The Effects of Banking Competition on Growth and Financial Stability: Evidence from the National Banking Era, with Stephan Luck and Mark Carlson (R&R Restud)
How do restrictions on banking competition affect credit provision and economic output? And, how do they affect financial stability? To identify the causal effect of banking competition, we exploit a peculiarity of bank capital regulation in the National Banking Era: opening banks in towns with more than 6,000 inhabitants required twice the equity as in towns below this threshold, thus leading to a locally exogenous variation of entry barriers.
We construct a novel comprehensive data set comprising the annual balance sheets of all national banks, and link it with the results of the decennial census. We show that banks operating in markets with lower entry barriers extend more credit and choose a higher leverage. The resulting local credit boom, in turn, is associated with an expansion in the local manufacturing industry. However, banks in markets with lower entry barriers are also more likely to default or go out of business during a major financial crisis. Altogether, we provide causal evidence that credit growth can cause both, economic growth and financial instability.
Did the Community Reinvestment Act (CRA) Lead to Risky Lending?, with S. Agarwal, B. Morais, E. Benmelech, N. Bergman and A. Seru (under revision)
We use exogenous variation in banks’ incentives to conform to the standards of the Community Reinvestment Act (CRA) around regulatory exam dates to trace out the effect of the CRA on mortgage lending activity. Our empirical strategy compares lending behavior of banks undergoing CRA exams within a given census tract in a given month to the behavior of banks operating in the same census tract-month that do not face these exams. We find that adherence to the act led to riskier lending by banks: in the three quarters preceding the CRA exams, lending in CRA-eligible census tracts increases by about 0.8 percent every quarter and delinquency rates by about 7.5 percent. These patterns are accentuated among large banks and in banks with previous non-satisfactory CRA evaluations.
Credit Supply Shocks, Consumer Borrowing and Bank Competitive Response: Evidence from Credit Card Markets
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.
Verifying the Existence of Maximum Likelihood Estimates for Generalized Linear Models, with Paulo Guimarães and Tom Zylkin
We expand on Santos Silva and Tenreyro (2010)’s observation that estimates from Poisson models are not guaranteed to exist by documenting necessary and sufficient conditions for the existence of estimates for a wide class of generalized linear models (GLMs).
We show that some, but not all, GLMs can still deliver consistent, uniquely-identified maximum likelihood estimates of at least some of the linear parameters at the boundary of the parameter space when these conditions fail to hold. We also demonstrate how to verify this condition in the presence of high-dimensional fixed effects, as are often recommended in the international trade literature and in other common panel settings.
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.
ppmlhdfe: Fast Poisson Estimation with High-Dimensional Fixed Effects, with Paulo Guimarães and Tom Zylkin (also see the software page)
In this paper we present ppmlhdfe, a new Stata command for estimation of (pseudo) Poisson regression models with multiple high-dimensional fixed effects (HDFE). Estimation is implemented using a modified version of the Iteratively Reweighted Least Squares (IRLS) algorithm that allows for fast estimation in the presence of HDFE. Since the code is built around the reghdfe package it has similar syntax and supports many of the same functionalities. ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo) maximum likelihood estimates.
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.