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Mälardalen University Västerås, 2011-06-02 School of Sustainable Development of Society and Technology (HST) Bachelor Thesis in Economics Tutor: Dr. Johan Lindén An investigation into the determinants of income inequality and testing the validity of the Kuznets Hypothesis Evaluating its relevance within Japan and China over time Yasir Khan Meenal Javed
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Page 1: An investigation into the determinants of income ...econ1.altervista.org/econ/edu/cup/reports/2011/inequality.pdf · Mälardalen University Meenal Javed Personal nr. 910506-0744 Bachelor

Mälardalen University

Västerås, 2011-06-02

School of Sustainable Development of Society and Technology (HST)

Bachelor Thesis in Economics

Tutor: Dr. Johan Lindén

An investigation into the determinants of

income inequality and testing the validity of

the Kuznets Hypothesis

Evaluating its relevance within Japan and China over

time

Yasir Khan

Meenal Javed

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Mälardalen University Meenal Javed Personal nr. 910506-0744

Bachelor Thesis in Economics Yasir Khan Personal nr. 881009-8130

2

Date: 2011-06-02

Level: C-thesis in Economics, 15 hp

Title: An investigation into the determinants of inequality and the validity of the

Kuznets hypothesis

Authors: Yasir Khan and Meenal Javed

Supervisor: Johan Lindén

Abstract

This study deals with testing some of the most widely discussed determinants of inequality

in literature within China and Japan, a developed and a developing country. The second part of the

paper deals with testing the Kuznets hypothesis in the two countries. First, we consider the

relevant literature on the topic and then employing at least three different functional forms of our

regression models, we conduct tests of the inverted-U relation for both countries. The results of

our study show that secondary education has a statistically significant and a negative relationship

with inequality while none of the determinants considered proved to be significant for Japan. A

Kuznets type relationship is also confirmed for China by using GDP and GDP2 as independent

variables while we find that the Japanese trend in inequality is better explained through a „cubic

hypothesis‟ suggested by Tachibanaki (2005). In our conclusion we speculate about a possible

recurring trend of income inequality following the pattern of: inequality-equality-inequality.

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Mälardalen University Meenal Javed Personal nr. 910506-0744

Bachelor Thesis in Economics Yasir Khan Personal nr. 881009-8130

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Contents Abstract

......................................................................................................................................................................... 2

Section 1: Introduction ................................................................................................................................... 5

1.1 Introduction .......................................................................................................................................... 5

1.2 Aim ........................................................................................................................................................ 6

1.3 Method .................................................................................................................................................. 6

Section 2: Why Japan and why China?............................................................................................................ 8

2.1 Reasons for focusing our study on these two countries ........................................................................... 8

2.2 Background of China and Japan ............................................................................................................ 9

Section 3: Data .............................................................................................................................................. 10

3.1 Data collection: ................................................................................................................................... 10

3.2 Delimitations of data and method: ..................................................................................................... 11

Section 4: Theoretical Framework ................................................................................................................ 12

4.1 Kuznets’ Hypothesis: ........................................................................................................................... 12

4.2 The Gini Coefficient as a measure of inequality: ................................................................................ 13

4.3 Gross domestic product (GDP) as a measure of development: .......................................................... 15

Section 5: Determinants of inequality: ......................................................................................................... 15

5.1 Why does income inequality exist? .................................................................................................... 16

5.2 The Determinants to be investigated ................................................................................................. 16

5.2.1 Average income per capita (GDP per capita) ............................................................................... 16

5.2.2 Foreign Direct Investment (FDI) inflows ...................................................................................... 16

5.2.3 International openness indicator ................................................................................................. 17

5.2.4 Urbanization ratio ........................................................................................................................ 18

5.2.5 Urban-rural income gap ............................................................................................................... 18

5.2.6 Average years of secondary and tertiary schooling ..................................................................... 18

5.2.7 Percentage of population aged 65 and above ............................................................................. 19

5.3 Empirical Analysis of Determinants .................................................................................................... 19

5.3.1 China ............................................................................................................................................ 20

5.3.2 Japan ............................................................................................................................................ 23

5.4 Determinants of inequality: results’ summary: .................................................................................. 26

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Mälardalen University Meenal Javed Personal nr. 910506-0744

Bachelor Thesis in Economics Yasir Khan Personal nr. 881009-8130

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Section 6: Kuznets’ Hypothesis ..................................................................................................................... 26

6.1 The debate on Kuznets’ Hypothesis: a review of relevant literature ................................................. 26

6.2 Empirical Analysis for Kuznets Curve .................................................................................................. 29

6.2.1 China ............................................................................................................................................ 30

6.2.2 Japan ............................................................................................................................................ 33

6.3 Analyzing and graphing the derived curves ........................................................................................ 35

6.3.2 China ............................................................................................................................................ 35

6.3.3 Japan ............................................................................................................................................ 40

6.4 Summary of results for Kuznets’ tests: ............................................................................................... 42

Section 7: Conclusion .................................................................................................................................... 43

Appendix: ...................................................................................................................................................... 45

References: ................................................................................................................................................... 51

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Mälardalen University Meenal Javed Personal nr. 910506-0744

Bachelor Thesis in Economics Yasir Khan Personal nr. 881009-8130

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Section 1: Introduction

1.1 Introduction

According to the IMF (International Monetary Fund, 1999), global output has grown by

more than 4% per year over the course of the past decade. World Bank estimates show that over

the period of 1981-2005, the number of people living on less than $1.25 per day has fallen from

1.4 to 1.9 billion. However, over roughly the same period from 1980-2002 the global inequality

has increased from 64.8 Gini points to 70.8 Gini points; an increase of approximately 9.3 %

(World Bank, 2009). Therefore, despite progress, the benefits of development have not been

experienced evenly. Regional disparities in standards of living and income inequalities are

mounting issues in both the developing and the developed world. This has raised serious

questions for policy makers globally on how to tackle disparities. There has been a heated debate

on the trends of inequality and its relationship with growth in different countries. The pivotal point

in this debate rests on one question that holds significant importance for those countries that have

been battling poverty and inequality: “Is there a trade-off between equity and growth or are they

complementary?” (IMF, 1999) This question is incorporated into a proposition formally known as

the Kuznets hypothesis.

Before delving into the subject of causes and analysis of inequality trends it is useful to

consider why inequality should be regarded worthy of investigation and what can be gained by

studying it:

Apart from being an interest area for policy makers who value the moral aspects of equality

such as social justice and fairness, inequality is widely researched due to its implications for

poverty reduction. It is known that for a given level of average income, the more unequal the

income distribution is the more people will live below the poverty line (Weil, 2009). Due to the

close relationship between income distribution and poverty; policies that promote equity can help

tackle income inequality. In a recent study, Fosu (2010) has suggested that inequality affects the

transformation of growth to poverty reduction, implying that a high level inequality would thwart

the increased benefits of growth. Regarding the responsiveness of poverty to economic growth,

Ravallion (1997) showed that growth elasticity of poverty decreases with inequality. However, the

mainstream literature on the topic maintains that the average level of GDP is the most important

determinant of well being of the poor (Dollar and Kraay, 2002). One argument that rejects growth

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as being always beneficial for the poor is represented by the Kuznets Hypothesis. It suggests that

in the early period of development income distribution worsens with growth and then improves as

the wider population catches up with the rise in income. If this were true it would suggest that

growth can be detrimental for the poor and the strategies for battling poverty would see a

significant change.

Thus, explaining the causes of inequality and its trends are truly worthy of investigation. It is

exactly these subjects which will be made the focus of this essay. The famous Kuznets hypothesis

will also be subjected to debate and tested in order to prove/disprove its validity within the case of

Japan and China; in addition the causes of income inequality in Japan and China will also be

touched upon in this paper.

1.2 Aim The aim of our paper is to first consider the determinants of inequality in both China and

Japan and to test the relevance of some of the most widely investigated causes of inequality in

literature. The second part of our paper tests the validity of the Kuznets Hypothesis in the two

countries. This is especially interesting since China is a developing country and Japan is an

industrialized, developed country. Finally, we hope to draw reasonable conclusions regarding the

following issues: Which is the most important determinant of inequality in case of each country?

Is the trend of inequality in both China and Japan characterized by the Kuznets Hypothesis or does

an alternate explanation exist?

1.3 Method We have first provided a foundation for our analysis by laying down the theoretical

framework for our investigation, through discussing the measurement of inequality and

development which are the main components of Kuznets hypothesis. Then we have reviewed the

relevant literature regarding the most extensively investigated causes of inequality which are then

tested in the two countries for their relative relevance; these include: Gross Domestic Product

(GDP), Foreign Direct Investment (FDI), International openness indicator, urbanization ratio,

urban-rural income gap, secondary and tertiary schooling and population aged 65 and above. In

order to test the effect of these control variables on inequality, a multiple regression model was

used with the figures for these variables obtained from World Bank, Ministry of Health, Welfare

and Labor of Japan and NBS (National Bureau of statistics of China); spanning the period 1985-

2005 for China and 1971-2005 for Japan.

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Then our paper tests the much debated Kuznets Hypothesis which has gained significant

popularity in the field of development economics as a universal socio-economic phenomenon.

Most of the studies in the past decades have concentrated on testing its validity in a cross-section;

however, we argue in light of the most recent literature that a cross-sectional test at a single point

in time is inappropriate for testing this hypothesis. In the past 10 years, it has become increasingly

possible to test Kuznets relationship over time, as many recent studies have, owing to the

availability of better quality and longer time series data thanks to the efforts of organizations like

World Bank. Thus, we have undertaken the task of testing the Kuznets relationship over time in

both Japan and China. For this purpose we have used three possible functional forms of a multiple

regression model to test the Kuznets relationship in the two countries.

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Section 2: Why Japan and why China?

2.1 Reasons for focusing our study on these two countries Simon Kuznets (1955) suggested that the level of economic development was the underlying

factor responsible for determining income inequality trends in a specific country. The position of a

country on Kuznets inverted-U curve trajectory depends on the stage of development that a

country has acquired. Thus, we have chosen to investigate China as a developing country and

Japan as an advanced, industrialized nation. According to the Kuznets process, a developing

country in the early stages of economic development must be in the increasing phase of the

inverted-U curve while the developed country should be in its decreasing phase. Thus, by

selecting a typical developed and a developing country we can investigate whether this

proposition is true.

China is especially an interesting choice as it is not only one of the fastest growing

economies in the world but also one with a rapidly increasing level of inequality; in 1990 the Gini

index was 35.5 and it reached 44.7 in 2004 which is a drastic rise of 26%. According to the Gini

index in World Bank Report 2004, China ranked 85th

out of 120 economies. Of the 35 countries

ranking below China, 13 had negative GDP per capita growth in 2002-2003, (Xiaolu, 2006). Thus,

it would be interesting for our investigation to consider the Chinese case of rising inequality and

infer whether it will reach its maximum and start to decline as the Kuznets process suggests. A

lack of such a maximum would show that we cannot expect economic growth to „correct‟

inequality by itself.

Interestingly, Japan has also displayed an increasing trend in income inequality in the past

decade despite being an advanced, industrialized nation. This can be observed from the difference

between the highest and lowest level of inequality experienced by Japan over the past three

decades; the Gini index of primary income was 0.349 in 1981 at its lowest and 0.498 in 2002 at its

highest, thus, yielding a difference of around 0.15 index points which amounts to a substantial

change in inequality (Tachibanaki, 2005). Japan is currently experiencing the same situation as

many other European developed countries such as Netherlands, Norway and UK where the level

of inequality has been rising since 1980s (Atkinson, Rainwater and Smeeding, 1994). Therefore,

in the case of Japan we can show how this recent increase has changed the direction of the overall

inequality trend and our study can infer whether the Kuznets relation is „dead or alive‟ in case of a

typical developed country. If rejected, we will suggest what alternate explanations exist.

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2.2 Background of China and Japan We pause here to consider the major changes in Chinese and Japanese economies in

relation to growth and economic development as both have under gone some drastic reforms.

China is currently the second largest economy after the United States with a Nominal

GDP of 4.985 trillion US$ in 2009 (World Bank). The GDP growth of China averaged around

9.5% for the last decade, making China one of the fastest growing economies. The GDP per capita

of China was 3,744 Yuan in 2009.

“The potential depicted in China‟s economy was not always like this, more than 30% of

Chinese lived under poverty in 1978” (Naughton, Ravallion and Chen, 2007). “The GDP per

capita in 1978 was 314 Yuan”, (Wei & Chao, 1982). The Chinese economy went under a

complete overhaul in 1978 and new economic reforms were implemented to fight poverty.

The Economic Reforms of 1978 saw the implementation of a „dual-track system‟ to grow

out of the planned economy and move towards market economy. “The „dual-track system‟, the

most distinctive element of the reforms, was to have a coexistence of traditional planned and

market channel system (Naughton, 1996). Apart from that “an „Open-Door Policy‟ was

implemented and changes in foreign trade policy system were made to enhance the foreign direct

investment in China” (Wei and Chao, 1982).

These economic reforms paid dividends.” In 1993 only 56% of the total labour force

worked in the agriculture sector, as compared to 71% in 1978. The overall average growth for the

same period was 9.5%. The integration of China into the world economy was equally dramatic:

Trade(exports plus imports) rose from 10% of GNP in 1978 to 36% in 1993 and the foreign direct

investment was $28 billion in 1993 compared to $2 billion in 1978 (Woo, Parker and Sachs,

1997).

Now, turning to Japan that has the third largest economy in the world, with a nominal

GDP of $5.068 trillion and a per capita GDP of $5.068 trillion in 2009. Japan has also experienced

drastic growth in the past century due to technological, industrial and structural changes but this

has recently slowed (CIA, 2011).

The start of Japan‟s historical growth and development can be tracked backed to the

famous Meiji Restoration of 1868. This initiated many important reforms where the feudal system

was stamped out and western legal and educational systems were adopted. However, the most

significant change came to Japan in the after math of the Second World War; structural reforms

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were implemented during the US occupation which changed the course of the Japanese society

and economy (Minami, 1998). These reforms included: the dissolution of ziabatsu monopolies,

decline in landlordism due to land reform of 1946-47, unionization of labor (which reached 50%

in 1940s), increase in farm income due to government policies of farm price support and

progressive tax reforms of 1946-51(which reduced inequality), (Minami, 1998). This resulted in a

fall in urban-rural disparity in post-war Japan and led to an era of rapid economic growth which

spanned the 1950s until the 1980s; during this period Japan experienced one of highest economic

growth rates in the world that averaged 10% in the rapid growth era of the 1950s-1960s

(Tachibanaki, 2005).

However, the economic growth slowed down dramatically when the “bubble economy”

collapsed in the 1990s. In the 1990s GDP grew at a rate of 1% yearly compared to the 4% yearly

growth in 1980s. Japanese economy later fell into one of the worst recessions in 2008, reporting

the GDP growth for that year of -5.2% in 2009 (CIA, 2011). The slow growth and recession

experienced after the end of the “bubble economy” has completely changed the lives of the

Japanese people. In Japan the number of the unemployed has risen to 4 million and the number of

homeless people has gone over 30,000 (Tachibanaki, 2005).

Section 3: Data

3.1 Data collection: The data on income inequality for China was obtained from WIID2c database provided

by UNU-WIDER (United Nations University – World Institute for Development Economics

Research) and the values for some years were also used from a study undertaken by Wu and

Perloff (2004) calculating the Gini measure of inequality from the data provided by surveys

carried out by NBS China (National Bureau of Statistics of China). The WIID2c database contains

a „high quality data-set‟ which has been subjected to various adjustments to facilitate

comparability. The Gini index values for income inequality range from 1981-2005. The WIID2c

database estimates for China are also based on surveys carried out by NBS China.

The data on income inequality for Japan has been obtained from the Income

Redistribution Survey (IRS) for the years 1971-2005, organized by the Ministry of Health, Labor

and Welfare for Japan. However, in the empirical analysis, when a longer time trend using

available observations from over the course of last century was to be considered; the Deininger

and Squire (1996) database provided by World Bank was used. The data from this database was

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used to provide values from the pre-war era and the early post-war era for which the IRS values do

not exist, which is the period spanning 1890-1959; the database values were also used to extend

the time series data beyond this period.

The World Bank online database provided data for GDP per capita, FDI (foreign direct

investment), Indicator of Openness, Population and Education. Data used for China was from

1981-2005 and for Japan it was 1971-2005.

The time series data for Gross domestic Product (GDP) in local currency units (LCU)

was obtained from World Bank online database for Japan and China. These values were deflated

using the GDP deflator provided for each country by World Bank; in order to control for

inflationary effects. The resulting real GDP was then divided by the total population of each

country to obtain the average income per capita (GDP per capita).

The international openness indicator has been calculated as the ratio of export plus

imports to GDP; the values of exports and imports were obtained from World Bank in local

currency units (LCU). These were deflated using the GDP deflator for the respective countries

before calculating the final value of the indicator.

The figures for the percentage of population aged 65 and above was calculated by

dividing the figure for population aged 65 and above by the total population. The urbanization

ratio has been calculated as a percentage of urban population to the total population.

The urban-rural income gap has been calculated by taking the ratio of per capita annual

disposable income of urban households and per capita annual net income of rural households. This

ratio was only calculated for China, as the data was readily available; while in case of Japan

because data regarding the average income in urban and rural households was not available

separately, therefore this variable was not included. The values used to calculate the ratio were

taken from NBS China for the period spanning 1985 to 2005.

3.2 Delimitations of data: The limitations in our data lie in the fact that in order to prolong the time series observations

for Gini index for both Japan and China we have used figures from two different sources which

might have an impact in terms of level of comparability of these values; since we have not

attempted any adjustments ourselves. Barro (2000) has also voiced these concerns: “Differences in

method of measurement arise due to aspects such as: whether the data is for individuals or

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households, whether inequality is calculated for income gross or net of taxes or for expenditure

rather than income”, (Barro, 2000, p.17). Again, it‟s the trend we are interested in, not the

exactness of measurement.

Section 4: Theoretical Framework

4.1 Kuznets’ Hypothesis:

Simon Kuznets (1955), a Nobel laureate, presented his now famous inverted-U hypothesis

which formally came to be known as the Kuznets Hypothesis. This proposition is one of the most

enduring arguments in the history of social sciences. It was first made public in Kuznets

Presidential Address to American Economic Association in 1954 and later discussed in his famous

paper in 1955 called: “Economic growth and income inequality” (Moran, 2005). In his discourse

on the size distribution of income and how it varies with the level of development, Kuznets

attempted to explain the decrease in income inequality in the 1920s in developed countries such as

USA, England and Germany after a period of stability in inequality trends. This was accompanied

by increases in average per capita income. He called this decrease a „puzzle‟ and suggested that

there were at least two forces supporting an upward trend in inequality; the concentration of

savings at the top of the income distribution and urbanization, as a consequence of

industrialization (Kuznets, 1955).

Kuznets attempted to explain the latter part of the puzzle by taking a simplistic approach, he

divided the economy into two sectors: agricultural/rural and industrial/urban. He further suggested

that the sectoral change, where there is a shift away from agriculture and towards other urban

sectors, should cause an increase in inequality. He explained the reason for this to be that the

exacerbation of the income gap between urban and rural citizens due to the shift from rural to

urban sectors. This expected worsening of income gap was justified in the following way: the

distribution of income in the urban sector is wider than in the rural sector; thus a shift of

population from a more equal (rural sector) to a more unequal (urban sector) would increase the

weight of the more unequal sector, thereby increasing inequality. Also, the average per capita

income is higher in the urban sector than the rural sector, thus increasing the urban-rural income

gap as more rural population shifts to the urban sectors. This should result in a rise in overall

inequality according to Kuznets.

However, through a numerical illustration based on several assumptions and inference based

on time series data on income distribution, Kuznets suggested that inequality rises at the early

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stages of development, then after reaching its peak, it levels off and then it starts to decline in the

mature stages of economic growth. He reasoned that the decrease in inequality in the advanced

stages of development is caused due to the rise in income of the poor in the non-agricultural

sector, as they benefits from urban facilities of education and health etc. Kuznets called this

phenomenon a “long swing in inequality” (Kuznets, 1955).

This theory implies that graphing the level of inequality as a function of the level of GDP

per capita would give an inverted-U relationship, formally known as the Kuznets curve. This

hypothesis sparked research in the area of growth and inequality as many economists have tried to

prove, disprove or explain the hypothesis (Weil, 2009).

However, Kuznets admitted to the lack of empirical data for testing this proposition. In his

paper he states “the paper is perhaps 5% empirical information and 95% speculation, some of it

possibly tainted by wishful thinking”, thus acknowledging the speculative nature of his

proposition (Kuznets, 1955).

Now, nearly 55 years later the theoretical and empirical standing of Kuznets proposition is

still debatable and subject to uncertainty.

4.2 The Gini Coefficient as a measure of inequality: The most extensively used measure of inequality is the Gini coefficient. In order to construct

the Gini coefficient for income inequality, the data on the incomes of all the households (or a

representative sample of households) is utilized. By first arranging the households form lowest to

highest income; we can then calculate the fraction of total income earned by the poorest 1%, 2%

and so on (Weil, 2009). This information is then used to graph the cumulative percentage of

Kuznets Curve

GDP per capita

Inco

me

Ineq

ual

ity

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households (from lowest to highest income) on the horizontal axis and the cumulative percentage

of income (or expenditure) on the vertical axis. Such a graph is known as the Lorenz curve

(Haughton and Khandker, 2009).

The Lorenz curve has a bowed shape owing to the level of income inequality. If the income

were distributed perfectly equally, the Lorenz curve would be a straight line with a gradient of 1.

This line is also known as the “line of perfect equality”. The degree to which the Lorenz curve is

bowed represents the level of inequality and this aspect is made the basis for calculating the Gini

Coefficient. The Gini coefficient is calculated by measuring the area between the Lorenz curve

and the line of perfect inequality and then dividing this area by the total area under line of perfect

equality. For an income distributed perfectly equally the Gini coefficient will have a value of 0

and if the income is distributed perfectly unequally then the Gini coefficient will have a value of 1

(Weil, 2009).

Since, either income or expenditure can be used to calculate the Gini index it is important to

keep in mind income is more unequally distributed than expenditure. Thus when comparing

inequality between different countries one must either use the Gini index based on household

expenditure or household income but not to mix the two.

Based on the criteria that form a good measure of inequality, the following are some of

attractive features of the Gini index: Gini coefficient is mean independent i.e. if all incomes are

doubled, the Gini value would not change. The Gini index is also independent of the population

Line of perfect equality

Lorenz curve

Cumulative Percentage of household income

Cumulative percentage of households

100

90

80

70

60

50

40

30

20

10

0

0 10 20 30 40 50 60 70 80 90 100

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size. In addition to that, Gini index satisfies the “Pigou Dalton Transfer sensitivity” which

suggests that the transfer of income from rich to poor should reduce inequality.

However, the drawback of using the Gini coefficient lies in its lack of decomposability.

Inequality may need to be broken down by population groups, income sources or other dimensions

for this purpose the Gini index cannot be used as it does not allow decomposability into additive

groups in this case measures like the „Theil‟s index‟ are more suitable. Nevertheless, for this paper

due to the sake of ease and availability of data; the Gini index will be used as a measure of

inequality (Haughton and Khandker, 2009).

4.3 Gross domestic product (GDP) as a measure of development:

The level of inequality is deeply related to level of economic development in a country as

initially emphasized by Simon Kuznets (1955) in his famous Kuznets Hypothesis. When

examining an inequality trend in a single country over time, its variation with economic growth

could provide some useful insight.

The level of development in a country can be measured through different indicators;

however, the most widely used measure is the Gross domestic Product (GDP). GDP represents the

value of all goods and services produced in a country in a year. It can be calculated either as the

value of output produced in a country or equivalently as the total income in the form of wages,

rents, interest and profits earned in a country. Thus, GDP is known as output or national income,

synonymously (Weil, 2009). In our paper we have employed GDP as a measure of development

owing to its wide use.

However, GDP is not a perfect measure of economic development. Many aspects of

economic welfare are not measured by GDP. This has also been pointed out by Simon Kuznets

(1973) in his paper “Modern economic Growth”. He stressed that the conventional measures of

GNP (Gross national product) or GDP (Gross domestic product) are not representative of the costs

of social and economic structural changes that a country faces in the process of economic growth.

In addition to that it also fails to account for other positive effects such as education and health

improvements etc. (Kuznets, 1973).

Section 5: Determinants of inequality:

However, before turning to explaining the trends in income inequality and testing the

Kuznets hypothesis in China and Japan, it is useful to consider the respective causes of income

inequality and why it exists.

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5.1 Why does income inequality exist?

The reason why income inequality exists is because people differ from each other in aspects that

have an effect on their incomes. Such differences can occur in possession of human capital,

physical capital, location of residence, specific skills etc. While considering the sources of income

inequality across countries, it is important to focus on “the distribution of different economic

characteristics among a population and (about) how different characteristics translate into different

levels of income”, (Weil, 2009, p.380). Therefore, “inequality in a given country could change

over time (both) because of a change in the way characteristics are distributed or (and) rewarded”,

(Weil, 2009, p.381).

5.2 The Determinants to be investigated In our study, we have selected a few explanatory variables that have been widely discussed

in literature in relation to inequality. These variables have been tested to evaluate their importance

for income inequality in Japan and China. These factors can be roughly divided into three

categories: the first category consists of factors related to economic growth, the second category

relates to provision of public goods and the third category relates to demographics.

For the first group of growth related determinants, the following factors have been

chosen to assess their effect on inequality in China and Japan: average per capita income (GDP

per capita) and its square, foreign direct investment (FDI), urbanization ratio, international

openness indicator and urban-rural income gap.

5.2.1 Average income per capita (GDP per capita)

GDP per capita and its square are used as development measures in order to examine their

relationship with inequality and (in a later section) to test the validity of the Kuznets hypothesis.

According to the Kuznets Hypothesis, the expected signs of the relationship between inequality

and GDP per capita and its square should be positive and negative respectively.

5.2.2 Foreign Direct Investment (FDI) inflows

The relationship between foreign direct investment and inequality has been extensively

investigated under the studies dealing with effects of globalization. Typically, income inequality

has been found to be positively related to FDI. Evans and Timberlake (1980) state that

dependence on foreign capital exacerbates income inequality by “distorting the occupational

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structure of developing economies, bloating the tertiary sector and producing highly paid elite and

large groups of marginalized workers”, (Lee, Nielson and Alderson, 2007).

Alderson and Nielson (1999) have found an inverted-U shaped relationship of inequality

with FDI stock per capita. They have explained this relationship by systematically linking the FDI

inflows and outflows with a country‟s level of development. As suggested by Dunning (1981),

less developed countries have little inward and outward FDI, countries at intermediate

development stage have excess inflows over outflows and developed countries have excess of

outflows over inflows. The curve therefore, portrays declining dependence on foreign investment.

Thus, we would expect a positive relationship between inequality and FDI inflows for China and a

negative relationship for Japan.

5.2.3 International openness indicator

The standard trade theory suggests that the effect of opening up an economy to international

trade on the income distribution depends on the factor endowments. For countries that are highly

endowed in human and physical capital, trade expansion would tend to lower the relative wages of

unskilled labor and thereby increase inequality. This would involve an increase in imports of

products intensive in unskilled labor and increase in exports of products intensive in human and

physical capital. For countries that are highly endowed with unskilled labor, international

openness would raise the relative wages of unskilled labor and lead to a lower degree of

inequality. This view suggests that openness would raise inequality in rich countries and lower it

in poorer countries (Barro, 2000).

However, this is in conflict with the general views of the popular debate on globalization

which suggests that international openness would mostly benefit the well off groups in society;

this effect would be more pronounced when the average income is low. Therefore, openness

would increase inequality in poor countries. Barro (2000) has shown that openness has a

statistically significant, positive relationship with inequality and that the openness ratio has a more

pronounced positive relation in poorer countries and gets weaker as the country gets richer (Barro

2000). Thus, we expect a significant, positive relationship of openness with inequality for China

and a very weak positive relationship for Japan.

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5.2.4 Urbanization ratio

Kuznets (1955) has pointed out in his hypothesis the inequality inducing effects of

urbanization in the earlier stages of development. During industrialization the migration from

agricultural sector to non-agricultural and urban sectors can cause low income groups to rise,

causing rising urban inequality without simultaneously reducing rural inequality. This factor is

expected to be more important for China which facing a rise in urbanization compared to Japan

which is largely an urban nation with only 4% employed in agriculture. Thus, we would expect a

positive relationship between inequality and urbanization, at least in China.

5.2.5 Urban-rural income gap

This factor has only been considered for China due to the availability of data and its relative

importance for China. Urban-rural income gap contributes to the overall inequality; this has been

shown to be true for China according to recent studies. Sicular, Yue, Gustafsson and Li (2006)

concluded that in 2002 the urban/rural income gap was the source of ¼ of the overall inequality.

Thus, we expect a positive relationship of income inequality with urban-rural income gap.

In the second category that relates to the provision of public goods, we have chosen to

examine the effect of secondary schooling on income inequality.

5.2.6 Average years of secondary and tertiary schooling

The provision of public education has been found to have a positive effect on efforts for

inequality reduction; this is because education helps increase the stock of human capital within

middle- and low- income groups and improves their chances of gaining employment (Xiaolu,

2009). In a recent study, Barro (2000) found a negative relationship (although not significant)

between inequality and average years of secondary schooling for aged above 15. While higher

education was found to have positive relationship with inequality (Barro, 2000). Thus, we expect a

negative relationship between inequality and secondary education and a positive relationship

between inequality and tertiary/higher education.

The third category, in which our last factor falls into, deals with demography. This factor is

based on studies, such as the one by Gustafsson and Johansson (1999), which argue that the

change in population structure can affect income inequality on the household level.

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5.2.7 Percentage of population aged 65 and above

If there are a large number of children or elderly people in a household the mean

disposable income would be low. Thus, a rise in number of elderly or young people would mean a

greater level of inequality. However, the government policies on welfare such as: pension policy

and family policy can affect the relationship between age and equivalent income. We have chosen

„percentage of population aged 65 and above‟ to investigate the effects of demographic structure

on inequality only in Japan; since this factor is more Japan specific. Japan‟s share of aging

population has been on the rise; in 1999 the percentage of population aged 65 and over was 16.7%

and in 2009 it was 21.9%.

Gustafsson and Johansson (1999) found a negative relationship for developed countries

between the percentage of population aged 65 and above and income inequality. However, the

negative coefficient could be owing to the policy measures related to income redistribution;

therefore it is not clear whether the expected relationship is negative or positive in advance.

5.3 Empirical Analysis of Determinants In Section 5.2 we have discussed the variables which could affect income inequality; in

addition to that specific variables for China and Japan were also mentioned. This Section would

show the relationship of these variables with income inequality (measured by the Gini coefficient)

by using regression analysis.

Recall that the factors affecting income inequality were:

1. Gross Domestic Product per Capita (GDPPC).

2. Foreign Direct Investment (FDI)

3. Indicator of Openness (IO)

4. Average years of Tertiary Schooling (TEDU)

5. Average years of Secondary Schooling (SEDU)

6. Urban Population (UP)

7. Urban/Rural Income Gap(U/R Y)[for China Only]

8. Population aged 65 and above (OAGE)[for Japan only]

The Regression analysis would be done by dividing the section in two parts. First the results will

be discussed for China and then for Japan.

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5.3.1 China To explore the impact of the previously stated variables on China‟s income inequality, the

following regression model is estimated:

As explained in Section 5.2; Gross Domestic Product per Capita (GDPPC), Foreign Direct

Investment (FDI), Indicator of Openness (IO) and Urban Population (UP), Urban/Rural Income

Gap (U/R Y) and Tertiary Education (TEDU) are expected to have a positive effect on income

inequality in China, indicated by the positive values of their Betas. The Secondary Education

(SEDU) is expected to have a negative impact on income inequality.

The data used to estimate the regression model spans the period 1985-2005 which is

provided in table (A) added in the appendix. Table 1 (below) shows the results of the test we had

done with the Gini coefficient as the dependant variable and other several factors; where we have

added and dropped some of the variables in each test. Both the tables in this section have some

values marked with a single star (*) and double stars (**). A single star (*) indicates that the p-

value attained for the relevant variable is relevant at 10% significance level, double star (**)

shows a significance level of 5%. The p-values for the variables are stated in brackets under the

coefficients of the variables.

Variables Test 1 Test 2 Test 3 Test 4

Urban/Rural income gap

0.049213* (0.084966)

0.049804** (0.021087)

0.048565** (0.007)

0.033937** (0.018551)

real GDPPC (LCU) 1.27E-05

(0.657624) 1.35E-05

(0.373933) 1.2E-05

(0.118598) 1.9E-05** (0.005363)

Urban population 0.020048

(0.121979) 0.019699**

(0.007061) 0.019166**

(0.000378) 0.023708**

(1.3E-06)

Average years of secondary schooling

-0.25455 (0.384186)

-0.24639 (0.110746)

-0.23275** (0.015444)

-0.32089** (0.000187)

Foreign direct investment

-0.00254 (0.44197)

-0.00262 (0.253319)

-0.00276 (0.1399)

Indicator of openness

-0.01302 (0.914084)

-0.01347 (0.907113)

Average years of tertiary schooling

0.086432 (0.97333)

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R Square 0.999614 0.999614 0.999614 0.999551

Standard Error 0.009303 0.008965 0.008666 0.009044

F in ANOVA 4810.305 6043.177 7761.969 8906.036

Observations 20 20 20 20 Table 1: The values stated under ‘Test’ columns are the coefficients for the stated variables in the respective rows. The values stated in brackets under the coefficients in italic are the corresponding P- values for the variables.

Before interpreting the results of the analyses we must run a global test. “The purpose of

running this test is to infer whether the independent variables, all of them, used in the regression

model effect the dependent variable at all. Stating it simply, we would test whether all the betas

are zero or not” (Lind, Marchel and Wathen, 2010).

The Null Hypothesis Test is:

H₀: β₁ = β₂ = β₃ = β₄ = β₅ = β₆ = β₇ = 0

The Alternative Hypothesis is:

H₁: Not all the Betas are zero.

Using the F distribution table at 5% significance level, we find that the corresponding value

for df (7,13) is 2.83, for Test 1,meaning that if the value of „F‟ in ANOVA table is smaller than

that, then the null hypothesis test is not rejected. But since from the table (1) we can see that the

value of F in ANOVA is 4810.305, i.e. is greater than 2.66, the null hypothesis is rejected.

The co-efficient of determination, stated as R-Squared in Regression Statistics, is defined as

“ The percentage of variation in the dependent variable explained, or accounted for, by the set of

independent variables”, (Lind, Marchel and Wathen, 2010). In the case of Test 1, R-Square value

is 0.9996, which states that 99.96% of the income inequality is due to the independent variables

we used for this model. “The R square‟s value could only lie between 0 and 1. The higher the

value is the better the model is” (Lind, Marchel and Wathen, 2010).

Up until now, the model looks really good but the p-values given in brackets in Table 1

under every coefficient value suggests otherwise. For example p-value of real GDPPC is 0.657 in

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Test 1. This value is interpreted as there is 65.7% chance that the co-efficient of „real GDPPC‟

might deviate from the stated value. Looking at the results only the „Urban/Rural Income GAP‟

would be acceptable at 10% significance level. So this result is rejected and no further

interpretation is required for these results.

If you see at Test 1, the variable with the highest p-value is „Average years of Tertiary

Education‟. For Test 2 we ran the regression analysis without this determinant, so that its removal

might decrease the p-values of the other determinants and consequently make the results more

reasonable.

The new estimated model for Test 2 is:

Before interpreting the data, we would follow the same prerequisites as we did for Test 1.

The global test indicates that we should reject the ´Null Hypothesis Test‟, as F distribution Value

for df (6,14) is 2.84 and less than value of F in ANOVA i.e. 6043. The R-Square is the same as it

was for Test 1, so the removal of „Average years of Tertiary Education‟ did not affect this model

at all. By now it would be clearly evident from the table that the p-values for many variables have

decreased in high proportion. The „Urban/Rural Income Gap‟ and „Urban Population‟ are now at

5% level of significance. But still other four variables have p-values that are statistically

insignificant. We would now remove the „Indicator of Openness‟, the variable with the highest p-

value. We would create a new model for Test 3;

This model easily passes the global test as the value of F in ANOVA is 7761.9 and the F

distribution Value for df(5,15) is 2.9. Again we see that the removal of the variable does not

change the relevance of our model at all, the R-Square is the same as it was in Test 1 and 2. But

two of the variables used in this model still have p-values which do not fall in the 10% or 5% level

of significance. However, it is obvious that the p-values for all the variables have started to show

significance.

A new model, which excludes the variable with the highest p-value, is formed for Test 4.

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This model is acceptable, as the table shows that all the variables are marked with double

stars (**). The F value in ANOVA is also very high. The R Square also suggests that 99.9% of the

change in China‟s Gini-coefficient is due to the independent variables used in this model. The

equation attained is:

Looking at the equation above, all the signs of the coefficients are exactly what we expected

them to be. GDP per capita (GDPPC), Urban/Rural Income Gap (U/R Y), Urban Population (UP)

have a positive impact on China‟s Gini coefficient, where as Secondary Education (SEDU) has a

negative impact on the Gini of China. It is worth mentioning here that as discussed previously, the

equation reflects the aspects shared by a growing economy.

Since our data consists of observations over time, we will conduct the Durbin-Watson test

for auto-correlation. Auto-correlation can cause the results of regression analysis to be inaccurate

and this problem mostly occurs with time series data (Lind, Marchel and Wathen, 2010). To

conduct the test for auto-correlation the null and alternate hypothesis are:

H0: No residual correlation

H1: Positive residual correlation

The decision rules for the Durbin-Watson test are: values less than dL lead to rejection of the

null hypothesis, values greater than dU will result in the hypothesis not being rejected and values

between dL and dU suggest that the result is inconclusive. At a 5% significance level, k (number of

independent variables) =4 and (sample size) = 20 observations (for test 5 in table 1); the critical

values for d are: dL=0.90 and dU=1.83. The value obtained for the Durbin-Watson test statistic is

2.5304 > 1.83= dU. Thus, the hypothesis is not rejected and there is no confirmation of auto-

correlation.

5.3.2 Japan The analyses for Japan would follow the same pattern as for China. The first test would

include all the determinants, if the results are insignificant we would remove the variable with the

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highest p-value, in order to obtain the model which is statistically significant. The original model

for Japan with all the estimated variables is:

The Gross Domestic Product per Capita (GDPPC), Foreign Direct Investment (FDI) and

Indicator of Openness (IO), all three of these variables could either have a negative or a positive

beta. The co-efficient of the respective betas depend on which stage of development the Japanese

economy is in. This has been explained in detail in section 5.2. Both the indicators of education

would behave in the same way as they did for China, i.e. tertiary positive and secondary negative.

„Population aged 65 years and above‟ and „Urban Population‟ are expected to increase Japan‟s

Gini coefficient when they increase.

The data used to estimate the regression equation spans 1971-2005 which is provided in

table B added in the appendix. The summary of regression analyses is given in table 2 (pg 25).

Japan Test 1, includes all the determinants of inequality, has F in ANOVA at 995 whereas F

distribution value for df(7,13) is 2.83. So the global test is passed. The R Square is at 0.9981 but

all of these indicators are statistically insignificant because not even one variable falls in 5%

significance level, all the determinants have very high p-values.

For Test 2 we exclude „Foreign Direct Investment‟, as it had the highest p-value in

Test 1. The new model is:

Variables Test 1 Test 2 Test 3

real GDPPC (LCU) -4.5E-08

(0.584185) -3.9E-08

(0.587567) -1.5E-08

(0.727409)

Average years of tertiary schooling

0.727778 (0.446482)

0.678496 (0.440966)

0.33443 (0.305518)

Average years of secondary schooling

-0.19001 (0.485545)

-0.17765 (0.483585)

-0.10054 (0.555935)

Urban population 0.014554 (0.273813)

0.013885 (0.2577)

0.010086 (0.207814)

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Indicator of openness -0.141 (0.506)

-0.12821 (0.504187)

-0.12569 (0.499901)

Population ages 65 and above

-0.00998 (0.667989)

-0.00946 (0.670501)

Foreign direct investment -0.018

(0.858229)

R Square 0.998137 0.998132 0.998107

Standard Error 0.023067 0.022257 0.021646

F in ANOVA 995.0008 1246.939 1581.829

Observations 20 20 20 Table 2, The values stated under ‘Test’ columns are the coefficients for the stated variables in the respective rows. The values stated under the coefficients in italic are the corresponding P- values for the variables.

Just as we had in Test 1; Test 2 passes the global test with F in ANOVA, 1247, is greater

than F distribution value for df(6,14), which is 2.84, the R square is at 0.9981, still none of the

determinants are statistically significant at 5% level.

The results for test 3 are shown in the table. This model passes the global test. It excludes

the variable with the highest p-value in the prior test, i.e. „Population age 65 and above‟. Still none

of the variables fall under 5% significance level. After Test 3 was concluded GDPPC was

rendered the most insignificant variable with the p-value of 0.727, which would suggest the

exclusion of one of the most important determinants of inequality, therefore the tests were

concluded. So it was clear that, according to the data none of the variables proved to be

statistically significant as determinants of the Gini coefficient in Japan. A possible reason for this

might be that these factors are either not important determinants of inequality in Japan or it could

be due to inaccuracy from using a simple estimated regression model for time series data. Perhaps,

the cause was auto correlation? We can compute the Durbin-Watson statistic for test 1 in table 2;

at 5% significance level, 20 observations and 7 independent variables, the critical values are:

dL=0.595 and dU=2.339. The value computed for the Durbin-Watson test statistic in this case was

2.3601>2.339 =dU. Thus, the null hypothesis is not rejected and the residuals are not auto-

correlated. Then perhaps these determinants are not important for Japan.

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5.4 Determinants of inequality: results’ summary: The tests for the determinants of inequality show that in China real GDPPC, urban-rural

income gap and percentage of urban population are significant determinants of inequality and they

all affect it positively. While secondary education has a negative effect on inequality in case of

China. The determinant which has the most influence on the Gini Index for China is secondary

education; an increase in the average years of secondary schooling by one year would decrease the

Gini index by 0.321. This might seem a bit drastic but this negative relationship has been

confirmed for China even by Xiaolu (2006) who found it to be an important factor. In case of

Japan no statistically significant results were obtained indicating that perhaps these determinants

are not relevant in case of Japan and that other factors which we have not been included might be

at play. All of these results have been tested for auto-correlation and no evidence of it has been

found.

Section 6: Kuznets’ Hypothesis

6.1 The debate on Kuznets’ Hypothesis: a critical review of relevant

literature The intellectual history of the Kuznets hypothesis can be broken down into three periods:

the first period spans from 1955-1970 where the hypothesis came to be treated as a „black box‟, an

undisputed fact, forming the foundation of the expanding field of development economics. In the

second period from 1980-1990, the inverted U-curve hypothesis was challenged leading to

contradictory findings and an inconclusive debate; also described as the “opening of the black

box”. This era led to a change in the way inequality was interpreted and understood in context of

development economics. The third period from the 1990s till today is shaped by continuing debate

on the validity of the Kuznets hypothesis, where some are still persistent on its soundness while

others have provided alternate explanations (Moran, 2005).

As stated previously, Kuznets inverted-U hypothesis can either be tested in a cross-section

(a group of several countries) at a single point in time or over time within a country. The

discussion below considers the debate which revolves around these methods used for the test.

During the first period spanning from 1955-1970, (due to the limited availability of over-

time observations) there was a surge in the number of cross-sectional studies which confirmed the

U-curve hypothesis; the Gini Index as a dependent variable was regressed with a quadratic term in

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GDP or GNP per capita as explanatory variables. The general view was that the countries forming

an inverted-U relationship got to this point by following an inverted-U trajectory of increasing and

then decreasing inequality over time (Moran, 2005).

One of the most well known cross-sectional studies is one by Montek S. Ahluwalia (1976);

he evaluated the empirical basis for the Kuznets hypothesis by using cross-country data. He

showed that an inverted U-shaped pattern is obtained through regressing the log of GNP per capita

and its square as explanatory variables (Ahluwalia, 1976).

By 1980s, the beginning of the second period (1980-1990) in the history of Kuznets

proposition, the following problems emerged which made the inverted U-curve hypothesis

increasingly questionable and made the support from cross sectional studies look like an

“empirical mirage”:

The empirical findings always showed an inverted U-curve in cross section whereas no such

pattern was observed longitudinally (in a single country over time) (Moran, 2005). This point was

also brought out by the results of a widely cited study by Deininger and squire (1998) which tested

the Kuznets relationship in individual countries and showed that for 40 out of 49 countries or 80%

of the sample, there is no statistically significant U- or inverted U-shaped trend. For the remaining

9 countries; 4 show a U-shaped curve instead of an inverted- U and the remaining 5 countries

(10% of sample) support an inverted-U shaped curve. Thus, this study does not find convincing

evidence of existence of a Kuznets relationship.

Li, Squire and Zou (1998) have further shown that inequality is largely stable within

countries. They showed that among their results 32 out of 49 countries or 65% of the sample

found no significant time trend of inequality. Of the 17 countries with a significant time trend, in

10 countries it was quantitatively small. Li, squire and Zou (1998) also point out that the stability

of inequality over time seems to run counter to the Kuznets hypothesis since significant increase

in average income has accompanied this stability.

Giorgio Gagliani (1987) has also questioned the earlier validation of the inverted-U curve

when tested in a cross-section, terming it as a “statistical composition effect”. Criticizing the

methodology of cross-sectional analysis, Gagliani states that any results of such a method would

depend on luck (Gagliani, 1987, p.323).

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Deininger and Squire (1998) have also criticized the earlier approach of using a cross-

sectional test of Kuznets hypothesis, stating that it is incorrect to test an intrinsically intertemporal

(over time) trajectory of development process by using cross-sectional data.

Furthermore, when country specific intercepts are introduced in the cross-country regression

over time, Deininger and Squire (1998) show that Kuznets relation loses significance and even

reverses sign, thus leading to a real U-shaped curve instead of an inverted U-shaped curve. Thus,

the concept of a universal cross-country Kuznets curve is also rejected by their empirical finding

which serves as a proper burial to the out-dated cross sectional method of testing Kuznets Curve at

a single point in time.

However, Barro (2000) has shown the existence of a Kuznets relation in a cross-sectional

regression over time and found a statistically significant inverted-U curve. He also showed that

filtering country fixed effects does not affect the significance of the results. This contradicts the

results obtained by Deininger and squire (1998).

Therefore, in line with the view that considers cross-sectional tests of Kuznets‟ Hypothesis

unfit; we will not conduct a cross-sectional test of the Kuznets curve. Instead we have chosen an

over-time approach to test the proposition within Japan and China.

Another challenge to the Kuznets relation came through observations of recent trends of

inequality in the developed world. The inverted-U phenomena gave way to increasing inequality

in the developed countries as shown by Atkinson, Rainwater and Smeeding (1994). They used the

Luxemburg Income Study (LIS) data to show that certain developed countries have seen a rise in

inequality since the 1980s; namely Finland, Netherlands, UK, Norway and Sweden; some

showing signs of a U-turn pattern instead of the inverted-U predicted by Kuznets hypothesis.

Further claiming that “we can no longer assume that all European countries are comfortably on the

downward part of the Kuznets curve, with inequality falling over time”, (Atkinson, Rainwater and

Smeeding 1994, p.16).

Since our focus is on Japan and China; the studies which have tested the Kuznets hypothesis

in the two countries also show some contradictory results to the Kuznets relation. Wang Xiaolu

(2006) used panel data covering the period 1996-2002, across 30 provinces to test the Kuznets‟

hypothesis. He did not find any concrete evidence of a Kuznets relationship and his findings

showed that the peak and the decreasing phase of China‟s Kuznets curve cannot be predicted with

certainty and there is no guarantee of its occurrence in the near future.

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In Japan‟s case Royshin Minami (1998) analyzing the trend in inequality over the course of

pre-war and post-war era in Japan found that even though the pre-war trend in inequality is

consistent with the first phase of the inverted-U curve, the post-war trend is not. The movement in

the Gini ratio has not been stable in the post war years; the decrease in inequality in 1962-80 is

„off set‟ by a subsequent increase till the 1990s. In addition to that, Minami states that the post war

stabilization of inequality cannot be fully attributed to Kuznets theory, it is indeed “as much a

consequence of economic growth as of the institutional changes and policy measures implemented

in the post-war era” (Minami, 1998, p.54); thus, rejecting the Kuznets relation as an explanation.

In another study on Japan, Tachibanaki and Yagi (1997) attempted an empirical test for the

Kuznets curve in Japan for the period 1963-1991 and they found a real U-curve instead of an

inverted one, again arguing against the existence of a Kuznets relation.

To sum up, the studies that support the Kuznets relation have been criticized and the general

view seems to be against terming the inverted-U phenomenon as a socio-economic „law‟.

6.2 Empirical Analysis for Kuznets Curve

This section deals with the empirical findings of the Kuznets‟ Curve for China and Japan. If

we are successful in finding significant results, we would analyze the respective results by

graphing the curve and finding the turning points in the next section. We have chosen the over-

time approach to test the hypothesis in China and Japan.

We would try to obtain a significant model with time or GDP per capita. As the Kuznets‟

curve should be an inverted U-shaped graph, we would have a model based on the quadratic

equation in which second degree variable would be expected have a negative coefficient. We

expect to derive a curve that has an increasing phase, then a turning point and followed by a

decreasing phase. Furthermore once an equation representing a Kuznets‟ curve is derived, we

would add the determinants we discussed in section 5. By doing so, the curve would give a better

fit and have a higher R-square value.

These empirical analyses would help us see if Kuznets‟ hypothesis is applicable on China

and Japan and if so, which portion of the curve their economies currently lies on? Whether the

turning point has been reached or not? It might be the case that the results for one country support

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the hypothesis and for the other they do not or it might be that we can explain the trend better by

using an alternate explanation.

As we did in section 5.3, we would divide our findings in separate parts for China and

Japan. We would first begin with China. The fundamentals of regression analysis are the same as

they were used in section 5.3, so they are not repeated here. As was the case in tables 1 and 2;

values marked with single star (*) are significant at 10% significance level, values marked with

double stars (**) are significant at 5% significant level.

6.2.1 China The table below summarises the results we have obtained from running the regression

analysis for China. Data used for China is from the year 1981 to 2005; the data used for the

regression is given in table C, added in the appendix. The results are further explained below:

Variables Test 1 Test 2 Test 3

Intercept 0.274405**

(5.58E-18) 0.240659**

(1.82E-12) 0.134687**

(5.54E-05)

Time 0.007281**

(0.001738)

Time² 2.19E-05

(0.792259)

Real GDPPC (LCU)

3.09E-05** (9.19E-05)

3.33E-05** (0.000128)

GDPPC²

-1E-09* (0.054881)

-2.4E-09** (0.000528)

FDI

-0.00738** (0.036889)

Indicator of openness

0.448334** (6.04E-05)

R Square 0.908252 0.882419 0.974161

F Value in ANOVA 108.8943 82.55289 93.02089

Standard Error 0.019079 0.021598 0.01492

Observations 25 25 25

Table 3: The values stated under ‘Test’ columns are the coefficients for the stated variables in the respective rows. The values stated in brackets under the coefficients in italic are the corresponding P- values for the variables.

The estimated regression model in Test 1 is:

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Just as we did before in section X, we would run a global test and see whether the „Null

Hypothesis Test‟ is rejected or if time and time² are not appropriate variables. The F Distribution

table value at 5% significance level for df(2,22) is 3.44 and the value of F in ANOVA table 108.9.

So the „Null Hypothesis Test‟ is rejected. The R Square vale is 0.908, stating that 91% change in

the income inequality of China is due to Time and Time².

The Equation is:

But as you can see that Time² is not marked with any of the stars(*) and has the P-value of

around 0.8 , indicating that it is insignificant at 10% rather than at a 5% level. So this model is

rejected and no further interpretation is required on this model.

Bear in mind that the Kuznets theory suggests that as the economic growth takes place in an

economy the income inequality first increases and then decreases, so a fair substitute for time in

this equation would be any indicator reflecting the economic growth. We have used Real GDP per

capita.

The estimated regression model in Test 2 is:

The „Null Hypothesis Test‟ is rejected, as F Distribution Value at 5% significance level, for

df(2,22): 3.44 which is less than the F value in the ANOVA table of 82.55. The value of R Square,

0.88, also represents a significant effect of real GDP per capita on Gini coefficient.

The estimated equation is:

The table suggests that the P-values in this case are very low and hence significant, GDPPC

marked two with stars (**) and GDPPC² with (*), indicating the model is statistically significant

and could be used to derive a Kuznets curve. The results achieved are also what we were looking

for, as GDPPC² has a negative coefficient and the coefficient for GDPPC is positive. This means

that the curve would first increase, reach a maximum point and then decrease.

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We can now perform the Durbin-Watson test statistics for auto-correlation. At 5%

significance level, 25 observations and k (number of independent variables) =2; the critical values

for the decision rule are: dL=1.21 and dU=1.55. The value computed for the test statistic is 0.99<

1.21dL. Thus, the null hypothesis is rejected and the values might be affected by auto-correlation.

Usually, to correct auto-correlation “we need to include one or more independent variables that

have some time ordered effects on the dependent variable”, (Lind, Marchel and Wathen, 2010).

However, owing to time limitations and the simplistic statistical approach taken, this was not

attempted.

As the above regression model satisfies the requirements of an acceptable regression model

to derive a Kuznets curve, we would now add more explanatory variables, stated in section 5.2, to

the model so that more factors, which affect income inequality, are considered for the Kuznets

curve. Consequently, we would try to keep the quality of the model as better as possible.

After test and trial method, the following regression model was obtained for Test 4:

Adding any other variables apart from the ones given in the above model, increased the p-

values of the model to an insignificant level.

The F value in the ANOVA table is 93.02 which is more than the F Distribution Value at 5%

significance level for df(4,20): 2.87.So null Hypothesis Test Is Rejected. The model accounts in a

better way for the change in inequality than the previous model as the value of R-Square value is

0.949, meaning that 95% change in China‟s inequality is due to the factors accounted for in the

model. Referring to table 3, we can see that all the variables are marked significant at a 5%

significance level.

For test 3; at a 5% significance level, k (number of independent variables) = 4 and 25

observations; the critical values for the Durbin-Watson test statistic are: dL=1.04 and dU=1.77. The

value for the Durbin-Watson test statistic in this case was computed to be 1.02< 1.04=dL. Thus,

the null hypothesis is rejected and auto-correlation might affect the results. However, again no

adjustment was attempted due to the simplistic statistical techniques adopted.

The estimated regression equation for the final model is:

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This model is favourable for drawing a Kuznets Curve, as GDPPC² has a negative

coefficient and GDPPC has a positive coefficient. The comparisons between the two models are

left for the next section.

6.2.2 Japan

We would replicate for Japan, what we did for China. We would create a regression model

for Japan with time and time², creating a quadratic function. If the model is acceptable, otherwise

we would substitute GDP per capita with time, we would add other determinants, discussed in

section 5.2, to the model so that we could have an equation that would help us derive a Kuznets

curve. The data used for Japan is from the year 1977 to 2005, which is in table 3 added in the

appendix.

Variables Test 1 Test 2 Test 3

Intercept 0.35009

(6.28E-16) 0.354656**

(0.04946) 0.328182**

(9.43E-16)

Time 0.006414

(0.005202) 0.008934**

(6.26E-06)

Time² -4.3E-05

(0.545676) -0.00021**

(1.34E-07)

Time³

1.25E-06** (7.5E-09)

Real GDPPC (LCU)

-3.2E-08 (0.773093)

GDPPC²

1.54E-14 (0.375568)

R Square 0.851198 0.824327 0.750502

F Value in ANOVA 48.62277 39.88548 35.09386

Standard Error 0.020357 0.022119 0.0282

Observations 20 20 38

Table 4, The values stated under ‘Test’ columns are the coefficients for the stated variables in the respective rows. The values stated under the coefficients in italic are the corresponding P- values for the variables.

The equation for Test 1 is:

The „Null Hypothesis Test‟ is rejected as F Distribution Value at 5% significance level for

df(2,17) is 3.6 and F value in the ANOVA table is 48.6. The R-square value is 0.85.

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Unfortunately, the P-value for time² (T²) is 0.54 and does not falls in 5% significance level, thus

we reject the model. However the equation derived is:

Now let us try with GDP per capita instead of time, we were fortunate enough to derive a

significant model for China. Hopefully, the data for Japan also helps us derive a Kuznets curve

with GDP per capita as an independent variable. Our model in GDPPC has the following

functional form:

In the global test, the F Distribution value at 5% significance level for df(2,17) is 3.6 which

is less than ANOVA table value of F, 39.8. So „Null Hypothesis Test‟ is rejected. The R square

value of 82.4 is but the P-values for both the independent variables are very high and statistically

insignificant at 5% level, therefore the model is not reasonable. Nonetheless the regression

equation is:

The results of our empirical model with time and time2 as the independent variable were

statistically insignificant, suggesting that a Kuznets inverted-U relation of rising and then falling

inequality is not valid in Japan; next we have attempted to test an alternative explanation

suggested by Tachibanaki (2006).

He has described his alternative proposition as the “cubic hypothesis”. He suggests that

instead of considering the past 30-40 years of observations of inequality which give a real U-curve

for Japan instead of an inverted-U (Tachibanaki and Yagi, 1997); a more interesting pattern can be

observed by taking a hundred years‟ perspective. He used the available observations spanning the

period 1900-2000 and suggested that a rough graph of these values overtime resembles a cubic

curve instead of a parabola as Kuznets suggested, this is due to the fact that inequality did not

continue a downward trend even after Japan had ended its phase of industrialization.

Therefore, we now test Tachibanaki‟s (2006) cubic hypothesis by extended our time series

data; instead of only considering the values of Gini index from 1971-2005 we prolong the time

series observations by using the values provided by the World Bank economists, Deininger and

Squire (1996) for the period spanning 1890-1962. Now, we test the trend of inequality from 1890-

2005 for Tachibanaki‟s (2006) cubic hypothesis. The expected equation is:

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This model has F in ANOVA, 35, greater than the F distribution table value for df(3,35):

2.85. The R-Square states that the independent variables, selected in the model, only explain 3/4th

changes in the dependant variable. The table also shows that all of the variables are relevant at a

5% significance level; their p-values are also extremely low, which makes this model reasonable.

So the equation of the Japan‟s estimated curve is:

At a 5% significance level, k (number of independent variables) = 3 and 39 observations; the

critical values for the Durbin-Watson test statistic are: dL=1.33 and dU=1.66. The value computed

for the Durbin-Watson test statistic in this case was 0.000123<1.33 =dL. Thus, the null hypothesis

is rejected and auto-correlation might affect the results in this case as well but no adjustments are

attempted the reason for which has already been stated.

6.3 Analyzing and graphing the derived curves In the previous section we carried out several regression analyses. However, only three

models, two for China and one for Japan were statistically significant enough to be accepted, the

rest of the estimated curves for the models that were rejected are presented in the Appendix

figures: A, B and C. In this section we would show the Kuznets curves we have derived from

these models and for the cubic hypothesis for Japan.

6.3.1 China The first model for China which proved significant was with GDP per capita and GDP per

capita² as independent variables. The resulting equation for China‟s Gini-coefficient with respect

to GDP per capita and GDP per capita² is as follows:

The equation above, describes a Kuznets curve for China. The regression coefficient for

GDPPC is positive and for that of GDPPC² is negative, indicating that the curve would first rise to

reach its maximum point and then start to decline in accordance with Kuznets inverted-U relation.

The estimated regression equation for China‟s inverted-U trend is graphed below:

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Figure 1

Primarily, the Kuznets curve above is divided into two parts. The blue part indicates the

estimated Kuznets curve for the data, which we used to obtain the equation. The red part of the

curve, marked as Predicted curve, is outside the scope of our data and represents speculation on

basis of our model. The scatter plot is the original data of China‟s Gini-coefficient plotted against

its GDP per capita, in order to see how well the estimated curve fits the actual data. The first phase

of the curve in blue seems to closely follow the observed Gini Index trends, thus giving a good fit.

From table C it can be seen that the last observation for GDP per capita in our data for China

which was used in estimation of the regression equation is: 12,124 Yuan and that‟s precisely

where the estimated part of the graph stops. The Maximum Value of the Gini-coefficient would be

calculated by differentiating the equation w.r.t. GDPPC. The differential is:

Taking

= 0, we get GDPPC = 15500 and corresponding value for the Gini Index at this

point is 0.48025 by plugging in 15500 Yuan into the estimated regression equation. The graph

above clearly shows that this value of the GDPPC lies in the predicted part of the curve. Our

turning point at GDPPC of 15500 Yuan has already occurred in 2008 according to the World Bank

data. However, even though the level of GDPPC of 15500 Yuan has been achieved; we cannot

confirm whether the highest level of Gini Index would be 0.48025, as estimated by our regression

equation because the Gini Index data for China ends at 2005. Thus, making it difficult for us to

confirm our inference.

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

0 10000 20000 30000 40000

Gin

i In

de

x

GDPPC

Estimated Curve

Predicted Curve

Scatter plot

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Chang (2002) has argued in favor of such a turning point on the basis that the urban sector in

China is not large enough to absorb all the surplus labor from the rural areas which amounts to

150 million. Thus, China will not be able to maintain a high level of inequality for an extended

period of time. However, Yang (1999) has argued that in presence of the persisting urban-biased

policies suppressing labor mobility and barriers to migration, inequality is not likely to decrease in

future.

From the differential of the regression equation we can also see that, currently, the Gini-

coefficient is increasing at a decreasing rate. The following table illustrates rate of change in the

Gini-coefficient with respect to GDPPC:

Year

GDPPC

Estimated Gini Index

Observed Gini Index

1981 1587,64 0,2867 0,2950

1982 1702,53 0,2898 0,2870

1984 2172,53 0,3026 0,2440

1985 2382,83 0,3082 0,3000

1990 3224,45 0,3296 0,3400

1995 5425,51 0,3788 0,3820

2000 7857,71 0,4218 0,4070

2005 12124,24 0,4689 0,4700

Table 5

Table (5) shows that the rate of change in the Gini-coefficient has fallen gradually over time

in relation to the level of GDPPC; suggesting an increase in inequality at a decreasing rate with

respect to the level of development. Thus, indicating that after the 15500 Yuan mark the Gini-

coefficient is bound to decrease (this is also depicted in the graph). However, one cannot rule out

the momentary fluctuations experienced by the Gini index from 1981-2005 apart from following

the inverted-U trajectory. These occurred due to the effects of drastic changes introduced by

various reforms during this period. For instance, even though our estimated curve gives a

significant inverted-U relation, from the table (5) above one can notice a decreasing trend in the

fifth column showing the observed Gini-coefficient during 1981-1984. This decrease was due to

the effect of rural reforms of (1979-1984) which consisted of “household responsibility system”

and other market oriented strategies to boost agricultural output, which decreased the urban-rural

disparity.

One reason why this is not reflected in the rate of change of Gini-index (calculated for our

estimated equation) in relation to GDPPC could be because of the square term in the regression

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equation through which “Gini coefficients are forced to decrease earlier or later once a concave

function is obtained” (Xiaolu, 2006, pp.6); thus, leaving little room for other possibilities for the

direction of the Gini trend.

However, the Gini trend in the period spanning 1985-2005 is consistent with the changes in

Chinese economy which comprised of: post-rural reform, decentralization of markets, opening up

to trade and foreign direct investment (Kanbur and Zhang, 2005). Even though these reforms led

to spectacular growth, they also contributed to inland-coastal disparity as well raising the urban-

rural divide in context of the barriers imposed on labor mobility (Kanbur and Zhang, 2005)

(Sincular, Yu, Gustafsson and Li, 2006); thus causing a rising trend in inequality.

The previous model for China only included GDP per capita and GDP per capita². But

another regression model used by us with Indicator of Openness and Foreign Direct Investment, in

addition to GDPPC and GDPPC² is also significant at a 5% level. The regression equation for the

model is:

In order to draw a Kuznets curve we need only GDPPC at the x-axis. The problem here is

that since we have two additional variables, Indicator of openness and foreign direct investment,

we cannot derive a Kuznets curve for this equation unless we convert these variables to constants.

So, we used mean of the two additional variables so that we could convert the variables into

constants and finally derive a Kuznets curve. The means for Indicator of Openness and Foreign

Direct Investment are, 0.367 and 2.54 respectively. The new equation for China‟s Kuznets curve,

with Indicator of Openness and Foreign Direct Investment is:

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The graph derived from the equation, with GDPPC marked on x-axis and Gini-coefficient on y-

axis, is:

Figure 2

As was the case in figure 1, the blue part of the curve is the estimated curve and the red part

is the predicted curve. This graph gives us some interesting observations. Once the effects of the

Indicator of Openness and FDI are taken into consideration, the Kuznets curve gives a completely

different picture.

The highest value of Gini-coefficient isn‟t as high as in the previous estimated curve, in

which only GDPPC was taken into consideration. Once again we would differentiate the equation

w.r.t. to GDPPC, so that we can find the highest value of the Gini-coefficient for the estimated

equation:

Taking

= 0, we get GDPPC as 6937.5 Yuan. So, the income inequality in China is at its

highest when GDPPC is 6937.5 Yuan, when FDI and Indicator of Openness are included in the

model. The corresponding value of the Gini-coefficient when GDPPC is 6937.5 is 0.3978 which is

around 0.4. This model suggests that the highest level of income inequality in China would be

40% if FDI and Indicator of Openness are considered. This is, less than the value of 48% for the

previous model without the two variables.

The most important difference between the two models is that the highest level of income

inequality in China has been already achieved in the start of the year 1999(World Bank), if we

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

0 5000 10000 15000 20000 25000

Gin

i In

de

x

GDPPC

Estimated Curve

Predicted Curve

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consider FDI and the Indicator of Openness, where as in the first model it is predicted to be

achieved in the year 2008(World Bank).

6.3.2 Japan The following is the regression equation obtained for the „cubic hypothesis test‟, as

suggested by Tachibanaki (2006). The equation for the curve and the estimated curve is given

below along with a scatter plot of Gini index to show the relative fit:

Figure 3

The graph above has two turning points, a maximum and a minimum, as the Gini Index

follows the pattern of: inequality-equality-inequality. Through our regression equation we can

infer the values of the turning points i.e. which years was the Gini Index at its maximum and

minimum for Japan as shown in the graph above. The differential for the regression equation

given above is:

In order to calculate the turning points, we set:

, ending up with the following simple

quadratic equation:

This can be solved using the quadratic formula:

where:

. Solving for T we get: T=83.13 and T=28.90. To find

0

0,1

0,2

0,3

0,4

0,5

0,6

0 50 100 150

Gin

i In

de

x

Time

Estimated CurveScatter plot

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41

which of these is the maximum and minimum, we compute the second derivative of the regression

equation which gives us:

Plugging in T=83.13 and T=28.90; we get:

.

Thus, at T=83.13 we have a minimum and at T=28.90 we have a maximum.

Thus, Gini index was highest around 1918 and lowest around 1973. In light of the literature

on Japan‟s inequality trend, this is indeed a reasonable result. The first turning point, (the

maximum) occurs in 1918 which according to Tachibanaki (2005) was a period of rising

inequality due to early stages of industrialization and urbanization where class differences

between blue-collar and white-collar workers were rampant, wealth inequality and landlordism

also existed.

The second turning point occurs at 1973, the annual GDP growth rate at the time was around

3% and the real GDP was 285 058 300 million yen (World Bank); this is also consistent with

changes being experienced in Japan in that era. Around 1973, Japan experienced the oil crisis but

it did not affect Japan for long as there was a quick turnaround followed by 3%-4% annual GDP

growth rate. Japan was fairly stable in the macroeconomic sense between 1970s to 1980s thus,

reflected by the inequality reaching its lowest in 1973. It was the mid-1980s that spelled the start

of Japan‟s “bubble economy” when the equity prices rose, leading to disparity among those who

owned real assets and financial assets or those who owned none at all (Tachibanaki, 2005).

We can also calculate the point of inflection i.e. the point which marks the transition

between where the graph is concave up and concave down. Setting

, we obtain the

inflection point: T=56 which is the year 1946. During this period some drastic economic and

social reforms were implemented under the US occupation, in the wake of the Second World War,

such as: the dissolution of family business conglomerates, land reform of 1946-7 (putting an end

to landlordism), unionization of labor (which rose to 50% in 1940s) and progressive tax reforms

such as the “Shoup tax-reform” (Minami, 1998). All these factors had an equalizing effect on the

income distribution and resulting in a turning point from a concave-up trend to a concave-down

trend.

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We can also identify and explain the periods of rapid rise and rapid decrease in Gini index

between the turning points. For instance, considering the rate of change in inequality during the

post-war years of 1954, 1975, 1986 and 1995; we can obtain the following values by plugging in

the corresponding T values (number of years since 1890) in our first order differential equation

discussed above:

Year

Gini index

1954 -0.00222 0.31

1975 -0.00245 0.37

1986 -0.00126 0.41

1995 0.00039 0.44

The rate of change of Gini index is decreasing in 1954 owing to the effect of the post-war

reforms mentioned before. The rate of change during 1975 is also negative due to the declining

trend in the Gini index which was more significant during 1960s; this is described by Minami

(1998) as a turning point from labor surplus to labor shortage. The author suggests that in periods

of growth, in presence of surplus labor, wage increases lag behind productivity increases; which

causes the relative income share of labor to be decreased. Since the poor dominate in labor

earnings and the rich dominate in asset earnings, a decrease in income share of labor would

increase inequality (Minami, 1998).

Thus, when a labor shortage occurred in 1960 inequality declined. The rate of inequality in

1986 is still decreasing, which is right before the inequality inducing effects of the bubble

economy surfaced. In 1995 the trend is positive indicating rising inequality which is due to the

existing changes in Japanese household structure owing to population aging, increase in number of

low paid part-time workers and the differences in income between small firm and big firm

employees (Tachibanaki, 2005).

6.4 Summary of results for Kuznets’ tests: The Kuznets type relationship between inequality and development could only be confirmed

in case of China. A statistically significant relationship was found for China through the model

with GDPPC and GDPPC2. While adding more explanatory variables did not affect the

significance and made the coefficients for GDPPC and GDPPC2 slightly higher. While on the

other hand no significant inverted-U relation was detected for China using t and t2. In case of

Japan, both the models with time and time squared and GDPPC and GDPPC2 were rejected as

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43

they were not statistically significant, which suggests that there is no Kuznets relationship for

Japan. Then we tested an alternative „cubic hypothesis‟ suggested by Tachibanaki (2005) which

modelled the Gini trend well and gave significant results. Also, auto-correlation might affect our

results for the test of Kuznets Hypothesis. However, we have kept to using simplistic statistical

techniques which is why we have not adjusted the models rigorously as already explained before.

Section 7: Conclusion

In light of our results, we have reached the following conclusions:

The determinants of inequality such as: real GDP, urban-rural income gap; urbanization and

secondary education proved to be significant for China; out of which secondary education has the

greatest impact on inequality. Thus, in terms of policy implications promoting secondary

education can act to reduce inequality and poverty in China. For Japan we found that none of

variables tested were statistically significant, indicating perhaps other factors might be responsible

which are not included in our data.

The Literature reviewed for the Kuznets hypothesis confirmed that a cross-sectional test of

Kuznets relation at a single point in time was inappropriate as the hypothesis models an

intertemporal relationship. Thus, we adopted an overtime approach with in Japan and China. In

case of the tests conducted for Kuznets hypothesis, we showed that China had a Kuznets curve

with GDPPC and its square and that adding openness and FDI as explanatory variables did not

change the significance of our results. In case of Japan no inverted-U relationship was found and

instead we confirmed an alternative „cubic hypothesis‟ which was suggested by Tachibanaki

(2005). Thus, in our study a typical developing country like China confirmed the Kuznets relation

but a typical developed country, Japan did not. Therefore, we can claim that Kuznets hypothesis is

not a universal rule and whether it applies or not depends on the time and place just as Kuznets

himself had warned that time and space matter. Japan was a thorough contradiction to the Kuznets

hypothesis, even though it follows an inverted-U trajectory in the beginning, it later over-turns to

reveal a concave up trend when it really should be decreasing according to the hypothesis, as it is

in a mature stage of development.

To sum it up, even though studies such as the ones by Xiaolu (2005), (Minami, 1998) and

(Tachibanaki and Yagi, 1997) have argued against the Kuznets curve for china and Japan; instead

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of taking an extreme approach and calling the hypothesis valid in China we can suggest that

Kuznets relation might be a recurring trend as shown by the confirmation of the „cubic hypothesis

for Japan. First, it follows a concave down trajectory and then concave up. Since, China is still

following the concave up trend at the moment, perhaps in around 60 years the „cubic phenomena‟

might be observed for China.

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Appendix:

Year Gini Index

FDI Openness GDPPC(Yuan) Tertiary Education

Secondary Education

Urban Population

Urban –Rural Income gap

1985 0.3000 0.541 0.225 2382.825 0.046 1.110 23 1.859

1986 0.3180 0.630 0.225 2603.213 0.048 1.156 23.88 1.954

1987 0.3310 0.856 0.289 2852.259 0.051 1.202 24.76 1.980

1988 0.3370 1.032 0.310 3103.418 0.054 1.247 25.64 2.054

1989 0.3560 0.986 0.292 3164.585 0.056 1.293 26.52 2.284

1990 0.3400 0.977 0.292 3224.455 0.059 1.339 27.4 2.200

1991 0.3730 1.151 0.317 3505.111 0.067 1.416 28.2 2.400

1992 0.3630 2.639 0.361 3917.101 0.074 1.494 29 2.585

1993 0.3800 6.246 0.420 4409.356 0.082 1.571 29.8 2.797

1994 0.3810 6.042 0.412 4992.602 0.089 1.649 30.6 2.863

1995 0.3820 4.924 0.388 5425.513 0.097 1.726 31.4 2.715

1996 0.3900 4.693 0.381 5904.937 0.106 1.812 32.28 2.512

1997 0.3750 4.644 0.390 6420.178 0.116 1.897 33.16 2.469

1998 0.3780 4.292 0.364 6864.654 0.125 1.983 34.04 2.509

1999 0.3890 3.577 0.377 7304.589 0.135 2.068 34.92 2.649

2000 0.4070 3.204 0.442 7857.711 0.144 2.154 35.8 2.787

2001 0.4150 3.339 0.431 8452.640 0.155 2.224 36.72 2.899

2002 0.4540 3.392 0.477 9124.326 0.166 2.295 37.64 3.111

2003 0.4490 2.869 0.569 10039.976 0.177 2.366 38.56 3.231

2004 0.4690 2.844 0.654 10916.438 0.188 2.436 39.48 3.209 Table A. Data Used for Regression Analysis of China’s Determinants of Inequality.

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Year Gini Coefficient

GDPPC ( Yen)

Urban population

Openness Tertiary Education

Secondary Education

Population ages 65

FDI

1977 0.3650 2186904.5 57.92 0.2426 0.39 2.95 8.32 0.00

1978 0.3652 2281458.7 58.48 0.2025 0.42 3.02 8.57 0.00

1980 0.3490 2434464.0 59.6 0.2794 0.47 3.17 9.05 0.03

1981 0.3491 2517479.4 59.8 0.2826 0.49 3.22 9.28 0.02

1983 0.3980 2645908.6 60.2 0.2574 0.54 3.34 9.72 0.03

1984 0.3975 2746539.4 60.4 0.2699 0.56 3.39 9.96 0.00

1986 0.4050 2966732.9 61.1 0.1845 0.61 3.47 10.51 0.01

1987 0.4049 3073436.1 61.6 0.1745 0.63 3.49 10.83 0.05

1989 0.4330 3441040.1 62.6 0.1909 0.66 3.53 11.56 -0.03

1990 0.4334 3620408.4 63.1 0.1977 0.68 3.55 11.97 0.06

1992 0.4390 3750393.0 63.7 0.1751 0.74 3.71 12.88 0.07

1993 0.4394 3747547.5 64 0.1601 0.77 3.78 13.38 0.00

1995 0.4410 3823312.9 64.6 0.1686 0.82 3.94 14.42 0.00

1996 0.4412 3914097.2 64.72 0.1912 0.85 3.98 14.97 0.00

1998 0.4720 3873856.8 64.96 0.1994 0.90 4.06 16.11 0.08

1999 0.4720 3861063.7 65.08 0.1897 0.93 4.10 16.68 0.28

2000 0.4980 3964608.7 65.2 0.2052 0.95 4.14 17.24 0.18

2002 0.4983 3964368.3 65.52 0.2142 1.00 4.22 18.34 0.23

2004 0.4350 4120519.9 65.84 0.2467 1.06 4.29 19.40 0.17

2005 0.5263 4199820.1 66 0.2728 1.08 4.33 19.92 0.07 Table B. Data Used for Regression Analysis of Japan’s Determinants of Inequality

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Year Gini Time Time² GDPPC GDPPC²

1981 0.2950 0 0 1587.644 2520613

1982 0.2870 1 1 1702.533 2898618

1983 0.2690 2 4 1879.637 3533036

1984 0.2440 3 9 2172.528 4719878

1985 0.3000 4 16 2382.825 5677855

1986 0.3180 5 25 2603.213 6776715

1987 0.3310 6 36 2852.259 8135382

1988 0.3370 7 49 3103.418 9631202

1989 0.3560 8 64 3164.585 10014595

1990 0.3400 9 81 3224.455 10397110

1991 0.3730 10 100 3505.111 12285800

1992 0.3630 11 121 3917.101 15343680

1993 0.3800 12 144 4409.356 19442422

1994 0.3810 13 169 4992.602 24926077

1995 0.3820 14 196 5425.513 29436196

1996 0.3900 15 225 5904.937 34868276

1997 0.3750 16 256 6420.178 41218681

1998 0.3780 17 289 6864.654 47123480

1999 0.3890 18 324 7304.589 53357020

2000 0.4070 19 361 7857.711 61743628

2001 0.4150 20 400 8452.640 71447123

2002 0.4540 21 441 9124.326 83253316

2003 0.4490 22 484 10039.976 100801113

2004 0.4690 23 529 10916.438 119168614

2005 0.4700 24 576 12124.241 146997218 Table C. Data Used for Regression Analysis of China’s Kuznets’ Curve

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Year Gini Time Time² GDPPC GDPPC²

1977 0.3650 0 0 2186904.5 4782551492501.19

1978 0.3652 1 1 2281458.7 5205053839018.07

1980 0.3490 3 9 2434464.0 5926615077864.38

1981 0.3491 4 16 2517479.4 6337702287571.04

1983 0.3980 6 36 2645908.6 7000832056540.54

1984 0.3975 7 49 2746539.4 7543478876713.97

1986 0.4050 9 81 2966732.9 8801504212915.22

1987 0.4049 10 100 3073436.1 9446009738439.92

1989 0.4330 12 144 3441040.1 11840756843945.50

1990 0.4334 13 169 3620408.4 13107357070076.30

1992 0.4390 15 225 3750393.0 14065447938410.00

1993 0.4394 16 256 3747547.5 14044111990316.50

1995 0.4410 18 324 3823312.9 14617721265336.60

1996 0.4412 19 361 3914097.2 15320156768471.80

1998 0.4720 21 441 3873856.8 15006766237964.20

1999 0.4720 22 484 3861063.7 14907812553708.60

2000 0.4980 23 529 3964608.7 15718121783560.40

2002 0.4983 25 625 3964368.3 15716216093104.90

2004 0.4350 27 729 4120519.9 16978684275716.10

2005 0.5263 28 784 4199820.1 17638488889633.60 Table D Data Used for Regression Analysis of Japan’s Kuznets’ Curve for Test 1 and 2.

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Year Gini index Time Time² Time³

1890 0.3110 0 0 0

1900 0.4170 10 100 1000

1910 0.4200 20 400 8000

1920 0.4630 30 900 27000

1930 0.4510 40 1600 64000

1940 0.4670 50 2500 125000

1954 0.3100 64 4096 262144

1956 0.3130 66 4356 287496

1959 0.3570 69 4761 328509

1961 0.3900 71 5041 357911

1962 0.3904 72 5184 373248

1966 0.3750 76 5776 438976

1967 0.3749 77 5929 456533

1971 0.3540 81 6561 531441

1972 0.3538 82 6724 551368

1973 0.3530 83 6889 571787

1974 0.3750 84 7056 592704

1975 0.3747 85 7225 614125

1977 0.3650 87 7569 658503

1978 0.3652 88 7744 681472

1980 0.3490 90 8100 729000

1981 0.3491 91 8281 753571

1982 0.3480 92 8464 778688

1983 0.3980 93 8649 804357

1984 0.3975 94 8836 830584

1986 0.4050 96 9216 884736

1987 0.4049 97 9409 912673

1989 0.4330 99 9801 970299

1990 0.4334 100 10000 1000000

1992 0.4390 102 10404 1061208

1993 0.4394 103 10609 1092727

1995 0.4410 105 11025 1157625

1996 0.4412 106 11236 1191016

1998 0.4720 108 11664 1259712

1999 0.4720 109 11881 1295029

2000 0.4980 110 12100 1331000

2002 0.4983 112 12544 1404928

2004 0.4350 114 12996 1481544

2005 0.5263 115 13225 1520875 Table E. Data Used for Regression Analysis of Japan’s Kuznets’ Curve for Test 3

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China’s Kuznets’ Curve with Time and Time²

Figure A

Japan’s Kuznets’ Curve with GDPPC and GDPPC²

Figure B

Japan’s Kuznets’ Curve with Time and Time²

Figure C

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0 10 20 30 40 50 60

Gin

i In

de

x

Time

Series1

Series2

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0 2000000 4000000 6000000

Gin

i In

de

x

GDPPC

Historic Curve

Predicted Curve.

0

0,1

0,2

0,3

0,4

0,5

0,6

0 10 20 30 40 50 60

Gin

i In

de

x

Time

Historic Curve

Predicted Curve.

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