Implementation Uncertainty and the Design of IMF Conditionality

1. Implementation Uncertainty and the Design of IMF Conditionality Martin S. Edwards Whitehead School of Diplomacy Seton Hall University

2. The Limits of Conditionality • 40% of International Monetary Fund lending programs are suspended for noncompliance • Program suspension has real costs for both IMF and the borrowing state • To what extent is the IMF to blame for the failings of conditionality?

3. Question • How does the IMF design loan programs if it does not know ex ante whether the borrowing state will honor its promises?

4. The Argument: • IMF devises conditionality both prospectively and retrospectively • Conditionality is front-loaded in states with approaching elections and federal states • Conditionality is front-loaded in states with a history of poor program implementation.

5. Does Implementation Uncertainty Matter for the IMF? • Joseph Stiglitz (2002:52): “Sometimes conditionality was even counterproductive, either because the policies were not well suited to the country or because the way they were imposed engendered hostility to the reform process.” • Stanley Fischer (1998): “We don't need to form very sophisticated judgments about the political forces in (those) countries. We basically have to form a judgment on whether the government will do what it says it will do in an overall satisfactory way.”

6. Forming international agreements • Variations in the form of international agreements are responses to uncertainty • Escape clauses / renegotiation are ways to adjust agreements • Agreements made under high transaction costs look different than those in which parties face low transaction costs • Ex ante commitments can be used to make promises more credible

7. Assumptions • Fund faces uncertainty about borrower commitment • Program causes domestic dislocation • Renegotiation costly for IMF • Renegotiation costly for borrower

8. If I’m right….. • Expect more conditions in states with a higher level of implementation uncertainty. • But these conditions should be imposed on states ex ante. • IMF imposes conditions (prior actions) in order for Executive Board to approve program

9. What shapes implementation uncertainty? • Approaching elections • We know that electoral cycles are common in developing countries, and that elections often coincide with program interruptions • Federalism • Economic adjustment more difficult in these states • Past performance • Failed programs lead to changes

10. Expectations • Prospective • Programs with approaching elections are more likely to have prior actions than those in which elections are not approaching • Federal states more likely to have prior actions • Retrospective • Programs negotiated in the wake of a suspended program are more likely to have prior actions than those negotiated following a successfully completed program.

11. Cases • Random Sample of 38 States under Programs from 3rd qtr 1997 to 2nd qtr 2003. • Asia (7 states), Latin America (8), Africa (11), Eastern Europe (12) • 183 agreements initiated in this time period • States required to submit new memoranda as part of program review process

12. Dependent Variable • Count of fiscal prior actions for each agreement • Range: 0-12 • Prior actions are those policy measures that are prerequisites for review by Executive Board

13. Examples of Fiscal Prior Actions • Measures adopted to reduce wage bill • Cambodia 99q3: freeze on new hiring for civil service • Measures to increase revenue • Russia 99q3: Delay law on VAT reduction • Pakistan 01q1: Mandated increases in rates for electricity and gasoline • Measures to reduce expenditure • Armenia 98q4: Publish decree detailing govt plan to reduce expenditures by 7 billion dram. • Passage of Fund-compliant budget by legislature

14. Why Fiscal Prior Actions? • Have to look at prior actions to ascertain whether Fund incorporates implementation uncertainty • Looking at performance criteria makes causal chain unclear • Fiscal criteria serves as “most likely” case • Electoral cycles manifest themselves through higher levels of spending • Federal states esp. prone to fiscal problems • Noncompliance stems most frequently from fiscal shortfalls

15. Independent Variables • Policy Stance • Lagged budget deficit / GDP (-) • Lagged growth of expenditures / GDP (+) • Lagged growth of reserves (-) • Lagged GDP (-) • Type of Agreement • Dummy for PRGF/ESAF (+) • US influence • Lagged US foreign aid / GDP (-)

16. Independent Variables • Approaching elections (Dummy) • Is an executive election six months away? • Federalism (Dummy) • Status of past program (Dummy) • Did the Fund interrupt the program for noncompliance?

17. Research Design • Confront substantial missing data problems • Missingness on fiscal variables approaches 40% of sample • Used multiple imputation (King et al 2000) to address missing data

18. Research Design • Empirical Test is a negative binomial model • Appropriate because data are counts • Distribution of count dictates model specification • Population averaged to address panel heterogeneity

20. Substantive – Discrete Change

21. Robustness Checks • Results hold in the presence of the following • Controls for Quota in IMF • Controls for Democracy • Trade Openness • Changes in Exchange Rate level • Results unchanged with nine-month window.

22. Summary • IMF does not lend “in the dark” • Expectations about electoral horizons affect program design • Programs look differently in federal states • Expectations about program compliance affect program design

23. A Further Question to Consider • Do prior actions make a difference ex post? • In fiscal performance? • In the probability of program suspension?