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RWP 22-04, May 2022; updated August 2022

When banks fail amidst nancial crises, the public criticizes regulators for bailing out or liquidating speci c banks, especially the ones that gain attention due to their size or dominance. A comprehensive assessment of regulators, however, requires examining all their decisions, and not just speci c ones, against the regulator's dual objective of preserving nancial stability while discouraging moral hazard. In this article, we develop a Bayesian latent class estimation framework to assess regulators on these competing objectives and evaluate their decisions against resolution rules recommended by theoretical studies of bank behavior designed to contain moral hazard incentives. The proposed estimation framework addresses the unobserved heterogeneity underlying regulator's decisions in resolving failed banks and provides a disciplined statistical approach for inferring if they acted in the public interest. Our results reveal that during the crises of 1980's, the U.S. banking regulator's resolution decisions were consistent with recommended decision rules, while the U.S. savings and loans (S&L) regulator, which ultimately faced insolvency in 1989 at a cost of $132 billion to the taxpayer, had deviated from such recommendations. Timely interventions based on this evaluation could have redressed the S&L regulator's decision structure and prevented losses to taxpayers.

JEL Classifications: C11, C38, G21, G33, G38

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Padma Sharma


Padma Sharma is an Economist at the Federal Reserve Bank of Kansas City. She joined the Economic Research Department in July 2019. Prior to joining the department, she completed …