“The silent revolution:” How the staff exercise informal governance over IMF lending


This paper examines how the staff exercise informal governance over lending decisions of the International Monetary Fund (IMF or Fund). The essential component of designing any IMF program, assessing the extent to which a borrowing country is likely to fulfill its policy commitments, is based partly on informal staff judgments subject to informal incentives and normative orientations not dictated by formal rules and procedures. Moreover, when country officials are unable to commit to policy goals of the IMF, the IMF staff may bypass the formal channel of policy dialogue through informal contacts and negotiations with more like-minded actors outside the policymaking process. Exercising informal governance in these ways, the staff are motived by informal career advancement incentives and normative orientations associated with the organization’s culture to provide favorable treatment to borrowers composed of policy teams sympathetic toward their policy goals. The presence of these sympathetic interlocutors provides the staff both with greater confidence a lending program will achieve success and an opportunity to support officials who share their policy beliefs. I assess these arguments using a new dataset that proxies shared policy beliefs based on the professional characteristics of IMF staff and developing country officials. The evidence supports these arguments: larger loan commitments are extended to countries where government officials and the Fund staff share similar professional training. The analysis implies informal governance operates in IOs not just via state influence but also through the evolving makeup, incentive structure, and normative orientations of their staffs.

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  1. 1.

    These data exclude administration staff.

  2. 2.

    In 2009 the IMF doubled the cumulative access limits to 600 % as part of its overhaul of its lending operations.

  3. 3.

    Based on author’s interviews with IMF staff, Washington, DC, June – August 2005.

  4. 4.

    See also Kahler (1992:126) and (1993:371, 377).

  5. 5.

    While the distinction between positive and normative analysis is an important one, even the most ostensibly positive models of economic behavior are saturated with normative and ethical implications (Best and Widmaier 2006).

  6. 6.

    See, for instance, Woods (2006) on Mexico in the 1980s and 1990s and Russia in the 1990s and Chwieroth (2010b) on Indonesia in the 1960s and 1970s.

  7. 7.

    See also IMF (1999:65) and IEO (2009:29).

  8. 8.

    I exclude loans made under the IMF’s Structural Adjustment Facility and Enhanced Structural Adjustment Facility – the two other principal IMF lending programs – because these programs are likely to be extended under different lending criteria due to their focus on long-term structural adjustment rather than short-term balance of payments support. While it would be worthwhile to analyze more recent loans, the time frame ends in 1998 due to data availability constraints on the IMF staffing profiles. Telephone directories, the source for IMF staffing data (see below), were not available after 1998 from the IMF Archives or from the IMF Communications Department. It is unclear whether this is due to selective deposit or selective survival. Notwithstanding this constraint, the time frame under analysis does permit investigation of the period when the IMF was most active in lending to developing countries.

  9. 9.

    Both the size of the loan and the quota are measured in millions of standard drawing rights (SDRs). Joseph Joyce generously shared these data, which were gathered from the IMF’s Annual Report.

  10. 10.

    There may be better methods for demonstrating shared policy beliefs, such as surveys of IMF staff and borrowing country officials, but these are empirically near impossible. However, as discussed, there is strong evidence based on extensive surveys of economists that indirectly confirms the link between common professional training and shared policy beliefs.

  11. 11.

    Countries include: Argentina, Bolivia, Brazil, Chile, China, Colombia, Costa Rica, Dominican Republic, Ecuador, Egypt, El Salvador, Ethiopia, Ghana, Guatemala, Haiti, Honduras, India, Indonesia, Iran, Iraq, Israel, Jordan, Korea, Liberia, Malaysia, Mexico, Morocco, Myanmar, Nicaragua, Nigeria, Panama, Pakistan, Paraguay, Peru, Philippines, Singapore, South Africa, Sri Lanka, Syria, Thailand, Tunisia, Turkey, Uruguay, and Venezuela.

  12. 12.

    See <>; <>; <>; <>.

  13. 13.

    While it is possible that staff members may self-select into area departments from their home region, to avoid conflicts of interest, the IMF Code of Conduct does not permit staff members to have an influence on lending decisions for their home countries (IMF 1998). Moreover, there are also organizational procedures, such as staff rotation at regular intervals across various departments, and cultural norms that weigh against staff going native (Woods 2006).

  14. 14.

    Babb’s data stop in 1991 and this trend may have been reversed over the past decade when the Fund has sought to recruit more staff with financial sector experience.

  15. 15.

    I take the natural log of the variables capturing a country’s reserve position, overall debt, and debt profile because each is positively skewed.

  16. 16.

    Since the observations are unequally spaced panel data, I ran a Baltagi and Wu (1999) locally best invariant (LBI) test, which failed to reject the null of no autocorrelation.

  17. 17.

    The results are unchanged if I instead use the cubic splines approach of Beck et al. (1998).

  18. 18.

    Anglo-American policy team is a slow-moving variable, which poses challenges for the use of conventional fixed effects models. I therefore ran a model using fixed effects vectors decomposition (Plümper and Troeger 2007; Beck 2011), which produced similar results as those reported here.

  19. 19.

    The negative coefficient for money supply as a proportion of reserves suggests that countries may be pursuing restrictive monetary policies and simultaneously borrowing from the IMF.

  20. 20.

    In addition to the fact that requests for loans are rarely rejected, this finding may result from the method used to address program participation. An alternative method, such as bivariate probit with partial observability, which models the decision of the country and the IMF separately, may generate different findings. Yet this method has a number of weaknesses, including poor convergence properties. Indeed, using the data set for this study and a range of specifications, convergence could not be achieved.

  21. 21.

    In terms of goodness of fit, in contrast to R2, a smaller number is better for the BIC and AIC.

  22. 22.

    An additional two cases are found in the Middle East (Jordan 1994, 1996) and Europe (Turkey 1994).

  23. 23.

    The cases are Argentina (1992, 1996) and Mexico (1983, 1995).

  24. 24.

    The cases are Argentina (1992, 1996), Chile (1983), Costa Rica (1995), El Salvador (1993), and Turkey (1994).


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Author information

Correspondence to Jeffrey M. Chwieroth.

Additional information

Earlier versions of this paper were presented at the Annual Meeting of the American Political Science Association, Chicago, IL, 30 August – 2 September 2007, the Annual Meeting of the International Political Economy Society, Stanford University, Palo Alto, CA, 9–10 November 2007 and the University Seminar on Global Governance and Democracy, Duke University and University of North Carolina, Durham/Chapel Hill, NC, 17 April 2008. The author is grateful for helpful assistance and comments on earlier drafts from Pablo Beramendi, David Brady, Benjamin J. Cohen, Mark Copelovitch, Axel Dreher, Christopher Gandrud, Randall Henning, Judith Kelly, Katharina Michaelowa, Layna Mosley, Thomas Oatley, and Randall Stone.

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Chwieroth, J.M. “The silent revolution:” How the staff exercise informal governance over IMF lending. Rev Int Organ 8, 265–290 (2013) doi:10.1007/s11558-012-9154-9

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  • IMF
  • Organizational culture
  • Professional training


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