Nonparametric Inference on State Dependence in Unemployment

• 成果类型: Article
• 署名作者: Torgovitsky, Alexander
• 署名单位: University of Chicago
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA14138
• 发表日期: 2019
• 页码: 1475-1505
• 关键词: dynamic discrete-choice; labor-force participation; partial identification; instrumental variables; ECONOMETRIC EVALUATION; Job applications; Heterogeneity; models; callbacks; selection

摘要

This paper is about measuring state dependence in dynamic discrete outcomes. I develop a nonparametric dynamic potential outcomes (DPO) model and propose an array of parameters and identifying assumptions that can be considered in this model. I show how to construct sharp identified sets under combinations of identifying assumptions by using a flexible linear programming procedure. I apply the analysis to study state dependence in unemployment for working age high school educated men using an extract from the 2008 Survey of Income and Program Participation (SIPP). Using only nonparametric assumptions, I estimate that state dependence accounts for at least 30-40% of the four-month persistence in unemployment among high school educated men.