What is the effect of trade openness on the income inequality in developing countries compared to OECD countries?

What is the effect of trade openness on

the income inequality in developing

countries compared to OECD countries?

Idse Kuipers

12 ec

10422358

Macro Economics

The effect of trade openness on the income distribution.

Abstract

Income inequality has been growing over the last decades. This thesis analyses the possible

relationship between trade openness and income inequality and compares OECD countries

with developing countries. Classical economic theory predicts a significant negative

relationship between trade openness and income inequality, but more recent researchers

are finding other factors to be more important for determining income inequality. In this

research, I use an unbalanced panel dataset of 59 countries. A fixed effects regression is

used to estimate the model parameters. The results show a positive effect of trade in OECD

countries. But no significant effect in developing countries. Technological advancement

seems to be of more importance.

This document is written by student Idse Kuipers who declares to take full responsibility

for the contents of this document.

I declare that the text and the work presented in this document are original and that no

sources other than those mentioned in the text and its references have been used in

creating it.

Table of contents

INTRODUCTION ... 4

LITERATURE REVIEW ... 6

M

ANY RESEARCH

,

ON DIFFERENT VARIABLES

. ... 6

METHODOLOGY ... 10

T

HE VARIABLES

... 10

T

HE DATA

... 15

T

HE REGRESSION MODEL

... 16

T

HE HYPOTHESES

... 18

EMPIRICAL ANALYSES ... 20

D

ESCRIPTIVE STATISTICS

... 20

C

ORRELATION STATISTICS

... 21

C

ONCLUSION AND PRACTICE

,

REJECT OR ACCEPT THE HYPOTHESES

... 27

CONCLUSION ... 27

REFERENCE LIST ... 29

APPENDIX ... 32

All country ... 32

OECD countries ... 41

Developing Countries ... 50

Introduction

“The distribution of wealth is too important an issue to be left to economists, sociologists, historians,

and philosophers. It is of interest to everyone” (Piketty, 2014, p.2). The 8 richest people dispose of

the same amount of money as the poorest half of the whole world (3.500.000.000 people) (Novib,

2017,. Income inequality is growing in a rapid pace and has negative effects; between countries and

within countries. Not only material but also psycho-social differences. People start to compare and

distrust each other and the government. It is the cause of migration.

Income inequality is a phenomenon the world has been dealing with since the old ages.

The world has been globalizing and the gap between rich and poor has been growing.

Some people believe that inequality is inevitable. Nothing can be done about it. Others believe that

it is naturally decreasing by free economics that lead to equilibrium levels. By ignoring that

something can be done of both camps challenge researchers to find the answers and solutions.

The subject of this paper is to shed light on the mechanisms responsible for income

inequality. If there is a better understanding of what causes income inequality, governments can

adjust their policy on it. The problem of income inequality must be taken very seriously, because of

the problems and instability it will cause.

According to Jaumotte (2013), trade openness and technological progress are widely

regarded as two of the main drivers of recent economic growth. Economic growth, in turn, is not

divided equally. All drivers of economic growth divide income differently. The exact effects are

ambiguous and show different results for countries belonging to The Organisation for Economic

Co-operation and Development (OECD) and developing countries. Most empirical research suggests a

positive effect of trade on the diminishing of income inequality. Trade openness reduces the gap.

The first research on trade and economic growth division between high-skilled and low-skilled labor

by Stolper and Samuelson (1941) supports this. OECD countries are high-skill-labor abundant and in

developing countries, low-skill-labor is the norm.

This creates an opportunity for trade, exchanging their abundant production factor. It is

interesting to compare these countries. What effect does the difference of the standard of labor

have on average income and the difference in income inequality? What type of country profits most

of the trade? What type of country distributes their wealth the most equal? What type of country

grows fastest? These questions can be answered by looking at data. The objective is to measure and

understand the relationship between trade and wealth; the central question in this paper is: what is

the effect of trade openness on the income inequality in developing countries compared to the

OECD countries?

In order to answer this question, it is important to first elaborate on earlier research. How

did major philosophers and economists write about this issue? This will be presented in the

literature review. After creating some understanding of the matter, the methodology chapter of this

paper will deal with the most important factors necessary for the research; what are the variables

and the corresponding data come from. The next chapter will give a broad description of the model

used analyzing the data and the regression output in the empirical analyses. Finally, I will provide an

answer to the central question and give a proposal for further research in the conclusion.

In this thesis, the effect of the level of trade openness on the level income inequality for

developing countries and OECD countries is analyzed. The relationship between trade openness and

income inequality will be tested by analyzing an unbalanced panel data set of OECD countries and

developing countries. The regions exist of respectively 30 and 29 countries with a time series from

1994 until 2014. This is the most recent time series available. For these regions plus the total data

set as a control region a fixed effects regression will be done. Income inequality will be regressed

against trade openness, foreign direct investment, technology and other control variables. For each

region control variables, will be added one by one to the simple model to generate the full model of

8 variables. The theoretical relationships of these variables to income inequality are explained in the

methodology section. This recent data set with the distinct comparison between OECD countries and

developing countries is what distinguishes this research from other research.

The results of the regression show that trade has a mildly positive relationship with income

inequality for OECD countries, but no significant effect in developing countries. The main findings are

that income inequality is mostly influenced by research and development. This is in contrast with the

expectation of the classical economic theory. Recent literature also predicts the effect of

technological advancement. The null hypotheses are rejected in this research, but to generalize

these findings further research is needed. The distinct difference between OECD and developing

countries is difficult to compare because of insufficient data for developing countries. Better data

series would improve the insight in the effect of economic environment. The new data series show

that, in a new era of technological advancement, the main causes of income inequality change. The

gain of this knowledge is of utmost importance when policy decisions are made considering the

income distribution.

Literature review

The cause of income inequality has been the subject of many researchers. Economists

and philosophers look for the connection between growth and income inequality. In this

section, the issue of income inequality and how it has evolved over time will be keynoted.

The main results will be addressed.

Many research, on different variables.

Distinguished economic philosophers like Ricardo and Marx already thought about

the division of wealth in the 18

_{century. Ricardo observed that increasing demand for one}

product can increase the costs of another. Population growth increases the demand for

food. Instead of increasing wages for farmers, the growing demand for land resulted in an

accrue of the prices of arable land. The landowners gain more than the farmers. Marx

observed the same effects related to the industrial revolution. And the increase in industrial

products resulted in higher profits for the industrialists, much bigger than a gain for the

factory workers. They observed how the income inequality grew between capital owners,

first landowners and later factory capital owners on the one hand, and the labor force, first

farmers and later factory workers on the other. Their predictions about the future were

pessimistic. Growing income inequality was inevitable. Although their theories were valued

and gave a great insight for further economics, their predictions did not turn out the way

they expected. However, Current researchers observe a widening of the income inequality

between high-skilled labor and low-skilled labor.

There were some changes in the world that Karl Marx did not expect. One was

globalization. Now that factories increased productivity, trade became more profitable.

New research was executed on trade and economic growth. Stolper and Samuelson (1941)

confirm and add to earlier economic theories from Hecksher and Ohlin. Hecksher and Ohlin

concluded that trade increases economic growth. Stolper and Samuelson found that if you

divide labor in high-skilled and low-skilled it has a different effect. They observed total

economic growth in combination with growing income inequality. By using the right

distributive matters economic growth can be more equally distributed. Together they

formed the HO-SS model to analyze two-factor trade and found the new division within

economic growth. They concluded that the loss of one factor of production is always less

than the gain from the other factor of production. Redistributive measures, specific

subsidies, for instance, can result in more equally distributed economic growth. In

developing countries, unskilled labor is abundant, so an export economy will emerge. In

developing countries, the increase in low-skilled labor increases wages and decreases

income inequality. (Meschi, & Vivarelli, 2009)

:

“Growth is a rising tide that lifts all

boats.” (Piketty 2014 p.11) Stolper and Samuelson added trade as a factor of economic

growth and connect it to income inequality.

The first empirical research on income inequality took place in the USA. It was Simon

Kuznets (1955), who gave definition to all the measures of income inequality and economic

growth. He used data, previously not available before, covering a period of 35 years, from

1913-1948. He found that when a capitalistic country is growing, income inequality rises

only to a certain level. Once the capitalistic country is so more developed income inequality

starts to decrease again. This resulted in “Kuznets curve”, still widely known. He was the

first using empirical evidence in a debate that had been going on for decades. The

pessimistic predictions of David Ricardo and Karl Marx are proven wrong.

Kuznets does mention to read his analysis with caution, for any exogenous shocks on

political front, like the two World Wars (1914-1918 and 1939-1945) could have influenced

his research. This weakened his conclusion and further research was needed. But only in the

late 20

_{century, when income inequality again started to increase again, researchers}

started to investigate the issue once more. Numerous researchers started to analyze

different countries. Piketty started on a program with many others to make an extension of

the Kuznets hypotheses. First for the United States of America and later for France (2013).

Piketty brought new knowledge about the division of wealth in the developed countries.

Though these researchers build the fundamental basis of research on income

inequality, the most of their theories are outdated and proven wrong by more recent

research. Researchers nowadays are more careful with generalizing the results of their

research. Another difference is that they have different approaches to the subject. In this

paper, I am interested in comparing income inequality within developing countries to

income inequality within OECD countries.

Branko Milanovic (2005) was the first who gave insight in the income inequality over

the whole world. He specifically compared emerging countries with the developed world.

Also interesting is his conclusion that financial trade has been growing. Though this shows

insignificant effects in his results. The new theory is that financial trade is a typical form of

high-skilled labor. People, who own capital in combination with the skills to trade, benefit

strongly of growing financial trade. Most of the capital in concentrated in the OECD. This

means it widens the gap between the rich countries and the poor. Branko Milanovic added

the factor of financial trade as an explanatory variable of income inequality.

More recently Jaumotte (2013), Meschi and Vivarelli (2009) also compared

developing countries with developed countries. They add technological advancement to be

a driver of economic growth. This is an interesting addition. They see developing countries

grow much faster because they profit from the innovative development of the western

countries went through. This positive effect is however in conflict with the next conclusion

of Meschi and Vivarelli (2009). They suggest that income distribution in developing

countries is worsened by trade with high-income countries. What they see is that a part of

the labor force in underdeveloped countries is picking up the advantages that high-skilled

technology brings. A majority of the population is not benefitting. The gap between have

and have not in these countries is widening.

Rafael Reuveny (2003) finds that income inequality is reduced by democracy. In a

democracy, the large numbers of low-skilled laborers have the same voting rights as a small

group of high skilled laborers. This results in more redistributive measures. Politicians have

to take into account the needs and demands of the majority, resulting in a more social

distribution of wealth. Coincidentally Sanjeev Gupta e.a.(2002) find evidence that corruption

is correlated to income inequality. Politicians who fight corruption and stimulate

redistributive measures, thus decrease income inequality.

Milanovic finds, in 2005, strong evidence stating that openness to trade in

developing countries increases income inequality. In the OECD, with its higher average

income levels, openness to trade decreases income inequality. This is an interesting

contradiction. Clarke (1995) finds that income inequality is negatively correlated with

growth in developing countries, but only has a small effect. Jaumotte (2013) finds that

financial trade is positively correlated to economic growth, but also positively correlated

with income inequality.

Branko Milanovic (2016) explains inequality is not the same as poverty. Poverty is, in

principle, reduced by economic growth. No such simple relationship exists between

economic growth and inequality. Li et al (1998) found an ambiguous effect of income

inequality on economic growth. But the gross domestic product (GDP) growth rate has a

positive effect on the income inequality. Lundberg and Squire (2003) found that income

inequality depends on economic growth and the other way around. Meaning that trying to

influence one factor will affect the other simultaneously.

Piketty (2014) investigates income inequality over the long run and tries to

generalize his findings. He concludes that inequality does not follow a predictable path.

Technology creates income inequality unless education levels can catch up with the rapid

progress. Whether the one or the other force is stronger depends on the institutions and

policies that countries choose to adopt.

Overall we can see that earlier research finds many different results. Where the

period of 1960 until the mid 1990’s show significant positive effects of trade on income

inequality (Lunberg and Squire 2003). Branko Milanovic (2005) finds a negative result of

trade openness. Meschi and Vivarelli (2009) find a positive result for trade openness in the

same time period. They conclude that technology alters the effect of trade. The more recent

decades show that technology on its own has impact on income inequality (Jaumotte 2013).

Piketty (2014) claims technology and education together are the main forces over the long

run but he does not show much empirical results. Since all researchers claim that different

factors are the most important in determining income inequality, this research will control

for most of these acclaimed influences. Jaumotte (2013) has the most comparable research.

Her method controls for most of the factors that have a supposed impact on the income

distribution according to earlier research. The proxies are different, the time series are more

recent and the developing country set consists of more extreme low income countries, but

the line of thought is similar to Jaumotte’s. The recent developments in the world ask for a

more complete research.

However, the world develops, more variables evolve that result in a faster growing

economy. Economic growth will per definition decrease poverty. This means that even poor

people profit from economic growth. The variables that cause economic growth, however,

all have a different effect on income distribution. The main variables are trade, financial

trade, technology and political regime. For the remainder of my thesis, I will follow the

methods of Jaumotte. Her paper is of high influence in the research world.

Methodology

This chapter provides the empirical framework and methodology of the research.

Instead of focusing on income inequality within a specific country, this paper focusses on

the comparison between developing countries and OECD countries. What is the relation

between income inequality and trade openness? Other factors that also influence income

inequality are discussed. The first section will elaborate on variables used and how they

affect income inequality according to the literature. In the second subsection, I will explain

how the eventual data set was established. In the third subsection, the regression model

used will be explained and justified.

In this paper, I will do a panel data research on developing countries compared to

OECD countries. Income inequality is the dependent variable, represented by the Gini

coefficient on the income distribution. On the right side of the equation, a set of growth

variables will be tested on significant positive or negative contribution to income inequality.

This model is based on the model of Jaumotte (2013). Her research presents a clear

distinction between developed and developing countries. Jaumotte is the first who

incorporates trade finance and technology together as an effect on income inequality. Her

variables are roughly the same. The data set to explain the variables, however, are different.

It consists of a different country sample.

The variables

According to economic theories and earlier research, the variables I used have some

level of influence on income inequality. Some variables with a significant effect might be

omitted. Those are either not accounted for, or the data were not available. In the end, I

used the most relevant and complete data to fit the model. The variables described are

almost complete sets that have the right time series for the right countries I want to

compare.

Variables

Explanation

Gini

The Gini coefficient, measuring income inequality.

Trade

The exports and imports as a percentage of GDP, measuring

trade openness.

FDI

Foreign Direct Investment as a percentage of GDP, measuring

financial trade openness.

R&D

Research and development as a percentage of GDP, proxy for

technological advancement.

Education

Government expenditure on education as a percentage of GDP,

proxy for the diffusion of knowledge.

Unemployment

Unemployment rate, percentage of total labor force

Government

expenditure

General government total expenditure, percentage of GDP

GDP growth

GDP per capita growth (annual %).

Inflation

Consumer prices (annual %)

Empirical evidence on the income distribution in developing countries is scarce and the

quality can be questioned. (Perottie, 1995) Thus researchers try to find different ways to

measure the income distribution. There is, however, no perfect way. Some have thorough

measurement ways but incomplete data, others have bigger data sets but simpler general

measuring tools.

The Gini Coefficient from Corrado Gini (1912) measures the distribution of income

and provides a measurable unit to income inequality. This is between 0 and 100. 0 meaning

perfect equality, everyone earns the same income. 100 meaning total income inequality,

only 1% of the people have all income. It is possible to use data from household surveys, or

the data from income tax registers. These both give a picture of the distribution of income.

According to Jenkins (Jenkins, 1991), it is best to measure social values in the most

consistent way. In this data set both income and consumption surveys are used. Some

countries switch from consumption to income surveys, countries change to one general,

most convenient and correct way of measurement. In the transition period, they use both

sources for a couple of years. This inconsistency is solved by using country fixed effects to

correct. Gaps in the data are linearly interpolated. This results in a data set of more than

1000 usable Gini data. Atkinson (1970) fought the Gini coefficient as a measurement of

income inequality. The properties of the Gini are unlikely to be accepted. But most

researchers, like Lundberg and Squire (2003) Li and others (1998), Jaumotte et all (2013) use

the Gini coefficient. A vast majority of available data on income inequality is measured by

the Gini coefficient.

Trade stands for the openness of a country. Trade openness is measured by imports

and exports as a percentage of GDP. In this case the only importance is how a certain level

of trade possibly influences the income distribution (Milanovic, 2005). The general theory is

that more openness decreases income inequality. As described in the literature review, the

HO-SS model explains how trade increases economic growth and how high-skilled and

low-skilled profit in a different way. Since the division of high-low-skilled and low-low-skilled is

substantially different in developing countries than OECD countries, trade should have a

different effect in developing countries than OECD countries. This is because OECD countries

are high-skill abundant and developing countries (DC) are low-skill abundant. This results in

a trade flow, consisting of high-skilled products from OECD to DC, and low-skilled products

from DC to OECD in reverse. This increases the demand for low-skilled labor in DCs, wages

go up and income inequality goes down. In the OECD, demand for high-skilled labor goes up,

high-skilled wages go up and low-skilled wages go down; the income inequality increases.

Though some of the assumptions of the HO-SS model are outdated, the connection between

trade and income inequality trough high-skilled and low-skilled labor is central to this issue.

More recent Branko Milanovic (2005) concluded that though trade may increase economic

growth and lift absolute poverty it had no favorable or neutral impact on income

distribution for developing countries.

Trade is an indicator for globalization. But globalization is not merely the result of

the flow of goods and services. Trade in the form of capital can also be globalization. The

implies it originates from high-skilled labor market and flows to DC. Goods are mostly

produced in dominantly low-skilled developing countries and find, as a result of trade

openness, their way easily to rich countries. According to the HO-SS model, this would mean

that a big supply of high-skilled labor would mean more advantage on financial trade.

Financial trade is mostly practiced by the rich. Only when the financial institutions are strong

and provide easy access to money for lower income groups it favors income equality. The

general theory suggests a positive effect of financial trade on income inequality.

The influence of technological advancement is more complicated. Technological

advancement is measured by expenditure on research and development (R&D) as a

percentage of GDP. This is the main source of the development and innovation. Where R&D

increases the high-skilled demand it increases the skill gap and accordingly income

inequality. The problem with using R&D as a proxy for technological advancement is that

most innovation comes from OECD countries. This technology is spread over the world by

trade. That is why the effect of technological advancement in developing countries is

actually underestimated. In developing countries, research and development is low but

technological advancement is high. For example in African countries most people own a

cellphone, they skipped landlines. Those countries skip a stage of development and lift on

the speed of R&D in developing countries. Data on ICT stock, as a percentage of total stock,

would capture the technological advancement in developing countries. Only ICT stock is

more complex to find. Therefore, I chose R&D as an alternative. Since it is measured as a

part of GDP, it is easy to compare with the other data and corrects for economic growth. In

short; Technology is high-skilled labor intensive. Causing both countries to increase demand

for high-skilled labor. Which increases income inequality. But the spread of technology also

increases the productivity of the low-skilled labor. Which decreases income inequality.

The main forces that determine the level of labor income inequality in the long-run

are, according to Goldin (2009) education and technology. These forces need equal growth

to maintain income equality. Higher level of education creates a bigger supply of high-skilled

labor. While technological development leads to a bigger demand for high-skilled labor.

whether education develops faster or technology occurs faster will cause income inequality

to respectively decrease or increase (Piketty, 2014).

Consider education as factor. Piketty (2014) called the spread of knowledge the main

divergence force. An increase in education implies an increase in the supply of skilled labor,

a decrease in the relative skilled/unskilled wage differential, and an overall decrease in

income inequality (Meschi and Vivarelli, 2009). I used government expenditure to indicate

the level of education in a country. Note that this measurement should be read with caution

because private expenditure on education is not included.

Another variable is inflation. This decreases real wages and effects the lower income

groups more than higher. Thus, inflation increases income inequality (Lundberg and Squire

2003). Inflation is added to capture the macroeconomic influence on income inequality.

The unemployment variable is taken as a percentage of the total labor force.

Unemployment is usually bigger at the bottom of the income distribution. This would mean

bigger unemployment rations bigger income inequality. In terms of high-skilled and

low-skilled, in a country with high unemployment rate, the high-skilled will be the first to get a

job.

GDP per capita growth, the annual percentage, is a proxy for economic growth.

Lundberg and Squire (2003) found that income inequality depends on economic growth and

the other way around. Meaning that trying to influence one factor will affect the other

factor simultaneously.

Another tool to use is "dummies". In order to exclude time trend effects, I used

"year" and "country dummies". Adding each year with a value of 0 or 1. So that every year

specific effect is caught in the dummy.

Total government expenditure affects the income distribution because the

expenditure is mostly on subsidies for weaker sectors and public goods. This increases the

income of the bottom part of the income distribution. This is a proxy for redistributive

policy.

Variables for democracy and corruption are omitted because measurement of

democracy strength is subjective and measurements of corruption are biased. These control

variables are insignificant in for example Branko Milanovic’s (2005) research.

The data

This subsection will elaborate on the origin and modification of the data to create

transparency. Most of the data I used are originating from the World Bank national accounts

data files. All data together form an unbalanced panel set. Meaning multiple observations

over multiple countries in multiple years.

For the Gini coefficient, the World Bank POVCAL data is used. The POVCAL is a

database of 176 available countries. After selecting a sample of developing and OECD

countries with sufficient data, a sample of 59 countries is left. I aimed for a time series from

1990 till 2014, but some data are incomplete. For example, before 2004 OECD countries are

not covered by the POVCAL. To extend this data to 1990, I used data from the World Income

Inequality Database (WIID). The WIID consists of a collection of all measurements on

inequality. For consistency, I used one GINI coefficient within a country. Remaining gaps of

maximal 4 years, I interpolated linearly. Some variables are less complete. All data are put in

logs, except inflation and GDP growth per capital (annual%) to make sure negative

observations are not dropped.

When adding more control variables to the model “Stata”, a complete, integrated

statistical software package, takes out the incomplete years. The trade variable, or trade

openness, is measured by imports as a % of GDP, plus exports as a % of GDP. Trade covers

the whole time-series. Financial trade is also downloaded from the World Bank. This data

set also provides a good coverage of the time series 1990-2014. R&D as a percentage of GDP

from the World Bank only has data starting from 1996.

The countries used in the regression are countries of which at least 2 years per

country cover all the variables used. To get a clear comparison between OECD and

developing countries, all countries are selected by either being part of the OECD or having

lower (lower-middle) average income. The World Bank classification is used to separate

these groups on Gross National Income (GNI): (L) Low-income economies ($1,025 or less)

and (LM) lower-middle-income economies ($1,026 to $4,035). OECD, members are drawn

from the member list at

https://www.oecd.org

. OECD countries are also listed by the World

Bank as high-income-economies ($12,476 or more)

The comparison between OECD and developing countries is interesting because

these country groups differ on many levels. OECD are high-skill abundant, and developing

countries are low-skill abundant. The difference in the amount of capital stock is huge.

Average of $142,24 billion in high-income countries and $17,924 billion in low-income

countries (World Bank, 2014). The technological development is mostly placed in OECD

countries but the market for technology is growing in developing countries. Even though the

differences, or because of the differences, the trade between these country groups has

been growing over the last decades and so is the income inequality within and between

these countries. The countries used are listed below. These are picked based on sufficient

available data. For the developing countries, the data set is contaminated with

measurement errors and therefore the data is not as complete as the OECD dataset. The

countries are listed below.

OECD countries (high-income-economies) (29)

Australia, Austria, Belgium, Canada, Czech Republic, Chili*, Denmark, Estonia, Finland,

France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Latvia, Luxembourg,

Mexico*, Netherlands, Norway, Poland, Slovak Republic, Slovenia, Spain, Sweden,

United-Kingdom, United-States.

*indicating countries that recently joined the OECD.

Developing countries

Armenia, Bangladesh, Benin, Bolivia, Burkina Faso, Burundi, Cambodia, Democratic Republic

of Congo, El Salvador, Ethiopia, Gambia, The, Honduras, Lao People's Democratic, Republic,

Indonesia, Malawi, Mali, Moldova, Mozambique, Niger, Pakistan, Philippines, Rwanda,

Senegal, Sierra Leone, Togo, Uganda, Vietnam, Zambia

The regression model

In this subsection, the econometric cross country model is constructed to find the

relationship between Trade and income inequality for OECD countries compared to

low-income countries. Before, we have established the variables that are included in the model

based on economic theory. As explained the dependent variable will be the income

inequality expressed in the Gini coefficient, GINI. The independent variable central to this

paper is trade or trade openness. Financial trade openness is the second independent

variable. Also, a series of control variables will be added to avoid biased coefficients. The

adjustments and modifications needed for econometric testing will be explained.

𝐺𝐼𝑁𝐼(it) = α + β

_{𝑇𝑟𝑎𝑑𝑒 𝑜𝑝𝑒𝑛𝑛𝑒𝑠𝑠}

𝑖𝑡

+ β

𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑜𝑝𝑒𝑛𝑛𝑒𝑠𝑠

+ β₃ 𝑅&𝐷

+ β

𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛

+ β

𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡

+ β

Govermet Exp

+ β

𝐺𝐷𝑃 𝑔𝑟𝑜𝑤𝑡ℎ

+ β

𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛

+ 

+ ∂

+ µ

where the subscript i is to indicate the country and t being the time series. The term 

is a

full set of country dummies, ∂

represents a full set time dummies, and µ

captures all the

omitted factors. These extra dummies are added to capture the fixed effects of countries

and years. This way shocks in a certain year, or other influences that don’t change much

over time are accounted for. By expressing the model in this way, a causal direction is

assumed. The variables affect the Gini and not the other way around. Also, all variables are

contemporaneous, meaning there is no lag assumed (Allison, P. D. 2009). Last all variables

are logged except the variables that contain negative observations, Inflation and GDP

growth.

To get a clear comparison between OECD and developing countries the regressions

will be done three times, for three different regions. Once for the whole data set. Once for

all OECD Countries and once for developing countries. For each region, 7 regressions are

done. One summary model regression with only trade and FDI as an independent variable. A

full model with all control variables. In between one by one all control variables are added

to increase the prediction value of the model.

I have chosen a fixed effects regression because this gives the most efficient results

and this method matches the best with the panel data set. A fixed effects model treats

unobserved differences between countries as a set of fixed parameters that can either be

directly estimated or partial out of the estimating equations (Allison, 2009). First, the

requirements for fixed effects regression. The dependent variable must be measured for

each country for at least 2 years. These data need to mean the same but change over time.

This is the case for the data set. Including the fixed parameters in the model indicate using

the fixed effects model.

For empirical prove, I conducted a Hausman test for the full model of all regions.

H0: Random effects is appropriate

H1: Fixed effects is appropriate

Hausman test for all countries region

chi2(26) =

66.55

Prob>chi2 =

0.0000

Hausman test for OECD countries region

chi2(26) =

113.91

Prob>chi2 =

0.0000

Hausman test for Developing countries region

chi2(26) =

30.68

Prob>chi2 =

0.1999

For both all countries and OECD countries the H0 is rejected. For the developing countries,

the H0 is not rejected but this is probably due to the reduced observations in the full model.

Because the countries drop to 6 observed countries it may be possible that the within-group

variation is not big enough. Therefore, a random effects regression should be pulled. Using

Random effects opens up to omitted variable bias. Because the model is probably much

more complicated than conducted in this research and the identifying assumption* is

assumed, I have chosen to use the fixed effects regression throughout all the regions and

models, thereby excluding the omitted variable bias.

*Identifying assumption: Unobservable factors that might simultaneously affect the

left-hand side and right-left-hand side of the regression are time-invariant.

The hypotheses

To understand how the central question will be answered a clear hypothesis is formulated.

What is the effect of trade openness on the income inequality in developing countries

compared to the OECD countries? So, the null hypotheses, based on the basic economic

theory, is that trade decreases income inequality in developing countries compared to an

increase in income inequality in OECD countries. The alternative hypotheses are that trade

increases income inequality in developing countries, like the results of Branko Milanovic

(2005). Trade has a positive effect on income inequality in OECD countries and trade

openness is positive for all country groups. Shortly: the rich are getting richer and de poor

don’t profit that much.

Three regressions will be done, so the hypotheses also exist of 3 parts.

H0: the β of trade openness is smaller than 0 for developing countries

the β of trade openness is bigger than 0 for OECD countries

the β of trade openness is unequal to 0 for all countries

H1: the β of trade openness is bigger than 0 for developing countries

the β of trade openness is bigger than 0 for OECD countries

Empirical Analyses

Descriptive statistics

First of all, let us look at some summary statistics.

OECD country

Variables

Obs

Mean

Std. Dev.

Min

Max

Country

621

15.37198 8.447855

1

30

Year

621

2002.089 6.857787

1990

2014

Gini

620

32.38892 7.394461

19.49

57.25

Trade

621

91.45078

53.4828 19.73534 352.9038

Foreign direct investment

590

5.349092 19.56188 -57.42675 430.6151

Research and development

443

1.689207 0.9216993

0.25923 4.40744

Education

483

5.241816 1.172526

1.92723 8.61797

unemployment

613

8.188155 4.183025

1.256

27.466

Government expenditure

567

43.85837 8.876292

18.708

68.313

GDP growth

613

2.113933 3.349974 -14.57305 18.62113

Inflation

591

4.903648 23.39716 -4.479938 555.3812

Developing country

variables

Obs

Mean

Std. Dev.

Min

Max

Country

398

15.03015 8.222602

1

29

Year

398

2002.608 6.511539

1990

2014

Gini

398

41.16154 8.549592

23.9

65.76

Trade

398

68.63267 31.84574 18.88983 169.5345

Foreign direct investment

388

3.278112 3.428731

-2.75744 32.41621

Research and development

93

0.250253 0.162252

0.00544 0.87295

Education

205

9.446889 7.093168

0.99855 26.69957

unemployment

168

8.57897 5.901036

2.4

38.4

GDP growth

373

2.905516

4.43955

-30.7128 14.44751

Inflation

360

9.679322 17.33336

-8.48425 183.312

Some results stand out, first of all, the independent variable, the Gini, in OECD countries this

is about 10 percentage points lower than developing countries. The standard deviations also

differ with more than 1. This is as expected, the income inequality is higher in developing

countries. The other important notice is that research and development is the galling factor

in the developing country dataset. This is probably because there is no real data on research

and development since research and development is less important in developing countries.

Most innovation takes place in OECD countries which explains the 1 percentage point

difference between OECD and DC's. The total number of observations is less for developing

countries but this should not matter for the regression.

Correlation statistics

In this section the results of the empirical regressions will be discussed, results are shown

for the regions models separately. The regions are all countries, OECD countries, and

Developing countries (DC). For every region, the regressions run for the full model to the

summary model dropping one control variable at the time. To check if there is no

correlation between variables a correlation matrix is shown below.

OECD

Trade

FDI

R&D

Educ

Unemp

Gov. EXP

GDP

growth

Trade

1

FDI

0.344

1

R&D

-0.2836

0.007

1

Educ

-0.0588 -0.0037 0.5379

1

Unemp

-0.1647 -0.1277 -0.1918 -0.2237

1

Gov. EXP

-0.1396 -0.0327 0.5602 0.4869

0.1659

1

GDP growth

0.1424 0.0877 -0.3133 -0.2192

0.0409

-0.3244

1

Inflation

0.0975 0.0427

-0.288 -0.1201

-0.0003

-0.0932 0.1834

Developing

countries

Trade

FDI

R&D

Educ

Unemp

Gov. EXP

GDP

growth

Trade

1

FDI

0.2668

1

R&D

0.2933

0.453

1

Educ

0.6668 0.4116

0.5616

1

Unemp

0.013 0.2023 -0.1245 -0.3618

1

Gov. EXP

0.7155 0.4466

0.6455 0.9564

-0.3124

1

GDP growth

0.1692

0.206 -0.0348

-0.1

0.2721

-0.1217

1

Inflation

0.1985 -0.0396

0.3875

0.237

-0.2528

0.2457 -0.0314

The correlation of research and development with education has a clear causation. A higher

level of education creates more high-skilled labor. This, in turn, leads to more research and

development. The correlation between R&D and government expenditure is possibly

because governments fund some research and development projects. Because R&D is

slightly correlated with the other variables it could be wise to take R&D out of the model.

But since the effect of research and development is of interest for this thesis. The other

variables will be dropped in order of theoretical importance. Overall the correlation levels

of the developing countries are higher, this is most likely due to the limited data points. So,

taken that in consideration only general government expenditure is highly correlated with

education. This is due to the fact that education is measured as government expenditure on

education as a percentage of total government expenditure.

The results of the models for each region are presented in the tables below. For

each group, the independent variables in the summary model are the same. The results

show that trade is positively related to income inequality in OECD countries and negatively

related in developing countries. This is in line with the economic theory. Though the effects

are only significant when most control variables are dropped. For Developing countries

trade is, trough out all the models, insignificant. This implies that the income of both poor

and rich grow from trade by the same relative percentage. The rise of the Gini coefficient is

due to other factors than trade.

FDI is not significant for any of the samples. This implies that the profit from financial trade

is equally divided over the income groups. Both in developing countries and OECD countries.

A reason for this could be that foreign direct investment creates easy excess to financial

capital.

Again analyzing the coefficients, we notice contradiction between the results and earlier

economic theory. Trade is not significant adding the control variables. Though the prediction

levels of the model increase R-squared.

All countries

Summary

model

Dropping

Variables

Full

model

VARIABLES

1

2

3

4

5

6

7

Trade

0.0236

0.0897***

0.0360

-0.00389

-0.0103

-0.0114

-0.00622

(0.0189)

(0.0287)

(0.0343)

(0.0368)

(0.0373)

(0.0374)

(0.0377)

Foreign Direct Investment

-2.67e-05

-2.69e-05

-0.000356 -0.000434 -0.000452 -0.000464 -0.000486

(0.000172) (0.000146) (0.000473) (0.000469) (0.000469) (0.000470) (0.000471)

Research and development

0.0255**

0.0159

0.00878

0.00779

0.00872

0.00615

(0.0129)

(0.0169)

(0.0199)

(0.0202)

(0.0203)

(0.0204)

expenditure on education

0.0141

0.000710

0.0430

0.0429

0.0458

(0.0351)

(0.0405)

(0.0477)

(0.0478)

(0.0483)

unemployment

0.0598*** 0.0583*** 0.0590*** 0.0640***

(0.0143)

(0.0154)

(0.0155)

(0.0159)

Total government expenditure

-0.00144

-0.00124

-0.00116

(0.00149)

(0.00153)

(0.00154)

GDP per capita growth

0.000799

0.00102

(0.00131)

(0.00132)

Inflation

0.00240

(0.00150)

Constant

3.414***

3.088***

3.262***

3.301***

3.329***

3.321***

3.259***

(0.0786)

(0.122)

(0.167)

(0.180)

(0.183)

(0.183)

(0.188)

Observations

977

527

413

395

385

385

382

R-squared

0.056

0.127

0.142

0.190

0.176

0.177

0.183

Number of country1

59

45

39

35

35

35

35

Standard errors in parentheses

There are several reasons this might not be the case in this empirical analysis. First of

all, it is possible that extending the data set with two decades would show other results

because of the larger variations in trade and income inequality. Statistically speaking a series

of 1991 until 2014 is big enough for testing but perhaps the relationship has weakened

because the influence of trade has decreased over the last couple of years and other

variables are now stronger forces of economic growth.

Developing countries

Summary

model

Dropping

variables

Full

model

VARIABLES

1

2

3

4

5

6

7

Trade

-0.00973

-0.00619

0.0102

0.0129

-0.0690

-0.103

0.0429

(0.0290)

(0.0672)

(0.0799)

(0.130)

(0.152)

(0.171)

(0.205)

Foreign Direct Investment

-0.00145

0.00442

0.00938

0.0109

0.0114

0.0110

0.0192

(0.00179)

(0.00490) (0.00648) (0.00983) (0.0110)

(0.0114)

(0.0130)

Research and development

0.0684*** 0.0602*

0.152**

0.127

0.144

0.122

(0.0183)

(0.0344)

(0.0696)

(0.0761)

(0.0856)

(0.0857)

expenditure on education

-0.0645

-0.233

-0.295

-0.336

-0.397

(0.0713)

(0.138)

(0.200)

(0.222)

(0.223)

unemployment

0.151

0.0572

0.0272

0.00941

(0.133)

(0.145)

(0.162)

(0.159)

Total government expenditure

0.00470

0.00989

0.00285

(0.0108)

(0.0153)

(0.0160)

GDP per capita growth

0.00301

0.00106

(0.00607) (0.00615)

Inflation

-0.00958

(0.00777)

Constant

3.714***

3.869*** 3.823*** 3.607*** 4.214*** 4.387***

4.310***

(0.120)

(0.288)

(0.387)

(0.846)

(0.912)

(1.003)

(0.983)

Observations

388

93

64

46

42

42

42

R-squared

0.122

0.509

0.571

0.708

0.753

0.758

0.788

Number of country1

29

15

10

6

6

6

6

Standard errors in parentheses

Second, it could be possible that the data set of developing countries has too many

missing values, this creates multi-correlation between the variables. This is somewhat

corrected in the reduced model. Still, the data from developing countries are less reliable

than the OECD data. Household surveys are difficult in poor parts of developing countries

and the tax system will have some flaws in correctly predicting the lower part of the income

distribution.

OECD countries

Summary

model

Dropping

Variables

Full

model

VARIABLES

1

2

3

4

5

6

7

Trade

0.0423*

0.0823**

0.0659*

0.0362

0.0417

0.0337

0.0519

(0.0241)

(0.0330)

(0.0398)

(0.0409)

(0.0415)

(0.0417)

(0.0432)

Foreign Direct Investment

-2.91e-05

-7.36e-05

-0.000574 -0.000580 -0.000560 -0.000596 -0.000615

(0.000139) (0.000131) (0.000436) (0.000432) (0.000433) (0.000433) (0.000434)

Research and development

-0.0759*** -0.0605** -0.0577** -0.0591** -0.0567** -0.0546**

(0.0223)

(0.0246)

(0.0244)

(0.0253)

(0.0254)

(0.0255)

expenditure on education

0.118**

0.0783

0.104*

0.0987*

0.107*

(0.0461)

(0.0480)

(0.0561)

(0.0561)

(0.0572)

unemployment

0.0394*** 0.0421*** 0.0451*** 0.0519***

(0.0146)

(0.0156)

(0.0157)

(0.0163)

Total government expenditure

-0.00103

-0.000537 -0.000641

(0.00150)

(0.00154)

(0.00155)

GDP per capita growth

0.00212

0.00231

(0.00147)

(0.00148)

Inflation

0.00279*

(0.00165)

Constant

3.234***

3.076***

2.939***

3.044***

3.023***

3.029***

2.907***

(0.101)

(0.140)

(0.201)

(0.203)

(0.206)

(0.206)

(0.217)

Observations

589

434

349

349

343

343

340

R-squared

0.207

0.194

0.207

0.226

0.216

0.222

0.230

Number of country1

30

30

29

29

29

29

29

Standard errors in parentheses

Third, measuring income inequality by the Gini coefficient may be more sensitive to

changes in the middle of the distribution. Changes in the upper and lower part of the

distribution are less visible in the Gini coefficient (Kakwani, 1980). Therefore, the Gini

coefficient may underestimate the changes in income inequality. The bad measurement of

inequality may cause bias of the variables towards zero. If the income inequality is

measured by quintiles of 20% income groups, the lower and upper-income groups can be

weighted. This would give a better insight into the specific distribution of economic growth

through globalization.

Last, it might be possible that the effect of democracy and corruption are

underestimated. These are theoretically speaking strong factors that could influence the

income distribution but also have influence on trade openness and FDI.

If we look at the regression output, we notice that research and development is

significant for the smaller models of developing countries (0.0684***) and for all models in

OECD countries (-0.0759*** model 2) ( -0.0546** model 7). These results show that

research and development has a positive effect on income inequality in developing

countries. Research and development increases the demand for high-skilled labor. Since

developing countries have relatively little high-skilled labor the already high wages increase

and the low-skilled labor is replaced by more productive technology and low-skilled wages

decrease. This explains the positive effect of R&D on the Gini in developing countries. In the

OECD country group, a 1% increase in R&D causes a 0.08% decrease in income inequality.

This is hard to fully explain with the same reasoning as above. Though OECD is high-skill

abundant, an increase in demand would cause income inequality either way. Perhaps the

low-skilled group profits more from the increased productivity. Since the educational level

of low-skilled labor is much higher than in developing countries. This is in line with the

reasoning of Piketty (2014) that income inequality is caused by the race between

technological advancement and education. Where the gap between the two is smaller in

OECD countries.

Although the positive effect of education is contradicting the divergence force of

knowledge diffusion. This is perhaps because of the use of the wrong proxy for education

which underestimates private expenditure on education.

Conclusion and practice, reject or accept the hypotheses

The regressions don't show very strong results but let's have a look at the benchmark

model, which is the second regression. Here trade, financial trade, and technological

advancement are regressed, the basis of Jaumotte’s research. We see that in fact, for OECD

countries, trade has a positive effect on the Gini, 0.0823**(5% significance level). When

trade increases with 1% the Gini will increase with 0.08%. For all countries, this effect is

almost the same only of a significance level of 1%. For developing countries, there is no

significant effect. Trade might actually be equally divided over the poor and the rich.

Observing this implies that the null hypotheses are rejected. As explained before some

flaws in the model and the dataset may be the cause of non-significant effects. It is possible

that the focus on trade is outdated. Though many researchers agree that globalization is a

driver of economic growth it may be that the more recent data sets show that trade is in

fact not that important anymore. Perhaps the effect of technological advancement, which is

a recent development of the 21

_{century, is underestimated in this central question and is}

of much more importance. Jaumotte (2013) already introduced technological advancement

to be an important control variable. This research agrees that technological advancement is

of much more influence. Leaving the classical economic theory behind and accepting the

theory of Jaumotte (2013). Piketty introduced education but in this research education has

no effect.

Conclusion

In this research, I have tried to find the relationship between trade openness and income

inequality and compare this effect in OECD countries with developing countries. The

literature review gives a short look into the ambiguity about this topic. General consensus is

far from reached. With the unique dataset, including the most recent decades of 59

countries, this research will give new insight and perhaps lead to a greater understanding of

the relationship between trade openness and income inequality.

The answer to the central question is clear but not very satisfying. In general, we find

that the benchmark model for all countries trade has a positive effect on income inequality.

In OECD countries, trade openness also has a positive effect on income inequality. For

developing countries there is no significant effect of trade on income inequality. The

classical literature suggested significant relationships between trade and income inequality

but it seems like the findings are more in line with new findings and modern economic

theory. The findings suggest that research and development is a more important driver of

income inequality than trade. Seeing trade as a manner of distributing technological

advancement rather than directly affecting income inequality. A reason for this could be

that trade divides growth quite equally and both rich and poor profit equally. Technology is

actually the factor that creates a skill-gap and therefor an income-gap. This is in line with

recent research, where technological development and education play large roles in income

inequality. The role of education is not clear in this research so technological advancement

is the number driver of income inequality.

It is important to look critically at the way of measurement. The comparison

between OECD countries and Developing countries is difficult because the data set for

developing countries is too small to create a strong reliable model. This is due to

inconsistent and insufficient measurement points of income in developing countries. The

data search is growing and perhaps the future can give more complete datasets of the

whole world.

In further research, it would be necessary to focus on technology, education and

political systems and policies. This is a more modern view on the matter. Putting these

factors central to the income inequality question means it is important to find the right way

of measurement for both developing and OECD countries. Another interesting question

would be to research the between country income inequality.

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Appendix

All country

SUMMARY:

Correlation matrix:

Inflationc~u 951 6.711474 21.42169 -8.484249 555.3812

GDPpercapi~l 986 2.413386 3.816255 -30.71279 18.62113

Generalgov~i 881 36.04717 13.31418 8.738 68.313

lnunemp 781 1.987651 .4957844 .2279321 3.648057

lneduc 688 1.707547 .5425073 -.0014511 3.284647

lnrnd 536 -.0094558 1.054006 -5.213976 1.483294

lntrade 1,019 4.275369 .5184768 2.938624 5.866195

FDIGDP 978 4.527476 15.3747 -57.42675 430.6151

lntrade 1,019 4.275369 .5184768 2.938624 5.866195

lnGini 1,018 3.549506 .2371977 2.969902 4.186012

Year 1,019 2002.291 6.726223 1990 2014

Country 0

Variable Obs Mean Std. Dev. Min Max

Inflationc~u 0.1624 1.0000 GDPpercapi~l 1.0000 GDPper~l Inflat~u Inflationc~u 0.0563 0.0083 0.0563 -0.3673 -0.1123 0.0128 -0.2319 GDPpercapi~l 0.1304 0.0822 0.1304 -0.2735 -0.1960 0.0947 -0.2971 Generalgov~i 0.0532 0.0260 0.0532 0.7206 0.6355 -0.0050 1.0000 lnunemp -0.1573 -0.1156 -0.1573 -0.2358 -0.2761 1.0000 lneduc 0.1585 0.0501 0.1585 0.5615 1.0000 lnrnd -0.0831 0.0569 -0.0831 1.0000 lntrade 1.0000 0.3351 1.0000 FDIGDP 0.3351 1.0000 lntrade 1.0000 lntrade FDIGDP lntrade lnrnd lneduc lnunemp Genera~i (obs=382)