Global COE Hi-Stat Discussion Paper Series 022

Research Unit for Statisticaland Empirical Analysis in Social Sciences (Hi-Stat)

An Empirical Analysis of Income Inequality between
a Minority and the Majority in Urban China:
The Case of Ningxia Hui Autonomous Region

Hi-Stat Discussion Paper

Li ShiDing Sai

January 2009

Hi-Stat
Institute of Economic Research
Hitotsubashi University
2-1 Naka, Kunitatchi Tokyo, 186-8601 Japan
http://gcoe.ier.hit-u.ac.jp

An Empirical Analysis of Income Inequality between a Minority
and the Majority in Urban China: The Case of
Ningxia Hui Autonomous Region *Li Shi
Beijing Normal University
andDing Sai
Institute of Ethnology and Anthropology
Chinese Academy of Social Sciences

Abstract: Based on the urban survey data of Ningxia Hui Autonomous Region in
2006, this paper studies the impact of ethnic characteristics on the income
determination mechanism in the same economic region. Using the decomposition
methods of Blinder and Oaxaca, Fields, and Morduch and Sicular, we analyze income
gap between employed Hui and Han as well as income inequality within the two
ethnic groups. The main conclusions are, first, that there is almost no income gap
between Han and Hui in Ningxia. But different ethnic characteristics have effects on
the income determination mechanism. Ethnic factors such as religion and social
capital have no obvious effect on the income determination.
Key words：Minority, Majority, Income Inequality
JEL Classification: D33, J15*

This research was partially supported by the National Philosophy and Social Sciences Foundation
under its program of Research on Minority Migrant Labor in Western Rural China (grant no.
06CMZ013). Financial supports for the preparation of this paper from the following research programs
are also gratefully acknowledged: Need-based Program for Area Studies “The Middle East within Asia:
Law and Economics”, Hitotsubashi University; Heiwa Nakajima Foundation; Fukino Project,
Hitotsubashi University. We also thank Mr. Alan Thwaits for his great help in preparing English
manuscript. Data for this study was collected in 2007 by the Ningxia Survey Team of the National
Bureau of Statistics, under commission from the Institute of Ethnology and Anthropology Chinese
Academy of Social Sciences.

11. Introduction
Over the past 30 years, income inequality has greatly expanded along many
dimensions in China. Relevant research has shown that in comparison with the past, income
inequality has greatly increased between urban and rural areas, among regions, within urban
areas, and within rural areas (Li Shi and Zhao Renwei 2007). At the same time, people havetaken increasing notice of large and small disparities in income among different classes ofpeople. China is a multiethnic nation, and this diversity of ethnic backgrounds is a basic
national characteristic. Each ethnicity has its own culture, historical tradition, lifestyle, andmodes of social interaction. These distinct characteristics form to some extent what the New
Institutional Economics calls informal institutions. They can also be understood as ethnicallycolored social capital. If we look at income distribution, income inequality among ethnic
groups has already become an undeniable fact, but such income inequality is attributable
more to differences in the levels of economic development of different regions. Especiallywhen one compares the income inequality of two ethnic regions, one finds that different
levels of economic development account for most of the differences in income between theethnic groups. Yet to paint a more complete picture, even when one sees that the level of
economic development of a region is lacking, one still ought to examine the effect that the
informal institutions of an ethnic group have on the income determination system and on
income distribution.

Theoretically, ethnic characteristics can have an effect on labor income at threedifferent levels. First is the effect on wage rates or income levels, second is the effect on theincome determination mechanism, and third is the effect on income inequality. Most research,
limiting itself to the first effect, compares income inequality across ethnic groups and offers
explanations on the causal factors. This paper, in addition to focusing on income differences
between the Han and Hui ethnic groups in the Ningxia Hui Autonomous Region, also
analyzes the second and third effects, that is, analyzes and explains the Han and Hui
income determination mechanisms and the size of income differences within ethnic groups.

1

To analyze the influence of ethnic characteristics on income of the employed byexamining the three effects mentioned above, the research presented here selected the
Ningxia Hui Autonomous Region and analyzed income differences between the Han and Huiethnic groups in this area. One of the reasons for doing so was to control the influence of thelevel of economic development so as to analyze more effectively the effects of ethnic
characteristics on income differences. In addition, data of the 2000 census show that the
population of Hui nonagricultural registered households (feinongye hukou) comprises 35.17
percent of the entire Hui population, eighth among China’s fifty-five ethnic minorities interms of portion, but first in terms of absolute numbers. Hence, comparing the income ofurban-dwelling Hui and Han will produce quite representative results.

This paper is structured as follows: The first section presents the background of thisresearch and the sources of our data. The second section gives a statistical description anddiscusses the research methodology. The third section compares the income determination
mechanisms within the Hui and Han ethnic groups and explains the formulas for estimating
incomes of the employed in the two ethnic groups. The fourth section assesses the factors
leading to income inequality within the two ethnic groups and compares the effects of thesefactors between the two ethnic groups. The fifth section presents the conclusions of thispaper.

2. Background of the Research and Sources of the Data

As is well known, in comparison with the Han ethnic group, many of the ethnic
minorities of China have their distinctive ethnic cultures and traditions. Especially in the fiveminority autonomous regions, ethnic populations are relatively concentrated, and ethnic
cultures and traditions receive their due respect and protection. Take, for example, the Hui of
Ningxia, who are the focus of this paper. Though they do not have their own language, mostof them believe in Islam, and they greatly differ from the Han majority on many points ofculture, such as customs and value orientation. In the labor force of the Ningxia Hui2

Autonomous Region, the distinctive customs and cultural background of the Hui can have aneffect on labor income and the income determination mechanism.

According to statistics of the fifth national population census in 2000, the Hui ethnic
group is the third largest ethnic minority after the Zhuang and Manchu minorities,
comprising 9,816,805 individuals, 19 percent of whom live in the Ningxia Hui Autonomous
Region. In 2006 the Hui population of Ningxia consisted of 2,140,000 individuals, whocomprised 35.46 percent of the population of this autonomous region 1 . In terms of economic
development, Ningxia is a relatively backward area, with a per capita gross domestic productequal to 74 percent of the national average in 2006, giving it a rank of twenty-first of all
thirty-two administrative regions 2 . The per capita disposable income of urban residents is 22
percent below the national average, giving the autonomous region a rank of twenty-sixth 3 .
We have not seen publicly published data on income inequality between Hui and Han urban
residents in the autonomous region. According to the data used in this paper, in 2006 percapita disposable income was an average of RMB 7,765.71 for Hui urban households and
RMB 9,646.67 for Han urban households, a difference of 24.22 percent. Because China has
a relatively lenient birth-control policy for ethnic minorities, the number of dependent
children is generally greater in Hui households than in Han households. Consequently, thedifference in household gross income and the difference in the earnings of the employed are
not as great between the two ethnic groups 4 .

Previous research has demonstrated that ethnic minorities in rural areas are
economically relatively disadvantaged. Bjorn Gustafsson and Li Shi (2003) have shown thatthough the per capita income of rural ethnic minorities grew from 1988 to 1995, its growthrate was clearly slower than that of the rural Han majority, a circumstance that lead to1

Ningxia Hui Autonomous Region Bureau of Statistics and Ningxia Survey Team of the National Bureau of
Statistics, ed. , Ningxia Statistical Yearbook 2007 , Beijing: China Statistics Press, October 2007.
2
See China Statistical Abstract, 2007 p. 28.
3
See China Statistical Abstract, 2007 p. 120
4The data of our survey sample show the following: average disposable income of urban households was
RMB 9,159.97. The average Han household population was 2.89 individuals, and the average Hui household
population was 3.55 individuals. The average household gross income of Han households was RMB 26,104.67,
and that of Hui households was RMB 25,471.12, a difference of 2.49 percent.

3

increasing income inequality between ethnic minorities and the Han majority 5 . The basic
cause of this inequality was that the two groups were dispersed in widely different regions. If
we compare the income levels of the Han majority and an ethnic majority within an ethnic
region, then the inequality nearly disappears. Ding Sai (2006) discovered that 2002 percapita net income in a minority village was 37.1 percent lower than that in a Han village and30.9 percent lower than the national average6 . Carrying out this comparison in different
regions, they found that inequality of per capita annual net income between an ethnic
minority and the Han majority was smallest in the Northeast, followed by the Northwest andthe Southwest, and in the worst region, South Central China, the Miao and other ethnic
minorities in Hunan Province had a per capita income that was only 50.38 percent of the Han
majority. We should point out that though some economic research on ethnic minorities
touches on the issue of urban income in regions where ethnic minorities concentrate, none ofit specifically analyzes income inequality of different ethnic groups 7 . Income inequality
among ethnic groups is not unique to China. Even in some advanced countries, it is common
for there to be income inequalities among races and ethnic groups and for this circumstanceto give rise to social friction and conflict. For example, in the United States from themid-1970s to the mid-1980s, the difference in annual income between black headedhouseholds and white headed households continued to increase, the ratio of the two fallingfrom 0.63 in 1976 to 0.59 in 1986 8 .

It is easy to see that research on income inequality among ethnic groups in China has
been directed mainly at rural areas, and that there is almost no research on the income ofurban-dwelling ethnic minorities and their income inequality with the Han majority. And in5

Bjorn Gustafsson and Li Shi, “The Ethnic Minority-Majority Income Gap in Rural China during Transition,”
Economic Development and Cultural Change 51 (2003): 805–822.
6
Ding Sai, “The Ethnic Minority-Majority Income Gap in Rural China”, China Labor Economics Vol. 3, no. 4(2006): 86–98.
7
See Li Junjie, “A Case Study of Economic Disparity in Ethnic Autonomous Regions and Its
Countermeasures”, Journal of the Central University for Nationalities (Philosophy and Social Science Edition)
2008, no. 1: 14–24; and Gao Xincai and Teng Tangwei, “An Analysis of the Economic Underdevelopment andIndustry Economy of the Ethnic Region of the Northwest of China:, Ethno-National Studies 2006, no. 1:
21–30.
8
William A. Darity Jr., Samuel L. Myers Jr., and Chanjin Chung, “Racial Earnings Disparities and Family
Structure,” Southern Economic Journal 65, no. 1 (July 1998): 20–41.

4

the literature on the urban labor force, there is very little research on income inequality
between the Han majority and ethnic minorities and on its determining factors. The primaryreason for this is that in comparison with the countryside, ethnic minorities dwelling in urban
areas are rather dispersed, their portion of the population is low, and hence the sample ofminority households in the data of an ordinary residential sampling survey is not sufficiently
representative. Moreover, some research on urban ethnic minorities focuses on differences in
ethnic groups in urbanization and participation in the labor force. For example, Deng Ai(2006) and Margaret Maurer-Fazio, James Hughes, and Dandan Zhang (2007) use data of thefifth national census in 2000 to analyze urbanization and the labor-force participation rate
among ethnic-minority populations 9 . Neither of these research papers attempted any analysis
of such related issues as the determination of labor-force wages or income inequality.

The research presented in this paper used data from a socioeconomic survey
conducted in 2007 in the Ningxia Hui Autonomous Region by the Ningxia Survey Team of
the National Bureau of Statistics for the Institute of Ethnology and Anthropology Chinese
Academy of Social Sciences. The urban sample of this survey consisted of 800 households(a total of 2,445 individuals) selected from a large sample pool of the region, households
located in five cities within the Ningxia Administrative Region: Yingchuan, Shizuishan,
Wuzhong, Guyuan, and Zhongwei. To increase the representativeness of the Hui sample, we
increased the sample weight of three cities in the southern part of Ningxia Hui Autonomous
Region where Hui households were relatively concentrated: Wuzhong, Guyuan, andZhongwei. Table 1 shows that the Hui sample made up 29.33 percent of the survey data, anincrease of 8.57 percentage points over the portion represented by the Ningxia urban Huipopulation in the 2000 census (20.76%) 10 .

Table 1 about here
9

Deng Ai, “A Positive Analysis of the Diversity of Ethnic Urbanization in Western China”, Ethno-National
Studies 2006, no. 2: 30–38. Maurer-Fazio, Margaret, James Hughes, and Dandan Zhang, “Gender, Ethnicity
and Labor Force Participation in Post-reform Urban China”, Feminist Economics 2007 13(3/4):189-212
10Department of Population, Social Science and Technology Statistics, National Bureau of Statistics, PRC and
Department of Economic and Development, State Ethnic Affairs Commission, PRC ed. , “Tabulation on
Nationalities of 2000 Population Census of China”, Beijing: Ethnic Publishing House, September 2003.

5

3. A Basic Statistical Description and Methodology

Within the sample we collected of 800 Ningxia urban households, in 2006 there were
1,104 individuals gainfully employed, 45.2 percent of the total number of individuals in thesample. Of these employed individuals, nearly 27 percent were Hui, about 1 percent were
Manchu, and 72 percent were Han. In comparison with the actual portion of the Hui ethnic
minority in the Ningxia population, the portion in our sample was somewhat high. To
eliminate the effect of sample bias on our analysis, when we calculated sample means anddifference indices, we weighted our sample with the actual portion of Hui in the population.

Table 2 gives some basic characteristics and income indices of Ningxia urban
employed Hui and Han. The table shows that the average income gap between employed Huiand Han is not large. The Hui income is somewhat lower than Han income, but thedifference is only 2 percentage points. In income differences by gender, one can see that theincome of Hui men is only somewhat higher than that of Han men by 3.2 percent, but theincome of Hui women is clearly lower than that of Han women, and this is the primaryreason for the income gap between Hui and Han. In education, it is worth noting that in thegroup with a postsecondary education, employed Han had clearly higher income thanemployed Hui, but at low levels of education the situation was reversed: the income of
employed Hui was higher than that of employed Han. Such income variations acrosspopulation groups require explanation. In addition, there are also structural variations amongoccupations and economic sectors. For example, Hui business owners and self-employedindividuals earn more than their employed Han counterpart, yet as managers in governmentorgans or enterprises, or as heads of departments, Han earn more than Hui. Again for
example, in the wholesale, retail, restaurant, and transport industries, Hui earn somewhat
more than Han, yet in the sectors of education, health, culture, and scientific research, Han
earn considerably more than Hui. In the same way, there are also structural differences inincome across enterprises with different ownerships. In state-owned enterprises, Hui6

employees earn 19 percent less than Han employees, but in urban collective enterprises, Huiemployees earn 17 percentage points more than Han employees (see table 2). But more
worthy of attention are differences in income between ethnic groups in different areas. In thethree county-level cities where the Hui ethnic group concentrates—Wuzhong, Guyuan, andZhongwei—only in Guyuan is the income of employed Han somewhat higher than that of
employed Hui. In the other two county-level cities, the income of Hui is clearly higher thanthat of Han. As shown in table 2, in Wuzhong the income of Hui is 6.4 percentage points
higher than that of Han, and in Zhongwei the income of Hui is nearly 20 percentage points
higher than that of Han.

Table 2 about here

From these basic differences in Hui and Han incomes, how can we discover thedifferent factors determining these income differences? To achieve this objective, we firstestimated an income function for the entire sample, setting Hui and Han as dummy variables,
and used this and other control variables to explain income differences among the employed.
The results tell us whether under other identical circumstances there are marked differencesin Hui and Han incomes. Next, to better understand whether, in the income-determining
process, different influencing factors have different effects between the two ethnic groups,
we estimated an income function for each of the two ethnic groups and, using theBlinder-Oaxaca method of decomposition and these results, decomposed the income gap into
the effects that different explanatory variables have in determining income. Let y1 and y0
represent the average incomes of employed Han and Hui, let x1 and x0 represent the meanvalues of the explanatory variables in the Han and Hui income functions, and let f1 and f0
represent the estimated values of the coefficients of the explanatory variables. Then theformula for decomposing the difference in Han and Hui incomes can be written as follows:−−−⎛− − ⎞
y1 − y0 = f1 ⎜ x1 − x 0 ⎟ + ( f1 − f 0 ) x 0
⎝
⎠

(1)

The first term on the right side of the equation reflects the difference between the mean

7

values of the explanatory variables (or determining factors), and the second term reflects thedifference between the coefficients (or determination system). A variant of thedecomposition formula is as follows:−−−⎛− − ⎞
y1 − y0 = f 0 ⎜ x1 − x 0 ⎟ + ( f1 − f 0 ) x1⎝⎠

(2)
Finally, we compared income inequalities within Hui and Han. We separately
calculated various indices of inequality in the incomes of employed individuals in the Huiand Han samples. At the same time, to compare the size of the influence of factors affecting
the inequalities, we also decomposed the Gini coefficients. The method of decomposition
used here has also been called “inequality-decomposition method based on regression
analysis.” For detailed explanations on this method, see Fields 1998 and Morduch andSicular 2002, and for an explanation of how to apply the method, see Deng Quheng et al.
2008 11 .

4. Analysis on Income Gap between Han and Hui

From the above one can see that in terms of average income, there is hardly any
income gap between employed Hui and Han. This is one of the important discoveries of thisresearch. Yet to make the incomes of the two ethnic groups comparable, we also sought tofind out whether income gap between the two groups was still insignificant when
individuals’ characteristics were controlled. In addition to income inequality between the two
ethnic groups, we also sought to understand differences between the two groups in theincome determination mechanism. To answer the first question, we estimated the income
function for the entire sample of the employed, into which we introduced two dummy
variables for the Hui and Han ethnic groups, as well as control variables representing

11

Fields, Gary, 1998, Accounting for Differences in Income Inequality, mimeo, Cornell University.
Morduch, Jonathan, and Terry Sicular, 2002, Rethinking Inequality Decomposition, with Evidence from Rural
China, Economic Journal, 112: 93-106
Deng Quheng, Li Shi, Yue Ximing and Weizhong, “The Reasons for the Change of Employed Earning
Inequality in Urban China: Based on the Regression Function Decomposition Analysis”, Working paper, 2008.

8

individual characteristics. To answer the second question, we estimated separate income
functions for the Hui and Han ethnic groups. From differences in the estimated values of thecoefficients of the income functions, we can discover differences in the income
determination mechanisms for the two groups. See table 3 for details of the results.
The first column in the table gives the explanatory variables of the income equation.
One can see that the explanatory variables include not only demographic and socioeconomic
characteristics, but also a religiosity variable and a social-capital variable. The second
column gives the earned-income for the sample of all employed individuals. In the equation
we introduced an ethnicity dummy variable to test whether ethnic status brought about
differences in income when other characteristics were the same. Our results showed thatwhen other characteristics were the same, the value of the Hui dummy variable is significant
at the 10 percent level. Then we can calculate from the estimated value of the coefficient thatthe earned income of employed Hui will be 10 percent higher than that of employed Han 12 .
This result has two implications: First, if individuals are endowed with the same
demographic and socioeconomic characteristics and have equal employment and career
opportunities, Hui labor is rewarded in the Ningxia urban labor market at a higher rate thanHan labor. Second, Han human capital is higher, and hence there are more opportunities for
Han individuals to select high-paying sectors and occupations. The simultaneous impact ofthese two factors creates a situation in which the average income of the employed in the two
ethnic groups is virtually the same, as we saw.
Worth noting are several special features determining labor income of in urban
Ningxia. First, the difference between men’s and women’s incomes is especially apparent,
exceeding the general national level. As shown in table 3, the earned income of employed
men was 35 percent higher than that of employed women. The next point is that returns tohuman capital favors work experience over education. This effect is reflected in the value ofthe coefficient for work experience being clearly higher than that for years of education.
From the results in table 3 we can calculate that the rate of returns to education is about 3.8
12

To convert the income-function coefficient value (C) to percentage (P), we can use the following formula:P = exp(C) − 1.

9

percent, but the rate of returns to work experience is as high as 5.2 percent. Another point isthat state-owned sector employees are privileged with high incomes. From the results one
can see that when other conditions are the same, those working in state-owned enterprises
earn 14 percent more than those working in enterprises with other forms of ownership.
Finally, we found no influence of religious belief on income. Though the coefficients of thereligiosity dummy variable in our analysis were negative, these results were not statisticallysignificant. This is an issue that requires further empirical investigation.

Table 3 about here

The last two columns of table 3 shows the results of regression analyses separately
carried out on the incomes of employed Hui and Han. From a comparison of the coefficientsof the two different income equations, one can see several features that determine theincomes of the two ethnic groups. First, the gender difference in income was clearly larger
among employed Hui than among employed Han. Even after controlling for other
determining factors, the income of employed Hui males was higher than that of employed
Hui females by 37 percent, whereas the difference by sex among employed Han was 34
percent. Second, among the human-capital variables, the rate of returns to education, washigher for Han than for Hui, that for the former being 4.3 percent and that for the latter being
3.1 percent. But the rate of returns to the work-experience was lower for Han than for Hui,the difference being 1 percentage point. The causes require further analysis. Is the rate of
returns to education lower among the Hui because they receive a lower-quality education?
Or does it stem from the selection process of the labor market? From the present data it is
impossible to give a definitive answer to these questions. Third, in the selection of sectors of
employment, employed Hui who work in the real-estate, finance, and insurance sectors havea higher income, while employed Han who work in the sectors of education, healthcare,
culture, and science and technology earn a higher income. Because real estate, finance, andinsurance sectors are somewhat monopolistic, entering these sectors requires more social
connections, that is, more social capital. This perhaps confers an advantage on Hui who grew10

up in this region. Working in the sectors of education, healthcare, culture, and science andtechnology requires more and higher-quality human capital. Employed Han are morecompetitive in this area. This point resonates with the higher rate of returns to education
among Han, as mentioned above.

The explanatory variables used above to explain income inequality within ethnic
groups can also be used to explain income inequality between ethnic groups of the employed.
To make the influence of various explanatory variables more distinct, we used theBlinder-Oaxaca method of decomposition to decompose income gap between employed Han
and Hui into each explanatory variable. See table 4 for the results of the decomposition13 .
Comparatively speaking, the differences between employed Han and Hui are not thatconspicuous in years of education, occupation, sector, and type of enterprise ownership, butthere are comparatively clear differences in their location, and employed Han have more
years of work experience. Worth emphasizing are the following implications of thedecomposition results. First, a negative value for the intercept term means that in general,
Hui status confers the advantage of higher income, not the opposite. This point is connectedwith policies toward ethnic minorities at various levels of government. Preferential policies
for minorities to obtain employment, start a business, and be promoted in the bureaucracy
are all reflected to some extent in our results. Next, the main factor leading to income gap
between the Han and Hui ethnic groups is the different rates of returns to education between
the two ethnic groups. The cause of this difference is worth further study. There is no
denying that one cause is a difference in the quality of education, which results in cases ofthe same number of years of education being compensated at widely different rates. Anotherfactor leading to the income gap between the two ethnic groups is that they received
different compensation in enterprises with different ownership structures. The main
expression of this fact is that employed Han received much higher incomes than employed
Hui received in state-owned enterprises. This point is also worthy of further study. Finally,

13

Because coefficient of the religiosity variable and the human capital variable were both insignificant, wedeleted these two variables from the decomposition analysis; that is, the estimates of the model do not include
them.

11

another important factor explaining income inequality is the location variation of employed
members of the two ethnic groups. As table 2 shows, average income was highest in
Yinchuan among the several cities in Ningxia. The Han portion of the Yinchuan sample isnearly three times higher than the Hui portion.

Table 4 about here

5. A Comparison of Income Inequality within the Hui and Han Ethnic Groups

In Ningxia Hui Autonomous Region, the difference between Hui and Han incomes is
not conspicuous, but income inequality within the ethnic groups is noticeable and easy todiscern. As shown in table 5, according to our survey sample, the distribution of income
among employed Hui in Ningxia had a Gini coefficient of 0.296, and that for employed Han
was somewhat higher, 0.313. Except for coefficients of variation, other indices of inequality
also show that income inequality within the Han ethnic group is greater than that within the
Hui ethnic group. For example, if we compare the average income of the highest decile withthat of the lowest decile, the Hui ratio is 7.6 times, and the Han ratio is 9.3 times.

Table 5 about here

To compare and explain the special characteristics of income inequality within thetwo ethnic groups, we used two methods for decomposing the Gini coefficients of income
inequality within the two employed ethnic groups. Table 6 gives the results of decomposing
the income inequality of the employed within the entire sample and the two ethnic groups.
One can see that the size of the effect of the various influential factors obtained from theFields decomposition method and from the Morduch-Sicular decomposition method isroughly the same. Consequently, in explaining the results, we focused on the decomposition
results derived from the Fields method. Some of the decomposition results were as we
expected. For example, the ethnic-group dummy variable accounted for almost none of the12income inequality of the entire sample. This means that the income difference between thetwo ethnic groups produced almost none of the income inequality among the employed in
the Ningxia region. Some of the results were consistent with our analysis, discussed above.
For example, gender difference in income explains a greater portion of income inequality
among employed Hui, accounting for nearly 10 percent of the Gini coefficient of income
inequality within the Hui ethnic group, whereas the corresponding figure for the Han ethnic
group is no more than-even 6 percent. To give another example, years of work experience
affected income inequality among employed Hui to a greater extent than that among
employed Han. And some decomposition results were unexpected. For example, the effect of
the years-of-education variable on income inequality among the employed was nearly thesame for the two ethnic groups. But in our analysis above we discovered that the rate of
returns to education was considerably higher for the Han ethnic group than for the Hui ethnic
group. Consequently, we expected that the education variable would explain more of theincome inequality within Han ethnic group than it did. Moreover, though the occupation
variables were an important factor influencing income inequality within each of the two
ethnic groups, it was not noticeably different between them. And the effects of the sector andownership variables were very limited. We should also note that the religious belief andsocial capital were not major factors explaining income inequality within either the Han or
Hui ethnic group. This finding was highly unexpected.

Table 6 about here

6. Conclusions

Income inequality between ethnic minorities and the Han majority has continuously
received people’s attention, but for different reasons. This research carried out a sample
survey on Ningxia urban residents in 2006 and performed a detailed analysis on income
inequality between employed Han and Hui in this Hui region using the data from the survey.
Out of this research came some results worth noting. First, there was almost no income gap

13between the Han majority and the Hui ethnic minority in this minority region. The results of
our analysis presented in this paper offer strong support for this conclusion. This means thatthe ethnic division of the labor force in this region has not brought about significant income
gap between the ethnic groups. If there is income gap between ethnic groups within a larger
region, especially gap consisting of higher income for the Han majority than for the Huiminority, such gap stems mainly from the geographical distribution of different ethnic
groups in different areas, and not from ethnic factors. Moreover, long-standing government
policies of giving various preferences to minorities have created a situation where Hui status
not only does not give rise to discrimination in income but even confers higher income onthese people.

Next, the special characteristics of different ethnic groups had a significant effect on
income determination mechanisms. In comparison with the Han ethnic group, sex clearly
had a somewhat high effect on the income of employed Hui. This shows to a certain extent
that Hui women have rather low economic status. We also discovered that the returns work
experience was clearly greater among Hui than among Han, and that the returns to education
was clearly lower. This result has two implications. One is that in determining wages, Huiattach more importance to experience and seniority, rather than education. The other isthat—owing to the influence of language, culture, religion, and customs—the quality ofeducation of employed Hui is clearly lower than that of employed Han. Consequently, withthe same number of years of education, Hui receive lower returns to education in the labormarket.

In addition, some variables of individual characteristics show that the type ofownership of the employing firm and the geographical distribution of employed individuals
conferred higher income on employed Han, while the distribution of sectors of employment
among the employed favored Hui with higher income. Moreover, in the selection of sectors,
employed Hui tended to enter high-paying monopolistic sectors, while employed Han more
readily entered sectors requiring more human capital. Furthermore, ethnicity did not clearly14

influence income through the factors of religion and social capital.

Finally, we discovered that income inequality within the Hui ethnic group was
somewhat lower than that within the Han ethnic group. This is perhaps because the Hui of
Ningxia are rather homogeneous. Though the variables of sex, human capital, occupation,
geographical location all are important in explaining income inequality within both the Han
and Hui ethnic groups, these variables exerted more influence within the Hui ethnic group.

15

References
Gustafsson, Bjorn and Li Shi. 2003. “The Ethnic Minority-Majority Income Gap in
Rural China during Transition,” Economic Development and Cultural Change 51:
805–822.
Deng, Ai. 2006. “A Positive Analysis of the Diversity of Ethnic Urbanization inWestern China”, Ethno-National Studies 2006, No. 2: 30–38.
Deng, Quheng, Li Shi, Yue Ximing and Wei zhong. 2008. “The Reasons for the
Change of Employed Earning Inequality in Urban China: Based on theRegression Function Decomposition Analysis,” working paper.
Department of Population, Social Science and Technology Statistics, National Bureauof Statistics, PRC and Department of Economic and Development, State Ethnic
Affairs Commission, PRC (eds.) 2003. Tabulation on Nationalities of 2000

Population Census of China, Beijing; Ethnic Publishing House.
Ding, Sai. 2006. “The Ethnic Minority-Majority Income Gap in Rural China,” China

Labor Economics,Vol. 3, No. 4: 86–98.
Fields, Gary, 1998. “Accounting for Differences in Income Inequality,” mimeo,
Cornell University.
Gao, Xincai and Teng Tangwei. 2006. “An Analysis of the Economic
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20–41.

17

Table 1：The distribution of sample in Ningxia Hui Autonomous Region
Numberof

Proportion

households

Numberof

Proportion

individual

Numberof

Hui%individual

Yinchuan city

200

25.00

534

21.84

59

11.05

Shuizuishan

150

18.75

433

17.71

31

7.16

Wuzhong city

200

25.00

649

26.54

326

50.23

Guyuan city

150

18.75

506

20.70

220

43.48

Zhongwei city

100

12.50

323

13.21

81

25.08

Sum

800

100.002445

100.00

717

29.33

city

Sources: the data are from a socioeconomic survey conducted in 2007 in the Ningxia HuiAutonomous Region by the Ningxia Survey Team of the National Bureau of Statistics for theInstitute of Ethnology and Anthropology Chinese Academy of Social Sciences.

18

Table 2：The characteristics and earnings of the employed in Ningxia Hui Autonomous
Region in 2006
Han

Hui

Percentage

Average

percentage

Average（%）

earnings in（%）

earnings

2006

Total
in

percentage

Average（%）

weighted

2006 (Yuan)

earnings(Yuan)
Total sample

10015562.4210015229.56

100

15494.63

Male

56.23

17346.63

59.46

17902.09

57.16

17491.30

Female43.77

13225.57

40.54

11334

42.84

12795.21

College or above

38.83

18945.27

44.14

16656.44

40.33

18326.09

Upper middle school or

34.74

14097.24

22.76

14230.44

31.49

14115.22

23.75

12688.39

24.4813280.55

23.94

12842.19

2.68

10912.86

8.62

16096.8

4.2413730.33

Education level

middle level professional
technical or vocational
schoolLower middle school
Primaryschoolandbelow
Occupational category for employment
Owner

of

privateor

9.36

17678.61

11.91

18276.97

9.92

17866.68

divisionin

7.41

22497.14

9.75

20596.11

7.93

21886.15

or

the
23.80

18278.25

18.41

17146.1

22.47

18096.26

individual enterprise
Head

of

institution
institution

professional or technicalworkerOffice worker
Worker,commercial

17.30

15697.69

31.05

15508.51

20.87

15692.73

42.13

12418.53

28.88

11281.23

38.81

11999.09

19.9212972.47

4.66

12514.77

16.00

13176.89

21.64

14457.98

27.96

14785.6

23.24

14530.27

13.06

16744.91

11.47

15894.09

12.67

16559.03

17.02

18362.15

22.94

15013.31

18.67

17240.508

12.27

16448.92

23.66

17051.74

15.14

16699.151

service worker or others
Economic sector for the work unit
Industry
Commerce

andtrade,

restaurants & catering,materials

supply,marketing,warehousing

and transportation
Realty business, financeandinsurance

Education, health, culture
and scientific researchgovernmentorgans,

and

Party

in

2006 (Yuan)social19

organizations
Other

16.09

15640.86

9.32

13668.24

14.29

15222.145

Ownership of the work place
State-owned units

41.59

17011.46

39.13

14341.56

41.44

16654.00

Collective units

28.32

12738.09

27.17

14922.68

28.29

13100.21

Others

30.09

15753.19

33.70

15402.66

30.27

15637.02

Yinchuan city

27.84

18211.33

8.97

17477.38

22.84

18146.90

Shizuishan city

24.14

14212.82

5.1712119.47

18.97

14155.36

Wuzhong city

18.01

13556.13

44.48

14427.55

25.32

13982.99

Guyuan city

15.84

15900.19

33.10

15552.39

20.35

15727.06

Zhongwei city

14.18

14829.19

8.28

17757.71

12.52

15363.62

Area

Note：the data in brackets are ratios for Han, Hui and total sample （%）。
Sources: the data are from a socioeconomic survey conducted in 2007 in the Ningxia HuiAutonomous Region by the Ningxia Survey Team of the National Bureau of Statistics for theInstitute of Ethnology and Anthropology Chinese Academy of Social Sciences.

20

Table 3：Earning function of the employed in Ningixa Hui Autonomous Region in
2006
Dependent variable：log personal earning of
the eamployed
Total sample

Hui sample

Han sample

Hui0.097*

---

---

Han

----

---

---

Male

0.303***

0.314***

0.293***

Female

----

---

---

Education year

0.037***

0.031***

0.042***

Work experience

0.051***

0.058***

0.049***

Square of work experience-0.001***-0.001***-0.001***

Communist party member-0.0030.096-0.043

Non communist party member

----

---

---

Owner of private or individual enterprise

0.115

0.196

0.101

Head of division in institution or the institution

0.194***

0.064

0.239***

professional or technical worker

0.109*

0.123

0.093

Office worker

----

---

---

Worker, commercial service worker or others-0.194***-0.220**-0.174***

Industry

0.012

0.144

-0.005

0.017

0.121

-0.018Realty business, finance and insurance

0.194

0.289**

0.064

Education, health, culture and scientific research

0.119*

0.027

0.183**

government and Party organs, social organizations-0.008-0.033

0.013

Other

----

---

---

State-owned units

0.130**-0.1560.179***

Collective units

----

---

---

Others0.045-0.055

0.057

Yinchuan city

----

---

---

Shizuishan city

-0.159***-0.330**-0.162***

Wuzhong city

-0.232***-0.155-0.268***Commerceandmaterials

supply,trade,

restaurants

marketing,&catering,

warehousingandtransportation

Guyuan city

-0.117**0.028-0.149**

Zhongwei city

-0.139**-0.020-0.167**

Religion beliefs-0.058-0.066-0.058Nullifidian

----

----

----

The best three friends are in same ethnic group

0.031

0.098

0.028

The best three friends are not only in the same ethnic

----

----

----

8.393***

8.44***

8.362***

group but also in other ethnic group
Constants
2

0.313

0.295

F-Value

22.10

15.80

Obs.

1067

Adj. R

289

778

21

Note: the omitted variables are female, non communist party member, professional or
technical worker, others in economic sectors for the work unit, collective units and Yinchuancity. * Denotes significance at 10 percent level; ** denotes significance at 5 percent level; ***denotes significance at 1 percent level.
Sources: the data are from a socioeconomic survey conducted in 2007 in the Ningxia HuiAutonomous Region by the Ningxia Survey Team of the National Bureau of Statistics for theInstitute of Ethnology and Anthropology Chinese Academy of Social Sciences.

22

Table 4：The results of earning gap decomposition between Han and Hui
Simulation I
Simulation II
f1(x1-x0)
x0(f1-f0)
f0(x1-x0)
x1(f1-f0)
Sex-40.91-152.45-47.99-145.37
Education year
78.72
2199.8526.22
2252.35Work years
184.64-541.9270.73-627.99
Communist party member
7.51-276.35-15.43-253.42
Occupational category-110.69
252.98-117.37259.65
Economic sectors-53.48-458.04-112.62-398.89
Ownership of the work place
42.74
1058.21-37.911138.85
Areas
407.63-577.59
65.61-235.46
Constants
0-1920.97
0-1920.97
Sum
516.16-416.26
31.24
68.75
Note: the high earning group is Han and the low earning group is Hui. Each item accounts for
the Han and Hui average earning gap.
Sources: the data are from a socioeconomic survey conducted in 2007 in the Ningxia HuiAutonomous Region by the Ningxia Survey Team of the National Bureau of Statistics for theInstitute of Ethnology and Anthropology Chinese Academy of Social Sciences.

Table 5：Inequality index of the employed in Ningxia Hui Autonomous Region in2006
Total employed sample
Hui
Han
0.308
0.296
0.314
Gini index
0.166
0.162
0.169
Theil index
0.179
0.168
0.184
MLD
0.629
0.641
0.627
Coefficient of variation
7.62
9.26
Ratio of top group/bottom 7.96
group in deciles
Sources: the data are from a socioeconomic survey conducted in 2007 in the Ningxia HuiAutonomous Region by the Ningxia Survey Team of the National Bureau of Statistics for theInstitute of Ethnology and Anthropology Chinese Academy of Social Sciences.

23

Table 6：The decomposition of the employed in Ningxia Hui Autonomous Region in2006
Unit：%

Fields method

Morduch-SicularmethodTotal Han
Hui
Total
Han
Hui
Hui
-0.03 -------0.04------Male
6.79
5.69
9.92
7.62
6.54
10.50
Education year
5.68
5.94
5.84
6.41
6.70
6.55
Work experience
20.68 18.55 27.87 24.26
20.99
34.95
Square of work experience-11.44 -10.49 -14.73 -13.86 -12.25 -20.08
Communist party member-0.04 -0.57 2.23
-0.05
-0.68
2.89
Occupational category
7.74
7.15
8.30
7.79
7.60
8.98
Economic sectors
0.76
2.00
-1.06 1.09
2.34
-1.93
Ownership of the work plac
0.72
1.34
0.04
1.03
1.88
0.06
Areas
1.90
1.99
3.23
1.80
1.64
4.06
Religion belif
0.14
0.11
0.21
0.12
0.04
0.26
The best three friends are in same 0.09
0.05
0.68
0.11
0.04
1.05
ethnic group
Residual
67.01 68.24 57.46 62.63
64.18
51.40
Constants
1.19
1.08
1.30
Sum
100
100
100
100
100
100
Sources: the data are from a socioeconomic survey conducted in 2007 in the Ningxia HuiAutonomous Region by the Ningxia Survey Team of the National Bureau of Statistics for theInstitute of Ethnology and Anthropology Chinese Academy of Social Sciences.

24