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Publications

"Mortgage Lock-In, Mobility, and Labor Reallocation,"  with Lu Liu

Accepted, Journal of Finance

We study the impact of rising mortgage rates on mobility and labor reallocation. Using individual-level credit record data and variation in the timing of mortgage origination, we show that a 1 p.p. decline in the difference between mortgage rates locked in at origination and current rates reduces moving by 9% overall and 16% between 2022–2024, and this relationship is asymmetric. Mortgage lock-in also dampens flows in and out of self-employment and the responsiveness to shocks to nearby employment opportunities that require moving, measured as wage growth within a 50 to 150-mile ring and instrumented with a shift-share instrument.

Media: Wall Street Journal (x 2), NPR Planet Money (The Indicator)Bloomberg (x 2), New York Times (The Upshot), NPR Planet Money, Yahoo Finance

"Financial Inclusion, Economic Development, and Inequality: Evidence from Brazil," with Adrien Matray 

[NBER working paper version]

Journal of Financial Economics, Vol. 156: 103854, 2024

We study a financial inclusion policy targeting Brazilian cities with low bank branch coverage using data on the universe of employees from 2000-2014. The policy leads to bank entry and to similar increases in both deposits and lending. It also fosters entrepreneurship, employment, and wage growth, especially for cities initially in banking deserts. These gains are not shared equally and instead increase with workers’ education, implying a substantial increase in wage inequality. The changes in inequality are concentrated in cities where the initial supply of skilled workers is low, indicating that talent scarcity can drive how financial development affects inequality.

Governments regulate debt collectors to protect consumers from predatory practices. These restrictions may lower repayment, reducing the supply of mainstream credit and increasing the demand for alternative credit. Using individual credit record data and a difference-in-differences design comparing consumers in states that tighten restrictions on debt collection to those in neighboring states that do not, I find that restricting collections reduces access to mainstream credit and increases payday borrowing. These findings provide new evidence of substitution between alternative and mainstream credit and point to a trade-off between shielding consumers from certain collection practices and pushing them into higher-cost payday lending markets.

 

Media: The Economist

"Financial Development and Labor Market Outcomes: Evidence from Brazil," with Bernardus Van Doornik

[Pre-publication version] [Internet Appendix]

Journal of Financial Economics, Vol. 143(1): 550-568, 2022

We estimate the effect of an increase in the availability of bank credit on the employment and the wages of high- and low-skilled workers. To do so, we consider a bankruptcy reform that increased the legal protections of secured creditors, which led to an expansion of bank credit to Brazilian firms. We use detailed administrative data and an empirical strategy that exploits cross-sectional variation in the enforcement of the new legislation arising from differences in the congestion of civil courts. We find that the expansion in credit led to an increase in the skill intensity of firms and in within-firm returns to skill. To rationalize these findings, we design a model in which heterogeneous producers face constraints in their ability to borrow and have production functions featuring capital-skill complementarity. We use this framework to generate an industry-level measure of capital-skill complementarity, which we use to provide direct evidence that the effect of access to credit on skill utilization and the skill premium is driven by a relative complementarity between capital and labor.

"Benchmarking Machine-Learning Software and Hardware for Quantitative Economics," with Victor Duarte, Diogo Duarte, and Alexis Montecinos

Journal of Economic Dynamics and Control Vol. 111, Feb 2020

We investigate the performance of machine learning software and hardware for quantitative economics. We show that the use of modern numerical frameworks can significantly reduce computational time in compute-intensive tasks. Using the Least Squares Monte Carlo option pricing algorithm as a benchmark, we show that specialized hardware and software speeds the calculations by up to two orders of magnitude when compared to programs written in popular high-level programming languages, such as Julia and Matlab.

Working
Papers

"Global Identification with Gradient-Based Structural Estimation," with Victor Duarte

Revise & Resubmit, Journal of Financial Economics

This paper develops a gradient-based optimization method to estimate stochastic dynamic models in economics and finance and assess identification globally. By extending the state space to include all model parameters and approximating the mapping between parameters and moments, we only need to solve the model once to structurally estimate parameters. We approximate the mapping between parameters and moments by training a neural network on model-simulated data and then use this mapping to find the set of parameters that minimizes a function of the distance between model and data moments. We show how the mapping between parameters and moments can also be used to assess identification globally, detecting issues that a local diagnostic would miss. We illustrate the algorithm by solving and estimating a dynamic corporate finance model with endogenous investment, costly equity issuance, and capital adjustment costs. In this application, our method reduces the estimation time from many hours to a few minutes.

"Simple Allocation Rules and Optimal Portfolio Choice Over the Lifecycle," with Victor Duarte, Aaron Goodman, and Jonathan Parker

[NBER working paper version]

Solicited for submission, Journal of Financial Economics

In many areas of economics, relatively simple models developed for insight are used as quantitative guides. We study the accuracy of such simple quantitative guidance in an area where it has been widely adopted — lifecycle portfolio choice among stocks, bonds, and liquid accounts — by developing a machine-learning algorithm to solve for optimal portfolio choice in a calibrated lifecycle model that includes many features of reality modeled only separately in previous work. Both for optimizing households and for households that under-save, the average fully optimal portfolio at each age conforms well to current simple age-dependent prescriptive rules until shortly before retirement, validating existing analyses. We further show that the consumption-equivalent losses from conditioning portfolio shares on age alone are substantial, around 2 to 3 percent of consumption. Fully optimal equity shares have substantial heterogeneity, particularly by wealth level, state of the business cycle, and dividend-price ratio, implying substantial gains to further customization in these dimensions.

 

Media: Wall Street Journal, Morningstar

U.S. mortgage borrowers are "locked in'': unwilling to sell their house and move, as that would require giving up low fixed mortgage rates for higher current rates. We study the general equilibrium effects of mortgage lock-in on house prices, mobility, and homeownership and evaluate policies aimed at unlocking mortgage lock-in. To do so, we design a spatial housing ladder model that captures moving patterns across different housing market segments. Households can move between locations differing in economic opportunity and cost of living, and within the housing ladder by deciding whether to rent, own a starter home, or own a trade-up home. In equilibrium, house prices and rents are endogenously determined by household mobility within and between locations, and are thus impacted by lock-in. We provide new empirical evidence on moving behavior along the housing ladder and over the life cycle and calibrate the model with rich microdata from 2024. While higher rates reduce the demand of households who would otherwise move up the housing ladder, we show that mortgage lock-in substantially reduces downsizing and exits from homeownership, increasing net demand for housing and resulting in higher house prices. We further evaluate the equilibrium effects of the proposed 2024 Mortgage Relief Credit, which would provide a $10,000 subsidy to sellers of starter homes. We find that the policy modestly increases first-time home buying and has larger effects on upward mobility at the top of the housing ladder. However, upward mobility within the housing ladder comes at the cost of renters and starter homeowners moving from high- to low-opportunity areas, as house prices in higher-priced areas increase.

Media: NPR Planet Money (The Indicator), NPR Planet Money, NPR Morning Edition

Work in
Progress

"How Much do Small Businesses Rely on Personal Credit?", with Jialan Wang

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