Accepted, Journal of Finance
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
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.
"Modern Frameworks 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.
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.
"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.
WORK IN PROGRESS:
"How Much do Small Businesses Rely on Personal Credit?", with Jialan Wang
Assistant Professor of Finance
University of Illinois at Urbana-Champaign