Retirement, Job Satisfaction and Mental Health : Partial Identification and Minimax-Regret Analysis with Panel Data

We study how retirement affects mental health using a partial identification framework for panel data. We consider identifying assumptions that restrict the process of selection, bind the amount of variation in counterfactual moments or limit the amount of interference within units along the time dimension. The identification regions that we estimate are then transformed into quantitative policy recommendations by adopting a minimax-regret decision rule. We find that retirement can have a moderate, positive effect on younger individuals whose pre-retirement mental health is below average of the corresponding age cohort. Our minimax-regret analysis further suggests that while retirement can be delayed without affecting the mental health of the majority of the population, it is advisable to facilitate the retirement of people with poor mental health