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Abstract
This article examines the gender differential in the payoff to schooling in rural China. The analyses are based on a framework provided by the over education/required education/under education literature, and the decomposition developed by Chiswick and Miller (Citation2008). It shows that the payoff to correctly matched education in rural China is much higher for females than for males. Associated with this, the wage penalty where workers are under qualified in their occupation is greater for females than for males. Over educated females, however, are advantaged compared with their male counterparts. These findings are interpreted using the explanations offered for the gender differential in the payoff to schooling in the growing literature on earnings determination in China.
Acknowledgements
We are grateful to Meng Xin, two anonymous referees, and to participants at the 2009 International Symposium on Contemporary Labour Economics, at the Wang Yanan Institute for Studies in Economics of Xiamen University, for helpful comments. Ren acknowledges financial support from the University of Western Australia via a University Postgraduate Award and from the Australian Research Council via a Postgraduate Scholarship. Miller acknowledges financial assistance from the Australian Research Council.
Notes
1. The literature that compares the return to education in urban and rural areas, but which does not make a distinction between males and females (for example, Li and Li, Citation1994; Li C., 2003), will not be considered here.
2. CHIP was a joint research project between the Institute of Economics, Chinese Academy of Social Sciences and the Ford Foundation. Support was also provided by the Columbia East Asian Institute and the City University of New York. The sample includes observations from rural areas in all of the 28 provinces other than Tibet, Xinjiang and Taiwan. Four surveys of this project were conducted in 1988, 1995, 2002, and 2007.
3. In comparison, in urban areas the return to education was 4.46 per cent for females and 2.78 per cent for males.
4. Four counties were involved in this survey: Wuxi county in Jiangsu province, Nanhai county in Guangdong province, Jieshou county in Anhui province and Shangrao county in Jiangxi province.
5. This measure follows Chen and Hamori (Citation2009), who use the same data set, but is narrower than the measure of earnings in Zhang et al. (Citation2005) and Li H. (2003), where bonuses, subsidies and other labour-related income were included along with basic monthly earnings.
6. The recent analyses by Chiswick and Miller (Citation2010b) indicate that the findings in this literature are quite robust to the choice of reference years of education.
7. This earnings effect is computed using the algorithm proposed by Halvorsen and Palmquist (Citation1980), namely g = exp(c)−1, where g represents the percentage effect on the dependent variable and c represents the coefficient of a dummy variable.
8. Treating individuals who do not report valid wage data in this study of selection bias as non-participants suggests that these data are missing at random.
9. As ln (892) = 6.7935, these figures are computed as earnings = exp (6.7935−3*0.06348) and earnings = exp (6.7935+4*0.06348), respectively.
10. Earnings = exp (6.7935−3*0.09346) and earnings = exp (6.7935+4*0.09346), respectively.
11. Earnings = exp (6.7935−3*0.00749) and earnings = exp (6.7935−3*0.07633), respectively.
12. Earnings = exp (6.7935+4*0.05778) and earnings = exp (6.7935+4*0.05832), respectively.
13. The earnings effects associated with correctly matched education and under education for males (0.0635 and −0.0075) and for females (0.0935 and −0.0763) mean that the earnings positions of under educated male and female workers are similar (they differ by around one percentage point per year of schooling). Hence, the greater earnings penalty associated with under education among females can be viewed simply as a consequence of their failure to reap the greater rewards for matching on the basis of level of schooling in the female labour market.
