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Need for speed: The lending responsiveness of the IMF

Abstract

How responsive a lender is the International Monetary Fund (IMF)? In this paper, I introduce new data on IMF loan approval periods: The days that transpire between when a borrower submits a “Letter of Intent” to the Executive Board requesting a loan and when the Board approves that request. The data reveal considerable variation across requests. Why are some loan requests approved swiftly while others wait much longer for approval? I argue that the financial interests of the G-5 economies drive variation in responsiveness contingent on when a request was made. I expect that during much of the 1980s, as G-5 commercial bank exposure increases, borrowers will face longer waits for approval. In such cases, the G-5 should have been more likely to press for the use of the “concerted lending” strategy. This protected G-5 financial systems by catalyzing private financing on behalf of those countries, but it also delayed loan approval. Into the 1990s, global capital flows grew more complex and catalyzing private capital flows required a swift response. Thus, during these years I expect increased G-5 bank exposure to be associated with shorter waits for approval. In such cases, the G-5 should have been more likely to press for accelerated approval. A quick response would have the best chance of attracting back private capital and reduce the threat posed by the crisis. Statistical analyses of 275 loan requests from 1984–2012 support these expectations.

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Notes

  1. 1.

    Bagehot asserted that during crises the Bank of England “should lend to all that bring good securities quickly, freely, and readily. By that policy they allay a panic; by every other policy they intensify it” (Bagehot 1873).

  2. 2.

    The G-5 includes the United States, United Kingdom, Germany, Japan, and France.

  3. 3.

    Mody and Saravia use a cutoff of one standard deviation which is, they admit, a “softer” approach than (Kaminsky and Reinhart 1999) which identifies speculative attacks at three standard deviations above the mean.

  4. 4.

    Severe crises are identified when EMP is 1.5 standard deviations above the country mean.

  5. 5.

    From a practical perspective, there is also no readily available record of when each country first began negotiations with Fund staff.

  6. 6.

    This procedure was confirmed in a phone conversation with a high level IMF official.

  7. 7.

    “While there were about 300 SBAs during this period, only 200 of them coincide with crises in the previous two years” (Mody and Saravia 2013, 192).

  8. 8.

    8 The IMF’s concessional loan programs are primarily designed to promote economic development and reduce poverty rather than respond to financial crises. Thus, from this paper’s perspective, they fall outside of the scope of the IMF’s international lender of last resort actions. By comparison, (Mody and Saravia 2013) only look at SBAs.

  9. 9.

    9 SBA dates were collected by the author at the IMF archives in Washington D.C.; EFF dates were collected by a research assistant via the Fund’s digital archive.

  10. 10.

    The only Managing Director during this period not from a G-5 country was Spain’s Rodrigo Rato who held the position from June 2004 to November 2007.

  11. 11.

    It should be noted that this position was created in 1994.

  12. 12.

    Boughton (2001) describes the moment this became the Fund’s new approach: “The turning point came at the November 1982 meeting in New York...at which the Managing Director informed the banks that the Fund would not approve Mexico’s requests for an extended arrangement until the banks provided him with written assurances that they would increase their exposure by enough to cover a substantial fraction ($5 billion) of Mexico’s scheduled interest payments for 1983” (406).

  13. 13.

    For a detailed description of the concerted lending approach, see Boughton (2001), 405-409.

  14. 14.

    Bird and Rowlands (2004) date the strategy from 1982 through 1986 while Caskey (1989) notes that the strategy was adopted during the Mexican debt adjustment program through 1987.

  15. 15.

    For example, between 1990 and 1994, roughly $670 billion of foreign capital flowed into countries in Asia and Latin America as investors around the world began putting their money into emerging stock markets and securities (Truman 1996, 201; Calvo, Leiderman and Reinhart 1996, 123).

  16. 16.

    Calvo (1998) referred to the crises that developed in the 1990s as capital account crises to distinguish them from current account crises which were the dominant variety in previous decades.

  17. 17.

    One notable exception to this point is the case of Korea during the Asian Financial Crisis. In January, 1998, the IMF implemented a concerted lending-like approach to forcibly catalyze additional lending from major banks. However, this approach was only used after the Fund’s initial, massive loan to Korea had already been approved in December 1997. Indeed, Korea’s SBA was approved one day after the Letter was filed making it the second shortest loan approval period in the sample. Thus, in the one known case where concerted lending was employed after 1987, it did not have the effect of delaying Board approval. Rather, it came following an incredibly swift vote. For more on the Korean crisis, see Boughton (2012, 557-565.)

  18. 18.

    This view of catalytic financing has been compared to as “Powell Doctrine” in military affairs: Once a decision is made to intervene, it must be done with “overwhelming force” if it is to succeed (Zettelmeyer 2000; Jeanne and Wyplosz 2001). See Morris and Shin (2006) for an alternative view on the necessity of large credits for catalytic financing. Empirically, Mody and Saravia (2006) find limited evidence that larger loans from the IMF are more effective at catalyzing private financial flows on behalf of borrowers.

  19. 19.

    For a excellent account of the Board’s debate surrounding the Mexican request, see Copelovitch (2010, 213-227).

  20. 20.

    The RFI is sometimes referred to as the Emergency Financing Mechanism. It is not a separate lending arrangement per se rather it is a mechanism which can be activated when a member country is “facing an urgent balance of payments need” yet does not need a “full-fledged program” (International Monetary Fund 2012d).

  21. 21.

    The RFI has been used on behalf of Korea and Indonesia in 1997, Turkey in 2001, Armenia, Georgia, Hungary, Iceland, Latvia, Pakistan, and Ukraine from 2008-2009, and Greece (twice), Ireland, Portugal, and Cyprus from 2010-2013.

  22. 22.

    After the Asian financial crisis in 1997-1998, the Fund debuted the Contingent Credit Line (CCL) which was designed to prevent contagion by providing precautionary lines of credit to countries at risk (Bird and Rajan 2002). More recently, in the aftermath of the Global Financial Crisis of 2008, the IMF has introduced three new facilities designed to provide speedier financing to member states facing balance of payments crises: the Flexible Credit Line (FCL), the Precautionary and Liquidity Line (PLL), and the and Rapid Credit Facility (RCL) (IMF 2012a, b). Even the SBA was recently overhauled in order to make the workhorse arrangement more effective for members who may not qualify for an FCL arrangement “by providing increased flexibility to front-load access” intended to improve its “crisis prevention and crisis resolution” performance (IMF 2009, 3).

  23. 23.

    The BIS consolidated bank claims data is the highest level aggregate data type that includes both private and public (sovereign) foreign debts.

  24. 24.

    Data available at http://www.bis.org/statistics/consstats.htm

  25. 25.

    This is calculated as follows: publicly guaranteed bond debt + private non-guaranteed bond debt/total public and publicly guaraneed debt owed to private creditors + total private non-guaranteed debt. All data are from WDI.

  26. 26.

    Bailey et al. (2015, 1) estimate dynamic national ideal points using voting data in the UNGA from 1946-2012. They show that ideal point estimates improve upon conventional dyadic similarity indicators such as Affinity or S-scores (Gartzke 1998; Signorino and Ritter 1999) by distinguishing UN agenda changes from changes in state preferences.

  27. 27.

    Data available at http://www.uni-heidelberg.de/fakultaeten/wiso/awi/professuren/intwipol/datasets.html

  28. 28.

    There is no systematic variation in the Board’s meeting schedule that should affect IMF responsiveness. Since its creation, the Board has functioned in “continuous session at the principal office of the Fund” and meets “as often as the business of the Fund may require (Articles of Agreement, Article XII, Section 3(g)). Van Houtven (2002, 14-15) explained that, at the time of his writing, “total Board meeting time averages more than 12 hours a week and over 600 hours per year, which demonstrates the intense oversight exercised by the Board on activities of the IMF. Nearly one-third of the Board meeting time is devoted to policy issues, about 60 percent to surveillance, and the remainder to administrative and budgetary matters.”

  29. 29.

    The variable Speculative Attack was created by the author based on the EMP index. It is calculated as follows:

    $$EMP_{i,t} = \frac{\Delta e_{i,t}}{\sigma_{{\Delta}_{e_{i}}}} - \frac{\Delta r_{i,t}}{\sigma_{{\Delta}_{r_{i}}}} $$

    e is the end-of-the-month U.S. dollar exchange rate of country i and r is the end-of-the-month non-gold reserves. A higher score on the index indicates increased pressure on a country’s currency. I identify a speculative attack as:

    $$\begin{array}{@{}rcl@{}} Speculative Attack_{i,t} &&= 1\, if\, EMP_{i,t} > 2\sigma_{EMP_{i}} + \mu_{EMP_{1}} \\ &&= 0\, otherwise \end{array} $$

    σ E M P and μ E M P are the country-specific mean and standard deviation of EMP, respectively.

  30. 30.

    Regional variables were created in Stata using the “Kountry” command based on the “Middle East broad” classification. Additionally, countries listed as “Oceana” were recoded as Asia resulting in five regional dummies: Africa, Americas, Asia, Middle East/North Africa (MENA) and Europe. In my statistical models, Europe is the baseline category to which other regions are compared.

  31. 31.

    In each of these cases, I add 1 to the raw data prior to transformation in order to prevent zero values from becoming missing observations after transformation as the log of zero is undefined.

  32. 32.

    For handling ties, I use the Efron method.

  33. 33.

    An advantage of the Cox model is that it does not make parametric assumptions about the baseline hazard rate. This contrasts with parametric models, including Poisson and Negative Binomial models, which assume the hazard rate takes on a specific shape. Box-Steffensmeier and Zorn (2001, 21, 46) explain that unless “there exists strong theoretical expectation regarding the ‘shape’ of the hazard rate (or by extension, survival times), conditional on covariates in the model” then non-parametric models, like Cox, are preferable to parametric models.

  34. 34.

    In my data collection efforts at the IMF archives, there were a total of three Letters of Intent filed for SBAs that I was unable to locate a corresponding approval date. In chronological order, these are: Malawi (8/11/1986), Brazil (9/13/1990), and Paraguay (1/9/1991). I was unable to determine whether these requests were rejected by the Board or withdrawn by the borrower countries. Because of this uncertainty and their rarity, I opted to exclude these cases from the analysis.

  35. 35.

    Specifically, I calculate Shoenfeld residuals as described in Box-Steffensmeier and Zorn (2001).

  36. 36.

    Nonetheless, the results are substantively unchanged when these are included.

  37. 37.

    Based on Model 1, I generated simulations using the R package Zelig to replicate estimates of survival rates at levels specified for covariates of interest while holding all other covariates at their means. Point estimates displayed are the median observation from the simulation at each day after the loan request based on 1000 sample replications. The same process was used to generate all other post-estimation figures.

  38. 38.

    The inclusion of Bond Debt leads to the omission of more than 30 observations which may bias these results against Hypothesis 4. Among these missing observations are most IMF credits to European countries since 2008. This includes credits to Greece (2010, 2012), Iceland (2008), Ireland (2010), Latvia (2008), and Portugal (2011). In each of these cases, G-5 bank exposure is above the mean and approval periods are quite swift.

  39. 39.

    This is a parametric model, in contrast to the Cox model. Thus, it assumes a particular “shape” of the hazard rate. However, as Box-Steffensmeier and Zorn (2001, 21) explain, “if the distribution of failure times is parameterized incorrectly” then the findings may be inaccurate.

  40. 40.

    The MENA (Middle East, North Africa) variable is absent because none of the borrowers in this sub-sample are from that region. The results are substantively unchanged if regional controls are removed.

  41. 41.

    I generated simulations using the R package Zelig to replicate estimates of counted days between request and approval at levels specified for covariates of interest while holding all other covariates at their means. Point estimates used are means from simulations based on 1000 sample replications. The same process was used to generate all other post-estimation estimates discussed here.

  42. 42.

    Mody and Saravia (2013) calculate the number of months between crisis and IMF program. However, measuring in days allows me to avoid losing information due to rounding up or down to the nearest month.

  43. 43.

    There is one caveat here: In a number of cases, a borrower negotiated a new IMF program less than two years after its previous program. In such cases, I use the earliest crisis since the previous IMF program as the starting point.

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Correspondence to Daniel McDowell.

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I would like to thank J. Lawrence Broz, Benjamin Cohen, Mark Copelovitch, Jeffry Frieden, Randall Henning, Christopher Kilby, Steven Liao, Tom Pepinsky, David Steinberg, Michael Tierney, and participants at the 2014 International Political Economy Society (IPES) conference for their helpful comments on this paper. I would also like to thank the Appleby-Mosher Fund for Research for their generous support of this project.

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McDowell, D. Need for speed: The lending responsiveness of the IMF. Rev Int Organ 12, 39–73 (2017). https://doi.org/10.1007/s11558-015-9240-x

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Keywords

  • International Monetary Fund
  • Catalytic financing
  • International lender of last resort

JEL Classification

  • F3
  • G2