Credit Supply Shocks, Consumer Borrowing and Bank Competitive Response: Evidence from Credit Card Markets (Job Market Paper)
I study local shocks to consumer credit supply arising from the opening of bank-related retail stores. Bank-related store openings coincide with sharp increases in credit card placements in the neighborhood of the store, in the months surrounding the store opening, and with the bank that owns the store. I exploit this relationship to instrument for new credit cards at the individual level, and find that obtaining a new credit card sharply increases total borrowing as well as default risk, particularly for risky and opaque borrowers.
In line with theories of default externality, I observe that existing lenders react to the increased consumer borrowing and associated riskiness by contracting their own supply. In particular, in the year following the issuance of a new credit card, banks without links to stores reduce credit card limits by 24–51%, offsetting most of the initial increase in total credit limits.
Linear Models with High-Dimensional Fixed Effects: An Efficient and Feasible Estimator (also see reghdfe and the slides)
I propose a feasible and computationally efficient estimator of linear models with multiple levels of fixed effects. This estimator builds upon the generalized within-estimator of Guimarães and Portugal (2010) and Gaure (2013), addressing its slow convergence properties with two contributions. First, I replace their projection methods by symmetric ones amenable to conjugate gradient acceleration, which guarantees monotonic convergence. Second, I reformulate the within-transformation problem into one of solving a Laplacian system, and apply recent breakthroughs in spectral graph theory (Spielman and Teng 2004; Kelner et al 2013) to implement a nearly–linear time estimator. This estimator performs particularly well in the cases where the conjugate gradient method performs at its worst.
Work in Progress
The Effects of Bank Location in SME Credit (with Philipp Schnabl)
Using a detailed GIS dataset of all Peruvian firms and bank branches, we study how bank proximity affects firms’ credit access, and at what scale do the effects occur. We find strong intensive and extensive effects of distance on credit, even after controlling for neighborhood, industry, and time fixed effects. This relationship is nonlinear, with proximity affecting up to a scale of 500 meters, with shorter distances being uncorrelated with credit access. This relationship is stronger for smaller firms, firms with opaque ownership structures and fewer legal representatives, suggesting that distance plays a stronger role when soft information is more valuable.
Maintaining singleton groups in linear regressions where fixed effects are nested within clusters can overstate statistical significance and lead to incorrect inference. Due to this problem, the reghdfe package now automatically drops singletons. However, a broader class of problems related to nested fixed effects and finite-sample adjustments remains.