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

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  • Christian Otchia
  • Simplice Asongu

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.

Suggested Citation

  • Christian Otchia & Simplice Asongu, 2020. "Industrial growth in sub-Saharan Africa: evidence from machine learning with insights from nightlight satellite images," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 48(8), pages 1421-1441, December.
    • Handle: RePEc:eme:jespps:jes-05-2020-0201
    DOI: 10.1108/JES-05-2020-0201
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    Cited by:

    1. Kouakou Jean Fidele SIÉ, 2023. "Industrial policy and labour productivity growth in Africa: does the technology choice matter?," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 12(1), pages 1-24, December.
    2. Simplice A. Asongu & Uchenna Efobi & Belmondo V. Tanankem & Evans S. Osabuohien, 2019. "Globalisation and Female Economic Participation in Sub-Saharan Africa," Working Papers of The Association for Promoting Women in Research and Development in Africa (ASPROWORDA). 19/005, The Association for Promoting Women in Research and Development in Africa (ASPROWORDA).
    3. Stein, Merlin, 2021. "Re-evaluating RCTs with nightlights - An example from biometric smartcards in India," University of Tübingen Working Papers in Business and Economics 152, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics.
    4. Pandey, Dharen Kumar & Hunjra, Ahmed Imran & Bhaskar, Ratikant & Al-Faryan, Mamdouh Abdulaziz Saleh, 2023. "Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022," Resources Policy, Elsevier, vol. 86(PA).

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    More about this item

    Keywords

    Industrial growth; Machine learning; Africa; I32; O15; O40; O55;
    All these keywords.

    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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