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Educations Effect on Income Inequality: A Further Look Ryan Wells
Abstract
Income inequality affects countries across all levels of development and with varying demographic, sociological, and economic characteristics. Utilizing a globalization framework this study contributes to the ongoing discussions concerning inequality, education, and development by reexamining the effects of educational and economic variables on income inequality and presents new information concerning previously unstudied relationships between educational and economic factors. This research shows that the effects of education on income inequality are affected by the level of economic freedom in a country, and specifically, that more economic freedom may limit the leveling effects of secondary enrollments. These findings imply that the level of economic freedom must be considered when creating policies intended to reduce inequality, that other complex relationships between education and economics must be considered when studying income inequality, and that further research in this area is warranted.
Introduction
Income inequality affects countries across all levels of development and with varying
demographic, sociological, and economic characteristics. Since Kuznets (1955), researchers
have studied the theoretical causes of income inequality in various ways. Education as a
determinant of income inequality has been studied, as have various economic variables.
However, past empirical estimates of the effects of educational and economic variables have
often been contradictory or inconclusive and complex relationships have been neglected.
In recent decades, the world has seen the rise of a potent, far-reaching form of
globalization. Inherent in any discussion of globalization is the increasing dominance of an
emerging global economic framework and the impact of this phenomenon on individual nations.
Likewise, globalization impacts educational policies and practices. Out of this globalization
arise new concerns about income inequality. Given this modern context, the income inequality
within any particular country must be examined through the lens of globalization. Past research
has failed to thoroughly account for these implications.
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Researchers have examined income inequality and its determinants both within and
between countries, and the causes and consequences of each level have been analyzed
(Firebaugh, 2003; Freeman, 2002). Recent research has shown that in the last twenty years the
proportion of the total world income inequality attributable to between-country effects, though
still the larger contributor, is waning while within-country effects are increasing in importance
(Firebaugh, 2002; Goesling, 2001).1 This finding gives renewed impetus to the examination of
factors affecting income inequality within nations.
Recent policy debates concerning development and pro-poor growth in less developed
countries have had to consider the effects of economic growth on inequality (Lopez, 2004b). For
example, Ravallion (2004b) has stated that growth will be quite a blunt instrument against
poverty unless that growth comes with falling inequality (p. 15). Education is often included in
these development discussions, most often as a factor that can increase economic growth, reduce
poverty, or reduce inequality. For Latin America, the bold claim has been made that increased
human capital could totally eliminate the excess of inequality in the region (Londoo, 1996).
Critical development issues such as these further the case for studying income inequality.
In this paper, I first examine previous research concerning the determinants of income
inequality with special attention paid to educational factors. I then examine the literature
concerning globalization with specific attention to globalizations effects on education. From this
I provide an outline demonstrating the ways in which globalization may contribute to within-
nation income inequality indirectly via the mechanisms of education. Using this framework, I
then build on previous research by analyzing national data from 1980, 1990, and 2000 to study
the potential leveling effects of education in relation to the economic liberalization forces
1 This viewpoint is contested, due in part due to differing methods which may or may not use population-weighted data (see Milanovic, 2002; Ravallion, 2004a).
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inherent in globalization. Secondary enrollments and economic freedom are explicitly examined
in this regard. From this analysis I contribute to the dialogue concerning determinants of within-
country income inequality and offer a new understanding of the complex relationships between
education, economics, and inequality. I also show how these relationships have changed in
recent decades, demonstrate that these types of previously-neglected relationships should not be
ignored in further research, and explain how these factors may impact individual nations and
their policies.
Literature concerning determinants of income inequality
There is a substantial literature that examines demographic and economic determinants of
income inequality.2 First among these was a pioneering study which determined an inverted U-
shaped curve for the association between economic development and income inequality
(Kuznets, 1955). In other words, increased economic development is associated with increased
inequality at lower levels of development, but then shifts at some point beyond which increased
development is associated with decreased inequality. Although regularly referenced and often
supported (see for example De Gregorio & Lee, 2002; Nielsen & Alderson, 1995), this
relationship is not universally accepted and has been challenged (Ram, 1988; Ravallion, 2004a).
Recent research has also suggested augmenting this curve to show that for the very highest-
income countries the relationship again reverses, in what Harrison and Bluestone (1988) call the
great U-turn (see also Alderson & Nielsen, 2002; Galbraith, Conceicao, & Kum, 2000).
Measures of economic freedom or its converse, economic restriction, have also been
examined for their effects on income inequality, but with inconclusive results. Some research
2 This literature review will primarily concentrate on studies that relate to the time period under consideration: 1980-2000.
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found that openness to trade, non-protectionist policies, and/or smaller government are
associated with greater income inequality (Barro, 2000; Lopez, 2004a; Savvides, 1998).3 Other
research came to the opposite conclusion, finding evidence that free trade and open economic
policies lead to increased equality (Dollar & Kraay, 2002; Edwards, 1997). Still others found no
relationship between economic freedom and income inequality (Li & Zou, 2002). Milanovic
(2002) found a more complex relationship whereby openness in low-income countries tended to
benefit only the rich, but openness in higher-income countries benefited the poor and middle
class to a larger degree. Looking specifically at tariffs, Milanovic & Squire (2005) found that
more liberal policies were associated with increased inequality in poorer countries, but with
decreased inequality in richer countries.
Research has also examined the link between income inequality and various measures of
education. Most studies have found a negative relationship between income inequality and a
countrys average or median educational attainment, (De Gregorio & Lee, 2002; Park, 1996;
Psacharopoulos, Morley, Fiszbein, Lee, & Wood, 1995; Ram, 1984). Others have found a
positive correlation between the two factors when wealth inequality is also included (Deininger
& Squire, 1998). Barro (1999) studied the effect of educational attainment on inequality, finding
a negative relationship for primary education attainment, but a positive relationship for higher
education attainment. Checchi (2000) concluded that when the distribution of educational
attainment was accounted for, the relationship between attainment and income inequality was
actually U-shaped.
The direct relationship between educational inequality (the unequal distribution of human
capital) and income inequality has also shown mixed results. Some have found a positive
3 Barro (2000) and Lopez (2004a) results are specifically for less developed countries (LDCs).
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relationship between the two factors (Braun, 1988; De Gregorio & Lee, 2002; Park, 1996).4
Others have found a negative relationship (Ram, 1984), such that incomes have diverged
despite substantial convergence in education levels (O'Neil, 1995, p. 1289).
Enrollments have also been examined for their effects on income inequality. Research
shows that higher enrollments at the secondary level are associated with decreased income
inequality (Alderson & Nielsen, 2002; Barro, 2000; Bourguignon & Morrisson, 1990; Nielsen &
Alderson, 1995; Papanek & Kyn, 1986) as they are for primary and secondary enrollments
combined (Tsakloglou, 1988). Barro (2000) also found a negative relationship between primary
enrollments and income inequality, but a positive relationship between higher education
enrollments and income inequality.
The relationship between secondary enrollments and income inequality may be thought of
as one which is inherently connected to development. In other words, increases in secondary
education and decreases in inequality may both be effects of increased development. In fact,
Crenshaw & Ameen (1994) argued that at the highest levels of educational expansion, when
development is also highest and Kuznets curve will have turned, that the relationship between
enrollments and inequality would become positive. This was not supported by Alderson &
Nielsen (2002), however, who found that the average level of education continues to exert an
important negative influence on income inequality in the advanced industrial societies (p.
1278). These results indicate that secondary enrollments have an effect independent of
development processes. In lesser developed societies the negative effect of secondary
enrollments could be theoretically attributable to an increased importance for education during
urbanization or a shift from agricultural to industrial societies. In any case, the significance of
4 Income inequality and educational inequality have also been studied comparatively for Brazil and South Africa (Lam, 1999).
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secondary enrollments is consistent with the logic of supply and demand, whereby an increase in
the supply of educated workers will tend to diminish the gap in wages, and thereby decrease
income inequality (Lecaillon, Paukert, Morrisson, & Germidis, 1984).
Sylwester (2002) has reported a negative relationship between inequality and government
expenditure on education. Others have found educational expenditures or educational growth to
be positively associated with inequality (Checchi, 2000; Deininger & Squire, 1998), though
causal relationships are left ambiguous. Other research has found no relationship between an
expanded educational system and a countrys income inequality (Shanahan, 1994 cited in
Chabbott & Ramirez, 2000). However, educational expansion may produce a widening gap in
returns to education, which in turn may contribute to increased income inequality (Bouillon,
Legovini, & Lustig, 2003).5
Other potential determinants of income inequality that have been studied, which may have
more complex relationships among them, include democracy (Barro, 2000; Burkhart, 1997),
political freedom (Li, Squire, & Zou, 1998; Simpson, 1990), capital/output ratio (Checchi, 2000),
percentage of the labor force in agriculture, sector dualism, the rate of population increase
(Nielsen & Alderson, 1995), decommodification, wage-setting coordination, union density,
female labor force participation (Alderson & Nielsen, 2002), mineral resources, agricultural
exports (Bourguignon & Morrisson, 1990), inflation, financial development (Dollar & Kraay,
2002; Li & Zou, 2002), and foreign investment dependence (Alderson & Nielsen, 1999).
Alderson & Nielsen (2002) studied the effects of three aspects of globalization specifically:
direct foreign investment, North-South trade, and migration. The incomplete or inconclusive
5 There is also literature examining the reverse causation discussed here, i.e., the effect of inequality on education. See Checchi (2003).
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nature of much of the existing literature, specifically in regards to educational and economic
factors, reveals the need for further studies concerning the determinants of income inequality.
Globalizations impact on education and income inequality
Globalization, in addition to its direct effect on economic policies and conditions, affects
educational systems in most nations of the world. The changing educational policies and
practices may in turn have effects on societal factors such as income inequality. I outline below
the way in which complex relationships between globalization, economics, and education affect
a countrys income inequality level. In the subsequent section I test these relationships using
national-level data.
Globalization has been defined as the widening, deepening, and speeding up of global
interconnectedness (Held, McGrew, Goldblatt, & Perraton, 1999, p. 14) or in terms of the
changing significance and position of national or regional borders in relation to economic,
political, or social transactions (Goesling, 2001, p. 757). Although political, cultural, social and
technological trends are important, this paper will primarily concentrate on economic aspects,
which may better fit the definition of globalization as the increasing freedom and ability of
individuals and firms to undertake voluntary economic transactions with residents of other
countries (Brahmbhatt, 1998, p. 1).
In addition to the often debated economic causes and effects, there are distributive
consequences of globalization (Petras, 1999) to consider. Globalizations prominent neoliberal
policies have a direct effect on the economies of individual nations, and may be a contributing
factor to increasing within-nation income inequality. Among the possible causes of rising
income inequality are the reduction of the redistributive role of the state, the decline in union
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presence in the workplace, the increased competition at the international level, technological
progress and all possible combinations of these (Checchi, 2000, p. 4). These factors are, at least
in part, globalization-related. By making the economy a global entity rather than a national one,
and therefore making national boundaries less significant in many ways, globalization may also
be partly responsible for the shift from inequality across borders to greater inequality within
borders (Goesling, 2001). In support of this new geography of inequality, Alderson & Nielsen
(2002) found globalization-related factors to be significant when examining the U-turn toward
increasing inequality in OECD countries.
An example of the global convergence in economic policies in the globalization era can be
observed via structural adjustment policies (SAPs), utilized by multinational lending institutions
such as the IMF and the World Bank and intended to improve and stabilize developing
economies. Critics claim that such policies are an extension of neoliberal hegemony whereby
polices reflect social and political relations in which capitalist countries in the North dominate
and decide in their favor (Stromquist, 2002, p. 29). According to The Global Poll (2003),
sizeable minorities of opinion leaders all over the world believe that the Banks actions have
increased the gap between rich and poor people in their countries (Goesling, 2001, p. 2), which
in part can be explained by Easterlys (2001) finding that adjustment lending lowers the
sensitivity of poverty to the aggregate growth rate of the economy (p. 15). Lopez (2004b)
interprets this research to be consistent with a positive relationship between increases in
inequality and the implementation of adjustment programs (p. 11).
Education, though given less attention than economics, may be a significant mechanism by
which globalization affects income inequality. Globalization-related policies affect education in
a number of ways, such as forcing national educational policies into a neoliberal framework that
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emphasizes lower taxes; shrinking the state sector and doing more with less; promoting market
approaches to school choice (particularly vouchers); rational management of school
organizations; performance assessment (testing); and deregulation in order to encourage new
providers (including on-line providers) of educational services (Burbules & Torres, 2000, p.
20). Other effects may be uncontested adoption of initiatives in developed countries along such
lines as decentralization, privatization, the assessment of student performance, and the
development of tighter connection between education and the business sector (Stromquist,
2002, p. 16). Through decentralization and privatization, globalization has encouraged the
commodification of education, such that it is now a commodity that, like any other, is to be
determined and bought on the market (Stromquist, 2002, p. 178).
SAPs, as an example, also impact educational systems by stipulating a number of fiscal
austerity measures for debtor countries, often including decreased public spending for education
(see for example Stromquist, 1999). However, SAPs do not simply lead to a decrease in
spending. Austerity measures also affect other sectors of the public economy which impact
personal economic choices. In Nigeria, for example, SAPs led to economic hardships for many,
ultimately leading to the poorest citizens being unable to afford school (Obasi, 1997). Similarly,
the economic situation of farmers is often worsened due to lower prices for agricultural goods
under SAP conditions (Assi-Lumumba, 2000) which may lower demand for schooling in rural
areas.6
Under SAPs, lower expenditures on education are often coupled with free-market reform
policies advocating cost-sharing strategies for education, or the expansion of private education.
Accordingly, students and their families are expected to pay increased school fees and share the
6 This disproportionate impact on poor households also leads to an inordinate impact on female education in many cases (Baden, 1993; Buchmann, 1996).
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burden of the cost of education. Assi-Lumumba (2000) sums up the concerns about such
policies: The application of the policy of decreasing public support in favor of users fees at the
lower levels of the systems would eliminate, from the very beginning, the most economically
vulnerable segments of the population, particularly first from farming areas and small traders in
urban areas whose opportunity cost for education is high even at a young age (pp. 115-116).
Cost-sharing has been shown to lead to lower enrollments in Nigeria, Ghana, Zimbabwe, and
Zambia (Appleton, 1999).
Even if national policies alone could expand access to education, levels of inequality may
not decrease. The sociological construct of maximally maintained inequality (MMI) (Raftery
& Hout, 1993) and structural theories of reproduction (Bordieau, 1990; Boudon, 1974) explain
how in some contexts the upper classes may be able to propagate their educational advantages,
whereby education would not decrease inequality.7 Even in cases where national enrollment
numbers hold steady or even increase, the proportion of students being served is likely to be
skewed, with the upper- and middle-class having greater access to the expansion than the lower
class. Tsakloglou (1988) cites earlier studies to support the claim that benefits from an
expansion in secondary enrollments go mainly to middle income classes (p. 519). This
possible educational imbalance due to free-market reforms may diminish the potential for
educational factors to reduce income inequality. Ironically, the World Bank often proposes
privatization of education as a means of increasing equity. They argue that since schools
(especially higher education) currently disproportionately serve the rich and upper-classes,
privatization would do away with such favoritism and allow anyone to achieve in school.
However, policies such as these wrongly assume a purely meritocratic educational environment
whereby each participant, both high- and low-class, starts from an equal position. 7 The closely related concept of effectively maintained inequality (EMI) (Lucas, 2001) is also relevant.
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Even when access to schooling appears to be equal between the upper and lower classes, on
the basis of enrollment statistics, there is still the potential for inequality. In response to
Tooleys (1998, 1999) insinuation that philanthropy and charity schools could sufficiently fill in
the void for lower-class access to education, Hill (2001) responds: The status and standards of
such schools would be unlikely to match those of fee-paying private schools (p. 45).8
Privatization policies have the inherent danger that they simply reinforce educational
inequalities by encouraging the wealthy sectors of society to create a private educational system
(Morrow & Torres, 2000, p. 40).9 Stromquist (2002) sums up the general argument: With
decreased state engagement in public education, most children of the poor are provided low-
quality services, while the more wealthy can afford a higher-quality private education, resulting
in an ever widening gap in educational attainment between the rich and the poor within
countries (Stromquist, 2002, p. xx). A poor-quality education will likely lead to less
development of productive skills and therefore lower paying jobs in the marketplace, while the
high-quality, private educations received by those that can afford it likely lead to higher paying
jobs. These effects of globalization are not reserved for developing countries whose economies
are weak. Hill (2001) cites studies showing that the United States, Britain, Australia, and New
Zealand all experienced increased inequality after making education a more free-market
commodity.10
8 For another reply to Tooleys arguments, see Winch (1998). 9 One problem with an educational agenda grounded in neoliberal economics, for both the developing and developed countries, is that there exists a fundamental mismatch between education and the capitalistic marketplace in terms of goals, motivations, methods, and standards of excellence (McMurtry, 1991). 10 Though no specific reference is made to education policies, Alderson & Nielsen (2002) discuss the causes of increasing inequality in OECD nations in recent decades.
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Summary of the literature and hypotheses
Several economic and educational factors have been posited as affecting income inequality.
For many of these variables, the effects have been contradictory or inconclusive over multiple
studies. In addition, many of these studies since 1980 have not adequately accounted for the
processes and effects of globalization, such as a convergence of worldwide economic policies.
Ultimately, these neoliberal policies and free-market reforms may directly impact a nations
level of inequality. In addition, these factors may affect educational access and quality, which in
turn may affect income inequality since education is associated with personal economic well-
being. However, it may be that the interaction between education and economic freedom
contributes to income inequality above and beyond the individual effects of either factor
individually.
Building on the above evidence, I test the relationships of traditionally-used educational
and economic variables with income inequality. Importantly, I also examine how the effects of
education on income inequality differ according to the level of economic freedom in a country.
Specifically, I test four hypotheses. In support of past research I hypothesize that:
1. Economic development is related to income inequality according to Kuznets
inverted U-shape.
2. Secondary enrollments are negatively related to income inequality.
Though pervious research is inconclusive, based largely on the literature concerning
globalization I also hypothesize that:
3. Economic freedom is positively associated with income inequality.
Finally, based on the literature concerning the effects of globalization on educational and
economic factors, I propose a previously untested hypothesis:
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4. Enrollments are more positively associated with income inequality in countries with
high levels of economic freedom than in countries with low or medium levels of
economic freedom.
Data and Methods
Using data from the World Income Inequality Database (WIID), the World Banks World
Development Indicators (WDI), Gwartney & Lawsons (2004) work on behalf of the Economic
Freedom Network, and Castell & Domnechs (2002) measure of human capital inequality, I
estimate the effects of selected educational and economic factors on income inequality by
analyzing cross-sectional data from 1980, 1990, and 2000.
Dependent Variable
The dependent variable for this study is income inequality, measured using the Gini
coefficient, which was obtained from the World Income Inequality Database (WIID) (WIDER,
2005).11 The Gini coefficient is an effective measure of inequality for two reasons. First, it is
the most common variable used in economic and inequality research, permitting more accessible
comparisons with prior research. Second, the Gini coefficient has an intuitive interpretation for
those that are not familiar with the technical details of inequality measures. In addition, the
alternatives to the Gini have drawbacks such as a high sensitivity to large rich nations (Theil
index) or to populous nations (MLD) (Alderson & Nielsen, 2002).12 I utilized Gini coefficients
for the years 1980, 1990, and 2000. Where there were missing values for the desired year, I
substituted the Gini coefficient from either one year earlier or one year later as a proxy for the
11 This dataset includes the often-used GINI data developed by Deininger & Squire (1996). 12 Further discussion concerning alternate measures of inequality can be found in Cowell (2000) and Firebaugh (2003).
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Gini coefficient in the year under examination. If one assumes serial correlation for these data,
this proxy value should serve as a fairly reliable estimate of the true value for the desired year.13
Independent Variables
I used the natural log of GDP per capita (in constant 2000 $US) as the variable for
economic development, as is common in the literature. This variable was retrieved from the
World Banks World Development Indicators (WDI). As is also common in the literature, I
included the squared value of this term as another variable, to test Kuznets' inverted U-shaped
hypothesis. I also included an aggregate measure of economic freedom which has been
developed by the Fraser Institute and the Economic Freedom Network (Gwartney & Lawson,
2004). Twenty-one factors are included in this measure in five main areas: size of government,
legal structure and security of property rights, access to sound money, freedom to exchange with
foreigners, regulation of credit, labor, and business (Gwartney & Lawson, 2004). I selected this
data source over more simplistic variables used in the past (such as the share of exports and
imports, or direct foreign investment, in GDP (Milanovic, 2002)) due to the comprehensive
aggregate nature of the variable.
Education expenditures as a variable was retrieved from the WDI and represents the
percentage of a countrys public expenditures for educational purposes. Public expenditure on
education consists of public spending on public education plus subsidies to private education at
the primary, secondary, and tertiary levels (World Bank, 2005). I also included an educational
variable for the gross secondary enrollment ratio.14 This variable was also obtained from the
13 Since the WIID database may have more than one value for any particular country-year, I followed a set of decision rules to ensure data with the highest integrity possible. These were based primarily on the quality ratings supplied by WIDER. 14 Primary and tertiary enrollments were originally included in models as possible predictors. Consistent with literature that excludes them, there was not significant association of these variables with income inequality in preliminary tests (not shown here). They were ultimately discarded due to the limitations of a small sample size.
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WDI and is the ratio of total enrollment, regardless of age, to the population of the age group
that officially corresponds to the level of education shown (World Bank, 2005). Castell &
Domnech (2002) have created a measure for the human capital inequality for a country in a
given year, which I have included as a representation of educational inequality. I selected this
measure because it is an improvement over other indicators of educational inequality used in the
past, such as the standard deviation of educational attainment. 15
To estimate the effects of an interaction between economic freedom and secondary
enrollments, I represented economic freedom by using dummy variables in the appropriate
models. I used this method, rather than interactions with a continuous variable, for two reasons.
First, grouping countries by level of economic freedom more accurately shows the differences
between leveled groups, rather than the effects of an incremental change in the economic
freedom measure. Second, the statistical interpretation for interactions with dummy variables is
more intuitive and better tests the hypothesis presented above. I created dummy codes for low
economic freedom representing countries in the lowest third of the distribution, and middle
economic freedom representing countries in the middle third. For each, high economic
freedom was the comparison group.
The data used in this study are subject to some limitations. The data for income inequality
in the WIID, though highly improved over data available in the past, have been gathered from a
variety of sources and are of variable quality. There are a number of countries that are not
consistently represented in the data due to intermittent coverage, as evidenced by variations in
estimates for the same country-years. The lack of available data, especially for the Gini
15 The original design also included a variable for the educational attainment of a country from the Center for International Development (Barro & Lee, 2000). This variable had to be eliminated due to a small sample size which restricted the number of variables that could be used, and due to collinearity problems with secondary enrollment and human capital inequality.
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coefficient, means that attempts at analyzing pooled time-series data would necessarily be
unbalanced and therefore difficult to interpret. To avoid these pitfalls, this study analyzes cross-
sectional data from 1980, 1990, and 2000 using OLS regression. This method is also subject to
some limitations, however. The lack of available Gini data means that for regression analyses
there will be relatively small samples sizes and therefore some models (especially Models 4 & 5)
can only include those variables that are most pertinent to my stated hypotheses. However, by
doing three separate analyses at different points in time, stronger generalizations may be drawn
than from other cross-sectional studies which have only analyzed one year.
In order to examine the determinants of income inequality, parallel regression models were
used for 1980, 1990, and 2000. Model 1 in each year represents only the economic variables
under consideration, and Model 2 represents only educational variables. Model 3 is a combined
model utilizing both economic and educational variables. To expand on past research, this study
also examines the specific hypothesis concerning education and economic freedom by including
interaction variables (secondary enrollment x economic freedom dummy) in the regression
analysis. Model 4 represents a baseline model in which the dummy coded variables for
economic freedom are used in the same model with secondary enrollment. This model can be
compared with Model 5, where the interaction variables are included. In all models variable
selection was based on past literature with the restricting condition of avoiding collinearity
problems. Given the limitations of the data, this research is still able to produce results that are
valuable on their own, and which will also serve as the foundation for more robust studies in the
future.
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Results
Descriptive results from this research support the assertions that there has been a general
trend toward increased within-country inequality in recent history (refer to Table 1 for
descriptive results). The average within-country GINI increased from 36.05 in 1980, to 38.24 in
1990, to 41.09 in 2000.16 Descriptive statistics also reveal that there was a trend toward greater
economic freedom in the world between 1980 and 2000, which is not surprising given the
globalization discussion above. The 10-point index used by Gwartney & Lawson (2004) rose
from a cross-country average of 5.1333 in 1980, to 5.4420 in 1990, to 6.4033 in 2000.17
Likewise, as expected, secondary enrollments have increased over the decades being studied.
The average gross enrollment ratio across all countries with available data increased from 50.25
in 1980, to 56.48 in 1990, to 71.67 in 2000. The results of regression analyses are presented by
year below, with a discussion of the combined results reserved for the subsequent section.
The results for 1980 (Table 2) are not significant for most variables. When the economic
variables are considered in model 1, they show support for Kuznets hypothesis: increased
economic development tends to increase inequality, but only up to a point. At some point, the
curve turns, beyond which increased development lessens inequality. When educational
variables are considered in model 2, secondary enrollments are significant in decreasing
inequality. When all of the variables are considered together in model 3, only the economic
development variables are significant, but the effects are weaker than in Model 1 implying that
they are somewhat confounded by education. When economic development is held constant for
1980, education variables have negligible effects on inequality. In models 4 and 5, in which
16 This result is similar to those found by Alderson & Nielsen (2002) using measures of income inequality other than the Gini (p. 164). 17 These are averages for the samples used in this study. The average for all countries analyzed by Gwartney & Lawson (2004) has also showed consistent increases since 1980.
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economic freedom is considered as a dummy variable rather than as a continuous variable, and in
which interaction variables are introduced, Kuznets hypothesis is supported, but no other factors
show significance.
The results for 1990 (Table 3) show more significant effects than those for 1980. In
models one through three, the economic variables support Kuznets hypothesis, and secondary
enrollments are consistently significant. This negative result indicates that higher secondary
enrollments may have an equalizing effect on within-country incomes. The variables for
economic freedom, education expenditures, and human capital inequality are insignificant.18
In models four and five for 1990, again the GDP variables show generally consistent
significance. In these models however, we also see interesting results of considering the
interaction effects of enrollment and economic freedom. In model four secondary enrollment is
significant, indicating that higher enrollments are associated with greater income equality. This
significance disappears however when interaction variables are introduced in model five. The
significance of the interaction of enrollment and low economic freedom indicates that countries
in the lowest third in terms of economic freedom show a negative association between secondary
enrollment and inequality that is significantly greater than countries with the highest levels of
economic freedom. In other words, most of the leveling effect of secondary enrollment on
income inequality is occurring in countries with low levels of economic freedom. Looked at in
yet another way, the association of greater secondary enrollments with lower income inequality
is diminished for countries with higher levels of economic freedom.
18 The results for human capital inequality are not significant, can not be assumed to be different than zero, and therefore do not warrant discussion here. Nevertheless, the negative sign in all years and models may be unexpected, though not without precedent (Ram, 1984). Other studies have found this negative relationship at a significant level and discuss possible reasons for this (Checchi, 2000; Park, 1996). ONeil (1995) explains this as the results of returns to education that are beneficial for developed countries, but not for developing countries.
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Due to the lack of available data for education expenditures and human capital inequality,
resulting in extremely small sample sizes, models one and three for 2000 (Table 4) are less
robust than in other years, and model two could not be analyzed. Nevertheless, there are still
some valuable results. Models one and three again show general support for Kuznets
hypothesis, but secondary enrollment is not significant. In addition, the year 2000 models show
positive significance for the economic freedom variables, indicating that open, free economic
policies were associated with higher income inequality. Interestingly, in models four and five
the economic development variables lose all significance for the first time in any model. In
addition, these 2000 models show results similar to those for the interaction variables in 1990;
the significance of secondary enrollment disappears when the interaction variables are
introduced, and most of the significance of that variables effect on reducing income inequality is
experienced by countries with low levels of economic freedom.
Discussion and Conclusions
This study confirms that there was a trend from 1980 to 2000 toward more economic
freedom, more within-country inequality, and higher levels of education. These findings are
consistent with arguments concerning globalization since 1980 whereby a given local condition
or entity succeeds in traversing borders and extending its reach over the globe and, in doing so,
develops the capacity to designate a rival social condition as local (Jenson & de Sousa Santos,
2000, p. 11).
The results concerning the non-linear relationship between a countrys level of economic
development and income inequality support past research with similar findings in support of
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20
Kuznets, as well as my first hypothesis. The negative association of secondary enrollment and
income inequality also largely confirms past research and supports my second hypothesis.
The significance of the economic freedom variable in 2000 models lends partial support to
my third hypothesis of a positive association between economic freedom and income inequality,
but only for that year. The increasing explanatory power of the economic freedom variable over
the years also indicates a possible shift in the determinants of income inequality based on time-
dependent contextual factors. This supports Firebaughs (2003) claim that the longitudinal trend
toward increasing inequality can be partially explained by globalization-related factors.
This research also lends partial support to my fourth hypothesis that in countries with high
levels of economic freedom the equalizing effect that school enrollments (and perhaps education
more broadly) may have on income inequality is less than in other countries. This is true for the
comparison between high and low-economic freedom countries in 1990 and 2000, but not for the
comparison between countries with high and medium levels of economic freedom. At least for
economies characterized by a large degree of economic freedom, these results lend credibility to
MMI and/or reproduction-based arguments which state that the wealthy may reap a
disproportionate benefit of education, and ultimately income. It also appears that the interaction
effect of secondary enrollment and economic freedom may be temporally sensitive. In 1980
there was no significance for the interaction effect, but models for 1990 and 2000 both showed
significance. This finding contributes to the wider discussion of globalizations effects, as
greater secondary enrollments worldwide coincide with shifts toward economic policies which
may restrict or stratify access and/or quality in some contexts.
From a policy perspective, this research leads to valuable insights. Inherent in the
globalization framework behind this papers hypothesis is the already debated warning
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21
concerning the effects of privatization, decentralization, and cost-sharing which result in the
commodification of education. However, this paper adds a new perspective to the discussion. It
can not be assumed that increasing secondary enrollments (or perhaps any other educational
factor) will have equal effects in all countries. This fact has been readily recognized in the past,
but primarily with regard to a countrys level of economic development. This research shows
that the level of a countrys economic freedom is also a salient factor for consideration. If
policies aimed at educational expansion are intended to level income inequality, policy makers
must realize that these effects may differ based on the economic policies of any given country.
Development organizations which promote both education and economic reform as means of
poverty reduction must realize that these factors are not independent. The interaction of the two
may also affect income inequality, and the two factors may even work against each other.
These observations need further support in the form of continued research. The
relationships between education and economic freedom should be studied via more robust
models utilizing secondary enrollment rates, with models studying enrollments at other levels,
and also with other educational variables. In addition, research should examine the trend
exposed in this research in a finer manner, within the decades discussed and also in the years
since 2000. Though subject to its own limitations, pooled time-series analysis should also be
conducted in order to further test these findings. In any future research including income
inequality and education, however, the interaction effects of education and economic freedom
should be considered. Finally, this research shows the need for more complex interactions,
mechanisms, and dynamic models of all kinds to be considered when studying within-country
income inequality in the future.
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22
Table 1
Descriptive statistics for key variables
1980 1990 2000
Mean Std Dev Mean Std Dev Mean Std Dev
Gini 36.05 9.88 38.24 11.42 41.09 10.90
Economic Freedom 5.13 1.13 5.44 1.36 6.40 1.07
Secondary Enrollment 50.25 31.75 56.48 31.39 71.67 31.80
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23
Table 2 Effects on GINI 1980 Model 1 Model 2 Model 3 Model 4 Model 5 Constant -40.2554
(34.558) 56.455*** (7.828)
-15.405 (41.086)
-30.004 34.897
-31.820 (43.014)
ln(GDP/cap)
23.800*** (8.833)
18.897* (9.684)
20.855** (9.379)
20.816* (10.983)
ln(GDP/cap)2
-1.717*** (.567)
-1.378** (.644)
-1.378** (.636)
-1.356* (.732)
Economic Freedom
.655 (1.268)
.441 (1.299)
Education expenditures
.838 (.727)
.974 (.706)
Gross Secondary Enrollment
-.272*** (.074)
-.131 (.094)
-.106 (.077)
-.102 (.089)
Human Capital Inequality
-13.200 (10.710)
-10.687 (11.457)
Low Econ. Freedom (dummy)
-.123 (3.317)
-2.697 (7.826)
Mid Econ. Freedom (dummy)
2.008 (3.221)
11.487 (8.424)
Sec Enroll * Low Econ Freedom
.077 (.139)
Sec Enroll * Mid Econ Freedom
-.174 (.138)
N 45 45 45 52 52 R2 .347 .311 .404 .356 .395 * = Significant at .10 ** = significant at .05 *** = significant at .01
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24
Table 3 Effects on GINI 1990 Model 1 Model 2 Model 3 Model 4 Model 5 Constant -68.789**
(33.924) 58.778*** (7.917)
-54.402 (35.787)
-13.292 (31.874)
-83.973* (47.177)
ln(GDP/cap)
29.105*** (8.786)
26.825*** (8.708)
17.751** (8.359)
34.030*** (11.479)
ln(GDP/cap)2
-2.063*** (.551)
-1.778** (.544)
-1.046* (..535)
-2.094*** (.747)
Economic Freedom
2.393 (1.507)
2.240 (1.493)
Education expenditures
.375 (.772)
.362 (.709)
Gross Secondary Enrollment
-.224*** (.068)
-.198** (.077)
-.268*** (.067)
-.135 (.112)
Human Capital Inequality
-16.573 (12.504)
-10.553 (12.476)
Low Econ. Freedom (dummy)
-3.701 (4.265)
14.176 9.766
Mid Econ. Freedom (dummy)
-1.526 (3.875)
3.948 8.831
Sec Enroll * Low Econ Freedom
-.321** (.153)
Sec Enroll * Mid Econ Freedom
-.073 (.142)
N 51 51 51 65 65 R2 .316 .229 .405 .374 .428 * = Significant at .10 ** = significant at .05 *** = significant at .01
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Table 4
Effects on GINI 2000 Model 1 Model 2 Model 3 Model 4 Model 5 Constant -56.107
(65.180) NA -65.203
(63.888) -17.572 (62.425)
-19.501 (84.221)
ln(GDP/cap)
20.552 (14.781)
NA 24.294*** (14.608)
21.671 (15.193)
21.544 (19.464)
ln(GDP/cap)2
-1.615* (.889)
NA -1.688*** (.869)
-1.466 (.898)
-1.583 (1.147)
Economic Freedom
6.150** (2.432)
NA 5.372* (2.421)
Education expenditures
NA
Gross Secondary Enrollment
NA -.137 (.082)
-.158* (.085)
-.063 (.111)
Human Capital Inequality
NA
Low Econ. Freedom (dummy)
-8.242 (5.645)
41.353 (25.168)
Mid Econ. Freedom (dummy)
-5.737 (4.549)
-.170 (14.173)
Sec Enroll * Low Econ Freedom
-.631* (.313)
Sec Enroll * Mid Econ Freedom
-.073 (.166)
N 41 NA 41 41 41 R2 .324 NA .373 .333 .406 * = Significant at .10 ** = significant at .05 *** = significant at .01
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26
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