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

Author

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  • Christian S. Otchia

    () (Hyogo, Japan)

  • Simplice A. Asongu

    () (Yaoundé, Cameroon)

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.

Suggested Citation

  • Christian S. Otchia & Simplice A. Asongu, 2019. "Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images," Working Papers 19/046, European Xtramile Centre of African Studies (EXCAS).
  • Handle: RePEc:exs:wpaper:19/046
    as

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    References listed on IDEAS

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    More about this item

    Keywords

    Industrial growth; Machine learning; Africa;

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General
    • O55 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Africa

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