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A Structural Analysis of the Correlated Random Coefficient Wage Regression Model

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  • Christian Belzil

    ()
    (GATE CNRS)

  • Jörgen Hansen

Abstract

We estimate a finite mixture dynamic programming model of schooling decisions in which the log wage regression function is set in a random coefficient framework. We also analyze the determinants of 3 counterfactual experiments (a college attendance subsidy, a high school graduation subsidy and an overall decrease in the rate of time preference) and examine a proposition often claimed in the “Average Treatment Effects” literature; that the discrepancy between OLS and IV estimates of the returns to schooling may be explained by the relatively higher returns experienced by those affected by exogenous policy changes. We find that the average return to experience upon entering the labor market (0.0863) exceeds the average return to schooling (0.0576) and we find more cross-sectional variability in the returns to experience than in the returns to schooling. Labor market skills (as opposed to taste for schooling) appear to be the prime factor explaining schooling attainments. We find little evidence in favor of a positive correlation between reactions induced by an exogenous experiment and the individual specific returns to schooling.

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Bibliographic Info

Paper provided by Groupe d'Analyse et de Théorie Economique (GATE), Centre national de la recherche scientifique (CNRS), Université Lyon 2, Ecole Normale Supérieure in its series Working Papers with number 0405.

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Length: 44 pages
Date of creation: Jan 2004
Date of revision:
Handle: RePEc:gat:wpaper:0405

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Keywords: Random Coefficient; Returns to Schooling; Comparative Advantages; Dynamic Programming; Dynamic Self-Selection;

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