Multilevel Analysis of Wage Inequality in Palestine

Anotace: Historical data exhibit the imbalance participation rate between genders in the Palestinian labour market in which female participation is among the lowest worldwide. On the other hand, occupational discrimination and wage inequality still exist between males and females. Combining both issues, this study seeks to examine the gender pay gap across occupational groups in Palestine. The data are collected from the Palestinian Labour Force Survey (PLFS) for the year 2017. The multilevel linear regression is applied to model the wage equation. For the robustness purpose, three estimation techniques are applied which are maximum likelihood (ML), restricted maximum likelihood (REML), and Bayesian estimation. The results reveal that occupational groups account for about 23.6% of wage differentials. The gender wage gap varies significantly across occupational groups, where it is decreased after correcting for self-selection bias. Moreover, the Bayesian estimation method provides more efficient estimates than ML and REML methods. Schooling, age, and other socioeconomic variables also contribute significantly to wage inequality in Palestine.