An Exploratory Analysis of Aggregates and Individual Wage Earnings in the Linked Employer-Employee Data (LEED)

Abstract

Longitudinal analyses with continuous repeated outcomes provide fuller insight into population and individual behaviour over time. Insights into processes of social change can thus e greatly enhanced through a more extensive use of longitudinal data. Using the Linked Employer-Employee Data (LEED), factors associated with wage earning were explored. Random effects models were investigated to identify fixed population effects as well as to help understand stochastic processes attributed to individual employee variations. It was found that wages vary significantly across region of residence, industry, age groups and gender. Random intercept adjustments provide an effective alternative for exploring earning variability over time.