Decomposing the structure of wages into firm and worker effects

Purpose The purpose of this paper is to show that for some key topics on labour economics such as the effect of seniority and job mobility in wages, it is important to explicitly consider firm fixed effects. The author also wants to test whether the importance of firm in explaining wage dispersion is higher or lower in Spain than in other European countries. Design/methodology/approach The author estimates an individual wage equation where firm and workers effects are considered and the estimation process control for censored wages. This exercise is performed for the Spanish economy over the course of a whole business cycle, i.e., 2000-2015. Findings The author demonstrates that Spanish firms contribute to explain around 27 per cent of the individual wage heterogeneity but more importantly around 74 per cent of inter-industry wage differentials. In both cases, this contribution is mainly related to large dispersion in firm’s wage policies. The process of positive sorting of workers across firms or industries does not play an important role. Interestingly, the importance of firm’s wage policies in explaining individual wage dispersion has increased over the current Big Recession. Practical implications The results confirm that firms set wages and, henceforth, are partially responsible for individual wage heterogeneity but more importantly for inter-industrial wage dispersion. Originality/value The exercise is performed under optimal conditions because the author uses a longitudinal matched employer-employee data set, observed wages are at a monthly frequency, and implements an estimation method suitable for censored models with two high-dimensional fixed effects. This is the first study that looks deeply into the role of firms in explaining wage heterogeneity at the individual and industry level in Spain and along the current Big Recession.

Using Poisson Class Regression To Analyze Count Data in Correctional and Forensic Psychology

The benchmark model for count data is the Poisson distribution, and the standard statistical procedure for analyzing count data is Poisson regression. However, highly restrictive assumptions lead to frequent misspecification of the Poisson model. Alternate approaches, such as negative binomial regression, zero modified procedures, and truncated and censored models are consequently required to handle count data in many social science contexts. Empirical examples from correctional and forensic psychology are provided to illustrate the importance of replacing ordinary least squares regression with Poisson class procedures in situations when count data are analyzed.