Machine Learning Decomposition of Gender and Rural–Urban Wage Gaps: Evidence from Uganda National Household Surveys

Abstract

Uganda's gender wage gap stands at about 32 percent (UN Women, 2024), with rural–urban differences of similar size. Published decomposition studies for Uganda have relied largely on the 2002/03 survey round and a linear wage equation that assumes constant returns to characteristics. This study examines whether a flexible functional form, applied to recent multi-round data, alters the share of each gap attributed to measured characteristics. Four Uganda National Household Survey rounds (2012/13–2023/24) were harmonised into a pooled sample of 14,963 formal wage earners aged 14–64. Ordinary least squares (OLS), LASSO, Random Forest and XGBoost were trained on a stratified 70/15/15 split, and SHapley Additive exPlanations (SHAP) were used to attribute predictions to worker characteristics. Predictions from the best-performing model replaced the linear fitted values in an Oaxaca–Blinder decomposition estimated for each round. XGBoost achieved the highest test R² (0.410), followed by Random Forest (0.399) and the two linear models (0.354); pooling all four rounds raised it from 0.286. Education was the leading predictor. The rural–urban gap was largely explained under both methods: 49–59 percent under OLS and 68–84 percent under the machine-learning decomposition. For the gender gap, OLS produced a negative explained share in three of four rounds, while the machine-learning decomposition attributed 14–57 percent to measured characteristics. The explained share of the gender gap is thus sensitive to the functional form of the wage model, which calls for caution in interpreting single-round linear estimates. The approach can be reproduced on successive survey rounds to track wage inequality over time. The unexplained component is reported as a residual and is not interpreted as a measure of discrimination.