Global vs. Local Banking: A Double Adverse Selection Problem

by Leslie Sheng Shen (UC Berkeley)

A summary of this research is provided here.

This paper provides a new theory of credit allocation in financial systems with both global and local banks, and tests it using cross-country loan-level data. I first point out that the traditional theory in banking and corporate finance of firm-bank sorting based on hard versus soft information does not explain the sorting patterns between firms and global versus local banks. In light of this puzzle, I propose a new perspective: global banks have a comparative advantage in extracting global information, and local banks have a comparative advantage in extracting local information. I formalize this view in a model in which firms have returns dependent on global and local risk factors, and each bank type can observe only one component of the firms’ returns. This double information asymmetry creates a segmented credit market with a double adverse selection problem: in equilibrium, each bank lends to the worst type of firms in terms of the unobserved risk factors. Moreover, I show that the adverse selection problem has important macroeconomic implications. When one of the bank types faces a funding shock, the adverse selection affects credit allocation at both the extensive and intensive margins, generating spillover and amplification effects through adverse interest rates. I formally test the model using empirical strategies that tightly map to the model set-up. I find firm-bank sorting patterns, and effects of US and Euro area monetary policy shocks on credit allocation that support the model predictions. This evidence reveals a novel adverse selection channel of international monetary policy transmission.

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