income inequalities in china: evidence from household survey data

World Development, Vol. 22, NO. 12, pp. 1947-1957,1994 Elsevier Science Ltd

Printed in Great Britain 0305-750x/94 $7.00 + 0.00

0305-750x(94)ootm-1

Income Inequalities in China: Evidence from Household Survey Data

ATHAR HUSSAIN London School of Economics

PETER LANJOUW The World Bank, Washington DC

and NICHOLAS STERN*

The European Bank for Reconstruction and Development

Summary. - On the basis of a household data set, this paper: compares household income inequality in urban and rural china, decomposes inequality into mtra- and interprovincial components; and analyzes the contribution of various income sources to total income equality. The main findings of the paper are, tirst, that unlike in most developing economies, income inequality in urban areas is lower than in rural areas. Second, nationwide income inequality is due mostly to intraprovincial inequality. Thiid, components of income associated with economic reforms are more unequally distributed than the rest.

1. INTRODUCTION

These studies of income distribution in China divide into three categories depending on the type of data they use: aggregate quantile income data, localized field surveys and wide-ranging household data. Most fall in the first two categories. The first systematic study based on quantile data is Adelman and Sunding (1987), which uses three sets of rural data (for 1952,1978 and 1983), and two sets of urban data (for 1981 and 1983) to analyze changes in income distribution in rural and urban areas, respectively. The study con6rms that income inequality in China has been exceptionally low and detects a U-shaped relationship between income inequality and growth. A later study, Ahmad and Wang (1991) based on yearly quantile data for 1981-88, comes to the conclusion that inequality has increased in the 1980s. This is recon- fumed for urban areas by Howes (1993) on the basis of the quantile data up to 1990, a study which also provides a careful evaluation of Chinese income data. The general conclusion of these studies are basically twcr: first, inequality in urban and rural China is very low by international standards, and, second, inequality has risen since 1978 both in urban and rural areas, though not steadily.

There are numerous studies drawing on local field surveys, all of rural areas. A pioneering study of the genre is Griffin and Saith (1981), which is baaed on a field visit in 1979 and is therefore focused on income

distribution in the pmdecollectivization period. The study finds a small inequality in per capita incomes in production brigades and teams (the constituent tiers of the erstwhile rural commune), though a large inequality across communes. An interesting finding of the study is the narrowing of intracommune income inequality since 1961. Given the impossibility of doing field work before 1979, almost all local studies deal with income distribution in the 19809, when collective agriculture was replaced with household farming. Drawing on data from a cross-section of rural units across China, Griffin and Griffin (1984) analyze the distributional impact of the rural reforms which began in 1979. A challenging conclusion of the study is that decollectivization rather than raising inequality may reduce it by widening income opportunities for poorer households. This conclusion tinds qualified support in Zhu (1991). This study, however, based on a survey of 90 households from three countries~ in the central Chinese province of Henan in 1985, finds that the reforms, while reducing intracounty inequality, also widened inequality between the counties. The implication is that the conclusion about the changes in

*We are grateful to Angus Deaton, Frank Cowell, Stephen Howes and two referees for comments and suggestions. This paper arises from research programs supported by the Ford Foundation and the Economic Social Research Council of the UK (Grant Nos. R 000 23 2297/3425). Final revision accepted: June 22.1994.

1947

1948 WORLD DEVELOPMENT

inequality may depend crucially on the scope of data. The most systematic longitudinal study of income distribution in rural locality is Putterman (1993), which is based on data from two rounds of a field survey of a commune (later a township) in the northern Chinese province of Hebei. The study provides a detailed breakdown of inequality by income sources and comes to the conclusion that income inequality in the locality arose significantly during 1979-85.

Both sets of studies, although they have contributed to our understanding of income distribution in China, are subject to limitations. Those based on quantile data are restricted by the form and the years for which data is supplied by the Chinese authorities. The quantile divisions have changed over time, and the data is bro- ken down only by urban and rural areas but not by provinces*. Localized surveys provide valuable insights into sources of inequality and changes in it over time but they beg the question of the generaliza- tion of findings to a diverse country such as China or even a province.

This paper presents an analysis of income inequality in China on the basis of a large household survey. The only other study of this kind is the one based on a nationwide household survey conducted by a team of foreign and Chinese researchers (GrifIin and Zhao, 1993). Although the data on which this paper is based suffer from certain problems (indicated later), they provide for the following:

- a comparison of household income inequality in rural and urban areas, which is possible for quantile data but not for local data; - a decomposition of total income inequality into intra and interprovincial inequality, which is neither possible for local data nor for quantile data as reported by the Chinese authorities; - a decomposition of income inequality by the sources of income, which is possible for local but not for quantile data.

We see the paper as a contribution to the stock of results on income distribution on China which can be used for comparison or to complement other studies. In section 2 we describe the data set, section 3 looks at urban and rural inequality, section 4 deals with intra- and interprovincial income inequality, section 5 analyzes inequality by income sources and section 6 con- cludes and points to the implications of the findings for policy.

2. DATA

The data set is based on a sample survey of 10,000 households, divided equally between rural and urban area, which was conducted in 1986 by the Chinese Academy of Social Sciences (hereafter referred to as CASS data). While the urban sample was drawn from 28 out of the then 29 provinces (excluding Tibet), the

rural sample was restricted to only 10 provinces due to a comparatively high cost of surveying rural households. The sampling frame is the one used by the State Statistical Bureau (SSB) for its annual household survey, findings of which am released in a highly aggre- gated form. Crosschecks with the population census data revealed both a mismatch in the provincial distribution of households and a bias in favor of the employees of the state sector and the better educated. Where relevant, we have reweighted the sample to compensate for these biases. A valuable feature of the data set is detailed information on households: its composition, sources of income and provincial loca- tion. For urban households, total income is divided into regular (mainly wages and bonuses) and irregular income and, for rural households, into income from fanning and nonfarming activities, and wage income. These details enable a three-way decomposition of income inequality: by rural and urban areas, by provinces and by income sources. A serious flaw in the data set,which with one notable exception (Griffin and Zhao, 1993) is common to all Chinese household income data, is the omission of income in kind. Of the 10,000 sampled households, the cleaning of the data set left us with a sample of 3,811 urban households and 3,653 rural households3.

Many of the interesting issues concerning income inequality in China involve comparisons between provinces and between rural and urban areas. Given that price levels vary widely between provinces and rural and urban areas, the analysis of unadjusted income is likely to yield misleading results about inequality. We have addressed this problem by con- structing separate price indices for urban and rural areas using available price data for cities and town- ships across China4. While price deflation does not alter the ranking of per capita income significantly, it reduces the coefficient of variation of average provincial incomes. This would suggest that undeflated incomes exaggerate income inequality.

3. RURAL AND URBAN INCOME INEQUALITY

In Figures 1 and 2 we present separately for rural and urban areas two Lorenz Curves for household incomes and for income per capita. “Household income” is as provided by the survey and “income per capita” is calculated by attributing to each individual the per capita income of his/her household, which for many will be very different from their earnings. As the sum of “income per capita” is equal to the sum of “household incomes,” their respective Lorenz curves are comparable. Both “household income” and “income per capita” raise problems. While the former overlooks variation in household size, the latter neglects the fact that intrahousehold distribution of income varies with age and sex.6 It is possible to adjust for discrimination by age and sex when data contain

1949 INCOME INEQUALITIES IN CHINA

Household income

Individual income

Poorest II%

Figure 1. Lorenz curvesfor rural incomes.

100 - - Household income

Individual income

g 8

60-

5

3 z

60-

l- (1) 0 40- z 0” t 20- a

0 I 0 20 40 60 60 100

Poorest II%

Figure 2. Lorenze curves for urban incomes.

both details on household composition and on expedi- ture of different categories of commodities,’ which neither the present nor the other available household data sets for China do.

The Lorenz curves for the two measures of income (Figures 1 and 2) demonstrate how a change in the unit of measurement alters the distribution of incomes. For both rural and urban areas, the Lorenz curve for “household incomes” lies outside the Lorenz curve for “individual per capita income.” This means that the former is more unequally distributed than the latter. This is a universal feature and is to be expected because of variation in household size. In the subse- quent discussion we shall confine our attention to the distribution of “income per capita” (hereafter referred to as individual incomes).

Tables 1 and 2 present three commonly used summary statistics of inequality, together with their respective standard error, by provinces and by urban

and rural areas. These include the coefficient of variation and the Gini coefficient and the Atkinson index. While most of our analysis is based on the Lorenz curve and the Gini coefficient, we use other indices to check the dependence of conclusions on particular inequality measures. It should be emphasized that all summary measures of inequality embody value judge- ments and that they may yield inconsistent conclusions.* The Atkinson measure is different from the rest in making the weight attached to inequality explicit - the higher the parameter the higher the aversion to inequality. In Tables 1 and 2 we report the Atkinson index for parameter values, 1, representing a low inequality aversion, and 2, representing a moderate inequality aversion. Most measured Gini coefficients for incomes lie in a limited range around 0.5, far from the end-points, 0 (complete equality) or 1 (maximum inequality). The end-points provide little help in assessing whether a measured value of the Gini coeffi-


Table 1. Urban areas

Gbservations Coef. of var.* (s.e.)t Gini (se.) Atkinson 1 (se.) Atkinson 2 (se.)

Beijing Shanghai

463 0.425

(0.022) 0.215

(0.009) 0.076

(0.006) 0.150

(0.012)

0.376 (0.017) 0.208

(0.010) 0.078

(0.008) 0.175

(0.022)

Nei Meng Liaoning

Tianiin

1278 0.432

(0.024) 0.205

(0.006) 0.07 1

(0.004) 0.139

(0.008)

Jilin

Hebei

527 0.437

(0.03 1) 0.205 (.OlO) 0.067

(0.006) 0.120

(0.010)

Hei’iian&

Shanxi

564 0.430

(0.015) 0.235

(0.008)

(::g) 0.210

(0.016)

Coef. of var. (se.) Giii (se.) Atkinson 1 (se.) Atkinson 2 (s.e.)

197 0.339

(0.015) 0.191

(0.009)

(::E) 0.124

(0.012)

788 0.361

(0.010) 0.197

(0.006) 0.067

(0.004) 0.140

(0.008)

585 0.384

(0.020) 0.200

(0.007) 0.067

506 662 0.493 0.45 I

(0.027) (0.016) 0.233 0.237

(0.011) (0.008) 0.088 0.096

(0.007) (0.006) 0.160 0.205

(0.012) (0.015)

Jiaogsu Anbui Zhejiang Fujian Jiangxi

Observations 403 305 Coef. of var. 0.335 0.385 (se.) (0.018) (0.028) Gini 0.180 0.198 (se.) (0.008) (0.011) Atkinson 1 0.056 0.065 (s.e.) (0.005) (0.006) Atkinson 2 0.117 0.128 (s.e.) (0.011) (0.011)

288 396 605 0.433 0.384 0.380

(0.023) (0.014) (0.017) 0.223 0.212 0.200

(0.011) (0.008) (0.007) 0.078 0.079 0.067

(0.007) (0.007) (0.005) 0.147 0.176 0.135

(0.012) (0.0178) (.009)

Henan Hubei Hunan Guangdong Guangxi

Observations 402 Coef. of var. 0.457 (s.e.) (0.025) Gmi 0.228 (se.) (0.011) Atkinson 1 0.092 (s.e.) (0.009) Atkinson 2 0.195 (se.) (0.019)

Sichuan

764 0.347

(0.013) 0.187

(0.006) 0.058

(0.003) 0.118

(0.006)

Guizhou

414 546 397 0.448 0.409 0.410

(0.0219) (0.013) (0.028) 0.231 0.219 0.204

(0.010) (0.007) (0.010) 0.091 0.077 0.073

(0.007) (0.005) (0.007) 0.186 0.158 0.151

(0.016) (0.016) (0.015)

YuNlarl

Observations 1126 Coef. of var. 0.494 (se.) (0.023) Gird 0.243 (se.) (0.007) Atkinson 1 0.102 (se.) (0.006) Atkinson 2 0.205 (se.) (0.010)

*Coef. of var.: Coefficient of variation t(s.e.): standard error SHei’jiang: Heilongjiang

358 712 0.463 0.390

(0.044) (023) .223 0.198

(0.013) (0.007) 0.089 0.068

(0.010) (.005) 0.195 0.138

(0.024) (0.009)

INCOME INEQUALITIES IN CHINA 1951

Table 2. Rural areas

ObSeNatiOns 788 1394 1566 Coef. of var.t 0.389 0.534 0.602 (se.)* (0.011) (0.015) (0.082) Gini 0.212 0.262 0.246 (s.e.) (0.005) (0.06) (0.009) Atkinson 1 0.069 0.107 0.098 (s.e.) (0.003) (0.005) (0.008) Atkinson 2 0.135 0.197 0.178 (s.e.) (0.006) (0.008) (0.010)

Beijing Shanxi Hei’jiang* Gansu Jiangsu

1345 0.458

(0.014) 0.242

(0.005) 0.098

1945 0.490

(0.031) 0.241

(0.005)

(.tJO5) 0.220

(0.017)

‘0.094’ (0.004) 0.180

(0.006)

Anhui Henan Hubei Guangdong Sichuan

Gbservations Coef. of var. (s.e.) Gmi (s.e.) Atkinson 1 (s.e.) Atkinson 2 (s.e.)

2,274 2,680 2,045 1,464 2,698 (%E) 0.642 0.538 0.553 0.639

(0.057) (0.044) (0.013) (0.088) 0.195 0.269 0.236 0.281 0.228 ww (0.007) (0.007) (0.005) (0.009) 0.061 0.118 0.091 0.118 0.087

(0.003) (0.006) (0.005) (0.004) (0.008) 0.112 0.226 0.171 0.211 0.148

(0.004) (0.009) (0.007) (0.007) (0.009)

*Hei’jiang: Heilsngjiang tCoef. of var.: Coefficient of variation t(s.e.): standard error

cient is high or low; this has to be based on a comparison with those obtained for other economies. Being a large and diverse economy such as China, India is an obvious benchmark for comparison. A rule of thumb is that a Gini coefficient of less than 0.3 is rare and denotes is very low inequality?

Turning to the results in Table 1, the Gini coefficients for urban incomes lie in the range, 0.146 (for Ningxia) and 0.289 (for Xmjiang), and the coefficients for 15 out of 28 provinces are clustered in the narrower range of 0.194.22. One way of interpreting these figures is that a Gini coefficient of, for example, 0.2 implies that on average the gap between per capita incomes is 0.4 (or 40%) of the mean income.*o The figures for urban China are exceptionally low by international standanW1, though comparable with the figures for Eastern Europe prior to the demise of the command economyl* Datt and Ravallion (1990) using data on per capita urban consumption (not income) for Indian states found Gini coefficients ranging between 0.25 and 0.39, with the exception of only 0.18 for Manipur (a very small state). In contrast, but for Xinjiang, all Chinese provinces, have a Gini coefficient or less than 0.25 (Table 1). Moreover, since consumption expenditure is generally more equally distributed than income13, the Gini coefficients for “per capita incomes” for Jndian states would be higher than the Gini coefficients for “per capita consumption.”

There are two notable features to the fIndings for income inequality in urban China (Table 1). First, the

provinces with a comparatively high Gini coefficient by Chinese standards (greater than or equal to 0.23) are all interior provinces and, predominantly poor in terms of per capita consumption.14 These are Shanxi (0.235), Heilongjiang (0.233), Shaanxi (0.237), Xinjiang (0.289), Hunan (0.231) and Sichuan (0.243). Nevertheless poverty does not always imply high inequality. The Gini coefficients for some of the poorest provinces in China, such as Ningxia (0.146), Qinghai (0.186), Anhui (0.198), Jiangxi (0.200), and Yunnan (0.198), are comparatively low. Second, given the interest in the impact of economic reforms on income inequality, it is worth noting that the Gini coefficients for Shandong (0.205), Jiangsu (0.218), Zhejiang (0.223), Fujian (0.212) and Guangdong (0.219), the coastal provinces in the forefront of the reforms, are. generally on the low side. This would suggest that the link between economic reforms and a rise in inequality is ambiguous.

Turning to rural income inequality in the 10 provinces covered by the data set (Table 2), the Gini coefficients lie in the range, 0.195 (Anhui) and 0.281 (Guangdong), compared to the range (0.146-0.289) for urban areas. As with urban areas, income inequality in rural China is remarkably low compared to other countries.15 For example, the Gini coefficients of “per capita consumption” in the rural areas of Indians states for a 1983 data set range between 0.20 and 0.34.16 More interesting is the contrast between income inequality in urban and rural China (Tables 1


and 2). For seven out of 10 provinces, income inequality in rural areas is greater than in urban areas not only in terms of the Gini coefficient but also, with minor exceptions, other measures. Anhui represent an ambiguous case: there rural inequality is lower than urban inequality in terms of the Gini coefficient, but higher in terms of the rest. Beijing and Sichuan are the only ones where urban inequality is higher than rural inequality in terms of the Gini coefficient. The difference for Beijing is small, however, and in the case of Sichuan the coefficient of variation and the Atkinson measures show rural inequality to be higher than urban inequality.

Given that these 10 provinces are drawn from all six regions (North-East, North, East, South-East, South-West and North-West) in which China is con- ventionally divided, there are reasonable grounds for concluding that in China rural inequality is higher than urban inequality. This is the reverse of the usual result for developing economies.17 An important implication of the finding is that when analyzing the redistributive impact of policies we should not only take into account the difference in average per capita income between urban and rural areas (intergroup inequality) but also income inequality within rural and urban areas (intragroup inequality).

The unusual pattern of urban and rural income inequality in China may be explained in terms of differences in the sources of personal income. An overwhelming majority of the urban labor force is either employed in the state sector or large collective enterprises, where differences in wages and salaries are not large, The private sector, including the Sino-foreign joint ventures, where differences in wages and salaries can be large, account for a very small share of total urban employment. I8 In comparison, personal incomes in rural areas depend on factors which vary widely, such as proximity to a city, the land quality and the possibilities of engaging in nonagricultural activities. Moreover, transitory fluctuations in personal income, which contribute to inequality, are prima facie more important in rural than in urban areas. For example, a cross-section of rural households would include many whose incomes are (tem- porarily) low because of adverse climatic factors. In contrast, unemployment, which is a major cause of low income in urban areas, has been exceptionally low in China.lg

It is important to add a qualification to the finding for urban areas. The urban sample does not include the “floating population”, immigrants from rural areas who do not have urban registration (hukou) and arc therefore not entitled to reside permanently in urban areas. The inclusion of the “floating population” raises important conceptual issues, and how it would affect income inequality in urban areas is prima facie not clear. The “floating population” is very heteroge- neous, including itinerant petty traders, casual labor-

ers, house maids, those in regular wage employment and in search of a job. In the Chinese context, there are no obvious criteria for deciding which of these should be counted as part of the urban population. Although much of the floating population is in low-paid jobs, a large percentage of them have their families in rural areas because they am not entitled to family housing. Therefore adjusted for their current household size in urban areas, their per capita incomes are not necessar- ily low. Moreover, compared to permanent urban residents the rate of unemployment in the floating pop- upulation is not necessary higher, even though they face a higher risk of being laid off. Those without employment are either forced to or voluntarily return to the countryside. The general point is that the growth of the “floating population” is a significant development, but we need to be cautious in drawing conclusions about their impact on income inequality without adequate research.

4. INTER- AND INTRAPROVINCIAL INCOME INEQUALITY

We turn now to an analysis of income inequality for all provinces taken together and examine the rela-

Table 3. Decomposition of inequality between and within provinces (Deputed urban individual for capita income)

Income Within Between inequality component component

Log variance 0.200 0.188 0.012

(94%) (6%) General entropy Measure o=o 0.0861 0.081 0.00514

(94%) (6%) o=l 0.08516 0.080 0.00516

(94%) (6%) 0=2 0.0931 0.088 0.005 1

(95%) (5%)

Table 4. Decomposition of inequality between and within provinces (De&ted rural individual for capita income)

Income Within Between inequality component component

Log variance 0.236 0.200 0.036 (85%) (15%)

General entropy Measure a=0 0.1233 0.019 0.024

(80%) (20%) CY=l 0.1328 0.108 0.0248

(81%) (19%) a=2 0.1805 0.159 0.0265

(85%) (15%)

INCOME INEQUALITIES IN CHINA 1953

tive contribution of inter- and intraprovincial inequalities, as before, for the urban and rural samples separately. Such decomposition is possible only for some measures of inequality, and the Gini coefficient is not among them.2o Tables 3 and 4 present several well- known decomposable measures of inequality, including log variance and the entropy (or Theil) measures for three values.2’ All these measures can be written as the weighted sum of two terms, one representing intraprovincial inequality and the other interprovincial.

Taking urban incomes first (Table 3), all four measures yield the identical result that urban income inequality in urban areas is almost all due to intraprovincial differences. A simple and likely explana- tion is that a vast majority of the urban labor force is employed in the state of the collective sector. In 1986 when the survey was conducted, wages and salaries in the sector were closely tied to a national schedule and were scaled up or down to take account of regional price differences, which we have filtered out through price deflation. The situation would be a bit different in 1993 as wage determination has since then been decentralized.

Turning to rural incomes (Table 4), the conclusion is that, as with urban inequality, most of the tural inequality is due to intraprovincial differences. For each of the four inequality measures, however, the between-province contribution is significantly larger for rural than for urban incomes (Table 3 and 4), which suggest interprovincial differences are more important for rural than for urban inequality. This would seem to be due to a greater dependence of rural incomes on factors which vary across regionally, such as the quality of agricultural land and climatic factors. The result for rural areas, although suggestive, carries less weight than the result for urban areas because of limited provincial coverage of the rural sample. It is difficult to say what the result would be if all provinces were sampled.

The overwhelming importance of the within- province component in urban and rural inequality, although significant, is not surprising. Most Chinese provinces are akin to large national economies in terms of population and heterogeneity. In some respects, this result is similar to a tinding that among a group of countries, most of international inequality is due to within-economy inequality. It is also interesting to note that decomposition exercises, such as the one here, tend generally to find that intragroup inequality dominates intergroup inequality.22 The result has two implications. First, conclusions about inequality in China on the basis of interprovincial differences in per capita income are likely to be highly misleading. Second, the principal focus of policies concerned with income inequality; though not neces- sarily poverty, should be on intra- rather than interprovincial inequality.

5. DECOMPOSITION OF INCOME INEQUALITY

The focus of this section is the respective contribution of different types of income to total income inequality, separately for each province and for urban and rural areas. The analysis is based on a decomposition of the Gini coefficient proposed by Shorrocks (1982). That is, the Gini coefficient for total income can be written as the weighted average of “pseudo Gini” coefficients for the components of total income.

where k denotes the kth income component (such as wages, bonus or income from farming) and ut is its mean, and p is the mean total income, both across the sampled households (urban or rural) in a province. G, is the “pseudo Gini” coefficient for the kth component of income, which is calculated by ranking individuals according their total income and not their income from the kth source, as in the case of the usual Gini coefficient. Thus the “pseudo Gini” is not equivalent to the usual Gini coefficient; it can also be negative. The contribution of the kth income component to total income inequality is given by (p~u) (GJG), which depends on both the sham of kth income in total, (pJu) and the ratio of the “pseudo Gini” for the kth income to the Gini coefficient for total income, (GJG). Tables 5 and 6 give the results for urban households and rural households, respectively, by the province. For each category of income, the tables give the percentage contribution of the income component to total inequality, the percentage sham of the income component in total income and the “pseudo” Gini coefficient. Our discussion of the results relies on a comparison of the percentage contribution of an income component to total inequality and its percentage share in total income. When the first is lower than the second, the income component could be said to have an equalizing influence and vice versa.

(a) Urban areas

Urban incomes are divided into (basic) wage, bonus, irregular income and residual. In Table 5 (the column labeled 8 share) we see that in all provinces (basic) wage is the largest component, though its share in the total income varies widely. Although an overwhelming proportion of the urban labor force in all provinces is employed in the state or the collective sector, the composition of urban income seems to vary widely accross provinces. The percentage share of (basic) wage in urban incomes is particularly high in the interior provinces of Shanxi (75.4), Nei Meng (Inner Mongolia) (75.6), Gansu (75.6), Ningxia (78.7). Qinghai (77.9), Jiangxi (74.6%) and Henan (74.5). These provinces have not been in the forefront


Table 5. Decomposition of urban income inequality by components of income

FJrovince. Gini Wage income Bonus income Irregular income MiSCellalleOUS

Beijing 0.215 57% Shanghai 0.208 41% Tianjin 0.205 56% Hebei 0.205 37% Shanxi 0.235 69% Nei Meng 0.191 64% Liaoning 8.197 47% Jilin 0.200 58% Heilongjiaug 0.233 43% Shaanxi 0.237 57% GsXlSU 0.216 63% Ningxia 0.146 76% Qinghai 0.186 49% Xinjiang 0.289 67% Shangdong 0.205 48% Jiangsu 0.180 65% Anhui 0.198 62% Zhejiang 0.223 43% Fujian 0.212 58% Jiangxi 0.200 65% Henan 0.228 56% Hubei 0.187 54% HllllZUl 0.231 48% Guangdong 0.219 41% Guangxi 0.204 43% Sichuan 0.243 42% Guizhou 0.223 58% YllMZUl 0.198 49%

% % Pseudo % % Cant* Share? Gini Cost Share

66.9 0.258 14% 12.2 68.4 0.177 12% 11.4 67.5 0.238 11% 10.1 65.3 0.195 14% 13.2 75.4 0.245 10% 11.3 75.6 0.256 12% 8.6 67.9 0.214 10% 10.8 71.7 0.223 12% 9.8 68.3 0.219 11% 10.0 70.1 0.268 7% 7.8 75.6 0.223 7% a.9 78.7 0.165 9% 10.5 77.9 0.204 6% 5.3 73.9 0.317 9% 8.0 67.0 0.224 12% 11.2 68.7 0.220 21% 13.8 70.0 0.211 11% 13.0 62.8 0.206 19% 12.5 65.8 0.264 13% 11.2 74.6 0.211 11% 10.0 74.5 0.215 10% 8.8 70.3 0.219 11% 10.0 68.1 0.232 9% 9.7 59.9 0.197 27% 22.1 64.7 0.200 13% 13.2 63.5 0.240 9% 9.7 67.0 0.258 8% 9.8 66.9 0.211 11% 10.4

Pseudo Gini

% Cont

0.484 20% 0.376 28% 0.459 27% 0.430 34% 0.403 17% 0.585 11% 0.425 27% 0.437 24% 0.595 23% 0.448 29% 0.479 24% 0.429 17% 0.622 25% 0.524 25% 0.479 24% 0.443 10% 0.378 23% 0.463 34% 0.471 18% 0.418 16% 0.497 31% 0.420 24% 0.496 31% 0.381 22% 0.416 30% 0.430 29% 0.385 15% 0.413 31%

% Pseudo Share Gini

6.8 0.852 9.8 0.825 9.5 0.874

10.2 0.884 6.7 0.852 4.6 0.839 7.5 0.879 9.0 0.834 9.2 0.855

10.5 0.870 7.2 0.876 4.6 0.846 a.5 0.849

10.7 0.865 8.2 0.844 4.1 0.840 9.5 0.814

11.0 0.845 8.8 0.768 6.0 0.851

10.6 0.863 7.9 0.831

11.8 0.817 7.8 0.829 8.7 0.874 0.107 0.851

10.0 0.802 9.3 0.863

% % Pseudo Cant share Gini

10% 14.1 0.666 19% 11.2 0.676 5% 12.8 0.710

15% 11.3 0.699 4% 6.5 0.747

13% 11.1 0.840 15% 13.7 0.651 6% 9.4 0.752

24% 12.4 0.795 7% 11.5 0.663 7% 8.3 0.761

-3% 6.2 0.687 20% 8.2 0.906 -1% 7.5 0.807 15% 13.5 0.681 4% 13.4 0.599 4% 7.4 0.683 5% 13.7 0.546

11% 14.1 0.678 8% 9.5 0.627 3% 6.3 0.746

11% 11.9 0.653 11% 10.4 0.702 10% 10.2 0.659 14% 13.4 0.568 20% 16.2 0.623 19% 13.3 0.666 9% 13.4 0.565

*Cant: Contribution tThe share of income component in total income &/I*)

Table 6. Decomposition of rural income inequality by components of income

Province Gilli Farming/Forestry Household activity Manufacturing/Construction Miscellaneous

% Cant*

Beijing 0.212 20% Shanxi 0.262 35% Heilongjiang 0.246 61% Gansu 0.242 66% Jiangsu 0.241 34% Anhui 0.195 75% Henan 0.269 43% Hubei 0.236 67% Guangdong 0.281 43% Sichuan 0.228 47%

% Pseudo Share? Gini

% cost

%

44.3 0.085 56% 43.6 53.4 0.091 43% 27.0 71.0 0.159 25% 15.1 65.9 0.159 21% 17.8 59.1 0.083 39% 23.8 77.3 0.144 12% 9.9 62.0 0.117 41% 23.1 76.9 0.158 15% 10.9 58.7 0.122 36% 23.6 69.6 0.107 37% 17.7

Pseudo Gini

% Cont

0.119 -4% 0.114 6% 0.061 1% 0.051 1% 0.094 17% 0.023 6% 0.110 8% 0.035 7% 0.102 8% 0.085 6%

% Share Gini

% Cont

% Pseudo share Gini

3.8 -0.008 8% 8.2 0.017 3.4 0.015 16% 16.1 0.043 1.9 0.002 13% 12.1 0.033 4.3 0.002 12% 11.9 0.030 7.2 0.041 10% 10.0 0.025 5.0 0.011 7% 7.9 0.014 5.7 0.022 8% 9.3 0.022 3.1 0.017 11% 9.2 0.026 3.6 0.023 12% 14.1 0.035 4.0 0.014 9% 8.7 0.021

*Cant: Contribution Whe share of income component in total income (CL&)

INCOME INEQUALlTIES IN CHINA 1955

of the economic reforms and are all comparatively poor. In contrast, the coastal provinces have all a comparatively low sham of basic wage in the total income, and Guangdong, which has taken a lead in economic reforms, stands out as having the lowest percentage share (59.9%). There would seem to be a broad rela- tion between the rank the province in terms of economic reforms and the composition of its urban incomes.

The contribution of (basic) wage to total income inequality (the column labeled “% cob” in Table 5) is between 45% to 65% in 18 out of 28 provinces. The notable exceptions are Hebei and Shanghai with the low values of 37% and 41%, on the one hand, and Shanxi and Ningxia with the high values of 69% and 76%. Basic wage would seem to be responsible for at least around half of income inequality (48% or more) in 20 out of 28 provinces. This finding has some significance because, when the survey was taken (1986), basic wage was mostly determined by a wage schedule carried over from the prereform period. Bonuses, which were reintroduced in the early 198Os, are of special interest because they are associated with the economic reforms and the sham of bonuses in total income has risen steadily. For a majority of provinces (17 out of 28), its share in total income falls in the nar- row range of lO-12%, which is close to the figure of 13% for the employees of the state sector in 1986.23 Qinghai stands out with a very low bonus share of 5%, which may be due to the fact that the province has little industry. At the other extreme is Guangdong, where the bonus share in total income is high 22%. The findings for bonuses are particularly interesting. Apart from Jiangsu, Zhejiang and Guangdong, the contribution of the bonuses to total income inequality does not seem particularly high. There is, however, an important difference between respective effects of basic wage and bonuses on total inequality. In all cases, the contribution of basic wage to inequality is lower than its share in total income. In contrast, in 17 out of 28 provinces the contribution of the bonuses to inequality exceeds bonus share in total income. Broadly whereas basic wage has an equalizing and bonuses a disequalizing in8uence on income inequality. The implication is that the steady rise in the bonus share in total income since 1980,” is likely to have widened urban income inequality.

Turning to “irregular income,” which is a wide category including income from second jobs, commer- cial activities, literary royalties, various types of rewards and hardship allowances its share and contribution to total income inequality varies widely among provinces. It is interesting to note that although the share of irregular income is invariably lower than the bonus share, the first contributes much more inequality than the second, except in Jiangsu and Guangdong. The implication is that urban inequality would rise with an increase in the possibilities of earning irregu-

lar income, which is likely to be the case as the Chinese economies shifts toward a market economy. Thus both the findings about bonuses and irregular income suggest, though do not establish, a trend toward a rise in urban inequality.

(b) Rural areas

The sources of rural income in the data set include farming, nonfarming activities of the household (labeled household activity), employment in manufacturing and construction and miscellaneous. Farming accounts for more than 50% of total income in all provinces except Beijing (Table 5), where rural is largely synonymous with peri-urban. Income from nonfarming activities of households (“household activities”) is the next most important component of rural income in all 10 provinces. While in Beijing its share is as high as 43.6%, the share in Anhui is only 9.9%. The provinces with a relatively high percentage share include Shanxi (27), and Jiangsu (23.8), Henan (23.1%) and Guangdong (23.6). The contribution of nonfarming activities of households to total rural income inequality ranges from 56% in Beijing to 12% in Anhui, and is in all cases higher than its percentage share in total income.

Turning to “manufacturing and construction,” which stands for employment in “town and village enterprises” (IVES), its contribution to rural income inequality is small in all provinces except Jiangsu, where it makes up 7% of total income but accounts for the comparatively high 17% of total income inequality. In Beijing its contribution to inequality is negative. The result for Jiangsu is of some significance because rural industry in the province is extensive and highly developed. Moreover, given the rapid growth of employment in TVEs in most provinces since 1986, the year of the survey, the result for Jiangsu points to the likely trend in rural China.

The notable feature of the result for rural areas is that income from farming in all provinces but Gansu and Anhui contributes substantially less to inequality than its share in total income. In this respect, it plays the same role as basic wages do in urban areas. In contrast, “nonfarming household activities” and “manufacturing and construction” together (all except fartn- ing income), account for over half of income inequality in six out of 10 provinces. Anhui, a comparatively poor province, is interesting in this respect. It derives the highest share of income from farming and has the lowest income inequality. These results would appear to corroborate the common observation in local studies that nonfarming income is more unequally distributed than farming income.25 An important implication of the tinding is that income inequality in rural areas would rise with a shift in labor from farming to nonfarmmg activities.


6. CONCLUSIONS AND THEIR POLICY IMPLICATIONS

The study confirms on the basis of household data the conclusions of studies based on quantile data and local surveys: by international standards income inequality is very low in both rural and urban China, though similar to that in Eastern Europe before the downfall of the command economy.26 Other notable conclusions of the paper are:

- Unlike in most developing economies, income inequality in urban areas is lower than in rural areas (section 3). - Both for urban and rural areas, income inequality at the national level is due predominantly to intraprovincial income inequality; and interprovincial income inequality is more important for rural than urban income inequality (section 4). - In Urban China, both bonus and irregular income, the components of income associated with economic reforms, contribute more to inequality than their share in total income. This suggests an increase in urban inequality with a shift toward a market economy. - In rural China, compared to farming income, nonfarming incomes are more unequally distributed, and, in general, their contribution to total income inequality is greater. The implication is that rural inequality would rise with a shift in labor from farming to nonfarming activities.

Turning to the policy implications of these findings, we concentrate on those areas where income distribution would be a central consideration, such as price

subsidies and taxation. Remarkably low income inequality in urban areas argues against the long- established policy in China providing low-price rations of necessities such as grain to urban residents. As shown by Weitzman (1977) the more equal the distribution of income (adjusted for household size) the weaker the social welfare case for such rations. It is important to note that the argument rests not on average income but on income inequality. This would lend support on welfare grounds to the government policy since 1991 of phasing out prices subsidies in urban areas.

Comparatively high income inequality in rural areas (section 3) suggests that in assessing and design- ing policies we should take into account their distributional impact within rural areas as well as between urban and rural areas. This applies particularly to the government pricing policies for agricultural products and inputs such as fertilizers and electricity, which affect rural households differentially. The result that both in urban and rural areas intraprovincial inequality contributes far more to nationwide inequality than interprovincial inequality (section 4) suggests that province-level schemes can be highly effective in dealing with nationwide income inequality. More- over, it also suggests that interprovincial transfers are much less important than they seem.

The indication that income inequality is likely to rise both in urban and rural areas suggests the need for the widening the net of the personal income tax, which does not apply to most of the labor force, and increas- ing the weight of distributional considerations in set- ting commodity tax ratesz7

NOTES

1. A county is the second tier of local government in rural 11. See Kakwani (1980, pp. 388-389). areas.

12. See Atkinson and Micklewright (1992, p. 112). 2. Details are provided in Howes (1993).

13. See Sundrum (1990, pp. 2627). 3. For details see Fan and Ludlow (1990)

14. See the per capita consumption figures for 1986 in State 4. For details see Howes and Lanjouw (1991) and Statistical Bureau (1988, p. 802).

Lanjouw and Ludlow (1990). 15. See Sundrum (1990, p. 96).

5. See Hussain, Lanjouw and Stem (1991). 16. See Datt and Ravaltion (1990).

6. For a review of literature see Sen (1992). 17. See Sundrum (1990, p. 96).

7. See Deaton (1988). 18. See State Statistical Bureau (1987, p. 115).

8. See Atkinson (1983) and Sen (1973). 19. See Fu et al. (1993).

9. See the Gini coefficients for a large sample of countries in Kakwani (1980, pp. 388-389). 20. See Fields (1980, pp. 101-102).

10. See Atkinson (1983, p. 53). 21. Gn the Theil measure see Fields (1980, pp. 103-104). Anand (1983, pp. 327-332) and Cowell(l977, pp. 55-62).

INCOME INEQUALITIES LN CHINA 1957

22. See, for example, Anand (1983) for a decomposition of 25. See for example Odgaard (1992) and Zhu (199.1). income inequality in Malaysia into intra- and intemthnic income inequality. 26. See Atkinson and Micklewright (1992, p. 112).

23. See State Statistical Bureau (1988, p. 182). 27. For a discussion of the implications of an increase in income inequality on taxes see Hussain and Stem (1992).

24. See State Statistical Bureau (1993, p. 105).

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