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Choosing international organizations: When do states and the World Bank collaborate on environmental projects?


While international cooperation research emphasizes institutional design, states mostly interact with existing organizations. How do states choose organizations for cooperation? We develop a theory of agency choice for development projects, emphasizing the importance of domestic institutions, the scope of cooperation, and the resources of the implementing agency. If states are to cooperate with funding agencies that have abundant resources, such as the World Bank, they must accept more stringent conditions on project implementation. We argue states accept the stringent conditions that resourceful organizations demand if the public goods from project implementation are highly valuable. Empirically, this is the case for democratic states, large projects, and projects that produce national instead of global public goods. We test this theory using data on 2,882 Global Environment Facility (GEF) projects, 1991–2011. The GEF offers an ideal case because various implementing agencies are responsible for the actual projects. States implement projects in collaboration with the World Bank, which has the most expertise and resources among the GEF’s implementing agencies, if their regime type is democracy, the project size is large, and the benefits are primarily national. Qualitative evidence sheds light on causal mechanisms.

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  1. 1.

    Originally, all GEF projects were implemented by one or a combination of three executing agencies: the UNDP, the United Nations Environment Programme (UNEP), and the WB. As of 2012, there are ten such agencies, including a number of additional UN agencies and regional development banks.

  2. 2.

    Based on a coding of 50 random GEF projects, we ascertain in the Online Appendix available on the journal’s webpage that GEF agencies contribute on average about 19 % of project funds, confirming that financial resources are one of the most important contributions that funding agencies bring to the table. Funding from other sources, such as private capital (12 %), NGOs (2 %), or other concessionary, non-recipient funding (5 %), is more limited.

  3. 3.

    See http://www.thegef.org/gef/project_detail?projID=2903. Accessed on May 3, 2013.

  4. 4.

    This database is available online at http://www.gefonline.org.

  5. 5.

    Occasionally, an additional agency is assigned to assist implementation. The GEF’s description of comparative advantages suggests that policy intervention is important in selecting a primary agency; focal area expertise, where it exists, is listed second.

  6. 6.

    Available online at http://www.aiddata.org/content/index/Research/research-datasets.

  7. 7.

    In terms of AidData Environment, our indicator is set to 1 if a project is coded either “environmental strictly defined — green” or “environmental broadly defined — green.” Our indicator is set to 0 if a project is coded “environmental strictly defined — brown,” “environmental broadly defined — brown,” “neutral,” “dirty strictly defined,” or “dirty broadly defined.”

  8. 8.

    In checking the robustness of our results, we also estimate random effects probit regressions. Our findings are robust to this specification; we present complete results in the Online Appendix.

  9. 9.

    As Fig. 1 shows, while partnering with the UNDP has been consistently less costly than cooperating with the WB, the difference shrank somewhat following the GEF’s adoption of the RAF.

  10. 10.

    We estimate both standard OLS and weighted regressions modeling each country’s variance as the inverse of the number of GEF projects. Both robustness tests validate the hypotheses.

  11. 11.

    Multiplicative interaction effects are not necessarily needed in non-linear models to account for non-linear effects. As suggested by Berry et al. (2010) we implemented likelihood ratio tests to check if the inclusion of interaction effects is warranted. Further details can be found in the Online Appendix.

  12. 12.

    The interview questionnaire and additional details on the interview process can be found in the Online Appendix.

  13. 13.

    For 1,000 draws from a multivariate normal based on model (1), we find a mean effect of -9 percentage points (-3 and -14 percentage points as lower and upper confidence bounds, respectively) for a change from local to global public goods. The mean effect for increases in total project costs by one standard deviation above the mean is 17 percentage points, with 12 and 23 percentage points as the lower and upper end of the 95 % confidence interval. The substantive effects for a change from an autocratic to a democratic regime type are weakest in absolute value, with an average increase of only 4 percentage points and a confidence interval ranging from 1 to 8 percentage points.


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An earlier version of this paper was presented at the 6th Political Economy of International Organizations Conference, held at the Universities of Mannheim and Heidelberg, February 7–9, 2013. We thank all conference participants for their comments and are particularly indebted to Katharina Michaelowa, Ulrich Wagner, Axel Dreher, and three anonymous reviewers for their helpful feedback.

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Correspondence to Johannes Urpelainen.

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Bayer, P., Marcoux, C. & Urpelainen, J. Choosing international organizations: When do states and the World Bank collaborate on environmental projects?. Rev Int Organ 9, 413–440 (2014). https://doi.org/10.1007/s11558-013-9184-y

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  • International organizations
  • International cooperation
  • Foreign aid
  • Environment
  • Donor-recipient interactions
  • Global Environment Facility

JEL Classifications

  • F55
  • F59
  • H87
  • Q56
  • Q58