Digital Connectivity in sub-Saharan Africa: A Comparative Perspective

September 27, 2019

Preview Citation

Format: Chicago

Emre Alper, and Michal Miktus. Digital Connectivity in sub-Saharan Africa: A Comparative Perspective, (USA: International Monetary Fund, 2019) accessed September 19, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

Higher digital connectivity is expected to bring opportunities to leapfrog development in sub-Saharan Africa (SSA). Experience within the region demonstrates that if there is an adequate digital infrastructure and a supportive business environment, new forms of business spring up and create jobs for the educated as well as the less educated. The paper first confirms the global digital divide through the unsupervised machine learning clustering K-means algorithm. Next, it derives a composite digital connectivity index, in the spirit of De Muro-Mazziotta-Pareto, for about 190 economies. Descriptive analysis shows that majority of SSA countries lag in digital connectivity, specifically in infrastructure, internet usage, and knowledge. Finally, using fractional logit regressions we document that better business enabling and regulatory environment, financial access, and urbanization are associated with higher digital connectivity.

Subject: Income, Information technology in revenue administration, Infrastructure, Machine learning, National accounts, Population and demographics, Revenue administration, Technology

Keywords: Account ownership, Africa, Connectivity index, Digital connectivity, Digitalization, EDAI SSA distribution, Fractional logit model, Global, ICT indicators database, Income, Information technology in revenue administration, Infrastructure, Machine learning, SSA countries lag, SSA economy, Sub-Saharan Africa, Unsupervised machine learning, WP

Publication Details

Pages:

44
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2019/210
Stock No:

WPIEA2019210
ISBN:

9781513514604
ISSN:

1018-5941

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