Heterogeneous Effects of Job Displacement on Earnings

TL;DR

This paper investigates how the impact of job displacement on earnings varies across individuals, revealing substantial heterogeneity and differences based on demographic factors using panel data and quantile regression methods.

Contribution

It introduces a novel approach to identify the distribution of individual effects of job displacement, accounting for unobserved heterogeneity and distributional features.

Findings

42% of displaced workers earn more than they would have without displacement

Substantial heterogeneity in effects across individuals and groups

Major differences in effects across education, sex, age, and earnings levels

Abstract

This paper considers how the effect of job displacement varies across different individuals. In particular, our interest centers on features of the distribution of the individual-level effect of job displacement. Identifying features of this distribution is particularly challenging -- e.g., even if we could randomly assign workers to be displaced or not, many of the parameters that we consider would not be point identified. We exploit our access to panel data, and our approach relies on comparing outcomes of displaced workers to outcomes the same workers would have experienced if they had not been displaced and if they maintained the same rank in the distribution of earnings as they had before they were displaced. Using data from the Displaced Workers Survey, we find that displaced workers earn about $157 per week less, on average, than they would have earned if they had not been displaced. We also find that there is substantial heterogeneity. We estimate that 42% of workers have higher earnings than they would have had if they had not been displaced and that a large fraction of workers have experienced substantially more negative effects than the average effect of displacement. Finally, we also document major differences in the distribution of the effect of job displacement across education levels, sex, age, and counterfactual earnings levels. Throughout the paper, we rely heavily on quantile regression. First, we use quantile regression as a flexible (yet feasible) first step estimator of conditional distributions and quantile functions that our main results build on. We also use quantile regression to study how covariates affect the distribution of the individual-level effect of job displacement.