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Econometrics pp 201-224 | Cite as

Selection Bias and Self-selection

  • James J. Heckman
Part of the The New Palgrave book series

Abstract

The problem of selection bias in economic and social statistics arises when a rule other than simple random sampling is used to sample the underlying population that is the object of interest. The distorted representation of a true population as a consequence of a sampling rule is the essence of the selection problem. Distorting selection rules may be the outcome of decisions of sample survey statisticians, self-selection decisions by the agents being studied or both.

Keywords

Population Distribution Reservation Wage Sample Selection Bias Empirical Content Market Wage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Bibliography

  1. Amemiya, T. 1984. Tobit models: a survey. Journal of Econometrics 24, 3–61.CrossRefGoogle Scholar
  2. Bjorklund, A. and Moffitt, R. 1986. Estimation of wage gains and welfare gains from self selection models. Review of Economics and Statistics 24, 1–63.Google Scholar
  3. Cain, G. and Watts, H. 1973. Toward a summary and synthesis of the evidence. In Income Maintenance and Labor Supply, ed. G. Cain and H. Watts, Madison: University of Wiscons in Press.Google Scholar
  4. Clark, K. and Summers, L. 1979. Labor market dynamics and unemployment: a reconsideration. Brookings Papers on Economic Activity, 13–60.Google Scholar
  5. Cosslett, S. 1981. Maximum likelihood estimation from choice based samples. Econometrica.Google Scholar
  6. Cosslett, S. 1984. Distribution free estimator of regression model with sample selectivity. Unpublished manuscript, University of Florida.Google Scholar
  7. Domencich, T. and McFadden, D. 1975. Urban Travel Demand. Amsterdam: North-Holland.Google Scholar
  8. Flinn, C. and Heckman, J. 1982. New methods for analyzing structural models of labor force dynamics. Journal of Econometrics 18, 5–168.CrossRefGoogle Scholar
  9. Gallant, R. and Nychka, R. 1984. Consistent estimation of the censored regression model. Unpublished manuscript, North Carolina State.Google Scholar
  10. Gill, R. and Wellner, J. 1985. Large sample theory of empirical distributions in biased sampling models. Unpublished manuscript, University of Washington.Google Scholar
  11. Goldberger, A. 1983. Abnormal selection bias. In Studies in Econometrics, Time Series and Multivariate Statistics, ed. S. Karlin, T. Amemiya and L. Goodman, Wiley, NY.Google Scholar
  12. Gronau, R. 1974. Wage comparisons–a selectivity bias. Journal of Political Economy 82 (6), 1119–1144.CrossRefGoogle Scholar
  13. Hall, R. 1982. The importance of lifetime jobs in the U.S. economy. American Economic Review 72, September, 716–724.Google Scholar
  14. Heckman, J. 1974. Shadow prices, market wages and labor supply. Econometrica 42 (4), 679–94.CrossRefGoogle Scholar
  15. Heckman, J. 1976. The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement 5(4), 475–92.Google Scholar
  16. Heckman, J. 1977. Sample selection bias as a specification error. Econometrica 47 (1), 153–62.CrossRefGoogle Scholar
  17. Heckman, J. and Honoré, B. 1986. The empirical content of the Roy model. Unpublished manuscript, University of Chicago.Google Scholar
  18. Heckman, J. and MaCurdy, T. 1981. New methods for estimating labor supply functions. In Research in Labor Economics, Vol. 4, ed. R. Ehrenberg, Greenwich, Conn.: JAI Press.Google Scholar
  19. Heckman, J. and Robb, R. 1985. Alternative methods for evaluating the effect of training on earnings. in Longitudinal Analysis of Labor Market Data, ed. J. Heckman and B. Singer, Cambridge: Cambridge University Press.Google Scholar
  20. Heckman, J. and Sedlacek, G. 1985. Heterogeneity, aggregation and market wage functions. Journal of Political Economy 93, December, 1077–125.CrossRefGoogle Scholar
  21. Heckman, J. and Singer, B. 1985. Econometric analysis of longitudinal data. In Handbook of Econometrics, Vol. III ed. Z. Griliches and M. Intriligator, Amsterdam: North-Holland.Google Scholar
  22. Lee, L.F. 1978. Unionism and wage rates: a simultaneous equations model with qualitative and limited dependent variables. International Economic Review 19, 415–33.CrossRefGoogle Scholar
  23. Manski, C. and Lerman, S. 1977. The estimation of choice probabilities from choice based samples. Econometrica 45, 1977–88.CrossRefGoogle Scholar
  24. Manski, C. and McFadden, D. 1981. Alternative estimates and sample designs for discrete choice analysis. In Structural Analysis of Discrete Data with Econometric Applications, ed. C. Manski and D. McFadden, Cambridge: MIT Press.Google Scholar
  25. Pearson, K. 1901. Mathematical contributions to the theory of evolution. Philosophical Transactions, 195, 1–47.CrossRefGoogle Scholar
  26. Rao, C.R. 1965. On discrete distributions arising out of methods of ascertainment. In Classical and Contagious Distributions ed. G. Patil, Calcutta: Pergamon Press.Google Scholar
  27. Roy, A.D. 1951. Some thoughts on the distribution of earnings. Oxford Economic Papers, 3, 135–46.Google Scholar
  28. Vardi, Y. 1983. Nonparametric estimation in the presence of length bias. Annals of Statistics 10, 616–20.CrossRefGoogle Scholar
  29. Vardi, Y. 1985. Empirical distributions in selection bias models. Annals of Statistics 13, 178–203.CrossRefGoogle Scholar
  30. Willis, R. and Rosen, S. 1979. Education and self selection. Journal of Political Economy 87, S7 - S36.CrossRefGoogle Scholar

Copyright information

© Palgrave Macmillan, a division of Macmillan Publishers Limited 1990

Authors and Affiliations

  • James J. Heckman

There are no affiliations available

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