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

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. [ABSTRACT FROM AUTHOR]