Labor Market Returns to Education and English Language Skills in the People's Republic of China: An Update

The People's Republic of China (PRC) saw a four-fold increase in the level of consumption per capita and unprecedented economic growth during 1980–2010. The country's transition to a market economy saw the dissolution of social safety net programs and the end of full employment. Substantial physical capital investment during this transition led to greater demand for high-skilled labor, thereby increasing the importance of education as a determinant of labor market earnings (Heckman and Yi 2012). In prereform years, wages were administratively set, which suppressed the true returns to cognitive skills and schooling (Fleisher and Chen 1997, Chen and Feng 2000, Démurger 2001, Fleisher and Wang 2004). Returns to schooling were low in the early years after the beginning of economic reform in 1978 but increased in the 1990s (Zhao and Zhou 2002).¹ Therefore, an updated analysis of how education is paying off in the labor market is important for understanding the evolution of income distribution in transition economies.

The PRC's rapid economic growth was accompanied by a considerable increase in earnings inequality.² Moreover, the country's postreform “open door” policy attracted foreign direct investment and multinational companies, leading to strong demand for skilled workers along the rapidly expanding industrial coast.³ Therefore, it is important to understand how skills and education are rewarded across rural and urban locations, and across coastal and inland cities.⁴

Understanding the determinants of rising returns to education—a labor market phenomenon in transition economies—can also help us understand the difference between the PRC and other transition countries in terms of labor market characteristics. Unsurprisingly, following the shift from an administratively determined wage system to a market-oriented one in the early 1990s, there has been a significant increase in research on the economic profitability of human capital investment in the PRC.

Most estimates of labor market returns correspond to the early years of reform and hence are unlikely to be a good guide given the unprecedented transformation of the PRC economy during the 1990s. Spatial differences in infrastructure growth and physical investment are also likely to have caused important variations in the way schooling impacts labor market earnings (Fleisher and Chen 1997). Therefore, we add to the existing literature by using the Chinese General Social Survey (CGSS) 2010 dataset and provide an up-to-date account of the labor market returns to education in the PRC.

Our empirical model accounts for two important determinants of earnings: health capital, which includes height, body mass index (BMI), and self-reported health status; and English language proficiency that were both ignored by most of the recent studies on the PRC. Moreover, our empirical analysis addresses concerns over endogeneity and sample selection biases. We use information on parental death during the respondent's childhood and parental schooling as excluded instruments to estimate the instrumental variable (IV) model. Nonrandom selection into waged work is modeled using data on various measures of nonlabor income. Lastly, we report estimates for various subgroups—men versus women, rural versus urban, and coastal versus inland provinces—to document the heterogeneous nature of returns to schooling and skills in postreform PRC.

The rest of the paper is organized as follows. Section II briefly reviews the literature. Section III and section IV describe the data and empirical framework used in our study, respectively. Section V presents our econometric results. We conclude in section VI.

Existing studies on the PRC have estimated a Mincer-type earnings function using a variety of micro datasets. Our review of the published literature on returns to education for the period 1987–2016 identified a total of 68 studies (Table 1).⁵

Of these studies, 52 included residents in urban areas, 8 included residents in rural areas, and 10 were rural–urban migrants, while only 6 covered both urban and rural areas. Most studies (59) used household survey datasets. These include the Chinese Household Income Project (CHIP, 27 studies); China Health and Nutrition Survey (CHNS, 5); Chinese Twins Survey (4); China Urban Labor Survey (CULS, 3); Panel Data of Urban Residents from 20 cities in six provinces (3); China Urban Household Income and Expenditure Survey (CUHIES, 2); and Urban Household Survey (UHS, 2). A total of 13 studies used data from other well-established household surveys, such as the Chinese Labor Market Research Project (CLMRP) and Rural Urban Migration in China (RUMiC), among others. The remaining 8 studies used data from several firm-based surveys, while only 1 study (Mishra and Smyth 2015) used data from both a household survey (China Household Finance Survey) and a firm-level survey (Shanghai matched worker-firm survey 2007). In this section, we discuss only those studies that used household survey datasets.

A stylized fact from the literature is that returns to education in the PRC labor market in the 1980s and early 1990s were extremely low compared with the average returns in other Asian countries (9.6%), low- and middle-income countries (11.2%–11.7%), and the world (10.1%) (Psacharopoulos 1994). The rate of return in studies using data from the 1986, 1988, and 1993 CHIP surveys ranged from 1.5% to 4.5% for urban areas (Knight and Song 1991, 1995; Xie and Hannum 1996; Johnson and Chow 1997; Liu 1998; Maurer-Fazio 1999) and 0%–4% for rural areas (Knight and Song 1993; Parish, Zhe, and Li 1995; Johnson and Chow 1997). Apart from the findings using CHIP dataset, researchers who employed data from other household surveys during this period found comparatively low rates of return to schooling, around 3.7%–5.9% for urban areas, compared with 2.3%–4.8% for rural areas (Jamison and Van Der Gaag 1987, Byron and Manaloto 1990, Yang 1997, Wei et al. 1999, Maurer-Fazio 1999, Zhou 2000, Zhao and Zhou 2002, Fleisher and Wang 2005).

Another stylized fact is that returns to education have increased since the mid-1990s, along with improvements in wages and workers’ contractual rights (Chan and Nadvi 2014). Studies that employed CHIP datasets found that the economic returns to each additional year of schooling increased to around 4.4%–8.9% in 1995 (Li 2003; Bishop and Chiou 2004; Li and Luo 2004; Bishop, Luo, and Wang 2005; Hauser and Xie 2005; Yang 2005), 4.1% in 1999 (Knight and Song 2005), 7.5%–8.1% in 2002 among urban residents (Appleton, Song, and Xia 2005; Wang 2013), and 3.6%–7.3% in 2002 among migrants (Démurger et al. 2009).

Findings from studies using non-CHIP datasets also indicate an increased rate of return after 1995. For example, research using another widely used dataset, CHNS, found that the rate of return rose sharply to 6.9% in 2000 (Qiu and Hudson 2010), 8.1% in 2004 (Chen and Hamori 2009), and around 9% in 2006 (Fang et al. 2012) in urban areas. Again, based on the CHNS dataset, Ren and Miller (2012) found that the returns to women increased from 2% in 1993 to 7% in 2004, while the returns to men increased from 0.8% to 3.1%. Similarly, Kang and Peng (2012) documented a larger increase in returns to education for Chinese women than men using the expanded CHNS dataset from 1989 to 2009. More precisely, the rate increased from 2.2% in 1989 to 10.3% in 2009 for women, but only from 2.6% to 7% for men. Additionally, these increased returns to schooling since the mid-1990s have been recorded in a large number of studies that used non-CHIP or non-CHNS survey datasets, including studies on rural workers (De Brauw and Rozelle 2008); migrant workers (Meng and Zhang 2001; Maurer-Fazio and Dinh 2004; De Brauw and Rozelle 2008; Deng and Li 2010; Frijters, Lee, and Meng 2010; Sakellariou and Fang 2016); and urban workers using the Chinese Twins Survey dataset (Li, Liu, Ma, and Zhang 2005; Zhang, Liu, and Yung 2007; Li et al. 2007; Li et al. 2012); CULS (Giles, Park, and Wang 2008; Cai and Du 2011; Gao and Smyth 2015); and CUHIES (Meng, Shen, and Xue 2013).

Apart from the overall returns to education, earlier studies looked into returns to specific education levels. Studies based on data from the period after higher education reform documented a sharp increase in returns to college education (Heckman and Li 2004; Fleisher et al. 2005; Giles, Park, and Wang 2008; Qian and Smyth 2008b; Zhong 2011; Li et al. 2012; Wang 2012; Carnoy et al. 2013; Meng, Shen, and Xue 2013), compared with those from before the reform period (Gustafsson and Li 2000, Knight and Song 2003, Li 2003, Bishop and Chiou 2004). Moreover, research on the postreform period argued that graduates from elite colleges earned a premium over other college graduates even after controlling for cognitive ability, academic major, college location, and students’ individual characteristics and family backgrounds (Zhong 2011, Li et al. 2012). Existing literature also found that women benefited more from a university education than men, and similarly, urban residents earned more than rural residents with the same college degree (Qian and Smyth 2008, Wang 2012).

The pattern of returns to education in different regions has also changed since the mid-1990s. In contrast to the finding of Liu (1998), Li (2003) observed that the rate of return was higher in less developed provinces, such as Gansu, than in high-income provinces, such as Guangdong.

There are additional stylized facts relating to methodological issues. First, recent research has employed an instrumental variable (IV) approach to solve the endogeneity bias in educational attainment.⁶ For the PRC, the IV estimates were higher than the corresponding ordinary least squares (OLS) estimates (Fleisher and Wang 2004; Heckman and Li 2004; Li and Luo 2004; Fleisher et al. 2005; Fleisher and Wang 2005; Zhang, Liu, and Yung 2007; Giles, Park, and Wang 2008; Chen and Hamori 2009; Zhong 2011; Fang et al. 2012; Kang and Peng 2012; Wang 2012; Mishra and Smyth 2013; Wang 2013; Gao and Smyth 2015; Mishra and Smyth 2015; Sakellariou and Fang 2016). Most of these studies used family-background variables to estimate the IV model. For instance, Heckman and Li (2004) used the 2000 CUHIES, and parental education and year of birth as instruments for an individual's education. Similarly, based on the 1995 CHIP data, Li and Luo (2004) estimated returns to schooling for young workers in urban areas using parental education and variables related to siblings as instruments. Moreover, using the 1988–2002 CHIP data, Fleisher et al. (2005) explored the private returns to schooling at the university level. They found that the IV and semiparametric estimates on the rate of return for college graduates were higher when parental schooling was the proxy for ability.⁷

In summary, while findings from existing research vary in terms of data sources, methods, and study periods, they generally confirm that gains from schooling have increased significantly. The estimated returns to schooling are higher in urban areas than in rural locations, and higher for female workers than for male workers. Moreover, the IV estimates that used parental education as instruments for an individual's schooling yielded higher returns than the OLS estimates. For the prereform period, the OLS estimates of the rate of return are around 1.4%–1.9% in urban areas, compared with 0%–2.6% in rural areas. For the postreform period, the OLS estimates show an increase of 3.3%–9% for the full sample, compared with the IV estimates of up to 20%. The OLS estimates also show an increase of 0%–4.8% for the rural sample, and OLS estimates of 1.5%–12.1% for the urban sample, compared with the IV estimates of 4.2%–22.9%.

In this paper, we use data from the CGSS 2010. The main advantage of CGSS over existing datasets (such as CHNS, CHIP, CLMRP, and RUMiC) is that, in addition to being representative of rural and urban areas of the PRC, it offers information on both language skills and health of the respondents. The CGSS 2010 sampled a total of 11,783 individuals, where 38.7% were from rural areas and 51.8% were women. Table A1 provides a breakdown of the sample observations across different groups and work status: (i) agricultural waged work, (ii) nonagricultural waged work, (iii) self-employed, (iv) in the labor force but unemployed, and (v) not in the labor force. Most studies relied on the second age group, females age 16–55 years and males age 16–60 years (16 is the youngest legal working age in the PRC, while 55 and 60 are the official retirement age). In this study, we follow Schultz (2002) and restrict the analysis to women age 25–55 years and men age 25–60 years. Our main analysis is restricted to individuals in waged work, both in agricultural and nonagricultural sectors. After ignoring cases with missing data, our working sample contains 4,223 waged workers. Table A2 summarizes all variables used in the regression analysis.

As explained in section II, past studies on the PRC rarely controlled for cognitive skills despite the fact that market reforms of the 1990s were likely to have increased demand for such language and numeracy skills. Although schooling is expected to capture returns to cognitive skills, recent research documents a systematic economic return to cognitive skills around the world independent of schooling completed (Hanushek et al. 2015). Therefore, it is useful to know, in the context of the PRC, the pathways through which schooling is rewarded in the labor market.

Similarly, individuals with more schooling may have higher wages because they have better health and healthier behaviors.⁸ At the same time, school attendance may ignore skills acquired through social channels and in the workplace. Existing studies on the PRC have not fully considered the interaction between schooling, skills, and health capital in determining labor market success. Recent studies have instead focused on the possibility that schooling is endogenous, owing to omitted health components, or that return to schooling is understated, because it does not capture the quality of human capital. Consequently, researchers have modeled schooling attainment as an endogenous determinant of earnings by employing instrumental variable techniques (Li and Luo 2004, Heckman and Li 2004, Mishra and Smyth 2013, Chen and Hamori 2009, Mishra and Smyth 2015, Gao and Smyth 2015, Sakellariou and Fang 2016). In addition, some researchers have accounted for nonrandom selection into waged work by employing Heckman's (1979) two-step procedure (Zhang et al. 2005, Chen and Hamori 2009).

Keeping the above issues in mind, we specify a Mincerian earnings function where the log of monthly employment income (measured in renminbi) is regressed on years of schooling; work experience; work experience squared; gender; marital status; and a series of additional control variables including ethnicity; hukou type; marital status; health factors (height, self-reported health status, and BMI); proficiency in English; and location dummies.⁹ In addition, we account for the endogeneity of years of schooling in the earnings function.

Existing studies on developed and developing countries such as the PRC have attempted to address the issue in an IV framework in two settings: experimental and nonexperimental. Experimental studies rely on various institutional reforms, such as changes in the minimum age of leaving school (Harmon and Walker 1995), which result in exogenous variation in educational attainment. Nonexperimental studies, on the other hand, use family background (Li and Luo 2004); parents’ education (Heckman and Li 2004, Mishra and Smyth 2013); and spouse's education (Chen and Hamori 2009, Mishra and Smyth 2013, Gao and Smyth 2015) as instruments for education in the PRC and other countries (Trostel, Walker, and Woolley 2002). In this paper, we follow the second approach.

Therefore, in addition to OLS estimates, we present IV estimates where we instrument schooling completed using the following as excluded instruments: whether a parent died when the respondent was 14 years old, father's education, and mother's education. Following Case, Paxson, and Ableidinger (2004) and Gertler, Levine, and Ames (2004), we assume that timing of parental death is exogenous and serves as a negative shock to the respondent's schooling. On the other hand, the father's and mother's education are not correlated to their children's inherent abilities but have influence on their children's education when we use them as excluded instruments. It should be noted that studies that used parental education as an instrument to estimate returns to education in the PRC have often done so only for a subsample. This is because of how the survey is designed, where the instruments are available only for the respondents whose parents are present in the same household (Wang 2013). Our dataset doesn't suffer from this problem as all respondents are asked about parental background in a retrospective manner.

Apart from the endogeneity problem, another common methodological concern is the sample selection problem. If individuals select into the labor force on the basis of some unobserved attributes that also affect their wages, OLS estimates would yield biased estimates of the correlation between education and wages. In this paper, we follow Heckman (1979) to correct for nonrandom selection into waged work. First, we estimate a probit function for labor force participation where a sample selection correction term, lambda, is computed. Then the earnings function is estimated with the selection correction term as an extra variable. For the purpose of identifying the lambda term, at least one variable needs to be excluded from the wage equation, which is otherwise included in the probit equation. In our model, we follow Duraisamy (2002) and Asadullah (2006) who used data on nonlabor income (i.e., income received from bequest) as an excluded variable, leaving it out of the wage equation.¹⁰

In this section, we estimate returns to education by adding additional controls for factors that are correlated with both wages and schooling. Moreover, we formally include a measure of English language skills alongside schooling.¹¹ Table 2 reports OLS estimates of the Mincerian earnings function for the full sample. To understand the true returns to education, we pursue a stepwise approach, sequentially adding controls for language proficiency and three measures of health—height, self-reported health status, and BMI—in the regression function. Four patterns follow from our analysis.

First, education has a significant and positive impact on earnings in the PRC even after we control for English language proficiency and health capital (specification 3). The rate of return to an additional year of schooling ranges from 7.8% to 8.8% in the full sample. Our OLS estimate is similar to the estimated average rate reported in existing literature on the PRC, which ranges between 7% and 10% (Chen and Hamori 2009, Mishra and Smyth 2015). The biggest decline in estimated returns to education (from 8.8% to 8%) occurs when we control for language proficiency (specification 1 versus 2). The decline in the rate of return to education after controlling for language skills may simply be because English is part of the institutional education received in school. Therefore, when such components of education are included in the regression, they underestimate the true returns to education.

Second, in contrast to Mishra and Smyth (2015) where language proficiency has no statistically significant relationship with wages in the PRC, our results indicate a clear correlation—individuals with good English speaking and listening abilities earn wages that are 30% higher than those who do not have these skills (column 5). This positive earnings premium from foreign language skills is consistent with existing studies focusing on both developed countries (Leslie and Lindley 2001, Dustmann and Fabbri 2003 on the United Kingdom, Bleakley and Chin 2004 on the United States) and other developing countries (Azam, Chin, and Prakash 2013 on India; Di Paolo and Tansel 2015 on Turkey). Moreover, compared with returns to other skills, the returns to a foreign language (i.e., English skills) are extremely high (Fasih, Patrinos, and Sakellariou 2013).¹²

Third, consistent with the literature for both developed countries (Case and Paxson 2008, 2009; Heineck 2008; Hübler 2006) and developing countries (Schultz 2002, 2003; Dinda et al. 2006), health capital matters for earnings in the PRC. The OLS estimates suggest an additional centimeter of adult height is associated with a 1.4% higher wage in the full sample. This result is very close to some of the recent studies on returns to health capital in the PRC, including Gao and Smyth (2010) who were the first to confirm the height–wage premium in the PRC using the CULS 2005 data. They found that the wage return to height in urban areas is 1.1% and 0.9% for men and women, respectively. A later study by Elu and Price (2013) documented a similar rate of return to height (1.1%) based on urban and rural sample data from the CHNS 2006. Besides the height–wage premium, the returns to self-reported health status in our paper are also close to the results found by Zhang (2011) and Fang et al. (2012).

Fourth, work experience is not rewarded in terms of higher wages in the full sample. Subsample estimates of the earnings function presented in Table 3 show that this is also true for rural areas of the PRC.¹³ However, we find a significant and inverse U-shaped relationship between experience and earnings in urban areas of the PRC. This is consistent with previous studies on urban areas of the PRC (Bishop and Chiou 2004; Appleton, Song, and Xia 2005; Gao and Smyth 2015). The return to work experience is low, only 2.7% in urban areas of the PRC using the CGSS 2010 dataset. This is in line with Appleton, Song, and Xia (2005), who document an increase in returns to education but a decrease in the returns to work experience in postreform PRC. Bishop and Chiou (2004) also report evidence of declining returns to experience in urban areas of the PRC between 1988 and 1995. One possible explanation for this declining return is that, unlike education, experience was overrewarded prior to the reform. Payments for seniority were a central feature of the prereform wage structure.¹⁴ The other possibility is that skills acquired in a socialist economy by older workers have declined in value following the PRC's labor market transition to one more market oriented.

We check the reliability of OLS estimates on the causal relationship between education capital and wages by comparing them with estimates using the IV and Heckman two-step models. Table 4 presents the returns to schooling based on OLS, IV, and Heckman sample selection correction estimation models for the full sample. Subsample specific results (female versus male, urban versus rural, and coastal versus inland regions) are also presented in the bottom panels of Table 4. All regressions control for personal characteristics, location dummies, and height, which is a predetermined health endowment (height). IV estimates are based on early parental death and parental education as excluded instruments. This serves as a way to address potential endogeneity bias in the estimated returns to education. On the other hand, excluding nonlabor income from bequest in the Heckman model identifies the selectivity term (lambda). Comparing OLS and selectivity-corrected Heckman estimates can help us understand the extent of sample selection bias in the OLS estimates.

In the OLS model, the estimated return is 7.8%. Furthermore, the result of the endogeneity test in column 2 rejects the null hypothesis that the OLS estimates are consistent. Using father's and mother's education and whether a parent died when the respondent was 14 years old as instruments, the IV rate of return yields 20.9%, which is 13.1 percentage points higher than the OLS return. Moreover, consistent with the international literature (Mendolicchio and Rhein 2014), we find that returns to education for female workers (OLS: 9%; IV: 23.7%) are higher than for male workers (OLS: 7.1%; IV: 17.9%) in both methods. The gender difference in returns to schooling increases by approximately 3% after correcting for endogeneity bias.

Table 4 also reports returns to schooling for urban versus rural residents, and coastal versus inland provinces. Returns to schooling are higher for urban workers (OLS: 12.2%) than their rural counterparts (OLS: 2.2%), which is consistent with earlier studies that report a clear gap in returns to education between urban and rural areas (Zhang 2011). Once again, the OLS estimates are smaller than the IV estimates in all of these subsamples. In addition, the true rate of return is underestimated by 9.7 percentage points for urban workers and by 14.1 percentage points for workers in the coastal region, compared with only 6.6 percentage points for rural workers and 4.7 percentage points for workers in the western area.

One explanation for the relatively larger size of the IV estimates is that the instruments are weak or nearly invalid, or both (Murray 2006, Wooldridge 2002). The first stage regression results of the IV model along with the diagnostic test results are presented in Table A3. The F-test statistic corresponding to the estimated coefficients of early parental death and parental education are both significant and large (19 and 151, respectively), implying that the instruments are strong and significant determinants of years of schooling completed. Results also show that if a parent died when the child was 14 years old, then his years of schooling are reduced dramatically.

Turning to Heckman estimates, we do not find significant evidence of sample selection bias in our analysis. The identifying variable in the probit model has the expected sign (see Table A3). Higher unearned income from bequest is found to significantly decrease labor market participation. Nonetheless, the lambda term is not significant.

Overall, results from Table 4 confirm that for CGSS data, we can rely on OLS estimates to examine the causal relationship between schooling and earnings. OLS, if anything, only leads to more conservative estimates of the true returns to years of education completed in the PRC.¹⁵ Therefore, the next section exclusively discusses estimates obtained from the OLS regression of wages to understand how returns to education and language skills vary in the PRC.

Next, we explore two particular channels through which returns to skills and schooling may have changed in postreform years. First, we reestimate returns to education and language skills for all subsamples. Second, we reestimate the returns to different levels of education vis-à-vis language skills for the full sample and all subsamples. Because the OLS method is shown to consistently produce a conservative estimate in the previous section, we use this to understand the heterogeneous nature of the returns in our data.¹⁶

Table 5 repeats the analysis presented in Table 2 for various subsamples, but only results specific to education and language skills are reported. The subsamples are female, male, urban, rural, eastern region, central region, and western region. First, we find that returns to education for female workers (9%) are still higher than for male workers (7.1%), even after controlling for personal characteristics; health indicators (height, self-reported health status, and BMI); and geographic locations, which is consistent with findings from previous studies (Kang and Peng 2012, Mishra and Smyth 2013, Wang 2013). The returns to women with good English skills (36%) are also higher than the returns to men (23%, see column 5). Second, in addition to this gender gap in returns to schooling, we observe a clear rural–urban gap in the returns. Our finding is consistent with Meng, Shen, and Xue (2013) who find that the rates of return to each additional year of schooling increased from 8% to 9.3% during 1988–2009. This increase is even larger in urban areas (about 3 percentage points higher), which is similar to the finding of Gao and Smyth (2015) for the period 2001–2010.

Turning to region-specific estimates, our analysis shows clear regional differences in the returns to education. The bottom three panels of Table 5 report estimates by region. We find that the eastern region of the PRC (i.e., coastal provinces) has a comparatively higher rate of return to schooling (10.7%) than the central (5.6%) and western regions (5.4%).

One explanation for this regional difference in returns to education might be the observed widening gap in the production of cognitive skills, assessed in terms of differences in per student recurrent expenditure, teacher quality, and physical conditions of schools between coastal and inland areas (Qian and Smyth 2008a; Cheng 2009; Bickenbach and Liu 2013; Yang, Huang, and Liu 2014; Whalley and Xing 2014). Zhong (2011) examined the relationship between college quality and returns to higher education in the PRC and confirmed that the returns vary significantly depending on school quality. Moreover, he found that the maximum earnings gap between recipients of high- and low-quality higher education is 28%, and the gap for annual returns reached 1.4% after controlling for ability. Thus, better education quality at both basic education level (Cheng 2009) and higher education level (Bickenbach and Liu 2013) has resulted in higher returns to education in coastal areas of the PRC.

Given such convexities in the earnings function, income inequality is unlikely to be reduced through school education unless equality in access to higher education is ensured.¹⁸ This is also confirmed by the fact that educational endowments (schooling as well as skills) are distributed unequally in the PRC. The average number of years of schooling in Shanghai is 13.8, which is clearly higher than in the full sample (9.7), the eastern region including Shanghai (11.6), the eastern region excluding Shanghai (11.4), the central region (8.9), and the western region (8.1). Moreover, the percentage of respondents that have good English skills in Shanghai is also higher (43.1%) than in the full sample (11.2%), the eastern region including Shanghai (20.1%), the eastern region excluding Shanghai (17.8%), the central region (6.3%), and the western region (5.4%).

In this paper, we have reexamined the economic returns to education in the PRC using a recent dataset that is representative of all provinces. When the endogeneity problem is not addressed, OLS estimates underestimate the true returns to schooling in the PRC. The IV estimates yield a much higher return to schooling—20.9% compared with the OLS estimate of 7.8%. In addition to commonly used instruments such as father's and mother's education, we used parental death when the respondent was 14 years old, which proved to be a strong excluded instrument in the first stage regression.

In general, our estimates are much higher than what has been reported in earlier studies on the PRC, particularly those that used prereform labor market datasets. This confirms that returns to education have steadily increased following the process of transition toward a market economy. Our evidence also confirms that individuals in coastal and urban locations (particularly nonstate sector employees) and young workers with market-relevant language skills were rewarded with higher returns to their education than their counterparts in rural and inland locations. The findings support the conclusions of recent studies that it took about 2 decades for the PRC to raise their workers’ respective returns to education to the 10% level (Hung 2008; Meng, Shen, and Xue 2013).

The transition of the Chinese labor market from a centrally planned to a market-oriented system has contributed to a significant increase in earnings inequality by increasing the rewards for education and work experience. The estimated return is much larger for higher education compared with secondary education. Market reforms may have also increased the price of unobserved skills (Meng, Shen, and Xue 2013). This may explain why we find a systematic labor market advantage enjoyed by those with English language skills and why the return is highest in coastal provinces where private sector jobs have the highest concentration. This finding is consistent with the evidence that schooling contributes to labor market performance in educationally advanced countries by enhancing labor market relevant functional literacy skills. Given our evidence on the convexities in returns to education and the significance of human capital as a determinant of labor market performance in postreform PRC, policies that improve access to cognitive skills are likely to reduce income inequality and boost economic growth in the coming decades.