Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/205016 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
AGDI Working Paper No. WP/19/046
Verlag: 
African Governance and Development Institute (AGDI), Yaoundé
Zusammenfassung: 
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.
Schlagwörter: 
Industrial growth
Machine learning
Africa
JEL: 
I32
O15
O40
O55
Dokumentart: 
Working Paper

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