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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.
Mälardalen University Bachelor Thesis in Economics
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
Mälardalen University Bachelor Thesis in Economics
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
Mälardalen University Bachelor Thesis in Economics
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 4: Regression result 2-a
Table 5: Regression result 1-b
Table 6: Regression result 2-b
Table 7: Summary statistics of human capital
Table8: Urbanization in China
Mälardalen University Bachelor Thesis in Economics
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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
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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
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related to land reform, different education levels and migration from rural to urban
areas.
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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
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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).
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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
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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
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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)
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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
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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
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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
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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.
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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
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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.
Table 3: Regression result 1-a
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
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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, >
Mälardalen University Bachelor Thesis in Economics
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and Ho is rejected. Additionally, =0.97524 implies a high quality of the
regression model.
Table 4: Regression result 2-a
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
Mälardalen University Bachelor Thesis in Economics
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Table 5: Regression result 1-b
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.
Table 6: Regression result 2-b
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
Mälardalen University Bachelor Thesis in Economics
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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.
Mälardalen University Bachelor Thesis in Economics
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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
Mälardalen University Bachelor Thesis in Economics
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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
Mälardalen University Bachelor Thesis in Economics
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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.
Mälardalen University Bachelor Thesis in Economics
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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
Mälardalen University Bachelor Thesis in Economics
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(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
Mälardalen University Bachelor Thesis in Economics
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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
Mälardalen University Bachelor Thesis in Economics
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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
Mälardalen University Bachelor Thesis in Economics
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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.
Mälardalen University Bachelor Thesis in Economics
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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
Mälardalen University Bachelor Thesis in Economics
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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.
Mälardalen University Bachelor Thesis in Economics
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Perotti, Robert (1992) Income distribution, Politics and Growth, The American
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Income Gap and Income Inequality in China/Understanding inequality and poverty in
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http://www.undp.org.cn/downloads/nhdr2005/NHDR2005_complete.pdf.
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China 1991-2000/Understanding inequality and poverty in China, United Nations
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http://finance.people.com.cn/GB/10246541.html
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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
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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
Mälardalen University Bachelor Thesis in Economics
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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?
Mälardalen University Bachelor Thesis in Economics
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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
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