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.
Abstract: Recent research has documented the prevalence and consequences of evictions in the U.S. However, 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 several 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 non-payment is common but is often tolerated by landlords, and that tenants frequently recover from default, 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 high, on the order of 2 to 3 months of rent, and for a majority of evictions, landlords evict only after learning a tenant is likely a persistent non-payer. As a result, while moderately-scoped 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 for delinquent tenants are more likely to prevent evictions of tenants who will pay going forward.

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.''

How Costly is Noise? Data and Disparities in Consumer Credit (2022) with Laura Blattner.
Abstract: We establish three facts about how credit scores drive disparities for disadvantaged groups in consumer credit markets. First, we show that widely used credit scores drive disparities in classification errors. That is, among disadvantaged groups, there are more applicants who are predicted to repay a loan but do not repay and more applicants predicted not to repay a loan who would have repaid. Second, we find that disparities in errors arise not because of (statistical) bias against a particular group, but rather because of noise, or higher variance of errors for disadvantaged groups. Third, we demonstrate that three plausible solutions do not help to reduce noise in credit scores. Developing better prediction technology based on machine learning techniques, building group-specific credit scoring models, and adding non-traditional credit bureau data all fail to eliminate the noise disparity we document. Rather, we provide evidence that reducing the noise disparity requires addressing higher credit report data sparsity for the disadvantaged groups.

The Arity of Disparity: Updating Disparate Impact for Modern Fair Lending (2024), with Spencer Caro. (Summary slide deck available here)
Abstract: Discrimination and protections against it are both rapidly evolving. To address issues raised by algorithmic screening and by recent economic research on competing notions of fairness, we formalize the last step in a disparate impact (DI) claim of discrimination — the Least Discriminatory Alternative (LDA) analysis — as an explicit constraint on choices over screening models and data inputs. The constraint restricts model-induced disparities in both parity and accuracy relative to a budget that depends straightforwardly on overall model performance. We also show how this trade-off leads to balancing other notions of fairness that are spanned by weighted combinations of parity and accuracy. We illustrate examples and develop a legal argument focused on DI under the Equal Credit Opportunity Act (ECOA). Our approach resolves the tension between DI's traditional focus on parity and ECOA's statutory emphasis on credit-worthy consumers, and we discuss implications for new frontiers in credit underwriting such as the use of machine learning and alternative data.

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.

Consumer Credit Reporting Data (2024), with Christa Gibbs, Benedict Guttman-Kenney, Donghoon Lee, Wilbert van der Klaauw, and Jialan Wang. Resubmitted to Journal of Economic Literature
Abstract: Since the 2000s, economists across fields have increasingly used consumer credit reporting data for research. We introduce readers to the economics of and the institutional details of these data. Using examples from the literature, we provide practical guidance on how to use these data to construct economic measures of borrowing, consumption, credit access, financial distress, and geographic mobility. We explain what credit scores measure, and why. We highlight how researchers can access credit reporting data via existing datasets or by creating new datasets, including by linking credit reporting data with surveys and external datasets.