Modelling heterogeneity and dynamics in the volatility of individual wages

Abstract

In this paper I consider a model for the heterogeneity and dynamics of the conditional mean and the conditional variance of standarized individual wages. In particular, I propose a dynamic panel data model with individual effects both in the mean and in a conditional ARCH type variance function. I posit a distribution for earning shocks and I build a modified likelihood function for estimation and inference in a fixed-T context. Using a newly developed bias-corrected likelihood approach makes it possible to reduce the estimation bias to a term of order 1 over T squared. The small sample performance of bias corrected estimators is investigated in a Monte Carlo simulation study. The simulation results show that the bias of the maximum likelihood estimator is substantially corrected for designs that are broadly calibrated to the PSID. The empirical analysis is conducted on data drawn from the 1968-1993 PSID. I find that it is important to account for individual unobserved heterogeneity and dynamics in the variance, and that the latter is driven by job mobility. I also find that the model explains the non-normality observed in logwage data