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ABSTRACT
Staff play a key part in designing IMF conditionality, and yet the literature provides a narrow view of their motivations. This article shows how the design of IMF conditionality is linked to the normative orientations of the staff and their common professional training. Professional ties from similar training help to bind the staff together around a shared set of normative orientations that inform the IMF's policy goals. When borrowing-country officials do not share these orientations, the staff are motivated to tighten conditionality. This behaviour also fits with staff concerns about time-inconsistency and moral hazard. I find robust statistical support for this argument using a dataset based on the professional ties that exist between the IMF staff and borrowing-country officials. Yet conditionality is not found to be more lenient when country officials share the normative orientations of the IMF staff. Staff concerns about time-inconsistent preferences and moral hazard likely weigh against more lenient treatment where normative adherence is stronger.
Notes
1. See also Independent Evaluation Office (Citation2007a, Citation2010) and Steinwand and Stone (Citation2008) on the IMF's uneven treatment of countries.
2. This guideline on conditionality has been reaffirmed in each subsequent review, the most recent of which was in 2012.
3. Scholars of epistemic communities describe a similar process; see Adler and Haas (Citation1992) and Haas (Citation1992).
4. See the seminal work of Kydland and Prescott (Citation1977) on credibility theory.
5. Data availability constraints on IMF staff profiles prevent the time-series of the analysis from extending beyond 1998. However, this time frame does permit analysis of the period when the IMF was most active in its lending to developing countries. Countries in the conditionality regression include: Argentina, Brazil, Chile, Costa Rica, Ecuador, Egypt, El Salvador, Guatemala, Indonesia, Jordan, Mexico, Morocco, Panama, Peru, Philippines, Thailand, Turkey, Tunisia, Uruguay and Venezuela. The full data set, which includes countries that did not borrow from the IMF, comprises 443 observations and 32 countries. This larger sample is used in the programme participation specification.
6. I also sought to explore the determinants of the number of prior actions, another element of IMF conditionality. However, a large number of countries had programmes where the number of prior actions equals zero. As a result, the use of fixed effects reduced the sample size to a number where the models would not converge.
7. The measure also takes into account the creation of two departments to manage relations with Europe (European 1 and European 2) and two departments to manage relations with Asia (South East and Pacific and Central Asia) in the 1990s.
8. 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.
9. See http://www.lib.umi.com/dissertations/seach; http://www.these.com; http://global.lexisnexis.com/; http://www.worldwhoswho.com/.
10. I take the natural log of the variables capturing a country's reserve position, overall debt, and debt profile because each is positively skewed.
11. The results are similar for the programme participation specification if I instead use the cubic splines approach of Beck, Katz and Tucker (Citation1998); and for the subsequent conditionality specification if I instead: (1) exclude the propensity score; (2) include a count of the number of years; (3) include a measure of debt outstanding to the IMF as a proportion of a country's IMF quota; and (4) include year fixed effects.
12. The results of the country fixed effects are not shown.
13. In Argentina (1984), Anglo-American staff was 52.95 and Anglo-American policy team was 0. In Egypt (1996), Anglo-American staff was 53.85 and Anglo-American policy team was 0. In each case, the IMF approved loans (Argentina: ten conditions; Egypt: seven conditions) that were more stringent than those provided in the mean borrowing country.
14. In Indonesia (1997/1998), Anglo-American staff was 60/65 and Anglo-American policy team was 66.6/66.6. The IMF approved loans with ten (1997) and nine (1998) binding conditions that showed little deviation from those loans provided to the mean borrowing-country during the Asian financial crisis.
15. The IMF Independent Evaluation Office (Citation2007b: 24) also finds ‘there is no evidence of a reduction in the number of structural conditions following the introduction of the streamlining initiative.’
16. The data on US military aid are from the Federation of American Scientists Arms Sales Monitoring Project. The OECD's Statistical Compendium provides the data on US economic aid. US trade data are from the IMF's Direction of Trade.
17. See also Fang and Stone (Citation2012).
18. For an opposing view, see Cline, Ford, and Vernengo (Citation2010) and Gabor (Citation2012).
19. See Nelson (Citation2014a) for a sceptical view of recent IMF streamlining of its lending programmes.
20. For an alternative view, see Grabel (Citation2011: 821); Van Waeyenberge, Bargawi and McKinley (Citation2010); and Weisbrot et al. (Citation2009).
