1
Ethnic Homogeneity, Group Antagonism, and State Financial Support for Higher
Education
John M. Foster
Southern Illinois University Edwardsville
Jacob Fowles
University of Kansas
PLEASE DO NOT CITE WITHOUT THE AUTHORS’ PERMISSION
2
Ethnic Homogeneity, Group Antagonism, and State Financial Support for Higher
Education
John M. Foster
Southern Illinois University Edwardsville
Jacob Fowles
University of Kansas
Abstract
A growing literature suggests that the presence of multiple ethnic groups could lead to lower
public expenditures. This inverse relationship may be attributable to the diversity in policy
preferences which tends to coincide with ethnic diversity. When policy preferences vary widely
among constituents, the subjective benefits of collective action are smaller. The subjective
benefits of public spending may also be lower when the beneficiaries tend to be ethnic outsiders.
For these reasons, ethnic diversity may steer the political process toward a lower level of public
spending. These negative effects of diversity may be offset by positive social contact between
ethnic groups. The authors investigate the effects of ethnic diversity on state higher education
spending with a model that allows the effect of ethnic diversity to be mediated by the degree of
positive social contact between groups, which we measure with the intermarriage rate. We find
that ethnic diversity only has a statistically-significant and negative impact on state
appropriations to universities in states with low intermarriage rates. The magnitude of the
negative effect diminishes with the rate of intermarriage
JEL: I23
KEYWORDS: Politics of race, higher education finance, redistribution
3
Introduction
An extensive literature has documented a large increase in income inequality in the
United States in recent decades. With the shift of production away from manufacturing and
toward the service and technology sectors, educational attainment has become a powerful
determinant of one’s position in the income distribution. Since the 1970’s, real earnings of adults
with only a high school diploma have declined significantly. Between 1972 and 2005, the
average income of high school graduates fell from $42,000 (in constant year 2003 dollars) to
$30,000, which is a decrease of about one-third. By contrast, real earnings for the collegeeducated have held steady; among women, they have risen (Dynarski 2008). Due to the growing
divide between those with and without a college degree, subsidies for higher education are likely
to be a prominent component of any policy platform geared toward reducing income inequality.
The political efficacy of public spending, including higher education subsidies, is likely
to be influenced by the ethnic demographic context (Alesina et al 2000). The presence of
multiple ethnic groups could increase the political costs of higher education subsidization for two
reasons. First, higher-income ethnic groups may be less inclined to support public spending
when the primary beneficiaries tend to be members of other ethnic groups. Second, preferences
for public spending vary among different ethnic groups. With a heterogeneous population, the
political process may be less likely to produce a fiscal platform that is close to one’s preferred
service package. Thus, ethnic heterogeneity may reduce the benefits that one expects to obtain
from numerous categories of public spending, including higher education subsidization, which
leads one to prefer a smaller public sector. On the other hand, tensions between ethnic groups
4
have subsided over the past thirty years. Consequently, biases against public spending driven by
ethnic divisions may be eroding.
We extend the literature by examining the effects of ethnic diversity and the amiability of
ethnic group relations on three categories of state higher education spending: state appropriations
to public universities, need-based student aid, and non-need-based aid. This study is the first
positive analysis of state higher education financial support that examines the effects of both
ethnic diversity and the amiability of ethnic group relations. We use data on the 50 states for
1980 to 2009. We measure ethnic diversity with the complement of a Herfindahl index of ethnic
group concentration. We use the intermarriage rate as a proxy for the congeniality between
groups. We utilize data on the fifty states for 1980 to 2009. We use the fixed effects specification
to control for time invariant state characteristics. We find that a state’s ethnic demographic
context influences state appropriations to public universities but does not affect need-based or
non-need-based student aid. Ethnic diversity is inversely-related to state appropriations. The
effect of ethnic diversity depends on the intermarriage rate. In states with relatively high rates of
intermarriage, the effect of ethnic diversity on state appropriations is not significantly different
from zero. The intermarriage rate has a positive effect on state appropriations which increases
with the level of ethnic diversity. Neither category of student aid is significantly affected by the
intermarriage rate, regardless of the level of ethnic diversity.
In the following section, we discuss the motives that states might have to subsidize higher
education along with the literature on the effects of ethnic heterogeneity and ethnic group
antagonism on the demand for public services. We then present our analysis of the effects of
state ethnic demographics on state financial support for higher education.
5
Motives for Subsidizing Higher Education and Voter Preferences in Multiethnic
Environments
In the United States, higher education is financed with a combination of user charges and
tax-financed subsidies. Experts and policymakers have prescribed subsidies for higher education
as a means for states to capture the positive spillovers associated with the activities carried out by
colleges and universities, to promote access to higher education for middle- and lower-income
households, and to retain high-quality students. States have subsidized higher education through
three mechanisms: transfers to public universities, need-based direct aid to students to cover all
or a portion of tuition, and tuition assistance based on criteria other than need such as academic
achievement, military service, or entry into a particular field of study such as education or
nursing. State appropriations to public universities constitute the vast majority of state subsidy
dollars, averaging 91 percent of state higher education spending in 2009. However, the share of
state higher education spending that is devoted to direct aid to students exceeds 15 percent in
seven states (Georgia, Florida, Pennsylvania, South Carolina, Tennessee, Vermont, and West
Virginia). State appropriations to public universities are used to reduce tuition and fees, support
research, and finance various services.
The positive spillovers attributed to higher education include research that confers broad
economic benefits and the enrichment of civic life that stems from the presence of a highlyeducated population. The products of research tend to become part of the public domain. Thus, a
pure market would not fully compensate researchers. As a result, individuals and firms acting
non-cooperatively would most likely underinvest in research in a pure market economy.
Governments can enhance efficiency by subsidizing research activities. The cost-benefit analyses
6
of academic research that have been conducted have found large net benefits. For example,
Griliches (1957) estimates the social rate of return on hybrid corn seed to be 700 percent and that
the rate of return of all agricultural research was between 35 and 75 percent.1
Economic theory suggests that positive spillovers can also be generated by instructional
activities. Educated citizens are likely to be more informed voters and more effective civic
leaders. Dee (2003) and Milligan et al (2004) find that educational attainment is positivelyrelated to voter participation. Dee (2003) also finds a direct relationship between educational
attainment and newspaper readership and support for free speech.
The effect of subsidies on the level of positive spillovers from higher education
instruction depends on the effects of those subsidies on college enrollment and degree
completion, or on the price elasticity of demand for higher education. Most studies find modest
responses to changes in the tuition price. Researchers have obtained a range of estimates that
suggests that a $1,000 reduction in tuition is likely to lead to increases of enrollment falling
between 3 and 7 percent (Deming and Dynarski 2009). Dynarski (2003) and Zhang and Ness
(2012) attribute large portions of these effects to reductions in the fraction of college-bound high
school graduates attending universities out-of-state. Dynarski (2008) finds that the large-scale
merit aid programs introduced in Georgia and Arkansas in the early 1990’s increased college
degree attainment by 3 to 4 percentage points. The effects are about twice as large for Hispanic
and nonwhite women, which suggests that disadvantaged groups are relatively sensitive to the
costs of higher education. These estimates suggest that the generation of positive spillovers from
instruction is probably not the most compelling motive for the subsidization of higher education.
1
See Rizzo (2004) for a review of the literature.
7
Even though subsidies for higher education do not have large impacts on enrollment and
degree attainment, they are important forms of in-kind redistribution. State appropriations to
public universities and direct student aid programs reduce the amount of debt that most students
take on in order to finance their studies. These subsidies may also reduce the amount of time that
they must spend working while studying which may enable them to complete their education
faster. Views on the progressivity of state funding for higher education have not been
unanimous. Hanson and Weisbrod (1969) find that the incidence of higher education financing in
California is regressive. However, the authors did not take the progressivity of the tax system
into account. Johnson (2006), analyzing data for all 50 states, examines the distribution of state
appropriations across income groups net of state taxes and finds a mildly progressive incidence
pattern since wealthy households pay relatively large shares of state taxes and are less likely to
utilize public universities. Systematic studies of the incidence of direct aid programs have not
been carried out. Because need-based aid is targeted to individuals from needy families, one
would expect the incidence of those transfers, net of taxation, to be highly progressive.
Why would state governments make these considerable in-kind transfers instead of
offering more cash assistance? In-kind transfers in the form of higher education subsidies may be
politically-desirable because they address what James Buchanan (1957) termed the “Samaritan’s
Dilemma.” The Samaritan’s Dilemma stems from two problems: (1) the moral hazard created by
transfers to those who are currently poor; and (2) the inability of the Samaritan, in this case the
government, to commit to a policy of zero future assistance for current recipients who could have
lifted themselves out of poverty but chose not to do so. Needy individuals who receive aid in the
present may reasonably expect to receive aid in the future if they continue to be poor. Bruce and
Waldman (1991) use the concept of the Samaritan’s Dilemma to show that providing at least a
8
portion of the transfer in the form of an illiquid investment, such as a subsidy for human capital
investment, enhances efficiency by reducing the likelihood that current recipients will need
assistance in the future.
State subsidies for higher education potentially promote a number of public objectives.
The optimal level of subsidization depends on the relative weight that the public attaches to those
objectives relative to other public service needs and private consumption. The relative weight
that states attach to higher education subsidization varies widely. In 2009, total state subsidies for
higher education per full-time-equivalent public university student averaged $7,732, ranging
from $3,743 in New Hampshire to $15,244 in Alaska. A scarcely-examined determinant of this
variation across states is their ethnic demographic context.
The presence of diverse ethnic groups could place downward pressure on the demand for
government intervention. If the population is divided into multiple ethnic groups with different
preferences for the level of public spending and the manner in which public programs are
delivered (e.g. divided opinion over what should be the curriculum of public schools), then the
subjective benefits of public provision are lower, entailing less public support (Alesina and
Spaloare 1997). Alesina, Baqir, and Easterly (1999) look at U.S. urban areas and find that shares
of spending on infrastructure and K-12 education are inversely-related to racial heterogeneity.
The concentration of ethnic others in the low end of the income distribution may also weaken
altruism among higher income ethnic groups. There is evidence that the ethnic congruence
between the poor and non-poor is directly-related to redistribution on the both the expenditure
and revenue sides. Orr (1976), Alesina et al (2000), and Luttmer (2001) find an inverse
relationship between ethnic heterogeneity and welfare spending among the U.S. states. Alesina et
al (2000) obtain similar results from a cross-country study and conclude that ethnic divisions are
9
a significant part of the explanation for why the United States never developed a European-style
welfare state. Foster (2013) finds that tax progressivity is lower in American states in which the
poor and non-poor tend to come from different ethnic groups, all else equal.
The effect of ethnic diversity on the demand for public spending is likely to depend on
the degree of antagonism between ethnic groups. Lind (2007) presents a theoretical model in
which the negative effect of ethnic diversity on public spending increases with the degree of
group antagonism. He examines the effects of group antagonism on support for welfare spending
with an analysis of data from the General Social Survey. Lind finds that white respondents who
had recently had African Americans as guests in their homes favored higher levels of welfare
spending, ceteris paribus. Other studies have found a direct relationship between positive social
contact between ethnic groups and support for policies that could generally be labeled
progressive. Boisjoly et al (2006) find that white college students who were randomly assigned
African- American roommates were more likely to be supportive of affirmative action policies.
In their study of the racial attitudes and policy preferences of Texas adults, Stein et al (2000) find
that white attitudes toward immigration were less favorable the more diverse the county if
contact with Hispanics was low but not if the diverse context led to frequent contact with
Hispanics, in which case there were more favorable attitudes to immigration. Roch and Rushton
(2011) find that white voters in more segregated Alabama counties were less likely to support a
state referendum that would have significantly increased the progressivity of the state tax system
and used the additional revenue for K-12 education. Foster (2013) finds that the average level of
residential segregation in a state is inversely related to state and local tax progressivity. Thus, the
effect of diversity on support for public spending appears to be associated with the degree of
social contact between ethnic groups. It is important to note that residential segregation could
10
also reflect the degree to which the most prosperous ethnic group is amiably-disposed toward
other ethnic groups in the first place. Whether social contact between different ethnic groups
causes or reflects congeniality between ethnic groups, we believe that measures of social contact
can function as proxies for the amiability of ethnic group relations.
Evidence provided by survey data and data on individual choices suggest that tensions
between ethnic groups have subsided considerably over the past three decades. In 1972, 37
percent of respondents to the General Social Survey (GSS) claimed to be in favor of a law
against interracial marriage. In 2002, only 10 percent of respondents expressed support for such a
law. One can argue that these changes in survey responses reveal more about mores of political
correctness than about actual preferences, but there have been accompanying changes in
behavior as well. Between 1980 and 2009, the percentage of married individuals with a spouse
from an ethnic group other than their own increased from around 5 percent to 8 percent, which is
an increase of almost 60 percent. The percentage of white GSS respondents who claimed to be
members of an integrated church rose from 34 percent in 1978 to 48 percent in 1994. It is
possible that the growing social integration of non-white groups has weakened biases against
public spending driven by ethnic divisions.
This study extends the literature by examining the effects of both ethnic diversity and the
amiability of ethnic group relations on state higher education spending. We estimate a model of
state higher education spending as a function of the overall ethnic diversity of the population, the
amiability of ethnic group relations, which we measure with the percentage of married
individuals who are married to a person from a different ethnic group (i.e. the intermarriage rate),
the population shares of the major minority groups, and the standard controls. We allow the
11
effect of ethnic diversity to depend on the intermarriage rate. The following section describes our
research design in detail.
Empirical Implementation
The data cover state spending on higher education by the 50 states for 1980 to 2009. We
model three types of state higher education spending (state appropriations to public universities,
non-need-based aid, and need-based aid) as functions of state demographic, economic, and
political characteristics from the previous year. The empirical model can be summarized by
𝑆𝑖𝑡 = 𝛽0 + 𝛽1 𝐸𝑖𝑡−1 + 𝛽2 𝐼𝑖𝑡−1 + 𝛽3 (𝐸𝑖𝑡−1 ∗ 𝐼𝑖𝑡−1 ) + 𝛽4 𝐻𝑖𝑡−1 + 𝛽5 𝐴𝑖𝑡−1 + 𝛽6 𝑂𝑖𝑡−1 +
(1)
𝛽7 𝑋𝑖𝑡−1 + 𝜃𝑖 + 𝑌𝑡 + v𝑖𝑡
The term Eit-1 denotes ethnic diversity while 𝐼𝑖𝑡−1 is the intermarriage rate. 𝐻𝑖𝑡−1 , 𝐴𝑖𝑡−1 , 𝑂𝑖𝑡−1 ,
denote the Hispanic, African-American, and “Other” population shares, respectively. The
standard control variables are given by 𝑋𝑖𝑡−1 . Lagging the determinants by one year allows for
the possibility that policymakers cannot immediately adjust state policy in response to changes in
economic, demographic, and political parameters. State and year fixed effects are given by 𝜃𝑖
and 𝑌𝑡 , respectively. The parameter v𝑖𝑡 is a mean-zero, random error term.
We measure ethnic diversity with the probability that two people randomly drawn from a
state’s population are from k different ethnic groups. This probability is given by:
Ethnic Diversity = 1- ∑∀𝑘(𝑃𝑜𝑝. 𝑆ℎ𝑎𝑟𝑒𝑘 )2
We expect ethnic diversity to be inversely-related to all three forms of higher education
spending. The intermarriage rate is the percentage of married individuals who married outside of
their own ethnic group. We expect the intermarriage rate to be positively-related to state higher
12
education spending since it reflects the cessation of tension between ethnic groups and should
reflect the erosion of differences in spending levels based solely on ethnic divisions. To derive
the intermarriage rate and the ethnic diversity measure, we divided state populations into
Hispanics, non-Hispanic whites, non-Hispanic African-Americans, and non-Hispanic, non-white
“others,” with “others” including Native Americans, Asians, Pacific Islanders, and a residual
category. We interact the ethnic diversity index with the intermarriage rate since the political
costs of public spending that benefits ethnic outsiders may be lower (higher) in states that are
characterized by low (high) degrees of ethnic tension.
Lind’s (2007) theoretical analysis demonstrated that the effects of an increase (decrease)
in the degree of antagonism between ethnic groups on public spending are directly related to the
level of ethnic diversity. Thus, the effect of intermarriage on state higher education spending
may increase with the level of ethnic diversity. The diversity index captures the extent to which
the population is ethnically fragmented. However, it does not provide information on the relative
size of each ethnic group. This information is likely to be pertinent since the policy preferences
of certain nonwhite groups tend to differ not just from those of whites but also from those of
other nonwhite groups. Analysis of survey data conducted by Alesina and La Ferrara (2003)
indicates that African-Americans are more supportive of redistribution, even with income held
constant. Brunner et al (2012) find that African-Americans, Hispanics, and respondents falling
into a residual, non-white, non-Hispanic category favor progressive fiscal policies more strongly
than whites. Preferences among the non-white groups are not uniform. African-Americans are
more supportive of government intervention than Hispanics while Hispanics are more supportive
of such policies than other non-white groups. Thus, the population shares of minority groups are
likely to be directly-related to the political attractiveness of government intervention, with the
13
African-American population share having the strongest effect. For these reasons, the models
include the African-American, Hispanic, and Other Non-Hispanic population shares.
The vector 𝑋𝑖𝑡−1 consists of measures of state economic, demographic, and political
characteristics. The natural log of state GDP per-capita (in $1,000s of year 2005 dollars) is
included to control for a state’s resources. The model also includes the state Gini coefficient. The
level of income inequality in a state is likely to affect its spending priorities. A high degree of
inequality may increase demand for fiscal platforms that transfer income from the wealthy to
middle and lower-income households. Thus, we expect the Gini coefficient to be positivelyrelated to both types of student aid. The effect of income inequality on state appropriations per
FTE public university student is difficult to predict since there is considerable variation within
many states in the socioeconomic groups served by public universities. An increase in income
inequality could lead to a shift in state appropriations away from public universities that serve
predominantly upper-income students and toward public universities that serve the less well-off.
The relative weight that state policymakers place on the subsidization of higher education
could depend on the distribution of state population across age groups. Thus, the models include
the population shares of three age groups that compete for sizable shares of state expenditure:
traditional elementary and secondary school-age youths (age 5-17), traditional college-age adults
(age 18-24), and the elderly (age 65 or older). The percentage of college students who are Pell
grant recipients is included since the receipt of Pell grants by students in a state may crowd out
state assistance. The percentage of adults with a college degree is included since it may influence
the demand for higher education. Alternatively, states with relatively large supplies of skilled
labor may be less inclined to subsidize higher education.
14
The model also includes measures of a state’s ideological orientation. We use the
measure of average voter liberalism developed by Berry et al (1998). We expect a greater degree
of liberalism among voters to be positively-related to need-based student aid. The effects of
average voter liberalism on non-need-based aid depend on the weight that liberal voters place on
transfers to middle-class households relative to redistributive programs that provide more
concentrated benefits to the poor and near-poor. The expected signs on average voter liberalism
are ambiguous in the state appropriations equation since changes in those regressors could lead
to shifts in the distribution of appropriations among institutions within states.
The models include the private university share of college enrollments. The prominence
of private universities is likely to be inversely-related to voter demand for state subsidies to
public universities. We include year indicators to control for national economic and policy
shocks. We carry out the fixed effects transformation to control for time-invariant state
idiosyncrasies. We experimented with the random effects specification and carried out the
Hausman test determine if the estimates differed significantly from those obtained with the fixed
effects specification. The tests strongly rejected the null hypothesis of zero difference for all
three equations. This suggests that at least one of the regressors is correlated with the state fixed
effects. Consequently, the fixed effects transformation is necessary to obtain consistent
estimates. The standard errors are clustered by state. Consequently, the inference statistics are
robust to heteroskedasticity and within-state autocorrelation. Summary statistics are presented in
Table 1.
Results
The ordinary least squares results are presented in Table 2. Ethnic diversity is
significantly and negatively related to state appropriations to public universities per FTE public
15
university student but is insignificant in the other models. The marginal effect of ethnic diversity
on any of the three state higher education spending equations depends on the interaction between
diversity and the intermarriage rate. Thus, we obtain the formula for the marginal effect by
differentiating the empirical model with respect to ethnic diversity, which yields:
𝜕𝑆𝑖𝑡
= 𝛽1 + 𝛽3 𝐼𝑖𝑡−1
𝜕𝐸𝑖𝑡−1
(2)
where 𝛽1 is the coefficient on ethnic fractionalization and 𝛽3 is the coefficient on the interaction
term.2
We evaluate equation (2) at all values of intermarriage for 1980 and 2009. We used the
delta method to obtain the standard error of each marginal effect. We present the results for all
50 states and for each year in Tables 3 and 4, respectively. We see that the marginal effect of
ethnic diversity on state appropriations to public universities is negative for almost all values of
the intermarriage rate, the sole exception being Hawaii in 2009, which has such a high rate of
intermarriage that an increase in ethnic diversity of one standard deviation has a small positive
effect. The magnitude of the negative effect and its statistical significance decrease as the
intermarriage rate increases. The highest intermarriage rate at which the marginal effect of ethnic
diversity on state appropriations is statistically significant is around 4.5 percent in 1980 and in
2009. For states with intermarriage rates less than or equal to 4.5 percent, an increase in ethnic
diversity of one standard deviation leads to reductions in state appropriations ranging from 12 to
15 percent. The number of states with intermarriage rates below the threshold of statistical
2
We conducted linear Wald tests of the joint significance of the interaction term and each of its constituent
variables. Ethnic diversity and the interaction term are jointly significant at the 95 percent confidence level in the
state appropriations equation but not in the student aid equations. We found the same significance pattern with tests
of the joint significance of the interaction term and the intermarriage rate.
16
significance fell from 27 in 1980 to 17 in 2009.3 Thus, the results indicate that the growing
amiability of ethnic group relations, which is captured so some extent by the intermarriage rate,
has weakened the influence of ethnic diversity on state appropriations to public universities in
many states. Controlling for other factors, ethnic diversity is not significantly related to either
type of student aid for any of the values of the intermarriage rate in the 2009 portion of the
sample.
The intermarriage rate is positively-related to state appropriations to public universities
per FTE public university student in all states except for those that are relatively homogeneous
(see Table 5). The lowest value of the ethnic diversity index that coincides with a significant
effect of the intermarriage rate on state appropriations is Ohio’s 2009 value of 0.31, which
corresponds to the 31st percentile of the 2009 distribution of the ethnic diversity index. The
effects of a one-standard-deviation increase in the intermarriage rate on state appropriations to
public universities among states with levels of ethnic diversity equal to 0.31 or greater range
from an increase of 2.5 percent for Ohio to 8.3 percent for California. Between 1980 and 2009,
the intermarriage rate increased from about 5 percent to about 8 percent, on average. This shift,
which presumably reflects an increase in the congeniality of ethnic group relations, increased
state appropriations to public universities by around 3 percent in states with the 2009 average
level of ethnic diversity. The estimated effects of the growth of intermarriage are considerably
higher in 5 states that are relatively heterogeneous and experienced above-average increases in
the intermarriage rate. In Arizona, California, New Jersey, Nevada, and Texas, the intermarriage
rate increased by around 5 percentage points, on average. The estimates indicate that state
appropriations to public universities were around 9 to 10 percent higher than they otherwise
3
Two of the 17 states—New Hampshire and West Virginia—are not interesting cases because their levels of ethnic
diversity are more than one standard deviation below the national average.
17
would have been. These estimated increases translate into dollar amounts per FTE public
university student ranging from $426 in Arizona to $817 in Nevada. The intermarriage rate is not
significantly related to either form of student aid for any value of ethnic fractionalization found
in the 2009 portion of our sample.
Other results are worth mentioning briefly. The African-American share is significantly
and positively related to state appropriations to public universities, but is insignificant in the
equations for student aid. The Hispanic share of the population and the share of the population
that consists of “Other” non-Hispanic individuals are not statistically significant in any of the
state higher education spending equations. Holding all other relevant variables constant, an
increase in the African-American population share of one standard deviation increases state
appropriations by about 44 percent. Non-need-based aid is influenced primarily by the certain
demographic characteristics. The Gini coefficient is inversely related to non-need-based aid. It is
possible that an increase in income inequality prompts state governments to divert resources
away from scholarships and toward programs that confer more immediate benefits to the poor
and near-poor. The percentage of adults with a bachelor’s degree is inversely related to nonneed-based aid. If a state has a relatively large college-educated population, then the benefits
from using scholarships to “stem the brain drain” may be smaller, all else equal. The population
share of traditional college-age adults is inversely related to both state appropriations to public
universities and non-need-based aid. The aggregate cost of a given per-student subsidy increases
with the share of the population constituted by potential students. The political costs associated
with this “price effect” of the population share of traditional college-age adults appear to
outweigh the political benefits of subsidizing that group. Need-based-aid is determined primarily
18
by state resources. State GDP per-capita is positively related to state appropriations with an
elasticity of 1.82.
Conclusion
Previous studies have identified two channels through which ethnic diversity can
influence voter demand for and government supply of public services. The presence of multiple
ethnic groups may put downward pressure on support for redistribution among all groups since
the political process is less likely to produce a public services bundle that is close one’s preferred
bundle. Additionally, altruism among higher income ethnic groups may decrease with the
proportion of beneficiaries from public spending who are members of other groups. These
negative effects of diversity may be offset by the rise of positive social interaction between
ethnic groups that took place over the latter decades of the 20th century.
This study extends the literature by using an empirical model that allows the effect of
ethnic diversity on state higher education spending to be mediated by level of antagonism
between ethnic groups. We gauge the degree of tension between ethnic groups with the
intermarriage rate. Consistent with previous studies, we find that ethnic diversity is inversely
related to state appropriations to public universities for states with low intermarriage rates. In the
most recent year in our sample, in states with intermarriage rates above roughly the 33rd
percentile of the distribution, the effect of ethnic diversity on state appropriations is not
significantly different from zero. Our estimates indicate that the rise of intermarriage over the
course of our 29 year sample has had a direct positive effect on state appropriations to public
universities. This direct effect is only substantial in magnitude in states that are characterized by
19
above-average levels of ethnic diversity. Increases in the intermarriage rate have supported state
higher education spending indirectly by reducing the number of states in which ethnic diversity
consistently inhibits state higher education spending. The literature on the political economy of
subnational budgets could be extended further by applying our empirical model to other
important areas of state spending such as K-12 education, Temporary Aid to Needy Families
(TANF), and state Medicaid programs. Positive social interaction between ethnic groups has
increased but additional analysis is necessary to determine whether the United States is evolving
into a society in which variation in fiscal policy among states is driven primarily by differences
in economic circumstances and in voters’ conceptions of economic justice.
20
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22
Table 1: Summary Statistics
Variable
State Appropriations
a
Non-Need-Based Aidb
Need-Based Aid
b
Intermarriage Rate
c
c
Ethnic Diversity
African American
Hispanic
c
c
Other Non-Hispanic
c
c
Gini Coefficient
Per-Capita GDP ($1,000 year
2005)d
Age 18-24
Age 5-17
Age 65+
c
c
c
Bachelor's Degree or Higher
Pell Recipients
e
Private Enrollment
e
f
c
Mean
STD
Min
Max
$6,891.79
$2,164.13
$2,796.41
$26,772.59
$70.71
202.44
0
$1,708.32
$229.92
$289.62
0
$1,785.88
5.72
5.04
0.73
37.35
0.32
0.16
0.03
0.67
9.5
9.23
0.207
37.1
6.67
8.29
0.03
44.99
5.46
9.01
0.32
68.34
0.39
0.03
0.32
0.49
$33.37
$9.13
$17.34
$99.66
10.4
1.6
5.6
17.4
18.84
1.65
15.09
26.57
11.8
2.12
2.23
18.4
16.36
8.75
2.34
40.83
23.24
8.13
4.62
56.98
21.27
12.41
0
57.87
Average Voter Liberalism
49.55
15.46
8.45
95.97
Sources:
a
Authors’ calculations based on appropriations data from Grapevine Surveys on state tax support
for higher education, FTE enrollment data from National Center for Education Statistics’
Integrated Postsecondary Education Data System, and state population data from the U.S. Census
Bureau.
b
Authors’ calculations from student aid data from the National Association of State Student Grant
and Aid Programs, FTE enrollment data from National Center for Education Statistics’ Integrated
Postsecondary Education Data System, and state population data from the U.S. Census Bureau.
c
Author’s calculations based on data from the U.S. Census Bureau, various years.
d
Author’s calculations based on data from the U.S. Bureau of Economic Analysis, various years.
e
National Center for Education Statistics’ Integrated Postsecondary Education Data System.
f
Richard Fording’s website: http://www.bama.ua.edu/~rcfording
23
Table 2: Regression Estimates, Ordinary Least Squares with State and Year Fixed Effects
(1)
State Appropriations
-0.972*
(0.015)
-0.006
(0.439)
0.039*
(0.045)
0.045*
(0.024)
0.012
(0.095)
-0.00003
(0.998)
-0.199
(0.484)
0.745***
(<0.001)
-0.852*
(0.036)
0.012
(0.254)
0.004
(0.347)
-0.009*
(0.010)
-0.005*
(0.037)
-0.004
(0.085)
(2)
Non-Need-Based Aid
-2.830
(0.717)
-0.191
(0.306)
0.235
(0.614)
0.056
(0.830)
0.052
(0.723)
-0.188
(0.375)
-9.566**
(0.004)
0.137
(0.888)
-12.58*
(0.026)
0.021
(0.887)
0.015
(0.830)
-0.093*
(0.040)
0.04
(0.183)
-0.0005
(0.989)
(3)
Need-Based Aid
3.656
(0.348)
-0.022
(0.677)
0.038
(0.767)
-0.186
(0.339)
0.019
(0.758)
-0.029
(0.780)
1.371
(0.556)
1.824**
(0.007)
-3.141
(0.205)
-0.008
(0.928)
0.035
(0.271)
-0.003
(0.852)
0.037
(0.251)
0.012
(0.516)
-0.0003
-0.008
0.009
(0.737)
(0.450)
(0.128)
Within R
0.52
0.36
0.44
N
1,450
1,450
1,450
Ethnic Diversity
Intermarriage Rate
Diversity*Intermarriage
African American
Hispanic
Other, Non-Hispanic
Gini Coefficient
Per-Capita GDP
Age 18-24
Age 5-17
Age 65+
College-Educated
Pell Recipients
Private Enrollment
Average Voter Liberalism
2
p-values in parentheses
* p<0.05
** p<0.01
*** p<0.001
24
Note: The coefficients on the state and year fixed effects are omitted for brevity. The variables
involving dollar amounts are in constant year 2005 dollars and are in natural logs. Because the
student aid variables take on zero values for some observations, we increased them by “1”
before logging. The standard errors are clustered by state.
Table 3: Marginal Effects of Ethnic Diversity on State Higher Education Spending, 1980
States
Ethnic Diversity
Intermarriage Rate
Marginal Effect of Fractionalization
P-Value
Minnesota
0.28
0.93
-14.98
0.019
New Hampshire
0.14
1.39
-14.68
0.022
Iowa
0.19
1.65
-14.52
0.023
Alabama
0.47
1.66
-14.52
0.023
South Dakota
0.26
1.76
-14.45
0.024
Connecticut
0.44
1.87
-14.38
0.025
Maine
0.11
1.9
-14.37
0.025
Vermont
0.1
1.97
-14.32
0.025
North Dakota
0.2
2.1
-14.24
0.026
Mississippi
0.52
2.13
-14.22
0.026
Nebraska
0.29
2.32
-14.1
0.028
Rhode Island
0.37
2.4
-14.05
0.028
Pennsylvania
0.33
2.48
-14
0.029
New Jersey
0.57
2.57
-13.94
0.03
Massachusetts
0.37
2.65
-13.89
0.03
Michigan
0.38
2.76
-13.82
0.031
Wisconsin
0.28
2.88
-13.75
0.032
New York
0.59
3.16
-13.57
0.035
Illinois
0.54
3.42
-13.4
0.037
Kansas
0.35
3.79
-13.17
0.041
Montana
0.23
4
-13.04
0.043
Delaware
0.5
4.02
-13.03
0.044
Utah
0.32
4.12
-12.96
0.045
Washington
0.42
4.19
-12.92
0.046
Indiana
0.31
4.45
-12.76
0.049
Virginia
0.52
4.51
-12.72
0.05
Maryland
0.59
4.54
-12.7
0.05
West Virginia
0.13
4.62
-12.65
0.051
Ohio
0.31
4.84
-12.51
0.054
Missouri
0.32
4.97
-12.43
0.056
25
Louisiana
0.52
5.06
-12.37
0.057
Tennessee
0.39
5.24
-12.26
0.06
Texas
0.63
5.39
-12.17
0.062
Idaho
0.27
5.45
-12.13
0.063
Oregon
0.35
5.76
-11.93
0.069
Georgia
0.58
6.05
-11.75
0.074
South Carolina
0.5
6.33
-11.57
0.079
Wyoming
0.25
6.55
-11.43
0.084
Table 3 Continued
Ethnic Diversity
Intermarriage Rate
Marginal Effect of Diversity
P-Value
Florida
States
0.58
6.66
-11.36
0.086
Arkansas
0.41
7.47
-10.85
0.105
North Carolina
0.5
7.68
-10.72
0.11
Arizona
0.57
8.23
-10.38
0.125
Kentucky
0.23
8.32
-10.31
0.128
Colorado
0.46
8.43
-10.25
0.131
California
0.67
8.7
-10.08
0.139
Alaska
0.51
8.84
-9.99
0.143
Oklahoma
0.47
8.91
-9.95
0.146
Nevada
0.6
10.23
-9.11
0.192
New Mexico
0.62
12.54
-7.66
0.295
Hawaii
0.53
16.6
-5.1
0.525
26
Table 4: Marginal Effects of Ethnic Diversity on State Higher Education Spending, 2009
States
Kentucky
Alabama
West Virginia
North Dakota
Tennessee
South Carolina
Mississippi
Indiana
New Hampshire
Minnesota
Pennsylvania
Michigan
Iowa
Nebraska
Ohio
Wisconsin
South Dakota
Maine
Rhode Island
Missouri
Delaware
Ethnic Diversity
0.23
0.47
0.13
0.2
0.39
0.5
0.52
0.31
0.14
0.28
0.33
0.38
0.19
0.29
0.31
0.28
0.26
0.11
0.37
0.32
0.5
Intermarriage Rate
1.81
2.69
2.86
3.2
3.5
3.69
3.75
3.81
3.86
4.08
4.18
4.19
4.38
4.46
4.47
4.48
4.57
4.61
4.92
5.16
5.28
Marginal Effect of Diversity
-14.42
-13.86
-13.76
-13.54
-13.35
-13.24
-13.2
-13.16
-13.13
-12.99
-12.93
-12.92
-12.8
-12.75
-12.74
-12.74
-12.68
-12.66
-12.46
-12.31
-12.23
P-Value
0.024
0.031
0.032
0.035
0.038
0.04
0.041
0.041
0.042
0.044
0.045
0.046
0.048
0.049
0.049
0.049
0.05
0.051
0.055
0.059
0.061
North Carolina
0.5
5.38
-12.17
0.062
Massachusetts
0.37
5.48
-12.11
0.064
Vermont
0.1
5.53
-12.07
0.065
New York
0.59
5.84
-11.88
0.07
Louisiana
0.52
5.86
-11.87
0.07
Virginia
0.52
5.93
-11.82
0.072
Arkansas
0.41
6.5
-11.47
0.083
Connecticut
0.44
6.59
-11.41
0.085
Illinois
0.54
6.74
-11.31
0.088
Montana
0.23
6.8
-11.28
0.089
Georgia
0.58
6.86
-11.24
0.09
Idaho
0.27
6.92
-11.2
0.092
Maryland
0.59
7.65
-10.74
0.109
New Jersey
0.57
7.75
-10.67
0.112
27
Utah
0.32
8.16
-10.42
0.123
Florida
0.58
8.26
-10.35
0.126
Kansas
0.35
8.7
-10.08
0.139
Texas
0.63
10.07
-9.21
0.186
Table 4 Continued
States
Oregon
Wyoming
Colorado
Washington
Arizona
California
New Mexico
Nevada
Oklahoma
Alaska
Hawaii
Ethnic Diversity
Intermarriage Rate
Marginal Effect of Diversity
P-Value
0.35
0.25
0.46
0.42
0.57
0.67
0.62
0.6
0.47
0.51
0.53
10.22
10.8
11.58
11.73
13.8
13.81
13.89
15.63
16.26
18.57
35.54
-9.12
-8.75
-8.26
-8.17
-6.86
-6.86
-6.81
-5.71
-5.31
-3.86
6.86
0.192
0.216
0.249
0.256
0.361
0.362
0.366
0.466
0.504
0.646
0.585
28
Table 5: Marginal Effects of the Intermarriage Rate on State Higher Education Spending,
2009
States
Ethnic Diversity
Intermarriage Rate
Marginal Effect of Intermarriage
P-Value
Vermont
0.1
5.53
-0.96
0.707
Maine
0.11
4.61
-0.74
0.764
West Virginia
0.13
2.86
-0.44
0.85
New Hampshire
0.14
3.86
-0.3
0.895
Iowa
0.19
4.38
0.48
0.806
North Dakota
0.2
3.2
0.64
0.737
Montana
0.23
6.8
1.1
0.521
Kentucky
0.23
1.81
1.2
0.474
Wyoming
0.25
10.8
1.44
0.363
South Dakota
0.26
4.57
1.62
0.288
Idaho
0.27
6.92
1.86
0.198
Minnesota
0.28
4.08
1.89
0.189
Wisconsin
0.28
4.48
1.96
0.166
Nebraska
0.29
4.46
2.19
0.106
Indiana
0.31
3.81
2.42
0.063
Ohio
0.31
4.47
2.51
0.05
Missouri
0.32
5.16
2.62
0.037
Utah
0.32
8.16
2.64
0.035
Pennsylvania
0.33
4.18
2.84
0.02
Kansas
0.35
8.7
3.09
0.009
Oregon
0.35
10.22
3.09
0.009
Massachusetts
0.37
5.48
3.49
0.003
Rhode Island
0.37
4.92
3.49
0.003
Michigan
0.38
4.19
3.67
0.002
Tennessee
0.39
3.5
3.72
0.001
Arkansas
0.41
6.5
4.08
0.001
Washington
0.42
11.73
4.24
<0.001
Connecticut
0.44
6.59
4.59
<0.001
Colorado
0.46
11.58
4.89
<0.001
Oklahoma
0.47
16.26
5.06
<0.001
29
Alabama
0.47
2.69
5.08
<0.001
Delaware
0.5
5.28
5.51
<0.001
South Carolina
0.5
3.69
5.62
<0.001
North Carolina
0.5
5.38
5.64
<0.001
Alaska
0.51
18.57
5.75
<0.001
Virginia
0.52
5.93
5.82
<0.001
Louisiana
0.52
5.86
5.93
<0.001
Mississippi
0.52
3.75
5.97
<0.001
Table 5 Cont.
States
Ethnic Diversity
Intermarriage Rate
Marginal Effect of Intermarriage
P-Value
Hawaii
0.53
35.54
5.98
<0.001
Illinois
0.54
6.74
6.2
<0.001
Arizona
0.57
13.8
6.73
0.001
New Jersey
0.57
7.75
6.79
0.001
Georgia
0.58
6.86
6.8
0.001
Florida
0.58
8.26
6.85
0.001
Maryland
0.59
7.65
6.99
0.001
New York
0.59
5.84
7.01
0.001
Nevada
0.6
15.63
7.26
0.001
New Mexico
0.62
13.89
7.51
0.001
Texas
0.63
10.07
7.77
0.001
California
0.67
13.81
8.31
0.001