Essays in Financial Economics and Econometrics

Abstract

This thesis comprises three essays in finance and econometrics. The first two essays focus on the role of credit access and liquidity in shaping real firm outcomes. The first essay examines the transmission of modern monetary policy through corporate asset markets. Exploiting quasi-experimental variation in the Central Bank of Brazil’s collateral framework and implementing a novel dynamic regression discontinuity design, it shows that monetary policy can ease expected future borrowing constraints, reduce firms’ precautionary cash holdings, and stimulate employment. The second essay analyzes how receivables financing through factoring helps firms smooth cash flows. Using a shift-share instrument and matched administrative data, it finds that cheaper liquidity leads firms to rely more on permanent labor. The third essay develops a new method for distributional inference—nonparametric quantile mixture models. This framework can be applied to financial settings such as tail risk estimation and density forecasting, as well as to causal inference when the objective is to estimate the distributional effects of interventions. It is used here to quantify the heterogeneous wage effects of a major environmental disaster.

The first chapter (joint with Luis Alvarez and Thiago Christiano Silva) studies how modern monetary policy tools, which increasingly operate through corporate asset markets, affect real firm outcomes. We exploit quasi-experimental variation from the inclusion of specific corporate debt instruments in the Central Bank of Brazil’s collateral framework and implement a novel dynamic regression discontinuity design. We find that eligibility increases firms’ debt issuance, modestly lowers spreads, and reduces cash holdings, reflecting a decline in precautionary savings. These effects translate into higher employment and greater supply chain liquidity. We interpret the mechanism through the lens of segmented financial markets: by relaxing firms’ expected future borrowing constraints, the policy acts as a persistent borrowing subsidy and liquidity injection. This encourages firms to reduce cash hoarding and expand production. Using a semi-structural framework calibrated to our reduced-form estimates, we find that an induced 0.8% borrowing subsidy leads to a 1% increase in debt issuance, a 0.2% reduction in cash holdings, and a 0.4% increase in the wage bill.

The second chapter (joint with Thiago Christiano Silva and Henry Zhang) shows that firms experience large increases in sales and purchases after receiving cheaper liquidity. We focus on factoring, defined as the supplier-initiated sale of receivables. In Brazil, receivables funds (FIDCs) securitize receivables for institutional investors. By assembling a novel transaction-level dataset of factoring with other credit operations for all registered firms and FIDCs, we construct a shift-share instrument for factoring financing supply based on FIDC flows. We then use a novel combination of electronic payments, trade credit, and employer-employee matched data to estimate the impacts. A flow-induced increase in receivables demand reduces firms’ factoring interest rate. In response, firms demand more permanent labor and less temporary labor. In our model, these effects arise from factoring’s purpose of reducing cash inflow volatility, helping firms match inflows to outflows, which firms otherwise achieve at an efficiency cost through substitution across labor types.

The third chapter (joint with Luis Alvarez) introduces nonparametric quantile mixture models as a computationally convenient and flexible alternative to standard nonparametric density mixtures, which are widely used in Statistics and Econometrics but face significant computational and inferential challenges. We propose a sieve estimator based on a generalized method of L-moments and develop a full inferential theory. In doing so, we contribute to the statistical literature by extending a numerical bootstrap method to high-dimensional settings. As a direct application of our theory, we provide the first inference method for the distributional synthetic controls of Gunsilius (2023), a novel tool for counterfactual analysis that previously lacked formal inference procedures. We apply this method to evaluate the effects of the Brumadinho dam collapse—a large-scale environmental disaster—on the local wage distribution. The results reveal substantial heterogeneity across the distribution, with evidence of displacement effects in which median-paying jobs are replaced by lower-wage contracts. JEL Codes: C1, E4, E5, G2, G3