Research

Working Papers

Private Information and Price Regulation in the US Credit Card Market (2023). Conditionally Accepted at Econometrica
Abstract: The 2009 CARD Act limited credit card lenders' ability to raise borrowers' interest rates on the basis of new information. Pricing became less responsive to public and private signals of borrowers' risk and demand characteristics, and price dispersion fell by one third. I estimate the efficiency and distributional effects of this shift toward more pooled pricing. Prices fell for high-risk and price-inelastic consumers, but prices rose elsewhere in the market and newly exceeded willingness to pay for over 30% of the safest subprime borrowers. On net, average traded prices fell and consumer surplus rose at all credit scores. Higher consumer surplus was partly driven by a fall in lender profits, and partly by the Act's insurance value to borrowers who could retain favorable pricing after adverse changes to their default risk. The relatively high level of pre-CARD-Act markups was crucial for realizing these surplus gains.

Unpacking the Black Box: Regulating Algorithmic Decisions (2024), with Laura Blattner and Jann Spiess. 'Reject and Resubmit' at QJE
Abstract: What should regulators of complex algorithms regulate? We propose a model of oversight over 'black-box' algorithms used in high-stakes applications such as lending, medical testing, or hiring. In our model, a regulator is limited in how much she can learn about a black-box model deployed by an agent with misaligned preferences. The regulator faces two choices: first, whether to allow for the use of complex algorithms; and second, which key properties of algorithms to regulate. We show that limiting agents to algorithms that are simple enough to be fully transparent is inefficient as long as the misalignment is limited and complex algorithms have sufficiently better performance than simple ones. Allowing for complex algorithms can improve welfare, but the gains depend on how the regulator regulates them. Regulation that focuses on the overall average behavior of algorithms, for example based on standard explainer tools, will generally be inefficient. Targeted regulation that focuses on the source of incentive misalignment, e.g., excess false positives or racial disparities, can provide second-best solutions. We provide empirical support for our theoretical findings using an application in consumer lending, where we document that complex models regulated based on context-specific explanation tools outperform simple, fully transparent models. This gain from complex models represents a Pareto improvement across our empirical applications that is preferred both by the lender and from the perspective of the financial regulator.

Nonpayment and Eviction in the Rental Housing Market (2024), with John Eric Humphries, Dam Linh Nguyen, Winnie van Dijk, and Daniel Waldinger. Submitted
Abstract: Recent research has documented the prevalence and consequences of evictions in the United States, but our understanding of the drivers of eviction and the scope for policy to reduce evictions remains limited. We use novel lease-level ledger data from high-eviction rental markets to characterize key determinants of landlord eviction decisions: the persistence of shocks to tenant default risk, landlords' information about these shocks, and landlords' costs of eviction. Our data show that nonpayment is common, is often tolerated by landlords, and is often followed by recovery, suggesting that landlords face a trade-off between initiating a costly eviction or waiting to learn whether a tenant can continue paying. We develop and estimate a dynamic discrete choice model of the eviction decision that captures this trade-off. Estimated eviction costs are on the order of 2 to 3 months of rent, and the majority of evictions involve tenants who are unlikely to pay going forward. As a result, while commonly-proposed policies can generate additional forbearance for tenants, they do not prevent most evictions. Compared to policies that create delays in the eviction process, increasing filing fees or providing short-term rent subsidies are more likely to prevent evictions of tenants who would resume paying.

How Costly is Noise? Data and Disparities in Consumer Credit (2024) with Laura Blattner. Submitted
Abstract: We show that widely used consumer credit scores exhibit disparities in classification errors: more minority applicants predicted not to repay a loan would have repaid, and more minority applicants predicted to repay do not. These disparities arise because of disparities in noise, not bias, in predicting risk. Three candidate solutions fail to reduce credit score noise disparities: machine learning prediction technology; building group-specific credit scoring models; and adding non-traditional credit bureau data. Rather, up to 70% of these disparities result from greater data sparsity for disadvantaged groups, suggesting that credit scoring can make historical disparities in credit access more persistent.

Information Design in Consumer Credit Markets (2022), with Laura Blattner and Jacob Hartwig.
Abstract: Over 30m US adults do not use formal consumer credit. How many of these are inefficiently excluded because they lack a credit history or have a poor credit score? We develop a framework to characterize the efficiency-maximizing system of credit histories and credit scoring, subject to the constraints imposed by the severity of adverse selection, and by the ability of credit histories to predict future risk. We find US consumer credit features a moderate amount of adverse selection and persistent consumer types. This adverse selection generates substantial welfare loss: a majority of today's non-borrowers would be first-best efficient to lend to. Credit reporting helps alleviate the costs of adverse selection, with the current US system recovering roughly two-thirds of the welfare that would be lost in a no-credit-reporting counterfactual, relative to a full-information first-best. We find that requiring histories to be shorter -- or to forget past default sooner -- would induce some market unraveling but also would help non-borrowing consumers escape the ``no history trap.''

Modernizing Fair Lending (2024), with Spencer Caro and Talia Gillis.
Abstract: Fair lending’s disparate impact doctrine aims to address lending disparities. But which disparities? Traditional fair lending has narrowly focused on equal outcomes—examining differences in loan approval rates or interest rates. However, this singular focus overlooks other critical dimensions of disparities that are essential for fair credit access. We challenge the conventional focus on equal outcomes, demonstrating how it has failed to address some of the most pernicious harms of traditional credit allocation and has stifled necessary machine-learning and alternative data innovations. We argue that disparities in validity of creditworthiness predictions —the accuracy with which a model identifies creditworthy applicants—severely impact equal access to credit and fail to equally extend credit to the creditworthy. Despite the mounting empirical evidence of the harm of validity disparities, traditional fair lending enforcement inadequately recognizes this disparity dimension, a gap that will become increasingly harmful as lending decisions rely on advanced statistical methods. Our updated, holistic account of disparities engages with how the competing fairness notions of equal outcomes and equal validity may be fundamentally in tension. Reducing differential validity by improving data quality and models for protected groups could increase outcome inequality. On the other hand, decreasing outcome inequality could exacerbate differential validity when noisy and inaccurate lending decisions are made for the most vulnerable borrowers. Using a lender simulation exercise, we demonstrate that these tensions can be addressed by balancing equal outcomes and validity, providing regulators the flexibility to weigh each dimension appropriately. We discuss how this approach can be practically implemented by lenders and regulators; we chart a path forward for considering other tensions within fair lending, such as balancing business benefit with discriminatory impact; and we address how fair-lending policies impact different protected groups. Our framework has direct implications for other domains—such as housing and employment discrimination—where competing disparity notions should be considered under discrimination doctrines.

Zoning for Profits: How Public Finance Shapes Land Supply in China (2023), with Zhiguo He, Yang Su, Anthony Lee Zhang, and Fudong Zhang.
Abstract: Public finance and real estate are uniquely intertwined in China, where local governments also serve as monopolist sellers of land. We shed new light on how land sale decisions and land prices depend on local governments’ financing objectives. First, we document the large (ten-fold) price premium paid for residential-zoned relative to industrial-zoned land and show this price premium can be explained by the greater future tax revenues generated by industrial land; the choice to sell land as industrial rather than residential generates an IRR of 7.70%, which is comparable to local governments’ cost of capital in bond markets. Second, local governments are sensitive to financing constraints: industrial land supply decreases with governments’ bond yields. Third, local governments’ land sales are sensitive to the intergovernmental tax sharing, such that industrial land sales increase with the share of taxes captured by local governments. Thus, shocks to local public finances can be expected to affect the Chinese real estate market and vice versa.