Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images

Christian Samen Otchia and Simplice Asongu

No 19/046, Working Papers from European Xtramile Centre of African Studies (EXCAS)

Abstract: This study uses nightlight time data and machine learning techniques to predict industrial development in Africa. The results provide the first evidence on how machine learning techniques and nightlight data can be used to predict 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.

Keywords: Industrial growth; Machine learning; Africa (search for similar items in EconPapers)
JEL-codes: I32 O15 O40 O55 (search for similar items in EconPapers)
Pages: 27
Date: 2019-08
New Economics Papers: this item is included in nep-big
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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http://publications.excas.org/RePEc/exs/exs-wpaper ... Machine-Learning.pdf Revised version, 2019 (application/pdf)

Related works:
Journal Article: Industrial growth in sub-Saharan Africa: evidence from machine learning with insights from nightlight satellite images (2020)
Working Paper: Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images (2019)
Working Paper: Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images (2019)
Working Paper: Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images (2019)
Working Paper: Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images (2019)
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