Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper documents the extent to which wage persistence can be explained by permanent worker, employer, and match heterogeneity. Standard methods used to perform such decompositions for industry or racial wage gaps are inappropriate for decomposing wage persistence in dynamic panel data models because of the incidental parameter problem. When we apply these methods without bias correction, we find that the majority, 59.3 percent, of wage persistence is explained by worker heterogeneity, with employer and match heterogeneity explaining 29.7 and 11.0 percent, respectively. We evaluate three methods for addressing incidental parameter bias using a Monte Carlo study. An empirical application to Portuguese linked employer–employee data shows that the uncorrected estimates tend to understate wage persistence by around 24 to 42 percent, depending on the choice of the bias correction estimator used, and overstate the extent to which wage persistence arises from permanent unobserved heterogeneity. Furthermore, results indicate that the uncorrected estimates overstate the role of permanent worker heterogeneity, and understate the role of firm heterogeneity.