This project seeks to provide some of the first causal evidence on how concentration in the meat-processing industry affects producers, workers, and consumers across racial and income groups in the United States. Rising concentration has been especially salient in the meat-processing industry, and recent research connects market power in the product market with monopsony power in the labor market. This project will build and expand on that literature by measuring the effect of consolidation by meat processors on monopoly power in the input and product markets, and monopsony power in the labor market, and then assessing what the implications of these market conditions are on racial inequality, specifically farmer profits, conditions for workers, and prices for consumers.

Individuals and their families use a variety of caregiving arrangements, but there is little research on who is likely to use which kind of arrangement. Furthermore, existing scholarship mainly focuses on the characteristics of the care recipient, without considering how family characteristics influence choices for caregiving arrangements. This mixed-methods study will explore how family characteristics and social infrastructure programs shape caregiving arrangements for older adults in the United States. The first two papers quantify and study the relationship between family characteristics and the size and scope of several social infrastructure and caregiving arrangements. The third paper will use semi-structured interviews with families who have eldercare responsibilities, focusing on Black females in the Chicago area, to shed light on their decision-making processes. Through these interviews, the author will explore how access to public programs affects their decisions, how they were selected as caregivers, what their preferences and future expectations are, and how employment plays into their decisions. The sample will include recipients (either care recipients or caretakers) of Social Security Insurance, Social Security Disability Insurance, Old Age and Survivors Insurance, Paid Family Leave, and Home & Community Based Services waivers from Medicaid.

This project is a data collection effort that seeks to fill an important gap: the undercounting of strikes. The author will collect data on all strikes in the United States regardless of size, duration, or whether workers are unionized or not. There is a perceived rise in collective action, but current data sources are not collecting information on the full universe of strikes. This effort will define a strike as “a temporary stoppage of work by a group of workers in order to express a grievance or to enforce a demand. Such a grievance or demand may or may not be workplace related.” A rigorous search and verification protocol by the author will ensure that a strike occurred. Data will be collected on other variables related to the strike, and all data will be publicly accessible on an interactive map. This project will extend recent research that has found strikes decreased in both amount and scale, and that strike effectiveness declined. It seeks to provide information on the outcomes of strikes for workers and their organizations, and under what conditions strikes are most effective. A notable extension is an investigation of whether strikes can respond to identity-based inequality, such as racial discrimination or sexual harassment, by studying noneconomic demands.

This project ties together neighborhood segregation, migration patterns, and local spending and policies to explore how “structural racism is built into physical infrastructure in cities.” The author will use granular satellite images to measure temperature in Black and White urban neighborhoods. The project will look at the degree to which neighborhoods are segregated and the level of surface imperviousness in each of those neighborhoods. The author also will map Great Migration patterns, White flight, and local government spending patterns. The research uses data from the University of Virginia’s Environmental Inequality Lab and builds on research on Northern cities’ responses to the Great Migration. Preliminary results show that historical Black migrant inflows increased the surface temperature of neighborhoods where Black households live, relative to the neighborhoods where White households live, as well as the Black-White gap in neighborhood imperviousness.

This project will examine the effect of common ownership of technology startups by venture capitalists on those firms’ outcomes, such as shutdowns, exits via mergers or acquisitions, and Initial Public Offerings. The author seeks to contribute to the literature on how common ownership may impact competition and innovation by studying spillovers among technology startups in the portfolios of multiple venture capital firms. It will explore two questions: Do venture capitalists’ common ownership of technology startups have anticompetitive effects, and by affecting startups’ outcomes, can common ownership impact the market structure of technology industries? The focus on the technology sector allows the author to look at competition between like firms. Because venture capitalists have a lot of decision-making power, the author theorizes that the effects could be strong since venture capital firms focus on the innovation pipeline. Therefore, the project expects to speak to how competition can be stifled in the seed stages of venture funding. The project will proceed in three stages: Develop a stylized analytical model to highlight the main incentives at play; present reduced-form evidence on the effects of common ownership on startups’ outcomes; and develop a structural matching model of venture capital firms and startups.

This project looks to determine the effect of monetary policy on wealth inequality in the United States. The author seeks to add to the literature demonstrating how portfolio heterogeneity is an important driver of wealth inequality. One contribution of the research is the use of the Distributional Financial Accounts. This dataset, collected by the Federal Reserve, reconciles the definitions of wealth between the Fed’s Survey of Consumer Finances and the quarterly flow of data from its Z.1 Financial Accounts of the United States, and then combines the datasets to create a series that tracks wealth for four quantile bins of households by wealth at a quarterly frequency. A key strength of the Distributional Financial Accounts is the ability to decompose quarterly wealth data into component asset and liability classes, allowing for the study of the channels by which monetary policy affects wealth for different groups of U.S. households.