Gibbons and Katz’s asymmetric information model of the labor market predicts that wage losses following displacement should be larger for layoffs than for plant closings. This was borne out in their empirical work. In this article, we examine how the difference in wage losses across plant closing and layoff varies with race and gender. We find that the basic prediction by Gibbons and Katz holds only for white males. We augment their asymmetric information model with heterogeneous human capital and show that this augmented model can match the data.

In this paper, we develop a model that captures key components of the Roy model, a search model, compensating differentials, and human capital accumulation on‐the‐job. We establish which components of the model can be non‐parametrically identified and which ones cannot. We estimate the model and use it to assess the relative contribution of the different factors for overall wage inequality. We find that variation in premarket skills (the key feature of the Roy model) is the most important component to account for the majority of wage variation. We also demonstrate that there is substantial interaction between the other components, most notably, that the importance of the job match obtained by search frictions varies from around 4% to around 29%, depending on how we account for other components. Inequality due to preferences for non‐pecuniary aspects of the job (which leads to compensating differentials) and search are both very important for explaining other features of the data. Search is important for turnover, but so are preferences for non‐pecuniary aspects of jobs as one‐third of all choices between two jobs would have resulted in a different outcome if the worker only cared about wages.

The goal of this paper is to use a semiparametric reduced form model to estimate the effects of various tuition subsidies. This approach expands on the tuition subsidy example in Ichimura and Taber (2000) in a number of dimensions. It has become common practice in the empirical literature to refer to any nonstructural empirical analysis as "reduced form." This is not the traditional sense of the phrase. A classic reduced form analysis (see e.g. Marschak, 1953) first specifies a structural model and then derives the reduced form parameters in terms of the structural parameters. While many recent studies have asserted to taking a reduced form approach, the structural parameters. While many recent studies have asserted to taking a reduced form approach, the structural model which the reduced form model should correspond is rarely specified. We explicitly specify a structural model and use the implied reduced form structure to estimate the effect of tuition subsidy policies. Specifying the underlying model has the advantage of being explicit about the assumptions that justify the analysis. This avoids Rosenzweig and Wolpin's (2000) criticism of work on natural 'natural experiments' that often leaves these conditions implicit. Our structural model is based on the model studied by Keane and Wolpin (1999). It is highly nonlinear and allows for more unobserved heterogeneity than the typical simultaneous equations framework that most previous work has used in reduced form estimation. Using hte specified structural model, we examine the assumptions discussed in Ichimura and Taber (2000) to justify reduced form estimation of the policy effects

In difference-in-differences applications, identification of the key parameter often arises from changes in policy by a small number of groups. In contrast, typical inference assumes that the number of groups changing policy is large. We present an alternative inference approach for a small (finite) number of policy changers, using information from a large sample of nonchanging groups. Treatment effect point estimators are not consistent, but we can consistently estimate their asymptotic distribution under any point null hypothesis about the treatment. Thus, treatment point estimators can be used as test statistics, and confidence intervals can be constructed using test statistic inversion.

We derive a formula to determine the degree to which a school choice program may harm public school stayers by luring the best students to other schools. The “cream skimming” effect is increasing in the degree of heterogeneity within schools, the school choice take-up rate of strong students relative to weak students, and the dependence of school outcomes on student body quality. We use the formula to investigate the cream skimming effect of hypothetical voucher programs on the high school graduation rate and other outcomes of the students who would remain in public school. We find small effects across a wide variety of model specifications and types of modest voucher programs.