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The Importance of Oil in the Allocation of Foreign Aid: The case of the G7 donors

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  • Cécile Couharde
  • Fatih Karanfil
  • Eric Gabin Kilamaa
  • Luc Désiré Omgbaa

Abstract

While it is often alleged that oil endowment might influence the destination of foreign aid, there is lack of empirical evidence of how and why such an effect may come into play, and even less so of the channels through which it works. This paper aims to bring evidence that contributes to address those points. Specifically, we investigate the role of oil in aid allocation of the G7 donors, over the 1980-2010 period. Results show that, unsurprisingly, aid allocated by these donors increases significantly with oil endowment of recipient countries. Looking more deeply, we interestingly show that their strategic interests in terms of oil security play a role in their provision of aid. More importantly, we find evidence for competition for access to oil supplies among this group of donors.

Suggested Citation

  • Cécile Couharde & Fatih Karanfil & Eric Gabin Kilamaa & Luc Désiré Omgbaa, 2017. "The Importance of Oil in the Allocation of Foreign Aid: The case of the G7 donors," EconomiX Working Papers 2017-40, University of Paris Nanterre, EconomiX.
    • Handle: RePEc:drm:wpaper:2017-40
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    References listed on IDEAS

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    Cited by:

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    2. Ludovic Feulefack Kemmanang & Jonas Juleo Dongmo Zamke, 2021. "Colonial spectre and foreign investments concentration: Does African growth escape?," Economics Bulletin, AccessEcon, vol. 41(1), pages 137-154.

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    More about this item

    Keywords

    aid allocation; G7 donors; oil competition; spatial lag model.;
    All these keywords.

    JEL classification:

    • F35 - International Economics - - International Finance - - - Foreign Aid
    • Q35 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Hydrocarbon Resources
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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