Research

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.

We study the removal of information from a market, such as a screening tool from labor markets or payment history from lending markets. Theoretically we show the effects of removal depend on a measure of signal precision scaled by the precision of other available information. This generates implications for incidence across groups: so long as the informed side of the market benefits from conveying any information at all, a signal ban harms the group for which, all else equal, the banned signal is most precise and other information sources are most noisy. Empirically, we illustrate these mechanisms in the context of banning the use of credit reports to screen job applicants. We estimate such bans have decreased job-finding rates for Black job-seekers by 3 percentage points, despite Black individuals having poorer average credit than other groups. We find this effect emerges primarily because other screening tools, such as referrals and interviews, have around 70% higher standard deviation of signal noise for Black relative to white job-seekers.

Since the 2000s, economists across fields have increasingly used consumer credit reporting data for research. We introduce readers to the economics and 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.

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.

Transfers paid through annual tax refunds are a large but uncertain source of income for poor households. We document that low-income tax-filers have substantial subjective uncertainty about these refunds. We investigate the determinants and consequences of refund uncertainty by linking survey, tax, and credit bureau data. On average, filers' expectations track realized refunds. More uncertain filers have larger differences between expected and realized refunds. Filers borrow in anticipation of their refunds, but more uncertain filers borrow less, consistent with precautionary behavior. A simple consumption-savings model suggests that refund uncertainty reduces the welfare benefits of the EITC by about 10 percent.

Using a unique, randomized information experiment embedded in a survey, this paper investigates how consumers’ inflation expectations respond to new information. We find that respondents, on average, update their expectations in response to (certain types of) information, and do so sensibly, in a manner consistent with Bayesian updating. As a result of information provision, the distribution of inflation expectations converges toward its center and cross-sectional disagreement declines. We document heterogeneous information processing by gender and present suggestive evidence of respondents forecasting under asymmetric loss. Our results provide support for expectation-formation models in which agents form expectations rationally but face information constraints.

We review the recent evidence on commitment devices and discuss how this evidence relates to theoretical questions about the demand for, and effectiveness of, commitment. Several important distinctions emerge. First, we distinguish between what we call hard and soft commitments and identify how soft commitments, in particular, can help with various dilemmas, both in explaining empirical behavior and in designing effective commitment devices. Second, we highlight the importance of certain modeling assumptions in predicting when commitment devices will be demanded and examine the laboratory and field evidence on the demand for commitment devices. Third, we present the evidence on both informal and formal commitment devices, and we conclude with a discussion of policy implications, including sin taxes, consumer protection, and commitment device design.