Regional Wage Inequality in China, 1996–2010

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Eurasian Geography and Economics, 2012, 53, No. 3, pp. 338–355. http://dx.doi.org/10.2747/1539-7216.53.3.338Copyright © 2012 by Bellwether Publishing, Ltd. All rights reserved.

Regional Wage Inequality in China, 1996–2010

Jessie P. H. Poon and Qingyan Shang1

Abstract: This paper examines regional wage inequality in China between 1996 to 2010, subdividing that time span into a period of increased wage inequality during 1996–2002 and one of stable to decreasing inequality for 2003–2010. Based on the interplay between wage levels and wage growth, the authors develop a typology whereby China’s provinces can be assigned to either peripheral, emerging, lagging, or leading regions. The particular character-istics associated with each type of wage region, and the factors underlying shifts of particular provinces from one category to another between the two periods, enable them to identify specific causes for the reversal (after 2002) of the trend toward increasing wage inequality (the causes especially reflect wage patterns in the mining and energy industries, information and communication technology industries, and foreign-invested enterprises). The recent wage convergence involves instances of both interior provinces advancing from the “emerging” to the “leading” wage category as well as the decline of a few “leading” coastal provinces to “lagging” status. Journal of Economic Literature, Classification Numbers: E240, J310, J380, 0180. 9 figures, 1 table, 47 references. Key words: China, wage inequality, labor productivity, endogenous regional development,

INTRODUCTION

China’s meteoric economic growth and development over the past two decades has been accompanied by significant regional inequality. Several scholars have observed that

under the government’s tripartite policy of globalization, marketization, and decentralization during the post-reform years, regional inequality in the country increased markedly during the 1990s (Wei, 2007; Fan and Sun, 2008; Chen and Groenewold, 2011), leading one prominent China scholar and his colleagues to conclude that “China has not only one of the highest rates of economic growth but also one of the highest degree of regional income inequality in the world” (Fleisher et al., 2010, p. 229).

Spatial and regional inequality has been predominantly studied in terms of income or GDP, but economic geographers are increasingly calling for a broadening of regional devel-opment perspectives beyond the income measure. Florida et al. (2008) maintain that income captures a region’s wealth, whereas wages directly reflect regional labor productivity. A region that is wealthy need not necessarily be economically productive, for example, if it is inhabited by many non-wage residents (e.g., retirees) or is a site that attracts capital flight. In other words, because income can come from sources other than wages, it may not adequately represent a region’s productive level and capacity. Yet there is a dearth of studies among geog-raphers on the role of wages in regional development. This paper seeks to address this gap. First, it examines the spatial distribution of wage level among China’s provinces. Second, it attempts to explain the factors that influence regional wage distribution and inequality.

1Respectively, Professor of Geography, University at Buffalo-SUNY, Amherst, NY 14223 (jesspoon@buffalo .edu) and Assistant Professor of Economics, University at Buffalo-SUNY, Amherst, NY 14260 ([email protected]).

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Recent theories of endogenous regional development have directed attention to the role of human capital, technological progress, and knowledge spillovers in enhancing the produc-tivity of workers, which in turn affects regional productivity (Lucas, 1988). One conclusion of endogenously driven regional development is that we can expect to see regional diver-gence, whereas older theories predict a decrease in regional inequality over time arising from neoclassical economic explanations of factor mobility (Martin and Sunley, 1998). Traditional economic theory suggests that regional income will converge in the long run as differences in technology and institutions narrow, and regions become more homogenous. In contrast, increasing returns under the endogenous growth theory are likely to yield the opposite out-come. Workers, particularly skilled workers, are expected to migrate to regions with already high levels of human capital, creating agglomeration forces that reinforce rather than dimin-ish regional inequality. The works of Glaeser (1994) and Scott (2010), for example, point to the geographical concentration of skilled workers in large cities. This reinforces the urban hierarchy and exacerbates spatial inequality between large and small cities.

In China’s case, policies also influence regional inequality (Fan and Sun, 2008). Early reforms in the 1980s targeted the coastal region in leading the country’s economic growth. More recently, policies have favored competitive regionalism by increasing the autonomy of local governments to set minimum wages and to pursue economic development. Increased responsibility among local officials in fiscal structure and economic development has in turn created intense competition among and within provinces (Wei, 2007). This is likely to increase wage differences, thereby promoting a process of regional divergence. On the other hand, the broader incentive structure remains in the hands of the central government. For example, the central government began a process of labor market liberalization in the 1990s that allows state-owned enterprises (SOEs) to set their own internal wage levels. In addition, labor unrest has led the central government to improve social welfare. In line with its 2006 push for a more harmonious society, new labor laws have focused on establishing a broad regulatory framework that gives more autonomy to enterprises in drafting labor contracts and work terms (e.g., firing and hiring), as well as the formalization of workers’ rights, such as working hours and social insurance (Wang et al., 2009). All this may have a converging effect across regions. In particular, the government’s 1999 Western Development Strategy (“Go West”) should have important implications for wage levels. First, several provinces announced an increase in minimum wages, including interior provinces such as Chongqing (Ulrich et al., 2011). Second, Fan and Sun (2008) note that the policy has had a favorable effect in halting regional income inequality in the years between 2003 and 2006, arresting the previous pattern of regional divergence.

Overall, forces of both regional divergence and convergence may simultaneously be at work. It is unclear whether central government policies that encourage convergence, and thereby a reduction in regional inequality, have yet had any effect. This paper will address this gap in our knowledge by examining the pattern of regional wage inequality in China from 1996 to 2010. Because the government accorded priority to a reduction of regional inequality in its Ninth Five-Year Plan (1996–2000), the year 1996 is a reasonable base year for the analysis in the paper. In addition, the mid-1990s represent a period of significant labor market reforms: minimum wages were introduced for the first time in the 1994 Labor Law to improve the earnings of unskilled workers (Demurger et al., 2012). Among skilled workers, the 1995 Education Law gave greater attention to human capital development that supports its modernization and innovation drive (Josephs, 1995).

In the next section, we elaborate upon the factors that contribute to wage levels and thereby regional wage inequality. This is followed by an analysis of the spatial distribution of

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wage levels and regional inequality. We also investigate the factors that significantly explain regional wage patterns. This is followed by sections presenting a discussion and the paper’s conclusions.

FACTORS INFLUENCINg WAgE INEQUALITY

The literature indicates that wage inequality arises from four major factors. First, theo-ries of international production associate the investment of multinational or foreign-invested enterprises (FIEs) with firm-specific assets that enable them to compete with local Chinese firms. One such asset relates to labor, which is thought to be more productive in foreign firms because the latter tend to be more skill-intensive (Ramstetter, 2004). Consequently, studies have found that FIEs pay higher wages: in Indonesia, for example, they have been found to pay between 10 to 50 percent higher wages in its manufacturing sector (Lipsey and Sjoholm, 2004). Similarly, Feenstra and Hanson (1997) found that FIEs in Mexico accounted for up to 50 percent of the increase in wages among skilled workers in the 1980s. Zhao’s (2002) study of cities in China demonstrated that a skill premium exists among FIEs vis-à-vis state-owned enterprises (SOEs). Workers who are trained in FIEs are in high demand by local firms, which seek the knowledge, expertise, and experience they have gained through working for for-eign firms. Ramstetter (2004) maintains that workers may also prefer local to foreign firms because of differences in labor practices and management culture. FIEs thus pay higher wages to prevent employees from leaving and switching from foreign to local employers. Higher wages also militate against employees starting up their own firms to compete with FIEs (Chen et al., 2011).

Second, wage inequality rises when a region undergoes rapid technological change. Technology changes the skill mix of the workforce and this in turn affects regional labor pro-ductivity. Technological changes are captured through a region’s industrial mix in this paper. Theory suggests that human capital investment is rewarded through higher wages (see further discussion below), and high-wage industries are associated with the employment of workers with a higher level of skills. In the United States, a decline in demand for unskilled workers and the concomitant rise in demand for skilled workers reflect major industrial shifts that are associated with the adoption of technology. Regional wage differences in industries reflect differences in technology use and skill level (Kim and Sakamoto, 2008). In particular, the rise of the knowledge economy has been accompanied by rising wages in professional occupa-tions among industries such as finance (Skinner, 2004) or technical professions among indus-tries like information technology (Majumdar, 2008). In the case of China, however, Owen and Yu (2008) found that wage differentials between skilled and unskilled industries are low, and attribute this to a strong demand for unskilled workers among labor-intensive export-oriented industries. On the other hand, Li and Haynes (2011) emphasize the importance of industrial mix and regional development in China. They show that industrial shifts between the interior and coastal provinces increased regional disparities from 1995 to 2004.

Third, differences in educational level contribute to wage inequality in industrialized countries (Hanushek and Welch, 2006). Educational level captures the human capital effect, which in turn influences the skill level of workers. Educated people are thought to be more adaptable to technological change and more inclined to adopt new ideas. In the U.S. and Germany, educational returns are reflected in the form of higher wages (Moretti, 2004; Heuermann, 2011). But returns from education in China the 1980s and early 1990s have been found to be low: that is, 4–7 percent compared to 15–20 percent in the U.S. (Fleisher et al., 2010). This implies that having a college degree does not translate into higher wages in China.

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Alternatively, wages are not reflective of a worker’s level of education, and this diminishes the contribution of human capital to regional productivity (Heckman, 2005). One reason is that university education was halted during the Cultural Revolution and only reinstated in 1977. Under pre-reform China, workers were embedded in a labor system that guaranteed lifetime employment. They were assigned to work units by the Labor Bureau; in turn, work units assumed responsibility for the worker’s welfare, and labor mobility was low. Differences in wages across occupations were small and college education offered few economic advan-tages. Moreover, wage increases privileged seniority over productivity. Hence Maurer-Fazio (1999) found no clear evidence of regional inequality between coastal and inland regions arising from educational differences.

As reforms intensified in the 1990s, the government established the 1995 Education Law that linked higher education to the country’s modernization drive. This was followed by a sharp rise of college admissions (Li et al., 2011). Since then, returns to education have become more positive and wage differences have also increased as a result. For instance, Zhang et al. (2005) found that the wage premium between college and high school education rose from 12.2 to 37.3 percent from 1988 to 2001. Likewise, Knight and Song (2003) have suggested that educational differences have increased income inequality in China. Maurer-Fazio (1999) found that younger educated workers are more likely to experience a wage premium compared to older workers who were hired under the previous system. Hartog et al. (2010) conclude that college graduates from prestigious universities earn more than those from less elite universities. Furthermore, while many cities are inhabited by migrants who earn relatively low wages because of their lack of urban hukuo status (Chan, 2012), highly educated Chinese citizens are more likely to be granted urban hukuo as local governments realize the benefits of human capital concentration in regional productivity growth (Fu and Gabriel, 2011). Fleisher and his colleagues (2010) have concluded that the central govern-ment will need to invest much more heavily in education and human capital in inland prov-inces in order to reduce regional inequality. This appears to have struck a chord with the government, as its recent five-year plans include policies that seek to move the country from a low wage to a more endogenous model of regional development that is based on human capital (e.g., see Fan, 2006).

The fourth and final factor that explains wage inequality is associated with trade and globalization. Traditional trade models indicate that as a country becomes more open and globally integrated, wage inequality rises. In their analysis of wage inequality, Hanson and Harrison (1999) show that Mexico’s reversal of protectionist policies following its entry to GATT in the 1980s was accompanied by a rise in wage inequality. Chiquiar (2008) examined the impact of the North American Free Trade Agreement (NAFTA) on Mexico’s regional wage levels, and found that wages in regions closer to the U.S. border rose substantially following NAFTA. These regions also experienced an increase in unskilled wages. Likewise, China’s accession to the World Trade Organization has seen a rise in wage inequality. Han and his colleagues (2012) show that regions that are exposed to trade (namely, Guangdong, Zhejiang, Beijing and Liaoning) all experienced rapid wage growth in the 1998–2008 period.

Taken together, trade, FIE investment, technology-intensive industries, and college education contribute to wage differences and thereby regional inequality through their effects on the share of skilled workers in a region. Because skills are associated with higher labor productivity, regions that have a concentration of skilled workers are also likely to be more productive. For this reason, economic geographers are paying greater attention to the locational choices of skilled labor and the nature of urban skill economies (Florida, 2002; Scott, 2010). More recently, Florida et al. (2008) also examined the impact of skilled labor


on regional productivity. These studies, however, focus on highly productive, skill-intensive regions. They ignore patterns of regional inequality that may arise as a result of uneven skill distribution across space. The rest of the paper attempts to address this issue. The principal source of data for the analysis below is derived from China’s National Bureau of Statistics which publishes annual statistical yearbooks that offer information on regional wage levels and other relevant economic statistics over the period being examined.2

ANALYSIS OF WAgE LEVELS AND REgIONAL INEQUALITY

Patterns of Regional Wage Inequality

Figure 1 presents the coefficient of variation (CV) of wage levels from 1996 to 2010. The period between 1996 and 2002 is marked by a steep rise in CV. However, after 2003 there is a noticeable halt in the growth of the CV. Indeed, the trend is generally downward, so that the CV in 2010 is about the same as it was in 1997. The figure thus points to an initial period of rising regional wage inequality (1996–2002) followed by a second period of declining regional inequality and convergence (2003–2010).

Figures 2A and 2B examine these trends in greater detail. For each year, the wage data is divided into four groups. The first quartile comprises provinces with the lowest wages, and the fourth quartile consists of provinces with the highest wages.3 Figure 2A reports wage levels in logarithmic terms for each quartile, and Figure 2B shows the wage gap in percentage terms between the different quartiles. Both figures shed some light on the widening and then narrowing of the wage gap over time. In 1996, the average wage of the fourth quartile is 70.0 percent higher than the average wage of the first quartile. This difference increased to 89.9 percent by 2001, but then subsequently decreased to 62.7 percent in 2010. Comparing wages

2More specifically we refer to annual editions of the China Statistical Yearbook (NBS, 1997–2011).3Data are divided into four equal groups by the 25th, 50th, and 75th percentiles.

Fig. 1. Coefficient of variation of provincial wages, 1996–2010. Source: NBS, 1997–2011.

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between two consecutive quartiles, the largest increase in wage gap in the first period, and the largest decline in the second period, are associated with the third and fourth quartiles. The trends for quartiles 1 and 2 as well as quartiles 2 and 3 remain relatively stable or decrease only marginally in the second period. Furthermore, Figure 2A shows that wage growth is larg-est among high-wage regions in the first period. This is illustrated by the steep slope of the fourth quartile and suggests a pattern of regional divergence and increased regional inequal-ity. In contrast, wage growth is higher among low-wage regions in the second period, which indicates a process of regional catch-up.

What factors contribute to the above trend? Figures 3 to 7 provide some preliminary answers. The figures map the logarithmic function of provincial average values of FIE investment and exports for four geographic regions as well as the average share of college graduates and the industrial mixes. Following Fleisher et al. (2010), these four regions are

Fig. 2. A. Average wage level (log) by quartile. B. Inter-quartile wage gap, in percent.


the coastal, northeast, central, and western regions.4 Figures 3 and 4 show that both FIE investment and exports increased markedly for all regions in the second period (2003–2010). In particular, the growth of FIE investment for both the central and western regions is as high as that of the coast, if not higher in recent years. Similarly, both the central and western regions have become more open to trade, with the central region catching up to the northeast region.

4The regions are defined as follows: (1) coastal: Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; (2) northeast: Heilongjiang, Jilin, and Liaoning; (3) central: Hebei, Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan; and (4) western: Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang.

Fig. 3. A. Average foreign-enterprise investment (log) by region. Source: NBS, 1997–2011.

Fig 4. Average export value (log) by region. Source: NBS, 1997–2011.

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Industrial effects are explored in terms of two major sectors. The first, FITST, consists of three industries, namely, finance, information technology, and science and technology. The second, ME, composes of both the mining and energy (e.g., electricity production) industries. FITST and ME are selected for three reasons. First, of the 19 industries listed in the China Statistical Yearbook, FITST is associated with the highest wages for coastal provinces. In 2010, the average annual wage of the finance industry in Shanghai was Rmb 155,763, which is five times that of lower paying service industries such as the hotel industry, and three times that of the manufacturing industry (NBS, 2011). One explanation is that workers in this sec-tor are likely to be more skilled, and hence the relatively high wage in this sector reflects a skill premium (Clark, 2002). Conversely, and second, the mining and energy industries are responsible for some of the highest wages in the western and central regions. For example, the average mining wages at Ningxia and Anhui were Rmb 71,684 and Rmb 57,315 respectively in 2010 (ibid). In contrast to the FITST industry along the coast, the ME industry pays the highest wages in these provinces (i.e., 30 percent more than their financial counterparts, and twice as high as that of the manufacturing industry). Third, whereas coastal provinces tend in general to command the highest wages among many industries, and wage rates tend to fall from east to west, the reverse is true for the mining and energy industries. In addition to Qinghai and Ningxia, many interior provinces including Inner Mongolia have higher wages in the ME industry than do coastal provinces such as Fujian or Zhejiang. A major reason is that in the interior the industries are dominated by state-owned enterprises (SOEs).5 SOEs not only tend to pay higher wages in general, they also pay higher wages to unskilled workers, whereas FIEs are more likely to reward skilled workers (Zhao, 2001; Demurger et al., 2012).

Figure 5 depicts the upward trend of the FITST industry for all four regions. Although it is in the coastal region that FITST accounts for the largest share of employment, the fastest growth occurs in the northeast, which exhibited a rise from 2.4 to 7.6 percent between 1996

5For instance, the Shenhua Ningxia Coal Group, an SOE in Ningxia, is China’s largest coal producer.

Fig. 5. The share of employment in the finance, information technology, and science and technol-ogy (FITST) industries by region. Source: NSSB, 1997–2011.


and 2010.6 In contrast, the industry’s shares of the central and western regions remained stable throughout most of the second period (2003–2010), so that the gap between them and the coastal region widened. For the ME industry (Fig. 6), the coastal region is responsible for a rather small share at less than 5 percent in the late 1990s, and this fell further to 3.5 percent in 2010. The highest share is found in the northeast region at nearly 14 percent in the late 1990s before declining sharply thereafter. However, the region’s decline is matched by an increase in mining and energy production in the western and central regions, indicating that firms had moved westward and increased their investments in these regions. Overall, the figures indicate that, in the interior, the industrial mix is characterized by relatively high levels of resource-based industries that pay high wages, whereas more skill-intensive FITST industries are responsible for some of the highest wages along the coast.

The share of college graduates is presented in Figure 7. As noted in the previous sec-tion, the college premium has only begun to yield some wage advantage in recent years, as the government is paying greater attention to human capital development in its five-year plans. All four regions show an upward trend, although the coastal region has clearly surged ahead, rising from 4.4 percent to nearly 14 percent over the period. Interestingly, the share college graduates in the northeast region was relatively high in the late 1990s—it was only 0.8 percentage points below that of the coastal region in 1996—but the gap increased to 3.7 percentage points by 2010. The shares of college graduates grew relatively slowly for both the western and interior regions rising from around 2 percent to 8 percent. The primary trend evident from Figure 7, is one of regional divergence in human capital production, with the coastal region increasing its lead over time.

6The industry classification scheme used in the China Statistical Yearbook was changed in 2003. Before 2003, the information industry was grouped together with transportation and storage, and was not considered in our calcula-tion. Since 2003, the information industry’s information was separated from transportation and storage and treated as an independent industry, and was thus included in our calculation. This explains the large jump in the FITST share between 2002 to 2003 that is shown in Figure 5.

Fig. 6. The share of employment in the mining and energy (ME) industries by region. Source: NBS, 1997–2011.

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Overall, Figures 3–7 reveal a simultaneous process of regional convergence and diver-gence. For example, the higher wages of skilled workers associated with college graduates and the share of FITST employment in the coastal region would suggest divergence and increased regional inequality. The interior and western provinces are becoming more open and export-oriented. They are also capturing more FIE investment, and are responsible for a larger share of employment in the ME industry, all of which might be expected to foster increasing wages and lessening regional inequality. We shall examine how each of these factors influ-ences spatial wage distribution and inequality in a later section using regression analysis.

Wage Regions: A Typology

One problem with conducting regional wage analyses based on the administrative geo-graphic units in Figures 3 to 7 is that there is considerable heterogeneity in wage levels and growth not only across regions, but within them. For example, while provinces in the coastal region generally display higher wages than provinces in other regions, Fujian and Shandong are characterized by lower wage levels on average (i.e., Rmb 32,340 and Rmb 33,321 in 2010, respectively) than more inland provinces such as Anhui (Rmb 33,341) or Chongqing (Rmb 34,727). Wages also grew more slowly in Fujian (328 percent) and Shandong (434 percent) than in Anhui (507 percent) or Chongqing (531 percent) over the 1997–2010 period. Hence, we need to develop a typology of wage regions that captures such diversity (Fig. 8), in which the provinces in each “region” (regardless of their actual geographic location) are relatively homogenous in terms of wage levels and wage growth. Accordingly, four wage regions can be defined:

Peripheral Region. This region, located in the lower left quadrant of Figure 8, con-sists of provinces with low initial wages and low wage growth. By “low,” we refer

Fig. 7. The share of college graduates (percent of total population) by region. Source: NBS, 1997–2011.


to wage levels and growth rates that are below the national median. The peripheral region is thus defined by provinces with both a low initial wage level as well as sub-sequent low wage growth.

Emerging Region. Located in the left upper quadrant of Figure 8, provinces of the emerging region are characterized by initial low wage levels but are distinguished from their peripheral counterparts by their high growth in wages (“high” here refer-ring to growth rates above the national median). The emerging region thus consists of provinces that should contribute to converging wages and a decrease in regional inequality. This is because high wage growth is likely to enable provinces in this region to catch up.

Lagging Region. The lagging region consists of provinces with initial high wage levels but that experience slow wage growth. Compared to the leading region immediately below, these provinces fall behind over time, as low wage growth enables provinces in the emerging region to catch up. Lagging provinces in this region thus potentially contribute to regional convergence.

Leading Region. In the fourth region (upper right quadrant), provinces are char-acterized by both high initial wage levels and high wage growth. Provinces in this region are thus most likely to contribute to regional wage divergence if they surge well ahead of provinces in other three regions in terms of median wage levels.

To understand the interplay among the four regions across the two subperiods in this study, we mapped shifts in regional wage dynamics between 1996–2002 and 2003–2010 in Figure 9. It reveals that Chongqing, Sichuan, and Liaoning, which were in emerging region in 1996–2002, advanced to become part of the leading region in 2003–2010. Shandong’s wage growth rate also increased in the latter period, and it leaped from the lagging region directly to the leading region. Meanwhile, Anhui, Henan, Hubei, Hainan, Jiangsi, Shanxi, and Guizhou left the peripheral region and became part of the emerging region. Conversely, Beijing, Shanghai, Jiangsu, Zhejiang, and Qinghai (all in the leading region in 1996–2002) fell into the ranks of the lagging provinces, while Hunan, Guangxi, Heilongjiang, and Gansu assumed peripheral status. Because of low wage growth in the first period, Hebei shifted from the lagging to emerging region; however its high wage growth in the second period prevented it from slipping into peripheral status.

Fig. 8. Typology of wage regions.

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Figure 9 sheds some light on the decline of the CV in Figure 1 during the second period. It is evident that wage growth in several high-wage provinces in the leading region (e.g., Beijing, Jiangsu, Shanghai, and Zhejiang) had begun to decelerate in the second period, a process paralleled by high wage growth and catch-up among emerging provinces (e.g., Chongqing, Liaoning, and Sichuan). The decline in regional inequality was also hastened by peripheral provinces such as Anhui, Henan, Hebei, Hainan, Jiangsi, Shanxi, and Guizhou, all of which moved up to become part of the emerging region. However, low wage growth in Gansu and Hebei caused both of these provinces to fall behind into peripheral or emerging status in the second period.

Regression Analysis

Earlier, we identified four factors known to affect wage levels: (1) FIE investment; (2) industry mix (FITST and ME industries); (3) educational level (i.e., college graduates); and (4) exports (which measure the extent of openness in the economy). To disentangle the influ-ence of these factors on wage levels, and to understand if their effects vary across the wage regions defined in Figure 8, we ran regressions for the two periods. The regressions are based on provincial fixed-effect regressions to account for unobserved intra-provincial effects.

Table 1 shows the effect of the aforementioned four factors on provincial wages.7 For the first period, the table shows that the level of exports and share of college graduates both signif-icantly and positively influence wage.8 The coefficients associated with FITST are significant and positive for the four regions (i.e., leading, emerging, lagging, and leading regions). How-ever regional differences also exist: the wage effect is highest for the emerging region (0.354),

7Wages, FIE investment, and exports are measured logarithmically, and the shares of college graduates and indus-try mix variables are measured as percentages.

8Regional interactions were conducted for college education as well, but no significant effect was found.

Fig. 9. Spatial shifts among regions, 1996–2002 and 2003–2010.


followed by the lagging (0.331) and peripheral (0.311) regions. It is lowest in leading region (0.125). This may explain why a few emerging provinces such as Liaoning were able to move to leading-region status in the second period (see Fig. 9). Despite FITST’s favorable effect on wages in peripheral and emerging regions, however, its relatively small shares in the overall industrial mix may not be sufficient to offset increased regional wage inequality overall.

Table 1. Factors Influencing Wages with Regional Effectsa

Dependent variable: wage (log) First period1996–2002

Second period2003–2010

Export (log) 0.235** 0.303***

(0.085) (0.053)College ( percent) 0.071*** 0.019**

(0.014) (0.008)FITST (peripheral) 0.311*** 0.235***

(0.028) (0.041)FITST (emerging) 0.354*** 0.189***

(0.064) (0.027)FITST (lagging) 0.331*** 0.032*

(0.045) (0.016)FITST (leading) 0.125** 0.185***

(0.054) (0.057)ME (peripheral) 0.022 –0.019*

(0.029) (0.010)ME (emerging) –0.077* 0.068**

(0.043) (0.030)ME ( lagging) –0.038** –0.048

(0.017) (0.048)ME ( leading) 0.010 0.045

(0.032) (0.079)FIE investment (peripheral) –0.268** 0.261***

(0.107) (0.070)FIE investment (emerging) 0.018 0.325***

(0.025) (0.078)FIE investment (lagging) 0.031 0.293***

(0.050) (0.072)FIE investment (leading) 0.196** 0.194**

(0.080) (0.091)Constant 6.602*** 5.327***

(0.406) (0.242)Observations 209 240R2 0.853 0.924

aBased on provincial fixed effect regressions. Robust standard errors are indicated in parentheses. * = significant at .10; ** = significant at .05; *** = significant at .01.

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The ME industry is positive but not significant for the peripheral region in the first period. It is significant and negative for both the emerging and lagging regions. Further investigation of the data indicates that the wage levels of this industry for emerging and lagging provinces were still quite low between 1996 and 2002. For example, 1997 mining wages ranged from Rmb 4,666 in Heilongqiang to Rmb7,891 in Anhui (NBS, 1998). These are much lower than in their coastal counterparts Shandong (Rmb 9,533) and Shanghai (Rmb 14,694). The situ-ation is reversed in 2003–2010 with wages rising dramatically among emerging provinces and overtaking provinces like Shanghai (see below). Another explanation is that SOEs were much more inefficient in the 1990s. Raizer’s (1997) survey of SOEs in the 1990s indicates that inland SOEs were subjected to more administrative interference from the central gov-ernment than those along the coast. Such interference meant that inland SOEs were granted less autonomy in decisions concerning employment and wage levels that in turn led to over-employment and inefficiency.

Rather surprisingly, FIE investment is significant but negative for the peripheral region during the first period, whereas it is significant and positive for the leading region. This would appear to be consistent with Ran et al.’s study (2007), which found that the net impact of foreign direct investment on western provinces where several of our peripheral provinces lie is negative. Although their findings show that only coastal provinces stand to gain from such investment, they did not provide an explanation; it is possible that the impoverishment of intellectual property rights among peripheral provinces has adversely affected FIE invest-ment’s effect on wages, as Wu (2001) has observed. Taken together, the negative coefficient for the peripheral region and the positive coefficient for the leading region go some way toward explaining the increasing regional divergence in the first period.

For the second period, both college education and exports continue to have a favorable effect on wages, while regional differences of the other factors appear to be stronger. The share of FITST employment is positive and significant for all four regions. However, its effect is now highest for the peripheral region, and lowest for the lagging region. Compared to the first period, 2003–2010 is characterized by lower wage effects of FITST for three regions, namely periphery (0.235 versus 0.311), emerging (0.189 versus 0.354), and lagging (0.032 versus 0.331) regions. On the other hand, the coefficient associated with the leading region increased from 0.125 to 0.185. Overall in the second period, the coefficient values of the peripheral and emerging regions are larger than the other two regions, which supports the process of regional catch-up.

Table 1 reveals that the ME industry favorably influences the emerging region’s wage level but has no effect on either the lagging or leading regions for the second period. It is only marginally significant and negative for the peripheral region. These results run counter to those in 1996–2002, in which a negative effect was observed for the emerging region. One explanation is that given the intensification of reforms and labor market restructuring in the late 1990s, SOEs may have become more efficient in the second period. In addition, Tong (2009) posits that SOEs have been forced to increase wages to compete with higher-paying foreign firms for skilled workers such as mining engineers. Hence the second period saw a relatively sharp rise in emerging region’s wage level in this industry, to one that surpasses its coastal counterpart.9

Another explanation lies in the nature of more recent investments. Much of the FIE invest-ment that has moved west is not necessarily associated with cheap labor but advanced goods

9For instance, whereas Ningxia’s wage in the mining industry was only about 60 percent of Shanghai’s in 1997, it had become 15 percent higher than Shanghai’s by 2010 (NBS, 1998, 2011).


(Moody, 2007). Moreover, these investments are predominantly directed toward exploration and development of natural resources. Attention has also been given to the modernization of the industrial system in the central region, including the establishment of technological zones in provinces such as Sichuan. Together with late-1990s reforms that involved massive labor retrenchment as SOEs hardened their budgets, industries in the mining and energy sectors may have become more efficient, which in turn has enhanced labor productivity in emerging provinces in more recent years. High wage growth in the ME industry likely enabled previ-ously peripheral provinces such as Anhui, Henan, Jiangsi, and Shanxi to advance to emerging status. Indeed the ME industry is responsible for a significant share of the employment in these provinces—i.e., 15 to 20 percent compared to 2 percent or less for their leading coun-terparts (Shanghai and Guangdong).

The most important difference in the second period is associated with the effect of FIE investment. Table 1 shows that it is positive and significant for all four regions compared to the first period, when it only benefited the leading region. Foreign investment participation’s contribution to wages is highest for the emerging region (0.325), followed by the lagging (0.293), peripheral (0.261), and leading (0.194) regions. This contrasts with the first period, in which FIE investment was either negative (peripheral region) or had no effect at all (emerging and lagging regions). From Figure 3, it is apparent that FIE investment rose rather sharply in the second period. One explanation for the positive effect of foreign investment may lie in the improvement of intellectual property rights since the 2000s, as inland provinces have become more open and integrated into the world economy (see Fig. 4). This should contribute to a decrease in regional wage inequality, as it helps provinces in peripheral and emerging regions to catch up.

Overall, the decrease in regional wage inequality in the second period would seem to be explained by three processes that have contributed to the catch-up potential of the periph-eral and emerging regions. First, employment in the FITST industry has a stronger effect on wages for the periphery than other regions. Second, FIE investment raises wages much more strongly in peripheral and emerging regions. And third, the relatively high share of employ-ment in the ME industry in the emerging region has contributed to higher wages. Hence, whereas the ME industry served as a source of wage growth for emerging provinces in the second period, it had a more deleterious effect in the first period.

CONCLUSION

China’s regional inequality has been a subject of investigation since the 1990s because of its implications for regional development. This paper differs from many of these stud-ies by focusing on wages instead of income inequality across regions and provinces. The advantage of this approach is that it offers a more nuanced picture of regional differences. Less-developed provinces in the interior may be more under-developed as highlighted by their lower income levels, but both their wages and growth rates may be higher than provinces in the coastal region. Consequently, instead of relying solely on conventional administrative regions as units of analysis, we developed a typology of wage regions that map regional wage dynamics and shifts over time.

The analysis reveals that regional wage inequality increased from 1996 to 2002 and then declined thereafter from 2003 to 2010. Regional divergence in the first period is character-ized by provinces in the leading region surging ahead of their peripheral and emerging coun-terparts. While FIE investment raised wages in the leading region during the first period, it adversely affected wages in the periphery and had no effect at all in the lagging and emerging

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regions. At the same time, inefficient SOE-run industries in the mining and energy sector resulted in negative wage outcomes for the emerging and lagging regions. The first period’s rise in regional wage inequality may thus be explained by the relatively unproductive eco-nomic activities of FIE investment and in the ME industry among provinces in the peripheral and emerging regions. Only FITST exerted a favorable effect on wages, but its employment share was too small to offset the overall negative trend.

By the second period, however, regional wage inequality was arrested and even declined. The regional convergence in wage levels was propelled by three factors. First, FIE investment as well as the FITST industries both exerted a higher positive wage effect in the peripheral and emerging regions than in the leading region, enabling wages in the former to grow faster than in the latter. Second, the ME industry had become more efficient, and was paying relatively high wages in provinces in the emerging region. Third, wage growth in the leading region was slowing down: FIE investment and employment share in the FITST industry contributed less to wage growth than their peripheral and emerging counterparts. The overall effect is a slowing of wage growth in the leading region and catch-up of the peripheral and emerg-ing regions. This resulted in important spatial shifts over the two periods: interior provinces such as Chongqing and Sichuan joined the ranks of the leading provinces while peripheral provinces such as Jiangxi, Henan, Shanxi, and others began to assume emerging status. At the same time, Beijing, Shanghai, and Zhejiang fell to lagging status.

The analysis in this paper has moved away from more traditional notions of regional shifts between the coast and the interior. It suggests that some interior provinces can become part of the leading region, and that coastal provinces may not always retain their lead as a result of slower wage growth. However, one caveat should also be noted. Wages are not a perfect indicator of labor productivity in China. As noted earlier, mobility continues to be low in certain sectors of the labor market, and the legacy of a socialist wage structure has meant that highly educated and skilled workers may still be under-compensated. Nonetheless, the analysis suggests that overall, policies that encourage the westward movement of FIE invest-ment, the restructuring of ME-oriented SOEs, and economic development of the industrial heartland would seem to be working, as these activities are having a favorable effect on regional development in terms of wage growth and levels.

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Regional Wage Inequality in China, 1996–2010

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