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The power of having powerful friends: Evidence from a new dataset of IMF negotiating missions, 1985-2020

  • SI: The Power of the Weak
  • Published:
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Abstract

When countries are confronted with a crisis and have no alternative but to turn to the International Monetary Fund (IMF), speed is of the essence. Why do some countries negotiate IMF loans more quickly than others? We introduce an original dataset on the timing and intensity of negotiations between the borrowing government and IMF staff for more than 700 IMF loans agreed between 1985 and 2020. Applying concepts from this special issue on the “Power of the Weak” (Snidal et al., 2024), we argue that although borrowing countries are in a weak position when they approach the IMF, they nonetheless sometimes achieve more rapid negotiations. In particular, we argue that borrowers can obtain speedier negotiations on the basis of their ties to major IMF shareholder states, specifically through shared membership in other international organizations and financial exposure. Importantly, we suggest that well-placed borrowers can hasten the conclusion of negotiations without compromising on the conditions attached to IMF programs. We use our original data and an illustrative case study of Côte d’Ivoire to support our claims.

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Data availability statement

The data used for the analyses in this paper will be made available upon publication.

Notes

  1. The following description of IMF program preparation draws on Copelovitch (2010a, 41-42), McDowell (2017), and Mody and Saravia (2013), as well as interviews with IMF staff.

  2. Occasionally, missions are held in Washington, DC or a third country.

  3. Sometimes, the head of the IMF mission will give a press conference to indicate that IMF staff and the borrowing government have agreed on the terms of an IMF loan.

  4. Based on the IMF’s “Lending Commitments” by country. Available at https://www.imf.org/external/np/fin/tad/extarr1.aspx.

  5. See Appendix A for information on the sample, available on the Review of International Organizations’ webpage.

  6. IMF Country Report No. EBS/86/237.

  7. For example, in Afghanistan’s 2016 program, “discussions were held in Delhi during May 18-26, 2016” (IMF Country Report No. 16/252 2016).

  8. For example, in Gabon’s 2017 program, “discussions were held in Washington DC during January 26-27, in Libreville during February 14-28, and in Washington DC during March 29-April 7.” (IMF Country Report No. 17/205 2017)

  9. For example, in Egypt’s 2016 program, “discussions were held in Washington DC in May, in London in June, and in Cairo in August” (IMF Country Report No. 17/17 2017).

  10. See for example research on why countries receive IMF loans at all (Dreher et al., 2009; Vreeland, 2003), how IMF loans are designed (Caraway et al., 2012; Dreher et al., 2015; Reinsberg et al., 2022b), and how IMF programs are enforced (Stone, 2004; Reinsberg et al., 2022b).

  11. France, Germany, Japan, the United Kingdom, and the United States

  12. Borrowers also have alternative strategies at their disposal. See, for example, Caraway et al. (2012) on domestic politics, and Nelson (2014) and Chwieroth (2015) on ideology. Here, we narrow our focus to best emphasize the contributions of the special issue. We highlight the most predominant arguments in the IMF literature and leave the impact of other factors to future work. See also Copelovitch and Powers (2021) on problematizing “important” countries in IMF research. Mechanisms that link measures of “friendship” like UNGA voting alignment to IMF programs may be theoretically and empirically imprecise.

  13. We rely here on the “causal” notion of power, in which actors have influence if they are able to affect the action of others in line with their preferences. Dahl and Stinebrickner (2003, 17) describe influence as “a relation among human actors such that the wants, desires, preferences, or intentions of one or more actors affects the actions, or predisposition to act, of one or more actors in a direction consistent with - and not contrary to - the wants, preferences, or intentions of the influence-wielders”.

  14. We use the authors’ weighted measure of IMF conditionality, which counts the number of conditions and weights them by how demanding each condition is.

  15. Here, we base assumptions on the vast literature on IMF conditionality. Understanding the circumstances in which speed trumps leniency, or vice-versa, is a fruitful future use for our data.

  16. The ordering of the variable is as follows: 4 - Fast and good, 3 - Slow and good, 2 - Fast and bad, 1 - Slow and bad.

  17. Data comes from the OECD, the BIS, and Bailey et al. (2017).

  18. Data come from the World Bank’s World Development Indicators and International Debt Statistics Database. Data on Public Debt comes from Abbas et al. (2010).

  19. Data is from the Varieties of Democracy Project and the Database of Political Institutions.

  20. Occasionally, senior representatives of the borrowing country will meet with IMF staff while they are in D.C., usually alongside the IMF annual meetings. These are almost always short bouts of negotiation, and they may do less to move a negotiation to conclusion than week-long missions. These D.C. meetings may therefore inflate the true count of meetings required to reach an agreement. If borrowers that have closer ties to major shareholders are also more likely to participate in these DC meetings, this may bias estimates of borrowers’ connectivity strategies.

  21. On average, covariates are missing roughly 15% of observations, but these do not overlap, leading to a high rate of missingness across the dataset as a whole.

  22. We implement this using the STATA mi impute chained command. See Appendix B for a description.

  23. Appendix Table A9 provides additional descriptive statistics for each of the seven programs we analyze.

  24. Mean exposure of French banks is approximately 0.004% of French GDP

  25. Executive Board Minutes EBM/87/172

  26. IMF Country Report EBS/87/249

  27. Executive Board Minutes EBM/89/151

  28. IMF Country Report EBS/89/212

  29. IMF Country Report EBS/94/12

  30. IMF Country Report EBS/98/36

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Acknowledgements

Thanks to Claas Mertens, Katharina Michaelowa, Karolina Milewicz, Bernhard Reinsberg, Sam Rowan, Randall Stone, participants of the APSA 2021 panel on “New Research on IMF Programs,” participants of the 2022 PEIO conference, organizers and participants of the 2022 “Power of the Weak” workshops, and especially the editors of the “Power of the Weak” Special Issue for comments on earlier versions of this paper. Thanks to Christina Lepore, Kamilah Mohammad, Doris Nyilidandi, Jay Palen, and Laura Sofía Rivera for excellent research assistance. Any errors are the authors’ own.

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Authors and Affiliations

  1. Department of Political Science, University of Mississippi, Oxford, Mississippi, USA

    Lauren L. Ferry

  2. Department of Political Science, Concordia University, Montreal, Canada

    Alexandra O. Zeitz

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Author contributions to research design and conceptualization: L.L.F (50%), A.O.Z (50%); statistical analysis: L.L.F (50%), A.O.Z (50%); writing: L.L.F (50%), A.O.Z (50%). Authors are listed alphabetically.

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Correspondence to Alexandra O. Zeitz.

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Ferry, L.L., Zeitz, A.O. The power of having powerful friends: Evidence from a new dataset of IMF negotiating missions, 1985-2020. Rev Int Organ (2024). https://doi.org/10.1007/s11558-024-09537-4

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