CV and Bio
I am an Assistant Professor of Finance at Rice University. My research focuses on real estate finance, household finance, and banking.
My CV is available here: CV
Email: dzhang@rice.edu
Working Papers
(with Yongqiang Chu and Tim Zhang)
Abstract (click to expand): The growth of FinTech and shadow banks could have unexpected spillover effects on bank lending. We study this possibility in the context of small mortgages, which are low balance mortgages disproportionately originated by brick-and-mortar banks and retained in their portfolios. Using a shift-share instrument after the introduction of Basel III, we find that areas more exposed to FinTech and shadow bank growth have significantly higher small mortgage denial rates despite similar application quality and local economic trends. We also find a corresponding reduction in small mortgage originations as well as lower owner occupancy shares among originated small mortgages. Further analyses suggest that reduced CRA requirements and shifts in bank business models as causal mechanisms. Overall, our results suggest that the rise of FinTech and shadow banks plays a key role in the declining availability of small mortgages, potentially contributing to lower homeownership rates among less affluent households.
(with Janet Gao and Livia Yi)
Abstract (click to expand): We study the effects of a policy that shifted from pure human underwriting to human-augmented algorithmic underwriting for low-credit-score, high-leverage mortgage borrowers. Estimating the bunching of loans around the policy's debt-to-income threshold, we find a large credit expansion to affected borrowers with little changes in default risks or interest rates among the affected group. Such effects are more pronounced among non-Hispanic White borrowers and higher-income borrowers. Consequently, low-credit-score households are more likely to move to better school districts. We use a structural approach to quantify the welfare implications of the policy change and isolate the credit supply channel. Overall, our results suggest that automated underwriting systems (AUS) can help increase financial inclusion while controlling risk. However, it can also generate disparate impact across racial groups and along the income distribution.
Invited Presentations: 2023 Lone Star Finance Conference, 2024 AEA*
Abstract (click to expand): I use a structural model to quantify the cross-subsidization in the US mortgage market due to heterogeneous borrower refinancing tendencies. In equilibrium, the presence of borrowers with high refinancing inertia reduces mortgage interest rates, particularly on lower upfront closing cost mortgages which have more valuable refinancing options. As a result, actively refinancing borrowers refinance excessively relative to a perfect information, no cross-subsidization benchmark, an effect that accounts for 36% of the rate and term refinances for 2013--2019 new purchase originations and generates deadweight losses due to administrative resource costs. Alternative contract designs can reduce transfers and increase total welfare.
Invited Presentations: 2022 NBER Summer Institute Real Estate, 2022 North American Summer Meeting of the Econometric Society, 2022 Asia Meeting of the Econometric Society, 2022 UEA Conference, 2022 Chicago Booth Household Finance Conference, 2022 CFPB Consumer Conference, 2023 AREUEA/ASSA Conference, 2023 UCLA/SF Fed Conference, Penn State University, 2023 Boulder Conference on Consumer Finance, 2023 Western Finance Association (WFA).
(with Paul Willen)
Revise & Resubmit at the American Economic Review
Abstract (click to expand): How should researchers test for differences in the menus of options that people face when given data on choices? In mortgage and labor contexts, we show how intuitively appealing regression-based approaches for assessing differences in menus can lead to misleading and contradictory results. To address this issue, we use pairwise dominance relationships in choices that can be supplemented by restrictions on the range of plausible menus to define (1) a test statistic for equality in menus and (2) a difference in menus (DIM) metric. We also derive a new procedure for inference on our class of problems. Finally, we apply our methodology to a novel data set linking 2018--2019 Home Mortgage Disclosure Act (HMDA) data to Optimal Blue rate locks. We find evidence for mortgage menu differences by race, particularly among Conforming mortgage borrowers who are relatively creditworthy.
Invited Presentations: 2020 System Econometrics Conference*, 2020 System Applied Microeconomics Conference, 2020 Econometric Society European Winter Meetings, 2021 Week After Conference on Financial Markets and Institutions*, 2021 Stanford Institute for Theoretical Economics (SITE), 2021 Financial Intermediation Research Society (FIRS), 2021 Society of Labor Economists (SOLE), 2021 IAAE Conference, 2021 Asian Meeting of the Econometric Society, 2021 OSU PhD Real Estate Conference (Best Paper Award), 2021 RES, 2021 SWFA, 2022 AREUEA National Conference, 2023 AREUEA/ASSA Conference, Zillow, FHFA.
(with Jeffrey Wang)
Abstract (click to expand): Many government support programs for small businesses are designed to pass through banks and credit unions. However, this poses barriers for minority communities that are less connected to financial institutions for obtaining this support. Using the latest program for supporting small businesses, the Paycheck Protection Program (PPP), as an example, we show that there was a large disparity in the density of PPP enrolled lenders by racial composition of the neighborhood. This difference is both due to a lower density of lenders in those neighborhoods in general, and by the fact that the banks and credit unions that do operate there are smaller, are less likely to have previous relationships with the Small Business Administration, and are less likely to enroll in the program. More heavily Black neighborhoods have significantly lower take-up of PPP loans particularly in lower population (more rural) areas where this disparity is most salient. Through an instrumental variables analysis, we show that the intensive margin of access to enrolled lenders can explain about 35% of the racial disparity in take up within the relevant areas. Our results suggest that government programs that provide "support through banks" can have undesirable distributional implications.
Invited Presentations: 2021 SFS Cavalcade North America.
(with Tianwang Liu)
Submitted
Abstract (click to expand): We examine whether law school alumni relationships between lawyers and judges affect case outcomes. We show that, in the context of medical malpractice lawsuits filed in Florida, having a plaintiff's attorney who attended the same law school as a randomly assigned judge increases the chances of recovery by 2%. We further show that the effect is more pronounced between lawyers and judges with a larger age gap, consistent with a mentorship hypothesis rather than a personal relationship effect from lawyers and judges who overlapped in school. Our results show that law school networks can influence case outcomes.
Media coverage:
Legal Theory Blog
Abstract (click to expand): I develop a new method for estimating counterfactuals in dynamic discrete choice models, a widely used set of models in economics, without requiring a distributional assumption on utility shocks. Applying my method to the canonical Rust (1987) setting, I find that the typical logit assumption on utility shocks can lead the researcher to conclude that the agent's counterfactual choice probabilities are much more sensitive to policy changes than what a semi-parametric model would suggest. Therefore, my method may be useful to applied researchers in generating policy counterfactuals that are robust to such distributional assumptions.
Invited Presentations: 2018 International Industrial Organization Conference
Publications
(with Kristopher Gerardi and Paul Willen)
Journal of Financial Economics, March 2023
Abstract (click to expand): Over the period 2005 to 2015, Black borrowers paid more than 40 basis points higher mortgage interest rates than Non-Hispanic white borrowers. We show that the main reason is that Non-Hispanic white borrowers are much more likely to exploit periods of falling interest rates by refinancing their mortgages or moving. Black and Hispanic white borrowers face challenges refinancing because, on average, they have lower credit scores, equity and income. But even holding those factors constant, Blacks and Hispanic white borrowers refinance less suggesting that other social factors are at play. Because they are more likely to exploit lower interest rates, white borrowers benefit more from monetary expansions. Policies that reduce barriers to refinancing for minority borrowers and alternative mortgage contract designs can significantly reduce racial mortgage rate inequality.
Editor’s Choice, March 2023
(with Rune Stenbacka and Oz Shy)
International Journal of Industrial Organization, September 2016
Abstract (click to expand): We analyze the Markov Perfect Equilibria of an infinite-horizon overlapping generations model with consumer lock-in to compare the performance of history-based and uniform pricing in growing and declining markets. Under history-based pricing, firms charge higher prices to locked-in customers and lower prices to new customers. We show that a high exit rate of consumers (sufficiently declining market) constitutes a sufficient condition for history-based pricing to generate higher average prices than uniform pricing, thereby harming consumer welfare. In contrast, a high consumer entry rate (sufficiently growing market) ensures that history-based pricing intensifies competition compared with uniform pricing.
(with Jun Ishii)
Managerial and Decision Economics, April 2016
Abstract (click to expand): We analyze how CEO stock options compensation can be used as a commitment device in oligopolistic competition. We develop a two‐stage model where shareholders choose managerial compensation to commit their managers to being aggressive in equilibrium. Our results may explain why some shareholders appear to incentivize ‘excessive’ risk taking through stock options compensation. We analyze how our results are impacted by product quality, marginal cost, product differentiation, and industry concentration. As motivation for our research, we show that there exists positive empirical correlation between industry concentration and options compensation vega within a sample of firms, as suggested by our model.
Other Writings/Media
ABC13 Houston, December 2023
FOX 26 Houston, August 2022
Philadelphia Tribune, January 2021
Wall Street Journal Central Banking, December 2020
Federal Reserve Bank of Boston Research Data Report, September 2016
Media coverage:
New York Post
* Asterisks indicate presentation by a co-author.
Website: This static website was built based on code from Gautam Rao, and the source code can be found on Github.
