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原文摘要
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, nonrandom treatment assignment, endogeneity, and discrete outcomes. This article 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 article constructs a novel bank-level dataset 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 article offers practical estimation tools to unveil new answers to important regulatory and policy questions.