This project will examine whether there are heterogenous inflation rates across the United States. If so, the authors plan to identify which areas face higher rates of inflation and what mechanisms drive the observed differences. More specifically, the authors will use regional Nielsen Retail Scanner data from 2006–2016 to measure food inflation. This project will also analyze the effects of local market concentration on inflation, adding to the evidence base needed to effectively measure the heterogeneous effects of inflation.

This project will create a database of hospital mergers over the past 40 years. The database will detail the following: where health systems are merging or divesting to understand which areas/people (including demographic differences) are affected; whether that differs between for-profit and nonprofit hospitals; and whether higher-priced hospitals continue to provide higher-quality care. Beyond the dataset construction, the project will map areas with 2+, 3+, and 4+ hospitals and produce descriptive statistics at various geographic levels. The research team will track the growth in health systems that resulted from acquisitions by analyzing whether for-profit health systems were strategically acquiring hospitals in regions with more affluent, privately insured patients. This project will contribute to our understanding of the impacts of hospital mergers on equity, patient access, and quality of care.

This project will examine whether regulations increase firms’ labor market power and how changes in labor market structure vis-à-vis regulation affect worker outcomes. Some laws limit how much existing firms must comply with new regulations, while new firms must comply. One example is the Environmental Protection Agency’s New Source Review permitting requirements. The research team seeks to understand how this regulation affects employers’ labor market power. They will use data from the U.S. Census Bureau to link individual earnings with demographic and geographic information. Further, they will use data on counties and industries from the EPA to conduct a difference-in-difference analysis and a two-stage instrumental strategy to estimate the effect of regulation on local labor concentration and worker outcomes. This research will broadly inform regulatory design with worker outcomes in mind.

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.

This project studies discrimination in online retail grocery stores. Do different consumers get charged a different price based on their perceived race? To answer this question, the author will implement a massive data collection exercise using web scraping. The data will combine firm-level data on products and prices with geography and aggregate socioeconomic indicators. This provides information on the underlying consumers in those areas. In the first phase of the project, the shopper’s race is based solely on geography. Future phases of the project will attempt to use browsing history to find racial differences in the shoppers. Web crawlers will be used to collect prices based on location to understand how online prices faced by consumers vary across socioeconomic and racial groups (imputed based on location). This research will identify whether online shopping allows retailers to price discriminate in ways that are harder to do in person.

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.