Analysis of gender wage gap using mixed effects models

Despite government interventions, the gender wage gap persists in workplaces. While reports on whether the gap is widening or narrowing vary, addressing this issue remains crucial. Traditionally, researchers have employed methods like the Blinder-Oaxaca decomposition and quantile regression to estimate the gender wage gap. However, these approaches often leave a high unexplained variance attributed to discrimination. In existing studies, gender wage gap estimates have typically been aggregated, and attempts to disaggregate the analysis have focused on broader levels such as occupations and salary bands. To delve deeper, human resource data from the National Department of Health in South Africa Eastern Cape province was leveraged. The goal was to analyze the gender wage gap for each job title using a novel approach: linear mixed effects regression. The linear mixed effects model captures both systematic trends and unexplained variability simultaneously to provide a more comprehensive understanding of the gender wage gap. Here are the key findings: 1. The unexplained variance in gender wage gap was remarkably low, accounting for only 3% of total variance. This indicates that the model captures most of the variability in the data as a result there is minimal unexplained variation. 2. Job titles emerged very significant by explaining 83% of the total random variance. This highlights the significance of considering specific roles when analyzing gender wage gap. 3. Over time, interesting patterns were observed. From 2010, the gender wage gap narrowed, but starting around 2015, it gradually widened again. 4. Encouragingly, 42% of the job title groups showed a gender wage gap in favor of women. Additionally, a substantial proportion of females occupied managerial and highly skilled positions. Therefore, incorporating random effects techniques through linear mixed effects regression enriched the analysis of gender wage gap. By examining job titles individually, detailed insights into this complex issue were gained. These findings underscore the importance of considering both fixed and random effects when studying wage disparities.

Grantor dc:publisher.institution

Department of Statistical Sciences

Year dc:date.issued

2025

Author dc:creator

• Chikanya, Magnolia M

Advisors dc:contributor.advisor

• Er, Sebnem
• Silal, Sheetal

• statistical sciences

Handle dc:identifier.uri

http://hdl.handle.net/11427/41610

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/41610