trends and inequalities of rural welfare in china: evidence from rural households in guangdong and...

Trends and Inequalities of Rural Welfare in China:Evidence from Rural Households

in Guangdong and Sichuan1

Tsui Kai-yuen

The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

E-mail: [email protected]

Received April 9, 1996; revised August 11, 1998

Kai-yuen, Tsui—Trends and Inequalities of Rural Welfare in China: Evidence from RuralHouseholds in Guangdong and Sichuan

This paper studies the trend of Chinese rural welfare and its inequality in the second half ofthe 1980’s using rural household survey data of Guangdong and Sichuan. In this connection,different indicators of economic well-being are used. Compared with other transitionaleconomies, rural inequality in China increased moderately in the second half of the 1980’s. Inspite of a more rapid pace of transition, Guangdong’s rural inequality did not increase. Nomonotonic relationship between the pace of transition and rural inequality is thus discernible.Finally, by decomposing overall rural inequality into its between-region and within-regioninequalities, we explore the relative importance of inter-provincial inequality.J. Comp.Econom.,December 1998,26(4), pp. 783–804. The Chinese University of Hong Kong,Shatin, N.T., Hong Kong. © 1998 Academic Press

Journal of Economic LiteratureClassification Numbers: O18, P2, R12.

1. INTRODUCTION

Rising inequality is often considered a likely outcome of the transition to amarket economy when individuals are increasingly being paid in accordance withtheir abilities and the existing social safety net is dismantled in the process. Thispaper is an attempt to further our understanding of the dynamics between

1 I thank the staff of the State Statistical Bureau for their help. In particular, I thank Zhu Xiangdongand Zhai Yan for their efforts in making this project a reality and Wen Jianwu for initiating thisproject. Valuable comments from Scott Rozelle, Loren Brandt, and Chan Ka Yan on earlier versionsof this paper are gratefully acknowledged. The financial support of the South China ResearchProgramme, the Hong Kong Institute of Asia-Pacific Studies of the Chinese University of HongKong, is gratefully acknowledged.

JOURNAL OF COMPARATIVE ECONOMICS26, 783–804 (1998)ARTICLE NO. JE981553

783 0147-5967/98 $25.00Copyright © 1998 by Academic PressAll rights of reproduction in any form reserved.

transition and inequality by studying the evolution of rural inequality in China inthe second half of the 1980’s.

While Eastern Europe and Russia have experienced a sharp increase ininequality in the initial stage of their transition (Milanovic, 1995), differentinstitutions and reform strategy in China have generated different transitionaldynamics. With the help of rural household survey data from Guangdong andSichuan, this paper studies the trends of rural inequality in the second half of the1980’s and analyzes the following set of questions. Was there a sharp increase inrural inequality in China as was experienced by many Eastern European coun-tries and Russia? Is rural inequality increasing faster with the speed of transition?As a vanguard of liberalization, was rural inequality in Guangdong increasingfaster than in Sichuan during the second half of the 1980’s? Does inter-provincialinequality account for much of the rural inequality in China since the coastalprovinces grow much faster than their inland counterparts? Departing fromprevious studies, which often use income as a measure of rural economic welfarein the measurement of inequality, inequalities with respect to consumption percapita and equivalent consumption are also derived.

Section 2 reviews the developments in rural China in so far as they exertimpact on rural inequality and raises a number of questions to be explored in thispaper. Measuring the level and inequality of rural well-being is the subject ofSection 3. Section 4 introduces briefly the rural household survey data ofGuangdong and Sichuan used in the empirical analysis and presents a back-ground on the two provinces. Section 5 contains the empirical findings, comparesChina’s experience with other countries, and discusses their implications. Theconcluding section summarizes the findings and suggests future research direc-tions.

2. ECONOMIC REFORM AND RURAL INEQUALITY IN CHINA

There is a general perception that transition to a market economy inducesrising inequality. While the experiences of Eastern Europe and Russia seem tohave corroborated such a hypothesis (Milanovic, 1995), the Chinese transitionaldynamics seem to be more complicated. For one thing, the pace of transition isrelatively gradual and it is not uniform across such a vast country. Furthermore,different forces were at work simultaneously in rural China during the 1980’s,exerting different impacts on rural inequality. This section reviews briefly thestructural transformation of rural China in so far as they may exert an impact onrural inequality and, in the course of the discussion, reviews previous studies onthe issue of rural inequality.

In the early 1980’s, the spread of the household responsibility system (HRS)against a background of equal land distribution stimulated spectacular ruraleconomic growth (Putterman, 1993). Furthermore, some poor regions grew fasterbecause they were among the first to espouse the new system. Official figures of

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the State Statistical Bureau (SSB) delineate a moderate increase in rural inequal-ity with a dip in 1982 (Tang, 1994).

Since the mid 1980’s, rural inequality seems to be on the increase. The officialvalue of the Gini coefficient increased from 0.2635 in 1985 to 0.3099 in 1990(Tang, 1994). Among the new developments considered to be the driving forcesbehind increasing rural inequality are, first, the changing economic structure ofthe rural economy. Unlike the inequality-reducing effect of non-farm employ-ment in such countries as Taiwan (Fei, Ranis, and Kuo, 1979), the burgeoning ofnon-agricultural activities, e.g., township and village enterprises (TVEs) in ruralChina, is often singled out as an important factor behind the changes in inequalitysince the mid 1980’s, e.g., Nongcun guding guancedian bangongshi (1997),Rozelle (1994, 1996), Zhu (1992), and Zhang (1992). The rapid growth of TVEsin coastal provinces is often believed to have exerted upward pressure oninterregional and thus overall inequality. Recent policies such as increasing loansto TVEs in the central and western provinces and the encouragement of east–west collaboration are responses to this concern (Guowuyuan, 1993).

Second, in the pre-reform era, interregional labor mobility was severelyrestricted by the household registration system. The expanding role of the markethas rendered the household registration system a less effective population controlmeasure (Zhang, 1995). Interregional factor mobility is often considered a potentforce in reducing interregional income inequality. Third, the equalizing impact offactor mobility is limited not just by the household registration system but alsoby local governments anxious to reserve jobs for local residents. For example,Zhu (1992) points out that TVEs often employ workers within the local com-munity as a means by the local government to increase employment. Fourth, inaddition to the uneven distribution of TVEs, the regional development strategyallowing the coastal provinces to get rich first in the second half of 1980’s hasoften been regarded as a reason behind widening interregional income gaps(Yang, 1990).

With the above backdrop in mind, we proceed to answer the questions raisedin the introductory section.

3. METHODS

3.1. Measurement of Rural Economic Well-being

In the final analysis, our concern is to measure the levels of, and the disparitiesin, economic well-being among rural households. Economic well-being is amultifaceted concept and may dependinter alia on the potential purchasingpower of a household, the goods and services consumed, and the assets accu-mulated, e.g., Atkinson (1983) and Atkinson et al. (1995). While income as ameasure of potential purchasing power is certainly the most readily availableindicator, it does not necessarily capture the various dimensions of economicwell-being. In so far as economic well-being depends on the consumption of

CHINESE RURAL INEQUALITY 785

goods and services, and households can dissave to finance current consumptionin a cyclical downturn of the economy, the level and inequality in consumptionmay be better indicators of long-run level of and inter-household variation ineconomic well-being. Furthermore, as predicted by the life-cycle hypothesis,consumption may be a better proxy of wealth or life-time income (Atkinson,1983; Atkinson et al., 1995; for evidence on China, see Wang, 1995). Anotherissue with inter-household welfare comparison is that households have differentdemographic characteristics. The economic well-being of two families with thesame income or consumption level may nonetheless be different if their demo-graphic compositions differ. For our sample, the average household size ofGuangdong in 1990 is 5.6 persons as opposed to 4.4 in Sichuan. With regard toage composition in 1990, 23% of those involved in the Guangdong sample arebelow 12 years old as opposed to only 15% in the Sichuan sample. In recentyears, work has been done on the derivation of an equivalent scale to take intoaccount demographic characteristics in welfare comparison (Coulter, Cowell,and Jenkins, 1992). We refer to consumption adjusted by the estimated equiva-lent scale as equivalent consumption.

There are thus at least three possible indicators.A priori, the level and trendof inequality may differ depending on the measures used. In so far as we areinterested in the level and trend of inequality purged of short-term cyclicalfluctuation and we want to take into account household demographic composi-tion in the valuation of household well-being, equivalent consumption seems tobe a better proxy. To check the robustness of the inequality trend, net peasantincome per capita, and consumption per capita are also used to gauge inter-household inequality in economic well-being.

While the computation of income per capita and consumption per capita isstraightforward, the derivation of equivalent consumption requires some expla-nation. For any variableX, let Xit

g be the value of theith household in thegthprovince at periodt. Equivalent consumption for theith household is defined as

ECitg 5 Cit

g/m~zitg!, (1)

whereCitg is nominal household consumption andm(zit

g) is the equivalent scale,which is a function of ak-vector of household characteristicszit

g :5 (z1itg , . . . ,

zkitg ). For some reference household with characteristicszr, m(zr) is normalized to

one so thatm(zitg) is the ith household’s equivalent units compared with the

reference household. Two households have the same welfare only if they havethe same equivalent consumption.

To estimatem(zitg), we resort to the widely used Engel approach (Coulter et al.,

1992). The Engel approach postulates that there is a functional relationshipbetween food shareFit

g and consumption; specifically, this paper adopts theLeser–Working functional specification,

Fitg 5 a0 1 a1ln~Cit

g/m~zitg!!, i 5 1, 2, . . . ,H, (2)

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where

m~zitg! 5 Nit

gexp@Oj51

k

b jzjitg #, (3)

is the functional form for the equivalent scale,Nitg is the household size;a0, a1,

bi, i 5 1, 2, . . . ,k, are parameters. Substituting Eq. (3) into (2),

Fitg 5 a0 1 a1ln~Cit

g/Nitg! 1 O

j51

k

d jzjitg (4)

wheredi 5 2a1bi. The estimated values of the parameters may be substitutedinto Eq. (3) to arrive at the estimated valuem̂(zit

g) of the equivalent scale. Thenominal equivalent income is then defined asCit

g/m̂(zitg). The estimates are then

deflated by a regional price deflatorPtg to arrive atreal equivalent consumption,

i.e., Citg/[m̂(zit

g)Ptg].

With regard to the estimation of Eq. (4),zitg includes the number of household

members in each of three age groups, i.e., age 0–5, age 6–11, and age above 11,the number of laborers in a household and dummy variables for counties.2 Ahousehold with two laborers above 11 years old in a specific county is chosen asthe reference household. Since consumption is likely to be endogenous, instru-mental variable estimation is employed. The estimated results are also correctedfor potential heteroscedasticity using White’s heteroscedastic consistentvariance-covariance matrix.

The estimates are reported in Table 1. As expected, the sign for consumptionper capita is statistically significant and negative, i.e., the expenditure share onfood declines as total expenditure increases or when the household size de-creases, holding all other things constant. The coefficients of the demographicvariables are negative and, in most cases, significant, suggesting that there arescale economies in consumption. The coefficient for the number of laborers issignificant for all the regressions and its sign is positive.

3.2. Measurement of Inequality

To explore the relative importance of inter-provincial inequality, we employinequality indices that may be decomposable in the following sense,

I ~x! 5 WG~x! 1 BG~x), (5)

where,x is a vector of household incomes.W(x) is the within-region inequality,which is the weighted average of the subgroup inequalities;B(x) is the between-

2 Without a finer breakdown of those above 11, it seems helpful to add the number of laborers ina household.


region inequality, which ignores intra-region inequalities effectively by assumingthat every household in a region has the mean income or consumption of thatregion. Although popular to use, the Gini coefficient is not susceptible to such adecomposition. The only class of inequality indices amenable to the abovedecomposition is the class of generalized entropy (GE) measures (Shorrocks,1984). We denote the GE measures byI(x) and their functional forms are

I ~x! 5 51

N Og51

G Oi51

Ng HS xig

mxD c

2 1J , c Þ 0, 1

1

N Og51

G Oi51

Ng S xig

mxD lnS xi

g

mxD , c 5 1

1

N Og51

G Oi51

Ng

lnSmx

xigD , c 5 0,

(6)

wherexig is the welfare indicator of theith household in regiong, mx is the overall

mean,G is the number of regions,Ng is the number of households in regiong,N is the total number of households,mx is the mean income of the households,and c is a parameter. Ifc is less than two, the measure is more sensitive totransfers at the lower tail of the income distribution.

TABLE 1

Estimates of Food-Share Equations

1985 1988 1990

Intercept 0.983 1.328 1.282(19.30) (27.36) (33.46)

Log of consumption per capita 20.059 20.123 20.110(27.06) (217.06) (219.36)

Age 0–5 0.001 20.007 20.007(0.55) (24.21) (24.15)

Age 6–11 20.918 20.016 20.015(26.35) (211.49) (29.98)

Age 12 and above 20.677 20.016 20.016(26.66) (217.16) (216.02)

No. of laborers 0.006 0.012 0.014(5.03) (12.51) (12.30)

AdjustedR2 0.20 0.49 0.33

Note. Sources: SSB’s rural household survey data of Guangdong and Sichuan. The food-shareequations are estimated using an instrumental variable estimation method with White’s heterosce-dastic variance-covariance matrix. The regression equations include dummy variables for countiesnot reported above. The dummies are in most cases significant.

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With regard to the class of generalized entropy measures,WG(x) 5¥g51

G wgI(xg) andBG(x) 5 I(mx1e1, . . . ,mx

GeG), wherexg is the vector of welfareindicators (e.g., income per capita, consumption per capita, or equivalent con-sumption) of those in regiong, g 5 1, 2, . . . ,G, eg is anNg dimensional vectorof ones andwg 5 (Ng(mx

g/mx)c)/N. In the present context, each subgroup corre-

sponds to a province. The share of interregional inequality is then defined asB(x)/I(x). This class of measures has been used extensively whenever decom-position of overall inequality by population subgroup is required (Anand, 1983;Fishlow, 1972).

Finally, we develop significance tests for changes in inequality. Assumingsome underlying statistical properties of the data, the asymptotic distributions ofI(x) BG(x) and WG(x) may be derived. Then chi-squared tests may be con-structed. The technical details are relegated to Appendix B.

4. DATA SET

Our findings are based on SSB’s rural household survey data of Guangdongand Sichuan (see Appendix A for details). The period under scrutiny is 1985–1990. While Guangdong is one step ahead of other provinces in economicreforms and has benefited most from the flexible policies (linghuo zhengce)granted by the central government to the coastal provinces, Sichuan is oftenconsidered lagging behind the coastal provinces (Li, Chen, and Peng, 1994).Over this period, there was also a distinct transformation of the economicstructure in rural Guangdong. Based on our data set, the share of non-agriculturalincome in Guangdong increased from 27.7% in 1985 to 35.49% in 1990. On theother hand, the share for Sichuan remained roughly constant at around 20%.Developments in Guangdong are typical of many rich coastal provinces whileSichuan’s experience is closer to the less-developed western provinces. Thedifferent development trajectories of the two provinces thus furnish an idealsetting for exploring the issues raised in Section 2.

Before considering the empirical results, some adjustments of the expendituredata are needed before they can be used. Expenditure figures include purchasesof consumer durables, exaggerating the level of consumption, thus providing animperfect proxy for current consumption. We estimate the stream of durableconsumption based on each household’s inventory of consumer durables. Detailson how the expenditure data are adjusted may be found in Appendix A. Anotherproblem specific to China’s rural household survey data set is that grains forself-consumption are valued at official prices before 1991.3 Appendix A explainsin detail how we re-value grains for self-consumption at market prices.

3 For a discussion of this problem, see Khan, Griffin, and Zhao (1992), Chen and Ravallion (1996),and Appendix A.


5. EMPIRICAL FINDINGS

5.1. Rural Economic Well-being

Panels A and B in Table 2 report the figures for net income per capita,consumption per capita, and equivalent consumption. How robust is one’sassessment of rural welfare with respect to the different welfare indicators? Asfar as inter-provincial welfarelevelsare concerned, the ratios of the provincialwelfare levels for each of the three welfare indicators are derived by dividing thefigures of Sichuan by the corresponding figures of Guangdong. The figures arereported in Panel B of Table 2. The ratios are lowest when net income is used anddecrease as consumption-related indicators are employed. For example, Shicu-an’s net income is only 70% of Guangdong’s net income in 1988, but thepercentage is 89.52% with respect to equivalent consumption (see the figures inbrackets reported in Panel B of Table 2).

With regard to the trends of rural welfare, the changes are largely significant,except for the change in net peasant income between 1988 and 1990. The figures

TABLE 2

Real Net Income per Capita, Consumption per Capita, and Equivalent Consumption

A. Guangdong B. Sichuan

1985 1988 1990 1985 1988 1990

Net peasant income per capita 924 1005 993 643 703 626(69.59) (70) (63.04)

Consumption per capita 729 888 939 568 694 644(77.91) (78.15) (68.58)

Equivalent consumption 703 783 866 582 701 649(82.79) (89.52) (74.94)

Chi-squared statistics for changes in welfare indicatorsC. Guangdong D. Sichuan

85–88 85–90 88–90 85–88 85–90 88–90Net peasant income per capita 18.89* 14.98* 0.43 87.79* 10.66* 138.82*Consumption per capita 214.27* 345.8* 18.99* 797.46* 313.64* 99.97*Equivalent consumption 67.62* 255.55* 66.05* 725.38* 239.66* 118.56*

Note.(1) All the figures have been deflated by the regional price index. For details, see AppendixA. Care must be exercised when comparing the figures for net peasant income per capita with thosein the official yearbooks. The figures in yearbooks have not been adjusted for inflation. Since the baseyear for the inflation adjustment is 1990, the 1985 and 1988 figures are higher than the official figures.The income differences between Guangdong and Sichuan are smaller because the figures have beenadjusted for inter-provincial price differences.

(2) The figures in brackets are the ratios of Sichuan’s indices of welfare to those of Guangdong.(3) Sources: Author’s calculation.* Indicates a 1% significance level.

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in Table 2 clearly illustrate how the trend may be affected by the measure ofwelfare used. The period under investigation includes a year of high inflationfollowed by a policy-induced recession. The cyclical fluctuation is nicely cap-tured by net peasant income per capita surging to a peak in 1988 and then fallingas a result of the government’s retrenchment policy. The trends for theconsumption-related measures are different due to consumption smoothing. InGuangdong, there was no decline in per-capita and equivalent consumption. Forthe period between 1985 and 1990, Guangdong experienced a persistent increasein consumption per capita and equivalent consumption. In Sichuan, per-capitaconsumption and equivalent consumption first increased and then decreased,although the response of consumption to the 1990 recession was less severe thanthat of income. Consumption-related measures are thus better proxies for long-term trends.

The levels and trends corresponding to consumption per capita and equivalentconsumption are similar. For provincial equivalent consumption to exhibit atrend diverging from that of provincial consumption per capita or net income percapita,m̂(zit

g) has to change over time and the changes have to be different fordifferent provinces. However, demographic variables change only slowly. Theperiod under investigation is probably too short form̂(zit

g) to make a difference.However, we speculate that for a longer period of time (e.g., one or two decades),the gains from using equivalent consumption are likely to be more evident.Among the Chinese provinces, Guangdong and Sichuan are not the most dispar-ate in terms of demographic composition. For the year 1991, when the figures forthe entire sample of the household survey are available, the average householdsizes for Guangdong and Sichuan are 5.51 and 4.33 while the range for ruralChina is between 3.49 and 6.15. The age composition also varies significantlyacross provinces (Guojia tongjiju nongdia zongdui, 1993). Although insignificantfor Guangdong and Sichuan, the difference between the two indicators mayconceivably be more prominent for other provinces.

5.2. Inequality in Rural Economic Well-being: Empirical Findings

This section presents the empirical results on rural inequality. To assess thedegree of inter-provincial inequality, overall inequality is decomposed intobetween-province and within-province inequalities. We rely on the class of GEmeasures to measure and decompose rural inequality with respect to equivalentconsumption. First, we check the robustness of the inequality trends with respectto consumption per capita and net peasant income per capita. To facilitatecomparison, the corresponding values of the Gini coefficient are also derived.

We examine in Table 3 whether inequality trends are sensitive to the indicatorof well-being used. Inequality is distinctly higher for net peasant income percapita as expected. The levels for the other two indicators are, however, quitesimilar with the inequality of consumption per capita being slightly larger than


TABLE 3

Comparison of Inequality Trends for Different Welfare Indicators

Overall inequality

Generalized entropymeasure,c 5 0 Chi-square statistics

1985 1988 1990 85/88 85/90 88/90

Net peasant income percapita 0.111 0.134 0.141 26.23* 51.25* 2.38

Consumption per capita 0.070 0.075 0.089 0.63 31.08* 31.84*Equivalent consumption 0.064 0.066 0.078 4.36** 48.30* 32.61*

Rural inequality within Guangdong


1985 1988 1990 85/88 85/90 88/90

Net peasant income percapita 0.149 0.161 0.157 1.70 0.84 0.20

Consumption per capita 0.081 0.079 0.086 0.23 0.67 3.17***Equivalent consumption 0.078 0.077 0.081 0.10 0.17 0.94

Rural inequality within Sichuan


1985 1988 1990 85/88 85/90 88/90

Net peasant income percapita 0.079 0.106 0.107 52.16* 87.61* 0.05

Consumption per capita 0.054 0.064 0.067 25.56* 42.08* 2.63Equivalent consumption 0.052 0.059 0.063 14.91* 32.77* 4.10**

Note.Source: Rural household survey data of the SSB and author’s calculation. The inequalityindex is the GE measure whenc 5 0, i.e.,

I~x! 51

N Og51

G Oi51

Ng

ln~mxg/xi

g!,

where the notation is the same as in the text.* Denotes a 1% significance level.

** Denotes a 5% significance level.*** Denotes a 10% significance level.

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that of equivalent consumption. The trends with respect to the three indicators arehowever roughly similar. Overall inequality increased during that period and theincreases are in most cases significant.

For the sake of comparison, we have also measured inequality with respect tothe Gini coefficient and the figures are presented in Table 4. The trends are, byand large, similar to those with respect to the GE measures and these trends are,thus, robust to different inequality indices. As in Table 3, Guangdong’s figuresare higher whereas Sichuan’s estimates are lower.

Since overall inequality trends seem to be robust with respect to both themeasures of well-being and the inequality indices used, the rest of this sectionwill focus on the inequality of equivalent consumption (see Tables 5 and 6). Withthe exception of the change in inequality between 1985 and 1988 whenc 5 2,

TABLE 4

Inequality Measured by the Gini Coefficient

Overall inequality

1985 1988 1990

Net peasant income per capita 0.260 0.284 0.291Consumption per capita 0.218 0.244 0.260Equivalent consumption 0.207 0.221 0.241

Guangdong

1985 1988 1990


Sichuan

1985 1988 1990


Rural China (official SSB estimates)

1985 1988 1990

Net peasant income per capita 0.264 0.301 0.310

Note.Sources: Rural household survey data of the State Statistical Bureau and author’s calculation;official statistics from Tang (1994).


overall inequality exhibits an increasing trend (see Panel A in Table 5), althoughthe change in inequality between 1985 and 1988 is insignificant regardless of thevalue ofc (see Table 6). For 85/90 and 88/90, the changes are all significant forthe transfer-sensitive measures. i.e.,c , 2. For the individual provinces, Table 6shows that the changes in Guangdong’s rural inequality are insignificant, i.e.,there is no change in rural inequality. Unlike Guangdong, Sichuan exhibits asignificantly upward trend. With regard to inter-provincial inequality, the trenddecreases from 1985 to 1988 but sharply rebounds in 1990 (see Table 5). Thechanges are statistically significant (see Table 6). The shares for different valuesof c, even at their highest levels, did not exceed 12%.

5.3. Discussion of Empirical Results

As shown above, the extent of rural inequality in China was moderate aftermore than a decade of reform and rural economic well-being improvedsteadily over time. The Chinese scenario contrasts sharply with that of

TABLE 5

Intra-provincial and Inter-provincial Inequalities of Equivalent Consumption

A. Overall inequality

c 5 0 c 5 1 c 5 2 c 5 21

1985 0.064 0.070 0.089 0.0651988 0.066 0.070 0.082 0.06771990 0.078 0.082 0.095 0.080

B. Intra-provincial inequality—Guangdong

1985 0.078 0.088 0.121 0.0791988 0.077 0.083 0.102 0.0771990 0.081 0.085 0.097 0.083

C. Intra-provincial inequality—Sichuan

1985 0.052 0.054 0.060 0.0541988 0.059 0.061 0.066 0.0621990 0.063 0.065 0.072 0.065

D. Inter-provincial inequality between Guangdong and Sichuan

1985 0.004 0.004 0.004 0.0041988 0.001 0.001 0.001 0.0011990 0.009 0.010 0.001 0.010

Note.Source: Rural household survey data of Guangdong and Sichuan and author’s calculation.The formulae for overall, intra-provincial, and inter-provincial inequalities are in Appendix B.

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Eastern Europe and Russia, For example, Russia’s Gini coefficient increasedby 50% between 1987–1988 and 1993–1994. Compared with other transi-tional economies experiencing sharp increases in inequalities shortly aftertransition began (Milanovic, 1995), the increase in rural inequality in Chinahas been less drastic.

Is rural inequality high by international standards? Comparisons are difficultbecause figures from other countries are often not confined to the rural sectoronly. Furthermore, only figures for income inequality are available. With thiscaveat in mind, the Gini coefficient for OECD countries in the mid-1980’s rangesfrom a low of 0.207 in Finland to a high of 0.341 in the U.S. (Atkinson et al.,1995, p. 46). As shown in Table 4, China’s rural inequality fell into this range in

TABLE 6

Chi-Squared Statistics for Tests in Significance of Changes in Inequalitywith Respect to Equivalent Consumption

A. Overall inequality

c 5 0 c 5 1 c 5 2 c 521

1985–1988 0.63 0.00 0.73 1.731985–1990 31.08* 5.04** 0.46 53.19*1988–1990 31.84* 6.02** 12.47* 42.61*

B. Inequality within Guangdong

1985–1988 0.10 0.21 0.47 0.161985–1990 0.17 0.13 0.85 0.811988–1990 0.94 0.05 0.32 2.40

C. Inequality within Sichuan

1985–1988 14.91* 9.77* 4.33** 17.55*1985–1990 32.77* 23.71* 13.09* 36.55*1988–1990 4.10** 3.96** 3.51*** 3.86**

D. Inter-provincial inequality

1985–1988 24.20* 24.14* 24.05* 24.23*1985–1990 45.43* 45.28* 45.06* 45.51*1988–1990 126.86* 125.53* 123.63* 127.63*

Note.Sources: SSB’s rural household survey data and author’s calculation. For derivation of thechi-squared tests, see Appendix B.

* Denotes a 1% significance level.** Denotes a 5% significance.

*** Denotes a 10% significance level.


the late 1980’s. China’s Gini coefficient is much lower than those of manydeveloping countries.4

In the case of the GE measures, comparable figures are hard to find. A previousstudy on Malaysian income inequality reports that the 1970 figure for Malaysiais 0.4798 whenc 5 0 and 0.5441 forc 5 1 (Anand, 1983) whereas thecorresponding figure for China in 1990 forc 5 0 was 0.141 (Table 3). The 1960figure for Brazil is even higher (Fishlow, 1972). The Chinese GE measure forc 50 in 1990 is quite close to that of the UK in 1986 (Jenkins, 1995). Thus,regardless of the inequality indices used, the Chinese figures in the second halfof the 1980’s were close to those of developed countries and lower than those inmany developing countries.

Notwithstanding the faster pace of market liberalization, our findings showthat rural inequality in Guangdong remained stable at least during the periodunder scrutiny. On the other hand, rural inequality in Sichuan increased moder-ately over the same period. No simple monotonic relationship between speed oftransition and inequality is thus discernible. As alluded to before, various forcesranging from the rapid growth of non-agricultural activities to labor mobilityhave been at work during that period and there is no consensus on their impacton inequality. An interesting extension of this research is to decompose inequal-ity according to the contributions by different sources of incomes so that theabove scenarios may be subject to rigorous testing.

With regard to inter-provincial inequality, the impression projected by officialreports and many previous studies, e.g., Zhu (1992), Rozelle (1996), World Bank(1996), is that the inter-regional income gaps account for much of rural inequal-ity. Are the shares of inter-regional to overall inequality high by internationalstandards? With most of the studies on China’s rural inequality using the Ginicoefficient, an index not susceptible to subgroup decomposition, comparablefigures are hard to find. Even for studies that focus on the decomposition ofinequality, the results are sensitive to the spatial units selected and, thus, theshares for different countries may not be comparable. Bearing these caveats inmind, the Chinese figures are not particularly high. The share of inter-regionalinequality for Malaysia is about 8% in 1970 (Anand, 1983). The share for Brazilis, on the other hand, substantially higher than the shares in China as it exceeds50% of overall inequality in 1960.

At least at the provincial level, our findings provide food for thought oninter-regional rural inequality. Our findings on the relative importance of inter-provincial inequality also shed light on the recent discussion about regionaldevelopment strategy in China that has focused on the disparities among theeastern, central, and western regions. Various measures have been introduced toreduce income gaps among the three regions. For example, the promotion of

4 See the data set on inequality compiled by Lyn Squire of the World Bank and available on theBank’s website.

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TVEs in the central and western regions is an attempt to reduce inter-regional andthus overall rural inequality. An objective of the recent Ninth Five-Year Plan isto reduce regional disparities among the eastern, central, and western regions (Li,1996). However, as our findings suggest, even for smaller spatial units such asprovinces, intra-provincial inequality is already very large. One cannot help butthink that the focus on the income gaps among the eastern, central, and westernregions is a reflection of the hierarchical structure of the state administration inwhich the provinces bargain with the central government for financial resources.It is not clear whether a shift in the central government’s development strategyin favor of the central and western regions would have a significant impact onoverall rural inter-household inequality. The findings also support the growingconcern that insufficient attention has been paid to growing sub-provincialinequalities; thus, the need arises to overhaul sub-provincial public finance(Wong, Heady, and West, 1997).

6. CONCLUDING REMARKS

In the introductory section, we identified a number of issues pertaining toChina’s rural inequality. Unlike the experience of other transitional economies,rural inequality increased only moderately in the 1980’s. By 1990, the degree ofrural inequality was similar to developed countries but less than that in manydeveloping countries. Notwithstanding the different paces of transition in Guang-dong and Sichuan, no simple relationship between the speed of transition andinequality is discernible. The changes in the overall rural inequality in the twoprovinces depends on various forces unleashed after mid-1980. A logical exten-sion of this paper is to decompose rural inequality into the contributions made bydifferent sources of income. There is already a small, but growing, literature onthis subject, e.g., Khan et al. (1992), Rozelle (1994, 1996), and Nongcun gudingguancedian bangongshi (1997). The SSB data set for Guangdong and Sichuanused in this study consists of detailed time-series and cross-section figures onincomes from different sources and is, thus, ideal for the factor decomposition ofrural inequality.

APPENDIX A: DATA AND DATA RECONSTRUCTION

Data Set

The empirical findings in this paper are based on the household data ofGuangdong and Sichuan collected by the Rural Survey Team (Nongcun diaochazongdui) of the State Statistical Bureau (SSB). The national sample is made upof more than 60,000 households. A short introduction of the sampling methodsemployed and the sampling frame may be found in Guojia tongjiju nongdiao


zongdui (1993).5 The price data for the years 1988 and 1990 are from Guan-dongsheng tongjiju (1991), Sichuansheng chengshi shehui jingji diaocha dui(1991). The prices of consumer durables are the average retail price datacollected by the urban survey teams of the SSB. Guangdong’s samples include2536, 2640, and 2560 households for the years 1985, 1988, and 1990, respec-tively. The corresponding figures for Sichuan are 5499, 5400, and 5500.

In the rest of this appendix, the definitions of the major variables used in ourempirical work are given.

Net Peasant Income

Net income (chun shouru) is equal to total income minus expenditures offamily-run businesses, depreciation of productive fixed assets, taxes, remittancesto the collectives, and survey subsidy. Net peasant income per capita is equal tonet income divided by household size.

Consumption

Consumption is estimated based on living expenses (shenghuo xiaofei zhichu).A number of adjustments have to be made:

(a) Adjustment of grain consumption data. Up to 1990, grains produced bypeasants for self-consumption were evaluated at official, and not market, prices.In so far as market prices are higher than official prices, consumption isunderestimated (Khan et al., 1992). We try to re-estimate grain consumption atmarket prices following the procedures suggested by Chen and Ravallion (1996).First, we derive the unit value of grains sold for each household. Then the medianvalue of the unit values is taken as the market price. This estimate is likely to behigher than the official price but lower than the market price because the unitvalue is essentially a weighted average of market and official prices. Next, weselect all those households that did not purchase grains from the market. One canthen infer that the grains they consumed are from their own production and werethus valued at official prices. For each of these households, the value of grainsconsumed is then divided by the total quantity consumed to arrive at an estimateof the official price for that household. The median of these estimates is thenchosen as the official price. Next, the value of grains for self-consumption isderived from the difference between the total value of grain consumption and theconsumption of grain purchased with cash. The portion of grains for self-consumption is then multiplied by the ratio of the median of the estimated marketprice to that of the estimated official price.

(b) Conversion of expenditure into consumption. Next, current expenditureson durable consumer goods and housing are replaced by estimates of current

5 For an in-depth discussion of the various aspects of the survey, see He et al. (1990).

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consumption. The value of each household’s dwelling is available. It is assumedthat the current consumption of housing is 5% of the value of the dwelling. Toestimate the current consumption of consumer durables, the values of consumerdurables are first estimated based on the inventories of consumer durablesreported. The stocks are then multiplied by the current prices of the correspond-ing consumer durables. Chen and Ravallion (1996) used the unit values ofconsumer durables derived from the data set. However, owing to the infrequentpurchases of consumer durables, the estimated unit values may be based on therecords of a few households and the prices may not be representative. Toovercome this problem, price data for 1988 and 1990 supplied by the SSB areused instead of unit values.6 When this paper was written, the SSB had not beenable to supply the relevant data for 1985. We were forced to use the unit valuesderived from the 1985 data set. Also, the prices of motorcycles are not availablefrom other sources and the unit values derived from the survey data have to beused. Once the values of the stocks of consumer durables are estimated, 10% ofthe value of the stock of consumer durables (as suggested by Chen and Raval-lion) is taken as a proxy of current consumption.

(c) Compilation of the regional price index. Finally, to arrive at real consump-tion and real income, a regional price index for 1990 with Guangdong as thereference point is derived. First, consumption goods are divided into categoriesaccording to the classification used in the questionnaire of the SSB’s ruralhousehold survey. For the base period, the formula for the regional price indexof the gth province is

P0g 5 O

i51

M

pi0g qi0

r /Oi51

M

pi0r qi0

r 5 Oi51

M

wi0r ~ pi0

g /pi0r !, wi0

r 5 pi0r qi0

r /Oi51

M

pi0r qi0

r ,

where pi0g and qi0

g are the estimated price and quantity consumed of theithcategory of goods in regiong in the base period 0, the superscriptr refers to thebase province,M is the number of categories of goods, andwi0 is the expenditureshare of goodi in the base province. Thus,P0

g is the price level ofg relative tothat of the base province;P0

g 5 1 wheng 5 r.To estimatepi0

g for the ith category of goods, we resort to information fromSichuansheng chengshi shehui jingji diaocha dui (1991) and Guangdongshengtonjiju, Guangdongsheng chengshi shehui jingji diaochadui (1991). In thesepublications, each category of goods may include products of different qualitiesand specifications, e.g., one category in the survey questionnaire is wine, butthere are different kinds of wine in the above publications. Then,pi0

g is the simpleaverage of the prices of that basket of goods.

6 The price data for the years 1988 and 1990 are from Guandongsheng tongjiju (1991) andSichuansheng chengshi shehui jingji diaochadui (1991). The prices of consumer durables are theaverage retail price data collected by the urban survey teams of the SSB.


With regard to the quantities for theM categories of goods, letqmi0r be themth

category of goods,m 5 1, 2, . . . ,M, purchased by theith household in the base

province, i.e., Guangdong. Then,qm0r is defined as~¥i51

N0r

qmi0r )/N0

r whereN0r is the

number of households in the base region and year.In the present context, the base year is 1990 and the base province is

Guangdong, i.e.,P0GD 5 1. The index for SichuanP0

SC is equal to 0.85 in the baseyear. Next, the index for the other years is derived using the formula

Ptg 5 Rt

g 3 P0g.

whereRt is the rural consumer price index of thegth region in periodt. Ptg is then

used to deflate consumption.

APPENDIX B

Tests of Significance

This appendix explains in detail how chi-squared statistics may be constructedto test whether changes in rural welfare and inequality are statistically significant.Before we proceed, an explanation of our notation is in order. The superscript ofa variable denotes region and the subscript denotes household. Letxg be a vectorwhoseith elementxi

g is the welfare indicator of theith household in regiong, g 51, 2, . . . ,G. Let Ng be the number of observations in regiong andN 5 ¥ Ng. Weassume thatf g 5 Ng/N is fixed asNg andN tends to infinity. Within each region,i.e., given g, xi

g is independently identically distributed with meanmxg and

variancenxg. In other words, the variablesxi

g, g 5 1, 2, . . . ,G, are within-regionhomogeneous but between-region independent and heterogeneous, i.e., cov(xi

g,xj

g) 5 0 for all i and j, wheneveri Þ j. These simplifying assumptions help toreduce the computational complexity. Letwi

g 5 (xig)c, yi

g 5 ln(xig), and zi

g 5xi

g ln(xig); their respective sample means are

mjg 5

1

Ng Oi51

Ng

j ig, j 5 x, w, y, z.

The sample variance ofxig is

mxxg 5

1

Ng Oi51

Ng

~ xig 2 mx

g!2.

To derive chi-squared tests for changes in welfare and inequality, we invoke awell-known result (Serfling, 1980, p. 122). Suppose thatun is AN(m, n21 ¥), i.e.,asymptotically normal with meanm and variance-covariance matrixn21 ¥. Letg(u) be a real-valued function having a nonzero differential atu 5 m, theng(u)is AN(g(m), n21d ¥ d*) whered 5 (g(u)/u1 . . . g(u)/un) is evaluated atm

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andd9 is its transpose. To find out whether there is a significant change in thevalue of g(u) between periods and t, the chi-squared test with one degree offreedom may be derived using the formula

~ g~us! 2 g~ut!!2

ds~n21 ¥!d*s 1 dt~n

21 ¥!d*t, x1

2.

The sample mean vector and the sample variance-covariance matrix may be substi-tuted into the above expression to arrive at the value of the chi-squared statistics.

(a) Test of significance of a change in rural welfare. It is not difficult to showthat, for some givenNg, mx

g is AN(mxg, nx

g/Ng). Then

~mxsg 2 mxt

g !2

nsg/Ns

g 1 ntg/Nt

g

is a chi-squared statistic with one degree of freedom and the subscriptss and trefer to two different periods. This statistic may be used to test whether there isa change in the mean of a variable. In practice, the sample momentsmx

g andmxxg

may be substituted into the above formula.

(b) Significance tests for changes in inequality. The class of generalizedentropy measures may be decomposed as

I ~x! 5 BG~x! 1 WG~x),

where

I ~x! 5 51

c~c 2 1! S mw

~mx!c 2 1D , c Þ 0, 1

mz

mx2 ln~mx!, c 5 1

ln~mx! 2 my, c 5 0

x 5 (x1, . . . , xG), mj 5 ¥g51G f gmj

g, j 5 w, y, z, f g 5 Ng/N. Similarly,between-group inequality may also be expressed in terms of moments,

BG 5 51

c~c 2 1! Og51

G H f gSmxg

mxD c

2 1J , c Þ 0, 1

Og51

G

f glnSmx

mxgD , c 5 0

Og51

G

f gmx

g

mxlnSmx

g

mxD , c 5 1


and within-group inequality may be expressed as

WG~x! 5 5Og51

G

f gSmxg

mxD cF 1

c~c 2 1! S mwg

~mxg!c 2 1DG , c Þ 0, 1

Og51

G

f g@ln~mxg! 2 my

g#, c 5 0

Og51

G f gmxg

mx mzg

mxg 2 ln~mx

g! , c 5 1.

Let m 5 (m1, . . . ,mG), mg 5 (mwg, mx

g, myg, mz

g), andmx 5 (mx1, . . . ,mx

G). Then,I(x), BG(x), andWG(x) may be denoted byd(m), b(mx), andv(m), respectively.

Next, the asymptotic distributions ofd(m), b(mx), v(mx), and v(m) arederived, from which we can construct their respective chi-squared statistics.7 Toapply the theorem above, it is to be noted thatmg is AN(mg, (Ng)21Vg), g 5 1,2, . . . ,G, wheremg 5 (mw, mx, my, mz) andVg is a 43 4 variance-covariancematrix of (wg, xg, yg, zg). Then,m is AN(m, N21V) wherem 5 (m1, . . . ,mG) andV is 4G 3 4G block diagonal such that

V 5 1~ f 1!21V1 0434 · · · 0434

0434 ~ f 2!21V2 · · · 0434

· · · · · · · · · · · ·0434g 0434 0434 ~ f G!21VG

2 ,

where 0434 is a 43 4 matrix of zeros. Furthermore,mx is AN(mx, N21Vx), where

Vx 5 1nx

1/f 1 0 · · · 00 nx

2/f 2 · · · 0· · · · · · · · · · · ·0 0 · · · nx

G/f G2 .

It follows that d(m) is AN(d(m), ¹d(m)N21V¹d(m)9), v(m) is AN(v(m),¹v(m)N21V¹v(m)9), and b(mx) is AN(b(mx), ¹b(mx)/N

21Vx¹b(mx)9) where¹d(m), ¹v(m), and¹b(mx) are the gradient vectors ofd(m), v(m), andb(mx),respectively.

To test whether there are changes ind(m), b(mx), andv(m), we may constructchi-squared statistics based on the fact that the three are asymptotically normal.Then,m, mx, V, andVx may be replaced by their sample counterparts.

7 The asymptotic distributions of the generalized entropy measures have been derived by Cowell(1989) for the joint distribution of household size and income.

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