TESTING THE DISTRIBUTIONAL DIVERGENCE IN THE PILLARS OF REGIONAL DEVELOPMENT IN MOROCCO

Abstract

This article examines the distributional divergence of the pillars of regional development in Morocco using a new hierarchical clustering algorithm from the unsupervised machine learning literature. The study utilizes a unique dataset of regional indicators to identify the major clusters of development and differences in regional performance in terms of economic transformation, social inclusion, and environmental sustainability. Results from the hierarchical clustering algorithm show that the Moroccan regions are highly differentiated and the clusters identified do not necessarily coincide with the traditional administrative divisions. This suggests that the policies implemented in Morocco have not been effective in achieving balanced development across all regions. The findings of this study provide important insights into the challenges of regional development in Morocco and the potential of using machine learning algorithms to better identify and address regional disparities.