Publication Title
World Development
Document Type
Article
Department or Program
Economics
Publication Date
9-17-2025
Keywords
Political economy, Ethnic voting, Machine learning, Sub-Saharan Africa
Abstract
This study constructs a novel dataset on the ethnicity of individuals in an ethnically diverse country in sub-Saharan Africa. We measure ethnicity using a machine learning algorithm that exploits variation in surnames across ethnolinguistic groups. We apply this approach to voter registration data from Uganda’s 2016 general election. The resulting data capture local variation in ethnicity over a wide geographic scale. We pair these data with election outcomes from polling stations throughout Uganda to estimate the relationship between ethnicity and voting behavior. Our regression analyses both control for location and include interactions between ethnic groups within the same polling station. Local variation in ethnicity is associated with voting behavior at the level of the polling station, and these relationships vary with the presence of other ethnic groups at the polling station. The results suggest the importance of studying ethnic voting using local variation in ethnicity at scale.
Recommended Citation
Bird, S. S., Michuda, A. 2025. "Ethnic diversity and voting behavior at scale: Evidence from Uganda." World Development. 196.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Copyright Note
This is the author's version of the work. This publication appears in Bates College's institutional repository by permission of the copyright owner for personal use, not for redistribution.
Comments
Original version is available from the publisher at: https://doi.org/10.1016/j.worlddev.2025.107181