Can mining countries take advantage of their mining rents? A question of abundance, concentration and institutions

ABSTRACT

A common puzzle in economics is whether natural resources are a ‘curse’ or a ‘blessing’ for economic development. Previous studies have suggested that resource booms can promote growth, but private rent-seeking can turn these booms into a curse if institutions are weak. We argue that private incentives differ depending on whether rents are diversified across different commodities or concentrated in a few of them, because greater diversification implies higher appropriation costs. By using SITC-4 level of export disaggregation to measure within-sector concentration in 131 countries during 1991–2015, we show that the effect of mining rents on economic growth is conditional on the level of concentration within the mining sector. Mining rents enhance growth for economies with low concentration and strong institutions but reduce growth for economies with high-concentration and extremely weak institutions.

KEYWORDS:

• Mining
• concentration
• rent-seeking
• institutions
• resource curse

JEL CODES:

• O13
• O40
• Q33

Acknowledgments

authors are grateful to the editor and two anonymous referees for constructive suggestions; to participants in the Fifth International Conference on Sustainable Development (Columbia University) for useful comments; to the Centro Latinoamericano de Políticas Económicas y Sociales (CLAPES UC) research team for accurate suggestions; and to Carmen Cifuentes for outstanding research assistance.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1. See Van der Ploeg (2011) for a comprehensive survey of the resource curse literature.

2. In order to meet the statistical condition of Roodman (2009) for GMM estimation (see Section 3.3), mining concentration and weak institutions were not included as individual variables, however, the inclusion of these variables does not change our findings and these results are available under request.

3. The 2000 s commodity boom began after 2001 for most mining exporters (see Section 5.1).

4. Appendix A contains the definition of all variables used in estimation and data sources.

5. The delta method expands a function of random variables around its mean, using a Taylor series approximation, and then truncates them to approximate the expected value of the original function, obtaining the associated variance.

Additional information

Notes on contributors

Felipe B. Larraín

Felipe B. Larraín is a Professor of Economics at the Facultad de Economía y Administración, Pontificia Universidad Católica de Chile, and a Senior Economist at the Centro Latinoamericano de Políticas Económicas y Sociales (CLAPES UC), where he was Director from 2014 to 2018. He served as Finance Minister of Chile from 2010 to 2014 and from 2018 to 2019.

Oscar P. Perelló

Oscar P. Perelló is an Associated Researcher at the Centro Latinoamericano de Políticas Económicas y Sociales (CLAPES UC) and a post-graduate student at the Department of Economics, University College London. He was Macroeconomic Advisor to the Finance Minister of Chile from 2018 to 2019.