Welcome! I am currently pursuing a Ph.D. in Finance at Temple University and am actively seeking job opportunities in Economist, Data Scientist, or Data Analyst roles within the private sector.
I have 5+ of empirical research experience in applied microeconomics (financial institutions and intermediation/small business lending). Last summer, I interned at Amazon as an Economist, where I assessed the impact of a new checkout technology on customer spending in Amazon's physical stores. This involved sophisticated causal inference techniques using both a staggered difference-in-differences (DiD) approach and Double Machine Learning (DML).
My expertise lies in delivering impactful, data-driven insights through rigorous quantitative analysis. I am proficient in causal inference, experimental design, and advanced machine learning techniques, with a strong command of Python and SQL.
Working Papers
The Impact of Lender Competition on Small Business Loan Pricing: Evidence from the SBA 7(a) Program (with Samuel Rosen)
We study the impact of lender competition on loan pricing using comprehensive loan-level data from the U.S. Small Business Administration (SBA) 7(a) program. Intended only for the most credit-constrained small businesses, SBA loans are originated in the banking sector and subsidized by the government through partial guarantees. In contrast to previous studies of small business lending markets, we find that greater competition is associated with lower SBA loan spreads. Further, we provide causal evidence for this relationship in a difference-in-differences analysis using bank mergers. Our results suggest that lending relationships are less important in the government-monitored-and-subsidized SBA loan market. As a result, hypothetical policies to encourage competition in the SBA loan market would benefit borrowers. Additionally, our findings support the use of product-market-specific concentration measures by regulators when evaluating bank mergers.
Mission-Driven Lenders (with Samuel Rosen and Tilan Tang)
The U.S. government created the Community Development Financial Institution (CDFI) certification to promote greater credit access in distressed communities. In this paper, we provide a systematic analysis of CDFIs and provide insights into why CDFIs are growing and how they are different from other lenders. Consistent with their mission-driven requirement, we document that CDFIs have expanded in counties with a greater reliance on government-subsidized business lending, higher unemployment rates, and a larger minority population. Within the universe of depository institutions, credit unions and minority depository institutions (MDIs) are more likely to become certified CDFIs as well as institutions with relatively low levels of cash and high leverage. After becoming certified, CDFIs tend to grow faster and lend more, which suggests that the resources available to CDFIs alleviate institution-level financial constraints. In our final analysis, we analyze the cost of CDFI lending using a novel loan-level dataset.
Works in Progress
Lender Strategic Behaviors in Government-Subsidized Credit Markets
Government-subsidized lending has been an essential credit source for financially constrained small businesses and underrepresented populations all over the world. It is common for government-subsidized lending programs to involve private-sector lenders for fund distribution given their expertise and information advantage. A key challenge, however, is how to incentivize more lending while avoiding costly lender rent-seeking behaviors. To provide insight, I study lenders' rent-seeking behaviors in the U.S. Small Business Administration (SBA) 7(a) markets, in which loans are originated in the banking sector and heavily subsidized by the U.S. government. Using the SBA loan-level data from 1991 to 2020, I find that lenders with more discretion make riskier loans on average and appear to hide their non-performing loans by renegotiation to keep a good record of loan performance. This hiding behavior could be a strategic response to the SBA's supervision and potential penalties. The evidence is concerning for SBA as it suggests that lending is riskier than captured by official statistics.