Estimation of worker and firm effects with censored data

The main contribution of this paper is to provide researchers with a new estimation method suitable for censored models with two high dimensional fixed effects. This new estimation method is based on a sequence of least squares regressions. In practice, use of this method can result in significant savings in computing time, and it is applicable to datasets where the number of fixed effects makes standard estimation techniques unfeasible. In addition, the paper both analyses the theoretical properties of the procedure and evaluates its practical performance by means of a Monte Carlo simulation study. Finally, it describes an application to the Spanish economy using a Spanish longitudinal match employer-employee dataset which provides wage information on the working population over a 13-year period. In particular, this paper contributes to the empirical literature on wage determination by providing the first decomposition of individual wages for Spain that takes into account both worker and firm effects after adjusting for censoring. This empirical exercise shows that the biases encountered when censored issues are not taken into account can be of sufficient magnitude as to overestimate the role of firm effects in wage dispersion. In our empirical research, individual heterogeneity explains more than 60% of wage dispersion.