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Estimation of Earnings- and Schooling Choice Relations: A Likelihood Approach

In this paper we develop a full information maximum likelihood method for the estimation of a joint model for the choice of length of schooling and the corresponding earnings equation. The model for schooling is assumed to be an ordered probit model, whereas the earnings equation is allowed to be very general with explanatory variables that are flexible transformations of schooling and experience. The coefficients associated with length of schooling and experience are allowed to be random and all the random terms of the model may be correlated. Under normality assumptions, we show that the joint probability distribution for schooling and earnings can be expressed on a closed form that is tractable for empirical analysis.

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Paper provided by Statistics Norway, Research Department in its series Discussion Papers with number 486.

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Date of creation: Nov 2006
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Handle: RePEc:ssb:dispap:486
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  1. Robert J. Willis & Sherwin Rosen, 1978. "Education and Self-Selection," NBER Working Papers 0249, National Bureau of Economic Research, Inc.
  2. David Card, 1993. "Using Geographic Variation in College Proximity to Estimate the Return to Schooling," Working Papers 696, Princeton University, Department of Economics, Industrial Relations Section..
  3. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, vol. 69(5), pages 1127-60, September.
  4. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, vol. 47(1), pages 153-61, January.
  5. James Heckman & Edward Vytlacil, 1998. "Instrumental Variables Methods for the Correlated Random Coefficient Model: Estimating the Average Rate of Return to Schooling When the Return is Correlated with Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 33(4), pages 974-987.
  6. Garen, John, 1984. "The Returns to Schooling: A Selectivity Bias Approach with a Continuous Choice Variable," Econometrica, Econometric Society, vol. 52(5), pages 1199-1218, September.
  7. J.D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc.
  8. Wooldridge, Jeffrey M., 1997. "On two stage least squares estimation of the average treatment effect in a random coefficient model," Economics Letters, Elsevier, vol. 56(2), pages 129-133, October.
  9. Gronau, Reuben, 1974. "Wage Comparisons-A Selectivity Bias," Journal of Political Economy, University of Chicago Press, vol. 82(6), pages 1119-43, Nov.-Dec..
  10. Griliches, Zvi, 1977. "Estimating the Returns to Schooling: Some Econometric Problems," Econometrica, Econometric Society, vol. 45(1), pages 1-22, January.
  11. Wooldridge, Jeffrey M., 2003. "Further results on instrumental variables estimation of average treatment effects in the correlated random coefficient model," Economics Letters, Elsevier, vol. 79(2), pages 185-191, May.
  12. Pedro Carneiro & Karsten T. Hansen & James J. Heckman, 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College," NBER Working Papers 9546, National Bureau of Economic Research, Inc.
  13. Vella, Francis & Verbeek, Marno, 1999. "Estimating and Interpreting Models with Endogenous Treatment Effects," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 473-78, October.
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