Working Papers:

"Financial Development, Labor Markets, and Aggregate Productivity: Evidence from Brazil," with Bernardus Van Doornik

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.

"Credit Access and Financial Health: Evaluating the Impact of Debt Collection," with Basit Zafar. Mentioned in: The Economist

Despite the prevalence of debt collection and the intense regulatory activity surrounding this industry, little is known about how these practices impact consumers. This paper conducts an empirical analysis of the effect of debt collection on consumer credit and on indicators of financial health, employing individual credit record data and a difference-in-differences research design that compares outcomes of consumers in states that increased the restrictiveness of legislation with those of consumers in the remaining states. We find consistent evidence that restricting collection activities leads to a decrease in access to credit and to a deterioration in indicators of financial health. Moreover, our estimated treatment varies considerably with the borrower's age and baseline credit score, with effects concentrated primarily among borrowers with the lowest credit scores.

"Modern Frameworks for Quantitative Economics," with Victor Duarte, Diogo Duarte, and Alexis Montecinos

Conditionally accepted, Journal of Economic Dynamics and Control

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.



Works in Progress:

"Firm Leverage and the Transmission of Consumer Demand Shocks," with Victor Duarte and Dejanir Silva

"Predicting Exchange Rates with Deep Learning," with Victor Duarte and Adrien Verdelhan


"Consumption and Portfolio Choice with Many Assets and Realistic Dynamics," with Victor Duarte and Jonathan Parker

Julia Fonseca

Assistant Professor of Finance

University of Illinois at Urbana-Champaign