In this dissertation, I delve into the impact of both local and national shocks on California's labor market and unemployment insurance (UI) program. Initially, I analyze the consequences of mass layoffs, which serve as a local employment shock, on neighboring firms. Later, I shift my focus to the effect of the COVID-19 shock on California workers, specifically those who receive UI benefits, and how UI assisted these displaced workers.
In the first chapter, I examine the spillover effects of mass layoffs on neighboring establishments, shedding light on the dynamics of agglomeration economies. I employ a difference-in-differences event study framework and leverage comprehensive administrative data encompassing all entities in California to study the indirect effects of mass layoffs on employment, earnings, and the number of nearby establishments. I exploit the geographic coordinates of establishments to define treatment and control areas based on their proximity to instances of mass layoffs. The findings reveal persistent and negative spillover effects on local employment levels, payroll, and the number of operating establishments four years after the events. However, there is no significant change in the average earnings of workers. Moreover, empirical evidence demonstrates that the spillover effects diminish with increasing spatial distance, effectively disappearing after 6km. In summary, a one percent employment shock results in a one percent indirect decrease in employment levels within a 6km radius four years later.
Furthermore, I contribute to the literature on agglomeration economies by assessing the mechanism of agglomeration economies in the observed spillover effects. I use economic distance measures to show the importance of industry linkages, knowledge spillover, and thick labor market as forces behind the spillover effects. My findings show that industries closely tied to the industry of mass layoff establishments experienced a more substantial employment decline, while those economically distant from the events show minimal changes.
In the second chapter, which is joint work with Alex Bell, T.J. Hedin, PeterMannino, Geoffrey Schnorr, and Till von Wachter, we answer this question: To what extent did jobless Americans benefit from unemployment insurance (UI) during the COVID-19 pandemic? We document geographic disparities in access to UI during 2020. We leverage aggregated and individual-level claims data to perform an integrated analysis across four measures of access to UI. In addition to the traditional UI recipiency rate, we construct rates of application among the unemployed, rates of first payment among applicants, and exhaustion rates among paid claimants. Through correlations across California counties and across states, we show that areas with more disadvantaged residents had less access to UI during the pandemic. Although these disparities are large in magnitude, cross-state analysis suggests that policy can play a salient role in mitigating them.
In the third chapter, coauthored with Alex Bell, T.J. Hedin, Peter Mannino, Carl Romer, Geoffrey Schnorr, and Till von Wachter, we leverage California's administrative longitudinal UI data to introduce two cumulative measures of the labor market health and use them to assess the impact of the COVID-19 pandemic on California's labor market and UI system. First, on the extensive margin, we measure the share of the pre-crisis labor force that applied for UI benefits. Second, on the intensive margin, we calculate the share of UI claimants who have received more than 26 weeks of unemployment benefits in the first year of the crisis. By combining the two measures, we show that the average member of the labor force spent nearly two months receiving regular UI benefits during the first year of the COVID-19 pandemic. Finally, we look into the demographic disparities in receiving UI benefits and show that more vulnerable workers experienced more weeks on UI than the more privileged.
