Firm-Level and Aggregate Effects of Cheaper Liquidity: Evidence from Factoring with Thiago Silva and Henry Zhang
We show 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 the supply of factoring financing 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. Using our model, we estimate that an aggregate decrease in the economy-wide factoring spread of 1 percentage point leads to 0.3 to 0.5 percentage point increases in aggregate output and wages.
Quantile Mixture Models: Estimation and Inference with Luis Alvarez
Awarded best Econometrics paper at the 2023 Brazilian Econometric Society meeting.
Nonparametric density mixture models are popular in Statistics and Econometrics but suffer from computational and inferential hurdles. This paper introduces nonparametric quantile mixture models as a convenient counterpart, discusses several applications, and proposes a computationally efficient sieve estimator based on a generalized method of L-moments. We develop a full inferential theory for our proposed estimator. In doing so, we make several contributions to statistical theory that allow us to extend a numerical bootstrap method to high-dimensional settings. We show that, as a direct byproduct of our theory, we can provide an inference method for the distributional synthetic controls of Gunsilius (2023), a novel approach to counterfactual analysis for which formal inference methods were not yet available. As an empirical application of the latter, we apply our proposed approach to inference in assessing the effects of a large-scale environmental disaster, the Brumadinho barrage rupture, on the local wage distribution. Our results uncover a range of effects across percentiles, which we argue are consistent displacement effects, whereby median-earning jobs are replaced by low-paying contracts.
Volatility and under-insurance in economies with limited pledgeability: Evidence from the Frost Shock with Thiago Silva and Henry Zhang
Using transaction-level data on payments, credit, and insurance, we measure the impact, propagation, and adjustment by coffee farmers to an extreme weather shock in Brazil. The severe frost of July 2021 primarily damaged the perennial coffee trees, a negative shock to farmers' capital stock that was large enough to increase world prices. Consistent with an increase in the marginal return to capital, we find that insured farmers increase expenditure on capital replenishment inputs and decrease expenditure elsewhere. Uninsured farmers reduced expenditure as well as both insurance and credit take-up after the shock. We show how this pattern is consistent with models of imperfect pledgeability of a firm’s collateral, where constrained firms neither insure (ex-ante) nor recover from a shock (ex-post). Limited commitment endogenously generates under-insurance through upfront payment of insurance premia and reduced borrowing capacity post-shock due to the decrease in total collateral. We discuss two equilibrium implications of this mechanism regarding the inefficacy of emergency credit lines in targeting liquidity constrained firms and the amplification of output volatility and drop due to an increase in risk of extreme weather shocks.
