توضیح تغییرات توزیع درآمد در چین

China has experienced one of the most remarkable increases in inequality over the last decade: the Gini coefficient increasing from 25.7 in 1984 to 37.8 in 1992. Using the recent developments in the theory of income distribution [J. Polit. Econ. 101 (1993) 274; Rev. Econ. Stud. 60 (1993) 35.] and a new panel data set about Chinese provincial-urban-level income inequality, this paper finds that inequality increased with the reduction of the share of state-owned enterprises (SOEs) in GDP, high inflation, growth, and (less significantly) the increasing exposure to foreign trade. We also find some evidence for the Director's Law: income redistribution tends to shift resources from the rich and the poor to the middle class. We do not find schooling and urbanization to be a significant explanatory factor.

Among developed countries, the UK has experienced an unparalleled rise in income in the 1980s, as Atkinson (1997) noticed: In the United States, the Gini coefficient of inequality for household income rose between 1968 and 1992 by three and a half percentage points … This is a significant increase, but if you want to see a big increase then it is to the United Kingdom that one has to look. Between 1977 and 1991, the United Kingdom Gini coefficient rose by 10 percentage points. Among developing countries, China shows a similar trend in rising income inequality. Starting from a relatively low Gini coefficient of household income of 25.7 on a scale of 100 in 1984, China reached a relatively high Gini coefficient of income of 37.8 in 1992. Over a short period of 8 years, the Gini coefficient in China increased 12 percentage points, and the rising trend has been continuing to the present. To illustrate the significance and uniqueness of the Chinese case, note that the Gini coefficient in India remained almost constant for 40 years (1951–1992) with a mean of 32.6 and a standard deviation of 2.0 (see Li & Zou, 1998 and Li et al., 1998, for more details on the evidence of intertemporal stability in the Gini coefficients for over 40 countries). The Chinese case is even more interesting when we consider its spectacular output growth since the economic reforms initiated in 1978. Over a period of 16 years (1978–1994), the average growth rate in terms of real GDP was 9.9%. This positive correlation between income growth and inequality immediately throws doubt on the significant negative association between income growth and inequality found in Alesina and Rodrik (1994) and Persson and Tabellini (1994) based on both theory and a cross-section of international data. This positive correlation between growth and inequality also contradicts an influential World Bank study The East Asian Miracle(World Bank, 1993), which found economic growth to be associated with low and declining levels of inequality for the eight East Asian countries excluding mainland China. On the other hand, this positive correlation seems to support the age-old Kuznets (1955) inverted U-curve and the more complicated theoretical relationship between economic growth and income inequality in Greenwood and Jovanovic (1990), Banerjee and Newman (1993), Galor and Zeira (1993), Perotti (1993), and Benabou (1996), among many others (see Benabou, 1996 and Atkinson, 1997, for a survey and further developments). What accounts for the rapid rise in income inequality in China? Recent advances in income distribution theory mentioned above provide plenty of channels. While many of these theoretical models have confronted cross-country data, very few examine individual countries. In this paper, we follow Banerjee and Newman (1993), Galor and Zeira (1993), and Atkinson (1997), and apply their theoretical insights to the case of China. Since 1978, China's experience provides a fertile field for testing the determinants of changes in income inequality explored in many recent theoretical contributions. We broaden the Benerjee–Newman and Galor–Zeira framework, and look at the role played by output growth, increasing exposure to international trade, urbanization, taxation and government spending, inflation, human capital formation, geography, and especially the sectoral structure of the economy (the share of state enterprises) in determining the changes of income inequality. Theoretical considerations of all these factors will be presented in Section 2. Section 3 provides a brief description of our provincial panel data on urban income distribution and all explanatory variables in our empirical study. We provide detailed regression analysis on the determinants of income inequality in Chinese provinces in 4 and 5. In Section 6, we summarize our findings and offer concluding remarks on the direction for further work.

Examining the experiences of 29 provinces over 11 years, we have focused on how the change of income distribution (and the income share of the rich, the poor, and the middle class) had been affected by the changing structure of economy (higher growth rates, increasing exposure to foreign trade), the role of the state (the reduction of SOE sector, and more decentralized fiscal expenditure), and increasing urbanization. We have found that inequality and Q5 increased with, and Q1 decreased with, the reduction of SOE share, higher inflation and growth rates, and (less significantly) the increasing extent of foreign trade. We have also found some evidence for Director's Law (Stigler, 1970): income redistribution through government spending tends to shift resources from the rich and the poor to the middle class, as witnessed by the positive sign on Q34, but generally negative signs of Q5 and Q1. Intriguingly, provinces farther from the coast had larger inequality, an observation probably reflecting the consequences of greater imperfection of capital market. Schooling and increasing urbanization did not play a significant role in the increasing income inequality during this period. The findings of this paper answer some questions, but elicits more questions. Is it inevitable that growth and openness will increase income inequality? Will the poor necessarily be the loser of the growth gains? Under what circumstances do the above scenarios arise? If the scenario is true, what can governments do to reduce the suffering of the poor during growth and global economic integration processes? In addition, given the nature of this data, and the short time-span we have, some questions evade answer. What determines, for instance, the level differences across different provinces? Further research is needed to answer these questions.