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The Economic Elements of Income Inequality

BSc (Hons) Economics and Business Finance

Social School of Sciences

Student Number: 1230887

Word count: 8,114

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Abstract:

This thesis studies the relationship of certain variables towards income inequality and measures

the significance and effect of these variables have on inequality. Inequality has a negative impact

on economic growth, making it essential to be able to control it. The study is based in 21

European countries over 21 years, 1992-2012. A cross-sectional panel data is used to estimate

the effects on inequality. Also a fixed country effect is set on the regression to eliminate the

constant variance of the countries being analysed. A discussion being raised by this paper is that

whether countries in the European Union have a lower inequality compared to countries outside

the European Union and inside Europe.

Acknowledgements:

A special thank you to Dr. Corrado Macchiarelli for his very valuable help towards this paper

and to my parents for the constant support.

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Contents

Part I Introduction .................................. 4

Part II Literature Review ................................... 5

1. Education ........................................................................................ 5

2. Trade Liberation .............................................................................. 7

3. Recent Trends in Income Inequality ................................................ 8

4. Tax ................................................................................................ 10

5. Inflation ........................................................................................ 11

Part III Data ................................... 12

1. Data .............................................................................................. 12

2. Descriptive Statistics ..................................................................... 13

3. Correlation Analysis .................................................................... 14

Part IV Methodology ................................. 15

Part V Results ...................................... 17

Part XI Conclusion ................................... 20

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Part I Introduction

Income inequality is the extent to which income is unevenly distributed among a population. It is

not a pretty sight for any economy. A highly uneven distribution in income can lead to a negative

impact on economic incentives, possibly slow down growth and it simply undercuts the ideal that

we, humans, were all created as equal and should live in an egalitarian society.

In the United Kingdom, Equalitytrust shows that only 0.1% of the U.K population earns £1

million while 10% earn £79,196 and the other 90% all make, on average, £12,969. A more

surprising statistic is that the average full-time pay for a CEO at a FTSE 100 company is a

staggering £4.3 million compared to the average U.K worker who received only £26,500. The

Gini Coefficient measured income inequality to be 0.24 in 1977 and increased to 0.34 in 2012

making the U.K the most unequal developed country, in terms of income. This shows how

income inequality does have a strong effect on the economy and if inequality had not changed

since 1977 the majority of the working population in the U.K would be earning more.

Strong government intervention could diminish inequality, through their tax systems and

expenditure on its economy. Increasing progressive tax rate is a very effective system of

equaling the rich with the poor. This dissertation sets out to find to what extent certain variables

such as education, growth and tax can affect income inequality. It also discusses if low inequality

is achieved by countries in the European Union compared to European countries outside the

European Union.

According to many economists, such as Perotti (1996), income inequality has a negative impact

on economic growth. It can also play an important role on the drive in financial development and

political stability. Since inequality can interfere with factors such as economic growth and

development, it is crucial to comprehend on how to affect and control inequality so as the

economy could potentially be better off.

There are different factors that may have an effect on income distribution and different methods

of reducing inequality. In the labor market, wages will be higher for jobs that require skilled

workers that are low in supply compared to jobs that require non-skilled workers that are high in

supply. This creates income inequality, being the gap between wages earned by non-skilled

workers to wages earned by skilled workers. One of the main factors that play the central role in

shaping up income inequality is education. An increase or improvement in education, of an

economy, helps increase the supply of skilled workers hence decreasing the wage premium of

high skilled workers through the law of demand and supply. This could be adjusted until the

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demand and supply of labor reach equilibrium, meaning skilled workers receive the same wage

as non-skilled workers, and income will be evenly distributed among the population eliminating

any inequality in income. Although the point of equilibrium is impossible to reach in terms of the

real economy, as it is just a model, it does have a comparable impact towards the economy.

Along with education, there are many other factors that affect inequality from globalization to

urbanization to crime and health. However this thesis will look at influences that play its part on

inequality through only an economic perspective.

Although there are many methods used to measure inequality, the Gini Coefficient is widely

used around the world and is taken as the primary indices in many of the past literature and

studies. It uses the Lorenz Curve to represent the cumulative percentage of income distribution

on the horizontal axis versus the cumulative number of people in an economy on the vertical

axis. This graphical representation was established by Max O. Lorenz in 1905 to measure

inequality of wealth. Pros of using the Gini coefficient are that it provides availability of the data

and the popular use of the Gini coefficient as a measure of income distribution in past literatures.

However a drawback is that World Bank Data provides a limited amount of data for the Gini

Index. However, this thesis will too use the Gini Coefficient as a measurement of income

distribution.

Part II Literature Review

1. Education

Schultz (1963) states that “increasing human capital as one way to lower income inequality and

increased support for public education might be one way to accomplish this.” Increasing

government expenditure on resources such as public education could potentially lower income

inequality and this is what Kevin Sylwester (2000) sets out to examine. He uses the Gini

Coefficient to measure the income distribution after changes in public education over 20 years.

His study shows that Egypt, Ecuador, France, Turkey and Italy benefited from a 20% decrease in

the Gini Coefficient meaning over the years, improvement in public education led to a decrease

in income inequality. However, Tanzania, Sri Lanka, U.K, Australia and New Zealand all

suffered from a 20% increase in the Gini Coefficient leading to an increase in income equality.

Sylwester’s final result concluded that countries that invest in public education tend to have

lower income inequality in succeeding years although the effect will be shown over the

long-term.

Knight and Sabot (1983) argue that in the short-term there is a composition effect being the

initial increase in inequality due to an in increase in the number of educated workers. However,

over the long-term there will be a compression effect which is the decrease in income inequality

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due to increase in the supply of educated workers and the fall in the wage premium. Their

argument supports the one of Kevin Sylwester (2000).

Abdul Abdullah (2013) conducted an MRA study in order to be able to tell us the average effect

of public education on income inequality. His 64 econometric studies found him that education

reduces the gap between the rich and the poor, hence lowering inequality. His study turned out to

produce more interesting results. He found that some of his results suggest that secondary school

has a more important influence on lowering inequality than primary and higher education.

Therefore, if an economy were in need of lowering inequality then reducing the funds of

secondary education would be effective. However, it should be known that this study is not

robust.

Günther Rehme (2007) stated, through his study of 6 of the G7 countries provided by the

Luxembourg Income Study (LIS), that increases in education first increase and then decrease

growth as well as income inequality, when measured by the Gini coefficient. His conclusion

supports the study of Sylwester (2000), Knight and Sabot (1983) and Abdul Abdullah (2013).

The studies used in these journals all use income inequality as its dependent variable. The

disadvantage here is the possible cause of reverse causation. Sylwester (2003) later on conducted

another study going deeper into the research. He compares the effect on income inequality with a

greater enrolment rate in higher education. Sylwester (2003) states that “what differentiates his

study from the others listed above is the use of the change in income inequality over time as the

dependent variable instead of using the level at a point in time.” He uses the Gini Coefficient to

measure the degree of income inequality. Using 50 observations in his study which was

conducted from 1970-1990, his results concluded that there is negative relation between

enrolment in higher education and income inequality. Therefore, supporting participation in

higher education is predicted to lower income inequality. However, through which higher

education will lower inequality is still unknown.

All in all, although studies were found that education was not highly correlated with inequality, it

can be concluded, with the support of a few economists, that past literature has found that there

does exist a relationship between education and income inequality. During the short-term, reports

show that education increases the gap between the rich and the poor. However, over the

long-term it can be seen that education has a negative impact on income inequality. Therefore,

past studies suggest that the government were to increase its expenditure on public education if

its aim were to lower income inequality. More specifically, through the research of Abdul

Abdullah (2013) and Sylwester (2003), lower income inequality could be met if the government

were to spend on secondary education and if there would be an increase in the participation rate

for higher education.

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2. Trade Liberation

The introduction of globalization could play a big role on how income is distributed among a

nations working population. Whether this process does have an effect on inequality is a

controversy among many economists and many argue that different outcomes will be produced

from different economic situations. According to some authors, both developing and developed

nations suffer from an increase in income inequality. Empirical evidence suggests that there has

been an increase in inequality and slow growth in most countries over the last two decades and

Cornia (1999) argues that globalization is a prime factor in this. Barro (2000) and Wood (1994)

both explain that globalizations lead to an increase in income inequality in developed countries

whereas income would be more evenly distributed among the population of a developing

country.

The trade theory, articulated in the Heckscher–Ohlin (HO) model, shows that developing

countries should experience a more equal trend in inequality as a result of globalization. One

theorem derived from this model is the Stolper–Samuelson (SS) theorem, which is significant as

it measures the relationship between the price of output and the real wage. So, if there is an

increase in the price of labor intensive goods then there will be an increase in real wages as a

result. However, an increase in the price of capital-intensive goods will lead to a decrease in real

wage as more capital is needed rather than labor to carry out the certain investment. In this case,

the introduction of globalization will increase the relative prices of unskilled workers in

developing countries hence leading to a more equal distribution in income. However, there are

too many limitations and assumptions implied towards this model that restricts it from predicting

the real economy.

Bergh and Nilsson (2010) conducted a study using the KOF index of globalization, that measures

the economic, political and social dimensions of globalization, and concludes that trade

liberalization does tend to increase income inequality in developed countries therefore,

supporting the results of Stolper- Samuelson theorem. Social globalization is the major drive of

high inequality for middle- and low-income countries. There are other studies by different

economists that also prove and support the predictions of the SS theorem, which include Wood

(1994) and Calderón and Chong (2001). They argue income inequality is decreased after

globalisation.

However, there stands a strong argument that allowing international trade among developing

countries may result in an increase in income inequality. This is because of the allowance of

developed countries to promote their new and advanced technology towards developing

countries. Therefore, there will be an increase in demand for skillful workers in order to manage

the new technology. This increase in demand for skillful workers, which developing countries

lack, will create a form of income inequality. Lee and Vivarelli (2004, and 2006b) put in their

own words that “if such is the case, then trade – via technology – should imply a counter-effect

to the SS theorem prediction, namely an increase in the demand for skilled labour, an increase in

wage dispersion and so an increase in income inequality.” This study, which contradicts that of

Wood (1994), is also supported by authors such as Barro (2000) and Ravallion (2001).

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Elena Meschi (2009) analyses the impact of trade on the inequality of income using a sample of

65 developed countries between the periods 1980-1999.Unlike previous studies, which were

characterized by cross-section methodology, this focuses on within-country income inequality as

it has shown to be more important in recent times. Her study uses the Heckscher-Ohlin (HO) to

analyse the effect of relative returns to the factor of production, such as Wood (1994). However,

Meschi chooses to relax the assumption of identical technologies across the two countries as this

will lead to the development of the process of technology diffusion across developing countries

through international trade, imports and exports. The more skilled-intensive technologies sent

from the developed country to the developing country will increase the demand for skilled

labour. This upward shift for skilled labour will lead to a more unequal income distribution.

Applying this model to her research shows that, consistent with previous studies, total aggregate

trade flows are not significantly related to within-country income inequality among developing

countries. However, what is interesting in her results is that trade with high-income countries

will increase income inequality in a developing country through imports and exports. Meschi

also states from her results, however not supported with theoretical evidence, that trade within

developing countries will reduce income inequality. Apart from this, Meschi concludes that the

level of human and economic development can impact within-country income inequality in a

developing country. This means that improvement in higher education and training policies in

developing countries will provide a more skilled labour force which will therefore reduce income

inequality.

3. Recent Trends in Income Inequality

Previous sections of this thesis analyse the effect of an independent variable on income

inequality. It shows the possible relationship between certain factors and inequality. In this

section however, changes in the pattern of income inequality will be covered between different

countries over a period of time. It will also study how significant each variable is on the changes

of income inequality.

Around 60 years ago, the average human capital earned enough to have a high standard of living

and be able to raise their family, in the United States. The economy grew at an increasing pace

and the country began to develop over time. 30 years after World War II, the United States had

the largest middle class sector in the world. In these times, the income of the average worker

doubled along with the size of the economy. However over the past 30 years, the size of

economy continued to double while the earnings of the average worker stayed the same. This

form of income inequality created, as what is known today, the Great U-Turn. Previous

literatures study the determinants of what cause this inequality and how this uneven income

distribution would affect the economy as a whole.

Freeman and Katz (1995, p. 13) stress on the fact that during the 1970’s income inequality has

rose drastically in advanced industrial societies, such as the United Kingdom and the United

Sates. This increase in income inequality raised awareness of social science researchers in

different fields. The reasoning behind this was because, firstly, the increase in income inequality

led to a reversal in the long-term decline in income inequality. Also the increase in inequality

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destroyed the Kuznets U-Shaped model which “represents the inverted U-curve relating

inequality to development conjectured by Kuznets” (1955; see also Lindert and Williamson

1985).

Stephanie Moller (2009) examines the change in patterns of income inequality in the United

States over 1970-2000. She also provides reasoning as to what effected income inequality and

how the economy would react. The data collected shows the distribution of family income

among 3098 U.S counties and therefore Moller uses U.S counties as a unit of analysis. County

data provides insight into social mechanisms that are causing inequality while data from

individuals or a national level does not offer that benefit. Her result agrees with Freeman and

Katz (1995) by showing that income inequality does increase significantly in U.S counties over

30 years. By the year 2000, income inequality was as high as the U.S has last experienced it in

late 1920s. The study shows that the strongest factor in rising income inequality is economic

development. There is a U-shaped relationship between economic development and inequality

where economic development in U.S counties initially decline as inequality increases but then

rises over the long-term. Education was recorded as the second strongest factor where counties

with higher levels of education, measured by high school completion, have a less unequal

income distribution compared to counties with lower levels of education. Race and ethnicity is

next in importance. There is a strong longitudinal effect which indicates that counties which had

an increase in the black population saw a more unequal income distribution. This finding shows

that race and ethnicity does contribute in increasing inequality. Other factors are shifts in the

labour force, urbanization, and change in women status and age composition. To conclude, this

journal has reported that the important determinants of income inequality are economic

development, demographic, and political-institutional variables.

Over the past few years, China’s income inequality has far surpassed the level of inequality in

the United States due to a rapid increase. Yu Xie and Xiang Zhou (2014) set out a study to

explain the possible effects of the increase in inequality in China. Data was collected from

the 2010 baseline survey of the China Family Panel Studies (CFPS). The 25 provinces of China

represent around 95% of the Chinese population. The Gini Coefficient increases from 0.530

based on the CFPS in 2010 to 0.611 based on the CFPS in 2011, showing the increase in income

inequality. Yu Xie and Xiang Zhou show that the increase in uneven distribution in income is

due to the urban areas being more preferred to rural areas. Since inequality is very high they

suggest that the government implements a policy that reduces the disparity between rural and

urban areas as to lower inequality.

In conclusion, over the years many developed and developing countries faced a substantial

increase in income inequality. Various factors played a role in rising inequality and this section

shows that different countries are affected by different factors. For example, Stephanie Moller

(2009) finds that inequality rises in the U.S because of an increase in economic development and

from different races and ethnicities in different counties. However, Yu Xie and Xiang Zhou state

that the gap between rural and urban areas raise income inequality in China.

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4. Tax

The tax system is a popular public policy instrument used to adjust income inequality.

In most countries today, income tax is progressive, that is income tax rates raise as income rises.

Theoretically tax has a significant impact on inequality and governments always attempt to

implement the best tax policy to minimize inequality. This section reviews the past literature on

how tax, such as income tax, could be used to control inequality in income of an economy.

Economists as Thon (1987) and Jakobsson (1976) show in their past studies that an increase in

the progressive tax system will evenly distribute income, after-tax, and Fellman (1976) states

that if income is progressively taxed, inequality will decrease.

Peter J. Lambert (1992) focuses his study on how income taxed progressively reduces inequality

and provides conditions that give significant descriptive and prescriptive values. Lambert

believes that a liability of tax occurs from attributes such as ones marital status, household

ownership and income which play a role on income inequality. His theory faces two problems:

1. Decreasing tax rates, towards a married couple for example leads to differences in tax

treatments among the population, towards a single man for instance.

2. The vice-versa effect, where the married couple would be taxed higher, could introduce an

adverse influence on income inequality.

Therefore, Lambert adds few conditions and limitations to motivate this theorem and to prove

that income tax leads to an overall decrease in inequality. His study shows that under the

assumption that the difference in tax treatment is not taken into account in an economy;

progressive income tax is a strong factor in reducing inequality as Fellman (1976) states.

Lambert says that his theory provides no explanation to why income tax reduces income

inequality. He explains his empirical findings illustrate that reduced inequality are the

achievements of tax policy-makers and designers rather than income tax.

Another study by Grace Anyaegbu (2011) shows that government interventions, tax and

subsidiaries, decrease household income in the United States from 1980 to 2010. Surprisingly

taxes made little difference on income inequality over the period. This was because direct taxes

reduced inequality while indirect taxes increase inequality hence almost equally diminishing the

impact on inequality. Her analysis shows that income inequality was reduced between 1980 and

2010 largely because of the increase in subsidiaries rather than tax. Government incentives

decreased inequality by an average of 15 percentage points, in terms of Gini Coefficient, over the

period. However, direct taxes reduced inequality by an average of 3 percentage points and

indirect taxes increased inequality by an average of 4 percentage points. She finds that direct tax

and subsidiaries appear to have a negative effect on income inequality however no relationship

holds between indirect tax and income inequality. Overall government incentives were the main

factor in reducing inequality as the Gini Coefficient shows. The effect of tax on income

inequality is very minimal however Anyaegbu concludes that there was an increase in the

redistributive effect of direct tax which was driven by the tax concentration coefficient therefore

leading to direct tax having only a small effect on lowering income inequality in the United

States.

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In theory, tax plays a significant role in adjusting inequality through tax progression as

Jakobsson (1976) argues. However, this does not seem the case in the real world as studies have

struggled to find strong evidence that supports the existence of the relationship between income

inequality and tax. More specifically, direct tax does have a small effect in lowering inequality

conversely an increase in indirect tax will increase inequality. Study shows that government

intervention through subsidies will be more effective, than direct tax, in lowering inequality

since Gini Coefficients showed to decrease by 15 percentage points compared to the 3

percentage point decrease from direct tax in the United States.

5. Inflation

Inflation is an under-researched topic when analysing the effects it has on income inequality.

This is surprising as the few researches done by Schultz (1969) and Blinder and Esaki (1978)

show that inflation has a recurring influence on income distribution in 12 developed countries.

This could possibly be because there are not many convincing hypotheses other than the simple

Kuznets hypothesis (Kuznets, 1955) which explains the non-linear relationship that exists

between income distribution and economic development. One alternative on measuring the effect

of inflation on inequality is an ad hoc augmentation of the Kuznets model. This alternative

method was established by Milanovic (1994, p.3) who explains “that income distribution is

determined ( 1) by factors that are in the short run, from the point of view of policy makers or

society as a whole, 'given,' and (2) by social (or public policy) choice.”

Using the original data of Milanovic, Aleš Bulíř (2001) uses a cross-country model of 75

countries to analyse the Kuznets hypothesis of income inequality by including inflation. He

states that it is not a coincidence that high inequality was recorded in South America, having

suffered from hyperinflation, and that there was low inequality in Asia where inflation is lower

than average. The study shows that inflation does have a positive effect on inequality and results

were robust. The positive impact of inflation on income inequality is non-linear since countries

who have recovered from hyperinflation had a more significant decrease in inequality compared

to countries already on low inflation rates that benefit less. This was then shown in the results

where countries with inflation rates between 5 to 40 percent achieve a higher decrease in

inequality compared to countries whose inflation rate is lower than 5 percent. He concludes that

disinflation has no negative costs and only benefits by improving income inequality. However it

cannot be compared with other alternative methods as there are only few cross-country studies

have used inflation as an effect on income inequality.

Another study done by Fahim A. Al-Marhubi (2007) agrees with previous studies in that

inflation has a positive impact on inequality. His analysis is based on a cross-country data

consisting 53 countries over the period 1975 to 1995. Data on the variables such as inflation and

high school enrolment are collected from the World Bank Data and inflation is transformed into

the logarithm of the average annual change in GDP spending. The study concluded that inflation

is significantly positively associated with income inequality and the results are robust.

Not much study is invested into the effect of inflation on income inequality therefore it is thought

that inflation does not play a role in adjusting inequality. However, the few studies that were

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applied show that inflation does in fact have a strong positive influence on inequality. Research

shows that countries that suffering from hyperinflation had a higher decrease in inequality when

re-adjusted to a steady rate of inflation compared to countries with lower than average inflation

rate which only had a small decrease in inequality. There are only a few alternate methods other

than the Kuznets hypothesis which is the reasoning to why not many studies were undertaken.

However, most of the studies consistently prove that inflation and inequality have a positive

relation, and to reduce inequality the economy has to reduce its inflation rate.

Part III Data

1. Data

Data for this thesis is collected, based on factors that are the main indicators on effecting income

inequality, from 21 countries in Europe over the period 1992-2012. Data of the Gini Coefficient

are collected from the World Data Bank and Eurostat along with OECD. Data of other variables

such as tax, inflation and education are solely collected from the World Data Bank. As stated

earlier this thesis will use the Gini Coefficient to measure income inequality.

Education is one of the variables that could potentially adjust inequality in an economy. Past

studies have shown that higher high school enrolment leads to a more even distribution in

income. The data collected for education in this thesis are labour forces that have completed

secondary or tertiary education. The inclusion of secondary education along with tertiary is

because previous analysis shows that developing countries lack the household income for tertiary

education and the majority of the population only completed secondary education. Data collected

on education will measure the percentage change in labour force of secondary and tertiary

education and if there exists a significant impact on inequality.

Economic growth is said to be an important factor on income distribution. This thesis uses a

logarithm transformation on the percentage change of GDP per capita as the main indicator to

evaluate what relationship it holds with income inequality. Theoretically most economists agree

that there is a Kuznets relation between growth and inequality. Using the data collected for GDP,

this thesis sets to prove if the same relation holds for countries in Europe or if it plays a more

significant positive role.

Inflation is also another factor affecting income inequality. It is the only variable in this study

that did not need first differencing as it was originally measured as the change in percentage of

consumer price index (CPI). There was lack of data on the World Data Bank therefore data for

inflation was also collected from Eurostat.

The data collected for tax had significant gaps between periods. Therefore, data was interpolated

meaning that data was automatically inputted by roughly calculating the mean between the gaps.

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Income tax was used as the measurement tool between tax and inequality because previous

studies show that income tax is the second most powerful government policy to have a positive

impact on income inequality. Income tax was then transformed to the annual percentage change

in direct tax.

Another influence on inequality is unemployment. Data was collected from the World Data Bank

and the data measures unemployment against the total labour force which is based on the ILO

estimate. It required first differencing so was transformed to the annual change in percentage of

unemployment, out of the labour force.

Growth in population has an indirect effect on income inequality. Population over the age of 65

(% of total population) is used to analyse the significance of population growth on inequality. It

was transformed to the annual change of population over 65 in percentage. This specific variable

was used in this study since an increase in retired human capital lowers inequality.

The effect of trade on inequality is the final variable involved in this study. Openness, through

globalization, allows imports and exports to flow leading to an effect on income inequality.

Therefore data collected for trade is measured as the difference between exports and imports

before being transformed to the annual change in the difference between exports and imports.

2. Descriptive Statistics

Descriptive statistics generally summarizes and defines the data that has been collected. The

standard deviation of most the variables in the result are significantly high. This indicates that the

data is not concentrated and is farther away from the mean. On the other hand, a low standard of

deviation is considered to be more consistent and concentrated around the mean.

One factor, for high results of standard deviation, is that the variables used are sensitive

instruments therefore leading to extreme changes in the annual value of the variable. For

example, from the data collected, GDP in Italy decreased from $23,175 to $18,683 which results

to a higher range between the minimum and maximum outliers thereby setting a higher standard

deviation. Net Trade has an exceptionally large standard deviation of 158.909 since it is being

measured as the difference between total exports and imports valued in billion U.S current

dollars. Italy again shows the high difference in change, annually, reporting a decrease in net

trade from -2.6472 to 11.9975. This, when transformed to the change in percentage of net trade,

equates to a staggering -209.92% and is an enormous decrease from the 67% achieved from 1995

to 1996 which will lead to a high value of standard deviation . Therefore, the data collected was

then transformed to a change in annual growth rate. The transformations led to a significant

decrease in standard deviation, although it is still considered high. Even though the results show

a high level of standard deviation, it can be accepted since the values of the variable collected

vary extremely over time and no effective methods could be used, other than transformation, to

lower standard deviation. Inflation records a standard deviation of 4.934. This is because the

difference between the minimum (-4.7%) and the maximum (45.3292%) is large. However, this

is expected since inflation is a sensitive instrument so countries suffering from hyperinflation

will set a higher maximum leading to an increase in standard deviation. Population growth has a

maximum growth of 3.7% and a minimum of -0.86%. The small difference in the maximum and

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minimum is because population is measured in terms of billions while population over 65 has a

much lower value. Therefore a substantial increase or decrease in the annual change of

population over 65 will have very minimal effects on the total population.

Table 1: Descriptive Statistics

3. Correlation Analysis

Correlation measures the statistical relationship of variables. This correlation analysis will

discuss what relationship variables hold with each other and with inequality. Figure 2 provides

some known relationships but does also make available interesting ones. For example, it is well

known that, an increase in unemployment will decrease GDP and this analysis shows that they

do hold a negative relation of -0.312934. This correlation seems weakly correlated but overall is

considered significant because there are many more factors that affect GDP per capita in an

economy. Alternatively, the correlation between Gini and the labour force with secondary

education, 0.011466, is very low to assume if there exists a strong relationship between the two.

However, analysing the relation variables hold with Gini shows interesting results. Past literature

studies have shown that direct income tax has a negative relationship with inequality, where an

increase in tax will lower the Gini. Figure 2 shows support for these past studies, by valuing the

relation between inflation and Gini to be 0.104190, showing the relationship to be positively

correlated. Also supporting past studies, to a certain extent, is the very weak negative relation

between tax and Gini, having a correlation coefficient of -0.064452.

The results of figure 2 show that there exists a low level of collinearity between Gini and the

independent variables, highest being inflation. This is positive since having high collinearity

increases the standard errors of their coefficients, and it may make those coefficients unstable

which would affect the performance of the control variables.

GDP GINI INFLATION LFSE LFTE TRADE POP TAX UNEMP

Mean 4.562 0.435 3.641 0.607 3.325 -0.609 1.008 0.0760 3.164

Median 5.331 -0.065 2.408 0.3250 2.815 5.155 0.980 0.050 0.000

Maximum 30.186 33.28 45.329 29.520 67.420 1170.010 3.700 52.790 150.910

Minimum -29.484 -27.09 -4.480 -19.180 -30.110 -1785.960 -0.860 -33.540 -30.430

Std. Dev. 9.863 6.838 4.934 4.126 7.275 158.909 0.866 6.885 19.0013

Skewness -0.258 0.4273 4.442 0.811 3.064 -3.627 0.238 0.3752 2.232

Kurtosis 2.920 6.334 27.592 16.575 32.864 56.923 2.631 14.419 13.682

Jarque-Bera 4.858 211.241 12192.38 3333.110 16574.370 52792.600 6.457 2335.795 2390.239

Probability 0.088 0.000 0.000 0.000 0.000 0.000 0.0396 0.000000 0.000

Sum 1952.677 186.350 1558.385 259.810 1423.030 -259.730 431.620 32.570 1354.390

Sum Sq. Dev. 41537.56 19965.06 10397.42 7270.792 22600.01 10782760 320.433 20241.570 154169.100

Observations 428 428 428 428 428 428 428 428 428

15

Table 2: Correlation Matrix

Part IV Methodology

A Panel Least Squares regression is applied to estimate the model of 21 countries over 21 years,

from 1992-2012. Using a cross-sectional panelled regression allows for both time and countries

to be used as an ID to observe changes in inequality and the possible effects that could arise.

Therefore, cross-sectional fixed effect is used to eliminate the constant variance of the countries

being analysed. Only countries in Europe were selected as this thesis discusses if countries in the

European Union, and who are part of the economic and monetary union (EMU), display a

significantly lower income inequality compared to countries outside the European Union.

This can be determined using the cross-section fixed effect, as the regression will interpret the

results in terms of dummy variables. The dummy variable will split the result in two, using the

variable “1” to represent countries that have a lower Gini coefficient than the average rate in

Europe therefore indicating low inequality. Conversely, using the dummy variable “0” will

represent countries with a higher income inequality than the average rate. The data of Gini Index

is used to value the dummy variables. The mean of Gini is 29.60468 and will be used to set the

dummy variable. Therefore, a country having an inequality over 29.6 will have a dummy

variable of 1 while a dummy variable of 0 is given to countries with lower inequality than 29.6.

Finland have achieved the lowest ever Gini Index, 19.7, while the United Kingdom acquired the

highest Gini Index in Europe, 38.7, over the past 21 years. However, the mean for Gini in table 1

is 0.435 since the descriptive statistic was inputted after the variables were transformed from its

original value.

To understand which variables need differencing, a unit root test should be applied to analyse if

variables are non-stationary over time. All the variables in this study needed first differencing in

order to overhaul the problem of omitted variables in this panel regression. GDP per capita

originally had a probability value of 0.8052 indicating that GDP is statistically insignificant to

Gini as it has a probability value higher than 0.05. Therefore, first differentiation under the

Levin, Lin & Chu Test is required to remove the factor of omitted variables.

GDP GINI INFLATION LFSE LFTE TRADE POP TAX UNEMP

GDP 1.000000

GINI -0.000419 1.000000

INFLATION 0.121829 0.104190 1.000000

LFSE 0.007336 0.011466 0.026629 1.000000

LFTE 0.020592 -0.051215 -0.032955 -0.384712 1.000000

TRADE 0.137426 -0.024440 0.193599 -0.008523 0.009113 1.000000

POP -0.078199 0.037387 0.225201 -0.020774 -0.036787 -0.017407 1.000000

TAX 0.094953 -0.064452 0.039555 0.082824 -0.073871 0.017952 -0.037476 1.000000

UNEMP -0.312934 0.066004 -0.025511 -0.002213 0.093742 -0.035001 0.044472 -0.185900 1.000000

16

Table 3: Unit Root Test (First Differencing)

The results in table 3 shows that first differencing, by the Levin, Lin & Chu Test, makes GDP

per capita more statistically significant as the probability value measured is 0.000.

GDP per capita was originally valued in current U.S dollars and was denoting higher values

compared to the other variables. It was also found that GDP per capita is highly skewed therefore

making the Log transformation ideal as it will produce a less skewed GDP per capita. It is then

transformed to the annual growth rate, in order for all variables to have equal measurements. The

equation used to transform GDP per capita is:

Log-GDP Growth = 100 * [Log(GDP(t)) – Log(GDP(t-1))]

, where “t” represents the current year and “t-1” represents the year before. All other variables are

transformed to the annual change in growth rate, except for inflation as it originally measures the

change in consumer price index (CPI). The equation used for this transformation is as follows,

“x” indicating all the variables excluding GDP per capita and inflation:

X Growth = 100 * [X(t)/X(t-1) – 1]

The panel regression used in this study is based in 21 countries and over 21 years, therefore there

should be 441 total observations recorded. However, table 4 shows the total panel of observation

to be 428. These few missing data were unable to be interpolated since there were no data

provided before the missing datum to estimate a mean. Nevertheless, the missing data will not

affect the result of the regression as it is a very small percentage of the total observations.

The model created by the panel regression equates to:

Gini = β₀ + β₁LogGDP + β₂INF + β₃POP + β₄LFTE + β₅LFSE +

β₆TAX + β₇UNEMP + β₈TRADE + ᶙi + Ԑ

Method Statistic Prob.** Cross-se

ctions

Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -11.9748 0.0000 21 420

Null: Unit root (assumes individual unit root process)

Im, Pesaran and Shin W-stat -10.1030 0.0000 21 420

ADF - Fisher Chi-square 178.144 0.0000 21 420

PP - Fisher Chi-square 198.073 0.0000 21 441

17

Gini measures income inequality, LogGDP is the natural log of GDP per capita in terms of

growth, INF accounts for the consumer price index, POP is the percentage change in population

over the age of 65, LFTE and LFSE are labour force in tertiary education and labour force in

secondary education respectively and they show the percentage change in growth of these two

variables. TAX indicates the growth of direct tax, UNEMP indicates the growth of

unemployment compared to the total population and TRADE shows the growth of difference

between exports and imports as a balance of payment. β represents the coefficients for different

variables , ᶙi represents country fixed effects and Ԑ represents error.

Part V Results

The results for the model, measuring inequality, are examined in table 4. R-squared is recorded

to be 0.0346 which implies that the model runs a low goodness of fit. The Adjusted R-squared is

lower, -0.033107, than the R-squared as expected. Adjusted R-squared provides a more accurate

analysis since it runs a standard error of the regression in order to run a balanced estimator and

adjusts the sample size and the number of coefficients to its correct value.

The probability value of all the variables is high, other than inflation, meaning that they are not

significant to income inequality. However this does not mean that the effect of the independent

variable on inequality is low as it is possible for a variable to be insignificant and still have a

high effect on inequality. Conversely, it is the coefficient that measures the size effect of the

independent variable on the dependent variable. In table 4, inflation shows to have an interesting

relationship with income inequality. The probability value of inflation is low, 0.0344, indicating

a significant effect on income inequality since it is lower than the 5% confidence interval. The

coefficient of inflation is 0.185 showing that there is a strong positive effect between the two.

Although it is a low value to assume a strong effect, inflation is one of the many factors affecting

inequality therefore only having a small effect towards inequality as a whole. The relationship

between inflation and inequality can be concluded by stating they are significant and there exists

a strong positive relation between the two. This is interesting because past literature has

consistently discussed inflation to be positively related to inequality. Fahim A. Al-Marhubi

(2007) reached conclusions in his study that stated inflation and income inequality is positively

related and the results are robust. This supports our study that an increase in inflation will

increase income inequality.

Another factor that has been achieving consistent results in past literature is tax. However, the

measurement of tax in this study is only in terms of income tax. Grace Anyaegbu (2011) analysed

that inequality was lowered by 3 percentage points through income tax. However, government incentives

show a more significant effect by lowering inequality by 15 percentage points in the United States.

Therefore, this study supports the results of Anyaegbu (2011), in that tax is insignificant due to the high

probability value being greater than the confidence interval. Also the coefficient of tax is highly relatable

18

to previous studies. Income tax has a negative relation of 0.0623 with inequality, indicating that a 1%

increase in income tax leads to a0.0623% decrease in income inequality. This is, to some extent,

equivalent to the decrease of 3 percentage points on income inequality. To conclude, this study and past

studies support one another stating there is no significant effect between tax and inequality and there is

only a weak negative relationship between the two.

The other variables used in the regression do not hold a strong or significant relationship with inequality.

An argument for this could be that economists found contradicting results between the variable and

inequality. For example, Wood (1994) states that the introduction to globalization will lower income

inequality while Lee and Vivarelli (2004, and 2006b) used the same model, Heckscher–Ohlin

(HO) model, to contradict Wood’s theory. There lacks strong evidence for some of the variables

while the others examined are under assumptions in theory. This could lead to the possibility that

some of the variables produce inaccurate or irrelevant estimates.

Education does not seem to have consistent results and not much could be analysed in terms of

the effect it has on inequality. However, it could be concluded that both variables, LFTE and

LFSE, are not significant. The fact that there is a difference between the two coefficients and two

probability values raises an interesting debate. The coefficient for the labour force that has

tertiary education is -0.0646 compared to secondary education -0.0272. Also, LFTE has a more

significant effect since it has a lower probability value than LFSE. This analysis could possibly

interpret that completion of tertiary education will have a more positive impact on inequality

compared to secondary education.

Another study this thesis sets to examine is whether countries in the European Union have a

lower income inequality compared to countries outside the European Union. Instead of running a

separate regression for a Least Square Dummy Variable, a fixed country effect is applied to the

current one adjusted for dummy variables. The purpose of a fixed effect is that it essentially adds

independent time effects to for every unit correlated to the regression. Comparing fixed effect

coefficients to the coefficient of the regression will possibly interpret if countries in the European

Union generally have a lower inequality against countries outside. A dummy variable of 1 will

represent countries that are above the coefficient -0.227 hence indicating low inequality. On the

other hand, a dummy variable of 0 will be given to countries below the coefficient indicating

high inequality. Table 4 shows the value of all fixed effect coefficients to be above the

coefficient of the regression hence interpreting that all countries have a dummy variable of 1

indicating low inequality. However, fixed effect shows no consistent or significant pattern in

order to adjust dummy variables. Therefore, it could be concluded that there is no significant

relationship between low inequality and countries in the European Union. Perhaps a more

effective study would be analysing the effect of inequality in developed and developing countries

since past and present studies have showed growth to be a main indicator for inequality.

19

Table 4: Panel Cross-Sectional (Fixed Effects) Regression

Dependent Variable: GINI

Total panel (unbalanced) observations: 428

Variable

Coefficient Std.

Error

t-Statistic Prob.

C -0.227769 0.704331 -0.323383 0.7466

GDP 0.020455 0.037518 0.545214 0.5859

INFLATION 0.185083 0.087212 2.122216 0.0344

NET_TRADE -0.002004 0.002213 -0.905778 0.3656

POPULATION 0.047883 0.479942 0.099769 0.9206

LFTE -0.064666 0.052284 -1.236814 0.2169

LFSE -0.027212 0.093918 -0.289738 0.7722

TAX -0.062327 0.050579 -1.232275 0.2186

UNEMPLOYMENT 0.026134 0.019347 1.350816 0.1775

Fixed Effects (Cross)

_AUSTRIA--C -0.002331

_BELGIUM--C 0.008257

_CZR--C 0.000584

_DENMARK--C 0.012560

_ESTONIA--C -0.007872

_FINLAND--C 0.007215

_FRANCE--C 0.004978

_GERMANY--C 0.006143

_GREECE--C 0.002437

_HUNGARY--C -0.004359

_IRELAND--C -0.003182

_ITALY--C 0.001366

_LUXEMBOURG--C -0.000664

_NETHERLANDS--C -0.009741

_POLAND--C -0.001394

_PORTUGAL--C -0.009018

_SLOVENIA--C 0.001913

_SPAIN--C -0.001178

_SWEDEN--C 0.008674

_SWITZERLAND--C -0.011113

_UK--C -0.004255

Effects Specification

Cross-section fixed (dummy variables)

R-squared 0.034638 Mean dependent var 0.435397

Adjusted R-squared -0.033107 S.D. dependent var 6.837878

S.E. of regression 6.950148 Akaike info criterion 6.780755

Sum squared resid 19273.52 Schwarz criterion 7.055789

Log likelihood -1422.082 Hannan-Quinn criter. 6.889378

F-statistic 0.511296 Durbin-Watson stat 2.811819

Prob(F-statistic) 0.983073

20

Part VI Conclusion

The main objective of this thesis was to find a relationship between the economic elements of

income inequality and inequality. Income inequality is a main indicator for economic growth,

therefore it is ideal to find the characteristics affecting inequality in order to increase economic

growth.

Data was collected from 21 different countries in Europe over 21 years. Regression was

estimated in order to analyse the significance and the size effect of the independent variables

against income inequality.

Certain limitations in this thesis restrict the fact for regression to provide a better analysis and

higher level of significance. For example, a simple Panel Least Square regression lacks the

advanced program and technique to measure accurate and significant values for the effect of

certain variables on inequality. Also, a variable could be lagged in order to achieve a more

accurate result. For example, a change in the inflation rate today will not immediately affect the

change in Consumer Price Index. It will take a certain amount of time for CPI to be adjusted to

the inflation rate, therefore lagging the variable would be ideal in order to set the outcome within

the time range of the original change. Assumptions are considered barriers to running a

regression as it only portrays the model and not the real economy. Meschi (2007) relaxes the

assumption of identical technologies allowing developed countries to transfer their advanced

technologies to developing countries. Relaxing this assumption, allows Meschi to examine the

increase in income inequality due to factors that was not available before the assumption was

dropped. Logically, lack of data provided will make the regression less accurate hence regression

can be improved by firms and databases making more data available to the public. This could

have been one of the cases for the regression ran in this study. For example, limited Gini Index

data is provided by the World Data Bank, going back to only 1992 and there were a few

significant data missing. Increasing the database will allow more accurate tests to be run and

most likely a better regression analysis, measuring to what extent independent variables effect

inequality. Although not used in the regression of this thesis, running a fixed time effect will take

into account economies suffering from global shocks. Therefore, a fixed time effect will increase

the accuracy of the regression. Adding all these techniques on the regression analysis will

improve the results and possibly show a stronger relationship and higher significant level

between income inequality and the dependent variable.

To conclude this thesis, a regression analysis was undertaken to see how dependent variables

affect income inequality and if the effects are consistent with previous studies. The regression

analysis for this thesis shows inflation and tax to be consistently and significantly related to past

studies. However, the other variables were found to be irrelevant due to not having a significant

effect nor a strong relationship with inequality. This could occur if past studies are tested without

sufficient evidence or if there are too many assumptions stressed on inequality. Therefore

applying lag or relaxing assumptions would possibly improve the results of the regression.

21

Perhaps during future studies, more data will be made available and new techniques will be

established to provide a strong and significant link between income inequality and the variables

affecting it.

22

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