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.

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.