Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects

44 Pages Posted: 18 Jan 2006 Last revised: 12 Jul 2021

See all articles by David Lee

David Lee

University of California, Berkeley - Department of Economics; National Bureau of Economic Research (NBER)

Date Written: October 2005

Abstract

This paper empirically assesses the wage effects of the Job Corps program, one of the largest federally-funded job training programs in the United States. Even with the aid of a randomized experiment, the impact of a training program on wages is difficult to study because of sample selection, a pervasive problem in applied micro-econometric research. Wage rates are only observed for those who are employed, and employment status itself may be affected by the training program. This paper develops an intuitive trimming procedure for bounding average treatment effects in the presence of sample selection. In contrast to existing methods, the procedure requires neither exclusion restrictions nor a bounded support for the outcome of interest. Identification results, estimators, and their asymptotic distribution, are presented. The bounds suggest that the program raised wages, consistent with the notion that the Job Corps raises earnings by increasing human capital, rather than solely through encouraging work. The estimator is generally applicable to typical treatment evaluation problems in which there is non-random sample selection/attrition.

Suggested Citation

Lee, David S., Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects (October 2005). NBER Working Paper No. w11721, Available at SSRN: https://ssrn.com/abstract=837164

David S. Lee (Contact Author)

University of California, Berkeley - Department of Economics ( email )

Berkeley, CA 94720-3880
United States
510-642-4628 (Phone)

National Bureau of Economic Research (NBER)

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Cambridge, MA 02138
United States

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