Institutions and the resource curse: New insights from causal machine learning

被引：1

作者：

Hodler, Roland ^{[1
,2
,3
,4
,5
]}

Lechner, Michael ^{[6
,7
,8
]}

Raschky, Paul ^{[9
]}

机构：

[1] Univ St Gallen, Dept Econ, St Gallen, Switzerland

[2] Univ St Gallen, SIAW HSG, St Gallen, Switzerland

[3] CEPR, London, England

[4] CESifo, Munich, Germany

[5] Univ Oxford, OxCarre, Oxford, England

[6] IAB, Nurnberg, Germany

[7] IZA, Bonn, Germany

[8] RWI, Essen, Germany

[9] Monash Univ, Dept Econ & SoDa Labs, Caulfield, Vic, Australia

来源：

PLOS ONE | 2023年 / 18卷 / 06期

基金：

澳大利亚研究理事会;

关键词：

NATURAL-RESOURCES; OIL; ECONOMETRICS; CONFLICT; GROWTH; MINE;

D O I：

10.1371/journal.pone.0284968

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

There is a widely held belief that natural resource rents are a blessing if institutions are strong, but a curse if institutions are weak. We use data from 3,800 Sub-Saharan African districts and apply a causal forest estimator to reassess the relationship between institutions and the effects of resource rents. Consistent with this belief, we document that stronger institutions increase the positive effect of the presence of mining activities on economic development and dampen the negative effect of mining activities on conflict. In contrast, we find that the effects of higher world mineral prices on economic development and conflict in mining districts are non-linear and vary little in institutional quality.

引用

页数：20

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