Reassessing the Resource Curse Using Causal Machine Learning

Working Paper: CEPR ID: DP15272

Authors: Roland Hodler; Michael Lechner; Paul A. Raschky

Abstract: 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.

Keywords: resource curse; mining; economic development; conflict; causal machine learning; Africa

JEL Codes: C21; O13; O55; Q34; R12


Causal Claims Network Graph

Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.


Causal Claims

CauseEffect
Mining activities (L72)local economic development (O29)
Mining activities (L72)likelihood of conflict events (D74)
Higher world market prices of minerals (L72)local economic development (O29)
Higher world market prices of minerals (L72)likelihood of conflict events (D74)
Mineral prices (L72)local economic development (O29)
Mineral prices (L72)likelihood of conflict events (D74)
Mining activities (L72)local economic development (conditional on institutional quality and ethnic diversity) (O17)

Back to index