The Causal Effect of Overqualification on Earnings : Evidence from a Bayesian Approach

This paper focuses on the causal effect of overqualification on earnings. Although the issue of overqualification has recently been addressed by quite a huge body of literature there are only few studies examining the causal effect of overqualification on earnings in the sense of Rubins potential outcome approach. Since for non-experimental data settings the incident of overqualification is not a random event, ignoring selfselection into overqualification leads to a misinterpretation of the empirical results and misleading policy conclusions, since the effect of overqualification on earnings cannot be interpreted causally. Using a cross-section of 1188 workers from the GSOEP we apply a Bayesian approach based on Markov chain Monte Carlo methods to estimate various treatment effects of overqualification on earnings. Our findings seriously question results ignoring selectivity effects and point out that on average for the overeducated an appropriate job match would not lead to higher earnings.