Economics at your fingertips  

Transmission channels of the resource curse in Africa: A time perspective

Alexandre Henry

Economic Modelling, 2019, vol. 82, issue C, 13-20

Abstract: Most sub-Saharan African countries share the following characteristics: a strong dependence on natural resources, weak institutions, and relatively low growth levels preventing them to catch up with the rest of the developing world. This paper aims to unfold the natural resource curse by introducing a time perspective: long-term versus short-term effects. Following the two-step Engle and Granger procedure, an error-correction model is performed after a cointegration estimation. In addition, the paper clusters the countries to differentiate the natural resource curse mechanisms by level of institutional quality. Results are three-fold. On the long run, the negative impact of the dependence is confirmed for all categories. Countries with weak institutions are more vulnerable to the curse because the resource dependence not only negatively impacts long-term growth but also adversely impacts the recovery process. Finally, in a strong institutional environment, results point towards a potential positive impact of natural resources during recovery process.

Keywords: Resource curse; Africa; Institutions; Transmission; Channel; ECM (search for similar items in EconPapers)
JEL-codes: O13 Q32 Q33 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

DOI: 10.1016/j.econmod.2019.05.022

Access Statistics for this article

Economic Modelling is currently edited by S. Hall and P. Pauly

More articles in Economic Modelling from Elsevier
Bibliographic data for series maintained by Haili He ().

Page updated 2020-05-24
Handle: RePEc:eee:ecmode:v:82:y:2019:i:c:p:13-20