Household electricity demand in Uganda

Description

This dataset was prepared to test the hypothesis that modern energy adoption influences household electricity consumption in Uganda, but that this relationship is shaped by structural conditions such as electricity reliability, housing quality, household size, urban/rural residence, regional location, and fuel market exposure. The data were derived from the Uganda National Household Survey 2019/2020 conducted by the Uganda Bureau of Statistics. The dataset contains cleaned household-level variables on electricity consumption, electricity expenditure, grid access, daily hours of power availability, household demographics, housing materials, regional location, urban/rural status, asset ownership, cooking fuels, stove types, and fuel sourcing behaviour. Several derived variables were created, including monthly electricity consumption in kilowatt-hours, a Modern Energy Adoption Index based on efficient lighting, efficient cooking and electric heating indicators, a Housing Quality Index based on roof, wall and floor materials, and an asset ownership index. Survey weights are retained to support nationally representative analysis. The data show that household electricity consumption in Uganda is highly right-skewed, with most households consuming relatively low quantities of electricity and only a small proportion recording high monthly consumption. Modern energy adoption also varies strongly across regions and settlement types, with urban households generally showing higher electricity use and stronger adoption patterns than rural households. The dataset supports descriptive analysis, principal component analysis, ordinary least squares regression, log-linear models, Gamma-log generalized linear models, quantile regression, instrumental-variable estimation, two-stage least squares, limited-information maximum likelihood, generalized method of moments, and urban–rural heterogeneity analysis. The results indicate that electricity reliability, urban residence, household size, and regional location are important structural determinants of electricity consumption. Modern energy adoption is strongly associated with dwelling characteristics, stove type, cooking fuel, and market exposure, but its causal effect on electricity consumption becomes weaker once endogeneity is addressed. The dataset can be used by researchers studying household electricity demand, energy poverty, modern energy adoption, electrification policy, and structural constraints in low-income energy systems. It should be interpreted as household survey-based evidence rather than metered engineering consumption data. Survey weights should be applied when making population-level inferences.

Steps to reproduce

The dataset was derived from the Uganda National Household Survey 2019/2020 conducted by the Uganda Bureau of Statistics. The original survey contained household-level information on demographics, housing conditions, electricity access, electricity payments, cooking fuels, stove types, appliance use, asset ownership, and regional location. The published dataset was prepared by extracting variables relevant to household electricity demand and modern energy adoption, followed by cleaning, recoding, and transformation for econometric analysis. Data preparation was conducted in Stata. The workflow involved retaining household identifiers, survey weights, regional and urban/rural indicators, electricity access variables, electricity expenditure, housing materials, household composition, stove-use variables, cooking fuel variables, fuel sourcing information, and asset ownership records. Monthly electricity consumption was calculated from reported electricity payments and the number of days covered by the last payment, normalized to a 30-day month and converted into kilowatt-hours using an assumed tariff of UGX 250 per kWh. Several derived variables were constructed. Modern energy adoption indicators were created from efficient lighting, efficient cooking, and electricity-based heating variables, then combined using principal component analysis to generate the Modern Energy Adoption Index. A Housing Quality Index was constructed from roof, wall, and floor material indicators using principal component analysis. Household size was computed from adult and child household members present during the reference period. An asset ownership index was created by summing household asset ownership indicators. Instrumental variables were generated from dwelling materials, stove type, cooking fuel category, and fuel sourcing behaviour. The empirical workflow included descriptive statistics, distributional analysis, ordinary least squares regression, log-linear regression, Gamma-log generalized linear models, quantile regression, two-stage least squares, limited-information maximum likelihood, generalized method of moments, diagnostic testing, predictive validation, and urban–rural heterogeneity analysis. Robust standard errors and survey weights were applied where appropriate. To reproduce the research, users should load the cleaned dataset into Stata, apply the survey weight, verify the derived variables, and follow the workflow from descriptive analysis and PCA index construction to baseline models, instrumental-variable estimation, diagnostics, and heterogeneity analysis. No laboratory instruments or reagents were used; the dataset is based entirely on secondary household survey data and statistical processing.

Institutions

• Uganda Martyrs University

  Central Region, Kampala

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