This project will utilize third-party scheduling data that is well-suited to investigate schedule volatility. Research in this area has been limited to surveys of workers, but with detailed time and attendance data from a payroll provider, this project seeks to document novel facts about worker schedules, evaluate the effect of predictive scheduling and minimum wage laws on schedule-related outcomes for firms and workers, and understand the welfare effects of schedule regulation on workers.

This project aims to provide new causal evidence on the economic implications of supply chain disruptions. The identification strategy leverages the fact that global supply chains rely on maritime trade, which depends on a few critical choke points. The author will identify disruptive incidents, which are plausibly exogenous to the U.S. economy, and then isolate the market impact of the disruption using high-frequency financial data. The author will then use these shipping cost surprises as an instrument in a structural VAR model of the U.S. economy to identify a structural supply chain shock.

Building on past research on the role of the Earned Income Tax Credit in supporting migration decisions, this research will evaluate the subsequent outcomes for both parents and children. Leveraging detailed linked administrative data—including the American Community Survey, Current Population Survey, and individual tax records—the author will conduct a longitudinal analysis of U.S. families’ migration patterns and economic outcomes. High-resolution geographic information provides information on the quality of neighborhoods families move to and from, with variables such as school quality, local poverty rates, incarceration rates, labor market opportunities, and measures of economic mobility. Linking individual tax records with survey data allows for an assessment of children’s educational attainment, employment, and earnings over time. Tax records provide information on family income, employment, and geographic mobility.

This project brings together two important U.S. economic policy areas: the housing shortage and market power. The author will use cutting-edge tools from industrial organization to test firm conduct and answer the question of whether market power among homebuilders can explain the under-supply of new housing, particularly entry-level units, or whether their economies of scale reduce costs.

This project studies how firms collude in labor markets, studying an overlooked potential mechanism: common leadership, in which the same person holds high-level leadership positions in two competing firms. Common leadership is prohibited by antitrust law, but until the past few years, enforcement was nearly nonexistent. This study focuses on collusion in labor markets in the form of no-poach agreements—specifically, how entry into such agreements affects worker mobility and career trajectories. Preliminary results find entry into collusive agreements in the years following connection through common leadership. No-poach agreements are extremely difficult to study since they are secret in nature. The author will use the largest known case of labor market collusion to overcome this data challenge. In the late 2000s, more than 50 tech companies entered into no-poach agreements. She will use three primary sources of data: court documents, data on worker histories, and biographical data on company executives.

This project seeks to answer the question of which labor market frictions prevent workers and communities from smoothly adjusting to local employment shocks and the extent to which investments in human capital address existing frictions. The author looks at this in the context of the transition to clean energy, focusing on the coal industry. She will merge worker-level administrative data with institution-level data on enrollment and completions from 2- and 4-year colleges to explore whether spatial proximity to postsecondary institutions serves to mollify longer-term earnings and employment outcomes.