Returns to Education in Off-farm Wage Employment in Rural China

1. Returns to Education in Off-farm Wage Employment in Rural China Zeyun Liu, Muyuan Qiu SEBA, BNU June 30th, 2011, Budapest

2. Introduction • Literature Review • Methodology • Data • Results • Conclusions

3. 1 Introduction • The human capital theory emphasizes the impact of human capital investment on labor market outcomes. As education is a major way to human capital accumulation, the economic returns to education (RTE) has been one of central topics in labor economics and economics of education. • In China, a rapidly growing transition economy, education is of primary importance to development. • As a majority of China’s population resides in rural areas and an even larger percentage of children are educated in rural areas, the effectiveness of rural education is of particular interest to policymakers and researchers.

4. 1 Introduction • Per capital annual net income of rural households in China (National Bureau of Statistics, 2010) • Wage income has been more and more important for rural residents • This paper attempts to estimate the impact of education on wage in rural China, based on three waves of rural household survey data during 1995-2007

5. 2 Literature Review • Estimating return to education in wage employment are based on Mincerian earning equation. • Previous empirical studies gave estimates of RTE in rural China during 1988 to 2007. The main finding is that RTE in rural China are very low. And we haven’t find a distinct picture of dynamic change in RTE in rural China during the two decades. Contrarily, RTE in urban China has been dramatically increased since late 1980s.

6. 2 Literature Review • Methodologically, previous studies have some limitations: • First, there is no a literatures can describe the long-term trend of RTE in rural China based on comparable data over time. • Secondly, many researches use unrepresentative samples of entire rural labor force. For examples, some papers only consider workers in one industry or economic sector (e.g. Meng, 1996; Gregory and Meng, 1995; Ho et al., 2002), some others only include migrant workers (e.g. Wang, et al., 2008). • Thirdly, most studies only use OLS and do not take account of econometric issues such as sample selection and omitted variables. • Finally, many studies use yearly or monthly wages rather than hourly wages.

7. 3 Methodology • We use three waves of cross-sectional household survey data conducted by the same research team. And in each wave, the sample is from the same nine provinces which are broadly representative of China’s regional development variation. So the results are comparable across time • Education has effects both on wage and on employment decision. Our sample includes all labor forces in rural areas, and we use two kinds of models to control for sample selectivity bias: • A standard Heckman selection model (Heckman, 1979) in which the employment decision is binary: a wage earner or not • The expanded selection model (Lee,1983; Durbin & McFadden, 1984; Dahl, 2002) in which employment decision is a multiple choice: a local wage earner, migrant wage earner, farmer or off-farm self-employee • We use a proxy for an individual’s ability to control for unobservable heterogeneities • We emphasize the need to use hourly wages, rather than monthly or yearly wages, in order to capture the effect of education on individual productivity

8. 3 Methodology (1) OLS • Base on the same model, RTE are estimated separately by three sub-samples: all wager earners, local wage earners and migrant wage earners

9. 3 Methodology (2) Two-step method with a correction for selectivity bias • Step 2: wage equation • Mother’s year of schooling is a proxy for an individual’s ability

10. 3 Methodology (2) Two-step method with a correction for selectivity bias • Step 1: selection equation • When estimating RTE for all wage earners, we use probit model to estimate selection equation. The outcome variable is a dummy variable indicating whether the individual is a wage earner or not • When estimating RTE for local wage earners and migrant wage earners, we use multinomial logit model to estimate selection equation. The outcome variable is a categorical variable indicating whether the individual is a local wage earner, migrant wage earner, farmer or off-farm self-employee. The reference category is farmers • In selection equations, covariates include all variables appear in wage equation. And two instruments, number of labors in the household and land area of the household, are also included as covariates

11. 4 Data • Three waves of rural household survey data from CHIP (Chinese Household Income Project) 1995, 2002, 2007 • In each year, the sample includes rural labors aged 16-60 from the same nine provinces • Developed provinces in eastern China: Zhejiang, Jiangsu, Guangdong, Hebei • Moderate developed provinces in central China: Hubei, Anhui, Henan • Less developed provinces in western China: Sichuan, Chongqing • 1995 data do not have information on educational attainment of household head and his/her spouse's parents, so we only use 2002 and 2007 data in selection models. • But we do use 1995, 2002 and 2007 data in OLS models, trying to describe the dynamic change pattern of RTE during 1995 to 2007

12. 4 Data • Observations

13. 5 Results (1) OLS models

14. 5 Results Preliminary results from OLS estimation • Returns to years of schooling for all rural wage earners declined from 3.9% in 1995 to 3.2% in 2007 • Returns to years of schooling for local wage earners are more stable than those for migrant wage earners • Compared with those without post-primary education experience, rural wage earners receiving upper-secondary education have a wage premium, but not for wager earners only complete lower-secondary education

15. 5 Results (2) Selection models • Selection equations: years of schooling *: p<0.1; **: p<0.05; ***: p<0.01 All regressions also include province dummies Regressions in column (1)-(4) use a multinomial logit model, and regressions in column (5)-(6) use a probit model

16. 5 Results (2) Selection models • Selection equations: educational level *: p<0.1; **: p<0.05; ***: p<0.01 All regressions also include province dummies Regressions in column (1)-(4) use a multinomial logit model, and regressions in column (5)-(6) use a probit model

17. 5 Results Determination of employment choice • Rural labor with higher educational attainment tends to be a local wage earner, but not to be a migrant wage earner • Rural labor with more labors in his/her household is more likely to be a migrant wage earner, but less likely to be a local wage earner • Rural labor with less land has higher probability to be a wage earner, either to be a local wage earner or a migrant wage earner

18. 5 Results (2) Selection models • Wage equations: years of schooling *: p<0.1; **: p<0.05; ***: p<0.01 All regressions also include province dummies Regressions in column (1)-(4) use a multinomial logit model to compute the inverse Mills ratio, and regressions in column (5)-(6) use a probit model to compute the inverse Mills ratio

19. 5 Results (2) Selection models • Wage equations: educational level *: p<0.1; **: p<0.05; ***: p<0.01 All regressions also include province dummies Regressions in column (1)-(4) use a multinomial logit model to compute the inverse Mills ratio, and regressions in column (5)-(6) use a probit model to compute the inverse Mills ratio

20. 5 Results Returns to education after controlling for sample selectivity bias • From 2002 to 2007, returns to years of schooling for all rural wage earners and local wage earners declined, but those for migrant wage earners increased • Compared with those without post-primary education experience, only people receiving upper-secondary education have a wage premium • Education has a effect on selection into the off-farm wage employment, but only help rural people find jobs inside their residential county

21. 5 Results (3) Returns to years of schooling based on different samples • CHIP rural household survey covers different provinces in the three years: 1995 survey covers 19 provinces, 2002 survey covers 22 provinces survey, and 2007 survey covers 9 provinces. How about if we do not restrict our samples to the same 9 provinces?

22. 6 Conclusions (1) Returns to education in wage employment in rural China are positive and statistically significant, but relatively low (2-5%) compared with those in urban China (>10%) and haven’t increased during 1995 to 2007 • Why? • Rural labor markets in China are under-developed • Rural wage earners primarily engage in temporary/seasonal jobs or low-end occupations such as blue-collar workers. Education is less valued in such jobs. • The quality of rural education is low.

23. 6 Conclusions (2) After controlling for ability bias and sample selectivity bias, return to education in 2007 is much higher for migrant wage earners than that for local wage earners. • Why? • Migrant wage earners mostly work in urban cities where labor markets are more market-oriented (3) In addition to raising hourly wages, education helps rural people access to wage employment. But only people who receive upper-secondary schooling or above can benefit from educational experience

24. 6 Conclusions (4) Methodologically • Nationally representative survey data which are comparable over time • Controlling for ability bias and sample selectivity bias • Using hourly wages, rather than daily or monthly wages, to control for the amount a person chooses to work, as highly educated people tend to choose to work less