Estimating Duration Dependence in Job Search: the Within-Estimation Duration Bias

TL;DR

This paper investigates the bias introduced by fixed-effects models in estimating duration dependence in job search data, revealing that the bias can be large and affects various longitudinal analyses.

Contribution

It derives conditions for valid fixed-effects estimates of duration dependence and demonstrates the potential for significant bias through simulations.

Findings

Fixed-effects models can induce large within-estimation bias.

Bias depends on specific conditions related to job exit outcomes.

Monte Carlo simulations show the bias can be substantial.

Abstract

Many recent studies use individual longitudinal data to analyze job search behaviors. Such data allow the use of fixed-effects models, which supposedly address the issue of dynamic selection and make it possible to identify the structural effect of time. However, using fixed effects can induce a sizable within-estimation bias if job search outcomes take specific values at the time job seekers exit unemployment. This pattern creates an undesirable mechanical correlation between the error term and the time regressor. This paper derives the conditions under which the fixed-effects estimator provides valid estimates of structural duration-dependence relationships. Using Monte Carlo simulations, we show that the magnitude of the bias can be extremely large. Our results are not limited to the job search context but naturally extend to any framework in which longitudinal data are used to measure the structural effect of time.