DP15272 Reassessing the Resource Curse using Causal Machine Learning

Author(s): Roland Hodler, Michael Lechner, Paul A. Raschky
Publication Date: September 2020
Date Revised: September 2020
Keyword(s): Africa, Causal machine learning, conflict, economic development, mining, resource curse
JEL(s): C21, O13, O55, Q34, R12
Programme Areas: International Trade and Regional Economics, Development Economics, Macroeconomics and Growth
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=15272

We reassess the effects of natural resources on economic development and conflict, applying a causal forest estimator and data from 3,800 Sub-Saharan African districts. We find that, on average, mining activities and higher world market prices of locally mined minerals both increase economic development and conflict. Consistent with the previous literature, mining activities have more positive effects on economic development and weaker effects on conflict in places with low ethnic diversity and high institutional quality. In contrast, the effects of changes in mineral prices vary little in ethnic diversity and institutional quality, but are non-linear and largest at relatively high prices.