My research interests include empirical corporate finance and household finance with work in banking and FinTech. I enjoy using data scraping techniques to create novel datasets, which along with the latest empirical techniques, including machine learning, allow me to answer new and interesting questions.

My job market paper studies the unintended consequences that arise from liquidity injection programs when firm heterogeneity is not considered. Direct lending programs from fiscal authorities and central banks have become standard tools to prevent liquidity crises that can originate from systemic risks in the banking sector. Despite the general success of these programs, significant inefficiencies and distributional effects occur as non-traditional lenders provide undifferentiated support to heterogeneous firms. I document these effects using novel data on over 300,000 food service firms from Yelp and Google Maps. In the context of the Paycheck Protection Program, I show how liquidity support had heterogeneous effects on these firms’ survival, the ultimate consequence of a liquidity shortfall, due to differences in firms’ liquidity needs, organizations’ abilities to process policy information, and the incentives of the intermediaries delivering the funds. In addition, I use a machine learning model to create a counterfactual of no liquidity support, showing that the distribution of firms shifted as a result of the program. Overall, the paper highlights the importance of the design of liquidity distribution to maximize its benefits.