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The Decline of Income Inequality in China: Assessments and Explanations*

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The Decline of Income Inequality in China: Assessments and Explanations* The Decline of Income Inequality in China: Assessments and Explanations* Guanghua Wan School of Economics Chongqing Technology and Business University 19 Xuefu Avenue, Nan’an District Chongqing, China, 400067 [email protected] Ting Wu Party School Tianjin Binhai Committee of CPC 540 Jiatai Road, Binhai New Area Tianjin, China, 300450 [email protected] Yan Zhang School of Economics Chongqing Technology and Business University 19 Xuefu Avenue, Nan’an District Chongqing, China, 400067 [email protected] Abstract This paper shows that the trend of worsening income distribution in China has been reversed. We ascertain the robustness of this decline using five nationwide household survey and different inequality indicators. Attempts are then made to uncover the underlying reasons. Major findings include: (1) The decline is largely due to improvement in the distribution of transfer income although its share in the total income is small and diminishing; (2) Occupational income, particularly its component of wage income, plays an important role; and (3) Other drivers include the expansion of the middle-income group, rapid urbanization, and the shrinking disparity within Eastern China. 1. Introduction Inequality is a major socioeconomic issue in China (Wan 2008; Wang, Wan, and Yang 2014). Its consecutive declines were noticed a few years ago (Wan 2013) but have re- ceived little analytical attention since then. The lack of research interest may be caused by uncertainty—whether this decline is part of the usual fluctuations or the beginning of a * The authors gratefully acknowledge financial support from the Natural Science Foundation of China (Project no. 71703088) and Shanghai Pujiang Program (Project no. 17PJC045). Yan Zhang is the corresponding author for this paper. Asian Economic Papers 17:3 © 2018 by the Asian Economic Panel and the Massachusetts Institute of Technology doi:10.1162/ASEP_a_00640 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/asep_a_00640 by guest on 02 October 2021 The Decline of Income Inequality in China: Assessments and Explanations new long-term trend. If one could confirm this trend, it is important to uncover the relevant driving factors. This paper attempts to ascertain the robustness of the declining trend of income inequality by using different inequality measures and different databases. The data sets include the China Health and Nutrition Survey (CHNS); China Household Income Project (CHIP), China General Social Survey (CGSS), China Family Panel Studies (CFPS), and China Health and Retirement Longitudinal Survey (CHARLS). The inequality measures used in- clude the Theil index, the mean log deviation (MLD), the Gini coefficient, and the Atkinson index (Wan 2006). In related work, using CHIP and CFPS data, Kanbur, Wang, and Zhang (2017) confirmed the decline and conducted several decomposition analyses. They found that the urban– rural gap peaked in 2004, the coast–inland disparity peaked in 2009, and the narrowing- down of wage distribution is the major inequality-reducing contributor. The authors merged two data sets, however—CHIP (1995–2007) and CFPS (2010–14)–whose con- sistency is debatable. For example, whereas CHIP consists of fewer than 40 percent of households from Eastern China in its 1995 and 2013 waves, around 50 percent of CFPS observations are from Eastern China in 2010 and 2014 (detailed results shown in Figure 7 later in the paper). As another example, the growth incidence curve (GIC) based on CFPS (2010–14) produces a broadly inverted U-pattern, whereas the GIC based on CHIP (2007– 13) exhibits a reverse S-shape. This data issue might be the cause for their very high Gini estimates for income components (0.583–1.128). Relying on the CHIP data, Zhuang and Li (2016) asserted that factors driving the inequality decline include decreased skill premium, increasing share of labor income, and the falling income gaps between urban–rural areas and across regions. These assertions were made with little analytical evidence. Also using CHIP data, Li (2016) attempted to confirm the trend of inequality decline by estimating the Gini index and the income mobility index, and then plotting the GIC. His findings were inconclusive as six different disparity indicators he used showed different trends. For example, the top–bottom 10 percent income ratio, the business versus wage income ratio, and the labor share demonstrate inequality increase, whereas the growth of income by groups, the Gini coefficient, and mobility estimates show inequality decline. In addition, several papers mentioned but did not analyze the recent inequality declines. These include Zhang (2015), Li et al. (2016), Fan, Kanbur, and Zhang (2011), Chan, Zhou, and Pan (2014), and Lee (2013). Our paper departs from the existing literature in the following aspects. First, we fo- cus on explanations that are lacking in the literature by conducting extensive analyses 116 Asian Economic Papers Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/asep_a_00640 by guest on 02 October 2021 The Decline of Income Inequality in China: Assessments and Explanations and decompositions. Second, as many data sources as possible are used to ensure ro- bustness of results and conclusions. Third, fresh and important findings are generated. For example, we find that transfer income is the main contributor to overall inequal- ity decline, whereas Kanbur, Wang, and Zhang (2017) concluded that wage income is more important. The rest of the paper is organized as follows. Section 2 assesses the robustness of the declin- ing trend of income inequality. Section 3 conducts preliminary analyses to uncover reasons of the declining inequality, using growth incidence curves. To further identify reasons or factors driving the recent inequality decline, Section 4 undertakes various decompositions. Finally, Section 5 concludes. 2. Is income inequality in China really declining? This section presents inequality estimates using different indicators as well as differ- ent databases. As is known, different inequality indicators are sensitive to different seg- ments of the Lorenz curve and different data sets could generate different findings. This section will also conduct a stochastic dominance analysis by plotting and comparing Lorenz curves. 2.1 Estimating different inequality indicators Based on the five data sets, Figure 1 depicts inequality trends using different inequality indicators. The results show that although the turning point differs slightly, the increas- ing trend since the mid 1980s peaked between 2006 and 2010 irrespective of data set and inequality indicator used except when using the CFPS data. In the latter case, the esti- mates of the MLD show no turning point. And the estimates of the Atkinson index indi- cate that the turning point occurred in 2012, later than the other indicators show. This is possibly because the MLD is more sensitive to low-income observations and CFPS collects more low-income observations than other databases (see more details in Section 2.2 on the Lorenz curve). The solid lines in Figure 1 correspond to the CHNS database, which was collected every two years from 1989 to 2011. They all exhibit an inverted U-shape, with inequality peaking in 2006. The dotted lines correspond to the CHIP database, which was collected approximately every five years from 1990 to 2013. They show that inequality peaked in 2007. The Gini estimate increased from 0.32 in 1990 to 0.50 in 2007. Other measures show declines from 1994 to 2001, followed by increases from 2002 to 2007 before declining again. For example, the Gini estimates decreased successively from 0.5 in 2007 to 0.42 in 2013. 117 Asian Economic Papers Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/asep_a_00640 by guest on 02 October 2021 The Decline of Income Inequality in China: Assessments and Explanations Figure 1. Income inequality in China: 1989–2014 Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13). The lines with triangles correspond to the CFPS database. In this case, the Gini and Theil estimates declined steadily between 2010 and 2014, the MLD estimates rise, and the Atkin- son estimates increased from 2010 to 2012 and then declined. The dashed lines with dots or diamonds show results from the databases of CGSS and CHARLS, respectively. They all share a similar trend of declining inequality. This is due to the late start of these two surveys, starting after China’s inequality began to decline. 2.2 Lorenz curve To confirm the inverted U pattern seen in Section 2.1, and to explore why CFPS data give inconsistent results, we plot various Lorenz curves. In Figure 2, the solid Lorenz curves correspond to the year when the relevant survey began, the short-dashed curves correspond to the year when inequality estimates peaked, and the long-dashed curves 118 Asian Economic Papers Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/asep_a_00640 by guest on 02 October 2021 The Decline of Income Inequality in China: Assessments and Explanations Figure 2. Lorenz curves based on different data, 1989–2014 Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13). correspond to the latest year. When using the CFPS data, the three curves correspond to 2010, 2012 and 2014, respectively. Several findings are worth mentioning. First, for the CHNS, CHIP, and CGSS data, the short-dashed curves (representing the peak of inequality) all lie in the bottom-right, con- firming the peaking of inequality in the relevant year. The declines afterwards primarily stem from the declining income shares of high-income groups. In the earlier phase of rising inequality, the income shares of high-income groups increased, the share of middle-income groups dropped, and the share of low-income groups did not change. Second, for the CFPS data, the Lorenz curves for 2010 and 2012 largely overlap, indicating little change in inequality over these two years.
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