  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.
