TY - JOUR AU - Heckman,James J. AU - Smith,Jeffrey A. TI - The Pre-Program Earnings Dip and the Determinants of Participation in a Social Program: Implications for Simple Program Evaluation Strategies JF - National Bureau of Economic Research Working Paper Series VL - No. 6983 PY - 1999 Y2 - February 1999 UR - http://www.nber.org/papers/w6983 L1 - http://www.nber.org/papers/w6983.pdf N1 - Author contact info: James J. Heckman Department of Economics The University of Chicago 1126 E. 59th Street Chicago, IL 60637 Tel: 773/702-0634 Fax: 773/702-8490 E-Mail: jjh@uchicago.edu Jeffrey Smith Department of Economics University of Michigan 238 Lorch Hall 611 Tappan Street Ann Arbor, MI 48109-1220 Tel: 734/764-5359 E-Mail: econjeff@umich.edu AB - The key to estimating the impact of a program is constructing the counterfactual outcome representing what would have happened in its absence. This problem becomes more complicated when agents self-select into the program rather than being exogenously assigned to it. This paper uses data from a major social experiment to identify what would have happened to the earnings of self-selected participants in a job training program had they not participated in it. We investigate the implications of these earnings patterns for the validity of widely-used before-after and difference-in-differences estimators. Motivated by the failure of these estimators to produce credible estimates, we investigate the determinants of program participation. We find that labor force status dynamics, rather than earnings or employment dynamics, drive the participation process. Our evidence suggests that training programs often function as a form of job search. Methods that control only for earnings dynamics, like the conventional difference-in-differences estimator, do not adequately capture the underlying differences between participants and non-participants. We use the estimated probabilities of participation in both matching estimators and a nonparametric, conditional version of the differences-in-differences estimator and produce large reductions in the selection bias in non-experimental estimates of the effect of training on earnings. ER -