Working Papers
"Giving Up": The Impact of Decreasing Housing Affordability on Consumption, Work Effort, and Investment (with Younggeun Yoo)
Abstract: Housing affordability has declined sharply in recent decades, leading many younger generations to give up on homeownership. Using a calibrated life-cycle model matched to U.S. data, we project that the cohort born in the 1990s will reach retirement with a homeownership rate roughly 9.6 percentage points lower than that of their parents’ generation. The model also shows that as households’ perceived probability of attaining homeownership falls, they systematically shift their behavior: they consume more relative to their wealth, reduce labor effort, and take on riskier investments. We show empirically that renters with relatively low wealth exhibit the same patterns. These responses compound over the life cycle, producing substantially greater wealth dispersion between those who retain hope of homeownership and those who give up. We propose a targeted subsidy that lifts the largest number of young renters above the "giving-up threshold." This policy yields welfare gains of 3.2 times greater than a uniform transfer and 10.3 times greater than a transfer targeted to the bottom 10% of the wealth distribution, while also improving homeownership rate, labor effort, and reducing social safety net reliance.
Coverage: Washington Post, Financial Times, Fortune 1, Fortune 2, Fox Business, New York Times, Realtor.com, Marginal Revolution, Macro Roundup, Le Figaro (in French)
Anticipatory Spending (with Scott Baker, Michael Gelman, and Lorenz Kueng)
Investing in Human Capital Incubation (with Bryan Seegmiller, Yufeng Wu, and Miao Ben Zhang)
Abstract: This paper examines the role of firms' intangible capital investments in fostering the growth of employee human capital and the subsequent spillover effects on other firms. Using U.S. Census Bureau employee-employer matched data, we provide the first direct evidence that firms' investments in intangibles significantly enhance their employees' human capital, designating these firms as "human capital incubators." We develop a model in which workers’ preferences for skill development influence their career decisions, thereby shaping labor supply to firms. Firms recognize this by investing in intangibles not only to boost output but also to attract talent. The model captures the facts that human capital incubation positively correlates with firm productivity, profitability, market power, and inflows of younger workers.
Disagreement, Subjective Uncertainty, and the Stock Market (with Jingoo Kwon and Younggeun Yoo)
Abstract: We propose a new method to separately quantify investor disagreement and subjective uncertainty at the firm level using equity analyst forecasts. Our approach exploits heterogeneity in how forecast dispersion responds to the arrival of signals that are widely perceived as informative and interpreted homogeneously across agents. Intuitively, for a given level of disagreement in point forecasts, a larger post-signal compression in dispersion indicates greater ex-ante subjective uncertainty in investors’ beliefs. Using these measures, we document differences in the economic roles of disagreement and uncertainty. Subjective uncertainty rises sharply prior to crises, while disagreement peaks during and immediately after crises. In the cross-section, stocks with higher disagreement earn lower subsequent returns and exhibit higher trading volume. These effects are significantly attenuated when uncertainty is high. In contrast, higher uncertainty is associated with higher expected returns and lower trading volume. Stock return volatility is strongly related to disagreement but only weakly related to uncertainty. Finally, firm characteristics are more closely linked to disagreement than to uncertainty: Smaller firms, firms with lower profitability, and firms with higher R&D intensity exhibit systematically higher levels of disagreement.
Signals from SMS: Alternative Data and Distributional Effects in Credit Scoring (with Kenneth Ryu and Jaehyeok Shin)
Abstract: Using data from 23 million loan applications from a FinTech lender in India, we demonstrate how transaction records and credit histories can be extracted from text messages and used to assess applicants' creditworthiness. We show that an alternative credit scoring system built on these data and machine-learning algorithms predicts consumer delinquency more accurately than traditional credit scores, with gains attributable to both algorithmic improvements and the use of alternative data. We find that this alternative credit scoring system expands credit access by approving loans to new-to-credit applicants with no credit bureau score. However, the benefits of this technological shift are skewed toward financially advantaged groups, due to differences in the amount of extractable information rather than due to algorithmic bias. Requesting additional information does not fully address this disparity: financially disadvantaged groups are less willing to share their data, and even when they provide their data, the additional datasets exhibit uneven coverage of financially informative signals. Our results suggest that underwriting systems need to address frictions in both data-sharing willingness and unequal availability of financial information to achieve truly inclusive credit assessment.
Publications
(with Minwoo Kang, Suhong Moon, Ayush Raj, Joseph Suh, David Chan, and John Canny), Conference On Language Modeling (COLM) 2025
Abstract: Large language models (LLMs) are increasingly capable of simulating human behavior, offering cost-effective ways to estimate user responses to various surveys and polls. However, the questions in these surveys usually reflect socially understood attitudes: the patterns of attitudes of old/young, liberal/conservative, as understood by both members and non-members of those groups. It is not clear whether the LLM binding is deep, meaning the LLM answers as a member of a particular in-group would, or shallow, meaning the LLM responds as an out-group member believes an in-group member would. To explore this difference, we use questions that expose known in-group/out-group biases. This level of fidelity is critical for applying LLMs to various political science studies, including timely topics on polarization dynamics, inter-group conflict, and democratic backsliding. To this end, we propose a novel methodology for constructing virtual personas with synthetic user "backstories" generated as extended, multi-turn interview transcripts. This approach is justified by the theory of narrative identity which argues that personality at the highest level is constructed from self-narratives. Our generated backstories are longer, rich in detail, and consistent in authentically describing a singular individual, compared to previous methods. We show that virtual personas conditioned on our backstories closely replicate human response distributions (up to an 87% improvement as measured by Wasserstein Distance) and produce effect sizes that closely match those observed in the original studies of in-group/out-group biases. Altogether, our work extends the applicability of LLMs beyond estimating socially understood responses, enabling their use in a broader range of human studies.
A Randomized Trial of Behavioral Nudges Delivered Through Text Messages to Increase Influenza Vaccination Among Patients With an Upcoming Primary Care Visit [Manuscript] [Online Appendix]
(with Mitesh Patel, Katherine Milkman, and 43 co-authors), American Journal of Health Promotion, 2023, 37(3): 324-332
Test-based Accountability and the Effectiveness of School Finance Reforms [Manuscript] [Online Appendix]
(with Christian Buerger and John Singleton), AEA Papers and Proceedings, 2021, 111: 455-59
Abstract: Recent literature provides new evidence that school resources are important for student outcomes. This paper examines whether school accountability systems that incentivize performance (such as No Child Left Behind) raise the efficiency with which additional resources get spent. We leverage the timing of school finance reforms to compare funding impacts on student test scores between states that had accountability in place at the time of the reform and states that did not. The results show that finance-reform-induced increases in student performance are driven by those states where the reform was accompanied by the presence of test-based accountability.
Coverage: Thomas B. Fordham Institute
Work in Progress
How Do Firms Allocate Capital and Labor? The Role of Headquarters Agglomeration (with Nuri Ersahin and Hyunseob Kim)
Access to and Gains from Selective Education (with Ying Shi and John Singleton)