bachelor thesis in economics -...

School of sustainable development of society and technology

Bachelor thesis in Economics

Economics Administration, basic level-300, 15 ESTC-points

Supervisor: Johan Lindén

Västerås 2010-06-02

Growing Unequally?

--- A study on the relationship between

income inequality and economic growth

in China

Group Members:

Jie Yang

Yanfei Yan

Bo He

Mälardalen University Bachelor Thesis in Economics

Bachelor thesis in Economics

Title: Growing Unequally?

Author: Jie Yang, Yanfei Yan, Bo He

Supervisor: Johan Lindén

Date: 2010-06-02

Abstract

The purpose of this paper is to study the relationship between the income inequality

and economic growth in China. Whether economic growth reduces inequality? How

does the income inequality affect economic growth in China? Is the gap between

urban-rural incomes the main contribution to the overall inequality? What are the

main causes of income inequality in China? We use the data from 1985 to 2002 in

China and we find that the income inequality will increase with the growth of

economic in China, and income inequality has negative effect on economic growth.

Moreover, we calculate the contributions of each group to overall income inequality

and the results show that the income gap within urban residents has become the most

important contributor to overall inequality. Finally, we analyze the causes to income

inequality in China, which includes land reform, different education levels and labor

migration from rural to urban areas.

Key words: Income inequality, economic growth, urban residents, labor migration.


Acknowledgement

This Bachelor thesis was written during the spring term of 2010 at Mälardalen

University with the Bachelor Program Business Administration.

First of all, we would like to pass the gratitude to our supervisor Johan. Thanks for his

great supervision. We could not have finished this study without his valuable advice.

Secondly, we would like to thank the school, Mälardalen University. Thanks for

offering us the opportunity to study here, as well as grateful gratitude to all the

teachers and nice classmates.

Last but not least, we are deeply grateful to our parents who have been encouraging

and motivating us throughout our study in Sweden. Without their support spiritually

and financially, we could not have finished our study abroad.

Thank you sincerely!

Yanfei Yan, Jie Yang, Bo He

June, 2010


Table of Contents

Chapter 1: Introduction ..................................................................................... 1

1.1 Introduction ....................................................................................................... 1

1.2 Aim and question .............................................................................................. 2

1.3 Method Framework ........................................................................................... 2

Chapter 2: Theory ........................................................................ 4

2.1 Previous research .......................................................................................................... 4

2.2 Theoretical Background ............................................................................................... 5

2.2.1 Gross Domestic Product (GDP) .............................................................................. 5

2.2.2 GINI coefficient and Lorenz curve ......................................................................... 6

2.2.3 Effect of income inequality on economic growth ................................................... 7

Chapter 3: Data Description ........................................................ 9

3.1 Data Collection .............................................................................................................. 9

3.2 Variables: ........................................................................................................................ 9

3.2.1 Economic Growth ................................................................................................... 9

3.2.2 Inequality .............................................................................................................. 10

3.2.3 Control Variables .................................................................................................. 11

3.2.4 Summay Statistics ................................................................................................. 12

Chapter 4: The Empirical Model ............................................... 13

4.1 Regression Model ....................................................................................................... 13

4.2 Empirical results ............................................................................................. 16

Chapter 5: China’s income inequality ....................................... 19

5.1 The overall inequality and the urban-rural income inequality ........................... 19

5.2 The causes of urban-rural income inequality ........................................................ 22

Chapter 6: Conclusion ............................................................... 27

Reference

Appendix


List of figures and tables:

Figure 1: Kuznets curve

Figure 2: Lorenz Curve

Figure 3: GDP and GDP per capita growth during 1985-2005 (are calculated at

current price)

Figure 4: Overall Gini during 1985- 2005

Figure 5: Urban Gini index during 1985-2002

Figure 6: Rural Gini index during 1985-2002

Figure 7: Urban-rural income ratio. Year 1985-2005

Figure 8: Urban-rural consumption ratio year 1985-2004

Table 1: Main control variables and units of measure

Table 2: summary statistics of variables

Table 3: Regression result 1-a


Table 5: Regression result 1-b


Table 7: Summary statistics of human capital

Table8: Urbanization in China


1

Chapter 1: Introduction

1.1 Introduction

In 1978, the Chinese government launched a massive reform program to fight poverty

and to liberalize the economy. Since then, China experienced one of the most

outstanding and enduring growth periods in the history of mankind (Brockmann,

Delhey, Welzel&Yuan, 2009). Over the last 30 years, the economy has grown at an

average annual rate of more than 8 percent, the growth has fundamentally improved

the living conditions of many of the 1.3 billion Chinese (Klein, L.R., & Özmucur, S,

2002).

This growth has many positive benefits, one of which is a substantial improvement in

average standards of living. Overall living standards in China have improved

significantly with rapid poverty reduction (Chen & Ravallion, 2007, 2008). The

average nominal income of the rural population more than tripled, jumping from 686

YUAN in 1990 to 2,253 YUAN in 2000 (The National Bureau of Statistics of China

2001). Measured by official line, rural residents who lived in poverty was reduced

from 250 million, more than 30 percent of the total population, in 1978, to 26 million,

less than 2 percent, in 2004, while the urban poverty only contributed a negligible

portion of national poverty (Naughton, Ravallion and Chen, 2007). Most people in

China have higher incomes, consume more and better goods, and live in better

housing than ever before. The luxury consumption in China accounting for 12 percent

of sales worldwide in 2006, which can fully states that there exists a rapidly

expanding middle and upper class. Also, life expectancy has grown, and as well as the

education levels (Gustafsson,Li Shi and Sicular, 2008).

On the other hand, income inequality has risen in the country. With the rapid growth,

a significant widening of income differences among households and individuals,

within/between urban and rural areas, and across provinces has occurred. The Gini

coefficient increased since the 1980s, from below 0.3 before 1986 to 0.447 in

2001(Naughton, 2007, UNDP and CDRF, 2005). From 1988 to 2007, the income gap

between the 10% of the highest income and 10% of the lowest income rises from 7.3

times to 23 times.1

As suggested by the hypothesis of Simon Kuznets, it implies that if we graphed the

level of inequality as a function of the level of GDP per capita, the data would trace

out an inverted-U shape, which means that as a country developed, inequality would

first rise and then later fall (David N. Weil, 2009, p375). According to his hypothesis,

1 http://finance.people.com.cn/GB/10246541.html

http://www.wikinvest.com/wiki/Rise_of_China%27s_Middle_Class


2

the inequality in China will increase at the initial stage of economic growth and will

decline in subsequent stages. However, many detractors argue that the empirical data

in his analysis was originated mostly from the development processes of richer

nations like the USA, Great Britain and Germany, in the 19th

and 20th

centuries, and it

cannot be applied to the current developments of poorer countries that have a colonial

mortgage and other social, economic conditions (Friedel, Roland&Pedro, 2006).

Since then, is there any relationship between economic growth and income inequality

would be discussed in this research.

1.2 Aim and question

In this research, the main purpose is to explain the relationship between China’s

economic growth and income inequality. Since studies of China’s inequality almost

universally report that the gap between urban and rural household incomes in China is

large and contributes substantially to overall inequality. We will explore how the

urban-rural income inequality contributes to the overall inequality.

To explain the aim of the research, the following sub questions will be answered:

Whether economic growth reduces inequality?

How does the income inequality affect economic growth in China?

Is the gap between urban-rural incomes the main contribution to the overall

inequality?

What are the main causes of income inequality in China?

1.3 Method Framework

Firstly, we collected the data from 1985-2002 from The National Bureau of Statistics

of China (NBS) and employed the multiple regression models. Based on the

econometric results, we estimated the effect of income inequality on economic growth

and how does economic growth affect income inequality.

Secondly, to identify how the urban-rural income gap contributes to the overall

income inequality, we used the coefficient of variation to decompose the factors of

China’s overall income inequality which includes: the income inequality among urban

residents, the income inequality among rural residents, and the income inequality

between the urban and rural residents. Based on the data from NBS, we will get the

result of contribution of each group on overall inequality. Due to lacking of data, we

collected the data from 1990 to 2001 for both urban and rural households.

Finally, we analyzed the main causes of China’s income inequality using the data


3

related to land reform, different education levels and migration from rural to urban

areas.


4

Chapter 2: Theory

2.1 Previous research

The first modern economic paper on the relationship between economic growth and

income distribution was written by Simon Kuznets (1955). He introduced the inverted

U-curve. It implies that if we graphed the level of inequality as a function of the level

of GDP per capita, the data would trace out an inverted-U shape, which means that as

a country developed, inequality would first rise and then later fall (Weil, 2009, p375).

Figure 1: Kuznets curve

Source: David N. Weil (2009). Economic Growth,

Barro (2000) studied the income inequality from a neo-classical economic growth

theory perspective which suggested that income inequality had a negative effect on

growth for countries with a low initial level GDP but a positive effect for countries

with a high level of GDP (Barro,2000). Barro used empirical data and models to find

the determinants of growth. It suggested that the initial level of GDP, human capital,

government expenditure, social stability, inflation and geographic location are

important factors to determine growth.

Promoters of neoliberal thought that only a part of the population profits from

economic growth. Growing inequality, however, is not considered completely

negative. Increasing the wealth of only one part of the population should make the

growing rich invest and consume more, which will increase the growth. Following

this approach, a part of this new wealth will trickle-down to the needy. This effect is

called as the trickle-down theory (Hackenberg&Morazan, 2006, p.34).

In Liang’s study (2008), he used panel data over the period 1991 to 2000 and applied

the generalized method of moment (GMM) method to investigate empirically the

effects of rural financial development on the distribution of income distribution in

rural China. It is found that rural financial development significantly contributes to


5

the reduction of rural inequality. Their findings strongly support the linear relationship

between finance and inequality instead of the Greenwood Jovanovic inverted

U-shaped relationship (Liang, 2008, p.86).

Wan, Lu and Chen (2008) obtained an income generating function based on both

human capital and production theory. It is found that globalization has a positive and

substantial share of China’s regional inequality and the share increases over time.

Besides, capital is another one of the largest and increasingly important contributors

to income inequality. Privatization during the economic reform period, which starts

from 1978, exerts a significant impact on regional inequality. However, the relative

contributions of education, location, urbanization and the dependency ratio to regional

inequality have been declining (Wan, Lu and Chen, 2008, p.19). They also said that

further globalization will lead to higher regional inequality in China unless Chinese

government put more efforts in promoting trade and FDI flows to west and central

China (Wan, Lu and Chen, 2008, p.19).

Sicular, Yue, Gustafsson and Li (2008) recalculated the size of the urban-rural gap and

its contribution to national inequality. They did so for considering China as a whole

and three major regions- the east, the central region and the west. They made three

modifications that would bring their measurement of the gap closer to international

best practice. Firstly, they used a fuller measure of income which means housing

related components of income was included in the calculation. Secondly, they

adjusted for spatial differences in the cost of living. Thirdly, they included

rural-to-urban migrants. They found that after recalculation, the urban-rural gap

income gap is substantially reduced. It followed that these adjustments also reduce the

contribution of China’s urban-rural gap to overall inequality. After recalculating

income and with migrants included, they found that in 2002 the urban-rural gap

contributes about one quarter of overall inequality, which is different with the

estimation of 50 percent or more in most studies (Sicular, Yue, Gustafsson and Li

2008,p25-26).

2.2 Theoretical Background

2.2.1 Gross Domestic Product (GDP)

Gross domestic product (GDP) is a measure of the value of all of the goods and

services produced in a country in a year. It can be considered as a measure of a

country’s overall economic output. GDP can be calculated as either the value of the

output produced in a country or equivalently as the total income earned in a country,

in the form of wages, rents, interest, and profits and so on. Thus, GDP is also known

as national output or national income (Weil, 2009, p.5).


6

2.2.2 GINI coefficient and Lorenz curve

Gini coefficient is the most frequently used measurement to compare income

inequality among countries or examine inequality trends in one country over time. It

is useful tool which has a single number that summarizes the degree of income

inequality in a country (Weil, 2009, p.373).

Weil (2009) said that to construct the Gini coefficient, data on the incomes of all

households in a given country would be collected. If we arrange the income of these

households from lowest to highest, we can then find what fraction of the total income

in the country is earned by the poorest 1% of households, by the poorest 2% of

households, and so on. And then we can do calculations for each fraction of

households through 100%. We could then produce a Lorenz Curve by graphing these

data.

The Lorenz curve is a bowed shape curve. If the poorest 20% of households would

receive 20% of total household income, the poorest 50% would receive half of total

household income, and so on, it implies that income were distributed perfectly equally.

In this case, the Lorenz curve would be a straight line with a slope equals to 1. This is

the “line of perfect equality”. We obtained the GINI coefficient by calculating the area

between the Lorenz curve and the line of perfect equality and dividing this area by the

total area under the line of perfect equality. The more bowed out is the Lorenz curve,

the more unequally income is distributed; the higher will be the value of the GINI

coefficient (Weil, 2009).

Figure 2: Lorenz Curve

http://en.wikipedia.org/wiki/File:Lorenz-curve1.png


7

2.2.3 Effect of income inequality on economic growth

Weil (2009) suggested that there are four different channels through which inequality

has been hypothesized to affect economic growth. These four channels are the

accumulation of physical capital, human capital, government redistribution policy and

sociopolitical instability (Weil, 2009, p.388).

Effect on the accumulation of physical capital

Saving rate is one significant channel through which income inequality can have a

beneficial effect on economic growth. Since saving can significantly affect economic

growth by leading to the accumulation of physical capital. If a country has a higher

saving rate, it will have a higher steady-state level of income per capita. For a country

that raises its saving rate will experience a period of transitional growth as it moves

toward a new steady state. Because saving rates tend to rise with income increases,

then income inequality is related to the saving rate.

The sum of saving by people in all different income groups is the total amount of

saving in a country. The more unequally income distributed, which means that the

higher the fraction of total income is earned by richer people, the higher will be total

saving. Thus, a more unequal distribution of income is beneficial for accumulating

physical capital (Weil, 2009, p.388).

Effect on the accumulation of human capital

The more unequally the income is distributed, the lower human capital accumulation

is. Human capital is owned by a specific person and it cannot be transferred from one

person to another.

The marginal product of the last dollar invested by the poor person is higher than that

invested by the rich person. If income is redistributed from the rich person to the poor

person, human capital accumulation will rise because the poor person will invest her

extra money in human capital; besides, total output will go up as well, because the

marginal product of human capital which is invested in by the poor person is higher

than the marginal product of physical capital that the rich person invests in.

Inequality has different effects on physical and human capital accumulation. It is

beneficial in the case of physical capital while it is harmful in the case of human

capital. It implies that inequality may have different effects on the pace of economic

growth at different stages of growth (Weil, 2009, p. 389-391).

Effect on income distribution and efficiency

Differences in the productivity are used to play an equally important role in

explaining income differences among countries. There exist two divisions of

productivity: one representing the available technology for combining factors of


8

production, and the other representing the efficiency with which available technology

and factors were used.

Income distribution could be the channel through which inequality can affect the

efficiency of production. Income distribution can be defined as the process by which

governments take money from those with high income away and give it to those with

low income. When incomes are unequal, governments will be under great pressure to

redistribute income. Governments accomplish this by taxation. Higher inequality

always leads to more redistribution and more taxation. So by raising the likelihood

that the government will want to use taxes to redistribute income, inequality can

indirectly lower the level of efficiency, and thus output (Weil, 2009, p. 392-396)

Sociopolitical unrest

Weil (2009) concluded that countries with a more unequal distribution of income

might have more pressure for distribution; however this does not lead to necessarily

more actual redistribution.

The pressure for government to redistribute can be expressed in several ways, but all

of them slow down the speed of growth. One expression is political instability;

different groups compete and fight for obtaining greater power. As a result, unstable

political situations will discourage investment. A second expression of the pressure for

distribution is crime. Poor people want to redistribute resources through channels like

property crime other than the political system. Other forms of social unrest also lead

to the destruction of property, such as rioting, which is motivated by severe inequality

(Weil, 2009, p. 396).

Barro (2000) said that the participation of the poor in crime and other anti-social

unrest causes a direct waste of resources because the criminals devote their time and

energy to crimes instead of devoting to productive efforts. Moreover, the threats to

property rights deter investment. An offsetting force is that economic resources are

required for the poor effectively to cause disruption and threaten the stability of the

established regime. Self-interested leaders would favor some amount of income

equalizing transfers if the net effect were a decrease in the tendency for social unrest

and political instability. The tendency for redistribution to reduce crimes and riots

would result greater income equality and hence enhance economic growth (Barro,

2000, p.5-6)


9

Chapter 3: Data Description

3.1 Data Collection

Data from this research were collected from statistical yearbooks, research articles,

journals and official reports. All the data in this research relies on the dataset of the

National Bureau of Statistics of China (NBS) and the World Income Inequality

Database 2 (WIID2) which is collected by United Nations University---World

Institute for Development Economic Research (UNI-WIDER). The dataset of NBS is

based on the China’s largest annual household’s surveys in rural and urban areas. The

surveys cover all 30 provinces in China and include 30,000 to 40,000 households in

urban areas and 60,000 to 70,000 in rural areas. The NBS selects the households with

a two-stage stratified systematic random sampling scheme. Each household remains

in the survey for three consecutive years and one third of the households are replaced

by incoming households every year. The income and expenditure of those households

are required to keep a record and the reports all published by NBS.

Unfortunately, we do not have access to the summary statistics for all regions and all

years. They only provide various interval summary statistics and publish those

statistics in the Chinese Statistics Yearbook. Our sample covers annual family

disposable income through 1985 to 2005 which has relatively consistent data.

3.2 Variables:

Variable Measure

Economic growth Real GDP and GDP per capita

Inequality Gini-coefficient

Human capital Share of population with above secondary school education

Urbanization Share of population living in urban areas

Investment Gross investment in fixed assets as a share of GDP

UR gap The relative difference between urban and rural incomes

Table 1: Main control variables and units of measure

3.2.1 Economic Growth

Different studies sometimes have different measures of economic growth, like the

growth in incomes, growth rate in real GDP. This research chose real GDP and GDP

per capita from 1985 to 2005 as measures of economic growth. All the values of GDP

are nominal and have been deflated by the World Bank Indicator. We collected all the

data from the National Bureau of Statistics of China. We can observe the trend of


10

GDP and GDP per capita growth in the following figure. The real GDP increases

from less than 40,000 million Yuan in 1985 to almost 160,000 million Yuan in 2005

while the GDP per capita grows from 2,000 Yuan in 1985 to more than 12,000 Yuan

in 2005.

Figure 3: GDP and GDP per capita growth during 1985-2005 (are calculated at

current price

Source: The State Statistics Bureau of China

3.2.2 Inequality

In most research, the income inequality is measured by Gini-coefficeient which has a

value between 0 and 1. A higher value represents a more unequal distribution. In this

research, we chose the Gini coefficient to measure income inequality. The Gini

coefficients were collected from the Chinese part of World Income Inequality

Database V2.0c (WIID). Since studies of China’s inequality almost universally report

that the gap between urban and rural household incomes in China is large and

contributes substantially to overall inequality. We selected the overall Gini

coefficients during 1985 to 2005, the urban Gini coefficients and the rural Gini

coefficients during 1985 to 2002 because the data from 2002 to 2005 in urban and

rural areas are not available. Due to the complicated residence registration system in

China, it is difficult to estimate the number of migrants from rural area to urban areas.

Thus, migrants are excluded in the urban residents. Figure 4 represents the trend of

overall income inequality in China and Figure 5 and 6 graph the urban and rural Gini

coefficients respectively during 1985 to 2002. The overall Gini coefficient increases

from 0.3 to more than 0.45. The urban Gini coefficient rises from less than 0.2 to

more than 0.3 while the rural Gini coefficient grows from 0.26 to 0.37.

0

40000

80000

120000

160000

200000

1985 1990 1995 2000 2005

GDP

0

2000

4000

6000

8000

10000

12000

14000

1985 1990 1995 2000 2005

GDPPC


11

Figure 4: Overall Gini during 1985- 2005

Figure5: Urban Gini index during 1985-2002 Figure6: Rural Gini index during 1985-2002

Source: World Income Inequality Database V2.0c, May 2010

3.2.3 Control Variables

Additionally, we chose different control variables to estimate the relationship between

income inequality and economic growth. Urbanization seems to play an important

role in affecting economic growth (Henderson, 2000). In this research, we used the

share of population living in urban areas during 1985 to 2005 as a measure. The

amount of capital is another important variable that affects economic growth. We

control this variable by using the amount of investments divided by GDP

(Voitchovsky, 2005). Human capital is important related to both economic growth and

inequality. The share of population that has an above secondary education is

estimated and employed as a control variable. To estimate the income gap between

urban and rural areas (URGAP), we define the income gap as the relative difference

0,2

0,25

0,3

0,35

0,4

0,45

0,5

1985 1990 1995 2000 2005

OGINI

0

0,1

0,2

0,3

0,4

1985 1990 1995 2000

UGINI

0,2

0,25

0,3

0,35

0,4

1985 1990 1995 2000

RGINI


12

between urban and rural incomes, UR gap=﹛(Rural Income/Urban Income)-1﹜*

100% . It shows how many percent more an urban resident has than a rural resident.

3.2.4 Summay Statistics

Variable Obs Mean Standard

Deviation

Min Max

GDP 21 72558.476 39818.562 25113.6 157896.7

GDPPC 21 5966.986 2921.902 2383.3 12053.8

Ogini 21 0.381857 0.43888 0.30 0.47

Ugini 18 0.222833 0.34362 0.183 0.317

Rgini 18 0.313778 0.25286 0.264 0.372

Invest.ratio 21 0.273938 0.13493 0.101262 0.562227

Urbanization 21 0.3147 0.061166 0.2371 0.4299

Education 21 0.04993 0.0098158 0.0399858 0.0668908

UR gap 21 1,607611 0,396858 0,858903 2,223755

Table 2: summary statistics of variables

Real GDP is in 100 million Yuan. Real GDP per capita and UR gap are in Yuan

As we can see from this table, the real GDP increased from 25113.6 YUAN in 1985

to 157896.7 YUAN in 2005 with an average growth rate of 9.96% per year. The GDP

per capita rose from 2383.3 YUAN in 1985 to 12053.8 YUAN in 2005 with a yearly

growth rate of 9 percent. Regarding income inequality, the overall Gini coefficient

increases from 0.3 to 0.47 from 1985 to 2005 and the urban Gini coefficient and the

rural Gini coefficient rose from 0.183 to 0.317 and 0.264 to 0.372 respectively during

1985 to 2002. The ratio of total investment in fixed assets to GDP had the same

development. The ratio reached 0.562227 by the end of 2005, which was more than

half of the total GDP. The speed of urbanization is quick too, proportion of urban

population increased from 0.2371 to 0.4299 during this period. Till the end of 2005,

the population who received above secondary education reached 85,810,000, which is

6.67 percent of the total population. The income difference between urban and rural

areas is becoming bigger and bigger, the urban residents earned 85.89 percent more

than rural residents in 1985, by the end of 2005, the incomes of urban residents is two

times more than that of rural incomes.


13

Chapter 4: The Empirical Model

4.1 Regression Model

To find out how the income distribution affects economic growth in China, many

studies follow Barro-style growth regression:

Growthi = Constant + 1GDPi + 2MSEi + 3FSEi + 4PPPIi + 5Ineqi + ei

In this equation, Growth stands for the growth rate of GDP per capita; GDP is income

per capita in the base year, MES is the average years of male secondary schooling in

the base year and FSE is the average years of female schooling in the base year, PPPI

stands for the PPP value of the investment deflator, Ineq is income inequality and ei

the country-specific error term. However, due to lacking of data for average years of

male and female schooling, we will use the proportion of population who received

above secondary education (EDU) instead of MSE and FSE as a variable. And we

will use the ratio of investment in fixed assets to GDP (Invest) instead of PPPI as a

variable.

Thus, in this research, we estimate the following equation:

(1) Growthi = Constant + 1GDPPCi + 2Investi + 3Edui + 4Ineqi + ei

The model used by Perotti (1996) found a negative correlation between Gini and

growth while Forbes (2000) found a positive correlation. The expected signs for these

variables in our thesis are: GDPPC should be negative; INEQUALITY could be

positive or negative, EDU should be positive and INVESTMENT should be negative.

The first statistical test we need to conduct in the evaluation of a multiple regression

model is to test that if there is linear regression relationship between the dependent

variable Y and any of the explanatory, independent variables Xi

A statistical hypothesis test for the existence of a linear relationship between Y and

any of the Xi is:

H0: 1 = 2=3=4=0

H1: Not all the are zero.

Test statistic: We can find that F=[Regression SS/(k-1)] / [Residual SS/(n-k)]

=4568,763＞F(4,16) from the table below. H0 is rejected which means there exist at

least one linear regression relationship between the dependent variable and


14

independent variable.

Heteroskedasticity is another factor that we should consider since it will affect at the

properties of least squares estimators. OLS estimators will not be BLUE (best linear

unbiased estimator). They will be Linear and unbiased but without the Minimum

Variance property, which means that they will not be efficient and consistent enough

to be the best estimators. Concerning the heteroskedasticity, we will use the White

Test for heteroskedasticity.

Ho: The variance is homoskedastic and constant.

H1: The variation of the disturbance term is heteroskedastic of an unknown form.

We can use Chi-square test where h=k-1 and in this case, the number of variables is 4

and h=3. We obtain the critical values for the test by using Chi-square tables.

=

=7.815. With the available values in Table 3, we could then obtain the

= 20.9816 with N= 21 and =0.999125. Thus, ＞ and Ho is

rejected.

To test the quality of our regression result, we have to use the multiple coefficient of

determination R². It measures the proportion of the variation in the dependent variable

that is explained by the combination of the independent variables in the multiple

regression model (Aczel, Sounderpandian, 2002, p.511).

Thus, it is important to measure how well the regression model fits the data. The

values of R² vary from 0 to 1. The higher the value of R², the more useful the model is.

We can observe from the Table below: =0.999125 which implies a good quality of

this regression model.


Summary statistics

Multiple R 0,999563

R Square 0,999125

Adjusted R Square0,998907

Standard deviation1316,683

Observations 21

ANOVA

df SS MS F Significance F

Regression 4 3,17E+10 7,92E+09 4568,763 3,08E-24

Residual 16 27738467 1733654

Total 20 3,17E+10


15

To explore empirically the impact of economic growth on income inequality in China,

the following regression model (equation 2) was estimated:

(2) GINIi = Constant + 1GDPPCi + 2GDDPC i ²+3 Urbi +4Edu i +5URGAP i +

ei

Gini was used to measure the overall income inequality and it was employed as the

dependent variable. GDPPC is the real GDP per capita as a measurement of economic

growth. To estimate if the growth of GDPPC would lead to an increasingly income

inequality, we add the square of GDPPC to the regression. The share of people living

in urban areas during 1985 to 2005 is a measure of urbanization (Urb). The share of

population that has an above secondary education is estimated and employed as a

control variable (Edu). URGAP is the ratio between the disposable income of urban

residents and net income of rural residents, which will be used to test the impact of

urban-rural income gap on the income inequality of the total residents.

The expected signs for those variables are: GDPPC should be negative, EDU should

be negative, UR gap should be positive and URB should be positive.

Firstly, we conduct a statistical hypothesis test for the existence of a linear

relationship between Y and any of the Xi is:

H0: 1 = 2=3=4= 5=0

H1: Not all the are zero.

Test statistic: We can find that F= [Regression SS/(k-1)] / [Residual SS/(n-k)] =

[0,043646/5] / [0,001108/15] = 118,1467 (Table 4)

Numerator degrees of freedom is df1=k (the number of variables) =5 while

denominator degrees of freedom is df2= n-k-1= the number of observations-the

number of variables-1 =21-5-1=15.

We can find the critical values of F for the 0.1 significance level: F=2.27

Thus, F=118.1467＞2.27. H0 is rejected which means there exist at least one linear

regression relationship between the dependent variable and independent variable.

Then we use the White Test for heteroskedasticity. Ho: The variance is constant. We

use Chi-square test where h=k-1=5-1=4 in this case. The critical values were obtained

from the test by using Chi-square tables. =

=9.49. With the available

values in Table 4, = 20.48004 with N= 21 and =0.97524. Thus, ＞


16

and Ho is rejected. Additionally, =0.97524 implies a high quality of the

regression model.


4.2 Empirical results

The result of regression 1 shows that:

Growthi = (-17110) + GDPPCi + Ineqi + Investi +

Edui + ei

Examining the results of regression1 (Table 5), the signs of those coefficients are not

the same with what we expected. Only the sign of education is consistent with the

expectation. We find that the GDP per capita, income inequality, investment and

education are all positive related to economic growth. The different result may due to

we use different measures to those variables. For instance, we use the ratio of

investment in fixed assets to GDP (Invest) instead of PPPI as a variable.

Additionally, from observing the p-values of all coefficients in the table below, the

coefficient of the constant is significant at 95% confidence level. The coefficient of

GDPPC is significant at 99% confidence level. The sign of coefficient of OGINI is

positive, whereas this result is not significant which means that we could not find any

significant effect of income inequality on economic growth. The coefficient of

investment at 99% confidence level and that of education is significant at 95%

confidence level.

Summary Statistics

Multiple R 0,987540696

R Square 0,975236627

Adjusted R Square0,966982169

Standard Error0,008595635

Observations 21

ANOVA

df SS MS F Significance F

Regression 5 0,043646 0,008729 118,1467 1,69E-11

Residual 15 0,001108 7,39E-05

Total 20 0,044755


17


The result of regression 2 shows that:

GINIi = 0.130584 GDPPCi + (7.06117E-10) GDDPC i ²- Edui

+URGAP i +Urbi + ei (R2= 0.963778)

Observing the p-values from the table 6, the coefficients of all those variables except

education are significant at 95% confidence level. The signs of these variables are

consistent with what is expected. This result indicates that with the growth of GDP

per capita, the Gini index decreased. The level of urbanization in China has increased

substantially, which makes a contribution to the income inequality. However, the

education level is negatively related to the income inequality, which means that if

more people are with above secondary schooling, the lower the Gini index will be. By

observing the p-value of coefficient of education, we found that this relationship

between education and income inequality is not significant. The income gap between

urban and rural area makes the overall inequality grows over time.


In brief, from the results of those two regressions, we found that the effect of overall

income inequality on economic growth could be either positive or negative. On

contrast, with the growth of economic, the income inequality increases as well.

However, heteroskedasticity is a major method critique of the regression model. Both

of the two regression results show that heteroskedasticty exist. Heteroskedasticity

does not affect the parameter estimates to be biased, but it can cause the variance and

CoefficientsStandard deviationt Stat P-value Lower 95%Upper 95%

Intercept -17110 7067,529 -2,42093 0,027738 -32092,5 -2127,52

GDPPC 11,3422 0,620415 18,28164 3,8E-12 10,02698 12,65742

OGINI 20950,83 19841,97 1,055884 0,306704 -21112,3 63013,93

INVSETMENT0,169547 0,05424 3,125834 0,006517 0,054562 0,284531

EDUCATION 198106 80723,92 2,454118 0,025958 26978,94 369233,1

CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%

Intercept 0,130584839 0,050782 2,571467 0,021272 0,022345 0,238825

GDPPC -1,67282E-05 7,41E-06 -2,25761 0,039309 -3,3E-05 -9,3E-07

GDPPC² 7,06117E-10 2,74E-10 2,572468 0,02123 1,21E-10 1,29E-09

Education -1,438335215 0,770046 -1,86786 0,081444 -3,07965 0,202979

URGAP 0,068774211 0,012375 5,557357 5,48E-05 0,042397 0,095152

Urbanization 0,894097756 0,326601 2,737583 0,015263 0,197964 1,590232


18

the standard errors of the coefficients to be underestimated. As a result, the t-values

for the estimated coefficients cannot be trusted. Thus, the regression results using

heteroskedasticity will still provide a valid estimate for the relationship between

income inequality and economic growth. However, it may judge the relationship to be

statistically significant when it is actually too weak to be confidently distinguished

from zero.


19

Chapter 5: China’s income inequality

5.1 The overall inequality and the urban-rural income

inequality

Previous studies almost universally report that the gap between urban and rural

household income in China is large and with a growing trend. Moreover, the urban

and rural income gap contributes substantially to the overall inequality (Sicular, Yue,

Gustafsson and Li, 2008).

According to the table below, we observe that the mean income in urban areas is more

than triple that in rural areas, which leads China to be one of the highest urban-rural

income ratios. Chang (2006) based on the dataset before the year 2000 and used the

coefficient of variation to calculate, found that the main reason why the overall

inequality in China is severe is because of the big difference between urban and rural

households’ incomes. The income inequality among urban residents and rural

residents are not quite severe. Based on his method, we will recalculate and estimate

the contribution of the urban-rural income gap to the overall inequality.

.

Figure 7: Urban-rural income ratio. Year 1985-2005.

Source: The State Statistics Bureau of China

0,00%

50,00%

100,00%

150,00%

200,00%

250,00%

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

Urb

an r

ura

l in

com

e g

ap

Year


20

Chang (2006) used the coefficient of variation to decompose the factors of China’s

overall income inequality. He decomposed the overall income inequality into three

groups:

(1) The income inequality among urban residents

(2) The income inequality among rural residents

(3) The income inequality between the urban and rural residents.

Firstly we have to prove that the coefficient of variation (the squared value) is

additively decomposable and consistent with the principles that all the groups are

independent from each other.

We use to stand for income of rural residents; is the income of urban

residents and is the income of all the residents. In addition, in the following

notation, we present the representatives of various variables:

: the total population in rural areas

nu : the total population of urban areas.

n: the total population, n=nr+nu

pr ：the proportion of rural population. rr

np

n

pu : the proportion of urban population. uu

np

n

r : the average income of rural residents.

u : the average income of urban residents.

: the average income of Chinese residents.

We decompose the variance of

2

1

1

1var( ) ( )

n

i

i

x xn

2 2

1

1( )

r un n

i

i

x nn

2 2 2

1 1

1 1( )

ur nn

ri ui

i i

x xn n

2 2 2

1 1

( )ur nn

urri ui

i ir u

ppx x

n n

2 2 2 2 2 2 2

1 1

1 1( ) ( ) ( )

ur nn

r ri r u ui u r r u u

i ir u

p x p x p pn n

var( ) var( ) var( , )r r u u r up x p x (1)

Here the national income is divided into 3 parts. Firstly, it is the variance of income


21

inequality within the rural residents (var(xr)) ,multiplied by the proportion of the

population in rural areas (pr). Secondly, it is the variance of income inequality within

the urban areas (var(xu)), multiplied by the proportion of population in urban areas

(pu). Then the last part is the variance of the inequality between the urban and rural

areas (var( r , u )).

Then Chang(2006) assumed that if all the rural residents(nr) received the same

income r , and all the urban residents received the same income u , the variation of

the urban-rural income gap would be:

2 2 2

1 1

1var( , ) ( )

ur nn

u ri ui

i i

r nn

2 2 2urr u

nn

n n

2 2 2

r r u up p

From the equations above, we get a conclusion that the variance of urban-rural

income gap is only affected by the urban average income and rural average income; it

is independent from the variance of income inequality within urban or rural areas. It

also implies that all the three groups are independent from each other.

Following step is converting the expression method from variation to coefficient of

variation (cv).

, CV is useful in analyzing different groups during

different time. The overall, rural and urban income coefficient of variations are

denoted by cv(x), cv(xr) and cv(xu). The overall coefficient of variation can be written

as following equation:

2 2 2 2 2 2[ ( )] ( ) [ ( )] ( ) [ ( )] [ ( , )]urr u u r ucv x p cv xr p cv x cv

(2)

In table 1-b in Appendix, we presented the national population/ income, the

proportion of urban population/ income, the proportion of rural population/income

and the urban-rural income gap. Table 1-c in the Appendix is the result which was

calculated from the formula (1) and (2). It shows the variation and coefficient of

variation of inequality within rural, urban areas and between rural-urban areas.

Obtaining the results from Table1-c, we found that during 1990 and 2001, the main

reason why the overall income gap becomes bigger and bigger is the increasing gap

between urban-rural income gap and the income inequality within urban residents.

The gap within rural residents did not deteriorate. Obviously, during the 1990s, the

income inequality within rural areas is one the main reason for the overall inequality.

However, the overall inequality grows substantially since 2000, and the inequality

within urban areas has gradually replaced the urban-rural income gap and has become

the main factor.


22

5.2 The causes of urban-rural income inequality

This paper has already examined the relationship between the income inequality and

the economic growth. However, the causes of China’s inequality were questioned. In

order to get a better understanding about the urban-rural income inequality, some

main causes are presented below.

5.2.1 Land Reform

Land institution plays a significant role in the economic growth, especially in the rural

regions in China. One of the successful land reform took place during the 50s in

China. The land distribution of all farmers reached the national level which increased

the level of the income in rural China, and it laid the foundation for the economic

growth in rural regions as well. There was another successful reform happened in

rural China in 1978 at the first time which is called household contract responsibility

system. This reform did great improvement in the rural regions at that time. With the

increased incentives for production, farmers’ income had a great improvement. At the

same time, China has promoted basic education and health services in these rural

areas which have created a considerable amount of human capital to support the

development of non-agriculture ventures, as well as provide educated farmers with

market opportunities during the period of reform and openness. Consequently, rural

poverty declined remarkably during that period (Heng, 2008).

Government studies indicate that more than 40 million farmers have been displaced

from their land, and the number is increasing by more than two million a year (Jialin

Zhang, 2010). This phenomenon has become the new rural poor which has worsened

the income distribution in rural regions.

Land reform is intuitively associated with lower income inequality. It does great help

to redistribute land assets from rich people to poor ones. But on the other hand, it is

inevitable that land markets are created during land reforms which make it possible

for people who are more productive to acquire more land from those who are less

productive. This could lead to higher landholdings inequality and income inequality

(Ayal, 2009).

5.2.2 Education

China has achieved remarkable achievement in education since the establishment of

the People’s Republic in 1949, with the most impressive progress being made since

the commencement of economic reforms. According to the Ministry of Education


23

(MOE) in China (2002), the adult literacy rate was 60 percent of the population; it

increased to 85.5 percent in 2001 (Qian & Smyth, 2005).

Education in rural household differs due to many reasons. It is universally

acknowledged that people who are well-educated earn high incomes. To some extent,

income differences are based on education. As higher earnings based on education

can be considered as compensation for investments in human capital or as the result of

household choice or merit. Education can be the tickets to a job in the formal

economy or in a more highly paid occupation. A person can be more productive in his

or her current job as well as getting promotion with sound training, or other forms of

education (Gustafsson., 2008, p.25-26).

In pre-reform China egalitarian policies compressed the wage distribution, and

education-based earnings differentials were comparatively small. China’s movement

toward a fully functioning labor market, combined with rising demand for qualified

personnel associated with China’s rapid growth and globalization, have changed the

returns to education. Concurrently, education levels have been rising. As reported, the

returns to education have been rapidly increased. For example, the premium for a

college degree over primary school was only 9 percent in 1988. In 1995, it increased

to 39 percent. Remarkably, it was 88 percent in 2002 (Gustafsson, 2008, p.26).

Gaps in education between rural and urban areas in China have been widely reported

(Hannum, Behrman, and Wang forthcoming; Knight and Song 1999). Wages and

incomes in China have become increasingly differentiated on the basis of education.

For example, by 2002, more than one-third of China’s income inequality was

attributable to education, up from about 10 percent in 1995, becoming a major source

of inequality in China. The importance of education to overall inequality reflects not

only the higher levels of education in urban China, but also differences in the returns

to education between urban and rural areas (Gustafsson, 2008, p.26).

On the other hand, access to education becomes increasingly important as the private

rewards to education rise. The access to education is not equal between urban and

rural regions. In China, school fees have been increasing and have constituted a

growing burden for low-income households, whose children typically complete

schooling at a relatively young age to begin work (Gustafsson., 2008, p.26).

According to Chen and Song (2006), urban workers are more educated as expected.

As shown in Table 7 below, urban workers have 3.57 more years of schooling than

rural workers on average, and the mean of urban workers’ age is 32.99 which is

slightly older than these workers who work in rural regions with the mean of 30.95.

The experience of urban workers is more than 4 years longer than rural workers.

Referring to the employment scale, the means of urban workers and rural workers are

514.28 and 327.20 respectively, which means that people who work in urban regions


24

have a larger employment scale than these who work in rural regions. This contributes

to the income inequality between urban and rural regions.

Continuous

variables

Urban Workers Rural Workers Difference in

mean Mean SD Mean SD

Age 32.99 9.85 30.95 8.72 2.04

Years of schooling 12.76 2.39 9.19 2.19 3.57

Experience 12.63 10.19 8.11 7.03 4.52

Employment scale 514.28 595.70 327.20 543.61 197.08

Table 7: Summary statistics of human capital (Chen & Song, 2006)

5.2.3 Migration

On the other hand, we estimate that labor mobility is another factor which leads the

increasing income inequality. We can observe this trend of labor mobility from the

statistics. There are two main trends which contribute to the growth in China’s urban

population, natural increase in the urban population and reclassification of the rural

population. Referring to reclassification, it occurs when rural residents migrate to

urban places and when rural places are reclassified as urban places. All of these

mechanisms have contributed to the population growth in urban China, but migration

seems to be the most significant factor (Sicular, Yue, Li and Gustafsson, 2008, p.44).

As shown in Table 8, according to these figures which are obtained from official

statistics, NBS, in 1990, there were only 26.41 percent urban population share, it

increased to 29.04 percent in 1995, and further reaching to 39.09 percent in 2002. As

we can see from the table, the urban population share increases with the decrease of

urban natural rate of growth, which indicates migration might be one of the attributors

for the urbanization.

Chan and Hu (2003) mention that the urban natural rate of increase has been low

(Table 8). According to their estimation, in the 1900s, the natural rate of growth of the

urban population contributed only about one third of total growth in the urban

population, and 22 percent was due to reclassification of rural places. Migration

contributed to the remaining 55 percent, which means more than half of China’s urban

growth was due to the migration during the 1990s (Sicular, Yue, Li and Gustafsson,

2008, p.44).

Urban population as %

of total

Urban natural rate of

increase

1990 26.41 1.043

1995 29.04 0.923

2000 36.22 0.510

2001 37.66 NA


25

2002 39.09 NA

Table 8: Urbanization in China

Source: Urban population shares from NBS (2003).

Kuznets (1995) has emphasized that the effect of the income disparity between sectors

on the overall inequality. It implies that the income disparity between urban and rural

areas causes a labor migration, which means that labor force in low-paid regions

would like to move to high-paid regions. This, additionally, leads to the labor

migration and results in growth in overall income inequality. (Xiaochuan Xi, 2008).

It is said that “measured consumption can serve a proxy for household permanent

income; it is proportional to permanent income” (X. Wu & M. Perloff, 2004). Thus in

this research paper, the urban-rural consumption ratio is used as a measure of the

urban-rural income inequality.

Figure8: Urban-rural consumption ratio. Source: SSB

Figure8 shows the data on the urban-rural household consumption ratio from 1985 to

2004 in China (Figures in detail are in Appendix). From the graph, it is obtained that

the urban-rural consumption ratio per capita changed slightly from 2.12, 1985 to 2.19,

1990 respectively. After 1990, the ratio of urban to rural increased rapidly, from 2.19

in 1990 to 3.29 in 2004, which indicated that during that period, the urban

consumption is more than triple of rural consumption. Thus then, labor migration

occurs. More and more people moving to big cities to seek for opportunities and

high-paid wages.

However, the survey data from Park and Wang (2010) shows that there exist

significant differences between rural migrants and local residents in many aspects. For

instance, compared to local residents, rural migrants have worse access to housing,

social insurance programs, social assistance and public services. Due to the strict

22,22,42,62,8

33,23,4

Year 1986 1988 1990 1992 1994 1996 1998 2000 2002

urb

an

-ru

ral

con

sum

pti

on

ra

tio

Year

Ratio of Urban to Rural


26

residence registration system in China, it is a barrier for rural migrants to obtain an

urban registered residence. Moreover, this barrier makes them hard to find a job with

high-paid wages and they cannot enjoy the equal social welfare benefits or subsidies.

As migration becomes increasingly permanent, it will be a great challenge to enable

migrant households to become equal members of urban communities (Park and Wang ,

2010). As a result, the overall inequality increases over time.

On the other hand, we can observe this effect from the formula (1) and (2) above, we

found that labor mobility is important to the increasing overall inequality. With more

and more people moving from rural areas to cities, the proportion of urban residents

(pu) increases which leads to the increase the weights of variance of urban inequality

(var(xu)). The overall income inequality will grow as well.


27

Chapter 6: Conclusion

In this study, we estimated the relationship between income inequality and economic

growth. We examined the China’s overall income inequality between 1985 and 2005.

Employing two regression models, we tried to find that if income inequality and

economic affect each other. The econometric results show a positive effect of

economic growth on income inequality, which means that with the growth of

economy, the income inequality in China will rise too. This, however, is not

consistent with Liang’s study (2008) which shows that the economic development

significantly contributes to the reduction of income inequality. From another

regression result, we found that the income inequality will reduce the economic

growth. However, this effect is not significant due to the small sample size in the

regression model. Moreover, heteroskedasticity exists in both of the two regression

results, which means that those results are less reliable.

Secondly, we adopted Chang’s method and used variation and coefficient to calculate

the contributions of decompositions of overall income inequality which includes: the

urban-rural income gap, the income inequality within urban areas and the income

inequality within rural residents. From the result, it is obtained that the urban-rural

gap is actually not the only factor of the overall inequality. The income inequality

within urban areas is deteriorating since 2000 while the gap within rural residents

reduced. This finding is consistent with the study of Sicular, Yue, Gustafsson and Li,

they found that the urban-rural gap contributes about one quarter of overall inequality

and it is not the only contributors to income inequality in China. The increasing

income inequality within urban areas, to some extent, is because of the labor mobility

from rural areas to urban areas.

Finally, the main cause of the China’s income inequality was analyzed in the paper.

We concluded three reasons for the increasingly inequality in China, which includes

land reform, education and labor migration. Land reform in 1978 helped to

redistribute land assets from the rich to the poor. On the other hand, people who are

more productive then could acquire more land from those who are less productive.

Thus then, landholdings then become inequality because of different productivity.

As a consequence, income inequality occurred. Since human capital contributes

substantially to production, different education levels of labor force plays an

important role in the income inequality. Gaps in education between rural and urban

areas in China have been widely reported which means average higher education level

in urban areas than that in rural areas. People with higher education level have higher

wages. Moreover, school fees have been increasing in China and left a heavy burden

for low-income households, whose children typically complete schooling at a

relatively young age to begin work. Consequently, the poorer becomes poorer and

income gaps increases over time. Migration from rural to urban areas is concerned as


28

one of the important factors. As suggested by Kuznets (1995), income disparity

between urban and rural areas causes a labor migration, which means that labor force

in low-paid regions would like to move to high-paid regions. However, due to the

strict resident registration system, it is difficult for rural migrants to get high-paid job

and enjoy the equal social welfare or subsidies. Thus, income inequalities between

rural-urban areas and within urban residents deteriorate.

In sum, with the development of economic, income inequality seems to increase as

well. We cannot make a conclusion that this inequality will decline. The effect of

income inequality on economic growth is not significant. It could be either positive or

negative. The urban-rural income gap is not the main contribution to the overall

inequality any more since the income gap within urban areas is becoming larger and

larger. The main reasons of China’s income inequality are land reform, education and

migration which caused by the urban-rural income disparity. It is quite necessary for

the Chinese government to adjust some public policies to reduce the increasing

income inequality.


29

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in China: Widening coast-inland gap or widening rural-urban gap? Department of

Economics, Monash University, Australia

Zhicheng Liang (2008), Financial Development and Income Inequality in Rural

China 1991-2000/Understanding inequality and poverty in China, United Nations

University 2008. P.86

http://finance.people.com.cn/GB/10246541.html

http://www.undp.org.cn/downloads/nhdr2005/NHDR2005_complete.pdf


32

Appendix: Figures

Table 1-a. Data Set

Real GDP and GDP per capita are calculated with GDP deflator provided by Word Bank Indicator

(Base year: 1997).

Source: NBS and WIID

Year GDP GDPPC(10

0million

yuan)

REAL

GROWTH

RATE%

GDPPC

GROWTH

RATE%

RGINI UGINI OGINI total

Investment(1

00million

yuan)

1985 25113,6 2383,3 13,5 9% 26,4 19 30 2543,2

1986 27768,6 2602,7 8,8 10% 28,8 18,9 31,8 3120,6

1987 30898 2851,3 11,6 9% 27,9 19,4 33,1 3791,7

1988 34174,5 3104,5 11,3 2% 30,1 20,1 33,7 4753,8

1989 35418,5 3164,6 4,1 4% 30,8 19,8 35,6 4410,4

1990 37436,6 3288 3,8 7% 28,8 19,8 34 4517

1991 40418,9 3505,6 9,2 14% 31,5 18,3 37,3 5594,5

1992 46443,6 3984,5 14,2 11% 31,7 20 36,3 8080,1

1993 51852,9 4408,8 13,5 23% 31,9 21,9 38 13072,1

1994 59393,2 5437 12,6 0% 30 22,9 38,1 17042,94

1995 64312,4 5425,8 10,5 9% 33,1 22,8 38,2 20019,26

1996 70851 5905,1 9,6 9% 31,6 22,1 36,9 22974,03

1997 78060,8 6420 8,8 7% 32,2 23,2 37,5 24941,11

1998 83862,9 6864,6 7,8 6% 32,1 23,9 37,8 28406,17

1999 90284,9 7305,1 7,1 8% 32,5 24,6 38,9 29854,71

2000 98000,5 7858 8 8% 33,9 25,8 39 32917,73

2001 105949,2 8452,9 8,3 8% 34,3 26,9 41,5 37213,49

2002 115626,9 9124,3 9,1 10% 37,2 31,7 45,4 43499,9

2003 128737,1 10040 10 9% 44,9 55566,61

2004 141227,2 10916,8 10,1 10% 46,9 70477,4

2005 157896,7 12053,8 9,9 9% 47 88773,6


33

Table 1-b Data set

Source: NBS

Yearpopulation

Average

income

unit:10,000personPopulation Proportion%Population Proportion%

1985 105851 25094 23,71 80757 76,29 478,6 739,1 397,6

1986 107507 26366 24,52 81141 75,48 540,5 899,6 423,8

1987 109300 27674 25,32 81626 74,68 599,2 1002,2 462,6

1988 111026 28661 25,81 82365 74,19 709,2 1181,4 544,9

1989 112704 29540 26,41 83164 73,79 806,7 1373,9 601,5

1990 114333 30195 26,41 84138 73,59 903,9 1510,2 686,3

1991 115823 31203 26,94 84620 73,06 975,8 1700,6 708,6

1992 117171 32175 27,46 84996 72,54 1125,2 2026,6 784,0

1993 118517 33173 27,99 85344 72,01 1385,1 2577,4 921,6

1994 119850 34169 28,51 85681 71,49 1869,7 3496,2 1221,0

1995 121121 35174 29,04 85947 70,96 2363,3 4283,0 1577,7

1996 122389 37304 30,48 85085 69,52 2813,9 4838,9 1926,1

1997 123626 39449 31,91 84177 68,09 3069,8 5160,3 2090,1

1998 124761 41608 33,35 83153 66,65 3250,2 5425,1 2162,0

1999 125786 43748 34,78 82038 65,22 3477,6 5854,0 2210,3

2000 126743 45906 36,22 80837 63,78 3711,8 6280,0 2253,4

2001 127627 48064 37,66 79563 62,34 4058,5 6859,6 2366,4

2002 128453 50212 39,09 78241 60,91 4518,9 7702,8 2475,6

2003 129227 52376 40,53 76851 59,47 4993,2 8472,2 2622,2

2004 129988 54283 41,76 75705 58,24 5644,6 9421,6 2936,4

2005 130756 56212 42,99 74544 57,01 6366,6 10493,0 3254,9

Urban

Income of Rural

Households

RuralPer Capita Annual

Net

Annual

Disposable

Income of Urban

Households(YUA


34

Table 1-c Inequality decomposition by urban, rural subgroups

Year

Estimator of variance Estimator of coefficient

of variance Urban

-rural

incom

gap

Overa

ll

Rural Urban Betwee

n rural

and

urban

Over

all

Rural Urban Between

rural

and

urban

1990 329330 172598 251146 135987 0.63 0.61 0.33 0.41 2.22

1995 3129541 1129580 2803779 1513774 0.75 0.67 0.39 0.52 2.72

1997 4637676 1533177 4725360 2085873 0.70 0.59 0.42 0.47 2.48

1998 5337862 1595756 5574282 2415268 0.71 0.58 0.43 0.48 2.52

1999 6604028 1686381 7000858 3069272 0.74 0.59 0.45 0.50 2.66

2000 8226306 1855453 8913972 3814257 0.77 0.60 0.47 0.52 2.80

2001 1.1E+07 1978280 11861201 4840485 0.80 0.59 0.50 0.54 2.92

2002 1.8E+07 2066342 23799262 7740583 0.91 0.58 0.60 0.59 3.30

Source: Data from the results of Gene H. Chang (2006), Decomposition of China’s Rising Income

Inequality: Is the Rural-Urban Income Gap Solely Responsible?


35

Table 1-d The urban-rural household consumption ratio in China from 1985-2004

Year Urban household

consumption per

capita

Rural household

consumption per

capita

Ratio of

Urban to

Rural

1985 673 317 2.12

1986 799 357 2.24

1987 884 398 2.22

1988 1104 477 2.31

1989 1211 535 2.26

1990 1279 585 2.19

1991 1454 620 2.35

1992 1642 660 2.49

1993 2111 770 2.74

1994 2851 1017 2.80

1995 3538 1310 2.70

1996 3919 1572 2.49

1997 4186 1617 2.59

1998 4332 1590 2.72

1999 4616 1577 2.93

2000 4998 1670 2.99

2001 5309 1741 3.05

2002 6030 1834 3.29

2003 6511 1943 3.25

2004 7182 2185 3.29

Source: Heng Quan, Income inequality in China and India

bachelor thesis in economics -...

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