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

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

[NBER working paper version]

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.

"Less Mainstream Credit, More Payday Borrowing? Evidence from Debt Collection Restrictions" 

Journal of Finance, Vol. 78: 63-103, 2023

[Pre-publication version] [Internet Appendix

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

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

[Pre-publication version] [Internet Appendix]

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.


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

Revise & Resubmit, 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 mortgage rate deltas (∆r), measured as the difference between the mortgage rate locked in at purchase and the current market rate, reduces moving rates by 0.68 p.p, or 9%. We find that this relationship is non-linear: once ∆r is high enough, households’ alternative of refinancing without moving
becomes attractive enough that moving probabilities no longer depend on ∆r. Lastly, we find that mortgage lock-in attenuates household responsiveness to shocks to nearby employment opportunities that require moving, measured as wage growth in counties within a 50 to 150-mile ring and instrumented with a shift-share instrument. The responsiveness of moving rates to wage growth is nearly three times as large for households who are less locked in (above-median ∆r) than for those who are more locked in. We provide causal estimates of mortgage lock-in effects, highlighting unintended consequences of monetary tightening with long-term fixed-rate mortgages on mobility and labor markets.

Media: Wall Street Journal (x 2), NPR Planet Money (The Indicator)Bloomberg (x 2), NYT The Upshot


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

[NBER working paper version]

We develop a machine-learning solution algorithm to solve for optimal portfolio choice in a detailed and quantitatively-accurate lifecycle model that includes many features of reality modeled only separately in previous work. We use the quantitative model to evaluate the consumption-equivalent welfare losses from using simple rules for portfolio allocation across stocks, bonds, and liquid accounts instead of the optimal portfolio choices. We find that the consumption-equivalent losses from using an age-dependent rule as embedded in current target-date/lifecycle funds (TDFs) are substantial, around 2 to 3 percent of consumption, despite the fact that TDF rules mimic average optimal behavior by age closely until shortly before retirement. Our model recommends higher average equity shares in the second half of life than the portfolio of the typical TDF, so that the typical TDF portfolio does not improve on investing an age-independent 2/3 share in equity. Finally, 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 of advice or TDFs in these dimensions.

Media: Wall Street Journal, Morningstar

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

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.


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

Julia Fonseca

Assistant Professor of Finance

University of Illinois at Urbana-Champaign

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