Hello! I am a Ph.D. candidate in Economics at Boston University.
My research interests are in industrial organization and household finance.
I make use of micro-data and employ structural techniques to quantitatively study the welfare implications of policies in the financial markets.
In my current research, I use novel models to study price dispersion and information asymmetry in the US mortgage market.
I document substantial price dispersion in the U.S. mortgage market, where each given lender charges observably similar borrowers different interest rates for similar loans. To explain the conditional price dispersion, I estimate a structural model that features borrowers' demand for mortgages and lenders' individualized optimal pricing decisions. I find evidence consistent with adverse selection in the form of a positive correlation between the unobserved determinants of borrowers' demand and lenders' costs. Moreover, lenders' ability to learn borrowers' private information from signals and tailor interest rates alleviates the severity of adverse selection. I find that the signals convey more information about lenders' costs rather than borrowers' demand, implying that the conditional price dispersion is explained more by lenders' optimal risk adjustments rather than demand-based price discrimination. I evaluate a counterfactual scenario where lenders must set the same interest rates for the observably same borrowers and loans. As a result, interest rates increase and consumer surplus decreases.
The effect is heterogeneous across income groups with higher-income borrowers' interest rates increasing more.
Talks: SGE, CES North America, IAAE China (scheduled), AMES (scheduled), EARIE (scheduled), BU Economics
In recent decades, there has been a shift from traditional banks' balance sheet lending to shadow banks' originate-to-distribute lending in the US mortgage market. This paper documents that shadow banks lend more to lower-income borrowers and charge lower interest rates than banks on average. Moreover, I study the impact of government loan guarantee schemes on shadow banks' growing dominance in the market by exploiting a regression discontinuity at the eligibility cutoff of the guarantee. I find that shadow banks' interest rates increase discontinuously and to a greater extent than banks at the eligibility cutoff. To explain why non-banks can offer lower interest rates, I estimate a structural model of the mortgage market featuring borrowers' heterogeneous preferences for mortgages and lenders' competition. The results suggest that although shadow banks incur greater marginal costs than banks, they charge lower markups, hence offering lower interest rates than banks. Shadow banks' costs increase to a lesser extent than banks' at the eligibility cutoff for the government guarantee. I conclude that the exploitation of the government loan guarantee is not likely to be the reason nonbanks can offer lower interest. Instead, nonbanks offer lower interest rates because they face fiercer competition and have lower market power than banks.
Talks: CES China, BU Economics
A Retrospective Analysis of U.S. Bank Mergers (with Tim Lee)
This paper conducts a retrospective analysis of bank mergers in the United States from 1994 to 2023 and examines the current bank merger review process. Policymakers have raised concerns about the growing concentration in the banking industry, particularly regarding the reliance on the HHI cutoff rule for assessing the competitiveness of a merger. Using a differences-in-differences approach, we first document that bank mergers generally reduce the number of local branches and decrease total deposits within the market. We also find that interest rates for various depository products, such as certificates of deposit, money market accounts, and savings accounts, decrease significantly after a merger. Moreover, we exploit the quasi-exogenous variation in the HHI cutoff rule to classify mergers as pro- and anti-competitive and explore their implications for banking competition. The results suggest that the HHI cutoff rule adequately screens anti-competitive mergers, as they lead to sharper declines in the number of branches, deposit amounts, and various depository rates than pro-competitive mergers.
Using a panel data set from the Survey of Consumer Payment Choices, this paper studies the effects of uncertainty and learning on the adoption and usage of payment instruments in the US. Consumers are heterogeneous in their `match' values with alternative payment instruments. Understanding how quickly consumers learn about their true match values is important for banks and card networks in designing card reward policies. I begin by presenting stylized facts that consumers exhibit substantial switching in the adoption of alternative payment methods over the years and they use card payments more frequently as time passes since adoption. Motivated by the stylized facts, I estimate a dynamic discrete choice model for consumer payment choice where consumers learn from experience about the usage values of alternative payment instruments. The structural estimates suggest that the adoption costs for all types of cards are positive with credit cards having greater costs than debit and prepaid cards, whereas the termination costs are heterogeneous across card types with only the credit cards having positive costs. The learning costs for credit cards than other types of card payments.
This difference in costs could be due to stricter credit requirements for credit cards than other card types.