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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 estim...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2025
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| _version_ | 1867613328515268608 |
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| access_status_str | Open Access |
| author | Chikanya, Magnolia M |
| author2 | Er, Sebnem |
| author_browse | Chikanya, Magnolia M Er, Sebnem |
| author_facet | Er, Sebnem Chikanya, Magnolia M |
| author_sort | Chikanya, Magnolia M |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/41610 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:34:23.309Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/41610 Analysis of gender wage gap using mixed effects models Chikanya, Magnolia M Er, Sebnem Silal, Sheetal statistical sciences 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. 2025-08-19T09:19:02Z 2025-08-19T09:19:02Z 2025 2025-07-31T07:00:20Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/41610 eng application/pdf Department of Statistical Sciences Faculty of Science Universiy of Cape Town |
| spellingShingle | statistical sciences Chikanya, Magnolia M Analysis of gender wage gap using mixed effects models |
| thesis_degree_str | Master's |
| title | Analysis of gender wage gap using mixed effects models |
| title_full | Analysis of gender wage gap using mixed effects models |
| title_fullStr | Analysis of gender wage gap using mixed effects models |
| title_full_unstemmed | Analysis of gender wage gap using mixed effects models |
| title_short | Analysis of gender wage gap using mixed effects models |
| title_sort | analysis of gender wage gap using mixed effects models |
| topic | statistical sciences |
| url | http://hdl.handle.net/11427/41610 |
| work_keys_str_mv | AT chikanyamagnoliam analysisofgenderwagegapusingmixedeffectsmodels |