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.

It is well understood that classical sample selection models are not semiparametrically identified without exclusion restrictions. Lee (2009) developed bounds for the parameters in a model that nests the semiparametric sample selection model. These bounds can be wide. In this paper, we investigate bounds that impose the full structure of a sample selection model with errors that are independent of the explanatory variables but have unknown distribution. The additional structure can significantly reduce the identified set for the parameters of interest. Specifically, we construct the identified set for the parameter vector of interest. It is a one‐dimensional line segment in the parameter space, and we demonstrate that this line segment can be short in practice. We show that the identified set is sharp when the model is correct and empty when there exist no parameter values that make the sample selection model consistent with the data. We also provide non‐sharp bounds under the assumption that the model is correct. These are easier to compute and associated with lower statistical uncertainty than the sharp bounds. Throughout the paper, we illustrate our approach by estimating a standard sample selection model for wages.

The bootstrap is a convenient tool for calculating standard errors of the parameter estimates of complicated econometric models. Unfortunately, the fact that these models are complicated often makes the bootstrap extremely slow or even practically infeasible. This paper proposes an alternative to the bootstrap that relies only on the estimation of one-dimensional parameters. We introduce the idea in the context of M and GMM estimators. A modification of the approach can be used to estimate the variance of two-step estimators.

We use the New York Fed Consumer Credit Panel data set to empirically examine how past house price growth influences the timing of homeownership. We find that the median individual in metropolitan areas with the highest quartile house price growth becomes a homeowner 5 years earlier than that in areas with the lowest quartile house price growth. The result is consistent with a life cycle housing-demand model in which high past price growth increases expectations of future price growth thus accelerating home purchases at young ages. We show that extrapolative expectations formed by homebuyers are a necessary channel to explain the result.