This research proposal seeks to answer three related questions regarding the barriers to accessing Unemployment Insurance benefits. First, what are the underlying causes of incomplete UI take-up for individual workers? Second, do the barriers to take-up disproportionately impact low-income, marginalized workers in a way that creates inequities in the UI program? Third, in what ways can outreach from workforce agencies boost UI take-up to better support workers and reduce inequities? To answer these questions, the author will utilize administrative data from the Washington State Employment Security Department. He will then field a survey of unemployed workers and combine findings with an analysis of administrative records. Together, this will inform the implementation of a field experiment, administered in partnership with the Employment Security Department, to explore how UI receipt can be increased, particularly for vulnerable populations.

This project will use administrative tax data to better understand the distributional consequences of the tax subsidy for private equity. The study includes three analyses: First, the co-authors will quantify the preferential tax treatment of private equity profits in several stages and then calculate the change in tax liability if carried interest was taxed as ordinary income. Second, they will compare tax on private equity income and loss flows to the counterfactual tax liability to understand how private equity firms make strategic decisions to distribute profits to nontaxable investors. Third, they will examine the consequences of private equity acquisitions for workers across the income distribution by tracking the long-run labor market outcomes of workers employed at firms acquired by private equity.  

This project seeks to empirically investigate the incidence and consequences of participation in multiple income support programs. The author proposes harnessing rich internal administrative records from the state of Virginia on program application and participation—covering the Supplemental Nutrition Assistance Program, Temporary Assistance for Needy Families, Medicaid, child care assistance, energy assistance, and other programs—to accomplish three objectives: Producing descriptive statistics on the frequency of multiple-program participation and the characteristics of enrollees; estimating the causal effects of multiple-program participation on various dimensions of family well-being, including earnings, the duration of program participation, and interactions with the criminal justice system for adults and children; and evaluating the efficacy of multiple-program participation and the combinations of programs that have the largest effects on improving the economic well-being of those served. Although many individuals participate in multiple income support programs, there is little research examining outcomes. The unique administrative data here will allow the author to examine these realities with a higher degree of accuracy over time than has been the case with survey data.

Due to federal American Indian policy, thousands of formerly enslaved people, or “freedmen,” and their descendants became landowners in present-day Oklahoma during the early 1900s. Over the following three decades, Oklahoma experienced an unexpected and unprecedented oil boom, from which a small fraction of these landholders profited. This research focuses on a specific subset of these landholders—Creek Freedmen—and estimates the causal effects of oil discoveries on their land on their socioeconomic outcomes and those of their descendants. In preliminary work, the author finds that oil discovery has effects in the very short term: Landholders who found oil at all appeared to be more geographically mobile, have higher-status occupations, and invest more in their children’s human capital. This research is poised to generate insight into how large exogenous influxes of resources into Black households may affect wealth and well-being in the near- and long-term. The focus on descendants of enslaved people links the historical work to contemporary discussions about reparations and racial wealth inequality.   

This research explores how worker-led lawsuits under California’s Private Attorneys General Act, or PAGA, impact firm size, survival, relocation decision, employment, and wages. The author will rely on two main data sources. First, she will leverage publicly available administrative data on PAGA claims and settlements from California’s Department of Industrial Relations. While PAGA claims data are publicly available online through PAGA Case Search tool, they have yet to be compiled and published in a way that facilitates research. Compilation, cleaning, and creation of this dataset to be made publicly available is an important contribution of this project. It is also critical to understand whether the Private Attorneys General Act is an effective tool for supporting worker rights.   

The COVID-19 recession was unique in that it was marked by a labor shortage in many U.S. sectors, leading policymakers to experiment with new tools to mitigate these losses. This project will study the impact of the child care spending provisions of the American Rescue Plan Act of 2021 on women's labor supply and child care utilization. The American Rescue Plan included $24 billion in child care stabilization grants, which were direct block grants to states to support child care services. The law also included the largest-ever expansion of the Child and Dependent Care Tax Credit, which increased by 400 percent the child-care-related transfers paid to some U.S. households in 2022, relative to past years. Unlike other child-related transfers, only children ages 13 and under are eligible for the Child and Dependent Care Tax Credit . Utilizing unique tax record data from the universe of all U.S. tax filers—which includes care providers, as well as CDCTC claims—the authors will use a regression discontinuity design in children's age to estimate the causal impact of this credit on household choices and how the impact varies with socioeconomic factors.