Many empirical questions in economics require the answer to some form of the following question: To what extent do past outcomes determine current and future outcomes? For example, if employers make hiring decisions based in part on the past employment outcomes of an applicant, then previous unemployment may cause an applicant to remain unemployed, if employers view this as a negative signal of the applicant's quality as a worker. Using data on employment outcomes to measure the extent to which this phenomenon occurs is a notoriously difficult problem. This proposal develops new statistical models for addressing this problem, both as it applies to employment dynamics, and to other similar problems in economics. The project advances the field by developing new methods for data analysis. The application to employment outcomes also advances the national prosperity by giving us better information about how unemployment affects long run job prospects.
The fundamental conceptual difficulty with measuring state dependence is that the effect of past outcomes will confound with temporally persistent unobservable heterogeneity across agents. For example, observing in a panel that previously unemployed agents are less likely to be employed could be due to a negative causal effect of past unemployment, but it could also result if unemployed agents are less likely to be employed due to other unobservable factors such as preferences or productivity. To date, the vast majority of statistical methods designed to measure state dependence use variants of highly-parameterized dynamic binary choice models. These models depend on many strong assumptions, including arbitrary functional form restrictions on the shape of heterogeneity. They are quite likely to be severely misspecified in many applications. The goal of this proposal is to develop and apply transparent nonparametric approaches for identifying state dependence from unobserved heterogeneity. Owing to the difficulty of point identifying state dependence, the proposed methods use partial identification techniques. In particular, the PI develops a new dynamic potential outcomes model, studies its properties, and applies it to questions of state dependence in employment outcomes.
