Essays in Macroeconomics and Labor Economics

Abstract

This dissertation presents three essays. In the first essay, we study the information advantages that employers hold over employees. Employers may have private information on impending separation shocks and may choose to withhold it if disclosing the information risks causing workers to leave prematurely. We examine whether employers have such information advantages by leveraging variations in the coverage of the Worker Adjustment and Retraining Notification (WARN) Act across states. The WARN Act requires employers to give advance notice of mass layoffs or plant closings to employees, reducing information asymmetry. We test whether there is excessive voluntary quits before WARN-covered mass layoffs or plant closings, as these quits indicate workers' knowledge about impending layoffs. Using confidential establishment-level labor turnover data, we observe an increase in voluntary quits leading up to WARN-covered plant closings, relative to trends in the control group, but results for mass layoffs are noisy due to a lack of statistical power. We also find evidence that WARN-covered establishments manipulate layoff scales to avoid triggering the advance notice requirement. Both findings suggest that employers hold information advantages over workers. We build an extended search-and-matching model to study the implications of such information advantages for equilibrium labor market outcomes. The second essay examines the role of social networks in shaping labor mobility, with a particular focus on how the sectoral backgrounds of an individual’s current coworkers influence job-switching decisions. To identify causal effects, we employ multiple strategies, including distinguishing between current-year and non-current-year coworkers, controlling for time-varying shocks specific to the industry pairs, and using unexpected death or retirement events to isolate idiosyncratic changes in coworker networks. Using German administrative matched employer-employee longitudinal data, we find a positive causal relationship between the proportion of coworkers from a sector and both the propensity of transitioning to that sector and the sensitivity to sectoral wage changes. To quantify the coworker mechanism's contribution to employment and reallocation, we develop and estimate a multi-sector, multi-firm general equilibrium model where perceived wages and adjustment costs for sector transitions depend on coworker shares. Our results show that the welfare effect of COVID-induced productivity shocks is higher when considering coworker networks compared to assuming no influence from coworkers. Maintaining worker-employer ties to reduce competition in positively shocked sectors can further increase welfare. In the third essay, we study the optimal policy for dual-use goods---items such as semiconductors or drones that have both military and civilian applications. We begin by empirically documenting that the regulation and trade flows of dual-use goods respond to changes in the security environment over time. To put structure on the national security externality, we introduce military procurement into a multi-country general equilibrium network model and add a military contest to the national welfare function. In a simple two-country case, optimal export taxes depend on a trade-off between the good's military centrality and its distortion centrality. Military centrality is a network-adjusted sales share to the foreign military; distortion centrality reflects taxation misallocation in the domestic economy from roundabout imports. Using U.S. defense procurement data, we construct a measure of military use across goods, which ranges from zero to one, by scaling the U.S. closed-economy military centrality by import demand elasticities. Our measure effectively evaluates policy restrictions and military content in trade flows. To quantify the macroeconomic magnitude of the consumption-security trade-off, we calibrate our model to a potential U.S.-China conflict. The revealed preference estimate of the value placed on the probability of winning the conflict equals 2.5 times the annual U.S. GDP.