Partial Identification of State Dependence

Abstract

This paper is about the empirical measurement of state dependence in dynamic binary outcomes. I propose a nonparametric dynamic potential outcomes (DPO) model and develop an array of parameters and identifying assumptions that can be considered in this model. I show how to construct sharp identified sets in the DPO model by using a flexible linear programming procedure that is valid for any of these parameters under any combination of identifying assumptions. Confidence regions for these identified sets are obtained by applying recent results from the literature on partially identified moment condition models. I apply the analysis to study state dependence in the labor force participation of married women using a well-known extract from the 1986 Panel Study of Income Dynamics (PSID), and to study state dependence in unemployment for working age high school educated men using a new extract from the 2008 Survey of Income and Program Participation (SIPP). Using conservative, non-parametric assumptions, I estimate that state dependence accounts for at least 28.1% of the observed persistence in yearly non-employment for women in the PSID sample. In the SIPP data, I find that state dependence accounts for at least 33.1% of the four-month persistence in unemployment among high school educated men. This contrasts with the findings of recent experimental studies based on fictitious resumes, which have found no evidence of short-term state dependence.