PUBLIC HIGHER EDUCATION SPENDING AND ECONOMIC GROWTH:
THE ROLE OF THE PRIVATE MARKET FOR HIGHER EDUCATION
AND THE MEDIATING EFFECT OF EDUCATIONAL ATTAINMENT
A Thesis
Presented to the faculty of the Department of Economics
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
Economics
by
Cary Garcia Jr.
FALL
2013
PUBLIC HIGHER EDUCATION SPENDING AND ECONOMIC GROWTH:
THE ROLE OF THE PRIVATE MARKET FOR HIGHER EDUCATION
AND THE MEDIATING EFFECT OF EDUCATIONAL ATTAINMENT
A Thesis
by
Cary Garcia Jr.
Approved by
__________________________________, Committee Chair
Suzanne O’Keefe, Ph.D.
__________________________________, Second Reader
Kristin Kiesel, Ph.D.
___________________________
Date
ii
Student: Cary Garcia Jr.
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
__________________________, Graduate Coordinator ___________________
Kristin Kiesel, Ph.D.
Date
Department of Economics
iii
Abstract
of
PUBLIC HIGHER EDUCATION SPENDING AND ECONOMIC GROWTH:
THE ROLE OF THE PRIVATE MARKET FOR HIGHER EDUCATION
AND THE MEDIATING EFFECT OF EDUCATIONAL ATTAINMENT
by
Cary Garcia Jr.
This thesis revisits the effect of public higher education spending on economic growth.
The data used covers all 50 U.S. States over the time period from 1989-2006. Unlike
previous studies, we have included the variation in the composition of the market for
higher education from state to state. In addition, a 2SLS model was specified, where
public higher education spending had an indirect effect on economic growth through
educational attainment. The results suggest that the relationship between public higher
education spending and economic growth is only positive in those states with the smallest
markets for private higher education. Also, results indicated that public higher education
spending has a negative relationship with educational attainment. However, this negative
effect decreases in states with larger markets for private higher education.
_______________________, Committee Chair
Suzanne O’Keefe, Ph.D.
_______________________
Date
iv
ACKNOWLEDGEMENTS
I would first like to thank my parents, my family and friends who have supported
me in all my endeavors. I also want to thank my partner, Katherine Valenzuela, for her
patience and encouragement. Lastly I want to thank the professors who have guided and
assisted me in writing this thesis, Suzanne O’Keefe and Kristin Kiesel.
v
TABLE OF CONTENTS
Page
Acknowledgements ............................................................................................................. v
List of Tables ................................................................................................................... viii
List of Figures .................................................................................................................... ix
Chapter
1.
INTRODUCTION ............................................................................................... 1
2.
LITERATURE REVIEW .................................................................................... 4
2.1
Human Capital Theory ......................................................................................... 4
2.2
Public Higher Education Spending and Economic Growth ................................. 5
2.3
The Market for Higher Education ........................................................................ 7
2.4
Educational Attainment ........................................................................................ 9
3.
EMPIRICAL MODEL AND DATA ................................................................. 12
3.1
Empirical Model ................................................................................................. 12
3.2
Data .................................................................................................................... 16
3.3
Summary Statistics and Time Trends................................................................. 18
4.
ESTIMATION ISSUES AND RESULTS ......................................................... 24
4.1
Estimation Issues ................................................................................................ 24
4.2
Initial Results...................................................................................................... 25
vi
4.3
Interaction Variable Results ............................................................................... 27
4.4
2SLS Results ...................................................................................................... 32
5.
CONCLUSION .................................................................................................. 38
5.1
Summary of Findings ......................................................................................... 38
5.2
Future Research .................................................................................................. 42
References ......................................................................................................................... 44
vii
LIST OF TABLES
Table
Page
Table 3.1 Variable Definitions and Sources ..................................................................... 18
Table 3.2 Summary Statistics ........................................................................................... 19
Table 4.1 Initial Estimates ................................................................................................ 27
Table 4.2 Interaction Variable Estimates .......................................................................... 29
Table 4.3 Higher Education Attainment Estimates (First Stage of 2SLS) ....................... 33
Table 4.4 Final 2SLS Estimates ........................................................................................ 36
viii
LIST OF FIGURES
Figure
Page
Figure 3.1 State Average Income Per Capita and Public Higher Education Appropriations
Per Capita ........................................................................................................ 21
Figure 3.2 State Average Income Per Capita and Higher Education Attainment ............. 22
Figure 3.3 State Average Public Higher Education Appropriations Per Capita and Higher
Education Attainment ..................................................................................... 23
Figure 4.1 Average Appropriations Per Capita and Average Share of Total Students
Attending Public Institutions for 50 States ..................................................... 32
ix
1
1.
INTRODUCTION
According to recent figures from National Center for Education Statistics, tuition
for public higher education institutions rose by 30% from 1980 to 1990. It then increased
by another 22% from 1990 to 2000. Finally in the most recent decade from 2000 to 2010
it increased by 42%. Although the amount of expenditure or spending on public higher
education by states has increased in the past decades, the National Association of State
Budget Officers points out that tuition has continued to rise and percentage of state
spending on public higher education as a percentage of total state budgets has decreased
from 13.1% in 1998 to 10.1% in 2011.
It seems that state governments are reducing their investments in public higher
education and are placing that burden more on students and their families. The role of the
government is important in the financing of higher education. Without government
intervention the role of financing higher education would be left mostly to students and
families. In a scenario with no public option for higher education, a financially
constrained family may be unable to access education in the private market. Families
unable to afford post-secondary education would then lag behind their more wealthy
counterparts who would be increasing their earning power and becoming more wealthy
with advanced skills and education. This inevitably leads to greater social inequality.
Therefore the government then has the role to either provide or subsidize education for
the public to reduce inequality (Becker, 1981; Bräuninger & Vidal, 2000).
With spending on public higher education being reduced relative to state
government budgets, it is important that those investments are efficient and therefore
2
provide the most benefit to the public. Many studies have investigated the relationship
between higher education spending by government and the impact it has on the economy.
The basis for these investigations has often been the work of Becker (1962) and Schultz
(1963).They proposed that spending on higher education is an investment in human
capital that positively impacts economic growth in a similar manner as physical capital
investments.
There are many studies that have investigated the relationship between
government investments in human capital through higher education spending across
countries, but there is no common understanding of what effect state government
spending on higher education has on economic growth. Furthermore, the link between the
products of those investments, an educated workforce, has not been clearly presented in
such a way that links all three parts; the investment, the human capital and the economic
impact of that investment. Consequently the purpose of this thesis is to conduct an
empirical analysis that would illustrate the positive causal link between each part.
This analysis is unique for two reasons. First it is one of the few studies that have
taken into account the private market for higher education in an analysis of public higher
education spending and economic growth. Second it may be the only study that has tested
the role of the private market for higher education in an analysis of public higher
education spending and educational attainment.
This thesis first begins with a review of the literature to provide a theoretical and
methodological foundation for the analysis. Next, we provide the empirical model and
summarize the data used. Here is where the general functional form of the statistical
3
models will be presented along with descriptions of the variables and corresponding
expectations. And finally, the results will be summarized and suggestions for future
research will be presented.
4
2.
LITERATURE REVIEW
Still very little has been done in terms of understanding the effects of higher
education spending specifically as a component of human capital theory and there is no
consensus on what effect this spending has on growth. It seems though that significant
and positive results may be found in models that take into account the composition of the
market for higher education. By including these differences along with the possible
mediating effect of educational attainment, we may be able to better examine how
education spending affects states’ economic growth.
Although the theoretical background exists to support the hypothesis that public
higher education spending positively influences economic growth and empirical evidence
exists in cross national studies, when tested empirically on the United States the results
are often mixed. This literature review begins with a section discussing human capital
theory and its importance to the analysis of public higher education spending’s effect on
economic growth. Then we will focus on empirical analyses that investigate the role of
the market for higher education and the mediating effect of educational attainment.
2.1
Human Capital Theory
Most studies that attempt to understand the relationship between education and
economic growth are dependent on the human capital theory pioneered by Becker (1962)
and Schultz (1963). Becker (1962) explains that by focusing on intangible characteristics
across countries, such as knowledge possessed, rather than differences in physical capital,
we can arrive at a better explanation of differences in income among people and across
5
countries.1 Since investments in human capital are closely tied with these intangible
characteristics it would seem that examining changes in human capital investments would
provide an explanation of differences in growth across countries. Furthermore, if the
primary benefits of human capital investments are to increase the physical and mental
abilities of the population so as to increase real income, then it is through this process that
investments in education improve the mental abilities of the workforce and in turn
increase economic growth (Barro, 2001; Becker, 1962; Lucas Jr, 1988).
2.2
Public Higher Education Spending and Economic Growth
If investments in education are a key component of economic growth then where
do these investments come from when looking at the United States? Bräuninger & Vidal
(2000) in their study of private versus public financing of education and inequality, call
education “the crossing of three institutions, the Market, the Family and the State.” Using
human capital theory as a basis for their analysis, Bräuninger & Vidal (2000) propose
that parents are largely responsible for the decision of whether or not to educate their
children.
The decision to educate is then based on three factors: the parents’ wage rate,
degree of intergenerational altruism, and the private cost of education. In the presence of
a borrowing constraint, the market is not a viable option for the financing of education,
thus the family and the state become the primary financiers of education (Becker, 1981).
If families were to finance education on their own, then those families with the greatest
wage rates would be more able to afford education for their children while less wealthy
1
The knowledge possessed by the workforce is often explained to increase either by learning by doing (onthe-job training) or through formal schooling (Becker, 1962; Lucas Jr, 1988)
6
families would forgo the decision to educate holding the other two factors equal. Thus the
State then has the role of financing education through loans and subsidization to lower
the financial burned placed upon families and reduce inequality (Bräuninger & Vidal,
2000).
In contrast with Bräuninger & Vidal’s hypothesis, some economists have found
that the effect of public higher education spending on economic growth is nil if not
negative. Wang and Davis (2005) studied the U.S. states excluding Alaska and Hawaii
over a 20 year period from 1980 to 2000 using a fixed effects panel analysis. Looking at
the major areas of government expenditure and the varying effects on economic growth,
they found that changes in public education expenditures, combining elementary,
secondary and higher education expenditures, did not have any positive effects on
growth. They agree that investments in education can improve the workforce and
increase income, but they blame the inefficiency of state governments’ use of funds for
the negative results of their analysis. Furthermore, Wang and Davis (2005) suggest that
ineffective state expenditure on education is crowding out private sector spending, thus
leading to low levels of growth.
Vedder (2004) too believes in this crowding out theory when it comes to public
higher education investments. Using state-level data for the United States including the
District of Columbia from 1977 to 2002, he measured total spending by state and local
institutions on higher education. He found that it was negatively related with growth in
real income per capita. Furthermore he found no significantly positive relationship
between higher education expenditure and enrollments. Vedder explains that his results
7
do not support the existence of positive externalities and equality and even suggests that
there may be negative externalities from spending on higher education i.e the crowding
out of private market spending. Moreover he argues that higher education institutions
may simply be screening devices for employers that lower information costs.
A key issue with the findings of Vedder’s analysis is that the results do not
include state fixed effects or time effects and therefor do not address across state
variation or time trends. It is possible that with 50 diverse states , there may be
unexplained variation from state to state that may distort results if using model without
state fixed effects. The same may be true for not accounting for any unexplained
variation over time as there may be large scale economic trends that vary over time and
affect the country as a whole.
2.3
The Market for Higher Education
Another part of this thesis focuses on the role the market for higher education
plays in the relationship between public higher education spending and economic growth.
In studies that have found positive effects from an increase in public higher education
spending by states, the research has generally focused on the non-homogeneity of states
such as the economic, polticial and demographic differences.
Returning to the work of Bräuninger & Vidal (2000), they constructed an
endogenous growth model to see what effect an increase in public or private funding on
higher education would have on economic growth. With their model they showed that a
mixed system of public and private financing of education would lead to lower economic
growth, whereas a system that financed exclusively by either public or private means
8
would experience greater growth. Furthermore they found that the greatest levels of long
term growth would be achieved in a system where education was completely subsidized
by the government.
Bräuninger & Vidal continue to further the thought that the effect of state
spending on higher education on growth is not homogenous across the country. They
found that in a system that was dominated by the public market, an increase in
subsidization from an already high level, would increase growth. On the other hand, in a
system that was dominated by the private market for education and therefore had a low
level of subsidization, an increase in the public subsidy had the opposite effect.
In observing the 50 U.S. states from 1970-2005 Curs, Bhandari, and Steiger
(2011) found that public education expenditure increased income growth per capita. Their
investigation was based on a state fixed effects and time fixed effects model that included
the interaction between the percentage of college students attending public institutions
and the public higher education expenditure. The percentage of college students attending
public institutions was used as a proxy measurement of the size of the private market for
higher education. For example, when comparing two states, a state with a smaller share
of students attending public institutions would suggest a larger market private market for
higher education within that state. They believed that omission of the market for higher
education in an analysis of public higher education expenditure’s effect on economic
growth would lead to negative omitted variable bias.
In agreement with Bräuninger and Vidal (2000) they discovered that spending on
higher education had different effects on growth based on composition of the market for
9
higher education in each state. Their results showed that states which had a larger market
for private higher education and thus a smaller percentage of college students attending
public institutions would see a smaller or even negative effect from public higher
education expenditure on economic growth. On the other hand, a state which had a
comparatively smaller private market for higher education would experience a more
positive impact from higher education expenditure by the government. Specifically they
found that percentage exceeding about 70% would produce positive effects. Although
using the share of students attending public institutions as a measure of the private market
for higher education does not give a complete picture of the true market size it seemed to
provide a good proxy measurement.
2.4
Educational Attainment
Some studies have hypothesized that increases in state public higher education
expenditure would increase higher education graduation rates and therefore educational
attainment of the population. Zhang (2008) examined this topic, focusing on institutionallevel graduation rates. Using a 6-year cohort survey of graduation rates for 2004 from the
Integrated Postsecondary Education System, Zhang developed a panel model controlling
for institution differences and time trends with the addition of institutional fixed effects
and time fixed effects.
Zhang explains that although some studies have found a positive link between
instructional expenditure only and graduation rates, the results of his analysis suggest that
at a broader level, state public higher education expenditure overall is the primary
determinant of graduation rates. Specifically he found that an increase in public higher
10
education expenditure of 10% per Full Time Equivalent student at 4-year public
institutions would increase the graduation rate about 0.64%. He argues that this is the
result of resource dependence, which states that internal organizational activities are
influenced primarily by the actions of external resource providers, i.e. state spending
budgets and public higher education systems. Furthermore, this would mean that a
decline in public higher education appropriations would lead to reduced instructional
expenditures for public higher education institutions and therefore a reduction in the
graduation rate.
Baldwin, Borrelli, and New (2011) took a different approach with their analysis
and developed a model that attempted to inlcude both the direct effect of public higher
education expenditure and the effect mediated by educational attainment. Using a path
analysis controlling for time trends for the 48 states from 1988-2005, they found that
college attainment levels was one of the most consistent predictors of GSP per capita
growth. Furthermore, they theorized that by using the path analysis they would find that
higher education can impact economic growth on two fronts: the direct impact of
increasing the funding of colleges and universities and what they consider an indirect
impact, increasing educational attainment of the population.2 Although Baldwin et al.
found that public higher education expenditure had a positive direct effect on economic
growth, they also discovered a negative mediating effect from educational attainment.
2
The direct effect of expenditure on higher education may be due to the current trend in higher education
institutions demonstrating that they are regionally focused institutions that provide more than educated
graduates by also providing research and development as well as technical services to nearby businesses or
government institutions and other economic development activities (Storm & Feiock, 1999).
11
The results of Baldwin et al. do not agree with Becker (1962), Schultz (1963)
Lucas Jr (1988) and human capitol theory. One possibility though, is that the omission of
the private market for higher education may also negatively bias public higher education
expenditure’s effect on educational attainment in similar way that was suggested by Curs
et al. (2011) when studying the direct effects of public higher education spending on
economic growth. Baldwin et al. also bring up this likelihood in the discussion of their
results.
12
3.
3.1
EMPIRICAL MODEL AND DATA
Empirical Model
In line with previous research, this paper will use a growth model where
economic growth is a function of state government spending on higher education. The
basic form of the model will be the following:
%∆𝐼𝑁𝐶𝑂𝑀𝐸𝑃𝐶𝑖𝑡 = 𝛼𝐸𝐷𝑈𝐸𝑋𝑃𝐶𝑖𝑡 + 𝛿𝑡 + 𝜑𝑖 + 𝜀𝑖𝑡
(1)
Specifically %∆𝐼𝑁𝐶𝑂𝑀𝐸𝑃𝐶𝑖𝑡 is the growth rate of real income per capita and
𝐸𝐷𝑈𝐸𝑋𝑃𝐶𝑖𝑡 is per capita dollars expended on state public higher education. 𝛿𝑡 and 𝜑𝑖 are
included as time fixed effects and state fixed effects respectively. The time fixed effects
will control for omitted variables that may change over time but do not vary from state to
state while the state fixed effects will control for omitted variables that may change from
state to state but do not vary with time. Lastly 𝜀𝑖𝑡 is the idiosyncratic error term. Based on
human capital theory we would expect the coefficient for state higher education would be
positive, but as explained in the review of literature and the various studies with both
positive and negative results the expected sign is somewhat ambiguous.
If we accept the findings of Curs et al. (2011), where the omission of the private
market for higher education negatively biases the effect of higher education spending,
then we must develop the above model further. It will thus take the form:
%Δ𝐼𝑁𝐶𝑂𝑀𝐸𝑃𝐶𝑖𝑡 = 𝛼𝐸𝐷𝑈𝐸𝑋𝑃𝐶𝑖𝑡 + 𝛽𝑃𝑈𝐵𝐿𝐼𝐶𝑆𝐻𝐴𝑅𝐸𝑖𝑡 + 𝜆𝐸𝐷𝑈𝐸𝑋𝑃𝐶𝑖𝑡 × 𝑃𝑈𝐵𝐿𝐼𝐶𝑆𝐻𝐴𝑅𝐸𝑖𝑡
+𝛿𝑡 + 𝜑𝑖 + 𝜀𝑖𝑡
(2)
In this equation we have included our proxy for the composition of the market for
education, the share of college students enrolled in public institutions,
13
as 𝑃𝑈𝐵𝐿𝐼𝐶𝑆𝐻𝐴𝑅𝐸𝑖𝑡 . Also we have the interaction between public higher education
spending and the public share in the market education. Goldin and Katz (1998) explained
that across time the shares of public to private enrollments are stable within each state.
Therefore, it cannot be expected that small changes in enrollments will have any
significant effects on economic growth. By using the interaction between enrollments and
spending we will be allowing the effects of states’ spending to vary across different state
enrollment patterns and thus different markets for higher education (Curs et al., 2011).
It is expected that because of the lack of variation in the composition of the
market for higher education within states over time, we will not find any significant
results for the share of students attending public higher education institutions. On the
other hand, we expect that the interaction between public higher education spending and
the percentage of students enrolled in public institutions will yield positive results. This
would then suggest that there is a difference in the impact of public higher education
spending which is dependent on the composition of the market for higher education in
each state. Specifically we should find that in comparison to states with larger private
markets for higher education, states that have a greater dependence on public institutions
for higher education would see a more positive effect from public higher education
spending per capita.
Along with explanatory variables focused on education, we will also include some
economic control variables. Our first variable will be the growth rate of the population
for each state, which may have a negative impact on income growth per capita if
population growth exceeds growth in income. Then we include total government
14
spending per capita, to control for other state spending. The result for this variable may
be ambiguous based on previous studies that did not find significant results for
government spending as a whole (Devarajan, Swaroop, & Zou, 1996; Hsieh & Lai,
1994).
Finally, we have variables for agriculture, manufacturing, mining, and the
finance, insurance, and real estate industries measured as a percentage of GSP.
Agriculture we would expect to be positive but may have a marginal impact on growth
and may slow growth for states largely dependent on agriculture output due to the fact
that agriculture output growth for the United States was just above one percent from 1990
to 2004 (United States Department of Agriculture, 2013). We expect a similar result with
the mining industry as the average growth rate across the nation was less than one percent
over the same time period (Bureau of Economic Analysis, 2013).
The next model will be treating educational attainment as a function of public
higher education per capita. This will take the following form:
𝐵𝐴25𝑖𝑡 = 𝛼𝐸𝐷𝑈𝐸𝑋𝑃𝐶𝑖𝑡 + 𝛽𝑃𝑈𝐵𝐿𝐼𝐶𝑆𝐻𝐴𝑅𝐸𝑖𝑡 + 𝜆𝐸𝐷𝑈𝐸𝑋𝑃𝐶𝑖𝑡 × 𝑃𝑈𝐵𝐿𝐼𝐶𝑆𝐻𝐴𝑅𝐸𝑖𝑡 + 𝛿𝑡
+𝜑𝑖 + 𝜀𝑖𝑡
(3)
Here our measure of educational attainment is 𝐵𝐴25𝑖𝑡 . This variable is measured
as the percentage of the population 25 years old and older that has achieved at least a
bachelor’s degree. Then we have simply included public higher education spending, the
percentage of students attending public institutions, the interaction term from the
previous equation as well state fixed effects and time fixed effects. Since Baldwin et al.
found a negative relationship between public higher education spending and educational
15
attainment we expect that the inclusion of the proxy measurement for the size of private
higher education market would remove negative omitted variable bias similar to
expectations for the second model presented.
The third model used in the two-stage least squares (2SLS) analysis, where the
model of educational attainment will become the first stage of the estimation and
educational attainment will be treated as an endogenous variable that will be
instrumented using the independent variables from the second equation that was
presented. If we are treating higher education spending as an investment in human
capital, it is reasonable that we should include the product of that investment in our
analysis. If it is expected that an educated workforce leads to economic growth, then
higher education spending and educational attainment are two parts of one process
(Barro, 2001; Becker, 1962, 1993; Lucas Jr, 1988).
As discussed in the review of the literature, Baldwin, Borrelli, and New (2011)
used a path analysis to examine the direct effect of higher education expenditure itself
and the indirect effects via educational attainment levels. Specifically when tested they
found that public higher education spending had a significant negative effect on
educational attainment. They proposed that this was likely due to not including the
market for private education in their model. We test this hypothesis by using the third
model as the first stage for the 2SLS model. We will essentially be examining the indirect
effects of public higher education spending on economic growth through the mediating
effect of educational attainment like Baldwin, et al. Specifically, we will be using public
higher education spending, the share of students attending public institutions and the
16
interaction between these two variables as the primary instruments for predicting
educational attainment.
3.2
Data
As mentioned previously, the data for this analysis covers the 50 United States for
18 years from 1989-2006. Table 3.1 lists definitions and sources for the key variables. All
dollars values used in this analysis are in real 2005 dollars.
The Grapevine surveys of financial officers of public higher education institutions
produced by Illinois State University’s Center for the Study of Education Policy provided
state-level data for state appropriations for public higher education including grants and
financial aid for all degree granting institutions, which will serve as our measure of
public higher education spending. The annual Digest of Education Statistics produced by
the National Center for Education Statistics (NCES) contained data for public and private
enrollments, of which the share of total college students attending public institutions was
calculated. Data for the last education related variable, higher education attainment of the
population 25 years old or older, was gathered from U.S. Census Bureau’s annual data
tables from their Current Population Survey.
The data for measuring income per capita growth rates and the size of
agricultural, manufacturing, mining and finance, insurance and real estate industries was
gathered from the Bureau of Economic Analysis’s interactive data tables. The data for
total government expenditure was gathered from annual volumes of the U.S. Census
Bureau’s Statistical Abstract of the United States. The BEA’s interactive data tables were
used to gather population data produced by the U.S. Census Bureau. This data was used
17
to derive the per capita measurements of income growth, higher education expenditure,
total government expenditure as well as the annual population growth rates for each state.
It is expected that since higher education expenditure is an investment over time,
it will have a delayed effect on economic growth. Therefore, a lagged three-year moving
average centered on t-5 will be used in the models tested. Data from the 2000/2001
Baccalaureate and Beyond Longitudinal Study by the NCES showed that among students
surveyed, the average time to graduation for students seeking 4-year degrees at public
institutions was 57.2 months or just under five years. Also, of those surveyed over 33
percent took six years or more to graduate (National Center for Education Statistics,
2003). 3
3
The variable for the share of college students attending public higher education institutions and total
expenditures per capita will also be used as lagged three-year moving average centered on t-5.
18
Table 3.1 Variable Definitions and Sources
Variables
Dependent
%ΔINCOMEPC
Definitions and Sources
Annual percentage change in real income per capita (BEA)
Explanatory
EDUEXPC
State higher education appropriations per capita ( BEA, Grapevine)
PUBLICSHARE
The percentage of total college students enrolled in public institutions (NCES)
BA25
The percentage of the population over 25 years old who has attained a
bachelor’s degree or more (U.S. Census Bureau)
%ΔPOP
Annual percentage growth in population ( BEA)
TEXPPC
Total government expenditure per capita (U.S. Census Bureau)
AG
The size of the agricultural industry as a percentage of GSP (BEA)
MAN
The size of the manufacturing industry as a percentage of GSP (BEA)
MIN
The size of the mining industry as a percentage of GSP (BEA)
FIRE
The size of the finance, insurance and real estate industries as a percentage of
GSP (BEA)
3.3
Summary Statistics and Time Trends
Table 3.2 provides summary statistics for the variables used in this analysis.
Wyoming, the Dakotas and Louisiana had the top average growth rates for income per
capita above 2.5%, with Wyoming experiencing an average growth rate of 3.6%.Alaska
and Michigan have the lowest growth rates of income per capita at 1.2% and 1.3%
respectively.
19
Table 3.2 Summary Statistics
Variables
Dependent
Mean
Std. Dev.
Max
Min
0.0204246
0.0203195
0.0989778
-0.0371158
EDUEXPC
184.6575
51.08501
337.479
64.04503
PUBLICSHARE
0.7946073
0.1210606
0.9935223
0.4251673
BA25
0.2366244
0.0501337
0.404
0.111
%ΔPOP
0.0112213
0.0098133
0.0732498
-0.0598613
TEXPPC
425.71
1487.04
13305.61
1585.72
AG
0.0199968
0.02052
0.1260822
0.0015239
MAN
0.15238
0.0657023
0.3146684
0.0185499
MIN
0.0244665
0.0534322
0.3760898
0.0001301
FIRE
0.179982
0.0555591
0.4653468
0.0739304
%ΔINCOMEPC
Explanatory
When looking at public higher education appropriations per capita from 19892006, Wyoming, Alaska and Hawaii have the highest annual average of over $300. The
next three highest states are New Mexico, North Dakota and North Carolina spending
$296, $268 and $265 per capita respectively. New Hampshire spends the least averaging
$77 dollars annually. The majority of the schools that spend the least per capita are in the
North Eastern region of the United States except for Missouri which spends less than
$145 per capita. The average across the country is about $184 per capita annually.
Total state government spending per capita, seems to have the highest values in
states with smaller populations. Alaska and Hawaii have averages of $11,367 and $6,036
per capita respectively. New York is an exception with total state government spending
20
per capita of $5,588. The states with the least amount of state government spending per
capita are all in the South. Missouri, Tennessee, Florida and Texas all have an average
below $3,200 per capita.
Turning to share of students attending public institutions, we find that only one
state, Massachusetts, has a majority of students attending private institutions. Over half
the states have at least 80% of college students attending public institutions. Five states
have over 90% of college students attending public institutions including Wyoming and
Nevada at average rates of 96%. Along with the North Eastern region of the country
spending less public funds they also expectedly have a greater reliance on private
institutions for higher education than the rest of the country.
As for educational attainment levels, the Southern region has the lowest levels of
higher education attainment. West Virginia has the lowest level of higher education
attainment at 14% of the population 25 years old and older achieving a bachelor’s degree
or more. The exceptions are Texas and Georgia. They averaged levels of 23%, close to
the national average. Colorado, Massachusetts, Connecticut and Maryland lead the
country with average college attainment levels over 30%.
Since the central focus of this thesis is the effect of higher education spending on
economic growth, we consider real income per capita for the 50 United States from 19892006. As Figure 3.1 shows, average income per capita grows fairly consistently at rate of
about 2% until 2000, after which growth averages 3% per year.
21
Figure 3.1 State Average Income Per Capita and Public Higher Education
Appropriations Per Capita
$40,000
$290
$270
$35,000
$230
$30,000
$210
2005 Dollars
2005 Dollars
$250
$190
$25,000
$170
$20,000
$150
Income Per Capita
Appropriations Per Capita
Figure 3.1 also illustrates the growth of higher education appropriations per capita
over the same time period. Appropriations grow at about the same rate as income per
capita, about two to three percent per year. This trend appears to change beginning in the
2003-2004 period as appropriations grow seven and eight percent for the next two years
while income per capita sees less dramatic growth. Overall this graph is unable to show a
clear picture of the lagged effect of higher education spending on the economy.
Examining the trends of income per capita and higher education attainment in
Figure 3.2, it appears that attainment grows at a similar rate as income per capita. You
may notice that a slight inverse relationship appears to emerge. In 1990 we see attainment
grow by seven percent while income decreases by one percent, while in 2000 we see that
22
attainment grows by only one percent and income increases by five percent. It is entirely
possible that during bad economic times more of the population returns to school and
obtains a degree to increase personal income which would explain this relationship. If we
expect educational attainment to have a positive impact on income growth per capita, the
trend of the graph suggests that we may not find a positive relationship in our analysis. In
addition, if we look at Figure 3.3 we can see that there is also no clear relationship
between educational attainment and higher education appropriations per capita.
Figure 3.2 State Average Income Per Capita and Higher Education Attainment
30%
$35,000
2005 Dollars
25%
$30,000
20%
$25,000
$20,000
15%
Income Per Capita
Attainment
Percentage of Populations > 25 Years Old
$40,000
23
Figure 3.3 State Average Public Higher Education Appropriations Per Capita and
Higher Education Attainment
$270
2005 Dollars
$250
25%
$230
$210
20%
$190
$170
$150
15%
Appropriations Per Capita
Attainment
Percentage of Populations > 25 Years Old
30%
$290
24
4.
ESTIMATION ISSUES AND RESULTS
For comparative purposes we began our estimation first testing no effects, state
fixed effects only, time fixed effects only and combined state fixed effects and time fixed
effects models for best fit. Next we present the estimation results for the addition of the
interaction between the share of total college students attending public higher education
institutions and public higher education spending per capita. Finally we will estimate the
2SLS model using public higher education spending per capita and the interaction term as
an instrument for educational attainment.
4.1
Estimation Issues
Before discussing the results of the estimation, there are several issues that need
to be addressed. First, the inclusion of state fixed effects may become an issue because
as mentioned previously, there is little within state variation when observing the share of
students attending public higher education institutions. Beck (2001) explains that with
variables that change slowly over time the use of fixed effects will make it difficult to
derive any meaningful or significant estimates. Therefore the addition of the interaction
term will let us measure public higher education spending’s effect in each state’s
particular mix of public and private college enrollments.
Another issue that is usually addressed in models that estimate the effect of any
type of government spending and economic growth is that government spending may be
endogenous. In this case it is possible that the growth of the economy may influence the
amount of funds spent on public higher education. Lags are often used to address such
issues. Since we are using a lagged three-year moving average centered on the t-5, based
25
on the findings from the National Center for Education Statistics (2003) discussed earlier,
we have alleviated this problem.
4.2
Initial Results
The results of our baseline (Model 1), fixed effects only (Model 2), time effects
only (Model 3) and the state fixed and time effects (Model 4) models are shown in Table
4.1. Table 4.2 contains models 5-7 which show estimates with the addition of the
interaction between public higher education spending and the share of college students
attending public institutions.
Looking at the results of our initial estimates, we can clearly see that there are
some differences across models using state fixed effects and time effects. It is important
to note the distinction in how coefficients are identified between the models with and
without the state fixed effects. In a model without state fixed effects the variation across
states is what determines the effect of the coefficients, whereas a model with state fixed
effects, the variation within states determines the effect.
Looking first at goodness of fit the model the two models with the lowest Rsquared values are the baseline model and the state fixed effects only model with Rsquared values of .073 and .215 respectively. The highest R-squared of 0.572 is the
combined state fixed effects and time effects with the time effects only model not far
behind with an R-squared of 0.486. Since it is likely that there are variations across states
as well as variations over time that we are not able to capture with the variables we have
specified in our analysis, we will use a combination of state fixed effects and time effects
to control for such unexplained variation. F-tests confirm that the state fixed effects and
26
time effects are justifiable in our specification, therefore the additional estimates after the
initial estimation are reported including both fixed effects. Also the discussion of the
initial estimates will focus on the inclusion of both fixed effects results unless otherwise
noted.
First looking at control variables, we find the population growth variable is not
statistically significant. Then we have the total expenditure per capita model which was
also a lagged three year moving average like the public higher education expenditure per
capita. This variable produces an insignificant negative coefficient. Finally we have the
industry size variables, agriculture, mining, manufacturing and the finance, insurance and
real estate industries as a percentage of GSP. All of these variables have positive
coefficients but only agriculture, mining, and manufacturing produce statistically
significant values. This suggests that increases in the size of any of these industries will
positively impact income growth per capita. In terms of magnitude an increase in
agriculture as a share of GSP has the biggest positive impact.
Now looking at higher education expenditure per capita, which was measured as
lagged three-year moving average of t-4, t-5 and t-6, we find negative coefficients in all
models including our preferred model, although the model with both fixed effects does
not produce a significant estimate. This is not surprising as we discussed the likelihood of
getting insignificant or even negative results for this particular variable without the
inclusion of the interaction between the share of students attending public higher
education institutions and higher education spending per capita.
27
Table 4.1 Initial Estimates
VARIABLES
EDUEXPC(t−4 ,t−5,t−6)
%ΔPOP
TEXPPC(t−4 ,t−5,t−6)
AG
MIN
MAN
FIRE
Constant
State Fixed Effects
Time Fixed Effects
Observations
R-squared
(1)
(2)
%ΔINCOMEPC %ΔINCOMEPC
(3)
(4)
%ΔINCOMEPC %ΔINCOMEPC
-7.72e-05***
(1.87e-05)
0.105
(0.124)
-5.88e-07
(8.23e-07)
0.213***
(0.0694)
0.0955***
(0.0289)
0.0284*
(0.0162)
0.0215
(0.0190)
0.0246***
(0.00712)
-0.000197***
(4.18e-05)
-0.0861
(0.297)
1.24e-05***
(2.32e-06)
0.827***
(0.168)
0.111
(0.0832)
0.295***
(0.0607)
-0.00893
(0.0981)
-0.0436
(0.0280)
-3.57e-05**
(1.63e-05)
-0.0102
(0.104)
-1.68e-06**
(7.58e-07)
0.156**
(0.0629)
0.0872***
(0.0282)
0.00604
(0.0138)
0.0319**
(0.0149)
0.0237***
(0.00577)
-6.64e-05
(4.19e-05)
0.0252
(0.256)
-2.06e-06
(2.90e-06)
1.038***
(0.159)
0.141*
(0.0786)
0.248***
(0.0536)
0.0793
(0.0930)
-0.0480*
(0.0256)
No
No
Yes
No
No
Yes
Yes
Yes
650
0.073
650
0.215
650
0.486
650
0.572
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
4.3
Interaction Variable Results
In Table 4.2 we have estimated Model 4, the two-way fixed effects model with
the inclusion of the interaction between the share of total students attending public higher
education institutions and the public higher education spending per capita. We include
Model 5 and Model 6 which present the introduction of the share of total students
attending public higher education institutions and then the interaction term.
First looking at Model 5, we find that including the share of students attending
public institutions produces a positive but insignificant coefficient of 0.0269. Next we
28
find that public higher education spending per capita is still negative but is now
statistically significant. The control variables then remain close to the same along with
the R-squared.
Looking at Model 6, we have now included the interaction between the share of
students attending public institutions and public higher education spending per capita.
First we can see that again the control variables remain the same and there is small
improvement in fit. Then we have the share of students attending public institutions
variable change signs but remain insignificant. But now we find that the interaction term
produces a positive and significant coefficient of 0.000771. In addition public higher
education spending itself produces a negative and significant coefficient of -0.000722.
These results agree with the findings of Curs et al. (2011). Although we measured public
higher education spending as a percentage of GSP, we obtained similar results with a
different specification using per capita measurements for public higher education
spending and income growth.
This result implies that variation in the share of students attending public higher
education across states has a significant effect on the relationship between public higher
education spending and economic growth. We will simulate marginal effects of the
interaction by testing the mean, maximum and minimum of the share of students
attending public higher education students to make these findings more clear.
29
Table 4.2 Interaction Variable Estimates
VARIABLES
EDUEXPC(t−4 ,t−5,t−6)
(4)
%ΔINCOMEPC
(5)
%ΔINCOMEPC
(6)
%ΔINCOMEPC
-6.64e-05
(4.19e-05)
-7.04e-05*
(4.21e-05)
0.0269
(0.0532)
0.0252
(0.256)
-2.06e-06
(2.90e-06)
1.038***
(0.159)
0.141*
(0.0786)
0.248***
(0.0536)
0.0793
(0.0930)
-0.0480*
(0.0256)
0.0232
(0.255)
-2.32e-06
(2.88e-06)
1.042***
(0.159)
0.141*
(0.0785)
0.247***
(0.0538)
0.0717
(0.0933)
-0.0697
(0.0515)
-0.000722***
(0.000233)
-0.103
(0.0747)
0.000771***
(0.000270)
0.0355
(0.257)
-3.26e-06
(2.99e-06)
1.042***
(0.161)
0.118
(0.0771)
0.231***
(0.0530)
0.0720
(0.0937)
0.0439
(0.0678)
-0.0001095**
(0.0000439)
Yes
Yes
Yes
Yes
Yes
Yes
650
0.572
650
0.572
650
0.578
PUBLICSHARE(t−4 ,t−5,t−6)
EDUEXPC x PUBLICSHARE(t−4 ,t−5,t−6)
%ΔPOP
TEXPPC(t−4 ,t−5,t−6)
AG
MIN
MAN
FIRE
Constant
Combined Effect4
State Fixed Effects
Time Fixed Effects
Observations
R-squared
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Going back to the summary statistics presented in Table 3.2, we find a mean value
of .79, a maximum of .99 and a minimum of .43 for the share of students attending public
institutions. If we hold higher education spending at a constant value we could see how
that effect changes based on the different values for the share of students. First let us
4
Combined marginal effect of EDUEXPC and EDUEXPC x PUBLICSHARE is shown at the mean value for
PUBLICSHARE. Combined marginal effects were also calculated at the maximum and minimum values.
30
assume a $1 increase in public higher education per capita for each state. If we then
assume the mean value of .79 for the share of students attending public institutions we
can calculate marginal effects. First the $1 increase in spending alone would have a
negative effect of -0.000722 based on the coefficient for public higher education
spending. Then looking at the interaction term we arrive at a positive value of 0.000609,
which is based on the $1 increase in spending multiplied by the mean value for the share
of students attending public higher education institutions. Combining the effect of the
interaction term and public higher education spending per capita we arrive at the
combined effect of -0.0001095. Based on our assumptions, this would suggest that for a
state where 79% of students were enrolled in public institutions the $1 increase in state
public higher education spending per capita would decrease growth of income per capita
by 0.01%.
Now if we look at the maximum and minimum values of the share of students
attending public higher education institutions we are basically looking at what occurs
when a state is heavily dependent on public higher education institutions to educate or
when a state is heavily dependent on the private market. Looking at the maximum share
of students attending public institutions, which is .99 and the same $1 increase in public
higher education spending per capita we find a positive combined effect of 0.0000439.
This indicates that the $1 increase in public higher education spending per capita would
increase income growth per capita by 0.004%. Using the minimum value of .43 then
gives a negative combined effect of -0.000394, which then suggests an opposite effect. In
31
this case the $1 increase in spending per capita would now reduce income growth per
capita by 0.04%.
These results agree with the endogenous growth model proposed by Bräuninger
and Vidal (2000) that suggested a higher education market dominated by the public
sector would see greater long run growth than a mixed system or one dependent on the
private market. As mentioned in the review of literature, they found that an increase in
higher education subsidy has a non-monotonic effect therefore an increase in the subsidy
from a low-level, such as with a state that is more dependent on the private market, would
have a negative effect on economic growth. On the other hand a state that already had a
high subsidy level and therefore may be more dependent on the public market would see
an increase in economic growth from an increase in the education subsidy.
If we look at Figure 4.1 we can see that states with a smaller share of students
attending public higher education institutions appear to spend less on public higher
education. So states at the left of this spectrum up to about .93 or 93% public share,
based on the results in Table 4.2, would see negative effects from increases in public
higher education spending, but those to the right would see positive effects. Looking at
the most recent year of data collected for the share of students attending public higher
education institutions which is 2006, only eight states are near 93%.5 Curs found that this
breakeven point at a lower level of about 72%, but the relationship remains the same.
5
These states are Alabama, Alaska, Arkansas, Mississippi, Montana, Nevada, New Mexico and Wyoming.
The only states to exceed 93% of students attending public institutions are Alaska and Wyoming.
32
Figure 4.1 Average Appropriations Per Capita and Average Share of Total Students
Attending Public Institutions for 50 States
$350
Appropriations Per Capita
$300
$250
$200
$150
$100
$50
0.35
4.4
0.45
0.55
0.65
0.75
Public Share
0.85
0.95
2SLS Results
Before we discuss the results of the 2SLS estimates, we will first regress
educational attainment on public higher education spending per capita and the interaction
between public higher education spending per capita and the share of students attending
public institutions. This will be the first stage of our 2SLS model.
Table 4.3 presents the results of the higher education estimates. In Model 8 we
have a baseline estimate. We then include Model 9 which includes the share of students
attending public institutions. And finally, Model 10 has the inclusion of the interaction
term.
33
Table 4.3 Higher Education Attainment Estimates (First Stage of 2SLS)
VARIABLES
EDUEXPC(t−4 ,t−5,t−6)
(8)
BA25
(9)
BA25
(10)
BA25
-0.000103**
(4.85e-05)
-9.12e-05*
(4.87e-05)
-0.0791
(0.0591)
-0.111
(0.165)
-7.23e-06**
(2.93e-06)
-0.0175
(0.123)
0.0717
(0.0750)
-0.0290
(0.0453)
0.0894
(0.0651)
0.203***
(0.0219)
-0.105
(0.163)
-6.47e-06**
(2.97e-06)
-0.0284
(0.123)
0.0701
(0.0737)
-0.0252
(0.0454)
0.112
(0.0705)
0.266***
(0.0521)
0.000588**
(0.000264)
0.0565
(0.0820)
-0.000803***
(0.000309)
-0.118
(0.162)
-5.49e-06*
(2.96e-06)
-0.0287
(0.125)
0.0946
(0.0723)
-0.00874
(0.0449)
0.111
(0.0696)
0.148**
(0.0712)
-0.0000505
(0.00005)
Yes
Yes
Yes
Yes
Yes
Yes
650
0.920
650
0.921
650
0.922
PUBLICSHARE(t−4 ,t−5,t−6)
EDUEXPC x PUBLICSHARE(t−4 ,t−5,t−6)
%ΔPOP
TEXPPC(t−4 ,t−5,t−6)
AG
MIN
MAN
FIRE
Constant
Combined Effect6
State Fixed Effects
Time Fixed Effects
Observations
R-squared
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
First looking at Model 8, we find a statistically significant negative coefficient for
the effects of public higher education. This significant negative effect is similar to the
results of Baldwin et al. (2011) using the path analysis model discussed earlier.
6
Combined marginal effect of EDUEXPC and EDUEXPC x PUBLICSHARE is shown at the mean value for
PUBLICSHARE. Combined marginal effects were also calculated at the maximum and minimum values.
34
In the second column (Model 9), we add the variable for the share of students
attending public higher education institutions. We find that the negative effect of public
higher education spending is reduced somewhat but the result is still negative and
significant. The coefficient for the share of students attending public higher education
institutions is negative but insignificant.
Looking at Model 10, where we have added the interaction term, we expected a
similar result as when used in the models focusing on economic growth. We find that is
not the case. In this model we find that the interaction term has a negative and significant
coefficient of -0.000803 and public higher education expenditure per capita then has a
positive and significant coefficient of 0.000588. This then suggests that holding other
variables constant, that an increase in the share of students attending public higher
education institutions would lead to decreased attainment levels. We will now look at
marginal effects of changes in the share of students attending public higher education
institutions at the mean, maximum and minimum as we have done in the previous
section.
As we have found, the mean, maximum and minimum values of the share of
students attending public higher education institutions are .79, .99 and .43 respectively.
Once again we will assume a $1 increase in public higher education spending per capita.
Looking first at mean share value, we find that a $1 increase in public higher education
spending per capita alone would increase educational attainment by 0.059%, but then
looking at the effect of the interaction term we find that the $1 increase in public higher
education spending per capita when the share of students attending public higher
35
education institutions is at the mean of 79%, would decrease educational attainment by
0.06%. The coefficient for the total marginal effect at the mean is then -0.0000505. This
means that a $1 increase in spending would reduce educational attainment is reduced by
about 0.01%.
Now we look at the effect of increasing public higher education spending per
capita by $1, at the maximum share of students attending public institutions based on the
data collected. In this scenario we still have a positive increase in educational attainment
of 0.06% but now the interaction term increases in magnitude, reducing attainment by
0.08%. The coefficient for the total marginal effect is -0.0002103. This then gives us a
reduction in attainment of about 0.021 %. Moving to the other end of the spectrum we
find that at the minimum share value of 43% we end up instead with a total marginal
effect coefficient of 0.0002463. This suggests a net increase in attainment of about
0.025%. The breakeven point for the share of students attending public institutions,
where public higher education spending has neither a negative effect nor a positive effect
on educational attainment, is at about 73%.
The results of Model 10 suggest that states that have a larger markets for private
higher education will see increases in educational attainment when public higher
education spending per capita increases whereas the states that have smaller markets will
see a decrease in educational attainment. This result will be discussed further in the
summary of findings.
Now moving on to Table 4.4 we have our 2SLS estimates, where we have
instrumented educational attainment using public higher education spending and the
36
interaction between higher education spending and the percentage of students attending
public higher education institutions. The first-stage estimate for educational attainment,
BA25, is based on results of Model 10.
Table 4.4 Final 2SLS Estimates
VARIABLES
BA25 (first-stage estimate)
TEXPPC(t−4 ,t−5,t−6)
%ΔPOP
AG
MIN
MAN
FIRE
Constant
State Fixed Effects
Time Fixed Effects
Observations
R-squared
(11)
%ΔINCOMEPC
-0.290
(0.237)
-4.25e-06
(3.36e-06)
0.0554
(0.223)
1.046***
(0.151)
0.147**
(0.0699)
0.238***
(0.0514)
0.104
(0.0956)
-0.00857
(0.0473)
Yes
Yes
650
0.534
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
In this model we have made the assumption that public higher education spending
per capita affects income growth per capita through educational attainment. We have
included estimated educational attainment explained by public higher education spending
and the interaction between public higher education per capita and the share of students
attending public higher education institutions as primary instruments. We find similar
estimates for the effects of the control variables as we found in the initial estimates
37
focusing on growth of income per capita. Now looking at educational attainment we find
a statistically insignificant coefficient of -0.290.
38
5.
5.1
CONCLUSION
Summary of Findings
The primary focus of this thesis is to investigate the relationship between public
higher education spending and economic growth, while a secondary analysis focused on
the role of educational attainment mediating the effect of that spending. We used the
interaction between spending and the share of students attending public higher education
institutions to control for variation in the size of the private market for higher education
in both analyses. In the first part of this analysis we tested for consistency with previous
research. In the second part we attempted to apply a new method of analysis using the
interaction proposed by Curs et al., in the investigation of the effect of public higher
education spending on educational attainment.
We begin by first looking at the results of the initial estimates and the results
when we included the interaction between public higher education spending and the share
of students attending public higher education institutions. In the initial two-way fixed
effects model we found an insignificant negative correlation between public higher
education spending and income growth per capita. Based on the results of Curs et al.
(2011) we expected that the addition of share of students attending public higher
education, which acted as a proxy for the size of the market for private education, would
remove negative bias associated with its omission. When we included the interaction
between public higher education spending per capita and the share of students attending
public higher education institutions and thus began controlling for variation in this share
across states, we arrived at a similar result. In contrast with the finding of Curs et al. the
39
point at which an increase in public higher education spending per capita was at a
significantly higher level of 93%.
These results suggested that in states with more than 93% of students attending
public higher education institutions and thus smaller markets for private higher education,
an increase in public higher education spending per capita would increase economic
growth. In states with shares of students attending public higher education institutions
below this level, the same increase in public higher education spending per capita would
have a negative effect.
It appears that increasing public higher education spending in a majority of states
except for the most heavily dependent on public systems, would not lead to the expected
improvements in economic growth. One possibility for this result is that the private
market for higher education may be providing a superior option in states that are not
completely dominated by public institutions. In comparison with the findings of Curs et
al the results in this study are more dire because only two states in this analysis would see
positive effects from increases in spending on higher education. It may be the case that
public institutions are inefficient or perhaps do not provide the same quality of education
that a private institution may provide. In addition private institutions may be targeting a
different pool of applicants and have a higher level of funding compared to public
systems.
In the second part of this thesis we examined public higher education spending as
one part of a two part process. We said that public higher education spending per capita
should positively influence educational attainment and therefore increase economic
40
growth. The attempt at modeling this causal relationship was based on the human capital
theory proposed by Becker (1962).and Schultz (1963).
In their path analysis Baldwin et al. (2011) discovered a negative relationship
between public higher education spending and educational attainment. Therefore the
indirect effect of public higher education spending on economic growth mediated by
educational attainment was negative. It was expected that as with the investigation of the
direct relationship between public higher education spending and economic growth, we
would find that the omission of the size of the market for private education would
negatively bias the results. Therefore the inclusion of the proxy variable for private
market size, the share of students attending public higher education institutions, would
show that public higher education spending’s effect on educational attainment was
dependent on variation in that share across states. We would then find that public higher
education spending per capita would increase attainment but only in those states that had
the smallest amount of students attending private institutions and therefore smaller
private markets for higher education.
The results of the analysis suggested otherwise. Instead we found that the states
with the larger markets for private higher education would see an increase in attainment if
there was an increase in public higher education spending. On the other hand a state
dominated by the public institutions would then see a decrease in attainment levels for the
same increase in spending. Specifically this negative effect on educational attainment
would be found in states that had over 73% percent of college students attending public
institutions.
41
It is not clear why increased spending on public higher education in a state with a
large market for higher education would increase attainment, whereas a state with little to
no market for higher education would see a decrease in attainment for that same increase
in spending. First we would think that immigration is playing a role here but it is not
likely that this is related to the immigration of college graduates. A special report by the
U.S. Census Bureau published in 2003, focused on the migration of single college
educated people between the ages of 25 and 39 from 1995-2000 (Franklin, 2003).
Comparing their findings to the one here, we find some consistency because the states
that show the highest percentages of net out of state migration of single college educated
young people occur in states that have large shares of students attending public
institutions. At the same time though states like Nevada and California that have larger
public higher education systems also show net into state migration. So the results of the
analysis do not present a clear picture.
In the last part of our analysis we conducted a 2SLS analysis in an attempt to
connect public higher education spending per capita, to educational attainment and then
to income growth per capita and address potential endogeneity. The basis of the 2SLS
model was that public higher education spending should increase educational attainment
and therefore add more skilled labor to the workforce. This increase in skilled labor
should then positively impact economic growth.
The results from the 2SLS model resulted in a statistically insignificant
coefficient for educational attainment. In future research it would be desirable to find
stronger instruments for educational attainment. The insignificant impact of attainment
42
on growth result may be due to migration that has not been accounted for in the model.
Potentially other important variables are also omitted, such as socio-demographics and
student achievement characteristics prior to entering college.
Generally cross national studies have shown a positive relationship between
investments in human capital and economic growth. One issue though is that many of
these investigations have focused on differences between developing countries and
developed countries. Although in the United States, individual states may differ
significantly, they are all generally well off economically in comparison to developing
and even other developed nations. Explaining differences across states may require a
different approach than simply applying models based on country-level theory and
analysis to the United States.
5.2
Future Research
There are several possibilities for future research that may make the relationship
between public higher education spending, educational attainment and economic growth
more clear. First controlling for migration would be useful. The 2003 Special Census
report shows significant movement of college graduates across states, so data on college
graduation migration could be gathered. For the purposes of this study, this data was not
available on an annual basis.
Second we could alter the structure of 2SLS model. Public higher education
spending and the interaction between spending and the share of students attending public
institutions showed signs of being weak instruments for linking educational attainment
and economic growth. It seems that the relationship between public higher education and
43
educational attainment is the most complex and additional variables should be considered
here as well.
Finally to generate a better link between attainment and economic growth, we
may want to conduct a microeconomic analysis that focused on the relationship between
public higher education spending and graduation rates in those institutions. This would
give us a better understanding of how state funding may affect student success at public
institutions. Also it may provide a better idea of what effects budget constraints have on
public institutions and how they may affect student progress towards degree completion.
44
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