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Selection Bias, Comparative Advantage and Heterogeneous Returns to Education


  • James J. Heckman
  • Xuesong Li


This paper uses newly available Chinese micro data to estimate the return to college education for late 20th century China when allowing for heterogeneous returns among individuals selecting into schooling based on these differences. We use recently developed semiparametric methods to identify the parameters of interest. We demonstrate that heterogeneity among people in returns to schooling is substantial. People sort into schooling on the basis of the principle of comparative advantage, which we document to be an empirically important phenomenon in modern Chinese labor markets. Standard least squares or instrumental variable methods do not properly account for this sorting. Using new methods that do, we estimate the effect on earnings of sending a randomly selected person to college is a 43% increase in lifetime earnings (nearly 11% annually) in 2000 for young people in urban areas of six provinces of China. The effect of college on those who go is 13%. Our evidence, and simple least squares evidence, suggests that after 20-plus years of economic reform with market orientation, the return to education has increased substantially in China, compared to the returns measured in the 1980's and the early 1990's.

Suggested Citation

  • James J. Heckman & Xuesong Li, 2003. "Selection Bias, Comparative Advantage and Heterogeneous Returns to Education," NBER Working Papers 9877, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:9877
    Note: ED LS CH

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    References listed on IDEAS

    1. Pedro Carneiro & James J. Heckman, 2002. "The Evidence on Credit Constraints in Post--secondary Schooling," Economic Journal, Royal Economic Society, vol. 112(482), pages 705-734, October.
    2. James J. Heckman & Edward J. Vytlacil, 2000. "Local Instrumental Variables," NBER Technical Working Papers 0252, National Bureau of Economic Research, Inc.
    3. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, February.
    4. James J. Heckman & Lance J. Lochner & Petra E. Todd, 2003. "Fifty Years of Mincer Earnings Regressions," NBER Working Papers 9732, National Bureau of Economic Research, Inc.
    5. Griliches, Zvi, 1977. "Estimating the Returns to Schooling: Some Econometric Problems," Econometrica, Econometric Society, vol. 45(1), pages 1-22, January.
    6. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
    7. Bjorklund, Anders & Moffitt, Robert, 1987. "The Estimation of Wage Gains and Welfare Gains in Self-selection," The Review of Economics and Statistics, MIT Press, vol. 69(1), pages 42-49, February.
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    Cited by:

    1. GOH, Chor-ching & LUO, Xubei & ZHU, Nong, 2009. "Income growth, inequality and poverty reduction: A case study of eight provinces in China," China Economic Review, Elsevier, vol. 20(3), pages 485-496, September.
    2. B. Müge Tunaer & Yaprak Gülcan, 2006. "Measuring Returns to Education in Turkey," Papers of the Annual IUE-SUNY Cortland Conference in Economics,in: Proceedings of the Conference on Human and Economic Resources, pages 66-71 Izmir University of Economics.
    3. Wagstaff, Adam, 2005. "Decomposing changes in income inequality into vertical and horizontal redistribution and reranking, withapplications to China and Vietnam," Policy Research Working Paper Series 3559, The World Bank.
    4. LILLO, Adelaida & CASADO-DÍAZ, José M., 2010. "On The Rewards To Education In Spain: Endogeneity And Regional Differences," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 10(3).

    More about this item

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models


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