Income Inequality and Public Expenditure on Non-Compulsory Education in
Western Europe from 1999-2010
by
Ryan R. Young
MPP Essay
Submitted to
Oregon State University
In partial fulfillment of
the requirements for the
degree of
Master of Public Policy
Presented May 22nd, 2012
Commencement June 17th, 2012
ii
Master of Public Policy essay of Ryan Young presented on May 22nd, 2012
APPROVED:
______________________________________________________________________________
Alison Johnston, representing Political Science
______________________________________________________________________________
Brent Steel, representing Political Science
______________________________________________________________________________
Mark Fermanich, representing Education
______________________________________________________________________________
Ryan R. Young, Author
iii
Abstract
Policies in Europe over the last half a century have steadily dismantled the inequalityeasing processes of the welfare state. Current conditions coupled with concerns related to the
recent economic downturn have heightened focus on the issue of income distribution. Education
has been identified as a resource to combat such ills. Funding for non-compulsory education
levels exhibits greater promise for wider support as it circumvents the welfare state. This paper
examines the relationship between public funding for upper secondary and tertiary education and
income inequality for fifteen Western European countries. Six time series models were used in
the analysis for the years 1999-2010. It was determined that increased tertiary education
expenditure is correlated with lowered levels of income inequality at a greater magnitude than
funding for upper secondary education. Therefore, policies should be enacted that take benefits
of advanced human capital accumulation into account. This paper recommends incomecontingent loan schemes that limit risk and enable cost sharing between taxpayers and graduates.
iv
TABLE OF CONTENTS
Page
INTRODUCTION...................................................................................................................
1
LITERATURE REVIEW.......................................................................................................
3
INCOME INEQUALITY.......................................................................................................................
EDUCATION AS A TOOL FOR EQUALITY.....................................................................................
3
7
HYPOTHESES.......................................................................................................................
11
DATA.......................................................................................................................................
12
UNIT OF ANALYSIS...........................................................................................................................
DEPENDENT VARIABLE...................................................................................................................
INDEPENDENT VARIABLES............................................................................................................
12
13
15
METHODS.............................................................................................................................
22
FINDINGS..............................................................................................................................
23
MODEL 1 – FIXED EFFECTS............................................................................................................
MODEL 2 – FIXED EFFECTS............................................................................................................
MODEL 3 – WELFARE REGIMES....................................................................................................
MODEL 4 – WELFARE REGIMES....................................................................................................
MODEL 5 – SKILL FORMATION.....................................................................................................
MODEL 6 – SKILL FORMATION.....................................................................................................
24
27
28
29
31
32
DISCUSSION.........................................................................................................................
33
POLICY IMPLICATIONS...................................................................................................
34
CONCLUSION.......................................................................................................................
36
LIMITATIONS......................................................................................................................................
FUTURE RESEARCH..........................................................................................................................
37
38
REFERENCES.......................................................................................................................
40
APPENDIX..............................................................................................................................
43
v
vi
LIST OF FIGURES
Figure
Figure 1. Effect of Increase of Different Factors on Growth Spell Duration..........................
Figure 2. Visual Representation of Gini Coefficient...............................................................
Figure 3. Gini Coefficient 1999-2010 by Country..................................................................
Figure 4. Tertiary Education Expenditure (% of gdp)............................................................
Page
5
13
14
16
vii
LIST OF TABLES
Tables
Table 1. Descriptive statistics – dependent variable 1999-2010..............................................
Table 2. Descriptive statistics – independent variables 1999-2010.........................................
Table 3. Effect of public expenditure type as a percentage of GDP on income
inequality (Gini index) in Western Europe, 1999-2010............................................
Table 4. Effect of social education expenditure type as a percentage of GDP on
income inequality (Gini index) in Western Europe, 1999-2010...............................
Table 1A. Effect of public expenditure on primary education as a percentage of GDP
on income inequality (Gini index) in Western Europe, 1999-2010.......................
Table 2A. Robustness checks for model 1 – Effect of public education expenditure
type as a percentage of GDP on income inequality (Gini index) in
Western Europe, 1999-2010..................................................................................
Page
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25
31
43
44
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1
Introduction
Income inequality is not an issue central to underdeveloped or developing countries
alone. Following the worldwide financial crisis in 2008, it is one of the most prominent issues in
the developed countries of the world. Even in recent years, income inequality has been complicit
in the galvanization of protests in Greece, the United Kingdom (UK), Spain, Portugal, France,
and other locales throughout Europe. The United States’ (US) originated ‘Occupy Wall Street
(OWS)’ movement has spurned a protest-type that may portend future debates on the issue. In
2011, OWS and affiliated protests took place in over 900 cities in Europe, North America, Asia
and Africa (Adam, 2011). Although recent events have emphasized the problem, the rise in
neoliberalism has long driven levels of income inequality.
Neoliberal macroeconomic policies have been complicit in increased levels of inequality
in the US and Europe. Emerging neoliberalism and the rise of inequality since the 1970s has
been tied to the decline of the welfare state (Coburn, 2000). Market inequalities are often offset
by strategy implicit of welfare policies. Lessening the impact of social safety nets affects the
income distribution as well as aspects of social cohesion and health. Daly and Duncan, et al.
(1998) declare, “political units that tolerate a high degree of income inequality are less likely to
support the human, physical, cultural, civic, and health resources needed to maximize the health
of their populations” (p. 319). Labor market policies, social welfare measures, and the
decommodification of ‘public goods’ are central tenets of the welfare state that work to relieve
inequality (Coburn, 2000).
2
Greater calls for welfare reform in recent years pose a particularly difficult problem for
some policymakers. Historically the development and implementation of policies associated with
the welfare state have been charged with alleviating issues related to income inequality.
However, as highlighted in the previous paragraph, neoliberal policies can limit movement on
income equality. Furthermore, recent austerity measures in Europe are another obstacle to
promoting welfare improving measures.
One institution that is seemingly divorced from the welfare state is that of noncompulsory
education. Policies related to public funding of education may hold a cornerstone of state
funding that neo-liberals are willing to embrace. Noncompulsory education may secure further
acceptance as it is instrumental in increasing human capital and promoting competitiveness
internationally.
This paper intends to highlight the both the determinants of income inequality and the
positive outcomes that arise in response to its alleviation. Furthermore, education has long been
linked to policies that advance equality at the national level. The analyses are conducted with
the idea that increased funding for higher levels of non-compulsory education, particularly
tertiary education, is correlated with more equal societies. Similar effects are absent in the
developed world for lower levels of noncompulsory education (i.e. upper secondary education).
The results are then utilized in order to prescribe policies, which can further underscore the
importance of higher education as a tool for easing income inequality. These policies call for
increased focus on equity at the point of access for tertiary education as well as a desire to
achieve overall efficiency in the public provision of funding.
3
Literature Review
Income Inequality
There have been various studies conducted on the subject of income inequality and
economic growth. More attention has been paid to the combined topic in recent years, and
investigations have yielded sometimes-differing results. Furthermore, the effect of income
inequality on political stability, public health and crime has received some attention. This
section intends to convey the various outcomes of the interactions listed here.
In a well-cited study, Persson and Tambellini (1994) used two cross section analyses; one
utilized historical evidence (dating back to 1830) from nine countries (Austria, Denmark,
Finland, Germany, the Netherlands, Norway, Sweden, and the US), and the other was a panel
analysis of 56 countries in the postwar period. They came to a conclusion that income inequality
negatively affects growth “The main theoretical result is that income inequality is harmful for
growth, because it leads to policies that do not protect property rights and do not allow full
private appropriation of returns from investment” (Persson and Tambellini, p. 617). The authors
explain that greater equality within a society decreases demand for redistributive policies, and
since redistributive policies have long been thought to inhibit growth, reduced income inequality
can lead to increased growth. Highlighting the reverse causality problem inherent in these
analyses, Iversen and Soskice (2009) explain, societies that redistribute more are also more
equal. Additionally, several researchers have expressed doubts with Persson and Tambellini’s
findings (Sylwester, 2002).
4
In a report written for the International Monetary fund Berg and Ostry (2011)
acknowledge that the economy is very complex and sometimes inequality may be good for
growth, however, they argue that for desirable long-term growth patterns, a lower magnitude of
income inequality is desired. They write, “too much inequality might be destructive for
growth…may amplify the potential for financial crisis, it may also bring political instability,
which can discourage investment” (Berg and Ostry, p. 4).
The idea that income inequality can perpetuate political instability is not a new one.
Alesina and Perotti (1996) established an inverse relationship between income inequality and
investment in 71 different countries. Higher income inequality raises socio-political instability,
which in turn decreases investment within a country (Alesina and Perotti, 1996). Therefore, there
exists an economic incentive to decrease income inequality at the national level. What is more,
the ability to react to external shocks may be hindered by political instability (Rodrik, 1999).
Finally, Berg and Ostry (2011) cite research conducted by Berg and Sachs (1988), which found
the ability of unequal societies to prevent severe debt crises in the 1980s was severely
handicapped. Such adverse conditions are likely due to the interaction of a variety of elements.
For instance, unequal societies may be intermediately developed, and in turn are plagued by
imperfect credit markets (Besley, 1994). Nevertheless, it seems political factors are influenced
by inequality in much the same way that economic indicators are.
Another concern that has long been linked to rates of income inequality is incidence of
crime. Two cross-country analyses have come to the conclusion that income inequality as
measured by the Gini index is an important determinant of crime. Fajnzylber, Lederman et al.
(2002) discovered that income inequality is a vital component in driving crime rates over time
and across different countries. In another article published the same year, the same researchers
5
concluded, “income inequality…has a significant and positive effect on the incidence of crime”
(Fajnzylber, Lederman et al., p. 25). The desire to lower crime rates at a societal level should
give policymakers the upper hand when prescribing strategies directed at ameliorating poverty
conditions.
Figure 1. Effect of Increase of Different Factors on Growth Spell Duration
Sources: Berg, Ostry, and Zettelmeyer (2008) and Berg and Sachs (2008)
A compelling argument for the reduction in income inequality in terms of growth is
posited by Berg, Ostry, et al. (2008) (see figure 1). The height of each bar represents the result
of different magnitude changes in each variable1. For instance, trade openness signifies a
dichotomous variable developed by Wacziarg and Welch (2008) in which a value of 1 indicates
that all trade has been liberalized and 0 indicates any other level of trade liberalization. When
the trade openness variable equals 1, it is associated with a 45 percent longer growth spell
duration (Berg & Ostry, 2011). The level of the income distribution bar indicates an almost 50
1
The variables representing income distribution, political institutions, trade liberalization, exchange rate competitiveness,
external debt, and foreign direct investment (FDI) are graphed according to their role in percent change in expected growth
duration.
6
percent longer growth spell duration. The increase in duration is due to a 10 percent decrease in
income inequality. Income distribution has a larger effect on growth duration when compared to
the variables; political institutions, trade openness, exchange rate competitiveness, external debt,
and foreign direct investment2. Berg and Ostry (2011) write “that income distribution survives
as one of the most robust and important factors associated with growth duration…[it] is thus a
more robust predictor of growth duration than many variables widely understood to be central to
growth” (p. 13).
Ravallion (2009) has written, as an example, that Brazil has implemented progressive
social policies and market-directed reforms focused solely on poverty reduction. Building off of
what was presented in the previous section, Berg and Ostry (2011) write “[statistical] estimates
would suggest that the resulting decline in Brazil’s Gini would, other things equal, increase the
expected length of a growth spell by some 40 percent” (p. 13). Increasing economic growth
magnitude and duration is an attractive means to pushing policy that deals with decreasing
income inequality.
Income inequality also appears to affect elements of public health at the national level as
well. Several studies have attempted to link levels of poverty to incidence of adverse health
conditions with differing results (Wagstaff & van Doorslaer, 2000; Subramanian & Kawachi,
2004). Inquiries have often been directed at overall epidemiological indicators. However, Pickett
and Williamson (2007) narrowed their research to indicators of child wellbeing in OECD
countries. In their discussion they indicate that “inequality and child relative poverty…are
almost equally and closely related to the Unicef measures of child health and
wellbeing…average income [is] unrelated to the overall index” (Pickett & Williamson, p.4).
2
Political institutions are measured in terms of “autocracy” (levels) according to the Polity IV database (Berg and Ostry, 2011)
7
Therefore, income inequality is correlated to the differentiation in child wellbeing among rich
countries and US states.
The IMF suggests that poverty reduction by means of closing the gap in income
inequality is central to establishing long run economic growth patterns. Economic concerns
coupled with the desire for political stability and overall social health and safety helps place
emphasis on poverty reduction policies. With this in mind, it would be beneficial to understand
past policies and programs initiated to combat income inequality. Furthermore, the effectiveness
of policy in the realm of poverty reduction deserves attention.
Education as a Tool for Equality
A major hurdle for any public policy is garnering enough support from the polis to allow
for its relatively long-term survival. This is no different for government expenditure on
education, whether it is compulsory or not. Increased support for public education eventually
allows for an increase in human capital, which helps to lower income inequality on its own
(Schultz, 1963). Furthermore, several have argued that public support for education lowers the
levels of income inequality over time (Saint-Paul and Verdier, 1992; Eckstein and Zilcha, 1994;
Zhang; 1996 ; Sylwester, 2000) Education may be utilized to help alleviate conditions associated
with high income inequality levels.
While support may result in a positive effect on policy duration, it is important to
understand that other factors play a role in the level of expenditure allocated for public
education. Sylwester (2002) identifies the fact that governments are under increasing pressures
to distribute scarce resources among a variety of priorities such as; national defense,
infrastructure, social programs, education, etc. Controlling for these variables amongst others,
8
Sylwester (2002) was able to conclude that when countries set aside more resources for public
education (measured as a percentage of GDP), they experience lower levels of income inequality
in subsequent years. Previous research has suggested that education expenditure is not correlated
with economic growth (Easterly and Rebelo, 1993; Sylwester, 1999) so it may be that education
expenditure is important in other aspects, notably those that reduce income inequality (Sylwester,
2002). Integrating this conclusion with that of a previous section about income inequality’s
effect of duration of economic growth spells, and one could deduce that income inequality may
act as an intermediary between public education expenditure and economic growth.
As it has been established that education expenditure can help decrease the distance
between income distributions, how does access to education play a role? Sylwester (2000)
developed a model in which it was observed that income inequality was lowered if individuals
had sufficient resources to bypass employment and pursue education. This would suggest that
credit markets are a major constraint in human capital investment. Naturally, those at the lower
end of the income distribution would have a more difficult time securing such circumstances,
due to lack of access or lack of knowledge on where to access credit. Building on this argument,
Sylwester (2002) writes: “[if those] are too poor to attend school, then promoting public
education can actually cause the distribution of income to become more skewed since the poor
are taxed for revenue but do not enjoy the benefits of the public education system” (p. 44).
The problem of access will obviously be more pronounced in poorer countries as
government policies often favor richer populations. Holsinger (2005) sums up the issue here:
The bias toward the rich in government spending on education may be of relatively small
consequence in wealthy nations whose citizens have a wide range of education options or
where quality rather than access is the principal victim of unequal public spending (p.
298)3.
3
This bias can be removed if the focus is solely on rich (OECD) countries.
9
It is assumed that there are many educational opportunities and programs that are accessible to
those individuals with lower income levels in more developed countries. This does not discount
the plight of the poor and policymakers need to be mindful of access issues when deciding to
allocate more spending to educational ventures.
Moving on from issues related to all education levels, upper secondary and tertiary levels
are then the focus. These levels will likely experience less participation, as they are not
generally required. If access is achieved, what makes noncompulsory education economically
viable? Is there a difference in returns between upper secondary and tertiary level completion?
The emphasis is then shifted from accessibility to attainment
Various OECD governments possess incentives to reach mass higher education targets.
This is due to the increased economic pressures relating to technology change, international
competition, and globalization (Clancy & Goastellec, 2007). There is a desire at the national
level to develop advanced human capital accumulation in order to remain competitive
worldwide. The European Union’s (EU) Lisbon objectives set the benchmark of at least 40%
tertiary attainment for the 30-34 year old cohort by 2020 (Commission of the European
Communities, 2009). Government education policies have shifted in order to increase
accessibility to previously underrepresented groups. This change has influenced participation in
noncompulsory education across countries. Students enrolled in tertiary education grew by
nearly 400 percent between 1970 and 2000, and by 2025 is expected to reach 150 million
worldwide (World Bank, 2000; Clancy and Goastellec, 2007).
OECD’s 2011 publication “Education at a Glance,” discusses the differences between
individuals that attain upper secondary education versus those that go on to complete a tertiary
10
level program. First off, the authors conclude the following when discussing higher-level
education fulfillment:
In all OECD countries, individuals with a tertiary-level degree have a greater chance of
being employed than those without such a degree; and higher education improves job
prospects, in general, and the likelihood of remaining employed in times of economic
hardship (p. 116).
Unemployment and income inequality effects are likely to emerge for those with greater
educational attainment. In fact, in 2009, the unemployment rate among OECD countries for
persons with a tertiary education sat at 4.4 percent while those with as much as an upper
secondary education were unemployed at a rate of 6.8 percent (OECD, 2011). This figure
reaches above 10 percent4 when looking at the population without as much as an upper
secondary education. Therefore, higher attainment is associated with lower unemployment
levels across the board. Furthermore, tertiary achievement is linked with higher rates of full time
employment than any other education level (OECD, 2011).
Naturally, it would be of interest to understand the differing levels of pay between
different attainment levels. The OECD (2011) reports that a person with a tertiary education can
expect to make 50 percent more than someone with an upper secondary education5. This
earnings premium is higher when compared to lower levels of educational completion. Earnings
premia “are typically defined as the percentage difference between the mean labor earnings of
people with different schooling levels” (Peracchi, 2004, p. 1). Returns to higher education are
pronounced and positive when compared to other levels of education. Perhaps increased
attainment of higher levels of education in the overall population of a country can cause income
4
5
On average for OECD countries (OECD, 2011)
On average for OECD countries (OECD, 2011)
11
inequality to decrease. Of course, as was previously presented in this paper, access to such
opportunities is required.
As increased public expenditure can help alleviate issues related to educational access
and ultimately drive attainment, how does spending at certain levels affect income inequality?
When speaking to recommendations for further study Sylwester (2002) writes that although it’s
known that education expenditure can help decrease income inequality, it would be beneficial to
understand which targeted level of expenditure is most effective. Furthermore, “this issue is
important if one is to better understand how to best allocate resources within education itself” (p.
49).
Hypotheses
The current study aims to answer the question posed by Sylwester (2002) and ultimately
the results hope to influence policy recommendations related allocation of public expenditure
within education. Using the previous research on social expenditure, particularly education
expenditure, and its effect on income inequality, the following hypotheses were formed:
H1: Increased public expenditure on tertiary education is expected to correlate with lowered
income inequality at the national level.
H2: The relationship between social expenditure levels on tertiary education and income
inequality is expected to be more pronounced than the relationship between social expenditure
levels on upper secondary education and income inequality if the sample is restricted to the
OECD. This is due to the fact that since secondary education is more or less universal for OECD
12
countries, it does not command the same wage premium as higher education, which is more
variable. The level of expenditure on education levels will reflect commitment at the national
level to decreasing income inequality.
Data
In the analysis, cross-sectional, time series data for 35 European countries from 1999 to
2010 was gathered. The variable selection process is listed in the following sections with
explanations as to their inclusion. Although data was originally collected for 35 European
countries, only 20 countries contained enough data to be considered in the time series analysis.
Of those countries, fifteen were ultimately used. The focus was shifted to include only data from
Western European countries. This was due the following reasons; 1.) data for former communist
countries (Eastern Europe) is fairly unreliable until the early 2000s; 2.) development in Western
Europe is more homogenized than in the East; 3.) the results produced are roughly the same; and,
4.) most welfare and educational theory rests on the assumption of highly developed or mature
welfare states, which the West has and the East does not.
Unit of Analysis
Fifteen European member countries of the Organization for Economic Cooperation and
Development are used for this study. The countries include; Austria, Belgium, Denmark,
Finland, France, Germany, Greece, Ireland, Italy, the Netherlands, Norway, Portugal, Spain,
Sweden, and the United Kingdom. They were chosen because they are representative of Western
European postindustrial nations and data is readily available for the years used in the analyses
(1999-2010).
13
Dependent Variable
The dependent variable for both analyses is the Gini coefficient (or index). The Gini
coefficient for each year from 1999-2010 is provided by the Eurostat of the European
Commission. The Gini coefficient represents within-country income inequality. Eurostat uses
equivalized disposable income for measuring income inequality. Equivalized disposable income
defined as “the total income of a household,
after tax and other deductions, that is available
Figure 2. Visual Representation of Gini Coefficient
Source: Wikipedia (2012)
for spending or saving, divided by the number of
household members converted into equalized
adults” (Eurostat, 2012). The income measure
also includes all social transfers. Equivalizing
members of a household involves adding weights
as determined by the OECD according to age.
The value used as the Gini coefficient is
the area between the line of equality and the Lorenz curve (see figure 2). This area (A) is
divided by the whole area underneath the line of equality (the triangle A+B) to produce the
coefficient (A / A+B). The Lorenz curve runs along plot points representative of wealth
accumulation as measured by quartiles of the country’s population. The poorest quartile of the
population is represented on the left side of the graph and the quartiles move up in wealth from
left to right, ending with the richest quartile on the far right side of the graph. The Lorenz curve
connects all four points. The line of equality represents perfect income inequality. For instance,
14
if everyone had the same income level, the Lorenz curve would become a straight line at the line
of equality. Correspondingly, a Gini coefficient of zero represents perfect income equality
whereas a coefficient of 100 would represent that all income was held by one individual. The
higher the Gini coefficient, the higher the inequality (see figure 3).
According to table 1, the Gini coefficient varies between 216 and 38.17 for the fifteen
countries studied across the years 1999-2010. The mean value for the dependent variable is
28.85 for the 157 observations. The observations indicate missing Gini coefficients for thirteen
yearly observations across the entire dataset.
Table 1. Descriptive statistics – dependent variable 1999-2010
Variable
N
Mean
Std. Dev.
gini
157
28.85
3.87
6
7
Denmark 1999.
Portugal 2005.
Min
21
Max
38.1
15
Independent Variables
The two independent variables that are of most interest in the analyses are the levels of
social education expenditure for non-compulsory education levels. Primary and lower secondary
education8 levels are compulsory and are of little interest to this study as their levels of funding
are likely higher and representative of the state’s adherence to welfare state compliance9. These
education variables represent the independent variables of interest and the remaining variables
are considered control variables.
Social expenditure on education variables used in the analyses include levels of
expenditure on tertiary (see figure 4) and upper secondary education10. These variables are
measured as a percentage of yearly gross domestic product (GDP) for each of the fifteen nations.
These values were generated by dividing the yearly government expenditure for each of tertiary
and upper secondary education (in millions of national currency) by the yearly GDP figures (in
millions of national currency). Both GDP and the education expenditure numbers were provided
by the OECD database. The emphasis on these variables is informed by the recommendation of
Sylwester (2002).
Welfare state redistribution activity is captured by the variable that uses social benefit
distribution. The social benefit variable is measured as a percentage of GDP. The variable is
provided by Eurostat and does not include social transfers in kind. The benefits represent cash
transfers made to households to relieve them of certain risks and are paid out by “social security
funds, other government units, non-profit institutions serving households (NPISHs), employers
8
9
A large amount of missing data for lower secondary education, likely due to country-specific reporting practices.
Models including public expenditure for primary education see appendix table 1A.
10
Upper secondary education (ISCED 3) corresponds to the final stage of secondary education in most OECD countries.
Instruction is often more organized along subject-matter lines than at ISCED level 2 and teachers typically need to have a higher
level, or more subject-specific, qualifications than at ISCED 2. The entrance age to this level is typically 15 or 16 years (OECD,
2011)
16
administering unfunded social insurance schemes, insurance enterprises or other institutional
units administering privately funded social insurance schemes” (Eurostat, 2010). Social benefits
represent the largest component of social protection expenditure and capture activities of the
wider welfare state. Welfare redistribution is highly correlated with poverty reduction across
nations and should influence the dependent variable (Moller, Huber et al., 2003)11.
Equality in education is captured partly by the variable that measures the percentage of
female involvement in tertiary education levels 5 and 6. The International Standard
Classification of Education (ISCED) defines level 5 to include both theory-based traditional four
year university programs and technical or vocational programs that typically require 2 years to
complete. ISCED level 6 programs are described as research programs that require at least 3
years on top of 4-year level 5 programs and lead directly to the award of an advanced research
11
Huber, Ragin and Stephens (1993) explain that high expenditures do not always promote high equality—Christian Democratic
welfare states have high levels of social insurance that are not redistributionary in nature.
17
qualification (e.g. Ph.D.). The variable was provided by Eurostat and was included to control for
gender equality in higher education. It was hypothesized that greater equality in education may
lead to greater income equality over the period 1999-2010.
Demand for advanced education by industry was controlled for by including the variable
that measures the percentage of total labor force (ages 25-64) that is employed in fields of
research and technology. In other words, the total amount of human resources in science and
technology out of the economically active population in the age group specified is represented.
Since science and technology fields generally require higher rates of education, it is hypothesized
that a higher percentage of this variable will correspond to more higher paying jobs amongst the
general population resulting in decreased income inequality.
Unemployment rate is used as a control variable because it has been shown to predict
levels of income inequality in the past (Moller, Huber et al., 2003). Furthermore, Gustafson and
Johannsson (1999) explain, “unemployment is more likely to hit those at the bottom of the
income distribution harder than others, therefore unemployment has an inequality-generating
effect” (p. 587). Since Eurostat provides unemployment rates for each month of the year, the
unemployment rate for June was used for each country for the years 1999-2010. This was not
done for any reason other than June being at the relative midpoint of the calendar year.
Political variables pulled for the analysis include cabinet composition data at the country
level. Three cabinet composition variables were gathered to determine their effect on both the
dependent variable and the significance of the independent variables of interest to this study
(tertiary and upper secondary expenditure). The variables represent percentage of parliamentary
seats of all governmental parties, weighted by the number of days the government was in office
in a given year (Armingeon, Careja et al., 2012). The cabinet composition variables represent
18
right-wing parties, left-wing (or social democratic) parties, and center oriented parties12. This
data was provided by the University of Bern’s Comparative Political Dataset III (CPD III).
Political variables are included as previous research as suggested that partisanship has a marked
effect on levels of welfare state redistribution and income inequality (Esping-Andersen, 1990;
Moller, Huber et al., 2003), with right-wing parties redistributing less and left-wing parties
redistributing more.
Trade union membership is controlled for in the analyses as it provides a measure of
employment and wage protection. The data on trade union membership is provided by Eurostat.
The variable indicates the percentage of wage or salary earners that are members of a trade union
out of all wage and salary earners in a nation. It is hypothesized that higher trade union
membership offers another level of protection that would inhibit growing rates of income
inequality via wage compression.
Educational attainment is captured in the variable that gauges tertiary education level
completion rates. The data is available from Eurostat and is the percentage of the total
population of 25-34 year olds that have completed a tertiary level education. Since the analyses
is concerned with the years 1999-2010, the age group used would present the best chance of
capturing effects of completion of tertiary education over the time period used. This variable is
hypothesized to act much in the same way on the dependent variable as the percentage of human
resources employed by science and technology measure. Therefore, it is expected that the higher
the percentage of the population that have completed tertiary level programs, the lower the
discrepancy between income distributions.
12
The party variables are highly correlated, so only one is used in the analysis.
19
In his seminal book Esping-Andersen (1990) defined three separate welfare regimes13;
liberal, corporatist-statist, and social democratic. The liberal welfare regime relies on meanstested assistance. Therefore, social benefits are generally ascribed to those in the working class
with lower incomes. Countries that subscribe to the liberal welfare regime include the US,
Canada, and Australia (Esping-Andersen, 1990). The liberal welfare regime is likely to exhibit
higher levels of inequality and serves as the reference category for the welfare state dummy
variables in the random effects regression models.
Corporatist-statist or conservative welfare regimes were generated to cater to the postindustrial class structure. Therefore, redistributive properties of this regime are negligible as the
state’s emphasis lies with upholding class differences. The Church14 has played a particularly
large role in the shaping of the regime structure. The state only interferes within in a family unit
if all private resources have been exhausted. The corporatist-statist regime is adhered to in
countries such as Austria, Germany, France, and Italy (Esping-Andersen, 1990). The
conservative welfare regime variable is likely to exhibit higher income equality levels than the
reference category, liberal welfare regime.
The social democratic welfare state has extended social rights to include both the
working class and the middle class. The working class experiences the same perks and benefits
as the middle class. There is a type of universalistic solidarity that Esping-Andersen (1990)
summed up succinctly here; “all benefit; all are dependent; and all will presumably feel obliged
to pay” (p. 169). Countries that utilized the social-democratic structure are well known to be
Sweden, Denmark, Finland, and Norway. Countries classified as social democratic welfare
13
Since welfare regimes are all encompassing in terms of social expenditure, these variables are not used in the models that
control for social benefits or in fixed effects models (as they absorb regime effects across countries). Using welfare regime
variables allows for the inclusion of Germany in the analyses.
14
In reference to the Christian democratic welfare regime.
20
regimes are hypothesized to have lower levels of income inequality than both conservative and
liberal welfare regimes.
Types of skill formation or skill formation regimes were captured with dummy variables
to highlight vocational education or training (VET) by country. These variables were included to
cover functional vocational systems. The definitions of separate skill formation typologies was
influenced by the chapter contributed by Busemeyer and Trampusch (2012). Liberal skill
formation is defined as a system which attributes skill formation to markets and the general
education system15 (Busemeyer and Trampusch, 2012). Advanced VET is acquired through
community colleges and other schools that require student tuition fees. These schools often have
“limited institutional linkages to the labor market” (Busemeyer and Trampusch, p. 13). The
liberal variable in this case is used as the reference dummy variable.
The statist skill formation system indicates heavy involvement of the government in VET.
As Busemeyer and Trampusch (2012) highlight, “public policymakers are much more committed
to supporting VET as a viable alternative to academic higher education” (p. 13). Although
advanced vocational training is heavily weaved into upper secondary education, there is little
participation from employers16. Therefore, statist skill formation systems experience heavy
public investment in VET with relatively low firm involvement (Busemeyer and Iversen, 2012).
This system is expected to represent lower levels of income inequality than the reference
category as it implies heavy support for advanced education from the public sector.
Finally, collective skill formation systems are utilized to control for their effect on income
inequality. They are defined as regimes with a “strong commitment of both the state and firms to
invest in the formation of vocational skills” (Busemeyer and Trampusch, 2012, p. 14). This is an
15
16
Basic vocational training is offered in upper secondary levels.
“…although policymakers have repeatedly tried to expand the workplace-based components of vocational training, even in
Sweden” (Busemeyer and Trampusch, 2011, p. 14).
21
important difference in that statist regimes have heavy public involvement resulting in reluctance
from firms, and segmentalist17 regimes enjoy high employer involvement with little state
participation. Therefore, cost for VET is shared between the public and private sector,
potentially elevating the availability of advanced education programs. When compared to the
reference category, liberal skill regime, collective systems are expected to exhibit greater
equality.
Table 2. Descriptive statistics - independent variables 1999-2010
Variables
N
Mean
Std. Dev.
tertiary expend.
147
1.40
0.49
secondary expend.
138
1.41
0.49
social benefits
180
15.23
2.87
women in tertiary ed.
164
54.59
3.01
HR share of sci/tech
169
39.79
8.65
unemployment rate
180
7.13
2.86
right cabinet comp. 165 35.71 39.06 trade union mem. 170 38.29 21.27 tertiary attainment 161 32.70 9.24 Min
0.75
0.63
7.70
47.40
17.10
2.50
0.00 7.62 10.60 Max
2.71
2.78
21.10
61.10
51.90
20.30
100.00 80.63 48.20 Table 2 lists the descriptive statistics for the continuous variables used in the analyses.
Public tertiary expenditure and upper secondary expenditure have a standard deviation of .49
with similar values at the low and high end of their distribution. Other variables fluctuate to a
greater magnitude across countries. For instance, the unemployment rate ranges from 2.5% to
21.1%, which captures the rate at different points in time18 for the Netherlands and Spain
respectively. Right cabinet composition spans between 0% and 100%. A value of 100%
indicates a completely right-partisan government. Other values of right cabinet composition
indicate government arrangements wherein left and/or center leaning members comprise a
17
Segmentalist skill formation systems are covered in this analysis as none of the countries used fell under this category. Japan
is an example of a segmentalist skill formation regime.
18
2.5% unemployment rate for the Netherlands in 2001; 20.3% unemployment rate for Spain in 2010.
22
portion of cabinet positions. The remaining variables can be understood in a similar way to
those discussed in this paragraph19.
Methods
Both fixed effects and random effects20 regression models were used to test the effect of
social education expenditures on income inequality as measured by the Gini coefficient. There
were six models generated in total to assess the different results of public spending on tertiary
education and public spending on upper secondary education. Spending at these levels was
desired as tertiary education is wholly non-compulsory in all fifteen countries studied, and upper
secondary education is mostly non-compulsory. Primary and lower secondary education are
compulsory across the same countries in Western Europe.
Data for the fifteen countries was declared time series data for the years 1999-2010 and
was found to be strongly balanced. The variables used in the baseline model exhibited
heteroskedasticity, and this was corrected by using country-clustered standard errors in the final
models.
The baseline model also exhibited serial correlation, therefore a lagged dependent
variable was introduced as a control. Finally a Hausman test determined that the model should
use fixed effects over random effects. The final baseline model was established and robustness
checks were conducted to determine each of the final fixed effects models (models 1 & 2).
Models 3, 4, 5 and 6 utilize random effects, as dummy variables are introduced. Random effects
are used for these models as much of the fixed effects are absorbed in the different regime
dummies, which are classified by country21
19
20
21
Refer to the definitions of independent variables that precedes this section.
The fixed effects and random effects models used country=clustered standard errors (CCSE).
The models employed generalized least squares (gls).
23
Findings
Each of the six models tests the determinants of income inequality for fifteen countries22
(see table 3). Models 1 and 2 examine the differences in significance and coefficients of public
tertiary education expenditure and public upper secondary education expenditure. The models
include the lagged dependent variable and control for; social benefits (other than social transfers
in kind) percentage of women in tertiary education; share of human resources in either research
or technology industries; the June unemployment rate for each year; right-wing cabinet
composition; percentage of workers with trade union membership; and percentage of secondary
students that move on to tertiary programs.
Models 3 and 4 illustrate comparisons between funding for tertiary and upper secondary
education. These models introduce the welfare state regime dummy variables. Since the liberal
variable is the reference, only conservative and social democratic welfare regimes are illustrated
in the models. The following variables were dropped due to their close correlation to aspects of
Esping-Andersen’s (1990) welfare regimes; social benefits (other than transfers in kind); women
in tertiary education; human resources in science and technology; the unemployment rate;
political cabinet composition; and trade union membership. Finally, the only other independent
variables to remain from the previous models are tertiary attainment levels and the lagged
dependent variable.
The final models (5 and 6) offer comparisons between funding for the two levels of
education while controlling for skill formation types. The skill formation regime dummy
variables are directed by typologies put forth by Busemeyer and Trampusch (2012). All control
variables used in the general models (models 1 and 2) were dropped except for the lagged
22
Germany was not included in models 1 and 2 due to missing data. Portugal, Spain and Greece were not included in models 3
and 4 because they are not characteristic of Esping-Andersen’s (1990) welfare regime typologies.
24
dependent variable. The analyses use the expenditure variables for upper secondary and tertiary
education and control for country-level skill formation systems.
Model 1 – Fixed Effects
This model corresponds to the original interest in the effect of tertiary education
expenditure on income inequality (see table 3). Only two variables were significant in the
model, tertiary education expenditure and right cabinet composition. The tertiary expenditure
inequality. The beta coefficient indicates that a one percent increase in tertiary education
expenditure is correlated with a reduction of 2.234 points in the Gini coefficient. This is due to
the negative sign of the beta coefficient. The standard deviation for the Gini coefficient over 14
countries is 3.93. It is difficult to establish causality in this case, however the correlation can be
reported.
The variable used to right cabinet composition is statistically significant (t = 2.37) at the
0.05 significance level (95 percent confidence interval). It was expected that right-wing cabinet
composition would correlate with a higher level of income inequality as right governments tend
favor less social benefit redistribution. According to the model, a one percent increase in right
cabinet composition corresponds to an increase in the Gini coefficient of .00784 points.
Therefore, a purely right partisan cabinet23 indicates an increase in the Gini index of .784 points.
The magnitude of the tertiary education expenditure variable is greater than the effect of the right
cabinet variable.
Three variables, although not significant, show surprisingly different beta coefficient
signs than were hypothesized during variable selection. Social benefits minus transfers in kind
23
Variable value of 1.00, or 100%.
25
Table 3. Effect of public education expenditure type as a percentage of GDP on income
inequality (Gini index) in Western Europe, 1999-2010.
(model 1)
(model 2)
(model 3)
(model 4)
Fixed-Effects
Fixed-Effects
Welfare Regimes
Welfare Regimes
-2.234+
(-2.00)
___
-2.007*
(-2.36)
___
___
-0.271
(-0.27)
___
0.170
(0.56)
0.257
(1.71)
0.0941
(0.62)
___
___
women in
tertiary ed.
-0.0953
(-0.44)
-0.193
(-0.82)
___
___
HR share of
sci/tech
-0.0770
(-0.68)
-0.0293
(-0.23)
___
___
unemployment
rate
-0.240
(-1.37)
-0.228
(-1.09)
___
___
right cabinet
comp.
0.00784*
(2.37)
0.00425
(1.73)
___
___
trade union
mem.
-0.0366
(-0.50)
-0.0475
(-0.66)
___
___
tertiary
attainment
0.0609
(0.76)
0.0527
(0.57)
0.00487
(0.48)
-0.0152
(-0.73)
lagged DV
0.174
(1.57)
0.167
(1.43)
0.600**
(6.01)
0.668**
(5.87)
conservative
WS
___
___
-1.249+
(-1.91)
-1.445+
(-1.70)
social dem. WS
___
___
-0.605
(-0.61)
-2.191*
(-2.19)
32.32*
(2.30)
91
7.492**
36.26*
(2.37)
84
2.892*
tertiary
expend.
secondary
expend.
social
benefits
_cons
N
F
t statistics in parentheses
+
p < 0.10, * p < 0.05, ** p < 0.01
χ2
14.68**
(3.79)
77
267.87**
10.89**
(2.66)
77
365.49**
26
and the percentage of 25-34 year olds who have completed higher education have positive
standard coefficients. Despite the lack of statistical significance, the signs indicate that higher
percentages of each variable correspond to an increasing Gini coefficient. While it was predicted
that social benefits paid out would likely affect a nation’s income inequality, the standardized
coefficient carries a positive sign. It was expected that social benefits decrease income
inequality (Moller, Huber et al., 2003).
This result may mean that welfare programs are reactionary in nature. Perhaps the
relationship is different than what is capture in the model. Higher income inequality rates would
indicate higher social benefit activity. This would explain the positive sign of the beta
coefficient in this case. In fact Huber and Ragin et al. (1993) touched on this very point when
describing Christian Democratic (or conservative) welfare regimes.
An increase in tertiary attainment levels was expected to mitigate inequality levels.
However, tertiary attainment also has a positive sign. Even more perplexing, the unemployment
rate carries a negative sign. This would point to a scenario where an increasing unemployment
rate would actually lower income inequality. In fact, it is hypothesized that the more of an
unemployed population, the greater the income inequality (the higher the Gini coefficient).
These are likely not concerning to this study as these variables proved to be insignificant.
However, they exhibit interesting activity that could be pursued in further research.
The remaining variables in the model were also not found to be significant. However, the
beta coefficient signs follow the previously stated hypotheses. Higher trade union membership
carries a negative beta coefficient. It follows that increased income protection strategies would
in fact lower income inequality. Secondly, an increased share of workers in the science or
27
technology field exhibits a negative sign. Increased human capital formation in high-skilled
professions likely drives down income inequality. Although not significant in this model, these
variables follow the expected trend highlighted in previous literature.
The F statistic for the time series regression (7.492) indicates that the independent
variables are jointly significant at the 0.01 significance level (99 percent confidence level).
Model 1 is the result of 91 combined observations across the 14 separate panels used in the
analysis. The first hypotheses (H1) related to outcomes of the overall analysis is supported by
this model. Robustness checks for this model are illustrated in the appendix to this paper (see
appendix table 2A).
Model 2 – Fixed Effects
Model 2 is similar to model 1 in that it utilizes many of the same control variables (see
table 3). The main difference between the two models is that social expenditure on upper
secondary education replaces social expenditure on tertiary expenditure in the analysis. This is
mainly done to illustrate the difference between the two variables and to satisfy the conditions
required by the second hypothesis (H2).
The beta coefficient for the expenditure on upper secondary education is not significant
in this model. The sign of the coefficient indicates that increased social expenditure on upper
secondary education may be associated with increased levels of income inequality. This is
difficult to explain and would be unwarranted due to the insignificance of the variable in this
analysis. None of the eight control variables (including the lagged dependent variable) is
significant in model 2. The signs of the insignificant coefficients also stay the same.
28
The model is jointly significant at the 0.05 significance level (95 percent confidence
interval). The F statistic of the model is 2.892. There are 98 combined observations across the
14 country panels in the model. This model, when compared to model 1, supports the second
hypothesis (H2). It appears that tertiary education expenditure is correlated more with income
inequality reduction than expenditure on upper secondary education.
Upper secondary education is partially universal across the countries used in the analysis.
That is, upper secondary education, unlike lower secondary education and primary education is
not wholly compulsory across Western Europe. Furthermore, for those electing to participate in
upper secondary education while forgoing tertiary education or advanced vocational training24 do
not experience the same wage premia than those that complete higher levels of education.
Therefore, financing upper secondary education may not result in the same inequality alleviating
effects that are experienced as a result of funding a noncompulsory entity such as tertiary
education.
Model 3 – Welfare Regimes
Model 3 is similar to model 1 in that tertiary expenditure is the independent variable of
note, however the majority of control variables have been dropped in favor of welfare regime
variables (see table 3). Esping-Andersen (1990) did not group Spain, Portugal, and Greece into
any of his three welfare regimes, therefore, they were dropped from the model. As mentioned
previously, the model controls for the following; the lagged dependent variable; tertiary
attainment; conservative welfare states; and social democratic welfare states.
24
Advanced vocational training is classified as international standard classification (ISCED) level 4.
29
The level of social expenditure on tertiary education is statistically significant (t = -2.36)
at the 0.05 significance level (95 percent confidence interval). Much like in table 1, the beta
coefficient is negative. In this case, a one-percentage point increase in percent of GDP funding
for tertiary education corresponds to a reduction of income inequality, as measured by the Gini
coefficient, of 2.007 points. The conservative welfare regime variable is statistically significant (t
= -1.91) at the 0.10 significance level (90 percent confidence interval). The sign and value of the
coefficient (-1.249) indicates that conservative welfare regimes, on average, are more equal than
the reference category, liberal welfare regimes. The literature on welfare states predicts this
outcome.
The social democratic regime variable is not statistically significant. However, the sign
of the coefficient is negative, indicating social democratic regimes are generally more equal than
liberal welfare regimes. This is also expected according to the literature, however, the
magnitude of the coefficient was expected to be larger than that of conservative welfare regimes.
The low t-statistic (-0.61) speaks to the relative weak explanatory power of the variable in this
model, therefore the perceived effects will not be pursued further.
The chi-squared value (267.87) of the model is statistically significant at the 0.01
significance level (99 percent confidence interval). Therefore, the null hypothesis is rejected.
There are 77 observations across the 12 countries used in the model. The significance of the
tertiary expenditure coefficient in the model supports the first project hypothesis (H1).
Model 4 – Welfare Regimes
Model 4 was created in the same vein as model 2. Its purpose is to serve as a comparison
to model 3 (see table 3). Both welfare regime control variables are significant in the model. The
30
conservative welfare regime coefficient is statistically significant (t = -1.70) at the 0.10
significance level (90 percent confidence level). The sign and value of the coefficient (-1.445)
indicates that conservative welfare regimes are more equal than liberal welfare regimes, on
average.
The social democratic regime coefficient is statistically significant (t = -2.19) at the 0.05
significance level (95 percent confidence interval). This is a different result from model 3 and
may suggest that there is an interaction between upper secondary expenditure and the social
democratic welfare regime variable that is not evident with tertiary education expenditure. The
sign of the coefficient (-2.191) suggests that social democratic welfare regimes experience less
income inequality than liberal welfare regimes. The magnitude of the coefficient’s value
indicates a more income equality than within conservative welfare regimes. This was the result
expected according to the literature, but the effect was not observed in the previous model.
Social expenditure on upper secondary education is not significant in the model. The beta
coefficient sign is positive and is difficult to explain. Once again its low t-statistic (0.56) signals
the coefficient’s low explanatory power and analysis of its effect is not merited.
Model 4 produces a chi-squared value (365.49) that is statistically significant at the 0.01
significance level (99 percent confidence level). Similar to model 3, there were 77 observations
across the 12 countries included in model 4. The magnitude of expenditure on tertiary education,
as compared to that of upper secondary education, is associated with greater reduction in income
inequality at the national level. This observation supports the second research hypothesis (H2).
31
Table 4. Effect of social education expenditure type as a percentage of GDP on income
inequality (Gini index) in Western Europe, 1999-2010.
25
(model 6)
(model 5)
Skill Formation
Skill Formation
tertiary expenditure
-1.331*
(-2.40)
____
secondary expenditure
____
-1.294**
(-2.90)
statist skill formation
-1.152
(-1.33)
-0.296
(-0.48)
collective skill formation
-0.798
(-0.93)
-0.424
(-0.56)
lagged DV
0.625**
(3.42)
0.704**
(4.73)
_cons
12.80*
(2.04)
45
828.82**
9.807*
(1.98)
45
717.46**
N
χ2
t statistics in parentheses
+
p < 0.10, * p < 0.05, ** p < 0.01
Model 5 – Skill Formation
The model was run to determine the effect of public tertiary education expenditure
controlling for state-level skill formation regimes (see table 4). The regimes capture advanced
VET programs that are otherwise absent in the previous models. The tertiary education-spending
variable is statistically significant (t = -2.40) at the 0.05 significance level (95 percent confidence
level). When controlling for skill formation systems, a one-percentage point increase in tertiary
expenditure (as a percentage of GDP) results in a reduction of the Gini index by 1.331 points.
The beta coefficient exhibits a lower magnitude in model 5 than in models 1 and 3. This could
25
The skill formation regime variables remain insignificant when the tertiary education expenditure variable is excluded from
the model.
32
be due to the fact that models 5 and 6 are limited in coverage compared to previous models26.
Therefore, they do not capture activity across all fifteen countries. However, this result supports
the first hypothesis (H1).
The skill formation dummy variables are not significant in model 5. Their beta
coefficients suggest that heavy investment in the form of statist or collective skill formation
regimes may indicate lowered levels of income inequality when compared to liberal skill
formation systems.
Model 6 – Skill Formation
The upper secondary education expenditure variable (see table 4) is significant (t = -2.90)
at the 0.01 significance level (99 percent confidence level)(. This is unexpected according to
previous model analyses. The beta coefficient (-1.294) suggests an increased expenditure of one
percentage point (as a percentage of GDP) on upper secondary education results in reduction of
the Gini coefficient by 1.294 points. This still supports the second hypothesis (H2), however, the
magnitude of difference between beta coefficients for tertiary and upper secondary expenditure is
compressed. Again, the decreased sample size required for the model analysis may not
encapsulate effects observed across all countries.
Similar to model 5, the regime variables in model 6 are not significant. The beta
coefficients for statist (-0.296) and collective skill formation regimes (-0.424) suggest higher
inequality alleviation than liberal skill formation regimes.
26
Limited to seven countries; Austria, Denmark, France, Germany, Ireland, the Netherlands, and Sweden.
33
Discussion
Several models were utilized in order to establish the relationship between public funding
for noncompulsory education and the levels of income inequality across fifteen OECD member
countries in Europe. The six models indicated that public expenditure on tertiary education is
highly correlated with income inequality. That is, an increase in expenditure for higher
education as a percent of GDP is associated with a tightening of the income distribution within a
country. Replacing the variable representing tertiary education expenditure in two of the models,
it was discovered that public expenditure on upper secondary education does not maintain
similar effects. The upper secondary education variable was significant in the final model
covering only seven Western European countries. Otherwise, public spending on upper
secondary education is not significantly correlated with the level of income inequality.
Therefore, it follows that upper secondary education’s ability to remedy levels of income
inequality does not sufficiently present itself within developed economies. Only spending on
tertiary education is significant across all models. The results suggest societies that allocate
more funding towards higher education are more equal. That is their Gini coefficients are
significantly lower across the board. Furthermore, it can be presumed that advanced human
capital accumulation is a determinant of inequality levels within Western European countries.
The evidence presented by the research offers an opportunity to affect policies directed at
education spending, particular higher education funding. These policies should be influenced by
the current economic conditions, the need for advanced human capital, and the importance of
maintaining a society with relatively low levels of income inequality. The following section
speaks to the policy implications derived from the analysis contained in this paper.
34
Policy Implications
Since lower levels of income inequality are highly correlated to public funding for higher
education across the fifteen countries studied, it would be simple to prescribe more spending.
However, governments simply do not possess the flexibility to raise subsequently greater
revenue or shift funding from other imperatives (Barr, 2004). Similarly, governments inherit a
large measure of risk when funding higher education. Poutvaara (2004) writes, “government has
to invest in the education of the young before they decide where to live, work, and pay taxes
after graduation” (p. 663). Therefore, alternatives to current funding practices are most desired.
Recently, income contingent loans have become popular in the literature as an alternative to
conventional loans.
Conventional loans borne out of need to finance human capital accrual carry heavy risks
for borrowers and lenders. The level of uncertainty for borrowers follows from the rather
ambiguous results of pursuing higher education. Variable outcomes include; failure in the
pursuit of qualifications; returns to investment are often inconsistent27; and, after achieving
graduation, the inability to sell the degree (qualification) if desired (Barr, 2004). Barr (2004)
contends that conventional loans are more appropriate for material investments and that in the
context of human capital development, they are highly inefficient.
Inefficiencies also find themselves on the side of the lender or the government in this
case. If a borrower defaults on their loan, the government cannot simply repossess the degree.
Therefore, it is expected that lenders will avoid risk by pursuing borrowers that provide security
(Barr, 2004). This places certain groups at a disadvantage, which perpetuates an inefficient loan
process. Barr (2004) writes that such practices disproportionately affect “people from poor
27
Although those pursuing higher education tend to be well informed (Barr, 2004).
35
backgrounds, women, and ethnic minorities, who may be less well-informed about the benefits
of a qualification and therefore less prepared to risk a loan” (p. 273). Since it is in the best
interest of governments to promote equity in access28, other avenues of funding education should
be explored.
Income-contingent loans provide an opportunity to increase access and limit the
shortcomings of traditional funding systems. Pressures to reform the traditional approach to
financing higher education are increasing “due to the evolution of the higher education market
structure, changes in the economic structure, demographic developments, and increased
competition within existing public service activities for finite public funds” (Vandenberghe and
Debande, 2007, p. 4)29. Furthermore, lifetime income heterogeneity validates an approach that
utilizes income-contingent repayment (Vandenberghe and Debande, 2007). The private benefits
incurred by graduates as a result of public funding for higher education warrants the
implementation of cost sharing measures30.
There are several desirable aspects of income-contingent loans over traditional loan
pursuits. Since repayment depends on income forthcoming, future low-earners are protected
from heavy financial burden31. Barr and Johnston (2010) write that income-contingent loans in
the UK increase efficiency and improve equity due in part to the following; variable fees
promote quality and strengthen competition; higher education is free at the point of use; and
programs designed to maintain students include grants from public sources. Similar elements of
the UK plan could be implemented across Europe, and for countries that already utilize an
28
29
It is understood that income equality is dependent on equity in access to higher education (Vandenberghe and Debande, 2007).
Countries typically respond by pursuing “three efficiency goals in higher education: larger quantity, higher quality, and
contained public spending “ (Barr and Johnston, 2010, p. 1).
30
The social benefits of higher education still require taxpayer subsidies (Barr and Johnston, 2010).
31
In the UK, repayment is 9% (yearly) of any income above £15,000 and loan forgiveness is enacted after 25 years (Barr and
Johnston, 2010).
36
income-contingent structure, certain components may be expanded. Furthermore, the US could
institute a similar loan program to combat high levels of graduate debt32. Cost sharing in the
public provision of higher education has the ability to raise overall expenditure. As the analysis
in this paper has concluded, higher expenditure on tertiary education is an essential component to
maintaining lower levels of income inequality.
Conclusion
The purpose of this research was to determine whether public funding for noncompulsory education levels in Western Europe demonstrate inequality-ameliorating effects in
related to income distribution. The literature indicated that a wide distribution of income
negatively affects growth, contributes to heightened levels of recorded crime, is correlated with
political instability, and has been tied to indicators of child wellbeing across countries.
Therefore, it was determined that a country would attempt to combat negative consequences with
equality raising measures. Education was identified as a conduit through which processes of
income inequality alleviation may be applied. The ability for education to raise equality was
supported by the literature.
From this conclusion, it was hypothesized that non-compulsory education had the
capability to affect income inequality at a greater magnitude than compulsory education, since
graduates of non-compulsory levels are more likely to enter the workforce with advanced
qualifications. Furthermore, funding for tertiary education levels was hypothesized to correlate
with lower levels of income inequality than funding for upper secondary education, because
graduates of tertiary programs carry larger wage premiums. Increased funding for tertiary
32
Total student loan debt in the U.S. topped $1 trillion in 2012 (Mitchell and Jackson-Randall, 2012).
37
education indicates desire for advanced human capital accumulation at the national level, which
in turn leads to decreased levels of income inequality.
Multiple time series models were run to test the hypotheses put forth. The models
controlled for separate sets of variables such as; those thought to correlate with income
inequality; Esping-Andersen’s (1990) welfare regime typologies; and finally, skill formation
regimes from Busemeyer and Trampusch (2012), which capture adherence to VET systems.
Tertiary education expenditure was found to be significantly correlated with reduction of income
inequality across all models of which it was included. Public expenditure on upper secondary
education was only significant in the model controlling for skill formation systems. However,
the beta coefficient for tertiary expenditure displayed a higher magnitude. Therefore, the
research hypotheses were satisfied across all models. Public expenditure on non-compulsory
education, particularly tertiary education, is correlated with compression of income distribution.
There is a substantial argument for fostering conditions that allow for increased advanced
human capital accumulation within nations. It can be argued that higher education is the best
way to create an environment that perpetuates such conditions. Since increased funding for
higher education is associated with lower inequality, it would be best to pursue an avenue that
expands available resources. Income-contingent loans, through cost-sharing and lowered risk,
may be the best way to navigate the domain of public finance for higher education while meeting
goals concurrent with the mitigation of income inequality.
Limitations
The availability of data conducive to time series panel data analyses is one possible
limitation. Data must exist across all variables for a particular year in order for the observation
38
to be included in the model. Although numerous data was gathered for analysis across twelve
years for fifteen countries, the highest number of observations across the six models was 9133.
This means that out of a possible 180 observations, nearly half were dropped. Some countries in
Western Europe that would have contributed to the research did not have data available for some
of the variables used. If data was missing over the entire twelve-year period, the corresponding
country was eliminated from the analysis. Finally, many of the variables chosen as controls did
not prove significant within models.
Another limitation to the analysis relates to the limited variation in the dependent
measure (Gini index) and the independent variables of interest (public funding for upper
secondary and tertiary education). Three year static averages were generated for each variable
and the fixed effects and welfare state models were rerun, however, each result proved
insignificant. This could be due to the relatively low sample size used for the analyses. When
the three-year averages were used, the sample size was condensed by a factor of three, further
compounding the limitation.
Future Research
Recommendations for future research call for the inclusion of greater measures of
funding for advanced VET programs34 besides general dummy variables. This would allow for
the comparison of possible inequality mitigating effects of public funding across upper
secondary, advanced vocational, and tertiary education levels. Furthermore, cultural variables
could add a further dimension to the research. One possible approach would control for cultural
33
34
Although there were 96 observations recorded for the primary education expenditure models in the appendix (see table 1A).
ISCED level 4 programs.
39
typologies described by Inglehart (1997)35. The analyses can also be opened up to include other
OECD countries not limited to Western Europe. Finally, private investment in non-compulsory
education and the effect on income inequality deserves attention. There are numerous potential
avenues for future research that analyzes funding structures of non-compulsory education
systems and its effect on income dispersion.
35
Confucian, Ex-Communist, Protestant Europe, Catholic Europe, English Speaking, South Asia, Latin America, Africa.
40
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APPENDIX
Table 1A. Effect of public expenditure on primary
education as a percentage of GDP on income inequality
(Gini index) in Western Europe, 1999-2010
(primary 1)
(primary 2)
Fixed-Effects
Welfare Regimes
primary
-2.363
-0.355
expend.
(-1.72)
(-0.72)
social
benefits
0.180
(0.61)
____
women in
tertiary ed.
0.225
(0.86)
____
unemployment
rate
-0.0303
(-0.17)
____
right cabinet
comp.
0.00196
(0.47)
____
trade union
mem.
-0.167
(-1.73)
____
tertiary
attainment
-0.0259
(-0.53)
-0.0198
(-0.93)
lagged DV
0.291+
(2.12)
0.742**
(8.22)
conservative
WS
____
-1.273*
(-2.49)
social dem.
WS
____
-1.575*
(-2.22)
_cons
15.84
(1.00)
96
3.09*
9.457**
(2.94)
96
790.97**
N
F
t statistics in parentheses
+
p < 0.10, * p < 0.05, ** p < 0.01
χ2
1A. Public expenditure on
primary education does not prove
to be significant in models
similar to those used in the body
of the paper. This is an
unexpected result. It could be
that the universal nature of
primary education and its
mandated (compulsory)
attendance inhibits any
significant mitigation of income
inequality across social strata.
44