Sample Selection and Treatment Effect Estimation of Lender of Last Resort Policies

Abstract

This article develops a framework for estimating multivariate treatment effect models in the presence of sample selection. The methodology deals with several important issues prevalent in policy and program evaluation, including application and approval stages, non-random treatment assignment, endogeneity, and discrete outcomes. This paper presents a computationally efficient estimation algorithm and techniques for model comparison and treatment effects. The framework is applied to evaluate the effectiveness of bank recapitalization programs and their ability to resuscitate the financial system. The analysis of lender of last resort (LOLR) policies is not only complicated due to econometric challenges, but also because regulator data are not easily obtainable. Motivated by these difficulties, this paper constructs a novel bank-level data set and employs the new methodology to jointly model a bank's decision to apply for assistance, the LOLR's decision to approve or decline the assistance, and the bank's performance following the disbursements. The paper offers practical estimation tools to unveil new answers to important regulatory and policy questions.

Analysis of Stigma and Bank Credit Provision

“…Any struggling bank could apply for assistance, the RFC reviewed the submitted applications in a reasonable amount of time (typically 2–5 weeks) and determined whether or not the bank was fit to receive assistance. Vossmeyer () reviews the RFC's selection procedures and finds that the RFC was successful at screening out helpless institutions and assisting those that could benefit from support.…”

“…The sampler does not simulate the outcomes that are missing due to the application selection mechanism and does not require the joint distribution for the potential outcomes, which result in an efficient sampler that maintains tractability in the sampling densities. A summary of the algorithm is offered below, however, the full derivation and details of the updating formulas are provided in Vossmeyer ()…”

“…For computational efficiency, a collapsed Gibbs sampler with data augmentation is employed which follows from Chib, Greenberg, and Jeliazkov (2009) and Li (2011). The particular algorithm that is utilized was developed in Vossmeyer (2016). The algorithm is attractive because of its excellent mixing properties, low storage costs, and computational speed.…”

“…For a thorough review of the determinants of RFC application and approval decisions and the RFC's effectiveness, see Vossmeyer ().…”

“…To capture realized stigma studying the RFC is the only viable option because alternative funding sources were not available for a majority of the banks. Previous studies on the RFC include Butkiewicz (1995Butkiewicz ( , 1999, Mason (2001Mason ( , 2003, Calomiris et al (2013), and Vossmeyer (2014Vossmeyer ( , 2016. While stigma is not the main focus of any of these papers, Butkiewicz (1995) and Mason (2001) address it in their analyses.…”