historical views on the purpose of education. This is to say that these relationships may vary
drastically depending on whether we are talking about a Chinese versus an American setting, for
example. Higher education autonomy is a force in flux, and will vary depending on the needs of
the communities being served, as well as the sociocultural trends specific to the region.
The review then moved on to a theoretical discussion of human capital, Endogenous
Growth Theory, and the measurement of human capital. Human Capital Theory and
Endogenous Growth Theory lay down the framework upon which this study and the
accompanying instrument are built, and so it is important to understand their basic concepts.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 59

More specifically, the inclusion of human capital as a factor of economic growth through the
development of new technologies and innovations argued that economic growth could be
internally driven. Higher education institutions, then, indirectly contribute to economic growth
by training graduates that are added to the pool of the human capital workforce. The theoretical
discussion then moved into the measurement of human capital. This issue is acutely significant
because if the contribution of universities and colleges is to the pool of human capital, then the
ability to measure human capital is necessary to gauge the performance of higher education
institutions.
Finally, the literature review ended with a practical discussion of the finance and
governance of the higher education industry of the United States. Although less theoretical, the
purpose of this section was to give the reader an idea of how higher education has become what
it is today, and the trends that continue to shape it. As this represents the new setting upon which
the dissertation seeks to apply the instrument, it is important to have a basic understanding of the
characteristics of the new environment. Doing so adds depth to the conclusions of this study,
and specifically on why some aspects of the instrument may translate more easily to the new
setting while others do not.

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Chapter 3: Methodology
Rationale
From a philosophical perspective, the inquiring forces driving this dissertation were
fueled by a combination of both postpositivist and pragmatic world views. Postpositivism
influenced the desire for measurable outcomes, to test and verify, to collect data and revise and
refine as necessary, evidence-based knowledge, and objectivity in explaining situations.
Pragmatism, on the other hand, imbued the research with a concern towards application, asking
what works, and problem-solving. Data collection is purpose-driven with a focus on the problem
issue rather than solely on the process or data (Creswell, 2014).
Practically speaking, these worldviews shaped the research process. The seed of an idea
that sprouted this qualitative narrative was a desire to provide quantifiable evidence of the
socioeconomic outcomes of public investments in higher education. During the research phase,
such an instrument was sought and found. Because the instrument was designed for European
Union countries, however, it would have to be tested and adjusted before it could be
appropriately applied to American states. The goal of this dissertation was to pilot the
instrument, in its original form, on a small number of American states in order to determine
which indicators would need adjusting and how. The model below charts the researchers
journey during the dissertation process.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 61

Figure 4. Process model. This chart represents the research process undertaken
for this dissertation.

As this dissertation was intended to replicate a European instrument in an American
setting, the priority in research design was to remain as similar to the original study as possible,
while still being contextually relevant to the sample at hand. This step is important to
determining the viability of the instrument in the new setting and highlighting any shortcomings.
In this case, the sample consisted of the American states of New York and California. Two
states were selected because the focus is on piloting the instrument itself. Any meaningful trends
could only be gained by analyzing all American states together, which would be appropriate if
the instrument was made viable for the new context. Future application to all American states
would require addressing shortcomings found in this dissertation so that the instrument, and
accompanying findings, may be considered valid. The motivation behind selection and other
aspects is discussed further in the sample section.
Some indicators of the instrument required minimal to no change. For example, the
expenditure per student relative to GDP simply needed a change in currency. Other indicators
required the identification of an American equivalent, such as the number of incoming Marie
Curie fellows. Because these fellowships only operate in Europe, it would not be appropriate or
applicable to the given sample. The study design was then required to identify an American
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 62

equivalent that also satisfied the original criteria for selection. Finally, some indicators were not
viable because the data was not available, and the replacement indicator was not appropriate.
One example included the lack of state-level autonomy surveys, and the inability to gauge
autonomy levels based on quantitative datasets. The aspects of how the study attempted to
translate all the indicators is discussed in the indicators section below.
Afterwards, how the indicators would be weighted and loaded in analysis is presented. It
is important to note, however, that given the small sample size of this research quantitative
analysis was not possible (Hoareau, Ritzen, & Marconi, 2012; Laerd Statistics, 2019) . The type
of factor analysis, known as Principal Component Analysis, requires a relatively large sample
size to be conducted appropriately (Laerd Statistics, 2019). While a sample size of two does not
satisfy this requirement, it would be possible to conduct the analysis on all 50 states should the
instrument be adjusted in a way to make it viable.
Sample
The primary concern of this sample was to determine the appropriateness of these sets of
indicators in an American context, which is to say that there is no real importance to which states
were selected. While both New York and California are large states with robust public and
private higher education systems, and are generally considered to be at the forefront in industries
such as business and technology, these are not necessary prerequisites or significant in selection.
The focus of this dissertation was instead the availability and type of data on federal databases
that could be consistently collected for all American states. The underlying assumption is that if
a datapoint from the National Center for Education Statistics, for example, is or is not available
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 63

for both California and New York, then it also would or would not be available for the remaining
states.
Indicators and Data Collection
Indicators are divided amongst the three dimensions of policy, performance, and
economic output. Each dimension is divided into factors, and each factor may contain anywhere
from one to five indicators. These are discussed in the following sections below, including any
necessary adjustments needed in the transfer of indicators from Europe to the United States. The
goal, however, was to keep the sets of indicators as close as possible to the original form.
Policy
Autonomy. The concept of autonomy in higher education has to do with the interactions
and relations between the university and the government. The original study, based on the work
of The Center for Higher Education Policy Studies (CHEPS), differentiates between three forms
of autonomy: organizational autonomy, financial autonomy, and policy autonomy.
Organizational autonomy refers to the degree a university may decide its own internal structure,
including its organizational structure, leadership, accountability measures, hierarchy or
responsibility (Hoareau, Ritzen, & Marconi, 2012). This notion of organizational autonomy
supports the idea of a supervisory board or committee that appoints leadership, whom then
appoint faculty and administrators. Policy autonomy refer to academic freedom in the selection
of students and staff as well as teaching and research programs (Hoareau, Ritzen, & Marconi,
2012). Finally, financial autonomy refers to the degree a university is able to raise and manage
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 64

its own funds (Hoareau, Ritzen, & Marconi, 2012). This includes raising money from alternative
sources and the freedom to decide internally how that money is spent, invested, or saved.
To code these indicators, the original investigators utilized data from the European
Commission (CHEPS; INCHER; NIFU-STEPS, 2008). CHEPS et al. (2008) coded the degrees
of organizational, policy, and financial autonomy through a process in which national experts
from each country provided their perceptions on the topics. Based on those assessments, CHEPS
et al. codes the degree of autonomy for all three dimensions from low to high. The original study
then recoded those measures with an ordinal scale of 1 to 5 for organizational autonomy, and 1 to
3 for both policy and financial autonomy. In regard to the factor loading of these indicators in
the original study, organizational autonomy was 0.83, policy autonomy 0.84, and financial
autonomy as 0.74 (Hoareau, Ritzen, & Marconi, 2012).
Before delving deeper, it is worth noting, as is also noted in the original study, that these
autonomy indicators and how they are measured are fuzzy (Hoareau, Ritzen, & Marconi, 2012).
For example, the original study cites how another set of assessments of autonomy, conducted by
the European University Association (European University Association, 2009), produced a weak
correspondence with the CHEPS study on the same topics. The reason for the selection of the
CHEPS dataset was because the European University Association dataset did not include all of
the sample countries of the study. The selection of indicators, while not always ideal, tends to be
guided by the availability of pertinent data. In addition, the nebulous nature of autonomy in
general makes it increasingly difficult to design an instrument that measures it in its entirety, as
opposed to certain and specific aspects of autonomy.
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For this dissertation, the indicators of organizational autonomy, policy autonomy, and
financial autonomy remained. The researcher sought sources of quantitative measures of each
form of autonomy that would be standardized across American states. Because the sources used
in the original study had an international focus on higher education, they did not have data for
the individual states within America. The study then searched for publicly available data on
these indicators from American sources, starting with governmental national organizations such
as the National Center for Education Statistics (NCES) (National Center for Education Statistics,
n.d.) and the Integrated Postsecondary Education Data System (IPEDS) (The Integrated
Postsecondary Education Data System, n.d.). State sources directly from California and New
York were also utilized to conduct a general overview of the individual state systems. Although
these reviews provided useful insights into aspects of autonomy for both California and New
York, they could not be used to provide a quantitative measure with an acceptable level of
validity.
Funding. With autonomy, the other half of the policy dimension addresses financial
resources of the university. In addition to the more typical funding indicators, such as the
expenditure per student, the authors of the original study also focus on funding for equity in
opportunities and access, as well as an analysis of incentives within the funding system. The
indicators were selected on the basis of national effort rather than directly on outcome student
expenditure.
Funding student’s education. The first funding indicator was the annual expenditure,
per student, for all services provided by tertiary education institutions as related to GDP per
capita. These include all core and ancillary services related to higher education. In addition,
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 66

research and development expenditures were included in the measure, whether they were private,
public, a combination of the two, or grants. The motive behind controlling for GDP was so the
investigators may have an idea of how much is spent on higher education relative to national
population and income. The original 2012 study used country data for the year of 2008 and
found the indicator to have a factor loading of 0.78 (Hoareau, Ritzen, & Marconi, 2012). For the
purposes of this study, it was not necessary to use data from the same year as the original study
but did use historical data from 2012. The year 2012 was selected because it was four years
prior to the initiation of this dissertation, and because the original study used data from four
years before its own date, the choice seemed consistent. The indicator itself remained the same,
and the greater consistency in currency was seen as a methodological advantage. Whereas the
indicators in the original study represented a national average, in this study they represented a
state average.
Funding for equity. The original authors sought an indicator that measured higher
education student equity in funding. The reason for analyzing equity in funding is because it is
typically the individuals and families whom are financially disadvantaged, or from differing
socio-economic groups, who are unable to access the resources of higher education (Dynarski &
Scott-Clayton, 2014). In addition, middle- and lower-income groups often do not have access to
capital markets with reasonable rates of interest (Hoareau, Ritzen, & Marconi, 2012). In order to
promote equity, the university may provide funding, at a reasonable rate, to alleviate the
financial burden of those who are struggling. For this reason, the investigators sought a measure
of the effort higher education institutions made to address socio-economic inequities. This effort
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 67

was measured by analyzing how institutions alleviate or subsidize the burden on potential
students.
The original study used public subsidies to households and private entities as a
percentage of total public expenditure on higher education for the year 2008, with data collected
from (Eurostat), and found it had a factor loading of 0.87 (Hoareau, Ritzen, & Marconi, 2012).
The indicator itself remained the same, but for this study they were at the state level and for the
year 2012. Data was collected from official public national sources such as the NCES and
IPEDS, as well as any other governmental sites with pertinent data.
Incentives in higher education funding. Funding formulas for institutions of higher
education are significant because they dictate how the financial resources of the institution will
be directed. The indicator of the original study analyzed the role of formulas and contracts in the
allocation of public funding. The assumption of this measure is incentive formulas and contracts
are more beneficial than negotiations and incremental increases (Hoareau, Ritzen, & Marconi,
2012).
The original study gathered data from CHEPS measuring incentives in funding. These
measures describe the degree to which European nations are moving away from incremental
increases and negotiations. The data gathered from CHEPS was then recoded by the original
research team with a score between 0 and 100. A score of 0 represents funding allocation that is
purely based on incremental increases and negotiations, and a 100 signifies a higher education
system with all funding based on formulas and contracts. It should be noted that the original
study found no correlation between the role of incentives and annual expenditure per student and
the indicator did not emerge as having a significant load on other factors.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 68

For the purposes of this study, Sources such as NCES and IPEDS were searched for
relevant quantifiable data related to incentives in funding mechanisms of higher education. In
addition, individual official state sources were searched for data as well. Similar to the
autonomy indicators, a brief review of the individual state systems was conducted to gain a
general idea of how these funding mechanisms work, but the information and data gathered from
the review were not applicable to the instrument. With the relevant data, each state would place
on a scale between 0 and 100.
Performance
Measuring higher education performance may be conducted in a number of ways
depending on the intent of the study. In the case of the original study, the investigators chose to
focus on the central tenants of higher education: education, research, and students. As these
represent the reasons for governmental investments and subsidies in the first place, with the
implicit assumption being that research produced may have “spillover benefits” and educated
students may improve civil society and generate external value (Deming, Goldin, & Katz, 2014,
p. 54).
Graduate employment and graduation. The assumption grounding this measure is that
a university educated individual should be able to function in both society and its institutions.
This measure is concerned with the ability of higher education institutions to contribute to the
labor market through their graduates.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 69

Graduate employment. In order to measure performance in this regard, the original
study used graduate employment within three years of graduation for individuals aged 18-34
years old. The indicator had a factor loading of 0.71 (Hoareau, Ritzen, & Marconi, 2012).
The indicator had to be adjusted for use in this dissertation. The reason for this was the
type of data collected from publicly available datasets such as IPEDS and the Bureau of Labor
Statistics. As there was no data of employment within three years of graduation, the study had to
use what was closest and available, which was employment by degree attainment of Bachelor’s
degree and higher for people aged 25 and older by state in relation to the civilian labor force for
the year 2012.
The throughput. This indicator has to do with the efficiency of the higher education
system. The measure looks at the number of graduates proportionally to the total number of
enrolled students (Hoareau, Ritzen, & Marconi, 2012). A high throughput does not necessarily
equate to performance, as it could also be indicative of loose standards. A low score, however,
does highlight inefficiencies in the system. The original study found this indicator to have a
factor loading 0.87 (Hoareau, Ritzen, & Marconi, 2012). This vulnerability is magnified in the
context of the original study where the socio-economic statuses of EU nations may vary widely.
For this dissertation, the throughput indicator remained mostly the same in the context of
New York and California. Data was gathered from IPEDS, but graduate level graduations had to
be excluded because that data was not available. The measure was represented as a percentage
of the total number of enrolled students for each state.
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Size of the student body. The governmental assumption underlying this measure is that
the labor force supply is best supported by encouraging higher education institutions to increase
the size of the student body. Based on this assumption, the original study included several
aspects related to the number and makeup of student bodies.
Student body and population. The first indicator directly looks at the size of higher
education student bodies as an indicator of performance. The investigators of the original study
focused on the size of student bodies in relation to the population aged 20 years. This indicator
had a factor loading of 0.78 (Hoareau, Ritzen, & Marconi, 2012). These results were presented
as a percentage.
For the purposes of this study, the indicator required a slight adjustment due to the type
of data available. Because demographic data from the US Census Bureau was only available for
individuals aged 20 to 24, the indicator had to be expanded to include all students aged 20 to 24
as opposed to only 20. Student data was gathered from IPEDS. The results were expressed as a
percentage of the total 20 to 24-year-old population within the state.
Transition students. This measure seeks to address the desire for differentiation within
the higher education setting. While the more traditional students may enroll in a university after
completing high school, many students transfer from professional or vocational backgrounds. In
order to represent this idea, the original study investigated the number of transitional students
from nontraditional background such as professional or vocational. In order to do so, the
investigators used data from Eurostudent for the year 2011. This indicator was found to have a
factor loading of 0.79 (Hoareau, Ritzen, & Marconi, 2012).
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 71

The measure, as is noted in the original study, has a shortcoming that is worth note.
Diversity or differentiation may often be achieved through the direct selection of applying
students (Hoareau, Ritzen, & Marconi, 2012; Office of Planning, Evaluation and Policy
Development, 2016). While this measure does provide a picture of differentiation within the
student body, that picture is incomplete because it excludes the diversifying role played by
traditionally enrolled students.
For the purposes of this study, the measure was mostly consistent with the original.
Because of the way data was available in IPEDS, the measure could only include full-time and
part-time transition students, which excludes those coming from a vocational or professional
background. This data was gathered for both states for the year 2012.
International students. International openness, and the number of incoming
international students, is seen as both an indicator of quality and performance. The assumption
being that the most attractive university systems would be the ones to appeal to mobile students.
In order to qualify as an international student, the individual must intentionally cross-national
borders for the expressed purpose of education. This indicator was not found to have significant
factor loading, instead relying on the number of incoming Marie Curie fellows and the number of
European Research Council starting grants as more significant indicators to research productivity
and attractiveness. The number of inward mobile students was expressed as a percentage of total
enrollment for each country.
For the purposes of this study, international or inward mobile students included students
that cross state borders, as well as the more traditional international students. This data was
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 72

gathered from IPEDS. It was presented as a percentage of the total enrollment for that state for
the year 2012.
Research productivity and attractiveness. One manner in which higher education
institutions are able to contribute to society is through innovative research. The creation of new
knowledge in a university may then be transferred to the private sector in order to improve
performance and efficiency (Romer, 1990). A number of indicators are used to measure research
performance of higher education systems.
Scientific publications. The first indicator related to research performance has to do with
the number of scientific publications in the 10% most cited scientific journals worldwide. That
number is then related as a percentage to the total number of scientific publications for the
country. This indicator had a factor loading of 0.9 in the original study (Hoareau, Ritzen, &
Marconi, 2012).
This indicator remained unchanged and included scientific publications within the 10%
most cited scientific publications worldwide as a percentage of total scientific publication per
state. To find such data, the study focused on IPEDS and citation and abstract databases. Data
for this indicator could not be gathered. After searching sources such as IPEDS and citation and
abstract databases, it was found that this type of data could not be narrowed down to the state
level. After further efforts, there was one possible source that may have had the level of data
needed, but it required an institutional subscription that was not available to the researcher. Even
if it were, however, that would go beyond the mandate of this study to only using publicly
available data.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 73

International visibility and attractiveness. In order to capture the international visibility
of higher education systems within a given country, the original authors decided to use a measure
of international rankings. This indicator looks at the presence of universities, within a nation,
amongst the top 500 institutions of the Academic Ranking of World Universities (ARWU) in
proportion to population size. This indicator had a factor loading of 0.94 (Hoareau, Ritzen, &
Marconi, 2012).
For the purposes of this study, the measure and ranking organization was consistent. The
results were displayed in proportion to the population of the state. The data was collected from
the ARWU.
Fellowships and grants. This measure has to do with the attractiveness of institutions to
researchers. In the original study, the investigators used the number of incoming Marie Curie
fellows as well as the number of winners of the European Research Council Starting Grants
while controlling for the population of each country. The former indicator had a factor loading
of 0.9 and the latter 0.94 (Hoareau, Ritzen, & Marconi, 2012).
Because the measures of these indicators are specifically European, they could not apply
to this effort. The spirit of the measure, however, was still maintained by shifting focus to
national American fellowships and grants. Towards that end, the study used the number of
incoming National Science Foundation (NSF) fellowships and grants per million inhabitants in
the host state. Data was collected from the NSF.
Connectivity. This measure has to do with the interactions higher education institutions
have with the communities that surround them, specifically the private sector. In order to
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 74

measure this connectivity, the original study looked at the number of public-private co-
publications. This included private higher education institutions, in addition to business and any
private entities that conduct research. This indicator was found to have a factor loading of 0.75
(Hoareau, Ritzen, & Marconi, 2012)
For the purposes of this study, the indicator remained the same. The investigators looked
for the number of public-private co-publication by state in relation to the state population from
sources at both IPEDS and citation and abstract databases.
Economic Output
In discussing the impact of higher education systems on economic growth, one must first
define how these interactions take place. It is noted that higher education institutions do not
impact economic growth directly, but instead impact the flow of graduates entering the labor
force. According to the original study, the inflow of new workers represents, at maximum, no
more than 5-10% of the total graduate work force (Hoareau, Ritzen, & Marconi, 2012). For this
reason, it may often take several decades of sustained quality graduate output before the impact
of higher education is economically visible.
Research, on the other hand, has a far shorter time-lag. Innovations developed at higher
education institutions may be translated into new products, processes, or businesses in relatively
short time (Hoareau, Ritzen, & Marconi, 2012; Romer, 1990; Becker & Toutkoushian, 2014).
Even in the case of quicker returns, however, the economic impact is often dependent on where
the nation or state lies on the technological frontier.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 75

Because the impact of higher education systems on cumulative economic growth is so
difficult to isolate, the investigators of the original study chose to focus on economic output in
terms of innovation. The assumption is not that innovation equates with economic growth, but
that innovation serves as a precursor to long-run growth (Hoareau, Ritzen, & Marconi, 2012).
Knowledge intensive activities. The first measure of economic output looks at the
percentage of employees working in knowledge intensive industries relative to total employment.
The assumption at play for this indicator is that the larger the knowledge intensive industry is,
the more innovative the national economy. This indicator had a factor loading of 0.84 (Hoareau,
Ritzen, & Marconi, 2012).
For the purposes of this study the indicator remained the same with a slight adjustment.
Data gathered from the Bureau of Economic Analysis did not have a specific heading for
knowledge intensive activities. Accordingly, the indicator was adjusted to be the percentage of
employees in knowledge intensive fields specifically defined as: professional, scientific,
technical services, and information industries. That data was then related to total employment
for the state.
Labor productivity. The indicator of labor productivity is focused on labor efficiency.
The assumption being that the more innovative an economy, the more GDP created by the
workforce. The measure looks at GDP per capita produced per hour worked in purchasing
power standard. This indicator was found to have a factor loading of 0.84 (Hoareau, Ritzen, &
Marconi, 2012). Labor productivity, combined with the size of the knowledge sector, is used to
measure the impact of higher education systems on innovation economies.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 76

For the purposes of this study, the indicator had to be changed to real GDP per capita.
While the Bureau of Labor Statistics did have data on labor productivity, it was only available at
either the national level or by industry and did not include labor productivity at the state level.
Accordingly, the study could only use the data available at the state level, which was GDP per
capita.
Quantitative Analysis
Analysis would have replicated the steps taken by the original study. This analysis
assumes a linear relationship between higher education policy, performance, and economic
output. Visually speaking, the model being applied is:
Policy → Performance → Economic Output
Figure 5. Linear Relationship

It must first be said that actual statistical analysis could not be conducted for this
dissertation. The reason has to do with the small sample size that is not compatible with the type
of statistical analysis used for this instrument. It is worth noting that the intention of the study
was not to conduct analysis through the instrument, but to determine the feasibility of the
instrument in the American state setting by piloting it in a small number of cases. The
dissertation is concerned with the ability of measures or indicators, related to the factor groups,
to be adequately collected given the availability of data in public databases. If the instrument
had proven to be a perfect fit regarding available data, then it would be feasible to apply it to all
50 states moving forward, which would be a large enough sample to conduct Principal
Component Analysis and gather useful information. The sections below present the steps for
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 77

statistical analysis to give the reader a full description of the application of the instrument. This
is followed by a brief discussion of the assumptions of Principal Component Analysis that
prohibit its application to such a small sample.
Factorizing Variables
Once all the data inputs for the indicators of the instrument were acquired, data analysis
would then be performed using statistical software. Z-scores are established by standardizing the
variables with a mean of 0 and standard deviation of 1, with minimum and maximum values set
at greater than 0. The next step then involves factorizing separately the variables of policy,
performance, and economic impact using Principal Component Analysis with a “direct oblimin”
(Hoareau, Ritzen, & Marconi, 2012, p. 52), which uses a non-orthogonal (oblique) rotation. This
produces a recognizable pattern where items load more heavily on some factors and lighter on
others. This allows factors to correlate between each other and establish correlation coefficient
gamma to exist between policy and performance factors, on one end, and performance and
economic factors on the other. The most relevant factors of the policy, performance, and
economic output variables would be chosen with an eigenvalue (>1) and a factor loading (≥0.6).
Factor Weights
After factorizing and selecting variables, weights would then be attached based on the
statistical strength of the relation between factors. These weights are the beta coefficients that
measure the impact of performance factors on economic output and the impact of policy factors
on performance, assuming a linear relationship. This step is accomplished through regression
coefficients for the relationships between performance factors and economic output, and the
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 78

relationship between policy factors and performance factors. The impact of the performance
factors of research productivity and attractiveness, graduate employment and graduation, and
size of the student body on economic innovation would be identified through the corresponding
coefficient. The p-value would also help determine the level of confidence for the regression,
which would be largely dependent on the sample size. The next step would involve presenting
the regression coefficients for policy factors, autonomy and funding, on the performance factors
that had coefficients greater than 0, which is to say that those performance factors with negative
coefficients would be excluded.
Assessing Policy Impact
The final step would involve calculating a score for state policy. Every policy variable
would achieve a certain score. These scores would be brought together to create three factors for
each state, based on the statistical factor loads. The final score for a state may then be assessed
by multiplying the size of each factor with the associated weight. The weights are the regression
coefficients that describe the impact policy may have on performance or the impact of
performance on economic output. Finally, factor variables are multiplied with their
accompanying weights, and added together to achieve a state score.
Limitations
The steps of analyzing the data would be simpler to comprehend with an example to
refer. However, given the intention of this study to test the feasibility of the instrument in a new
setting, it was not possible to conduct a statistical analysis. Principal Component Analysis, the
factorization method used to measure the impacts of factors on each other, has several associated
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 79

assumptions. The main limiting assumption that could not be fulfilled in this study is the need
for a large sample size. The assumption relates to the other requirements of a linear relationship
through the development of a scatterplot, and that there be no outliers (Laerd Statistics, 2019). It
is not possible to visually portray the relationship or recognize any outliers in a scatterplot with a
sample of two. Conducting even the first step of establishing z-scores is not possible given the
small sample. The other assumption of continuous and ordinal variables would not present any
issues.
Analysis and Discussion
The final step in the study involved an analysis and discussion of the findings. Because
the original study sample consisted of 32 European nations, much of the discussion revolved
around a critical ranking of nations that could also be grouped together under certain categories.
For the purposes of this dissertation, however, ranking was not possible with a sample of two
states and the lack of quantitative analysis. The discussion instead focused on the
appropriateness of the individual indicators and the adjustments needed to make the instrument
viable for American states.

PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 80

Chapter 4: Results and Discussion
A significant portion of the literature review focused on theoretical concepts and
historical examples related to governmental inputs intended to boost certain industries or fields
for the purposes of positive socioeconomic development. In that regard, this study sought to test
the viability of a European instrument on American states in order to identify those aspects that
would require adjustment before the application could be considered valid. The future
development of an appropriate instrument would provide greater insight into the impacts of
public investments in higher education on economic growth in the United States.
Part of the value of the instrument came not from measurements attributed to a single
European country, but how these measurements compared amongst all EU countries. While
American states are not countries unto themselves, the ability to apply an instrument that
quantifiably compared the economic outputs of states, given certain inputs, via higher education
performance, would add value and clarity to how these systems interact (Hoareau, Ritzen, &
Marconi, 2013). The purpose of this study was to pilot the instrument in California and New
York to highlight shortcomings and vulnerabilities, as a sort of stress test. Those weaknesses
would later need to be addressed before the instrument could be adequately applied.
It was important that the data be gathered from publicly available datasets originally
collected by government or government affiliated institutions to ensure consistency and
reliability across states. Many of these institutions included: The National Science Foundation
(NSF), The National Center for Education Statistics (NCES), the Integrated Postsecondary
Education Data System (IPEDS), the United States Bureau of Economic Analysis (USBEA), the
United States Census Bureau (USCB), and the United States Bureau of Labor Statistics
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 81

(USBLS). This is consistent with the original study, which used official European Union
organization to gather publicly available data (Hoareau, Ritzen, & Marconi, 2012). In addition,
state governmental sources were used to gather more specific state-level data, such as details
about their specific higher education systems, the various boards and committees associated with
such structures, and funding mechanisms and budgets. In cases where the types of universities
could be specified, such as with IPEDS, the Carnegie Classifications were used and narrowed
between mixed baccalaureate/associate’s granting universities, at the lower end, and doctoral
universities, at the higher end, with master’s granting colleges and universities in-between. Data
was gathered for the year 2012 for consistency and to ensure availability, but this date could be
adjusted for future studies as any year with available data could be used. Finally, each group of
indicators that consist an instrument factor is followed by a table summarizing the results, how
the indicator may or may not have changed from its original form, the underlying theoretical
framework, and the sources of data.
Results
Policy
The policy dimension refers to the systemic relationship allowing, or obstructing, the
ability of higher education institutions to pursue their mission (Hoareau, Ritzen, & Marconi,
2012). The focus is on how aspects of policy effect the performance of these institutions. Policy
is divided into two factors, autonomy and funding.

PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 82

Organizational, policy, and financial autonomy.
Although each is a unique variable, the organizational, policy, and financial autonomy
indicators are discussed together for the sake of efficiency and to avoid repetition, as the results
for each is the same. The original study based scores for the autonomy variables via a
questionnaire to various national experts as well as interviews with relevant stakeholders,
conducted by The Center for Higher Education Policy Studies, for each European nation. After
conducting multiple searches, no equivalent standardized survey of autonomy could be found
that was implemented across all American states. Although measures of higher education
autonomy certainly exist, they tend to take the form of case studies, comparative studies, or
interviews (McLendon, Deaton, & Hearn, 2007; McCombs, 2003; Taira, 2004). What is lacking
is a standardized survey or instrument that has been utilized across states. Although a brief
review of the state systems of California and New York was conducted, it could not provide a
valid measure of these indicators because the result would be neither standardized nor valid.
Limitations of autonomy scale. Although the measures of autonomy in the original
study had their own share of shortcomings mostly related to de jure vs. de facto autonomy
(Hoareau, Ritzen, & Marconi, 2012) , they were at least consistent and reliable across European
nations, and they could be quantified. Despite shortcomings, it is still a tool that provides a
certain level of useful insight and data. The lack of a standardized survey tackling the issue of
autonomy across American states represents a significant shortcoming in application. The table
below summarizes the findings and symbolizes the way data would be organized if it were
available for these indicators of autonomy. Overall, these datapoints proved unavailable in
public governmental databases for American states.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 83

Table 1. Autonomy Factor Indicators
Policy
Theoretical
Framework
Indicator
(European
Countries)
Indicator
(American
States)
California New York
State
Source
Human
Capital
Theory and
Endogenous
Growth
Theory
Organizational
autonomy (1-5)
Organization
Autonomy (1-5)
NA NA NA
Policy Autonomy
(1-3)
Policy
Autonomy (1-3)
NA NA NA
Financial
Autonomy (1-3)
Financial
Autonomy (1-3)
NA NA NA

Funding. Funding refers to the degree in which the government enables the higher
education institution to achieve their mission through financial investments (Hoareau, Ritzen, &
Marconi, 2012). In this case, the instrument is more concerned with the effort put in rather than
the actual amount of expenditure. The first indicator is the actual expenditure per student
relative to GDP per capita. Controlling for GDP is intended to give an idea of the proportion that
is spent on higher education relative to income and population size. The next indicator focuses
on expenditure on financial aid as a percentage of total public expenditure on education at the
tertiary level. In this case, the measure is meant to gain an idea of the financial effort placed
towards equity in higher education relative to other expenditures (Hoareau, Ritzen, & Marconi,
2012). The final indicator has to do with incentives in higher education funding. This focuses
on the role of formulas and contracts in allocating state funds, as opposed to incremental
allocations and negotiations.
Expenditure per student relative to GDP per capita. This indicator took the form of cost
per full time enrollment (FTE) relative to GDP per capita by state. The focus of the costs was at
the state level of tertiary education. The data was able to be accessed from the Integrated
Postsecondary Education Data System (IPEDS), a part of the National Center for Education
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Statistics. The indicator includes the expense per 12-month FTE for: instruction, research,
public service, academic support, all other core expenses, institutional support, and student
services (National Center for Education Statistics, 2018). The averages of those individual
statistics were added together for a cumulative total. That total was then proportioned in
reference to per capita real GDP for the states of California and New York for the same year of
2012. Per capita real GDP data was obtained from the Bureau of Economic Analysis (Bureau of
Economic Analysis, 2018) and was the all industry total. The resulting percentage represented
the value of effort placed on tertiary student education.
The original measure of this indicator included the annual expenditure per student by
institution for all services related to tertiary education relative to GDP per capita. Accordingly,
the use of cost per FTE is consistent and satisfies the original intent of the instrument.
Expenditure on financial aid as a percentage of total public expenditure on education
at the tertiary level. Data for this indicator was gathered from the publicly available budgets for
each state. The amounts spent by each state on financial aid, in the form of grants and
scholarships, was set in proportion to the total amounts spent on higher education (California
Department of Finance, 2014; New York State Division of the Budget, 2012).
Role of formulas and contracts in funding mechanism X. The purpose of this indicator
is to focus on incentives in higher education funding (Hoareau, Ritzen, & Marconi, 2012). The
measure assumes that shifting funding for higher education institutions away from negotiations
and incremental allocations towards contracts and funding formulas that include performance-
based indicators are beneficial to higher education performance. This measure is based on a
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 85

score from 0 to 100, where 0 means all funding is based on incremental allocations and
negotiations, and 100 if all funding is based on contracts and performance-based formulas.
Data for this measure, in the original study, was gathered from the Center for Higher
Education Policy Studies. No equivalent source for data could be found for American states.
While state-level performance funding does exist and has been implemented in a number of
states (Dougherty, Natow, Bork, Jones, & Vega, 2014), no standardized survey of this topic
could be found that would allow for a scale-based quantitative measure. The best that could be
found was whether performance funding existed in a state or not, which is a binary measure that
does not satisfy the needs of this indicator in its current form. That being said, a look at the
funding mechanisms for higher education in California and New York showed the majority
emphasis is on incremental increases and negotiations as opposed to performance-based
incentives (Academic Planning, Planning and Coordination Department, 2009; New York State
Education Department, 2019; Dougherty, Natow, Bork, Jones, & Vega, 2014). Still, without a
reliable survey to refer to, it is not possible at this time to include this measure when applying the
instrument to American states. The table below summarizes findings for the indicators related to
funding.

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Table 2. Funding Factor Indicators
Policy
Theoretical
Framework
Indicator
(European
Countries)
Indicator
(American
States)
California New York
State
Source

Human
Capital
Theory and
Endogenous
Growth
Theory
Expenditure per
student relative
to GDP per
capita (tertiary)
Cost per full
time enrollment
(FTE) relative
to GDP per
capita
49.10% 37.89% IPEDS
Bureau of
Economic
Analysis
Expenditure on
financial aid as
a percentage of
total public
expenditure on
education, at
tertiary level of
education.
Expenditure on
financial aid as
a percentage of
total public
expenditure on
education, at
tertiary level of
education (state
level)
7.05% 9.42% State
Budget
Databases
Role of
formulas and
contracts in
funding
mechanism X
(Scale of 1-100
Role of
formulas and
contracts in
funding
mechanism X
(Scale of 1-
100)
NA NA NA

Performance
This dimension of the instrument has to do with performance indicators of higher
education institutions. Performance is analyzed through the lenses of research and education.
This is consistent with the services generally provided by higher education institutions, which
include instruction, research, and public service, the degree of each being dependent on the
mission of the institution (Becker & Toutkoushian, 2014). For the instrument, higher education
performance is divided into research productivity and attractiveness, graduate employment and
graduation, and size of the student body.
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Research productivity and attractiveness looks at higher education performance in regard
to research. This factor contains five separate indicators, which are translated for this study
accordingly. First are scientific publications within the 10% most cited scientific publication
worldwide as a percentage of total scientific publications per state. Second are the number of
universities in the top 500 ARWU ranking per million inhabitants per state. Third are the
number of incoming National Science Foundation Grants per million inhabitants per state.
Fourth are the number of awardees of the Graduate Research Fellowship Program in proportion
to state populations. Fifth are the number of public-private co-publications in proportion to
population (in millions) by state.
Two more factors make up the rest of the performance dimension. The first factor is
graduate employment and graduation, which is measured with graduate employment and the
graduation rate in proportion to total enrolled per state. The second factor is measured by
indicators related to the size of the student body (Hoareau, Ritzen, & Marconi, 2012). This
includes the inflow of foreign students, students from non-university backgrounds, and the
proportion of student of a certain age to the greater population.
Some of these indicators had to be adjusted when seeking out the data. How these
measures were adjusted is discussed in the results for each individually.
Research productivity and attractiveness.
Scientific publications within the 10% most cited scientific publication worldwide as a
percentage of total scientific publications. Finding data related to this measure was not
possible. In attempting to gather this data, the researcher contacted Scopus (Elsevier B.V.,
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 88

2018), an abstract and citation database, and was told that this specific level of data was not
available. There may have been some availability at another affiliated database that would allow
for reference to the most cited publications, but that it would not be possible to narrow the
definition of the searches to the state level, which is what would be needed. In addition, the
mentioned database requires an institutional subscription that was not available to the researcher
and is not consistent with the condition to use publicly available data set at the outset of this
study.
Number of universities in the top 500 ARWU ranking per million inhabitants per state.
The Academic Ranking of World Universities are published by the Shanghai Ranking
Consultancy (Shanghai Ranking Consultancy, 2012), which is an independent organization
focused on research in higher education. This is the same ranking used in the original study.
The rankings from 2012 were used in conjunction to population numbers, for each state, obtained
from the United States Census Bureau (United States Census Bureau, 2012).
Although the method for this measure proved feasible in this study, it is worth
questioning whether it is the most relevant form. Because the original study was focused at the
national level, a global ranking of higher education institutions was appropriate in order to gain
comparisons between countries. In the case of American states, however, it may be more
appropriate to use a ranking system that is slightly narrower in focus, such as the Americas as a
whole, North America, or simply the United States.
Number of incoming National Science Foundation grants per million inhabitants. The
original instrument used the number of annual European Research Council Starting grants, but as
those are focused solely on Europe it was necessary to switch the indicator to an appropriate
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 89

American measure. The National Science Foundation (NSF) acts as an independent federal
agency focused on funding research and education in areas related primarily to science and
engineering.
Data for this measure was gathered directly from the NSF Budget Internet Information
System (National Science Foundation, 2012), which provides statistical and funding information.
Using this database, award summary data could be gathered by state and year, in this case
California and New York. For the sake of consistency, the year 2012 was used. These numbers
were then set in proportion to population numbers for each state for the same year, gathered from
the U.S. Census Bureau (United States Census Bureau, 2012).
Number of incoming National Science Foundation Graduate Research Fellowship
Program awardees. The original instrument used the number of incoming Marie Curie fellows
per million inhabitants per country. For this study, data was collected about the number of
fellowships awarded by the NSF Graduate Research Fellowship Program (GRFP) (National
Science Foundation, 2012) for the year 2012. The GRFP is a subdivision of the NSF that
supports graduate students in science, technology, engineering, and mathematics fields pursuing
master’s or doctoral degrees. Data for this indicator was gathered from NSF FastLane system
(Graduate Research Fellowship Program, 2012). Through this system an award year could be
entered, in this case 2012, and a list of those offered awards as well as their current institutions
was generated. The list of current institutions was then combed through for California and New
York universities. Once these were compiled, they were set in proportion to the state population
per million inhabitants (United States Census Bureau, 2012).
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 90

Number of public-private co-publications by state in proportion to population. The
purpose of this indicator was to act as a measure of the connectivity between higher education
institutions and the private sector. The search for this datapoint did not yield any usable
information. The search included the typical databases for higher education statistics, but areas
related to public-private research partnerships seems to be generally lacking in publicly available
data of higher education performance. The table below summarizes the findings of indicators
related to research productivity and attractiveness.

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Table 3. Research Productivity and Attractiveness Factor Indicators
Performance
Theoretical
Framework
Indicator
(European
Countries)
Indicator
(American
States)
California New York
State
Source

Human
Capital
Theory and
Endogenous
Growth
Theory
Scientific
publications
within the 10%
most cited
scientific
publications
worldwide as a
percentage of
total scientific
publications per
country
Scientific
publications
within the 10%
most cited
scientific
publications
worldwide as a
percentage of
total scientific
publications per
state
NA NA NA
Number of
universities in
the top 500
ARWU ranking
per million
inhabitants
Number of
universities in
the top 500
ARWU ranking
per million
inhabitants (per
state)
0.35 0.72 ARWU
US Census
Bureau
Number of
yearly
European
Research
Council
Starting grants
wins per
million
inhabitants
Number of
incoming
National
Science
Foundation
Grants per
million
inhabitants
67.52 76.20 National
Science
Foundation
Number of
incoming Marie
Curie fellows
per million
inhabitants in
the host country
per year
Number of
incoming
National
Science
Foundation
Graduate
Research
Fellowship
Program
awardees
8.30 7.43 National
Science
Foundation
Number of
Public-private
co-publications
Number of
public-private
co-publications
NA NA NA
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Graduate employment and graduation.
Employment rate by degree attainment of bachelor’s degree and higher in proportion
to the civilian labor force. The original form of this measure was the employment rates of 18-34
years old within three years of leaving formal education in proportion to total enrollment.
Maintaining the measure in its original form presented a number of obstacles that could not be
overcome. In searching employment statistics, it was not possible to find data that satisfied the
criteria of this study. For example, it was not possible to find data on employment within 3 years
of graduation. Data was found on employment levels based on degree attainment, but even this
data was at the regional level as opposed to the state level. The databases used included the
Integrated Postsecondary Education Data System (National Center for Education Statistics,
2018), the Bureau of Labor Statistics (Bureau of Labor Statistics, 2018) , and the U.S. Bureau of
Economic Analysis (Bureau of Economic Analysis, 2018); unfortunately none of these sources
were able to provide satisfactory data for this measure.
Although employment by degree attainment statistics are available, this measure does not
satisfy the original intention of the measure and cannot be used with an adequate level of
confidence with this instrument. The lack of employment data within three years of graduation
represents another shortcoming of translating the original instrument for an American setting,
which will be discussed in the discussion section.
Graduates in proportion to the total enrolled students per state. The original form of
this measure was graduates in International Standard Classification of Education (ISCED) 5 and
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6, including all genders and ages, in proportion to enrollment. ISCED 5 and 6 includes both
levels of tertiary education (UNESCO Institute for Statistics, 2012), which translates into both
undergraduate and graduate levels of higher education in the US.
Data for this measure was gathered from IPEDS (National Center for Education
Statistics, 2018). Although the number of undergraduate graduates was available, there was no
data at the graduate level. Because data was only available for undergraduates, these numbers
were set in proportion to total undergraduate enrollment. The table below summarizes the
findings related to graduate employment and graduation.
Table 4. Graduate Employment and Graduation Factor Indicators
Performance
Theoretical
Framework
Indicator
(European
Countries)
Indicator
(American
States)
California New York
State
Source

Human
Capital
Theory and
Endogenous
Growth
Theory
Graduate
employment
rates of 18-34
years old, 3
years or less
after leaving
formal
education
(ISCED 5 and
6) in relation to
the labor
market
Employment rate
aged 25 and
older by degree
attainment of
bachelor's degree
and higher in
relation to labor
market
70.20% 73.00% Bureau
of Labor
Statistics
Graduates in
ISCED 5 and 6
all gender all
ages per
enrollment
Undergraduate
graduates in
proportion to the
total enrolled
undergraduate
students per state
13.49% 16.32% IPEDS

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Size of the student body.
Full time and part time transfer students. Originally this measure is concerned with the
transition students entering higher education through an alternative route. Using IPEDS
(National Center for Education Statistics, 2018), data was gathered for the number of transfer
students in California and New York for the year 2012. More specifically, this included transfer-
in degree/certificate-seeking undergraduate enrollment, which are categorized as students whom
are known to have previously attended a postsecondary institution at the same level. The
original measure also included students transitioning from a vocational school, and it is not clear
how those students are represented for this measure in IPEDS. While the measure would ideally
include transition students from a vocational background as well, the measure itself satisfies the
original intent. These numbers were then set in proportion to total undergraduate and graduate
enrollment.
Undergraduate and graduate students aged 20-24 in proportion to state population of
the same age. This measure required a certain amount of adjustment from its original form
largely due to the way the data is collected for the United States. The original measure was
concerned with the number of ISCED 5 and 6 students aged 20 as a percentage of the
corresponding population at the same age. Demographic data in the United States, gathered from
the US Census Bureau (United States Census Bureau, 2012), is narrowed down by ages between
20 and 24, as opposed to only 20 years. Accordingly, the age category of fall enrollment data,
gathered from IPEDS (National Center for Education Statistics, 2018), also had to be collected at
between the ages of 20 and 24 years old.
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The intent behind this measure had to do with the availability of skilled labor. The
enrollment of more students in higher education is equated with a greater supply of skilled labor
(Hoareau, Ritzen, & Marconi, 2013). For this reason, the students enrolled in higher education
in proportion to the corresponding demographic is seen as an indicator to higher education
performance. Although more specific demographic data from the US Census Bureau would have
been ideal, the age range still addresses the same concerns, and was a necessary accommodation
given the form of the statistic.
Inward mobile students as a percentage of the student population in the host state. The
measure in its original form only included students from abroad. Because the instrument is being
applied to states, as opposed to countries, both out-of-state students and students from abroad
were included for the indicator. The data was gathered from IPEDS (National Center for
Education Statistics, 2018), and set in proportion to the student population of the host state. The
table below summarizes finding for indicators related to size of the student body.

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Table 5. Size of the Student Body Factor Indicators
Performance
Theoretical
Framework
Indicator
(European
Countries)
Indicator
(American State)
California New York
State
Source

Human
Capital
Theory and
Endogenous
Growth
Theory
Percentage of
students entering
higher education
through an
alternative route
Full time and
part time
transition
students
11.74% 9.35% IPEDS
Students (ISCED
5-6) aged 20 as a
percentage of the
corresponding
population
undergraduate
and graduate
students aged
20-24 in
proportion to
state population
aged 20-24
14.57% 21.35% IPEDS
US
Census
Bureau
Students from
abroad - Inward
mobile students
as percentage of
student
population in the
host country
Students from
abroad in
addition to
students from
other American
states - Inward
mobile students
as a percentage
of student
population in the
host state
2.57% 5.02% IPEDS

Economic Output
The final dimension of the instrument is concerned with measuring economic output. In
reference to higher education performance, this output is more concerned with research rather
than contribution to the labor force. The reason for this is that higher education contribution to
labor is often less impactful and takes place over an extended period, and that is under ideal
conditions of sustained high-quality higher education (Hoareau, Ritzen, & Marconi, 2012).
Cumulative economic growth can also often be attributed to fiscal issues such as indebtedness
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 97

and budget deficits, again making the economic contribution of higher education a murky factor
to measure.
Focusing on research, however, allows for the benefits to be more apparent at an earlier
stage. These benefits often takes the form of new products, firms, and services or developing
existing products and services into new forms (Hoareau, Ritzen, & Marconi, 2013; Romer,
1990). The assumption being that an innovative economy is more primed for sustained
economic growth (Romer, 1986). For these reasons, the indicators of the economic output are a
factor of innovation and are essentially related to employment in knowledge intensive fields and
labor productivity.
Innovation.
Percentage of employees in knowledge intensive fields related to total employment.
The underlying assumption of this measure is that the more workers participating in knowledge
industries or sectors, the more innovative and competitive the economy (Hoareau, Ritzen, &
Marconi, 2012; Dill, 1997; Becker & Toutkoushian, 2014). The original measure looked at the
percentage of employees in knowledge intensive industries relative to overall employment. This
measure was able to remain relatively consistent with its original form. However, in accessing
data from the Bureau of Economic Analysis (Bureau of Economic Analysis, 2018), there was no
single measure for knowledge intensive industries. Instead, those knowledge intensive sectors
were selected individually as part of the measure, and these included: professional, scientific,
technical services, and information industries. These sectors were combined and set in
percentage to overall employment for the state. Beyond some potential debate about which
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 98

sectors to include as knowledge intensive industries, this measure was compatible with its
original form.
Real GDP per capita. Originally, this indicator used labor productivity as a measure for
innovation. The availability of data at the state level proved a hindrance to keeping form with
the measure. While the Bureau of Labor Statistics (Bureau of Labor Statistics, 2018) does
contain labor productivity data, it is focused at the industry or sector level for the whole country.
This includes the U.S. business sector, the nonfarm business sector, and the manufacturing
sector. Given the lack of accessible state level labor productivity data, real gross domestic
product (GDP) per capita was used in its place and retrieved from Bureau of Economic Analysis
(Bureau of Economic Analysis, 2018). GDP per capita was also used in the original instrument
to interpolate missing data for the measure in the single case of Croatia.
Although it is feasible to interpolate GDP per capita in place of labor productivity, it does
represent a shortcoming of instrument translation. Methods of calculating state level labor
productivity would be worth discussing in developing the instrument. Alternatively, the Bureau
of Labor Statistics could supplement their existing data by adding labor productivity at the state
level. Currently, the existing data does not satisfy the needs of the instrument in its current form.
The table below summarizes finding for indicators related to innovation.

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Table 6. Innovation Factor Indicators
Innovation
Theoretical
Framework
Indicator
(European
Countries)
Indicator
(American
States)
California New York
State
Source

Human
Capital
Theory and
Endogenous
Growth
Theory
Employment in
Knowledge
Intensive
Activities as a
percentage of
total
employment
Percentage of
employees in
knowledge
intensive fields
related to total
employment for
the state -
Professional,
scientific,
technical
services, and
information
industries
11.20% 10.47% Bureau of
Economic
Analysis
GDP per hour
worked in
purchasing
power standard
(labor
productivity)
Real GDP per
capita
52,974 62,841 Bureau of
Economic
Analysis

Discussion
The findings have shown that the application of the instrument to a new setting has
presented several difficulties that must be addressed before it can be adequately implemented.
These shortcomings do not have to do with the conceptual framework of the instrument, but with
the form of the associated measures.
The instrument itself is composed of a linear relationship between governmental policy,
higher education performance, and economic output in the form of innovation (Hoareau, Ritzen,
& Marconi, 2012). Each of these dimensions is made up of certain factors, and each factor is
composed of a group of indicators or measures. These indicators compose the main body of the
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 100

instrument, and each is an ordinal or continuous variable associated with a specific piece of data.
The ability to fill in those datapoints is dependent on how data is gathered in the first place.
Differences in how data is collected in the European Union versus the United States is where
much of the dissonance lies.
The results have successfully highlighted those indicators that require adjustment to make
the instrument appropriate for American states. Specifically, the hurdles that would need
addressing include measures of autonomy, the role of formulas and contracts, employment data
of graduates, scientific studies within top publications, public-private partnerships, and state-
level labor productivity statistics.
These indicators, in their original form, do not work for two reasons. The first is related
to how some public data is collected for American states. The second reflects the need to adjust
specific aspects of the instrument to make it more suitable to the new context and availability of
data. For example, the use of a higher education autonomy survey of experts and practitioners,
for each state, would provide a wealth of useful data. In another case, such as public-private
partnerships, it may be helpful to adjust the measure to focus on aspects of technology transfer
offices. Now that the shortcomings are clear, it is possible to both make recommendations on
data collection, as well as adjusting specific measures to make them more appropriate while
maintaining the conceptual framework of the instrument. This could be accomplished by using
indicators that are both available for American states and attributable to their associated factor
even if that differs from the indicator used in the original instrument.

PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 101

Chapter 5: Conclusion
The problem of focus for this dissertation was the lack of instrumentation to
quantitatively measure the relationships between public policy, higher education, and economic
growth. Rather than being concerned with varied aspects of these areas in isolation, this
dissertation sought to explore the relationships between these forces, and the impacts that each
has on the other. As was discussed during the literature review, governmental inputs have
historically sought to spur activity resulting in positive socioeconomic change (McMahon, 2009;
Dill, 1997), and it is important to design quantitative measures of public investment outcomes to
support decision making and justify spending.
This dissertation asked if the instrument used in a European study could be applied to
American states. If it were feasible, one could use such an instrument on all states, with their
accompanying higher education systems and economies, and garner certain insights about the
interactions between governmental policy and higher education. More specifically, how those
interactions could be optimized toward the benefit of economic output in the form of innovation.
Before such quantitative analysis could be conducted, however, the instrument itself would need
to be investigated, the purpose being to identify those indicators that fell short, and that would
need adjusting before the instrument could be appropriately applied to a new setting.
As with any translation, the application of an instrument developed for European
countries to an American state setting would present challenges. Foreseeing the existence of
those challenges, this study sought to pilot the instrument with only two states. More
specifically, to discover which aspects of the instrument would remain functional given the
available state level data and which would not. This was necessary to determine how the
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 102

instrument needed to be changed to fit the new context. The remainder of this chapter will begin
with a summary of the findings of this dissertation. The next section discusses implications for
practice, followed by recommendations for further research and the conclusion.
Summary of Findings
The translation of the instrument presented several challenges that need to be addressed if
this instrument is ever to be used appropriately with American states. The majority of these
obstacles have to do with differences in the data collected at the state level, in the United States,
versus the national level for the European Union. While not all of the indicators presented issues
when translating the instrument to the new context, the ones that did do need addressing. The
following section will discuss the findings of this study by highlighting the shortcomings of the
instrument.
Autonomy as a whole proved to be a difficult measure on which to find quantitative data.
More specifically, standardized quantitative data on organizational, policy, and financial
autonomy were generally lacking. Ideally, a state level survey or questionnaire would be
distributed to high level higher education practitioners and experts, across all states, to measure
their experiences with the various forms of autonomy. No such measure could be found.
The indicators for funding were more manageable to ascertain. Both cost per full time
student and the expenditure on financial aid as a percentage of total public expenditure on higher
education were attainable. The only difficulty was with the role of formulas and contracts in the
funding mechanism. No standardized quantitative measures could be found related to how
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 103

funding for the higher education systems is typically conducted at the state level, whether these
are the results of incremental allocations and negotiations or formulas and contracts.
Research productivity and attractiveness indicators also varied in results. Scientific
publications within the 10% most cited scientific publications was not possible to ascertain at the
state level, and the associated database was also not publicly available, which presented another
shortcoming. The number of universities in the top 500 Academic Ranking of World
Universities was collected at the state level, but it was worth asking whether that ranking
publication would be the most appropriate when only being applied to American states. The
number of yearly European Research Council Starting grants had to be translated to the number
of incoming National Science Foundation Grants, but this translation was both feasible and
appropriate. Such a translation was also necessary for the number of incoming Marie Curie
fellows, where the number of National Science Foundation Graduate Research Fellowship
Program awardees were used. Finally, it was not possible to find data on the number of public-
private co-publications by state, representing a shortcoming of data related to the connectedness
between higher education research and the private sector.
Graduate employment and graduation indicators provided a disappointing lack of nuance
in the availability of data for American states. The employment rates of graduates specifically
three years or less after graduation could not be found. The employment rate by level of degree
attainment, at the state level, was the closest measure that could feasibly be used, but translating
the indicator in this way does not directly address employment after graduation, which would be
a stronger reflection of higher education performance. Graduates in proportion to the total
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 104

enrolled students per states was attainable, but unfortunately graduation statistics only included
undergraduate students and excluded graduate level students.
Size of the student body indicators could be gathered with some shortcomings.
Transition student data could be gathered, but these only included those coming from other
tertiary institutions, without reference to those with a vocational background. Undergraduate
students aged 20 years old as a percentage of the corresponding population had to be expanded to
20-24 years old to accommodate the availability of demographic data at the state level. Data on
inward mobile students was gathered and included both students from other states as well as
other countries.
Economic output indicators related to innovation also presented certain challenges.
Percentage of employment in knowledge intensive industries could be found, but these industries
had to be defined and identified by the researcher. The alternative would include a single
statistic on knowledge intensive activities, with those sectors defined, but the measure is still
feasible in its current form. Labor productivity, or GDP per hour worked, was not possible to
ascertain at the state level. While this data was available, it was either at the national level or
sector specific. Data on labor productivity at the state level could not be found. Instead, this
indicator was interpolated with real GDP per capita for the state. While this replacement is
feasible, it does not directly address the impact on innovation, which represents a shortcoming
that needs addressing.

PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 105

Implications for Practice
The instrument in its current form is not ready for application to American states. It still
has the potential to contribute to the understanding of the impact of policy on economic output
through higher education. In order to feasibly design such an instrument, however, two separate
avenues would need to be pursued. The first has to do with the data that is collected for
American states, and how adding new measures could expand our understanding. The second
has to do with the instrument itself, and essentially tweaking it so that it is more appropriate for
the American setting, while also maintaining the conceptual framework of its foundation. The
instrument is built upon the tenets of Endogenous Growth Theory, and that investments in
human capital, innovation, and knowledge can lead to endogenous economic growth (Romer,
1994). As was discussed in the literature review chapter, much of Endogenous Growth Theory is
concerned with the measurement of human capital, and a fully developed instrument would
successfully add to that pool of knowledge. So long as the indicators, even if changed from their
original form, remain consistent with the theoretical conceptual framework, then the instrument
would be able to be applied to American states and expand upon the knowledge that drives the
existing literature.
Many of the shortcomings that resulted from gathering data for the instrument also act as
recommendations for how data collection and reporting could be enhanced. Doing so would
provide quantitative nuance to the functioning of policy, higher education performance, and
economic output. Areas related to autonomy, funding mechanisms, scientific publications,
public-private connectedness, graduate employment, and labor productivity could all be
enhanced by incorporating these measures for state-level data collection and reporting. Clearly,
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 106

the collection of more nuanced data means more effort on the parts of the reporting and
collecting institutions, but these efforts would conceivably be rewarded by providing more
detailed quantitative evidence of their economic impact, both in regard to policy and higher
education performance.
The second implication for practice has to do with those aspects of the instrument itself
that can be better adapted for the American state setting. The use of the Academic Ranking of
World Universities would be one example for potential change. Another example was shifting
the indicators from European scientific grants and fellowship to American grants and
fellowships. It is possible to develop the instrument to be a better fit to the American setting
while still maintaining the foundations of the conceptual framework (Romer, 1994).
Developing both avenues of data collection and instrument optimization would have
positive implications for practice. On the one hand, state policy would have more insight into
how they can optimize their interactions with and funding of higher education to positively
impact economic growth by developing the pool of human capital. On the other hand, higher
education institutions would have greater understanding of how to focus their energies to
improve performance and be able to provide evidence for the positive impacts of higher
education investments on economic growth. Such evidence would also contribute to the existing
literature of autonomy in higher education. Finally, the citizenry would benefit from optimized
use of tax money that spurs the economic growth of communities.

PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 107

Recommendations for Research
Research recommendations essentially involve those areas of the instrument that fell
short. Shortcomings include research on methods for gathering quantitative data on the forms of
higher education autonomy, relating to the nature of the relationship between the state and higher
education. Such research should also reflect differences inherent in the American system,
specifically incorporating how accreditation impacts certain aspects of higher education
autonomy in addition to the state. Research on quantifying measures of the funding mechanisms
of higher education, and how to gauge where these take place on the spectrum between
incremental allocations and negotiations, on one hand, and funding formulas and contracts on the
other.
Further research on more nuanced collection and reporting of higher education
performance metrics, at the state level, would also be advantageous. The inclusion of
employment data of graduates within three years of higher education completion. Research on
transition students, specifically students transferring from a vocational background into tertiary
institutions. Strategies on quantifying higher education research productivity at the state level, as
in published studies and public-private partnerships, would also be beneficial for our
understanding of state-level higher education performance as a whole. This area is especially
acute when considering the rise of private industry in higher education, as was discussed in the
literature review. Finally, more research into labor productivity statistics at the state level is
needed. As focus is centered on the innovation economy, it is important to have more nuanced
data related to efficiency at the state level.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 108

As the content of this dissertation has largely been qualitative in nature but quantitative in
subject, it is recommended that the next step of developing the instrument take the form of a
mixed methods approach. The purpose of such research would be to survey higher education
experts and practitioners across all American states with the goal of identifying those indicators
that would be more suitable and available to measure the same concepts. Analyzing such data
would greatly aid in the development of a more tailored instrument that could assist
policymakers in decision making.
Conclusion
A number of obstacles were presented in attempting to gather the various datapoints for
the instrument. While obstacles do not totally prohibit the potential use of the instrument in an
American setting, it does call for certain adjustments and accommodations before it can be
appropriately applied. As was noted in the literature review, where the notion of autonomy can
vary depending regional context and community demands, there must also be adjustments in the
measurements of these trends to accurately reflect the accompanying environment.
As is the case with different languages, a direct translation of words will often lead to
misunderstandings and miscommunications. It is the meaning itself, rather than the words, that
must be of focus in order to communicate the same message. For example, the number of yearly
European Research Council Starting grants could adequately be replaced with the number of
incoming National Science Foundation grants. While not a word-for-word translation, one could
adequately replace the other because the meaning behind the measure was consistent.
PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 109

In this case, a word-for-word translation of the European instrument to an American
setting was not possible. On the one hand, certain indicators required adjustment in order to be
applicable to the new context, and some of these adjustments were more appropriate than others.
On the other hand, the translation put on display certain shortcomings of American data
collection and reporting that should be addressed to improve our own understanding.
This dissertation reflects the qualitative first step in translating a complex instrument
from a European context to an American setting. In order to achieve an applicable instrument
that will convey meaning, further work will be required. This research has managed to highlight
those shortcomings and where energy should be focused to optimize the instrument for use in the
United States. So long as any changes made to the indicators align with the conceptual
framework of the instrument (Hoareau, Ritzen, & Marconi, 2012; Romer, 1990) and the tenets of
Endogenous Growth Theory, then there is no reason why potential changes could not be applied.
The endeavor would have value in contributing to our understanding of how these differing
systems work together, as well as providing direction towards their improvement. Although this
would still represent a small piece of a greater puzzle, it is a significant one that would be worth
pursuing to improve the overall wellbeing of communities through governmental investment in
higher education.

PUBLIC POLICY HIGHER EDUCATION AND ECONOMIC IMPACT 110

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Appendix
Theoretical Framework Alignment Matrix

Research Question Theoretical Framework Data Instrument Dimension
How to quantitively gauge
the impact of governmental
investments in higher
education performance?
Endogenous Growth Theory/
Human Capital Theory
Dimensions of Public Policy
and Higher Education
Performance
How does higher education
performance impacts
economic growth?
Endogenous Growth
Theory/Human Capital
Theory
Dimensions of Higher
Education Performance and
Economic Output
(innovation)

Abstract (if available)

Abstract The dissertation sought to pilot a European quantitative instrument on two American states in order to highlight indicator shortcomings. The purpose being to identify those aspects of the instrument that would require adjustment before appropriate application to all states. The theoretical framework of the instruments consists of Human Capital Theory and Endogenous Growth Theory, and quantitatively explores the impacts of public policy on the innovation economy through higher education performance. Results display those indicators in need of adjustment, how data collection practices could be improved, and how the instrument could be made more suitable for American states. This dissertation represents a qualitative first step in the development of quantitative instrument focused public policy, higher education, and economic growth.

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Asset Metadata

Creator Islam, Sharif Sadad (author)

Core Title Public policy in higher education for economic progress: a qualitative study of quantitative instrumentation on California and New York

School Rossier School of Education

Degree Doctor of Education

Degree Program Education (Leadership)

Publication Date 10/04/2019

Defense Date 06/24/2019

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Autonomy,California,data,Economic development,economic growth,Endogenous Growth Theory,Europe,Higher education,higher education performance,Human Capital Theory,innovation,innovation economy,instrumentation,measurement,New York State,OAI-PMH Harvest,Public Policy,qualitative,quantitative,replication,Technology

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Tobey, Patricia Elaine (committee chair), Filback, Rob (committee member), Michael, Steve (committee member)

Creator Email shislif@gmail.com,ssi@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-223905

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