We use a panel of survey responses linked to administrative data in Germany to measure the depreciation of skills while workers are unemployed. Both the reemployment hazard rate and reemployment earnings steadily fall with unemployment duration, and indicators of depression and loneliness rise substantially. Despite this, we find no decline in a wide range of cognitive and noncognitive skills while workers remain unemployed. We find the same pattern in a panel of American workers. The results imply that skill depreciation in general human capital is unlikely to be a major explanation for duration dependence.

We systematically review studies of how unemployment benefits affect unemployment duration. Statistically significant findings are eight times more likely to be published. Correcting for publication bias cuts the average elasticity by a third. Meta-analysis is a data-driven way to aggregate estimates across policy contexts and generalize sufficient statistics methods to compute the global optimal policy. Although existing consumption drop-based approaches typically imply an optimal replacement rate near zero, our corrected estimates imply an optimal replacement rate of 28% in the US. We are unable to reject the hypothesis that the “micro” elasticity is equal to the “macro” elasticity.

Approximately 10 percent of Unemployment Insurance (UI) claimants in the United States are denied benefits after being deemed at-fault for their job loss by a government examiner. Using administrative data from California and an examiner leniency design, we estimate the causal effects of extending eligibility to marginally at-fault claimants— those whose job separation reason would be deemed UI-eligible by some examiners but UI-ineligible by others. Approving a marginally at-fault claimant increases UI benefits paid by over $3,000 and lengthens the nonemployment spell by just under two weeks, but it does not decrease labor income. We combine these estimates and other relevant claimant responses to calculate the fiscal externality of expanding eligibility on this margin and find that it accounts for 16 percent of the expansion’s total cost. Using two regression kink designs in the same data, we show that other more commonly studied UI benefit expansions have significantly larger fiscal externalities. We provide suggestive evidence that lower efficiency costs for the at-fault eligibility expansion are driven by smaller responses among lower-income claimants who are disproportionately affected by at-fault eligibility criteria.