African Mining, Gender, and Local Employment
By Andreas Kotsadam, Anja Tolonen. 2014
226 citations recorded by the source corpus.
It is a contentious issue whether large scale mining creates local employment, and the sector has been accused of hurting women’s labor supply and economic opportunities. This paper uses the rapid expansion of mining in Sub-Saharan Africa to analyze local structural shifts. It matches 109 openings and 84 closings of industrial mines to survey data for 800,000 individuals and exploits the spatial-temporal variation. With mine opening, women living within 20 km of a mine switch from self-employment in agriculture to working in services or they leave the work force. Men switch from agriculture to skilled manual labor. Effects are stronger in years of high world prices. Mining creates local boom-bust economies in Africa, with permanent effects on women’s labor market participation.
Subfields: Building and Construction, Economics and Econometrics, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/d8ce2d0d5e7495851b1295d03e9e29c44830043f
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Citations in the corpus
- Racing to the bottom? Chinese development projects and trade union involvement in Africa (2018, background): “Our baseline set of 12 See also Kotsadam and Tolonen (2016) and Isaksson and Kotsadam (2016).”
- This Mine is Mine! How Minerals Fuel Conflicts in Africa (2015, unknown): “Other recent research using the RMD data includes Kotsadam and Tolonen ( 2016 ) , who study gender and local labor market effects of mining, as well as Knutsen et al. ( 2017 ) , who assess the impact of mining on local corruption.”
- Chinese aid and local corruption (2018, unknown): “11 See also Kotsadam and Tolonen (2016). 12 Instead calculating marginal effects after probit regressions does not change the interpretation of any results (results are available upon request).”
- Development aid and infant mortality. Micro-level evidence from Nigeria (2018, methodology): “To do so we build on the spatial-temporal strategy presented in Kotsadam and Tolonen (2016) and Knutsen, Kotsadam, Olsen and Wig (2017) and use a difference-in-differences method.11 The model compares the likelihood of dying before the age of one, both before and after the intro- 12 When running…”