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.

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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

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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.

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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

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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. The situation changed in 2014, however. Although the income shares rose for the middle- and upper- income groups (the long- dashed curves for these two groups moved closer to the 45-degree diagonal line), those

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for the low-income groups declined slightly. Overall, the level of inequality in 2014 was lower than in the earlier two years.

Third, for the CHARLS data, the Lorenz curve for 2013 moved slightly closer to the diago- nal line than the 2011 curve, indicating an improvement in income distribution.

3. Preliminary analytical results using the GIC tool

This section examines changes in income shares of different income groups using the growth incidence curve (GIC). Proposed by Ravallion and Chen (2003), the GIC is widely applied to gain insights into changes in income distribution (Lin 2003) or pro-poor growth (Chen and Wang 2001; Honohan 2008; Goh, Luo, and Zhu 2009; Huang and Quibria 2013). The GIC is defined as

= yt (p) − , gt (p) 1 (1) yt−1(p)

where the subscript t indicates year, g(p) is the rate of income growth for the pth percentile population, and y denotes income. If data span multiple periods, the average income growth rate can be calculated as

  1/n = yn(p) − , gavg(p) 1 (2) y0(p)

where n is the number of intertemporal periods, and the subscript 0 indicates the base pe- riod. Insights can be gained by comparing income growth for different income groups (rep- resented by the percentile of the population) on the GIC. The higher the income growth rate, the greater the rise in the income share of the relevant group, and vice versa.

3.1 GIC of total income We divide the sample into 12 subsamples: the poorest 5 percent, 10 percent, 20 percent, 30 percent, then the middle group of 40 percent, 50 percent, 60 percent, 70 percent, the richer 80 percent, 90 percent and 95 percent, and the top 5 percent. Figure 3 shows the GICs for the inequality rise phase (left) and the decline phase (right).

The results from the CHNS data show that: (1) In the inequality-rising phase of 1989–2006, the GIC sloped upwards monotonically, implying that the richer the household, the higher the income growth. The richest 5 percent achieved an average rate of income growth of 6.75 percent, whereas this rate for the poorest 5 percent was only 2.09 percent, which is 2.45 percentage points lower than the average of the whole sample. (2) In the inequality- declining phase of 2006–11, the GIC shows an inverted U-shape, meaning that the middle

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Figure 3. GICs of total income, by different data sets

Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13).

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class1 gained more than others, at 7.28 percent, significantly higher than the sample aver- age. The top 5 percent achieved the lowest growth, averaging 3.33 percent per annum, and the growth rate was 3.7 percent for the poorest 5 percent. A growing middle class appears to be underlying the improvement in income distribution.

The results from the CHIP data show that: (1) In the phase of rising inequality (1995–2007), the GIC is like that of CHNS. The wealthiest 5 percent recorded an average income growth of 7.09 percent, 1.47 times the overall average growth of 4.83 percent. Whereas the top 15 percent of households experienced above-average growth, the remaining 85 percent ex- perienced below-average growth. Those located in the bottom 15 percent experienced negative growth, averaging –6.83 percent. This phase can be characterized as a period of “pro-rich” growth. (2) In the period of inequality decline (2007–13), the sample average growth rate was 5.54 percent. Different from the results based on CHNS, the decreasing trend of the GIC is monotonic: The richer the household, the lower the income growth. For example, the richest 5 percent grew annually by only 2.12 percent, whereas this rate is 6.98 percent for the middle-income group, slightly higher than CHNS data show. The declining phase demonstrates the feature of typical “pro-poor” growth.

The results from the CGSS data show that: (1) In the phase of worsening income distri- bution (2006–08), the GIC displays an increasing trend. Income for the richest 5 percent grew at 2.69 percent, whereas the bottom 85 percent reported lower than average growth. As with the CHIP data, the growth rate of low- and middle-income families is negative, with about 65 percent of households experiencing negative income growth. This may be related to the adverse impact of the global economic crisis on the middle class, whose in- come dropped by 1.26 percent, as indicated by the notch on the GIC. (2) The GIC fluctuates slightly during the period of inequality decline (2008–13). The income of the middle-class grew by 4.83 percent, relatively higher than the average of other groups (4.29 percent). This is like the results based on CHNS, in which the growth of middle-income groups is the main driver for improving distribution. Unlike what other data show, however, the richest 15 percent in CGSS enjoyed a 4.29 percent growth rate, which is identical to the sample average.

The results from the CFPS data show that the growth rate of middle-class was significantly higher (6.44 percent) than that of other groups during the period of inequality decline (2010–14). This feature is common to CHNS and CGSS, confirming the role of an expanding middle-class in improving incomes distribution. The bottom 5 percent experienced nega- tive growth, however, which could explain why the MLD based on CFPS rose.

1 The middle class or middle-income group refers to households whose average income falls into the range of the 25th to 85th percentile of the whole sample.

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The results based on the CHARLS data are almost identical to those based on CHIP (2007– 13). The growth rate of the poor is much higher than that of the middle- and high-income groups, exhibiting “pro-poor” feature.

To sum up, a worsening income distribution can be largely accounted for by the relative losses of the poor in sharing the growth dividend in the earlier period. And the recent de- cline in inequality is mainly attributable to the expansion of the middle-class. One worri- some finding is that even in the period of inequality decline, the growth rate of the poor remains low, and often lower than that of the wealthy.

3.2 GICs of income components This section serves two purposes. First, it helps identify the role of transfer income in af- fecting income distribution. The transfer income mainly comprises government price sub- sidies, and provision of public pension and social assistance. Second, it may shed light on whether China has reached the Lewis turning point by examining the distributional trend of labor income.

According to Lewis (1954), in the early period of takeoff, there is unlimited supply of surplus rural labor to the urban factor market, meaning stagnant labor income or wage growth for rural-to-urban migrants. Because the native urbanites are likely to en- joy the usual wage growth, income disparities in the urban sector tend to enlarge. Af- ter reaching the Lewis turning point, disparities become stable or smaller due to the depletion of the surplus labor in the rural areas. Note, however, that labor income is an evolving concept in transition economies like China and it contains receipts other than wages. Therefore, here we examine distributions of both wage and, more broadly, occupational income.2

Different surveys adopt different income definitions. It is thus necessary to adjust and merge data to minimize inconsistency of data from different surveys. In this paper, we use CHNS as the benchmark and classify total income from CHNS into four components: wage, business income, transfer income, and other income. This classification is in line with that of CHIP (1995–2013) and CFPS (2010–14). CGSS (2006–13) does not provide ob- servations on component incomes. They only distinguish earnings from other incomes. CHARLS (2011–13) categorizes incomes into quite a few components, however observa- tions for component income are mostly missing. Thus, we group these income compo- nents into occupational income and other incomes, where occupational income is the sum

2 Occupational income consists of wage and business income. Wage refers to the remuneration paid to employees for work performed or service provided. Business income refers to the gain obtained from business activities.

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of wage and business income. Figures 4 and 5 plot the GICs of component incomes from CHNS and CHIP, respectively.

The following findings are discernable from Figure 4: (1) Inequality of occupational in- come, including wage and business income, declined. Its growth for middle- and lower- income groups was significantly higher than that for the wealthy. As the most important component of income in China (see Figure 6) with its share growing over time, improving distribution of occupational income plays a vital role in driving the trend of overall income inequality. (2) The distribution of occupational income improved slightly in the period of inequality decline (2006–11) when the middle-class gained more than others. This could be the main reason for the drop in the overall income inequality. (3) The distribution of trans- fer income significantly improved. The growth rate of government transfers is much higher for the poor and reduced considerably for the rich. (4) Figure 6 shows a small share of other income that naturally exerts little effect on the overall income distribution. From Figure 4, in the phase of rising inequality, the growth rates of other income for different groups are roughly the same. In the phase of inequality decline, except for the bottom 15 percent, who enjoyed slightly higher growth of other income, the remaining 85 percent see little differ- ence in their growth rates of other income.

Figure 5 displays the GIC for income components based on CHIP database. The results are broadly consistent with those from Figure 4, indicating that the improvement of occu- pational income distribution is the main driver for the overall inequality decline. More- over, both CHNS and CHIP show that the transfer income becomes more pro-poor in the phase of inequality decline, although the shapes of GICs differ. The growth rate of trans- fer income decreases monotonically with income quantiles in CHNS, whereas in CHIP it appears higher for middle classes than other groups.

4. Decomposing income inequality and its changes

This section uses the MLD index and the Gini coefficient to explore sources of inequality and its changes. Suppose the sample can be divided into G population subgroups; the MLD index can then be expressed as:

  G G = + pg , MLD pgMLDg pgLog v (3) g g=1 g=1

v where pg is the population share of subgroup g, g is the corresponding income share, G MLDg is MLD index within group g. g=1 pgMLDg is the sum of income disparity within G pg each group, and = p Log( ) is the income inequality between subgroups. The former is g 1 g vg often termed within-group inequality and the latter between-group inequality.

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Figure 4. GIC of component income: CHNS

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Figure 5. GIC of component income: CHIP

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Figure 6. Composition of total income

Source: Authors’ calculation based on CHNS (1989–2011) and CHIP (1995–2013).

According to Schauer (1996), a change in the MLD index can be expressed as:

        = + + pg + pg MLD pgMLDg pg MLDg pgLog v pg log v g g      ≈ + + λ − λ + v − pgMLDg pg MLDg k log ( k ) pg g pg log ug

contribution  of mobility  contribution of changes of within−group inequality      = + λ − λ + MLDg k log ( k ) pg pgMLDg

− contribution of changes of  between group inequality  + v − , g pg log ug (4)

Where the overbar indicates the average of the relevant variable in the beginning and end v periods. λ = g ; u is the mean income for group g. k pg g

Conversely, suppose total income consists of K components, the Gini coefficient G can be expressed as (Wan 1998):

K = , G SiCi (5) i=1

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where Si is the share of the i-th component in total income, and Ci denotes its concentration index.

A change in the Gini coefficient can be decomposed based on equation (5):

K K = − G Sit+1Cit+1 SitCit i=1 i=1

K K = + , Cit Si Sit Ci (6) i=1 i=1

= − , = − where Si Sit+1 Sit Ci Cit+1 Cit . Equation (6) indicates that a change in the Gini K coefficient can be decomposed into two parts: (1) i=1 Cit Si denotes the contribution K by changes in income shares, which can be called the structural effect;and(2) i=1 Sit Ci indicates the contribution by changes in the concentration index, which can be called the concentration effect.

4.1 Inequality decomposition by population subgroups Equation (3) will be used to decompose inequality in this subsection. We set the poorest and the richest 5 percent as the first and last subgroup. The remaining sample is equally divided into nine subgroups. Table 1 summarizes the results.

Focusing on the second and third rows from the bottom and the results under the column heading of Relative Contribution (percent), the contribution by between-group inequality dominates, accounting for no less than 90 percent of the overall inequality. This implies that gaps between different income classes are significant in China and that stratification is severe. This component shows an inverted U-pattern over time.

As far as contributions by different within-group disparities are concerned, the poorest 25 percent, and especially the poorest 5 percent, contribute significantly more than other subgroups. This means that those at the bottom of China’s income ladder not only face poverty, but also suffer from more severe within-group gaps. In contrast, the middle-class contributes less to the overall inequality and the contribution is stable over time. There- fore, promoting the expansion of the middle-class represents an important policy option to fight high income inequality. Second to the poorest, the top 15 percent subgroup also has a relatively large within-group disparity.

Table 2 reports decomposition results of changes of income inequality, using equation (4). Like Table 1, the between-group component dominates changes in the total income

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Table 1. Decomposition of MLD, by income group

CHNS CHIP Absolute Relative Absolute Relative contribution contribution (%) contribution contribution (%) Groups 1989 2006 2011 1989 2006 2011 1995 2007 2013 1995 2007 2013 [0,5] 0.008 0.017 0.015 2.62 3.06 2.98 0.015 0.004 0.003 4.18 0.99 1.08 (5,15] 0.002 0.003 0.004 0.65 0.52 0.90 0.006 0.014 0.001 1.65 3.36 0.35 (15,25] 0.001 0.001 0.001 0.18 0.12 0.22 0.001 0.001 0.000 0.40 0.15 0.11 (25,35] 0.000 0.001 0.000 0.12 0.10 0.08 0.000 0.000 0.000 0.12 0.06 0.05 (35,45] 0.000 0.000 0.000 0.07 0.05 0.06 0.000 0.000 0.000 0.09 0.04 0.14 (45,55] 0.000 0.000 0.000 0.05 0.06 0.05 0.000 0.000 0.000 0.08 0.04 0.06 (55,65] 0.000 0.000 0.000 0.04 0.05 0.05 0.000 0.000 0.000 0.07 0.05 0.06 (65,75] 0.000 0.000 0.000 0.05 0.06 0.05 0.000 0.000 0.000 0.08 0.08 0.06 (75,85] 0.000 0.000 0.000 0.05 0.08 0.05 0.000 0.001 0.000 0.07 0.16 0.10 (85,95] 0.000 0.001 0.001 0.15 0.19 0.16 0.001 0.003 0.001 0.22 0.62 0.23 (95,100] 0.003 0.007 0.005 1.10 1.24 1.02 0.005 0.004 0.004 1.41 1.02 1.27 Within 0.016 0.030 0.028 5.07 5.52 5.64 0.030 0.027 0.011 8.36 6.56 3.50 Between 0.292 0.517 0.468 94.93 94.48 94.36 0.328 0.382 0.309 91.64 93.44 96.50 Total 0.308 0.548 0.495 100 100 100 0.358 0.409 0.321 100 100 100 CGSS CFPS Absolute Relative Absolute Relative contribution contribution (%) contribution contribution (%) Groups 2006 2008 2013 2006 2008 2013 2010 2014 2010 2014 [0,5] 0.015 0.003 0.003 4.03 0.68 0.76 0.000 0.002 0.05 0.31 (5,15] 0.006 0.005 0.001 1.59 0.95 0.25 0.001 0.002 0.24 0.45 (15,25] 0.001 0.001 0.000 0.39 0.12 0.08 0.001 0.003 0.12 0.50 (25,35] 0.000 0.000 0.000 0.12 0.06 0.04 0.000 0.001 0.09 0.25 (35,45] 0.000 0.000 0.000 0.09 0.02 0.10 0.000 0.000 0.04 0.07 (45,55] 0.000 0.000 0.000 0.07 0.01 0.04 0.000 0.000 0.05 0.04 (55,65] 0.000 0.000 0.000 0.07 0.03 0.04 0.000 0.000 0.08 0.04 (65,75] 0.000 0.000 0.000 0.07 0.07 0.04 0.000 0.000 0.07 0.06 (75,85] 0.000 0.000 0.000 0.07 0.06 0.07 0.000 0.000 0.11 0.06 (85,95] 0.001 0.002 0.001 0.21 0.33 0.16 0.001 0.001 0.30 0.15 (95,100] 0.005 0.013 0.004 1.36 2.54 0.90 0.005 0.003 1.16 0.54 Within 0.030 0.024 0.011 8.06 4.88 2.48 0.011 0.012 2.30 2.48 Between 0.341 0.474 0.442 91.94 95.12 97.52 0.447 0.488 97.70 97.52 Total 0.371 0.498 0.453 100 100 100 0.457 0.501 100 100 CHARLS Absolute Relative contribution contribution (%) Groups 2011 2013 2011 2013 [0,5] 0.000 0.000 0.01 0.02 (5,15] 0.000 0.004 0.07 0.58 (15,25] 0.003 0.000 0.45 0.08 (25,35] 0.002 0.000 0.30 0.08 (35,45] 0.001 0.000 0.07 0.04 (45,55] 0.000 0.000 0.02 0.06 (55,65] 0.000 0.000 0.02 0.03 (65,75] 0.000 0.000 0.05 0.06 (75,85] 0.000 0.000 0.03 0.07 (85,95] 0.002 0.004 0.26 0.60 (95,100] 0.008 0.002 1.06 0.37 Within 0.017 0.012 2.34 1.96 Between 0.708 0.613 97.66 98.04 Total 0.725 0.625 100 100

Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13).

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Table 2. Decomposition of changes in the MLD, by income group

Absolute contribution Relative contribution (%) Data source Period Within group Between group Within group Between group CHNS 1989–2006 0.031 0.209 12.88 87.12 2006–2011 −0.005 −0.048 9.18 90.82 CHIP 1995–2007 −0.008 0.059 −16.34 116.34 2007–2013 −0.026 −0.062 29.14 70.86 CGSS 2006–2008 0.014 0.113 11.03 88.97 2008–2013 0.103 −0.148 −229.33 329.33 CFPS 2010–2014 0.005 0.040 10.23 89.77 CHARLS 2011–2013 −0.003 −0.097 2.69 97.31

Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13).

inequality.3 Specifically, during the phase of rising inequality, its contribution amounts to at least 87 percent. In the phase of inequality decline, its contribution is also dominant. Over time, however, the within-group contribution declined based on the results from all datasets other than CGSS.

4.2 Inequality decomposition by regions The sample can be divided into east, central, and west regions to analyze the spa- tial composition of income inequality, showing the contributions of within- and between-region disparities.

The geographic coverage differs for different surveys: (1) CHNS covered eight provinces in 1989: Liaoning, Heilongjiang, Jiangsu, Shandong Henan, Hubei, Hunan, Guangxi, and Guizhou. Heilongjiang was added in 2007 and Beijing, Shanghai, and Chongqing were added in 2011. It seems East and West China are undersampled. (2) CHIP (1995–2013) covers more provinces. In 1995, it covered 19 provinces: Beijing, Hebei, Shanxi, Liaoning, Jilin, Jiangsu, Zhejiang, Anhui, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Sichuan, Guizhou, Yunnan, Shaanxi, and Gansu. In 2007, it covered 17 provinces, with the addition of Shanghai, Chongqing, and Fujian, and the deletion of Jilin, Jiangxi, Shan- dong, Guizhou, and Shaanxi. In 2013, it covered 14 provinces: Beijing, Shanxi, Liaoning, Jiangsu, Anhui, Shandong, Henan, Hubei, Hunan, Guangdong, Chongqing, Sichuan, Yun- nan, and Gansu. (3) CGSS (2006–08) covered 28 provinces: Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Ningxia, and Xinjiang. Inner Mongolia, Hainan, and Qinghai were added in 2013. (4) CFPS (2010–14) covered 25 provinces: Beijing, Tianjin, Hebei, Shandong, Shanxi, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Anhui, Zhe- jiang, Fujian, Guangdong, Guangxi, Henan, Jiangxi, Hubei, Sichuan, Yunnan, Guizhou, Hunan, Shaanxi, Gansu, and Chongqing. (5) CHARLS (2011–13) covered 28 provinces:

3 Theoretically speaking, the contributions attributable to population mobility in this context sum to nil and will not be discussed here.

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Figure 7. Sample composition, by region and survey

Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13).

Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shanghai, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Zhuang, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, and Ningxia.

Following the National Bureau of Statistics of China, East China refers to Beijing, Tian- jin, Hebei, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan. The central region includes Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan. West China includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. Figure 7 shows the proportion of households in the three regions in the starting year, inequality-peaking year, and the ending year.

To avoid sampling bias, we use population shares of different regions in China’s total pop- ulation, not the population shares in the survey data, as weights in computing and decom- posing inequality. Table 3 reports the decomposition results, which, together with Figure 8, indicate that the within-region inequality is the primary contributor to the overall inequal- ity. It drives both the rises and declines of China’s inequality.

Focusing on the within-region contributions, the following points are noteworthy: (1) From 1989 to 2014, disparities within East China contributed the most to the overall disparity,

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Table 3. Decomposition of income inequality, by region

CHNS CHIP Absolute Relative Absolute Relative contribution contribution (%) contribution contribution (%) Region 1989 2006 2011 1989 2006 2011 1995 2007 2013 1995 2007 2013 East 0.126 0.220 0.173 40.98 40.11 34.99 0.191 0.191 0.134 53.43 46.81 41.92 Central 0.088 0.168 0.151 28.58 30.69 30.50 0.069 0.092 0.080 19.31 22.60 25.06 West 0.087 0.131 0.144 28.09 23.90 29.02 0.095 0.101 0.083 26.59 24.77 25.91 Within 0.301 0.519 0.468 97.65 94.70 94.50 0.356 0.385 0.298 99.34 94.18 92.89 Between 0.007 0.029 0.027 2.35 5.30 5.50 0.002 0.024 0.023 0.66 5.82 7.11 Total 0.308 0.548 0.495 100 100 100 0.358 0.409 0.321 100 100 100 CGSS CFPS Absolute Relative Absolute Relative contribution contribution (%) contribution contribution (%) Region 2006 2008 2013 2006 2008 2013 2010 2014 2010 2014 East 0.158 0.185 0.164 42.61 37.14 36.27 0.208 0.251 45.53 50.22 Central 0.095 0.127 0.118 25.59 25.52 25.98 0.106 0.113 23.15 22.63 West 0.089 0.143 0.130 24.02 28.73 28.63 0.129 0.115 28.29 22.89 Within 0.342 0.455 0.412 92.22 91.40 90.88 0.443 0.479 96.97 95.74 Between 0.029 0.043 0.041 7.78 8.60 9.12 0.014 0.021 3.03 4.26 Total 0.371 0.498 0.453 100 100 100 0.457 0.501 100 100 CHARLS Absolute Relative contribution contribution (%) Region 2011 2013 2011 2013 East 0.348 0.272 48.05 43.49 Central 0.148 0.159 20.43 25.36 West 0.213 0.186 29.33 29.78 Within 0.709 0.616 97.80 98.63 Between 0.016 0.009 2.20 1.37 Total 0.725 0.625 100 100

Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13).

amounting to 33 percent to 57 percent. This contribution rose during the inequality-rising phase when CHNS, CHIP, and CGSS are used. During the phase of inequality decline, its contribution decreased when CHNS, CHIP, CGSS, and CHARLS (not CFPS) are used. (2) Disparities within Central China seldom changed from 1989 to 2014. Results from CHNS, CHIP, CGSS, and CFPS show that the direction of inequality change within Central China is consistent with the that of overall inequality. Based on CHARLS data, it rose dur- ing the phase of inequality decline, and its contribution to the overall inequality increased significantly. (3) Disparities within West China are considerably lower than those in East China during 1989–2014, and the changes over time are relatively small.

What about inequality changes? Table 4 summarizes the decomposition results, which are mixed or inconsistent, depending on whether the data set comes from follow-up or non–follow-up surveys. Specifically, the contribution of mobility is smaller when using follow-up surveys (CHNS, CFPS, and CHARLS) relative to that when using non–follow-up surveys (CHIP and CGSS). This could be caused by the changes in the survey samples for CHIP and CGSS. In what follows, we will only use data from follow-up surveys to conduct decomposition analysis.

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Figure 8. Decomposition of income inequality: By region

Source: Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–2014), CGSS (2006–13), CHARLS (2011–13).

Table 4. Changes in income inequality: Decomposition by regions

Absolute contribution Relative contribution (%) Data Within Between Within Between source Period Mobility group group Mobility group group CHNS 1989–2006 0.0077 0.2403 −0.0080 3.21 100.14 −3.35 2006–2011 −0.0020 −0.0598 0.0087 3.72 112.75 −16.47 CHIP 1995–2007 0.0206 0.0271 0.0033 40.37 53.09 6.54 2007–2013 −0.0212 −0.0669 0.0001 24.14 76.02 −0.17 CGSS 2006–2008 −0.0237 0.1394 0.0112 −18.64 109.79 8.85 2008–2013 0.0186 −0.0655 0.0019 −41.44 145.64 −4.20 CFPS 2010–2014 0.0014 0.0423 0.0003 3.27 96.14 0.58 CHARLS 2011–2013 0.0008 −0.0892 −0.0116 −0.78 89.19 11.59

Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13).

The results in Table 4 show that irrespective of the phases of inequality change, the within- region contributions to inequality changes dominate. Leaving aside the results based on CHARLS, migration aggravates inequality. In the phase of inequality decline, the between- region contribution rose when using CHNS but decreased when using CHARLS.

4.3 Rural–urban decomposition A special case of spatial inequality is the rural–urban divide, for which the decomposi- tion results are reported in Table 5. We find that: (1) the between contributions are signifi- cantly smaller than earlier estimates (Wan 2007). For example, based on CHNS and CFPS,

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Table 5. Urban–rural decomposition of income inequality

CHNS CHIP Absolute Relative Absolute Relative contribution contribution (%) contribution contribution (%) Group 1989 2006 2011 1989 2006 2011 1995 2007 2013 1995 2007 2013 Urban 0.060 0.148 0.163 19.43 27.08 32.85 0.074 0.039 0.065 20.72 9.43 20.37 Rural 0.225 0.370 0.303 72.98 67.56 61.20 0.198 0.166 0.159 55.34 40.51 49.64 Within 0.285 0.519 0.466 92.41 94.64 94.04 0.272 0.204 0.225 76.05 49.94 70.01 Between 0.023 0.029 0.029 7.59 5.36 5.96 0.086 0.205 0.096 23.95 50.06 29.99 Total 0.308 0.548 0.495 100 100 100 0.358 0.409 0.321 100 100 100 CGSS CFPS Absolute Relative Absolute Relative contribution contribution (%) contribution contribution (%) Group 2006 2008 2013 2006 2008 2013 2010 2014 2010 2014 Urban 0.158 0.237 0.207 42.64 47.57 45.69 0.212 0.289 46.40 57.67 Rural 0.137 0.165 0.171 36.77 33.10 37.73 0.236 0.195 51.72 38.93 Within 0.295 0.402 0.378 79.41 80.66 83.42 0.449 0.484 98.12 96.59 Between 0.076 0.096 0.075 20.59 19.34 16.58 0.009 0.017 1.88 3.41 Total 0.371 0.498 0.453 100 100 100 0.457 0.501 100 100 CHARLS Absolute Relative contribution contribution (%) Group 2011 2013 2011 2013 Urban 0.205 0.309 28.32 49.50 Rural 0.441 0.288 60.82 46.07 Within 0.646 0.597 89.14 95.57 Between 0.079 0.028 10.86 4.43 Total 0.725 0.625 100 100

Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13).

it was less than 10 percent. Using CHIP, the contribution rose to 50.06 percent in 2007 but dropped to 29.99 percent in 2013. Except when using CFPS, the between contribution ex- hibits an inverted U-shape over time. (2) Disparity within rural China, according to CHNS and CHIP, is more serious than urban inequality, which is consistent with the existing liter- ature (Li 1999; Wan 2013), although the opposite can be concluded when using CGSS and CFPS. In the latter case, urban inequality is the most important contributor to total inequal- ity. (3) Different data sets produce different results regarding changes in within-urban inequality and within-rural inequality.

Table 6 reports the urban–rural decomposition results for changes in inequality. Sev- eral points are worth noting. First, except CGSS, the impact of urbanization4 on income inequality, as represented by the mobility component, is consistent over different data sets. Based on CHNS, CFPS, and CHARLS, in the phase of declining inequality, urbaniza- tion helps reduce inequality by 9.7 percent, 16.69 percent, and 27.5 percent, respectively. Based on CHIP, urbanization is the most significant contributor to inequality decline, with a contribution as high as 150.9 percent. Second, based on CHNS, CGSS, and CFPS, changes of within-inequality contribute the most to the overall changes of inequality,

4 The urbanization process leads to the increase of the proportion of urban residents.

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Table 6. Urban–rural decomposition of changes of income inequality

Absolute contribution Relative contribution (%) Data Within Between Within Between source Period Mobility group group Mobility group group CHNS 1989–2006 0.004 0.232 0.004 1.48 96.74 1.78 2006–2011 −0.005 −0.047 −0.001 9.72 89.27 1.02 CHIP 1995–2007 0.017 −0.101 0.136 32.98 −198.96 265.97 2007–2013 −0.133 0.0108 0.0342 150.90 −12.22 −38.68 CGSS 2006–2008 −0.2552 0.3097 0.0725 −200.93 243.86 57.07 2008–2013 0.0005 −0.0314 −0.0141 −1.08 69.81 31.27 CFPS 2010–2014 0.0073 0.0327 0.0039 16.69 74.40 8.91 CHARLS 2011–2013 −0.0275 0.0066 −0.0791 27.50 −6.57 79.07

Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13).

Table 7. Shares of income components

Data Government source Year Occupation Wage Business Transfer transfer Others Total 1989 66.11 34.07 32.04 31.75 22.25 2.14 100 CHNS 2006 67.09 45.98 21.11 23.71 21.27 9.20 100 2011 83.43 41.09 42.34 10.65 9.23 5.92 100 CFPS 2010 75.53 – – 15.17 9.30 100 2014 95.46 – – 2.48 2.06 100 1995 84.46 54.74 29.73 14.51 11.60 1.02 100 CHIP 2007 81.52 60.02 21.50 15.44 11.88 3.03 100 2013 75.61 57.00 18.60 15.88 – 8.51 100 2006 83.04 – – 16.96 100 CGSS 2008 88.78 – – 11.22 100 2013 77.22 – – 22.78 100 CHARLS 2011 39.51 – – 60.49 100 2013 52.79 – – 47.21 100

Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13).

whereas CHARLS shows that the between component drives the decline of the overall income inequality.

4.4 Decomposition by income components Based on the discussions in Section 4.2, total income can be broken down into four compo- nents. Table 7 reports the shares of these components. Occupational income is the major source of total income and its share in total income rises over time according to CHNS, CFPS, and CHARLS. According to Wang et al. (2018), an increase in the share of labor (occupational) income leads to a decline in income inequality. Regarding transfer income, it trends downward according to CHNS and CFPS, whereas CHIP shows a slight increase. Later computation will show that transfer income is not in fact pro-poor, but it did at least become less pro-wealthy over time.

Table 8 reports the estimates of the concentration index for different income components. The index reflects the distribution of the component, as well as the correlation between the component and the total income. A negative estimate implies that the poor (who have less

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Table 8. Concentration index of income components

Data Government source Year Occupation Wage Business Transfer transfer Others Total 1989 0.372 0.528 0.207 0.306 0.010 0.512 0.354 CHNS 2006 0.510 0.553 0.414 0.520 0.465 0.376 0.500 2011 0.437 0.521 0.354 0.405 0.313 0.356 0.428 CFPS 2010 0.494 – – 0.605 – 0.307 0.494 2014 0.490 – – −0.06 – −0.110 0.464 1995 0.394 0.598 0.018 0.537 0.602 0.850 0.419 CHIP 2007 0.521 0.605 0.287 0.692 0.612 0.668 0.552 2013 0.498 0.554 0.326 0.433 – 0.696 0.505 2006 0.470 – – – – 0.318 0.444 CGSS 2008 0.525 – – – – 0.390 0.510 2013 0.511 – – – – 0.339 0.472 CHARLS 2011 0.753 – – – – 0.470 0.582 2013 0.747 – – – – 0.387 0.577

Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13).

Table 9. Decomposition of inequality by income components: Absolute contribution

Data Government source Year Occupation Wage Business Transfer transfer Others Total 1989 0.246 0.180 0.066 0.097 0.002 0.011 0.354 CHNS 2006 0.342 0.254 0.087 0.123 0.099 0.035 0.500 2011 0.364 0.214 0.150 0.043 0.029 0.021 0.428 CFPS 2010 0.373 – – 0.092 – 0.029 0.494 2014 0.468 – – −0.001 – −0.002 0.464 1995 0.332 0.327 0.005 0.078 0.070 0.009 0.419 CHIP 2007 0.425 0.363 0.062 0.107 0.075 0.020 0.552 2013 0.377 0.316 0.061 0.069 – 0.059 0.505 2006 0.390 – – – – 0.054 0.444 CGSS 2008 0.467 – – – – 0.044 0.510 2013 0.395 – – – – 0.077 0.472 CHARLS 2011 0.298 – – – – 0.284 0.582 2013 0.394 – – – – 0.183 0.577

Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13).

total income) receive more of the component income and vice versa. Regrettably, govern- ment transfers do not seem to benefit the poor more as its concentration is positive, with only one exception that corresponds to the 2014 CFPS with a small negative value.

Over time, the concentrations of income components tend to display an inverted U-shape, thus supporting the inverted U-pattern of total income inequality. In the declining phase of total inequality, the occupational income became less concentrated. Focusing on changes over time, concentration of transfer income changed quite significantly. The direction of change in the concentration of wage income is consistently U-shaped across different data sources.

The Gini estimates of total income can be decomposed using equation (5). See Tables 9 and 10. Broadly speaking, the largest contributor is occupational income and its contribution changes little over time. The contribution of wage income is larger than that of business

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Table 10. Decomposition of inequality by income components: Percent contribution

Data Government source Year Occupation Wage Business Transfer transfer Others Total 1989 69.49 50.74 18.75 27.41 0.60 3.10 100 CHNS 2006 68.41 50.90 17.50 24.68 19.80 6.92 100 2011 85.01 50.02 34.99 10.07 6.75 4.92 100 CFPS 2010 75.61 – – 18.59 5.79 100 2014 100.81 – – −0.32 −0.49 100 1995 79.33 78.06 1.27 18.59 16.67 2.08 100 CHIP 2007 76.96 65.79 11.17 19.36 13.62 3.68 100 2013 74.64 62.62 12.02 13.63 – 11.73 100 2006 87.86 – – 12.14 100 CGSS 2008 91.42 – – 8.58 100 2013 83.64 – – 16.36 100 CHARLS 2011 51.13 – – 48.87 100 2013 68.34 – – 31.66 100

Source: Authors’ calculation. Data from CHNS (1989–2011), CHIP (1995–2013), CFPS (2010–14), CGSS (2006–13), CHARLS (2011–13).

Table 11. Structural and concentration components of inequality rise

Government Data source Occupation Wage Business Transfer transfer Others Total CHNS Total effect 0.096 0.075 0.021 0.026 0.097 0.024 0.146 (1989-2006) Concentration 0.091 0.010 0.055 0.059 0.099 −0.008 Structural 0.004 0.064 −0.034 −0.033 −0.002 0.031 CHIP Total effect 0.092 0.036 0.056 0.029 0.005 0.012 0.133 (1995-2007) Concentration 0.106 0.004 0.069 0.023 0.001 −0.004 Structural −0.013 0.032 −0.013 0.006 0.004 0.015 CGSS Total effect 0.076 – – – – −0.010 0.066 (2006-2008) Concentration 0.048 – – – – 0.010 Structural 0.029 – – – – −0.020

Source: Authors’ calculation. Data from CHNS (1989–2006), CHIP (1995–2007), CGSS (2006–08).

Table 12. Structural and concentration components of inequality rise (%)

Government Data source Occupation Wage Business Transfer transfer Others Total CHNS Total effect 65.78 51.30 14.48 18.02 66.48 16.20 100 (1989–2006) Concentration 62.81 7.12 37.80 40.81 68.07 −5.31 Structural 2.97 44.18 −23.32 −22.79 −1.60 21.51 CHIP Total effect 69.48 27.06 42.42 21.80 4.01 8.72 100 (1995–2007) Concentration 79.62 3.12 51.87 17.49 0.92 −2.78 Structural −10.14 23.93 −9.44 4.31 3.08 11.50 CGSS Total effect 115.33 – – – – −15.33 100 (2006–2008) Concentration 72.08 – – – – 15.44 Structural 43.25 – – – – −30.77

Source: Authors’ calculation. Data from CHNS (1989–2006), CHIP (1995–2007), CGSS (2006–08).

income, although the latter increased broadly over time. The contribution of transfer in- come is small and declined over time, due to its small and declining share.

In the phase of rising inequality (Tables 11 and 12), the occupational income (especially wage income) contributed the most, mainly because of the rise in the disparity of business income. To some extent, however, the transfer income helped improve income distribu- tion through the significant decrease in its share. The results that transfer income is not

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Table 13. The structural and distributional components of inequality decline

Government Data source Occupation Wage Business Transfer transfer Others Total CHNS Total effect 0.022 −0.040 0.062 −0.080 −0.070 −0.013 −0.071 (2006–2011) Concentration −0.055 −0.014 −0.019 −0.020 −0.023 −0.001 Structural 0.077 −0.026 0.082 −0.060 −0.047 −0.012 CHIP Total effect −0.048 −0.047 −0.001 −0.038 – 0.039 −0.047 (2007–2013) Concentration −0.018 −0.029 0.008 −0.040 – 0.002 Structural −0.030 −0.017 −0.009 0.002 – 0.037 CGSS Total effect −0.001 – – – – −0.084 −0.085 (2008–2013) Concentration −0.1 – – – – 0.022 Structural 0.1 – – – – −0.107 CFPS Total effect 0.095 – – −0.093 – −0.031 −0.030 (2010–2014) Concentration −0.004 – – −0.059 – −0.024 Structural 0.098 – – −0.035 – −0.007 CHARLS Total effect 0.097 – – – – −0.102 −0.005 (2011–2013) Concentration −0.003 – – – – −0.045 Structural 0.100 – – – – −0.057

Source: Authors’ calculation. Data from CHNS (2006–2011), CHIP (2007–2013), CFPS (2010–14), CGSS (2008–13), CHARLS (2011–13).

Table 14. The structural and distributional components of inequality decline (%)

Government Data source Occupation Wage Business Transfer transfer Others Total CHNS Total effect 31.14 −56.20 87.34 −112.3 −98.03 −18.89 −100 (2006–2011) Concentration −77.08 −19.44 −26.83 −27.67 −32.40 −2.07 Structural 108.22 −36.76 114.18 −84.58 −65.63 −16.82 CHIP Total effect −101.98 −99.95 −2.02 −81.03 −160.19 83.01 −100 (2007–2013) Concentration −37.75 −62.68 16.89 −86.29 – 3.40 Structural −64.23 −37.28 −18.92 5.25 – 79.61 CGSS Total effect −1.18 – – – – −98.82 −100 (2008–2013) Concentration −117.65 – – – – 25.88 Structural 116.47 – – – – −125.88 CFPS Total effect 318.88 – – −314.8 – −104.13 −100 (2010–2014) Concentration −12.08 – – −198.1 – −80.06 Structural 330.96 – – −116.7 – −24.07 – CHARLS Total effect 1881.33 – – – – −1981.33 −100 (2011–2013) Concentration −58.70 – – – – −872.78 Structural 1940.03 – – – – −1108.55 –

Source: Authors’ calculation. Data from CHNS (2006–2011), CHIP (2007–2013), CFPS (2010–14), CGSS (2008–13), CHARLS (2011–13).

pro-poor and its share declines over time are disappointing but consistent with earlier find- ings (Wan 2008).

In the phase of declining inequality (Tables 13 and 14), transfer income contributes to the decline of income inequality. But both the magnitude of the contribution and the share of transfer income declined over time. The distributional effects of occupational income and wage income are negative and consistent across different data sources, seemingly imply- ing that China has reached the Lewis turning point. This conclusion could be controversial because the institutional barriers to labor mobility imposed by the hukou system have cer- tainly contributed to the negative distributional effects. Transfer income, especially the government transfer, contribute significantly to the overall inequality decline, and the oc- cupational income is the second contributor.

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5. Conclusion

Based on five sets of household survey data (CHNS, CHIP, CFPS, CGSS, CHARLS), this paper investigated the changing trend of income inequality in China. The results show that income distribution began to improve in the past 5 to 10 years, after many years of sharp rises. Drivers underlying the improvement include (1) expansion of the middle class, (2) rapid urbanization, and (3) the decline of inequality within East China.

We also found that (1) the decline is mainly due to improvement in the distribution of transfer income although its share in total income is small and diminishing; (2) occupa- tional income, particularly wage income, plays an important role in driving the declining trend; (3) the poor gained relatively less, and in some cases suffered losses amid China’s miracle growth; (4) there is a lack of mobility among different income groups; (5) liter- ally, China seems to have reached the Lewis turning point as wage or occupational income shows a consistently negative distributional trend, using different data sets; and (6) in- equality within East China declined significantly although income gaps between provinces rose over time.

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