Speaker
Description
As Africa continues to navigate rapid political, economic, and institutional change, understanding patterns of governance across the continent is essential. Attaining a clear and exhaustive picture of governance dynamics in Africa is critical in analyzing the continent’s broader economic development and investment landscape. Governance quality influences key economic outcomes, including market stability, infrastructure delivery, and investor confidence. As a multidimensional concept, it offers a valuable framework for assessing the institutional environments that shape long-term growth potential and capital flows across countries.
This paper investigates governance trends across the African continent through an exploratory clustering analysis, using longitudinal data from 2005 to 2023, spanning 39 countries, 11 countries were left out of the studies due to lack of data availability. The Governance Index was used as an aggregate indicator for clustering. It captures five key dimensions: difficulty of management, steering capacity, resource efficiency, consensus building, and international cooperation.
Hierarchical clustering (Ward’s method) was applied to classify countries into governance-based groups, with K-means clustering used to validate the results. The analysis identifies three distinct clusters—high, moderate, and low governance quality—with minimal geographical coherence. This suggests that governance performance is influenced more by historical, institutional, and political factors than by regional proximity. Additionally, to enhance interpretability, a geospatial visualization of the clusters was created using Python, providing an clear graphic representation of governance classifications across the continent. The study highlights the diversity of governance trends among African countries and illustrates the potential of exploratory data techniques to reveal regional patterns and institutional dynamics.
This research contributes to the already existing body of studies, by providing a more exhaustive understanding of governance in Africa. Its findings are relevant for policymakers, development partners, and regional bodies that are seeking to design more appropriate governance interventions. More broadly, the study demonstrates the usefulness of unsupervised learning techniques for classifying institutional environments and contributes to data-driven approaches in comparative development economics.
