Socioeconomic Drivers of Household Electricity Access in Rwanda
# Socioeconomic Drivers of Household Electricity Access in Rwanda
## Evidence from EICV 7
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## Authors
| Name | Role | Institution |
|---|---|---|
| Jean Pierre NIYOMUGABO | First Author | University of Rwanda, CBE |
| Dr. Jules NGANGO | Supervisor & Co-author | University of Rwanda, CBE |
**Institution:** College of Business and Economics (CBE),
University of Rwanda, Huye Campus, Rwanda
**Target Journal:** Scientific African (Elsevier)
**Status:** Under preparation — May 2026
---
## Abstract
Rwanda's household electricity access expanded from 34.4% in
2016/17 to 72.0% in 2023/24. Despite this progress, significant
socioeconomic and spatial disparities persist. This study examines
the household-level socioeconomic drivers of electricity access
using EICV 7 (2023/24) microdata (n = 15,054 households) across
all 30 districts of Rwanda. Binary logistic regression with average
marginal effects is estimated as the baseline model, with probit
and linear probability model (LPM) robustness checks. GIS spatial
mapping is employed to visualize geographic disparities across
provinces and districts.
---
## Key Statistics
| Metric | Value |
|---|---|
| Dataset | EICV 7 (2023/24) |
| Sample size | 15,054 households |
| National electricity access | 72.0% |
| Urban access | 88.5% |
| Rural access | 66.1% |
| Highest province | City of Kigali (92.3%) |
| Lowest province | Southern Province (65.0%) |
| Richest quintile (Q5) | 92.2% |
| Poorest quintile (Q1) | 54.1% |
| AUC-ROC | 0.811 |
| McFadden R² | 0.2271 |
| Variables in model | 14 (all VIF bibtex -> xelatex x2"
}
```
---
## Python Environment
```bash
# Required packages
pip install pandas numpy matplotlib seaborn
pip install statsmodels scikit-learn
pip install geopandas folium scipy
```
### Python Version
- Python 3.13.6
---
## Data Sources
| Dataset | Source | Year |
|---|---|---|
| EICV 7 microdata | National Institute of Statistics of Rwanda (NISR) | 2025 |
| Rwanda shapefiles | GADM administrative b …