Bounds on Treatment Effects in the Presence of Sample Selection and Noncompliance: The Wage Effects of Job Corps

Abstract

Randomized and natural experiments are commonly used in economics and other social science fields to estimate the effect of programs and interventions. Even when employing experimental data, assessing the impact of a treatment is often complicated by the presence of sample selection (outcomes are only observed for a selected group) and noncompliance (some treatment group individuals do not receive the treatment while some control individuals do). We address both of these identification problems simultaneously and derive nonparametric bounds for average treatment effects within a principal stratification framework. We employ these bounds to empirically assess the wage effects of Job Corps (JC), the most comprehensive and largest federally funded job training program for disadvantaged youth in the United States. Our results strongly suggest positive average effects of JC on wages for individuals who comply with their treatment assignment and would be employed whether or not they enrolled in JC (the “always-employed compliers”). Under relatively weak monotonicity and mean dominance assumptions, we find that this average effect is between 5.7% and 13.9% 4 years after randomization, and between 7.7% and 17.5% for non-Hispanics. Our results are consistent with larger effects of JC on wages than those found without adjusting for noncompliance.

KEY WORDS:

• Instrumental variables
• Nonparametric partial identification
• Principal stratification
• Program evaluation
• Training programs

ACKNOWLEDGMENTS

Detailed comments from the Editor, Associate Editor, and three anonymous referees greatly improved the article and are gratefully acknowledged. We are also grateful for comments from Marianne Bitler, Eduardo Fajnzylber, Alfonso Flores-Lagunes, Laura Giuliano, Guido Imbens, Fabrizia Mealli, Oscar Mitnik, Christopher Parmeter, Jeffrey Smith, Zhong Zhao, James Ziliak, and conference/seminar participants at University of Miami, Renmin University of China, the Theory and Practice of Program Evaluation Workshop at CEPS/INSTEAD, the 2012 Impact Evaluation Network Meeting at Harvard University, the 2012 Midwest Econometrics Group Meeting at University of Kentucky, the 2014 Southern California Conference in Applied Microeconomics at Claremont McKenna College, and the 2014 International Association for Applied Econometrics Conference at Queen Mary University of London. Flores acknowledges summer research support from the Orfalea College of Business at California Polytechnic State University. All errors are our own.