Existing research consistently finds that unemployment insurance (UI) benefits delay job finding with limited effects on job quality, but focuses on changes in UI generosity while holding fixed access to re-employment services. Using employer-employee matched data from Washington State and a fuzzy regression discontinuity design around the eligibility threshold for UI, we find that benefit receipt minimally delays re-employment but substantially improves labor market outcomes. UI increases cumulative hours worked by approximately 15 full-time weeks and earnings by $14,000 in the two years following job loss, representing 37% and 50% increases, respectively. These gains are driven by improved job quality, as recipients experience longer tenure and higher wages with their next employer. Effects are larger for workers living near public employment offices, suggesting that access to re-employment services enhances search productivity. Expanding UI access by lowering the eligibility threshold is much more cost-effective than raising benefit levels or extending potential duration, as workers benefit from more stable, higher-paying re-employment that partially offsets its cost.

This paper investigates how the generosity of unemployment insurance (UI) affects take-up and optimal policy design. Standard models of UI begin their analysis with benefit receipt, yet take-up is highly incomplete: only around half of eligible workers claim benefits in the United States. We develop a model with incomplete take-up explained by the hassle of applying. More generous benefits induce workers on the margin to claim benefits, creating a fiscal externality without providing additional insurance value. Our optimal policy condition extends the Baily-Chetty formula to include the take-up elasticity, which proves quantitatively important. Using administrative data from Washington State and a regression kink design (RKD), we find that a 10% increase in the weekly benefit increases take-up by 4.7%, which drives a 6.2% increase in the number of benefit payments. Conditioning on benefit receipt captures only the elasticity of claim duration and ignores this take-up response, thus substantially underestimating the fiscal cost. Combining our theory and empirical results, we show that endogenous take-up reduces the optimal benefit level by 29% and the cost-effectiveness of raising benefits by 27%. Together, these results highlight that incomplete and endogenous take-up is a first-order consideration in the optimal design of social insurance.

How do worker beliefs about layoff risk due to generative artificial intelligence compare to the realized labor market impact of the technology, and what shapes those beliefs? We field a large-scale, multi-wave survey measuring workers' perceptions of AI-related job loss risk and AI adoption in the workplace, which we estimate at 30-40% of U.S. workers through the first half of 2026. The average U.S. worker, regardless of whether they use the technology at work, believes there is a 21% chance of losing their job to generative AI within two years, which is comparable to workers' realized risk of job loss from any cause over a similar horizon. Using a continuous-treatment difference-in-differences design, we find that these fears are not borne out, as job postings and layoffs in more exposed occupations show no statistically significant response to the diffusion of generative AI. Instead, fear rises with usage intensity (more hours per week) and is concentrated among those who deploy AI for core occupational functions such as programming rather than for drafting tasks such as writing.

This paper develops and evaluates the concept of revenue-neutral seasonal consumption taxation in a tourism-dependent economy. Building on a model with heterogeneous elasticities and tax salience, we establish two theoretical results: (i) when the peak season is dominated by tourists with less elastic and less salient demand, there exists an efficiency-dominant seasonal tax schedule that yields the same revenue as a uniform rate while reducing deadweight loss; and (ii) such schedules shift a greater share of the tax burden from residents to tourists. Calibrating the model with monthly sectoral sales data for Maine, a state with a strong seasonal tourism industry, we find clear U-shaped efficiency curves and shifts in incidence, confirming the theoretical propositions. Implementing the efficiency-dominant schedule would reduce annual deadweight loss by about $0.4 million, lower residents’ tax burden by $19 million (about $14 per capita), and raise tourists’ burden by the same amount, with no adverse effects on business sales (which rise by 0.06%). These results suggest that seasonal taxation can simultaneously improve efficiency, alleviate residents’ tax burden, and preserve fiscal capacity in tourism-intensive economies.

We develop a method to jointly measure the response of worker search effort (micro effect) and vacancy creation (macro effect) to changes in the duration of unemployment insurance (UI) benefits. To implement this approach, we exploit an unexpected cut in UI durations in Missouri and provide quasi-experimental evidence on the effect of UI on the labor market. The data indicate that the cut in Missouri significantly increased job-finding rates by raising the search effort of unemployed workers and the availability of jobs. The latter accounts for around one-third of the total effect.

We investigate the role of firm pay premiums in explaining the large, persistent earnings losses of displaced workers. We first estimate that long-run earnings for displaced workers from 2002 to 2008 in Ohio are depressed by 22%. Drawing upon empirical approaches from the displaced worker and firm heterogeneity literature, we then estimate that one-quarter of this earnings loss can be explained by the forfeiture of a favorable employer-specific pay premium. Such firm rents are more salient for those laid off from manufacturing firms, explaining half of their lost earnings. Nevertheless, we add to early evidence that firm rents do not explain the majority of earnings losses sustained by displaced workers in the United States.

Displaced workers suffer large and persistent earnings losses. These losses can be mitigated by returning to school, yet the extent to which such workers enroll in post-secondary education in response to displacement is poorly understood. Using employer-employee-student matched administrative data from Ohio, we provide the first direct evidence of workers’ enrollment responses following mass layoffs in the United States. We estimate that for every 100 displaced workers, only 1 is ever induced to enroll in a public college. This effect is concentrated almost entirely among displaced manufacturing workers, who enroll at a rate of 2.5 per 100. Workers with relatively low earnings at their layoff firms are the most likely to enroll in public institutions.

The study of labor unions in the United States is significantly constrained by the coarseness of the Current Population Survey (CPS), whose geographic granularity is limited to the state level or large metropolitan areas. This absence of fine geography means that recent approaches to studying labor market shocks that use shocks to within-state labor markets cannot incorporate union density as an outcome, covariate, or source of heterogeneity. This paper overcomes these problems by marshaling numerous survey and administrative data sources to construct a novel dataset on union membership and density at the local labor market level for the period 1980-2020. We validate our granular density measure across regions and over time with use cases such as the 1980s recession and the China shock.