Industrial growth in sub-Saharan Africa: evidence from machine learning with insights from nightlight satellite images

Abstract

Purpose

This study uses machine machine learning techniques to assess industrial development in Africa.

Design/methodology/approach

This study uses nightlight time data and machine learning techniques to assess industrial development in Africa.

Findings

This study provides evidence on how machine learning techniques and nightlight data can be used to assess economic development in places where subnational data are missing or not precise. Taken together, the research confirms four groups of important determinants of industrial growth: natural resources, agriculture growth, institutions and manufacturing imports. Our findings indicate that Africa should follow a more multisector approach for development, putting natural resources and agriculture productivity growth at the forefront.

Originality/value

Studies on the use of machine learning (with insights from nightlight satellite images) to assess industrial development in Africa are sparse.

Citation

Otchia, C. and Asongu, S. (2021), "Industrial growth in sub-Saharan Africa: evidence from machine learning with insights from nightlight satellite images", Journal of Economic Studies, Vol. 48 No. 8, pp. 1421-1441. https://doi.org/10.1108/JES-05-2020-0201