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Author (up) Otchia, C. S. & Asongu, S. A. url  openurl
  Title Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images Type Journal Article
  Year 2019 Publication African Governance and Development Institute Abbreviated Journal  
  Volume Issue Pages  
  Keywords Remote Sensing  
  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.
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  Area Expedition Conference  
  Notes Approved no  
  Call Number IDA @ intern @ Serial 2627  
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