As AI transforms health care, workers must have a say in its development, design, and implementation to ensure fair outcomes for both employees and patients. The proposed study examines the Labor Management Partnership at Kaiser Permanente, a unique collaboration between front-line workers and their employer, as a test case for integrating worker voice into AI decision-making. Through action research and a structured case study, the researchers will assess whether jointly negotiated AI strategies improve job quality and patient care. This work builds on a four-phase model of worker participation, addressing AI from its initial development to workforce training and transition planning. Given that U.S. labor law often limits workers' input on technology adoption, this study has broader policy implications, demonstrating pathways to stronger worker engagement in AI governance.

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 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.

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 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 proposes to estimate a structural equilibrium model of the U.S. child care sector to use for counterfactual subsidy design, with the goal of finding an optimal cost-neutral subsidy design. The project consists of two parts. First, the author will evaluate the effect of reimbursement rate policies on local maternal labor force participation, child care worker wages, child care prices, and quality of care. Second, the author will use the estimated model to simulate the effects of counterfactual subsidy policies on parent utility, worker wages, mark-ups, and the distribution of quality.