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  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

  19. Substantive – Discrete Change

  20. 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.

  21. 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

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