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Using rich Swedish administrative data, we apply causal machine learning methods
to study how earnings losses after job displacement vary with observable characteristics
that may be relevant for targeting policy interventions for workers. Heterogeneity
in effects is as large within as across worker groups defined by age and
schooling, and as large within as across establishments. A substantial portion of
cross-establishment heterogeneity can be explained by industry and local labor market
characteristics, suggesting a role for place- and industry-based targeting. The
largest losses are concentrated among already vulnerable workers, indicating that
well-designed targeting policies can improve both efficiency and equity.
