Regional Inequality of Gansu in China:
A Focus on Rural-Urban Income Difference, 1990 – 2005
Jing Feng
Department of Geography
University of Ottawa, Canada

ABSTRACT China has been experiencing an incredible growth since the economic
reforms of 1978. These reforms not only accelerated economic development, but also
resulted in regional inequalities among China’s provinces, especially between Western
China and the coastal provinces. The Chinese Government values equity as a central issue
in economics, sociology and politics. The People’s Congress listed regional inequality as
one of the most pressing problems to be targeted during the ninth five-year plan andincome distribution is often used to understand regional inequality in the Chinesecontext. Gansu, as one of the poorest provinces in China, is characterized by severe
natural conditions, a large number of minority groups and low urbanization rates. In orderto understand the spatial patterns of regional inequality in Gansu, this study measures the
distribution of income within the region. Descriptive statistics are used to distinguishbetween urban and rural counties on the basis of several socio-economic variables. GIS
Spatial Analysis is also used to illustrate the geographical relevance of the income
disparity measures. The findings of this study suggest that income disparity is strongly
correlated with urbanization rates, revealing a consistent urban-rural disparity within theprovince.
INTRODUCTION
China has been experiencing a considerable growth since the economic reforms of
1978, while simultaneously facing a growing gap between its developed eastern coasts,
especially the ultramodern metropolises and the littoral zones, and with the poor
hinterlands in the Western regions. The great majority Of some 48 million people living
under the official poverty line in China are located in the Western China (World Bank,
2001), where poverty is most pronounced in rural, ethnic minority areas, while urban
areas enjoy a much higher standard of living. In 2000, The Chinese Government has
made the development of the Western provinces’ social and economic conditions anational priority (China’s Western Development Priority). Since then, however, thisdevelopment has been hindered by a lack of understanding of many factors that havecreated this disparity in Western China.
The development experience during the last century has shown that economic
progress alone does not achieve either the social equity or the multi-sector dynamics,
essential for the long-term advancement of human welfare. After decades of development,
many populations are still facing “decreased opportunity, political and economic
disempowerment, and general insecurity when it comes to food, social safety, politicaland legal representation, as well as financial well-being” (Jones, 2004 p.145). It has
become clear that economic growth cannot be sustained without taking into consideration1

the social and political development of vulnerable populations, particularly minority
populations.
China is facing more and more challenges since the late 1980’s because thebenefits of its economic growth continue to be distributed inequitably, as is often the casewith the core-periphery territorial organizations. While the urban area is certainly the coreof developing activities, continued polarized development in China underlies numerous
social and demographic shortcomings at the county level, as in the Western minority
counties (Cao et al., 2000).
This research focuses on Gansu, as a study region, exploring the urban-rural
income disparity at the county level, and examining the evolution and spatial distributionof the disparity for last two decades in the province. The following text will first review
the existing regional disparities in China, then it will provide a brief introduction to thestudy region and will introduce the method applied in this research as well; thirdly, thearticle will study the evolution and spatial distribution of the urban-rural income disparity
during the last 15 years; fourthly, the factors that contribute to the increasing regional
disparity in the province will be presented; finally, a summary of the research findings
will be provided.

REVIEW OF REGIONAL DISPARITIES IN CHINA
The Chinese government considers equity to be a central issue in economics,sociology and politics (Yang, 1999). Since 1990, the People’s Congress listed regional
inequality as one of the most pressing problems needing to be addressed; it is listed forthe eighth “five-year” plan.
It is widely recognized that the reforms that have taken place in China since 1978,
though overall very successful in achieving high economic growth rates, have beenaccompanied by a substantial increase in income disparity. This income disparity has
manifested itself as follows:• inter-regionally, including coastal versus inland regions, northern versus southern
regions, and inter-provincial;• intra-regionally, that is urban versus rural areas;• between groups of individuals, especially between the rich and poor. Those getting
rich through successful business endeavours or by the abuse of power are leaving many
people behind struggling to meet their basic needs.
For the purposes of this research, the focus is on the intra-regional disparity, inparticular urban-rural income disparity.
Urban-rural income disparity has been at the centre of scholarly and policy
debates since the establishment of the People’s Republic of China in 1949 (Chang, 2002;
Ma, 2003; Wu and Perloff, 2004; Xiang, 1998), largely due to the diverse views on thenature and practice of socialism (Wu, 2002). A number of studies have emphasized therelationship between urban growth and urban-rural income disparity. Lu (2002) attempts
to determine the validity of the Kuznets-Williamson model 1 in order to explain
1

Kuznets (1955) had proposed an inverted U-shaped relationship (also called a Kuznets curve) between thelevel of urbanization and income level, measured by the average income between urban and rural sectors,
using scant sample data of the United States, England and Germany. He concluded that, in general, theurban-rural disparity would increase at the beginning of the urbanization process, as the population moved
away from the rural sector to the urban sector in search of higher urban incomes; gradually the income
disparity would decrease as the population settled down in the urban sector, receiving progressively higher

2

urban-rural income disparity in China. He finds that the provinces where economic
growth raises per capita consumption, urban-rural consumption differences are likely tobe smaller, or at least, increase very slowly. Unlike other developed countries, China has
not followed Kuznetz’s principles because the governing registration system, Hukou
policy, has restricted the mobility of rural labourers. Lu and Chen (2006) suggest thereason why China has not followed the Kuznets curve is richer rural areas are the first to
be urbanized; because residents in these areas have a greater possibility of getting ahigher paying job, they are more likely able to afford higher education for their children.
Xue (1997) also notes that the high per capita income of urban workers, the availability ofmultiple subsidies, and the restrictions on migration from rural to urban areas, explain
most of the differentials of the urbanization effect. Yang and Zhou (1999) observed thaturban-rural per capita income and consumption experienced a U-shaped change after the
economic reforms were launched in the late 1970s. From 1978-1990, the differentials
decreased. Afterwards, however, they increased rapidly. Changes traced through the
1990s, indicate that consumption differentials peaked around 1993-1994, followed byanother U-shaped change. Given the multi-U-shaped experience of China’s urban-ruralinequality, it is especially interesting to investigate the validity of a Kuznets-Williamson
hypothesis in this particular country’s urban–rural context.
Apart from economic development, other factors have also contributed torural–urban income disparity. Predominantly, as Yang and Zhou (1999) have shown,inter-sector gaps in marginal productivity of labour, as well as barriers to inter-sector
reallocation of labour are major sources of urban-rural disparity (Lu, 2002).
Todaro (1969) suggested that the urbanization process narrows the disparity
between urban and rural sectors. When rural labour forces look for higher income inurban areas, their migration contributes to the urban-rural income disparity because of the
expectation for higher wages. At the same time, the rural to urban migration accelerates
the urbanization process (Lu and Chen, 2006; Bruecknera and Zenoub, 1999; Yang, 1998).
Chen (2002) examined the correlation between variables such as GDP per capita,
urban-rural income gap, and levels of urbanization and industrialization by using the
Chinese provincial data of 2000. His study confirms that the regions with higher GDP per
capita and higher levels of urbanization and industrialization, such as Jiangsu, Zhejiang,
Liaoning, Shanghai, Beijing and Tianjin, have a smaller urban-rural income gap.
Provinces and autonomous regions with lower GDP per capita, such as Tibet, Yunnan,
Guizhou, Shaanxi, Qinghai and Gansu, usually have higher urban-rural income disparities.
Consequently, it is concluded that urbanization contributes to reducing both urban-rural
income and regional disparities.
Yang (1999) attributed the rise in urban-rural disparity after 1990 to what hecalled the “urban-biased policy mix”, which included increased subsidies, investments,
and banking credits for urban regions. These policies brought about higher inflationary
taxes on rural earnings. Johnson (2000) summarized three other major policy areas thatadversely affected rural incomes: restrictions on rural-to-urban migration, frequent
inaccessibility to education, and the urban-biased allocation of investment and credit.
Moreover, the individual effect of household composition has increased income
inequality (Lanjouw and Ravallion, 1995; Brandolini and D’Alessio, 2001). Larger
families tend to be poorer because they have more children; consequently, they achieve alower level of welfare.
Little is known about the relative importance of potential factors contributing tourban-rural inequality (Wan, 2007). Nevertheless, it is clear that urban-rural income
incomes.

3

disparity is considerably wider in Western China, where minority populations are mosthighly concentrated (Xue, 1997). Ethnic inequality is an important concern because of
the implications that it might have on economic development and the functioning ofsociety in China. It has been confirmed that the income disparity in minority populations
is the result of socioeconomic inequality between minority and majority (non-minority)groups (Cao et al., 2005; Frisbie and Neidert, 1977).
METHODOLOGYSTUDY REGION. The province of Gansu is located in Western China (see Figure 1) andat the intersection of the Loess Plateau, the Inner Mongolian Plateau and the
Tibetan-Qinghai Plateau. Each of these plateau regions have distinct cultures, creating in
the province of Gansu distinguished socio-economic and cultural characteristics. Gansu
adjoins three Minority Autonomy Provinces: Inner Mongolia (Mongolian Minority) in thenortheast, Ningxia (Hui Minority) in the north, and Xinjiang (Uyghur Minority) in thenorthwest. It also connects three other provinces where a significant number of minority
populations live: Qinghai (45.5% minority proportion, including Tu, Hui, Tibetan,
Mongolian, etc.), Sichuan (5.6% minority proportion, including Tibetan, Qiang, etc.) andShanxi (0.6% minority proportion, including Hui, etc.).
Although Gansu is not recognized as a Minority Autonomy Region, it counts 45
ethnic minority groups among its total population of 2.5 million people. This minority
population accounts for 8.75% of the total population in Gansu. Most of these ethnic
groups live in one of the 22 government-designated minority counties (Figure 1), which
represent nearly 20% of the total counties in Gansu province. The geographicaldistribution of the minority counties is characterized by the neighboring provinces, which
are the source of the different ethnic minority groups. The Kazaks, Salars and Mongols
located in the northern autonomous territories; for example, originate in the Inner
Mongolia and Xinjiang provinces. The Hui and Tibetans located in the southern part ofthe province come from Qinghai province. The cohabitation of the majority population,Han, and the various ethnic minorities listed above, make Gansu a very interesting region
for a case study of Western China.
Aside from its unique placement at the intersection of three plateaus, and its rolein adjoining three minority provinces, the impoverished socioeconomic characteristics ofGansu also make this province distinct. Nearly 40% of Gansu’s total population isconsidered relatively poor; an additional 10% is considered very poor. In 2000, theaverage GDP per capita in Gansu was 3490 yuan (US $426), which represented only 54%of the national GDP per capita average of China. According to the Chinese government,of the 80 counties in the province, 41 were poverty stricken; 12 of these were populated
mostly by minority groups. The average annual net income of the 0.9 million people
living in these 12 minority poverty counties was below 1,000 yuan (US $125) in 2000.
Figure 1
Gansu Province and its Minority Regions4

2 administrative areas of Subei
CHINA

Xinjiang±

Akesi

GANSU Inner Mongolia
Qinghai^ Beijing

Shanxi

Tibet

SichuanChongqing
Guizhou
Yunan

Hui Minority Counties:
1, Yongjing 2, Jishishan 3, Dongxiang 4, Linxiashi
5, Linxiaxian 6, Hezheng 7, Kangle 8, Guanghe
Tibetan Minority Counties:
9, Xiahe 10, Zhuoni 11, Lintan 12, Diebu 13, Zhouqu
14, Luqu 15, Maqu

Ningxia

Guangxi

3 administrative
areas of Sunan
Tianzhu

1
2
5
9

4

^ Lanzhou

3

6 7
8
10
11

Zhangjiachuan

14
Gansu and its Regions

15

Tibetan

12

Hui

13

Mongolian
Kazaks0

Projection: Regional Conformal Projection (China)
Softwear: ArcGIS 9.0

75

150 KM

Non Minority Region

Feng Jing
Candidate of M.Sc. in Geography
University of Ottawa
Oct. 10. 2006

METHOD AND DATA. Since the analysis of the urban-rural income disparity contains
two steps, first, the evaluated the urban-rural income disparity and its special distribution
from 1990 to 2005; second, examining the contributed of the socio-economic variables tothis disparity, there are different methods apply to each steps. Income disparity will bepresented by urban-rural income ratio which is calculated by urban income / rural income.
Moreover, a GIS analysis will present the special distribution of this ratio over each of thegeographical region of the province. Finally, a multi-regression analysis will examine thefactors that had contributed to the disparity.
County as the geographical unit will be used in this research. There are 80
counties in the province as shown in Figure 1. Data used in this research are base on theGansu Statistical Yearbook (GSY) and the Gansu Statistic Bureau (GSB) database.
EVOLUTION OF THE URBAN-RURAL INCOME RATIO IN GANSU, 1990-2005
To find out the evolution of how the disparity occurred in each county during thelast 15 years, an elaborate examination of the urban-rural income ratio was considered.
Table 1 shows the average urban-rural income ratios of the province. This table illustrates
that, despite slight decreases at various times, the overall urban-rural income disparity

5

increased between 1990 and 2005. Throughout the province, the average ratio was 5.1431,
but in 1995 it decreased to 4.9397. The ratio kept increasing over the next five years,
reaching 5.0088 in 2000 and 6.8260 in 2005. Between 1990 and 2005, therefore, theurban-rural income ratio grew at a rate of 32.72%. Minority counties’ average ratio isgreater than the provincial average. The average ratio of minority counties grew from5.9086 to 6.0015 between 1990 and 1995. There was a slight decrease income ratio of
0.0461 between 1995 and 2000; in 2000 the ratio was 5.9554. By 2005, the urban-ruraldisparity in the minority counties had widened once again, attaining a ratio of 8.1146.
Non-minority counties’ urban-rural income ratios were better off than both the overall
province and minority counties ratios. In 1990 the average ratio for non-minority counties
was 4.9514; it decreased to 4.6023 in 1995. A minor increase in income disparity
occurred in 2000 when the ratio reached 4.7068. Again, urban-rural income disparity
increased in non-minority counties in 2005. The highest ratio was recorded in Dangchang:a ratio of 12.9607 in the year 2005. As is expected, the counties of Dangchang also
experienced a great increase in income disparity; in 2000, the ratio was 11.1591. Kanxian
had the highest ratio in 1995 at 10.2078, and Minxian had the largest disparity of 10.6653
of the urban-rural income ratio in 2005. These counties are all located in the south of theprovince.
Table 1. Average Urban-rural Income Ratio in the Province of Gansu, 1990-2005
1990

1995

2000

2005

87 counties

5.14314.93975.00886.8260Minority counties

5.90866.00155.95548.1146Non-minority counties

4.95144.60234.70686.4019

Data has been calculated by author
Source: Gansu Statistical Yearbook 2001 and 2006; Statistic Gansu Documents

In 1990, there were six counties (see Table 2) with very high urban income
disparity ratios, incomes eight times greater than the average rural incomes of theprovince. These six counties are located in the south of Gansu. Among these counties,
four are minority counties (Zhouqu, Lintan, Jishishan and Dongxiang) dominated by
Tibetan and Hui minority populations. Furthermore, there were 26 counties, representing
30.23% of the total counties, with high urban income ratios between 6.0 and 7.9 times
greater than the rural income ratios. Among these 26 counties, six were minority counties,
again, all located in the southern part of the province. A high proportion of the counties(33.72%) had a relatively high ratio of income disparity in 1990 that is 4.0-5.9. Amongthese counties, five were Tibetan minority autonomous counties (Luqu, Xiahe, Diebu,
Guanghe and Tianzhu) four were located in the south, and one, Tianzhu, was located in
the north. There were eleven counties in the ratio category of 3.0-3.9; only two of thesewere minority counties (Linxia and Maqu). In 1990, only fourteen counties were found tohave an urban-rural income ratios lower than 2.9. Three of these counties were minority
counties, namely Sunan, Subei and Akesai (Mongolian and Kazaks nationalities).
In 1995, there were two more non-minority counties that had a ratio greater than
8.0. These higher ratio counties represented 9.30% of the total number of counties in the6

province. There were seventeen counties in the second 6.0-7.9 ratio category; more thanhalf of these counties, nine, to be exact, were minority counties. That same year, 30
counties, that is 34.88% of the total number of counties in the province had a high ratio of
disparity – these counties fell between 4.0 and 5.9 which represented 34.88%. However,
only one minority county, namely, Zhangjiachuan placed itself in the 4.0-5.9 ratio
category. Fourteen counties found themselves in the 3.0-3.9 ratio category. Two of thesewere minority counties, Linxia and Luqu, both located in the southern province. The
number of counties finding themselves in this, at 2.9, the lowest category of urban-rural
income ratio, had thus grown.
Table 2. Number of Counties in Different Categories of Urban-rural Income Ratio
Province

1990

< 8.0
6.0 – 7.9

6
26

6.98%
30.23%

817

9.30%
19.77%

7
21

8.05%
24.14%

31
26

35.63%
29.89%

4.0 – 5.9
3.0 – 3.929
1133.72%
12.79%

30
14

34.88%
16.28%

317

35.63%
8.05%

12
14

13.79%
16.09%> 2.9

14

16.28%

17

19.77%

21

24.14%

4

4.60%

minority
counties

1995

1990

2000

1995

2005

2000

2005

< 8.0

4

20.00%

4

20.00%

4

19.05%

15

71.43%

6.0 – 7.9

630.00%

945.00%

1047.62%

2

9.52%

4.0 – 5.9

525.00%

1

5.00%

3

14.29%

1

4.76%

3.0 – 3.9

210.00%

2

10.00%

-

-

2

9.52%

> 2.9

3

15.00%

4

20.00%

4

19.05%

1

4.76%

Data has been calculated by author
Source: Gansu Statistical Yearbook 2001 and 2006; Statistic Gansu Documents

In the year 2000, the number of counties found in the first category of income
ratio, that is greater than 8.0, had decreased. There were four minority counties in thiscategory; three of these (Zhouqu, Lintan and Dongxiang) were in the same ratio category
as in 1995. Zhouni County, however, replaced Jishishan, which became another county
suffering from a large urban-rural income disparity. About 24.11% of the counties had anurban-rural income disparity ratio between 6.0 and 7.9; about half of them were minority
counties. A greater number of counties found themselves in the 4.0-5.9 ratio category. Of
these, only three were minority counties. Fewer counties had ratios between 3.0 and 3.9
and none of these were minority counties. The counties finding themselves in the smallest
disparity ratio category had increased to 21. The same minority counties, namely, Sunan,
Subei and Akesai remained in this category, and were joined for the first time by
Lianxiashi County.
In 2005, income disparity throughout Gansu became greater than ever before.
There were 31 counties (35.63%) with a ratio greater than 8.0; about half of these wereminority counties. These particular minority counties were all located in the south, mainly

7

in the Linxian Hui and Gannan Tibetan Autonomous Prefectures. About 26 counties aratio between 6.0 and 7.9; these were located throughout the province. Only two wereminority counties, they are Luqu and Maqu. These particular counties had a substantial
Tibetan population. Twelve counties’ urban-rural income disparity ratio was between 4.0
and 5.9, only one minority county, Linxiashi, among them.
Among the fourteen
counties with a ratio between 3.0 and 3.9, two were minority counties, namely, Subei and
Akesai, which, in the past, always had lower income disparity ratios than other minority
counties. In 2005, only four counties found themselves in the lowest ratio category, SunanCounty having the lowest disparity of all.
FACTORS INFLUENCING URBAN-RURAL INCOME DISPARITY
In order to examine the individual effects of each variable on urban-rural income
disparity, represented by urban-rural income ratio, a series of multiple-liner regression
analyses were carried out. The selected variables were based on our literature review. The
multi-regression analyses conducted include three models: Model 1 was applied for all
the counties in the province in 2000; Model 2 tested the relation between the dependentand the independent variables, as tested in model 1, in the 60 counties of the southern
province; Model 3 examined if the independent variables also had the same effect on the
16 minority counties of the south, where performance an unfavorable socioeconomic
condition than the other counties.
The dependent variable in these regression analyses is: the urban-rural income
ratio for each county of Gansu in 2000. The independent variables are: township density,
female illiteracy rate, immigration rate, household size (more than 6 people), and per
capita GDP.
Results of the Multi-regression Analysis
The multi-regression analysis confirms that the there is a strong association
between urban-rural income disparity and predictors among the 80 counties of theprovince (Model 1: R=0.776, R2=0.602, Adjust R2=0.575, F=22.348) in the year of 2000.
Among the five independent variables, four variables, namely township density,
female illiteracy rate, immigration rate and household size, contribute to urban-rural
income disparity (see Table 3). Two variables have positive relations with urban-rural
income disparity. To begin, the female illiteracy rate has a very strong correlation withincome disparity, which means that a higher proportion of female illiteracy results in ahigher urban-rural income disparity throughout the province. Household size also has
positive correlation with income disparity, signifying that counties that have larger size
households usually have higher urban-rural income disparity. The other two variables
have negative relations with income disparity. The correlation with the immigration rate
shows that a higher mobility among the population results in less disparity in a county.
The same is true of the township density variable – counties with higher township density
have less significant income disparity.
The second model of the regression analysis tested the same variables as thosementioned above on income disparity in the 60 southern counties (Model 2: R=0.750,
R2=0.563, Adjust R2=0.522, F=13.892). Three variables have contributed to the8urban-rural income disparity in 60 south counties.
Female illiteracy had a positive relation with income disparity, while the other two
variables, namely township density and immigration rate, had a negative correlation with
the income disparity.
Table 3. Results of the Multi-regression Analysis
Model 180 Counties

Independent Variables

Model 260 Southern
Counties

Model 316 Southern
Minority Counties-0.674(-1.804)*
0.078(3.954) ***-0.082(-3.767)***-1.037(-3.097)***0.086(4.902)***-0.172(-3.149)***-1.305(-2.532)***0.046(1.304)-0.242(-2.452)***0.045(1.895)*-0.021(-0.653)-0.013(-0.524)-0.018(-0.535)-0.066(-1.449)-0.878(-0.439)

4.104

5.823

9.123

R
R2

(8.412)
0.776
0.602

(10.489)
0.750
0.563

(5.941)
0.853
0.727

Adjusted R2
F Statistic
Sample of Size

0.575
22.34880

0.522
13.892
60

0.591
5.328
16

Township Density
Female Illiteracy Rate
Immigration Rate
Household Size (> 6 pop.)
per Capita GDP
Model Summary
Constant (C)Note:

1. Dependent variable: Urban-rural income Ratio in 2000
2. In the brackets are the t statistics. *, **, *** represent the level of significancegreater than or equal to 0.10, 0.05, 0.01 respectively

The third model concentrated on the 16 minority counties of the province’s south.
The aim of this model was to test whether the same independent variables had the same
influence on income disparity. In Table 4.6, it is clear that Model 3 was highly significant(Model 3: R=0.853, R2=0.727, Adjust R2=0.591, F=5.328). Only two variables, townshipdensity and immigration rate, are correlated with income disparity in the 16 southernminority counties, both of them are negatively related with the dependent variable.

9Analyses on the Influential Variables of Urban-rural Income Disparity
TOWNSHIP DENSITY Researchers have argued that the urbanization process will help
to reduce both urban-rural and regional income disparity, meaning as the level ofurbanization increases, the disparity will decrease. It has been shown that a region with ahigher urbanization level has a smaller urban-rural income disparity (Chen, 2002). The
regression analysis supports this fact: when all other conditions are equal, regions witha higher township density have a smaller urban-rural income disparity.
Figure 2 shows the distribution of urban-rural income disparity in relation to thedensity of townships in the province of Gansu in 2000. Figure 2.3-1 shows that while all
the counties of Gannan prefecture had low township density, they also had high income
disparity. Due to close proximity to the provincial capital, Lanzhou, there are some highdensity townships in the minority counties in Linxia. Although one of the counties
Linxiashi, a city level county, had a little income disparity, the rest of the counties in
Linxia had both high density townships and high level of income disparity.
FEMALE ILLITERACY Often there is a correlation between parents’ level of educationand the educational level of their child. Indeed, negative attitudes towards education areusually linked to the parent’s low level of education. . Low levels of education among
women are often associated with high levels of fertility, which reduces the probability of
having enough money for the education of all children. Parents, especially mothers, areconsidered the first teachers of their children. Subsequently, children’s attitudes toward
education are likely to reflect those of their parents. Children from families whose parents
have not been educated may lack a thirst for knowledge because they have not beenencouraged to learn. These children lack the skills necessary to learn academics, such as
the ability to focus. Furthermore, they do not see the value of education.
Figure 2-2 shows the distribution of female illiteracy. Generally, female illiteracy
has a positive relation with income disparity with income disparity. As is made clear in
the regression results in Table 3, female illiteracy contributes positively to the income
disparity at both the provincial and southern county levels. Thus a higher-educatedpopulation, especially female population, could reduce income disparity between urban
and rural regions.

Figure 2. Spatial Distribution of Urban-rural Income Disparity and its Influential
Variables in Gansu Province, 2000

10

Income Disparity

Township Density±
Township Density
0
1
2-3

(1)
Female Illiteracy

Urban-rural Income Ratio
0.84 - 2.99
3.00 - 3.99
4.00 - 5.99

Illiteracy Rate (%)

6.00 - 7.99
8.00 - 11.16

1 - 10075

11 - 25150 Km26 - 54

Immigration Rate

Household Size

0.14 - 5.00

1.44 - 10.00

5.01 - 20.00

10.01 - 25.00

20.01 - 47.19(3)

Projection: Regional Conformal Projection (China)
Software: ArcGIS 9.0
Source: Gansu Statistical Yearbook 2001

25.01 - 43.18

per Capita GDP

per Capita GDP(1000 USD)Househould>6 (%)

Immigration (%)(2)

0.138 - 2.000
2.001 - 8.000(4)8.001 - 40.639(5)Feng Jing
Candidate of M.Sc. in Geography
University of Ottawa
Nov. 18, 2007

MOBILITY Large flows of immigrants seem to help reduce urban-rural income disparity,
as highlighted by Fu (2004). Migration helps reduce urban-rural income disparity in two
ways. First, urban wages are generally higher than rural ones. In an idealized labour
market, rural labourers flow into urban areas to seek higher paying jobs. The excess
labour supply lowers urban wage rate, while the reduced labour supply in the rural labour
market increases rural wages. Ideally, this process continues until rural and urban wages
are equal. In this way labour mobility could decrease income disparity. Second, rural
household incomes heavily depend on transfers from migrants. Li and Wei (1999) foundthat the outflow of rural labourers not only increased the income of rural residents
because of the remittance to their hometowns but also raised the productivity of theremaining labourers due to the reduced labour surplus.
Figure 2-3 shows the distribution of the immigration rates in Gansu. As theregression results indicate, immigration rate had very strong relation with income
disparity at all three geographic levels – Counties with more immigrants have less income
disparity.

11

SIZE OF HOUSEHOLD The structure of households is a closely linked with the
distribution of income among its members (Brandolini and D’Alessio, 2001). In particular,
the size of a household makes for a strong positive correlation in developing countries(Lanjouw and Ravallion, 1995). The part of rural China that is less developed still holds
to the tradition of the son as the hope of the family. Most rural families ignore China’s“One Child” policy; they typically continue producing children until they have a boy.
Consequently, household size has been increasing greatly. As the household size grows
the demand for goods and food increases, less money, thus, is left over for the educationof children. Boys become the priority, and female illiteracy increases.
Figure 4-4 shows the distribution of lager household sizes (more than six people in
one household). It is clear that larger households are mostly found in minority counties;
higher urban-rural income disparity is also found in these counties
ECONOMIC CONDITIONS Regions with higher per Capita GDP have smallerurban-rural income disparities, while regions with lower per Capita GDP, have higher
urban-rural income disparities (Chen, 2002). Chen suggests that the growth of GDP has
become an important force in reducing urban-rural disparity. Since the Chinese central
government has defined the poverty-stricken counties according to the people’s annual
income, families’ yearly revenue has become crucial in measuring the development of acounty. In 2000, Gansu had 41 government-designated poor counties, in which theaverage income of rural residents was 1,945 yuan (US $243). The poor counties with thelowest incomes were generally concentrated in the eastern, interior and southern parts ofGansu, as well as in the southern minority counties. Figure 4-5 shows the distribution of
GDP per Capita in all the counties. Generally, minority counties do not have a high GDP
for their region due to the domination of the lower productive agricultural industry.
CONCLUSION
In this research, the urban-rural income disparity has been reviewed from 1990 to2005 on a county level over the province of Gansu. The distribution of this disparity isquite different. The southern province and especially the southern minority counties havea higher disparity. Overall the province, the income disparity between urban and rural had
a slight decrease from 1990 to 2000, but an increase in the last five years between 2000 to
2005, however, the minority counties evolution has a different trend which the disparity
was keep growing during the last 15 years.
It is also been found that there are several socio-economic variables that havestrongly influenced this urban-rural income disparity: First, the township density had asignificant contribution to reduce the income disparity between urban and rural; Second,
the proportion of the female illiteracy is one of main raisons of the disparity; Third,
mobility as one of the most important one had a significant negative relation with thedisparity, which also means that the population’s mobile could reduce the income
disparity in the minority counties of Gansu.
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