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.

This project asks how the racial composition of workers’ professional networks affect their wage growth and access to outside opportunities. Existing research has estimated that the inclusion of underrepresented workers in the U.S. economy since 1960 significantly contributed to U.S. GDP growth. Yet racial divides in earnings and opportunities persist. Understanding the sources of these gaps is critical for understanding growing income inequality and fostering broad-based growth. While many explanations have been proposed, relatively little is known about the extent of U.S. labor market segregation and its consequences today. This project will use restricted firm-level data to explore the role of workers’ professional networks in the persistence of race disparities in the U.S. labor market. Building on recent work showing that workers are able to renegotiate their wages when labor demand increases in their networks, and that referred workers are often similar to referrers in terms of age, gender, and race, the author proposes to estimate the impact of changes in the composition of the professional networks of workers to investigate whether the impact of an increase in local labor demand is sensitive to the size of the professional networks for underrepresented groups. This analysis will then be used to calibrate a search model that estimates the contribution of labor market segregation to the wage gap.

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 explore the impact of natural disasters on small businesses and whether effects differ depending on the demographics of the entrepreneurs running those businesses. Understanding how the composition of local economies changes after disasters because of the demise of some firms and the startup of others can explain a lot about what makes a particular place more or less resilient. Research shows that there is a lot of startup activity following a natural disaster, but this could be misleading if it is generated from rebuilding and doesn’t fully factor in displaced workers or shuttered businesses, which will have larger long-term effects. This project will look at the impact of natural disasters on firms’ entry/exit, survival, and financial performances. It will also look at whether these effects vary by entrepreneur demographics, their socioeconomic backgrounds, the types of firms and in which industries, market power, and competitive setting.

This project explores how low-income people with immediate needs for cash make borrowing decisions in states where payday lending is heavily restricted versus states where it is not. It takes a qualitative approach to exploring the experiential processes that unfold across varying state policy contexts. As the author notes, there is a burgeoning line of scholarship on payday loans and states’ attempts to restrict them, but with mixed evidence on the effects on low-income borrowers. On one hand, these loans come with predatory lending rates that are often compounded for borrowers who are unable to pay back the loan in the original period and therefore roll it over, incurring more fees and often resulting in the borrower owing many times over what they originally received. On the other hand, credit is highly constrained for low-income individuals, with payday loans filling the gap. Yet there remains neither a consensus on the utility of such loans for low-income borrowers nor an understanding of how low-income individuals make decisions about borrowing. This gap limits policymakers from addressing the dual needs of credit access for low-income borrowers and the need to reduce the deleterious effects of payday lending, a gap this research will shed light on.