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Counterfactual distributions of wages via quantile regression with endogeneity

  • Martinez-Sanchis, Elena
  • Mora, Juan
  • Kandemir, Ilker

Counterfactual decompositions allow researchers to analyze changes in wage distributions by discriminating between the effect of changes in population characteristics and the effect of changes in returns to these characteristics. Counterfactual distributions are derived here by recovering the conditional distribution via a set of quantile regressions, and correcting for the endogeneity of schooling decisions using a control function approach. This makes it possible to isolate the effect on the wage distribution of both changes in the conditional and unconditional distribution of schooling and changes in the distribution of unobserved ability. This methodology is used to analyze the sources of changes in wage distribution that took place in the United States from 1983 to 1993, using proximity to college for different parental background as instruments. The results show that the change in the distribution of ability had a negative effect on wages at the low quantiles, which almost compensates for the positive effect of the change in the schooling distribution over this period. The impact on wages of changes in the conditional distribution of unobserved ability is found to be larger than the impact of changes in the conditional distribution of distance to college.

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Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 56 (2012)
Issue (Month): 11 ()
Pages: 3212-3229

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Handle: RePEc:eee:csdana:v:56:y:2012:i:11:p:3212-3229
DOI: 10.1016/j.csda.2012.02.024
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