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.
Publisher
Journal of Empirical Economics and Social Sciences, Bandirma Onyedi Eylul University
Reference42 articles.
1. Aggarwal, A. (2019). SEZs and economic transformation: towards a developmental approach. Transnational Corporations Journal, 26(2).
2. Badraoui, M., & Dahan, R. (2011). The Green Morocco Plan about food security and climate change. Food Security and Climate Change in Dry Areas, 61.
3. Bakucs, Z., Fertő, I., & Benedek, Z. (2019). Success or waste of taxpayer money? Impact assessment of rural development programs in Hungary. Sustainability, 11(7), 2158.
4. Baldwin, R. E., & Martin, P. (2004). Agglomeration and regional growth. In Handbook of regional and urban economics (Vol. 4, pp. 2671-2711). Elsevier.
5. Becheikh, N. (2021). Political stability and economic growth in developing economies: Lessons from Morocco, Tunisia and Egypt ten years after the Arab Spring. Insights into Regional Development, 3(2), 229-251.