We examine how students of varying abilities sort (and are sorted) into colleges of varying qualities. Our data indicate substantial amounts of both academic undermatch (high-ability students at low-quality colleges) and academic overmatch (low-ability students at high-quality colleges). Student application and enrollment decisions, rather than college admission decisions, drive most deviations from academic assortative matching. Financial constraints, information, and the public college options facing students all affect this sorting, but mainly via college quality rather than the match between ability and quality. More informed students attend higher-quality colleges, even when doing so involves overmatching.

Existing studies of the effects of college quality on wages typically rely on a single proxy variable for college quality. This study questions the wisdom of using a single proxy given that it likely contains substantial measurement error. We consider four econometric approaches to the problem that involve the use of multiple proxies for college quality: factor analysis, instruments variables, a method recently proposed by Lubotsky and Wittenberg, and a GMM estimator. Our estimates suggest that the existing literature understates the wage effects of college quality and illustrate the value of using multiple proxies in this and other similar contexts.

This article decomposes the participation process of a prototypical program into eligibility, awareness, application, acceptance, and enrollment. With this decomposition, we determine the sources of unequal participation for different groups and demonstrate that variables often have very different effects at different stages in the participation process. Our analysis shows that personal choices substantially affect participation and that awareness of program eligibility is a major source of variation in participation.

This paper re-creates three of the samples from LaLonde’s famous 1986 paper that began the literature on “within-study designs” that uses experiments as benchmarks against which to assess the performance of nonexperimental identification strategies. In particular, we recreate the experimental data for the target group of women on welfare from the National Supported Work (NSW) Demonstration and two of the corresponding comparison groups drawn from the Panel Study of Income Dynamics (PSID). The loss of these data resulted in the (sizable) subsequent literature devoting its attention solely to the NSW men. In addition to repeating LaLonde’s analyses on our recreations of his files for the AFDC women, we apply (many of) the estimators from later papers by Dehejia and Wahba and by Smith and Todd to these data. Our findings support the general view in the literature that women on welfare pose a less difficult selection problem when evaluating employment and training programs. They also call into question the generalizability of some of the broad conclusions that Dehejia and Wahba and Smith and Todd draw from their analyses of the NSW men.

Reconciling Conflicting Evidence on the Performance of Propensity-Score Matching Methods by Jeffrey A. Smith and Petra E. Todd. Published in volume 91, issue 2, pages 112-118 of American Economic Review, May 2001

This paper explores issues that arise in the evaluation of social programs using experimental data in the frequently encountered case where some of the experimental treatment group members drop out of the program prior to receiving treatment. We begin with the standard estimator for this case and the identifying assumption upon which it rests. We then examine the behavior of the estimator when the dropouts receive a partial “dose” of the program treatment prior to dropping out of the program. In the case of partial treatment, the identifying assumption is typically violated, thereby making the estimator inconsistent for the conventional parameter of interest: the impact of full treatment on the fully treated. We develop a test of the identifying assumption underlying the standard estimator and consider whether exclusion restrictions produce identification of the mean impact of the program when this assumption fails to hold. Finally, we discuss alternative parameters of interest in the presence of partial treatment among the dropouts and argue that the conventional parameter is not always the economically interesting one. We apply our methods to data from a recent experimental evaluation of the Job Training Partnership Act (JTPA) program.

This paper develops and applies semiparametric econometric methods to estimate the form of selection bias that arises from using nonexperimental comparison groups to evaluate social programs and to test the identifying assumptions that justify three widely-used classes of estimators and our extensions of them: (a) the method of matching; (b) the classical econometric selection model which represents the bias solely as a function of the probability of participation; and (c) the method of difference-in-differences. Using data from an experiment on a prototypical social program combined with unusually rich data from a nonexperimental comparison group, we reject the assumptions justifying matching and our extensions of that method but find evidence in support of the index-sufficient selection bias model and the assumptions that justify application of a conditional semiparametric version of the method of difference-in-difference. Fa comparable people and to appropriately weight participants and nonparticipants a sources of selection bias as conveniently measured. We present a rigorous defin bias and find that in our data it is a small component of conventially meausred it is still substantial when compared with experimentally-estimated program impa matching participants to comparison group members in the same labor market, givi same questionnaire, and making sure they have comparable characteristics substan the performance of any econometric program evaluation estimator. We show how t analysis to estimate the impact of treatment on the treated using ordinary obser