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.

2

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.

4

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).

14

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.

15

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

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

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.

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

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

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

25

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

26

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