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: Bloomberg Businessweek, Washington Post, Financial Times, Fortune 1, Fortune 2, Fox Business, Bloomberg Markets, New York Times, The Atlantic, World Economic Forum, Realtor.com, Investopedia, Marginal Revolution, Macro Roundup, Le Figaro (in French)
Conference: Financial Intermediation Research Society, Annual Workshop in Real Estate Finance (Leibniz Institute for Financial Research SAFE and Frankfurt School of Finance & Management), Urban Economics Association (North American Meeting), Georgia Tech–Atlanta Fed Household Finance Conference (Young Scholars Poster Session), Macro Finance Research Program Summer Session for Young Scholars (Poster)
Anticipatory Spending: Identifying Consumer Types with Pre-Payment MPCs (with Scott Baker, Michael Gelman, and Lorenz Kueng)
Abstract: How much of the heterogeneity in marginal propensities to consume (MPCs) reflects transitory financial circumstances versus persistent behavioral traits? We address this question by jointly estimating novel "pre-payment" MPCs—spending increases prior to payment arrival—and traditional post-payment MPCs. Using high-frequency transaction data, we leverage multiple pre-announced income changes per person across a range of fiscal policies. We find a robust positive correlation between pre- and post-payment responses, which exhibits substantially higher within-person persistence than predicted by canonical models with liquidity constraints. Building on this evidence, we use k-means clustering to classify individuals into four distinct types: Active Savers (43%), Passive Savers (23%), Active Spenders (9%), and Passive Spenders (25%). By examining auxiliary dimensions such as liquidity, payment size, and event salience, we map these empirical types to established consumption models such as rational consumption smoothing, inattention, mental accounting, and present bias. Our findings suggest that persistent behavioral traits are important drivers of consumption dynamics and help explain the empirical attenuation of forward guidance.
Conference: NBER Summer Institute (Household Finance), CEAR-RSI Household Finance Workshop, American Economic Association
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. In our model, firm investments generate improved human capital as a byproduct, allowing firms to attract workers at lower wages and creating an additional return to investment. We document a positive relationship between intangible investment and subsequent growth in workers’ portable earnings capacity, with evidence supporting a productive human-capital interpretation. We use these findings to discipline the quantitative model. Eliminating workers’ ability to differentially value firms’ learning opportunities reduces the steady-state intangible stock by 17% and aggregate income by 2.9%. For firms in the top tercile of human-capital growth, about 43% of the value generated by incubation is captured through wage savings, while the remainder accrues to workers and subsequent employers.
Conference: NBER Firms, Technology, and Economic Implications Meeting, Finance, Organizations and Markets (FOM), CSEF-CEPR Conference on Labor and Finance, Labor & Finance Group, Western Finance Association, Financial Intermediation Research Society
Agglomeration at the Top: Headquarters Clusters, Productivity, and Resource Allocation (with Nuri Ersahin and Hyunseob Kim)
Abstract: This paper studies the agglomeration economies of corporate headquarters: how they affect productivity, resource allocation, and the internal organization of multi-unit firms. Using restricted-use US Census Bureau microdata, we show a robust positive relationship between headquarters agglomeration and plant and firm productivity — beyond the well-documented association between local plant and worker agglomeration and productivity. Plants managed by more agglomerated headquarters exhibit greater investment and capital deepening, more specialized labor and product portfolios, and more productivity-sensitive churning. Notably, the productivity gains do not translate into higher wages for plant employees; instead, the wage gap between headquarters and plants widens, and the plants' labor shares decline. Using openings of large headquarters across competing locations as a quasi-experimental source of variation, we provide evidence that greater headquarters agglomeration increases plant productivity and induces capital deepening. These results identify headquarters clustering as an important, previously understudied channel through which agglomeration economies shape the performance of multi-unit firms. Given the prominent role of multi-unit firms and the spatial concentration of headquarters in the economy, the findings have implications for aggregate productivity and the allocation of value-added across regions.
Conference: Urban Economics Association (North American Meeting), Federal Reserve System Committee on Regional Analysis Conference, Federal Statistical Research Data Center Annual Conference
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 variation in how much forecast dispersion falls after the arrival of a signal that most agents use and interpret in the same way. Intuitively, for a given level of disagreement in point forecasts, a larger decrease in dispersion after the signal indicates greater subjective uncertainty in agents' prior beliefs. Using these measures, we document differences in the economic roles of disagreement and uncertainty. In the cross-section, stocks with higher disagreement earn lower subsequent returns and exhibit higher trading volume. Both effects are 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. We extend the extrapolative bubbles framework to show that the model's predictions closely match our empirical findings and provide intuition for the observed relationships. Finally, firm characteristics are more closely linked to disagreement than to uncertainty: smaller, less profitable, and more R&D-intensive firms exhibit systematically higher levels of disagreement.
Conference: Annual UIC Finance Conference, World Congress of the Econometric Society, Financial Intermediation Research Society, Economics Graduate Student Conference, Yiran Fan Memorial Conference (Poster)
The Cost of Leverage: Retail Demand and Option Prices (with Giyoung Kwon, Jingoo Kwon, Seongkyu Gilbert Park, Younggeun Yoo, and Haesung Kim)
Abstract: Retail investors often use options to obtain leveraged exposure. Using account-level administrative data covering the universe of KOSPI 200 index option trades from 2010 to 2014, when Korea hosted one of the world’s largest options markets, we examine how retail demand shapes option prices and investor performance. Retail investors buy to open more than 80% of their positions in both calls and puts, concentrating their purchases in short-dated, out-of-the-money options. This buying tendency is highly persistent at the investor level and more consistent with demand for leverage than with directional views or capital constraints. Options with greater retail demand exhibit higher implied volatility but not higher subsequent realized volatility, indicating larger risk premia. This pricing relationship is strongest for out-of-the-money options: a one-standard-deviation increase in retail demand corresponds to an estimated price increase of 3.8-4.3%, compared with 0.3-0.5% for at-the-money options. We provide causal evidence for the role of retail demand by showing that implied volatility declined following an exogenous policy shock that reduced retail participation. As buyers bear these premia, buy-opened positions lose money on average for both retail and institutional investors, while sell-opened positions earn profits. Retail investors’ concentration on the buying side thus helps explain why 76% of retail accounts lose money, whereas more than 60% of institutional accounts earn profits. These findings reveal a hidden cost of leverage: retail investors’ collective demand raises the price of the leveraged exposure they seek and contributes to their trading losses.
Signals from SMS: Alternative Data and Distributional Effects in Credit Scoring (with Kenneth Ryu and Jaehyeok Shin)
Abstract: Using data from 22 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.
Conference: Financial Management Association, Korea Economic Review International Conference (ESWC Pre-Conference), Asia-Pacific Association of Derivatives
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
Access to and Gains from Selective Education (with Ying Shi and John Singleton)
Teacher Shortages, Relative Pay, and College Access: Evidence from Texas (with Kirabo Jackson and Matthew Kraft)