Chapter

CHAPTER 10 What Determines the Implementation of IMF-Supported Programs?

Author(s):
Alessandro Rebucci, and Ashoka Mody
Published Date:
April 2006
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Author(s)
Anna Ivanova Wolfgang Mayer Alex Mourmouras and George Anayiotos1

The paper provides a quantitative analysis of the factors that determine successful implementation of IMF-supported programs. To this end, we construct new measures of program implementation and compliance with conditionality for 170 IMF programs approved between 1992 and 1998. The main hypothesis tested is whether IMF effort and the design of conditionality significantly affect the probability of successful implementation of IMF-supported programs. We find that program implementation depends primarily on the borrower’s domestic political economy. Strong special interests in the parliament, political instability, inefficient bureaucracies, lack of political cohesion, and ethno-linguistic divisions weaken program implementation. IMF effort, the design of conditionality, and initial and external conditions do not materially influence program prospects.

Introduction

At the heart of the International Monetary Fund’s operations are conditional lending programs that give borrowing countries breathing space while they correct their macroeconomic and structural imbalances. These programs provide mutual assurances. On the one hand, member countries are assured that they will continue receiving IMF financing if they meet the specified conditions. On the other hand, conditionality ensures that adjustment is undertaken in ways that are conducive to national and international prosperity, providing guarantees to the IMF that it will be repaid and that the world’s financial system will not suffer from disruptive systemic crises.

In order for the effects of IMF-supported programs to be fully realized, however, the policies they envisage must be implemented to the fullest possible extent. Many programs are in fact interrupted amid political or economic turmoil, in circumstances in which it is not possible to agree on conditionality to underpin new or revised programs. The implementation record of IMF-supported programs has been rather disappointing. About 44 percent of all programs approved between 1992 and 1998 were not completed, experiencing irreversible interruptions.

In this paper, the quality of implementation of IMF-supported programs is linked to three groups of factors: (a) the political characteristics of borrowing countries; (b) IMF conditionality and the human and financial effort the IMF invests in programs; and (c) internal economic conditions in countries implementing programs and the external conditions affecting them. Implementation, as we use the term, is to be contrasted with overall program success, defined as the achievement of its macroeconomic and structural objectives. Previous econometric studies (Bird, 2000, provides a review) commonly assessed the success of IMF-supported programs by examining macroeconomic indicators such as budget deficit, international reserves, inflation, and growth before and after the program. However, there is no reason to expect that a program will realize its macroeconomic and structural objectives if implementation consistently falls short of program intentions. Understanding the factors that affect program implementation is thus the first step in understanding the determinants of overall program success.

Our analysis focuses on program implementation for a sample of countries that made conscious decisions to enter into agreements with the IMF. We do not address the prior questions of what makes a country commit to an IMF-supported program and whether countries have a better chance of succeeding in their macroeconomic and structural adjustment by having an IMF program in place. To answer these questions would require an assessment of economic performance of countries whose adjustment programs were not supported by the IMF, and comparable information is not readily available.

The literature offers several clues indicating that the primary factors influencing the implementation of IMF-supported programs lie in the domestic political economy of borrowing countries. Interruptions of programs supported by the IMF’s concessional facilities—the Structural Adjustment Facility (SAF) and the Enhanced Structural Adjustment Facility (ESAF)—were primarily caused by domestic political economy factors, not poor program design (Mecagni, 1999). Similarly, the success of World Bank—supported adjustment programs is attributed to favorable domestic political conditions and institutions, including lack of ethnic and linguistic divisions, government stability, and democratic regimes (Dollar and Svensson, 2000). It is important to note that World Bank conditionality and resources allocated to program design and monitoring did not seem to matter at the margin. Finally, case study evidence suggests that in some countries, the ambivalence of the top political leaders and resistance by senior officials and special interests were key to the failures of IMF-supported programs.2 When lack of political commitment resulted in stop-and-go program cycles, the imposition of large numbers of prior actions had limited success, pointing to the need for greater selectivity in lending. In other countries, participatory processes that actively involved the country’s top leadership were instrumental in overcoming domestic divisions, building ownership for programs, and ensuring program success.

Our analysis of the determinants of program implementation is made possible by the availability of new datasets. First, political scientists have in recent years developed several quantitative indicators of political conditions in borrowing countries. Second, during the last decade, the IMF has improved its monitoring of programs and internal resource allocation. This allows us to develop indicators that capture program conditionality, the quality of its implementation, and the IMF’s human and financial effort in program countries.

In ascertaining the impact on program implementation of variables under IMF control, a key empirical issue is the need to properly account for the endogenous nature of IMF decisions. A second key issue, based on the findings of recent theoretical work on conditionality and program ownership, is to test for the impact on program implementation of special interests resisting reforms.3 We develop an index of the power of special interests in parliament and examine the impact on program implementation of parties representing religious, nationalistic, regional, and rural interests.

Our main results are easily summarized. On the one hand, the implementation of IMF-supported programs is strongly influenced by recipient countries’ domestic political economy. Weak program implementation was strongly associated with strong special interests in parliament, lack of political cohesion, inefficient bureaucracies, and ethno-linguistic divisions. The strong association between program implementation and political economy variables is robust across different econometric specifications. On the other hand, initial economic conditions, IMF effort, and the breadth and depth of conditionality do not seem to materially influence program prospects when they are properly instrumented for. Other recent studies corroborate this finding. Program implementation is not related to the number of conditions or the number of prior actions4 (IMF, 2001c; and Thomas, 2003). Structural conditionality does not influence medium-term fiscal developments (Bulíř and Moon, 2003).

The rest of the paper is organized as follows. The second section describes the sample and various implementation measures. The third section describes the econometric methodology and presents the main results. The fourth section is the conclusion.

Characteristics of IMF-Supported Programs

Measuring Program Implementation

We analyzed the implementation of 170 IMF-supported programs approved between 1992 and 1998 (Table 1). The choice of the time period was determined by the availability of information on conditionality in the MONA database5 and the difficulty in assessing programs approved after 1998, some of which were still ongoing when the paper on which this chapter is based was prepared. The largest collection of programs (about 48 percent of the total) in the sample were Stand-By Arrangements (SBAs). The second-largest group of programs (38 percent) were programs under concessional fa-cilities,6 followed by programs under the Extended Fund Facility (EFF) (15 percent).

Table 1Program Implementation by Type of Arrangement
Type of ArrangementNumber of Programs1Number of Programs Excluding Precautionary Arrangements and Cancelled and Ongoing Programs1Share of Programs Having Inter-ruptions2,3Share of Programs Having Irreversible Inter-ruptions3,4 (in percent)Average Macro Implementation Index3,5,6Average Structural Implementation Index3,6,7Average Overall Implementation Index3,6,8Average Share of Committed Funds Disbursed1
(in percent)
EFF2513684087.075.483.372.1
PRGF/ESAF645173.445.377.171.372.977.2
SBA814167.943.281.060.876.063.7
Total17010570.043.580.367.475.871.3
Notes: EFF denotes Extended Fund Facility; PRGF denotes Poverty Reduction and Growth Facility; ESAF denoted Enhanced Structural Adjustment Facility; and SBA denotes Stand-By Arrangement.

Multiyear arrangements are treated as one program. This is a sample of programs approved between 1992 and 1998 and available from the IMF’s Monitoring of IMF Arrangements (MONA) database. (Our sample is missing 16 SBAs, one ESAF, and one EFF program approved in 1992.) The sample of EFF programs is quite small to use for drawing reliable conclusions regarding relative performance of EFF programs compared with ESAF and SBA programs. The average share of disbursed funds is computed across a sample of programs that excludes arrangements that were either precautionary upon approval or later became precautionary, as well as canceled and ongoing programs.

An interruption occurs if an SBA program review was delayed by more than three months or not completed at all; if a program review for ESAF/PRGF programs was delayed by more than six months or not completed at all; if there was an interval of more than six months between two subsequent years of a multiyear arrangement; or if at least one of the annual arrangements was not approved (exceptions are programs that were canceled and replaced by another program, in which case noncompleted reviews and nonapproved annual arrangements are not counted as interruptions).

The macroeconomic and structural implementation indices were computed from information available in MONA. Since MONA questionnaires are sent only for programs for which IMF Executive Board meetings are scheduled, implementation information is not available on many conditions for programs with noncompleted reviews. Since these were typically interrupted programs, the macroeconomic and structural indices overstate program implementation. Interruption indices were constructed using additional information from country documents and other sources.

An irreversible interruption occurs if either (a) the last scheduled program review was not completed (all programs); or (b) all scheduled reviews were completed but the subsequent annual arrangement was not approved (ESAF/PRGF arrangements).

The macroeconomic implementation index for a given macroeonomic performance criterion is equal to 100 percent if the said criterion was met or met after modification and it is equal to zero if the said criterion was not met, not met after modification, waived, or waived after modification. The macroeconomic implementation index for a program is the average of the macroeconomic implementation indices across all macroeconomic performance criteria for this program.

The sample size for implementation indices was smaller (150 programs), which corresponds to the sample constructed for “Structural Conditionality in IMF-Supported Programs”; we simply extended the structural index used in this paper to macroeconomic and overall implementation indices.

The structural implementation index for a given structural condition is equal to 100 percent if the structural condition was met or met with a small delay for structural benchmarks; it is equal to 50 percent if the structural condition was partially met or delayed for performance criteria; and it is equal to zero if the structural condition was not met. The structural implementation index for a program is the average of the structural implementation indices across all structural conditions for this program.

The average overall implementation index for a given program is the average of macroeconomic and structural implementation indices over all conditions in this program.

Notes: EFF denotes Extended Fund Facility; PRGF denotes Poverty Reduction and Growth Facility; ESAF denoted Enhanced Structural Adjustment Facility; and SBA denotes Stand-By Arrangement.

Multiyear arrangements are treated as one program. This is a sample of programs approved between 1992 and 1998 and available from the IMF’s Monitoring of IMF Arrangements (MONA) database. (Our sample is missing 16 SBAs, one ESAF, and one EFF program approved in 1992.) The sample of EFF programs is quite small to use for drawing reliable conclusions regarding relative performance of EFF programs compared with ESAF and SBA programs. The average share of disbursed funds is computed across a sample of programs that excludes arrangements that were either precautionary upon approval or later became precautionary, as well as canceled and ongoing programs.

An interruption occurs if an SBA program review was delayed by more than three months or not completed at all; if a program review for ESAF/PRGF programs was delayed by more than six months or not completed at all; if there was an interval of more than six months between two subsequent years of a multiyear arrangement; or if at least one of the annual arrangements was not approved (exceptions are programs that were canceled and replaced by another program, in which case noncompleted reviews and nonapproved annual arrangements are not counted as interruptions).

The macroeconomic and structural implementation indices were computed from information available in MONA. Since MONA questionnaires are sent only for programs for which IMF Executive Board meetings are scheduled, implementation information is not available on many conditions for programs with noncompleted reviews. Since these were typically interrupted programs, the macroeconomic and structural indices overstate program implementation. Interruption indices were constructed using additional information from country documents and other sources.

An irreversible interruption occurs if either (a) the last scheduled program review was not completed (all programs); or (b) all scheduled reviews were completed but the subsequent annual arrangement was not approved (ESAF/PRGF arrangements).

The macroeconomic implementation index for a given macroeonomic performance criterion is equal to 100 percent if the said criterion was met or met after modification and it is equal to zero if the said criterion was not met, not met after modification, waived, or waived after modification. The macroeconomic implementation index for a program is the average of the macroeconomic implementation indices across all macroeconomic performance criteria for this program.

The sample size for implementation indices was smaller (150 programs), which corresponds to the sample constructed for “Structural Conditionality in IMF-Supported Programs”; we simply extended the structural index used in this paper to macroeconomic and overall implementation indices.

The structural implementation index for a given structural condition is equal to 100 percent if the structural condition was met or met with a small delay for structural benchmarks; it is equal to 50 percent if the structural condition was partially met or delayed for performance criteria; and it is equal to zero if the structural condition was not met. The structural implementation index for a program is the average of the structural implementation indices across all structural conditions for this program.

The average overall implementation index for a given program is the average of macroeconomic and structural implementation indices over all conditions in this program.

IMF-supported programs are complex in nature, making it difficult to arrive at a single metric of program success. In genera1, a program is considered to be successful if its principal macroeconomic and structural objectives are met. Lacking a single indicator of success for IMF-supported programs, such as the one produced by the World Bank’s Operations Evaluation Department for Bank-supported programs, we focus on the narrower measures of successful program implementation, which is a prerequisite for overall program success.

Our strategy was to construct multiple measures of implementation for each program in our sample. These measures capture program performance from different angles.7 Viewed from this narrower perspective, implementation is measured by the extent to which the program was completed without undue delays, the extent to which macroeconomic and structural conditionality was met, and the extent to which funds committed by the IMF were disbursed.

Our first indicator of program implementation is a binary variable measuring program interruptions. This variable captures both major and minor interruptions and is motivated by Mecagni’s work. We say that an interruption occurred if an SBA review was delayed by more than three months or not completed at all, if a program review for EFF/PRGFs was delayed by more than six months or not completed at all, if there was an interval of more than six months between two subsequent years of a multiyear arrangement, or if at least one of the annual arrangements was not approved.8

The second indicator is a binary variable identifying irreversible program interruptions. This measure captures programs that went off track and were not revived subsequently (i.e., were either canceled or were allowed to lapse because of policy slippages). More precisely, we say that an irreversible interruption occurred if either the last scheduled program review was not completed (all programs) or all scheduled reviews were completed but the subsequent annual arrangement was not approved (ESAF/PRGF arrangements). Third, we constructed a quantitative indicator of implementation of IMF conditionality, the overall implementation index, which represents the average fraction of macroeconomic and structural conditionality implemented. This indicator is an extension of the structural conditionality index developed in the IMF’s Policy Development and Review Department (PDR) during the 2000–2002 review of conditionality (IMF, 2001c). Finally, we also computed the ratio of disbursements to commitments. The last two indicators are continuous variables that take values between zero and 100.

Each of our indices captures an important dimension of program implementation. The macroeconomic and structural implementation indices provide quantitative information on implementation rates by type of condition. Their main drawback is that they overstate the degree of implementation because, as is well known, MONA fails to capture information on interrupted programs that were not subject to further Board reviews.9 The interruption dummies, which are based on MONA data and additional information from program documents, complement the macroeconomic and structural implementation indices by capturing significant program stoppages. The share of disbursed funds provides useful information on the proportion of approved assistance actually delivered for non-precautionary arrangements and also on the actual versus scheduled duration of the program. The implementation indices and interruption dummies provide useful information about precautionary programs, canceled programs, and some unusual cases in which no drawings were made despite good results.

Descriptive Statistics

Table 1 summarizes program implementation by type of arrangement. About 44 percent of all programs experienced an irreversible interruption, while 70 percent of all programs experienced either a major or a minor interruption. Nonetheless, approximately 71 percent of committed funds were disbursed on average (excluding precautionary arrangements, and canceled and ongoing programs). The average implementation index for programs for which information is available in MONA is 76 percent. The macroeconomic implementation index is significantly higher than the structural implementation index (80 percent versus 67 percent). However, implementation indices most likely overstate program performance. MONA collects data only for program test dates subject to Board approval or review. Information on later stages of some programs experiencing major interruptions is, therefore, not available.

The four measures of program implementation are significantly correlated (Table 2).10 However, the correlation coefficients are not very high in most cases, reflecting the fact that the various implementation measures capture quite different angles of program performance. The correlation coefficient between the macroeconomic and structural implementation index is only 0.2. This is consistent with the recent finding by Bulíř and Moon (2003) that the implementation of fiscal measures in IMF-supported programs was not strongly correlated with the implementation of structural measures.

Table 2Correlations of Implementation Indices(excluding arrangements precautionary on approval)
Pearson CorrelationAverage Macroeconomic Implementation Index1,2Average Structural Implementation Index2,3Average Overall Implementation Index2,4Interruption Index5Irreversible Interruption Index6Average Share of Committed Funds Disbursed
Average macroeconomic implementation index1,21.000
Average structural

implementation index2,4
0.2111.000
(0.01)
Average overall

implementation index3,4
0.7820.6531.00
(0.00)(0.00)
Interruption index50.286−0.0500.301.00
(0.00)(0.56)(0.00)
Irreversible interruption index60.2630.2790.390.551.00
(0.00)(0.00)(0.00)(0.00)
Average share of committed funds disbursed0.2110.3460.380.420.751.00
(0.01)(0.00)(0.00)(0.00)(0.00)
Notes: The two-tailed significance level appears in parentheses. Figures significant at the 0.05 level are in boldface. Multiyear arrangements are treated as one program. These programs were approved between 1992 and 1998 and are taken from the IMF’s Monitoring of IMF Arrangements (MONA) database.

The macroeconomic implementation index is equal to 100 percent if macroeconomic performance criteria were met or were met after modification; and it is equal to zero if macroeconomic performance criteria were not met, not met after modification, waived, or waived after modification.

The macroeconomic and structural implementation indices were computed from information available in MONA. Since MONA questionnaires are sent only for programs for which IMF Executive Board meetings are scheduled, implementation information is missing on many conditions for programs with noncompleted reviews. Since these were typically interrupted programs, the macroeconomic and structural indices overstate program implementation. Interruption indices were constructed using additional information from country documents and other sources.

The structural implementation index is equal to 100 percent if structural criteria were met or met with a small delay for structural benchmarks; it is equal to 50 percent if structural criteria were partially met or delayed for performance criteria; and it is equal to zero if structural criteria were not met.

The average overall implementation index is the average of macroeconomic and structural implementation indices over all conditions for a given program.

An interruption occurs if a Stand-By Arrangement program review was delayed by more than three months or not completed at all; if a program review for Enhanced Structural Adjustment Facility (ESAF)/Poverty Reduction and Growth Facility (PRGF) programs was delayed by more than six months or not completed at all; if there was an interval of more than six months between two subsequent years of a multiyear arrangement; or if at least one of the annual arrangements was not approved. (Exceptions are programs that were cancelled and replaced by another program, in which case noncompleted reviews and nonapproved annual arrangements are not counted as interruptions.)

An irreversible interruption occurs if either (a) the last scheduled program review was not completed (all programs); or (b) all scheduled reviews were completed but the subsequent annual arrangement was not approved (ESAF/PRGF arrangements).

Notes: The two-tailed significance level appears in parentheses. Figures significant at the 0.05 level are in boldface. Multiyear arrangements are treated as one program. These programs were approved between 1992 and 1998 and are taken from the IMF’s Monitoring of IMF Arrangements (MONA) database.

The macroeconomic implementation index is equal to 100 percent if macroeconomic performance criteria were met or were met after modification; and it is equal to zero if macroeconomic performance criteria were not met, not met after modification, waived, or waived after modification.

The macroeconomic and structural implementation indices were computed from information available in MONA. Since MONA questionnaires are sent only for programs for which IMF Executive Board meetings are scheduled, implementation information is missing on many conditions for programs with noncompleted reviews. Since these were typically interrupted programs, the macroeconomic and structural indices overstate program implementation. Interruption indices were constructed using additional information from country documents and other sources.

The structural implementation index is equal to 100 percent if structural criteria were met or met with a small delay for structural benchmarks; it is equal to 50 percent if structural criteria were partially met or delayed for performance criteria; and it is equal to zero if structural criteria were not met.

The average overall implementation index is the average of macroeconomic and structural implementation indices over all conditions for a given program.

An interruption occurs if a Stand-By Arrangement program review was delayed by more than three months or not completed at all; if a program review for Enhanced Structural Adjustment Facility (ESAF)/Poverty Reduction and Growth Facility (PRGF) programs was delayed by more than six months or not completed at all; if there was an interval of more than six months between two subsequent years of a multiyear arrangement; or if at least one of the annual arrangements was not approved. (Exceptions are programs that were cancelled and replaced by another program, in which case noncompleted reviews and nonapproved annual arrangements are not counted as interruptions.)

An irreversible interruption occurs if either (a) the last scheduled program review was not completed (all programs); or (b) all scheduled reviews were completed but the subsequent annual arrangement was not approved (ESAF/PRGF arrangements).

Several differences stand out between successfully implemented and interrupted programs (Table 3). First, countries that implemented their IMF-supported reform programs were experiencing much higher inflation at the start of the program than countries whose programs were interrupted. Although the difference in inflation rates was not statistically significant in the year in which the program was approved, inflation was significantly higher in countries with successfully implemented programs one year before the program started. Countries that implemented their programs started with substantially smaller budget deficits (2½ percent of GDP on average) compared with countries in which programs were interrupted (4¾percent of GDP on average). In countries with interrupted programs, terms of trade shocks were stronger, the strength of special interests was higher, and the degree of political cohesion was lower. Interestingly, the effort invested by the IMF and the extent and structure of conditionality are similar in interrupted and implemented programs.

Table 3Features of Successfully Implemented and Interrupted IMF Programs
Successfully ImplementedInterruptedt-test for Equality of Means1
AverageNumber of programsAverageNumber of programst-statisticsp-value
Political economy characteristics
Ethnic fractionalization46585150−0.860.39
Political instability 24.75675.6857−1.160.25
Executive index of electoral competitiveness (in percent)3628656660.690.49
Time in power (years)5.73864.52661.000.32
Strength of special interests4166625541.740.04
Index of political cohesion52.36852.06662.450.01
Quality of bureaucracy61.72671.8157−0.680.50
Change of chief executive 718.189928.3874−1.590.11
Variables under IMF control
IMF effort per program year (in millions of U.S. dollars)81.01991.03680.130.90
Total number of conditions per program year409538650.480.63
Share of quantitative program criteria (PCs) waived (percent)8.33997.22730.600.55
Share of structural conditions (percent)37954068−0.740.46
Loan size (agreed amount, in millions of SDRs)62095526690.300.76
Macroeconomic characteristics
Initial GDP per capita per year (U.S. dollars)1,494981,291740.810.42
Initial debt to the IMF (actual holdings as a percentage of quota)17799159741.160.25
Initial central government balance (percent of GDP)−2.5088−4.74683.700.00
Reserve holdings (as percent of imports)936.728132.98680.850.40
Initial inflation (percent per year)809853740.89100.37
Initial current account balance (percent of GDP)−5.3298−5.87740.420.67
Terms-of-trade shock (growth rate during program period, in percent)11−9098−15741.300.10
Notes: Bold figures indicate significance at the 5 percent level; bold italic figures indicate significance at the 10 percent level. PCs denote performance criteria; SDRs denotes Special Drawing Rights.

The null hypothesis is stated as follows: H0: mean (implemented)− mean (interrupted) = 0. The alternative hypothesis was different for different cases. In cases in which the means were significantly different we report t-statistics for the relevant one-sided alternative hypothesis—for example, for the index of political cohesion we report t-statistics for the null hypothesis as specified above versus the alternative hypothesis (that the degree of political cohesion is higher for successfully implemented compared with interrupted programs), which in fact, cannot be rejected at the 5 percent significance level), otherwise we report t-statistics for the alternative hypothesis (that the difference in means is not equal to zero).

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from 0 to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF program.

Dummy variable, which equals unity if the executive index of electoral competitiveness is equal to seven, and zero otherwise. The executive index of electoral competitiveness is from the Database of Political Institutions at the World Bank. It ranges from unity to seven, with higher values corresponding to more competitive elections.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems, a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems, a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable was interacted with the dummy indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

The variable, change of chief executive, is equal to 100 if the chief executive changed during the program period.

IMF effort is the estimated dollar cost of IMF programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

Although the average inflation rate in the approval year was not significantly different for successfully implemented and interrupted programs in the year preceding the approval year, the average inflation rate for implemented programs was significantly higher than for interrupted ones.

Reserves here do not include gold.

Average growth rate of dollar export prices multiplied by the initial share of exports in GDP minus average growth rate of dollar import prices multiplied by the initial share of imports in GDP over the course of the program.

Notes: Bold figures indicate significance at the 5 percent level; bold italic figures indicate significance at the 10 percent level. PCs denote performance criteria; SDRs denotes Special Drawing Rights.

The null hypothesis is stated as follows: H0: mean (implemented)− mean (interrupted) = 0. The alternative hypothesis was different for different cases. In cases in which the means were significantly different we report t-statistics for the relevant one-sided alternative hypothesis—for example, for the index of political cohesion we report t-statistics for the null hypothesis as specified above versus the alternative hypothesis (that the degree of political cohesion is higher for successfully implemented compared with interrupted programs), which in fact, cannot be rejected at the 5 percent significance level), otherwise we report t-statistics for the alternative hypothesis (that the difference in means is not equal to zero).

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from 0 to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF program.

Dummy variable, which equals unity if the executive index of electoral competitiveness is equal to seven, and zero otherwise. The executive index of electoral competitiveness is from the Database of Political Institutions at the World Bank. It ranges from unity to seven, with higher values corresponding to more competitive elections.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems, a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems, a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable was interacted with the dummy indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

The variable, change of chief executive, is equal to 100 if the chief executive changed during the program period.

IMF effort is the estimated dollar cost of IMF programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

Although the average inflation rate in the approval year was not significantly different for successfully implemented and interrupted programs in the year preceding the approval year, the average inflation rate for implemented programs was significantly higher than for interrupted ones.

Reserves here do not include gold.

Average growth rate of dollar export prices multiplied by the initial share of exports in GDP minus average growth rate of dollar import prices multiplied by the initial share of imports in GDP over the course of the program.

Correlation of Implementation Measures with Macroeconomic Performance

IMF-supported programs aim to strengthen the borrowing countries’ balance of payments and overall macroeconomic performance. This section presents a preliminary assessment of whether program implementation improves macroeconomic performance, both over the course of programs and after their expiration.

Figures 13 show how the main macroeconomic magnitudes evolved in uninterrupted and interrupted programs from the year in which the program was approved until three years after the program ended. The variables plotted are the average changes in inflation, the ratio of reserves to imports, and real GDP growth. The eyeball test (Figure 1) indicates that inflation for both implemented and interrupted programs continued to decline after the program ended, but the reduction in inflation (compared with the approval year) was greater for implemented than for interrupted programs.11 However, this difference was significant only for the end year of the program, as indicated by solid dots on the graph. The average level of inflation itself in the end year was also significantly lower for uninterrupted than for interrupted programs but was not significantly different in later years. The high variability of inflation in the data contributed to the differences in the changes in inflation being indistinguishable between implemented and interrupted programs in later years. Completed programs were associated with better performance, at least as far as the evolution of the reserve coverage of imports was concerned (Figure 2). Reserves in relation to imports experienced significantly higher growth, over the course of the program, in uninterrupted programs than in interrupted ones. Changes in the reserve cover of imports were also significantly and positively correlated with the share of disbursed funds and, in one case, with the no-interruption dummy. However, the correlation of the reserves-to-imports ratio with the overall implementation index took the “wrong” sign (it was negative), although it was insignificant in almost all cases.

Figure 1Inflation Dynamics for Successfully Implemented and Interrupted IMF Programs

Figure 2Reserves-to-Imports Dynamics for Successfully Implemented and Interrupted IMF Programs10

Figure 3Real GDP Growth Dynamics for Successfully Implemented and Interrupted IMF Programs

Countries that completed their IMF-supported programs started with deeper recessions (more negative GDP growth rates) but grew faster than countries where programs were interrupted, both right after the programs expired and for a couple of years after that (Figure 3). However, these differences in growth rates were not statistically significant. Once initial GDP and inflation are controlled for, only the overall implementation index was significantly positively correlated with growth in the program’s end year.

What, then, is the association between program implementation and macroeconomic performance? Although not especially strong in our sample, these results provide some evidence that countries that complete their IMF-supported programs also manage, on average, to reduce inflation, increase their relative reserve holdings, gain export competitiveness, and accelerate growth more than do countries where programs are interrupted. These results are generally consistent with those of the literature: program implementation helps countries strengthen their current account, external reserves, and balance of payments.12 Economic growth, which is depressed in the short run as program reforms begin to “bite,” also improves eventually. One noteworthy difference with previous studies concerns inflation performance. Whereas previous studies generally have been inconclusive regarding the impact of IMF-supported programs on inflation, inflation performance improves with program implementation in our sample.13

Econometric Analysis

Model Specification

We identify three major groups of factors that might affect the prospects of successful implementation of IMF-supported programs. These are political economy variables, variables describing IMF behavior, and initial and external conditions.

On the political economy side, we collected data from various sources, namely, the Political Institutions Database at the World Bank (Beck and others, 2001), the International Country Risk Guide (ICRG), the Polity IV dataset, and the CIA World Factbook. The main hypothesis that emerges from the theoretical model presented in a companion paper (Mayer and Mourmouras, 2002) is that the implementation of reforms is affected by the strength of special interest groups in countries using IMF resources. In practice, it is difficult to identify and measure the strength of organized lobbies. To develop a suitable measure of the strength of special interests, we relied on the observation that in many countries political parties represented in government or the legislature (or both) sometimes represent specific interests. Legislatures are crucial players in policymaking: legislative approval is required for successful implementation of almost all key reforms.14 While many different organized interest groups can and do block reforms, special interest groups in parliament seem a natural candidate.

The Political Institutions Database (Beck and others, 2001) identifies four groups of parties in parliament that represent nationalistic, rural, regional, and religious special interests. Key components of the platforms of these parties are the creation or defense of a national or ethnic identity and of rural, regional, or religious issues. Sometimes nationalistic special interests have persecuted minorities (nationalist special interests), with disastrous consequences for economic development. In any event, special interests in parliament influence government policy choices through the exercise of their political power and, perhaps, through monetary exchanges.

An important question is whether the interests of the political parties representing these interest groups run counter to the reform objectives of IMF-supported programs. While the motives of each of these four types of parliamentary groups are different, each is clearly committed to promoting the interests of only a segment of the population. As such, these parties are likely to support policies favored by the groups they represent even if they harm aggregate welfare. In short, special interests in the parliament serve as our proxy for special interest groups in the theoretical model. To test whether the presence of influential lobbies lowers the probability of successful program implementation, we use the maximum share of seats in parliament held by parties that represent nationalistic, religious, rural, and regional interest groups as a measure of the strength of special interests.

Regarding the remaining political economy variables, we include political instability, ethnic frac-tionalization and ethnic fractionalization squared, political cohesion, and the interaction term of the quality of bureaucracy and the change of chief executive. (See the appendix for more details on the definitions of the political variables and their sources.) Program implementation might be jeopardized by political instability, which measures the degree of internal conflict and the extent of drastic political change, such as the installation of a new chief executive. Ethnic fractionalization may lead to tensions in society and is, therefore, a potential threat to reforms. Political cohesion emphasizes the heterogeneous nature of the government and the legislature. In countries with poor bureaucracies, changes in government tend to be traumatic as they are often accompanied by disruptions in policy formulation and day-to-day administrative functions, which can have a negative impact on program implementation. A high-quality bureaucracy has the strength and expertise to govern without drastic changes in policies and, therefore, can act as a shock absorber to reduce policy deviations from program goals when governments change. Since the importance of bureaucracy is more sharply felt in times of government change, we included only a term that interacts the strength of the bureaucracy with the dummy variable indicating a change of chief executive.15

To test how factors under IMF control affect program implementation, we included three major groups of factors in our regressions: measures of IMF effort, the extent of IMF financing, and measures of the extent and structure of conditionality.

To test the hypothesis that more IMF support improves the prospects of programs, we constructed three variables. The first is IMF effort, measured by the dollar cost of each program. This variable is based on (a) internal IMF data on staff hours allocated to Use of Fund Resources (UFR) work, which is program related, and staff hours devoted to technical assistance and support tasks in member countries; (b) information on average staff salaries by grade; and (c) the costs of running the IMF’s resident representative offices in member countries with programs (data were provided by the IMF’s Office of Budget and Planning (OBP) and Office of Personnel Management (OPM)). The second variable is the number of IMF staff missions, and the third is the number of mission days.16

It also has been argued that the size of IMF loans may not be large enough to induce substantial changes in domestic policies. To test how the extent of IMF financing influences program implementation, we included loan size as a percentage of quota in our regressions.

Box 1.List of Instrumental Variables

It is difficult to find instruments for all endogenous variables simultaneously. Out of all IMF variables, the share of structural conditions in the total number of conditions seems the least subject to later revisions in the course of the program, so we treat this variable as exogenous. For the remaining IMF variables, we use the following IVs (see Table 6 for first-stage regressions). F-statistics on the IV set for all endogenous variables were significant.

The average share of bilateral aid provided to the country by the Group of Seven (G-7) before the start of the program.1 This variable is positively correlated with the loan size in relation to quota and with the share of quantitative performance criteria (PCs) waived, although these correlations are not significant even at the 10 percent significance level.

Approval year. Since the number of conditions per program year has been increasing over time, it is positively correlated with the approval year and we can use the latter as an IV.

Expected program duration. A program’s expected duration is positively correlated with the loan size in relation to quota and negatively correlated with the share of quantitative PCs waived. The longer the program, the larger is the loan and the more time the IMF has to adjust its conditionality.

IMF quota (log). The quotas of members with IMF-supported programs are significantly positively correlated with IMF effort per program year and with the share of quantitative PCs waived. A higher quota is associated with greater IMF effort in a program and a higher share of quantitative PCs waived for two main reasons. First, the quota determines the size of the IMF loan to a member and the amount “at stake” for the IMF. Second, the quota also determines the member’s voting power in the IMF.

GDP per capita (log). This variable is negatively correlated with IMF effort per program year. Richer countries require less IMF effort, get higher loans as a percentage of quota, receive fewer waivers, and get fewer conditions per program year. (This coefficient is significant at 10 percent significance level only.) This is the only initial condition included in the IV set.2

Regional dummies. IMF effort per program year is higher in Latin America and the Caribbean compared with Europe and the Middle East. Compared with the other regions, loan size in relation to quota is higher in Latin America and the Caribbean, sub-Saharan Africa, and East Asia. The share of quantitative PCs waived is higher in East Asia (significant at the 10 percent significance level only).

Population (log). This variable is negatively correlated with the share of quantitative PCs waived and positively correlated with the loan size as a percentage of quota.

1 Since G-7 members comprise 45 percent of the IMF’s voting power, this variable could be related to the “weight” the IMF puts on particular borrowers. See Mayer and Mourmouras (2002) for details.2 This variable was not significantly correlated with successful program implementation when we included it in the original regression.

To analyze the impact of conditionality on program implementation, we employed the following measures: (a) the number of conditions per program year, which measures the extent of overall conditionality; (b) the share of quantitative performance criteria waived, which measures the strength of enforcement and associated flexibility of conditionality; and (c) the share of structural conditions in the total number of conditions, which measures the weight that programs put on structural reforms. As an alternative to the last measure, we also included the number of structural conditions per program year in the regressions to capture the extent of structural conditionality. As the results were unaffected, they are not reported separately.

Variables under IMF control are endogenously determined. Hence, a list of appropriate instrumental variables (IVs) must be employed in order to glean the impact of IMF variables on the probability of successful implementation of IMF-supported programs. These instruments must be correlated with variables that are under IMF control, be uncorre-lated with the shocks hitting programs, and not be direct determinants of program implementation. The choice of instruments is described in more detail in Box 1.

Another key issue is the impact of initial and external conditions and shocks on the implementation of IMF-supported programs. One possibility is that countries that start with unfavorable initial conditions or are hit by unfavorable shocks have a harder time meeting program targets. Alternatively, these countries could face stronger incentives to reform and might be more successful in implementing IMF-supported programs. A third possibility is that programs are designed and negotiated optimally, taking into consideration all the relevant factors, including initial conditions and the frequency, intensity, and nature of economic and other shocks. If programs are tailored to the circumstances of each member country, differences in initial or external conditions and in exposure to shocks may not play a big role in program implementation.17 It turns out that it is not possible to distinguish empirically among these three possibilities. All we can say is that the data are consistent with the notion that initial and external conditions do not represent a major stumbling block for program implementation.

Variables included as initial conditions in our regressions were as follows: the central government fiscal balance in relation to GDP, the current account balance in relation to GDP, the level of gross reserves at the start of the program, initial inflation, initial GDP per capita, and initial debt to the IMF in relation to a member’s IMF quota. To control for external conditions, we use the term “trade shock”—namely the difference between the growth rate of dollar export prices times the share of exports in GDP and the growth rate of dollar import prices times the share of imports in GDP.

Econometric Methodology

Our strategy is to relate the various indicators of implementation, either in isolation or in a pooled sample, to various right-hand-side variables. These “explanatory” variables include observable characteristics of borrowing countries, such as initial conditions and features of their domestic political economy, and variables under IMF control, as described in the previous section.

Our choice of econometric technique was guided by the need to make efficient use of the information contained in our implementation indicators and by data availability. One complication is that one of our indicators is a binary variable while the other two vary continuously, which makes it difficult to combine all three in a single model. Limited availability of political economy data is an additional consideration. Even though implementation measures are available for 170 programs, political economy variables are available for only about 60 programs. Crucially, some of the political economy data are not available for all former centrally planned economies for the period under consideration.18 The limited sample also forced us to set aside problems of prolonged use of IMF resources. As some of the countries in the complete sample had multiple programs with the IMF, there is strong cross-sectional correlation between observations in the entire sample. But since 56 percent of our working sample comprised countries with only one program, and only 8 percent of countries in the sample had three or more programs, we could not apply panel data techniques.

Owing to the small sample size, we estimate several specifications to check the robustness of our conclusions. Our approach is to first apply the Multiple Indicators and Multiple Causes (MIMIC)19 model (see Joreskog and Goldberger, 1975), which combines three implementation measures in one econometric model. We then re-estimate models that feature each implementation measure separately using proper econometric techniques. Amemiya’s IV probit method is employed to estimate regressions where the left-hand-side variable is a binary indicator. Amemiya’s IV tobit is used in regressions of the share of disbursed funds and the overall implementation index.

Formally, our model can be described as follows. If yi is the unobservable probability of successful program implementation, then

where Pi is a vector of country i political economy variables; Fi is a vector of variables under IMF control; ay, γy, and βy are vectors of coefficients; and εyi is a stochastic disturbance term. The variables controlled by the IMF are given by

where αF, γF, and λF are vectors of coefficients; εFi is another error term; and Zi is a vector of exogenous variables that are correlated with donor behavior but do not systematically influence the probability of successful implementation. Since the IMF responds to shocks hitting programs by adjusting its effort and conditionality, εyi and εFi are correlated. We use IV techniques to obtain consistent estimates of the coefficients in equation (1.1).

Since we do not observe yi, we cannot estimate equation (1.1) directly. However, we have three indicators of implementation, which are correlated with yi. We can relate our observed measures of implementation to the unobserved probability of successful implementation as follows:

where yi1, yi2, and yi3 are our three implementation measures, and Ui1, Ui2, and Ui3 are measurement errors that are possibly mutually correlated.

Equations (1.1)–(1.5) represent a special case of the MIMIC model analyzed in Joreskog and Goldberger (1975). To estimate this model, we first substitute equation (1.2) into (1.1) and (1.1) into (1.3)–(1.5) to obtain a system of equations that can be treated as seemingly unrelated regressions. This system can be estimated to obtain reduced form coefficients that we can use to recover the parameters γy and βy. To calculate the variance of γy and βy we employ the delta method. This approach requires normalization of one of the coefficients δ to one. Since the model is overidentified, we also had to impose nonlinear constraints to obtain unique parameter estimates. Because of computational complexity, we estimate the general form of the MIMIC model (1.1)–(1.5) including only one variable under IMF control, namely the IMF effort.

A computationally convenient version of this model arises if the coefficients δ are all unity. In this case, substituting (1.1) into (1.3)–(1.5) and setting the δs to one, we have:

The system (1.6)–(1.8) is a random-effects model with random effect εyi. If IMF effort were not simultaneously determined with the probability of successful implementation, then the random effect εyi would be uncorrelated with the set of regressors in Fi and be Pi. We could then obtain consistent estimates of this model by pooling the three implementation measures in one variable and regressing it on the same set of political economy and IMF effort variables for a particular program using the random-effects estimator. However, since IMF effort is simultaneously determined with the probability of successful implementation, we apply the random-effects IV estimator to obtain consistent estimates of the coefficients on political economy and IMF effort variables.20

To summarize, we proceed as follows: we first estimate linear-in-probability and tobit regressions that combine three implementation measures in one model employing the random-effects estimator—that is, equations (1.6)–(1.8). Two variants are examined, one that ignores the endogeneity of variables under the IMF’s control (Table 4) and another dealing with this endogeneity through IV techniques (Table 5). The set of IVs employed is specified in Table 6. Table 5 also reports a third, more general, version of the MIMIC model. This is specification (1.1)–(1.5) with only one endogenous variable, namely, IMF effort per program year (Table 5, column 1).21 We then re-estimate our chosen specification of political economy variables with each of the implementation indices in isolation, not taking into account endogeneity of the variables under IMF control (Table 7) and instrumenting for these variables (Tables 810).22

Table 4Random-Effects Model: Linear in Probability and Tobit Regressions
Regression Number(1)(2)(3)(4)
Dependent variable: Program implementation indicesLinear in probabilityTobitLinear in probabilityTobitLinear in probabilityTobitLinear in probabilityTobit
Number of observations240240170170179179167167
Political economy variables
Ethnic fractionalization0.12

(0.25)
0.31

(0.34)
1.07

(2.07)
1.99

(2.34)
1.16

(2.46)
2.14

(2.64)
1.31

(2.70)
2.45

(2.94)
Ethnic fractionalization (squared)0.00

−(0.17)
0.00

−(0.39)
−0.01

−(1.34)
0.02

−(1.76)
0.01

−(2.03)
0.02

−(2.30)
0.01

−(2.38)
0.02

−(2.59)
Political instability1−0.39

−(0.41)
−1.31

−(0.75)
−1.49

−(0.69)
−5.47

−(1.53)
2.85

−(1.87)
5.87

−(2.23)
3.11

−(2.03)
5.54

−(2.15)
Executive index of electoral competitiveness29.64

(1.27)
15.18

(1.11)
8.23

(0.88)
18.85

(1.28)
12.69

(1.57)
22.36

(1.64)
13.25

(1.67)
23.33

(1.79)
Time in power0.34

(0.30)
1.90

(0.91)
1.32

(0.73)
3.70

(1.29)
1.22

(0.78)
3.54

(1.33)
2.24

(1.46)
4.88

(1.90)
Time in power (squared)0.00

−(0.08)
−0.04

−(0.80)
−0.07

−(1.21)
0.18

−(1.92)
−0.07

−(1.29)
0.16

−(1.84)
0.10

−(1.93)
0.20

−(2.35)
Strength of special interests331.72

−(2.14)
69.73

−(2.87)
34.46

−(3.08)
68.41

−(3.53)
36.39

−(3.19)
70.49

−(3.62)
Index of political cohesion49.66

(1.98)
19.52

(2.50)
11.49

(2.95)
20.58

(3.11)
13.22

(3.20)
22.85

(3.32)
Quality of bureaucracy interacted with change of chief executive512.82

(1.19)
33.25

(1.85)
16.25

(1.92)
31.30

(2.13)
20.77

(2.52)
36.82

(2.63)
Initial and external conditions Central government balance (percentage of GDP)0.93

(0.76)
0.98

(0.50)
Level of reserves0.00

(0.46)
0.00

(0.72)
Inflation0.00

(0.01)
−0.27

−(0.97)
Current account balance (percentage of GDP)−95

−(1.26)
−123

−(1.02)
GDP per capita (log)5.39

(0.81)
4.75

(0.44)
Debt to the IMF (percentage of IMF quota)4.62

(0.37)
13.21

(0.63)
Terms-of-trade shock6−0.01

−(1.15)
−0.01

−(0.34)
Variables under IMF Control
IMF effort per program year (log)73.81

(0.78)
11.91

(1.48)
Loan size as percentage of quota (log)7.01

(1.63)
11.45

(1.55)
Number of conditions per program year (log)−6.43

−(0.99)
−13.80

−(1.29)
Share of quantitative PCs waived (percent)0.49

−(1.98)
1.05

−(2.56)
Share of structural conditions (percent)0.16

(0.98)
0.21

(0.77)
Wald chi-square statistics2.955.0034.2942.6333.3238.3145.8648.33
p-value0.810.540.010.000.000.000.000.00
Notes: Bold figures indicate significance at the 5 percent level; bold italic figures indicate significance at the 10 percent level. PCs denote performance criteria.The model was estimated on a pooled sample of three implementation measures as left-hand-side variables, ignoring the endogeneity of variables under IMF control. The measures of program implementation used are (a) a binary variable indicating no irreversible program interruption; (b) the share of funds committed by the IMF under an arrangement disbursed (we excluded the measure of committed funds disbursed for arrangements precautionary on approval; canceled programs that did not have irreversible interruption; and arrangements that turned precautionary were treated as fully disbursed (100 percent)); and (c) the average share of conditions implemented. Regression also included the constant term, which is omitted in the table.

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from zero to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Dummy variable equals one if the executive index of electoral competitiveness is equal to seven, and zero otherwise. The executive index of electoral competitiveness is from the Database of Political Institutions at the World Bank. It ranges from one to seven, with higher values corresponding to more competitive elections.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable is interacted with the dummy indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

Average growth rate of dollar export prices multiplied by the initial share of exports in GDP minus average growth rate of dollar import prices multiplied by the initial share of imports in GDP over the course of the program

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

Notes: Bold figures indicate significance at the 5 percent level; bold italic figures indicate significance at the 10 percent level. PCs denote performance criteria.The model was estimated on a pooled sample of three implementation measures as left-hand-side variables, ignoring the endogeneity of variables under IMF control. The measures of program implementation used are (a) a binary variable indicating no irreversible program interruption; (b) the share of funds committed by the IMF under an arrangement disbursed (we excluded the measure of committed funds disbursed for arrangements precautionary on approval; canceled programs that did not have irreversible interruption; and arrangements that turned precautionary were treated as fully disbursed (100 percent)); and (c) the average share of conditions implemented. Regression also included the constant term, which is omitted in the table.

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from zero to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Dummy variable equals one if the executive index of electoral competitiveness is equal to seven, and zero otherwise. The executive index of electoral competitiveness is from the Database of Political Institutions at the World Bank. It ranges from one to seven, with higher values corresponding to more competitive elections.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable is interacted with the dummy indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

Average growth rate of dollar export prices multiplied by the initial share of exports in GDP minus average growth rate of dollar import prices multiplied by the initial share of imports in GDP over the course of the program

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

Table 5Random-Effects (IV) and MIMIC Models: Linear-in-Probability Regressions
Random Effects IV Regressions1MIMIC Model2
Regression Number(1)(2)(3)(4)(5)(6)
Number of observations165165165165165165
Dollar and Svensson variables
Ethnic fractionalization1.08

(2.09)
0.91

(1.70)
0.94

(1.75)
1.25

(2.44)
0.99

(2.05)
2.50

(3.55)
Ethnic fractionalization (squared)0.01

−(1.76)
−0.01

−(1.42)
−0.01

−(1.48)
0.01

−(2.07)
0.01

−(1.68)
0.03

−(3.53)
Political Instability33.91

−(2.49)
3.82

−(2.43)
3.93

−(2.50)
4.58

−(2.90)
4.48

−(2.82)
4.23

−(2.20)
Other political economy variables
Strength of special interests439.78

−(3.57)
33.86

−(2.80)
34.01

−(2.81)
45.47

−(3.99)
37.38

−(3.24)
38.69

−(2.48)
Index of political cohesion510.03

(2.74)
10.55

(2.87)
8.78

(2.17)
9.86

(2.43)
9.69

(2.39)
14.02

(2.86)
Bureaucracy quality interacted with change of chief executive621.39

(2.68)
21.36

(2.68)
22.02

(2.75)
24.97

(3.11)
24.50

(3.02)
21.05

(2.16)
Variables under IMF control
IMF effort per program year (log)7,81.54

(0.23)
−2.14

−(0.29)
−0.49

−(0.07)
7.06

(0.99)
−9.29

−(1.06)
Loan size as a percentage of quota (log)86.89

(1.26)
7.35

(1.33)
6.58

(1.30)
Number of conditions per program year (log)8−9.87

−(1.05)
−15.77

−(1.55)
−13.70

−(1.43)
Share of quantitative PCs waived (percent)8−0.67

−(1.59)
−0.47

−(1.17)
Share of structural conditions (percent)0.14

(0.83)
0.11

(0.62)
0.06

(0.32)
0.16

(0.92)
0.14

(0.77)
Wald chi-square statistics26.9228.5029.5731.6231.93
p-value0.000.000.000.000.00
Overidentifying restrictions test12.6710.428.838.046.91
Degrees of freedom98777
p-value0.180.240.270.330.44
Hausman t-test0.380.921.040.840.83
p-value0.540.630.790.840.84
Notes: Boldfaced figures indicate significance at the 5 percent level; bold italic figures indicate significance at the 10 percent level. PCs denote performance criteria.

This model was estimated on a pooled sample of three implementation measures as left—hand-side variables, using a random-effects IV estimator with the set of instruments as specified in Table 6. The measures of program implementation used are (a) a binary variable indicating no irreversible program interruption; (b) the share of funds committed by the IMF under an arrangement actually disbursed. We excluded the measure of committed funds disbursed for arrangements precautionary on approval; canceled programs that did not have irreversible interruption; and arrangements that turned precautionary, which were treated as fully disbursed (100 percent); and (c) the average share of conditions implemented. The regression also included the constant term, which is omitted from the table.

This model comprises equations (1.1)–(1.5) in the text. It is essentially a system of seemingly unrelated regressions which can be estimated to obtain reduced-form parameters. Since the model is overidentified, we had to impose nonlinear constraints to obtain unique estimates of coefficients. Then the structural parameters were computed using estimates of reduced-form parameters and their variance was estimated using the delta method. (More details are available from the authors upon request.)

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from 0 to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the Appendix for a more detailed definition. This variable is interacted with the dummy variable indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

Treated as an endogenous variable in this regression.

Notes: Boldfaced figures indicate significance at the 5 percent level; bold italic figures indicate significance at the 10 percent level. PCs denote performance criteria.

This model was estimated on a pooled sample of three implementation measures as left—hand-side variables, using a random-effects IV estimator with the set of instruments as specified in Table 6. The measures of program implementation used are (a) a binary variable indicating no irreversible program interruption; (b) the share of funds committed by the IMF under an arrangement actually disbursed. We excluded the measure of committed funds disbursed for arrangements precautionary on approval; canceled programs that did not have irreversible interruption; and arrangements that turned precautionary, which were treated as fully disbursed (100 percent); and (c) the average share of conditions implemented. The regression also included the constant term, which is omitted from the table.

This model comprises equations (1.1)–(1.5) in the text. It is essentially a system of seemingly unrelated regressions which can be estimated to obtain reduced-form parameters. Since the model is overidentified, we had to impose nonlinear constraints to obtain unique estimates of coefficients. Then the structural parameters were computed using estimates of reduced-form parameters and their variance was estimated using the delta method. (More details are available from the authors upon request.)

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from 0 to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the Appendix for a more detailed definition. This variable is interacted with the dummy variable indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

Treated as an endogenous variable in this regression.

Table 6First-Stage Regressions
Dependent VariableIMF Effort Per Program Year (log)1Loan Size as Percentage of Quota (log)Number of Conditions Per Program Year (log)Share of Quantitative PCs Waived (percent)
Number of observations57575757
Political economy variables
Ethnic fractionalization−0.021

−(1.56)
0.006

(0.58)
0.008

(0.84)
0.031

(0.12)
Ethnic fractionalization (squared)0.000

(1.39)
0.000

−(0.51)
0.000

(0.620)
−0.002

−(0.57)
Political instability2−0.034

−(0.66)
0.008

(0.20)
−0.012

−(0.34)
0.316

(0.34)
Strength of special interests30.391

(1.15)
−0.05

−(0.20)
−0.042

−(0.18)
−9.424

−(1.51)
Index of political cohesion4−0.008

−(0.06)
−0.092

−(0.86)
−0.093

−(0.98)
−0.060

−(0.02)
Bureaucracy quality interacted with change of chief executive50.007

(0.02)
0.009

(0.04)
0.055

(0.29)
0.391

(0.08)
Variables under IMF control
Share of structural conditions (percent)−0.003

−(0.30)
0.007

(1.11)
0.009

(1.59)
0.064

(0.42)
Instruments
Average share of bilateral aid by Group of Seven (G-7) to the country before the program start0.060

(0.49)
0.153

(1.60)
−0.038

−(0.45)
3.009

(1.35)
Approval year0.119

(1.55)
0.052

(0.86)
0.110

(2.04)
−0.199

−(0.14)
Expected program duration0.123

(0.93)
0.251

(2.40)
0.059

(0.63)
5.512

−(2.27)
IMF quota (log)0.469

(2.01)
−0.128

−(0.70)
0.219

(1.33)
17.421

(4.06)
Dummy for ESAF/PRGF0.306

(0.80)
0.167

(0.56)
1.063

−(3.95)
16.555

(2.37)
GDP per capita (log)0.442

−(2.45)
0.418

(2.93)
0.218

−(1.71)
8.443

−(2.55)
Latin America and Caribbean1.095

(2.29)
0.784

(2.08)
0.313

(0.93)
0.303

(0.03)
Sub-Saharan Africa0.222

(0.40)
0.968

(2.24)
−0.046

−(0.12)
13.041

(1.30)
East Asia0.724

(1.31)
1.609

(3.70)
−0.005

−(0.01)
18.280

(1.81)
Population (log)−0.020

−(0.11)
0.291

(2.02)
−0.121

−(0.94)
10.740

−(3.21)
R20.560.750.570.549
F-statistic on instruments3.379.483.552.94
p-value0.000.000.000.01
Notes: PCs denote performance criteria; ESAF denotes Enhanced Structural Adjustment Facility; and PRGF denotes Poverty Reduction and Growth Facility. Estimated by ordinary least squares (OLS) with robust standard errors. Regression also included the constant term, which is omitted in the table. Bold figures indicate significance at the 5 percent level; bold italic figures indicate significance at 10 percent level.

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from zero to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable is interacted with the dummy indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

Notes: PCs denote performance criteria; ESAF denotes Enhanced Structural Adjustment Facility; and PRGF denotes Poverty Reduction and Growth Facility. Estimated by ordinary least squares (OLS) with robust standard errors. Regression also included the constant term, which is omitted in the table. Bold figures indicate significance at the 5 percent level; bold italic figures indicate significance at 10 percent level.

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from zero to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable is interacted with the dummy indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

Table 7Linear-in-Probability and Probit/Tobit Regressions on Three Implementation Measures Separately Ignoring Endogeneity of Variables Under IMF Control
Our Specification of Political Economy Variables +Variables Under the IMF’s Control
Dependent VariableNon-interruption dummyShare of committed IMF disbursed1Average overall implementation index
ModelLinear in probabilityProbitLinear in probabilityTobitLinear in probabilityTobit
Number of observations575753535555
Dollar and Svensson variables
Ethnic fractionalization1.50

(1.58)
0.05

(1.43)
1.13

(2.28)
1.98

(2.24)
0.61

(2.47)
0.66

(2.91)
Ethnic fractionalization (squared)−0.01

−(1.18)
0.00

−(1.06)
0.01

−(2.05)
0.02

−(1.91)
0.01

−(2.34)
0.01

−(2.84)
Political instability28.13

−(3.22)
0.31

−(2.52)
3.79

−(2.10)
6.31

−(2.51)
−0.37

−(0.41)
−0.30

−(0.44)
Other political economy variables
Strength of special interests373.98

−(3.92)
2.83

−(3.03)
32.17

−(2.92)
60.48

−(3.02)
17.07

−(2.96)
17.25

−(3.37)
Index of political cohesion420.52

(3.26)
0.83

(2.50)
11.67

(3.00)
16.35

(2.70)
−0.83

−(0.50)
−1.17

−(0.65)
Bureaucracy quality interacted with change of chief executive545.851.8317.2828.474.364.28
(4.16)(2.51)(2.29)(2.20)(0.97)(1.21)
Variables under IMF control
IMF effort per program year (log)616.93

(1.79)
0.65

(1.85)
−0.88

−(0.17)
6.37

(0.77)
2.42

(1.29)
2.88

(1.30)
Loan size as percentage of quota (log)4.92

(0.54)
0.15

(0.56)
3.29

(0.64)
3.10

(0.42)
2.28

(1.63)
2.34

(1.19)
Number of conditions per program year (log)−10.71

−(0.93)
−0.49

−(0.97)
−7.53

−(1.08)
−13.18

−(1.28)
11.44

−(4.57)
12.93

−(4.14)
Share of quantitative PCs waived (percent)−0.87

−(1.45)
0.03

−(2.02)
0.09

(0.41)
−0.21 −(0.48)0.56

−(6.21)
0.58

−(5.12)
Share of structural conditions (percent)0.22

(0.73)
0.01

(0.86)
0.15

(0.98)
0.28

(1.00)
−0.12

−(1.60)
0.14

−(1.78)
R20.410.460.58
Predictive ability of the model (percent)775.44
Notes: Boldfaced figures indicate significance at the 5 percent level; bold italic figures indicate significance at the 10 percent level. PCs denote performance criteria.

For the regression of the share of committed funds disbursed, we excluded arrangements precautionary on approval. Canceled programs that did not have irreversible interruption and arrangements that turned precautionary were treated as fully disbursed (100 percent).

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from zero to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable is interacted with the dummy variable indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (including both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

The predictive ability of the model is computed as follows: if the predicted value from the probit regression was higher or equal to ½, we count this prediction as no interruption; otherwise, we count it as an interruption. We then compare the actual outcome with the predicted outcome and compute the share of correct predictions.

Notes: Boldfaced figures indicate significance at the 5 percent level; bold italic figures indicate significance at the 10 percent level. PCs denote performance criteria.

For the regression of the share of committed funds disbursed, we excluded arrangements precautionary on approval. Canceled programs that did not have irreversible interruption and arrangements that turned precautionary were treated as fully disbursed (100 percent).

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from zero to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable is interacted with the dummy variable indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (including both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

The predictive ability of the model is computed as follows: if the predicted value from the probit regression was higher or equal to ½, we count this prediction as no interruption; otherwise, we count it as an interruption. We then compare the actual outcome with the predicted outcome and compute the share of correct predictions.

Table 8IV Regressions for Non-Interruption Dummy Taking into Account Endogeneity of Variables Under IMF Control
Dependent VariableNon-Interruption Dummy
Regression number(1)(2)(3)(4)
Number of observations61616161
Dollar and Svensson variables
Ethnic fractionalization0.042

(1.32)
0.033

(1.09)
0.027

(0.86)
0.037

(1.17)
Ethnic fractionalization (squared)0.000

−(1.03)
0.000

−(0.70)
0.000

−(0.49)
0.000

−(0.72)
Political instability10.256

−(2.40)
0.251

−(2.31)
0.255

−(2.31)
0.318

−(2.42)
Other political economy variables
Strength of special interests22.329

−(2.95)
1.887

−(2.51)
1.974

−(2.50)
2.479

−(2.97)
Index of political cohesion30.636

(2.33)
0.632

(2.33)
0.484

(1.54)
0.856

(2.44)
Bureaucracy quality interacted with change of chief executive41.355

(2.12)
1.435

(2.36)
1.345

(2.16)
1.656

(2.14)
Variables under IMF control
IMF effort per program year (log)5,60.271

(0.57)
Loan size as percentage of quota (log)60.464

(1.34)
Number of conditions per program year (log)6−0.356

−(0.52)
Share of quantitative PCs waived (percent)6−0.045

−(1.34)
Predictive ability of model770.4972.4166.6763.93
Notes: Boldfaced figures indicate significance at the 5 percent level. PCs denote performance criteria. ESAF denotes Enhanced Structural Adjustment Facility, and PRGF denotes Poverty Reduction and Growth Facility.For IV estimation on each of the implementation measures separately, we use shorter sets of IVs:

  • For IMF effort per program year (log): expected program duration, quota (log), and GDP per capita (log);
  • For loan size as percentage of quota (log): expected program duration, GDP per capita (log), and population (log);
  • For number of conditions per program year (log): approval year, dummy for ESAF/PRGF, and GDP per capita (log); and
  • For share of quantitative PCs waived (percent): quota (log), GDP per capita (log), and population (log).

IV regression for non-interruption dummy was estimated using the two-stage Amemiya GLS procedure (IV probit).

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from 0 to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable is interacted with the dummy variable indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

Treated as an endogenous variable in this regression.

The predictive ability of the model is computed as follows: if the predicted value from the probit regression is higher or equal to ½, we count this prediction as no interruption; otherwise, we count this prediction as an interruption. We then compare the actual outcome with the predicted outcome and compute the share of correct predictions.

Notes: Boldfaced figures indicate significance at the 5 percent level. PCs denote performance criteria. ESAF denotes Enhanced Structural Adjustment Facility, and PRGF denotes Poverty Reduction and Growth Facility.For IV estimation on each of the implementation measures separately, we use shorter sets of IVs:

  • For IMF effort per program year (log): expected program duration, quota (log), and GDP per capita (log);
  • For loan size as percentage of quota (log): expected program duration, GDP per capita (log), and population (log);
  • For number of conditions per program year (log): approval year, dummy for ESAF/PRGF, and GDP per capita (log); and
  • For share of quantitative PCs waived (percent): quota (log), GDP per capita (log), and population (log).

IV regression for non-interruption dummy was estimated using the two-stage Amemiya GLS procedure (IV probit).

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from 0 to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable is interacted with the dummy variable indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

Treated as an endogenous variable in this regression.

The predictive ability of the model is computed as follows: if the predicted value from the probit regression is higher or equal to ½, we count this prediction as no interruption; otherwise, we count this prediction as an interruption. We then compare the actual outcome with the predicted outcome and compute the share of correct predictions.

Table 9IV Regressions for the Average Share of Committed Funds Disbursed Taking into Account Endogeneity of Variables Under IMF Control
Dependent VariableShare of Committed Funds Disbursed
Regression number(1)(2)(3)(4)
Number of observations55555555
Dollar and Svensson variables
Ethnic fractionalization1.234

(1.38)
1.478

(1.88)
1.553

(1.95)
1.165

(1.45)
Ethnic fractionalization (squared)−0.010

−(1.03)
−0.012

−(1.46)
−0.013

−(1.54)
−0.008

−(0.97)
Political instability16.022

−(2.42)
5.351

−(2.26)
5.925

−(2.45)
6.289

−(2.48)
Other political economy variables
Strength of special interests249.287

−(2.58)
49.505

−(2.80)
51.833

−(2.97)
50.597

−(2.77)
Index of political cohesion316.820

(2.95)
17.807

(3.18)
13.194

(2.03)
18.703

(2.87)
Bureaucracy quality interacted with change of chief executive426.162

(2.00)
23.426

(1.88)
25.209

(1.99)
27.253

(2.03)
Variables under IMF control
IMF effort per program year (log)5,6−1.311

−(0.10)
Loan size as percentage of quota (log)64.349

(0.49)
Number of conditions per program year (log)6−22.011

−(1.30)
Share of quantitative PCs waived (percent)6−0.638

−(0.78)
Notes: Boldfaced figures indicate significance at the 5 percent level. PCs denote performance criteria; ESAF denotes Enhanced Structural Adjustment Facility, and PRGF denotes Poverty Reduction and Growth Facility.

For IV estimation on each of the implementation measures separately, we use shorter sets of IVs:

  • For IMF effort per program year (log): expected program duration, quota (log), and GDP per capita (log);
  • For loan size as percentage of quota (log): expected program duration, GDP per capita (log), and population (log);
  • For number of conditions per program year (log): approval year, dummy for ESAF/PRGF, and GDP per capita (log); and
  • For share of quantitative PCs waived (percent): quota (log), GDP per capita (log), and population (log).

IV regression for the share of committed funds disbursed was estimated using two-stage Amemiya (1978) GLS procedure (IV tobit).

For the regression of the share of committed funds disbursed, we excluded arrangements precautionary on approval. Canceled programs that did not have irreversible interruptions and arrangements that turned precautionary were treated as fully disbursed (100 percent).

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from 0 to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems, a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems, a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable is interacted with the dummy variable indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

Treated as an endogenous variable in this regression.

Notes: Boldfaced figures indicate significance at the 5 percent level. PCs denote performance criteria; ESAF denotes Enhanced Structural Adjustment Facility, and PRGF denotes Poverty Reduction and Growth Facility.

For IV estimation on each of the implementation measures separately, we use shorter sets of IVs:

  • For IMF effort per program year (log): expected program duration, quota (log), and GDP per capita (log);
  • For loan size as percentage of quota (log): expected program duration, GDP per capita (log), and population (log);
  • For number of conditions per program year (log): approval year, dummy for ESAF/PRGF, and GDP per capita (log); and
  • For share of quantitative PCs waived (percent): quota (log), GDP per capita (log), and population (log).

IV regression for the share of committed funds disbursed was estimated using two-stage Amemiya (1978) GLS procedure (IV tobit).

For the regression of the share of committed funds disbursed, we excluded arrangements precautionary on approval. Canceled programs that did not have irreversible interruptions and arrangements that turned precautionary were treated as fully disbursed (100 percent).

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from 0 to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems, a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems, a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable is interacted with the dummy variable indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

Treated as an endogenous variable in this regression.

Table 10IV Regressions for Average Overall Implementation Index Taking into Account Endogeneity of Variables Under IMF Control
Dependent VariableAverage Overall Implementation Index
Regression number(1)(2)(3)(4)
Number of observations55555555
Dollar and Svensson variables
Ethnic fractionalization0.525

(1.63)
0.695

(2.49)
0.697

(2.32)
0.668

(2.69)
Ethnic fractionalization (squared)0.006

−(1.74)
0.008

−(2.63)
0.008

−(2.40)
0.007

−(2.59)
Political instability1−0.329

−(0.38)
−0.076

−(0.09)
−0.080

−(0.09)
−0.417

−(0.56)
Other political economy variables
Strength of special interests214.183

−(2.12)
15.304

−(2.41)
16.680

−(2.52)
18.900

−(3.42)
Index of political cohesion3−1.675

−(0.77)
−0.747

−(0.35)
−0.911

−(0.34)
1.143

(0.55)
Bureaucracy quality interacted with change of chief executive43.457

(0.77)
3.375

(0.77)
3.069

(0.64)
4.369

(1.11)
Variables under IMF control
IMF effort per program year (log)5,6−5.095

−(1.15)
Loan size as percentage of quota (log)63.188

(1.01)
Number of conditions per program year (log)62.126

(0.32)
Share of quantitative PCs waived (percent)60.600

−(2.51)
Notes: Boldfaced figures indicate significance at the 5 percent level. PCs denote performance criteria; ESAF denotes Enhanced Structural Adjustment Facility, and PRGF denotes Poverty Reduction and Growth Facility.For IV estimation on each of the implementation measures separately, we use shorter sets of IVs:

  • For IMF effort per program year (log): expected program duration, quota (log), and GDP per capita (log);
  • For loan size as percentage of quota (log): expected program duration, GDP per capita (log), and population (log);
  • For number of conditions per program year (log): approval year, dummy for ESAF/PRGF, and GDP per capita (log); and
  • For share of quantitative PCs waived (percent): quota (log), GDP per capita (log), and population (log).

IV regression for the average overall implementation index was estimated using the two-stage Amemiya GLS procedure (IV tobit).

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from 0 to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable is interacted with the dummy variable indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

Treated as an endogenous variable in this regression.

Notes: Boldfaced figures indicate significance at the 5 percent level. PCs denote performance criteria; ESAF denotes Enhanced Structural Adjustment Facility, and PRGF denotes Poverty Reduction and Growth Facility.For IV estimation on each of the implementation measures separately, we use shorter sets of IVs:

  • For IMF effort per program year (log): expected program duration, quota (log), and GDP per capita (log);
  • For loan size as percentage of quota (log): expected program duration, GDP per capita (log), and population (log);
  • For number of conditions per program year (log): approval year, dummy for ESAF/PRGF, and GDP per capita (log); and
  • For share of quantitative PCs waived (percent): quota (log), GDP per capita (log), and population (log).

IV regression for the average overall implementation index was estimated using the two-stage Amemiya GLS procedure (IV tobit).

This index is computed based on the index of internal conflict provided by the International Country Risk Guide (ICRG) on a scale from 0 to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of the IMF-supported program.

Computed as the maximum share of seats in the parliament held by parties representing special interests (Political Institutions Database, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: in presidential systems a high degree of political cohesion is said to exist if the same party is in control of the executive and legislature; in parliamentary systems a high degree of political cohesion means a one-party majority government. See the appendix for a more detailed definition.

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. See the appendix for a more detailed definition. This variable is interacted with the dummy variable indicating that there was a change of chief executive (Political Institutions Database and CIA World Factbook for most recent years).

IMF effort is the estimated dollar cost of IMF-supported programs computed based on the IMF’s Budget Reporting System (BRS) data on hours spent by the staff on program implementation (which includes both preparation and supervision of the program) and estimated average salaries of the staff by grade. We also made use of data provided by the IMF’s Office of Personnel Management (OPM) on the dollar costs of resident representatives.

Treated as an endogenous variable in this regression.

Results

Main Findings

Program prospects depend on the domestic political economy. In particular, strong vested interests in parliament, lack of political cohesion, poor quality of bureaucracy, and ethnic divisions significantly undermine program implementation. We first estimated random-effects regressions on a pooled sample, both for linear-in-probability and tobit specifications (Table 4, column 2). The coefficient on the strength of special interests is negative and significant at the 5 percent significance level. The strong empirical evidence of the adverse role of special interests on reforms is reassuring because it comes from a sample that excludes some transition economies. The coefficients on the index of political cohesion as well as on the interaction term of the quality of bureaucracy and the change of chief executive is positive and significant. Interestingly, once we added to the regression three more political economy variables,23 which might affect the probability of successful implementation, the coefficients on ethnic fractionalization and ethnic fractionalization squared became significant.

The impact of ethnic fractionalization on program performance is nonlinear. Large and small ethnic divisions are both bad for program implementation.24 The results remain essentially the same when we re-estimate the model using the more general MIMIC specification given by equations (1.1)–(1.5) (Table 5) and when each of the implementation measures are considered in isolation (Tables 710).25

Neither incumbents’ democratic credentials nor their newness in office is associated with better program implementation (Table 4).26 The coefficients on democratic election of a leader (dummy variable) and time in power were insignificant in almost all specifications. Likelihood-ratio tests for the tobit specification confirmed that these exclusions did not substantially worsen model performance. The first result corroborates anecdotal evidence that the implementation of IMF-supported programs does not suffer in countries with authoritarian regimes.

The magnitude and even direction of impact of most reforms is ambiguous, especially at the outset, making them unpopular with policymakers and their public even as these reforms enhance welfare in the long run. This may lead to democratic administrations in developing or transition countries having a harder time than dictators marshaling the support they need to pursue successful reforms. The absence of significant correlation between a government’s length of tenure and the probability of successful program implementation is also intriguing. It suggests that one should not expect too much of new, reform-minded governments implementing IMF-supported reforms in countries with adverse political economy characteristics. Perhaps the lack of correlation also reflects public sector characteristics we have not captured.

Initial and external economic conditions do not seem to influence program implementation much once political economy variables are taken into account. The coefficients on all initial and external conditions in the random-effects regressions came out individually and jointly insignificant (Table 4).27 Initial conditions were insignificant in the IV regressions as well—to save space, we do not present these results here. The coefficients on the political economy variables do not change appreciably when the estimation excludes initial conditions (Table 4, column 3). As already mentioned, the fact that initial conditions do not affect the probability of program implementation does not necessarily imply that IMF-supported programs are optimally designed. It does, however, indicate that unfavorable initial or external conditions per se do not compromise programs’ prospects of being successfully implemented.

Variables controlled by the IMF, including financial and human effort and the breadth and depth of conditionality, do not affect program implementation once domestic political economy variables are taken into account. IMF effort was measured by the dollar cost of staff hours spent on UFR and on technical assistance tasks per program year and the loan size in relation to a country’s IMF quota. The extent of conditionality was captured by the total number of conditions per program year, the share of quantitative performance criteria waived, and the share of structural conditions in conditionality.28,29 Once their endogeneity was accounted for, IMF-related variables did not significantly affect the probability of successful program implementation (Tables 6, 810).30 The overidentifying restrictions test confirmed the validity of including additional IVs in the regressions. The Hausman test suggests that IV random-effects regressions were not much different from the simple random-effects model.

The coefficients on IMF-related variables were insignificant in many regressions when their endo-geneity was ignored (Tables 4 and 7, column 4). We note two exceptions. First, the share of quantitative performance criteria waived was, in several cases, negatively correlated with the probability of successful program implementation. This partly reflects the nature of the implementation index, which is assigned a value of zero if the condition is waived. Second, IMF effort was positively correlated (at the 10 percent significance level) with the index of completion of IMF-supported programs (Table 7). This correlation vanished when the endogeneity of these two variables was taken into account.

Illustration

It is helpful to illustrate the estimated impacts of political economy variables on the probability of program implementation. Consider the marginal effects of improved political stability, political cohesion, and the quality of bureaucracy, based on the IV regression of the no-interruption dummy (Table 8, column 1). For a country that enjoys perfect political stability and no special interests in parliament, the probability of program implementation is very high (96 percent). If political stability is only average, the chances of successful program implementation decline to 70 percent (Figure 4). If parties representing special interests occupy 20 percent of the seats in parliament, a program only has a 50–50 chance of implementation.

Figure 4Probability of Successful Implementation, Strength of Special Interests and Political Instability

Note: Probabilities are evaluated at the means of other explanatory variables.

Lack of political cohesion reduces the probability of program implementation by 50 percentage points (from 70 percent to 20 percent) when there are no special interests. If 20 percent of the seats in parliament are controlled by special interests, the probability of program implementation drops another 10 percentage points (Figure 5). The impact of a country’s bureaucracy on program implementation is also substantial. On the one hand, in the absence of special interests, the probability of program implementation increases from 50 percent when the quality of the bureaucracy is low to 74 percent when the bureaucracy is of average quality (Figure 6). If, on the other hand, special interests control 20 percent of the seats in parliament, the probability of program implementation increases from 33 percent when the quality of the bureaucracy is low to 50 percent when the bureaucracy is of average quality.

Figure 5Probability of Successful Implementation, Strength of Special Interests, and Political Cohesion

Note: Probabilities are evaluated at the means of other explanatory variables.

Figure 6Probability of Successful Implementation, Strength of Special Interests, and Quality of Bureaucracy

Note: Probabilities are evaluated at the means of other explanatory variables.

Robustness Checks and Limitations

Although our relatively small sample size makes it difficult to reach definitive conclusions, our findings appear to be robust to the specification of regressions, the choice of left-hand-side variable, and the choice of the measure of IMF effort. As already demonstrated, our main conclusions regarding the effect of political economy and IMF-related variables on program implementation are robust to the precise specification of the econometric model. Estimating random-effects models on a pooled dataset and re-estimations using the appropriate probit and tobit technique for each of our three implementation measures separately lead to similar conclusions.

Our basic conclusions also are robust to alternative specifications of IMF effort. We tried various alternatives to our primary IMF effort variable (the dollar cost of IMF hours invested in country work between the approval and actual end dates of the program). Various other measures, such as the number of missions per program year and the number of mission days per program year, yield qualitatively similar results. Even though the number of missions and mission days are positively and strongly correlated with program implementation when their endogeneity is not accounted for, this association disappears in the proper IV regressions.31 We also considered a measure of IMF effort scaled by the loan size, correcting for their strong correlation. While the IMF exerts greater effort in monitoring larger loans (as measured by staff hours per dollarlent), this does not lead to better program implementation. Finally, investing more IMF effort into program preparation, as measured by the dollar cost of staff hours and the number of missions or mission days to a country three and six months before program approval, does not affect program implementation either.

While useful, our approach is not without limitations. To begin with, the linear-in-probability specification may not be an appropriate statistical model for the irreversible-interruptions indicator. Moreover, the assumption of constant variance needed to apply the random-effects model is hard to justify in the linear-in-probability model. We believe that these drawbacks are outweighed by the substantial informational advantages from pooling the implementation indicators in one econometric model (see, for example, Lubotsky and Wittenberg, 2001). As additional political economy data become available, it should be possible to extend our dataset and provide a more thorough check of the robustness of our results.

Concluding Remarks

This paper makes a start on providing an economet-rically informed assessment of the factors influencing the implementation of IMF-supported programs. This approach fills a gap in a literature that, until recently, has evaluated the macroeconomic and structural impacts of these programs without making adequate distinctions between implemented and non-implemented programs. The paper presents a variety of (new and old) statistical indicators of program implementation and the groups of factors that could affect it, including (a) quantitative measures of the political environment in borrowing countries, (b) the conditionality and financial and human resources invested by the IMF in programs, and (c) initial economic conditions and subsequent shocks in borrowing countries. The main findings are as follows:

  • Failures in program implementation are associated with a small number of observable political indicators in borrowing countries, including the strength of special interests in parliament, lack of political cohesion in the government, ethnic fragmentation in the broader society, and the combination of political instability and an inefficient bureaucracy.
  • Indicators of the IMF’s investment of financial and human effort in programs and the depth and breadth of conditionality are not good predictors of program implementation. This is an uncomfortable conclusion, although it could be partly due to imprecise measurement of IMF inputs into programs.
  • There is no association between initial and external conditions and the probability of program implementation, indicating that program targets may incorporate realistic goals and be related effectively to a member’s initial “position.” Interestingly, and despite previous evidence to the contrary (see Killick, 1998), a member’s initial indebtedness does not affect the outcome of IMF-supported programs.

The strong empirical link between political variables in borrowing countries and the outcomes of IMF-supported programs documented in the paper suggests some changes in the way the IMF approaches the extension of its financial support. First, the IMF could take political information and constraints in borrowing countries into consideration systematically. With the adoption of new conditionality guidelines in 2002, the IMF has streamlined its conditionality and is more carefully tailoring programs to members’ circumstances. The IMF also has committed itself to changing its interactions with borrowing countries to put them in the driver’s seat in designing and implementing reforms. Second, to make systematically informed political judgments, the IMF could methodically collect the growing numbers of political indicators made available by research in quantitative political science. Such information could be used much like economic information, as one input in forward-looking quantitative assessments of program prospects and risks in individual countries. Third, the close connection between the strength of special interests and weak program implementation documented in the paper underscores the need for programs to take measures to inform and defuse resistance to reforms. These actions are described in detail elsewhere (see Boughton and Mourmouras, 2004). Related to this, the paper’s results strongly suggest that programs need to take into account more systematically than in the past the way legislatures and other key domestic players affect the implementation of reforms. While this will undoubtedly make programs more complex to design and negotiate, the additional payoff in terms of improved implementation may be well worth the extra effort.

The paper’s results are also relevant in addressing the issue of selectivity in IMF financing. How high should the IMF set the bar in approving (or continuing) programs if objective political indicators and other evidence (including prior IMF experience with failed programs) indicate that these programs would have a low probability of implementation, despite the IMF’s anticipated best efforts? In some cases, the IMF may have no choice but to stay involved, if only because broader considerations are involved. This could be the case, for instance, in some low-income countries in which donor aid, including support under the debt initiative for Heavily Indebted Poor Countries (HIPCs), is predicated on the presence of an active IMF-supported program. In other cases, however, if the probability of implementation is judged to be below some acceptable threshold, the IMF and its membership might fare better if the IMF exercised greater selectivity in providing financing.

The combination of more selectivity, streamlined conditionality, and enhanced ownership would enable the IMF to counter criticisms that it grants too many waivers or is otherwise lax in its enforcement of conditionality. This combination would also improve the quality of IMF-supported programs as signals and catalysts of private investment. The IMF also could become a better catalyst for change in borrowing countries that do not meet the threshold required to receive its assistance. Even though the IMF would not be providing loans to these countries, it would continue being active through surveillance, economic education, and technical assistance, and encouraging open debate about policy options and trade-offs. Especially useful in this regard would be dialogue with reform-oriented groups in borrowing countries, both explaining the IMF’s points of view and hearing their perspectives (see Birdsall, 2000).

Future work in this area will involve both a more systematic collection of information on IMF-supported programs and more careful econometric modeling of these programs’ impacts. The top priority is establishing on a firmer basis the relation between program implementation and macroeconomic impact. Even though this paper presented some evidence that improved program implementation was associated with strengthened economic performance, econometric research on the connection between program implementation and macroeconomic success is at an early stage. A more definitive econometric study is needed to measure the impact of improved program implementation on fiscal and balance of payments outcomes, and inflation and growth. The connection between IMF efforts in borrowing countries and program outcomes needs to be reassessed as well. The indicators of IMF effort need to be refined by, among other things, examining in greater detail how missions and staff inputs are related to specific programs and their outcomes. One would hope that the IMF’s Independent Evaluation Office would follow the example of the World Bank’s Operation Evaluations Department in collecting and analyzing information on lender efforts at the program design, negotiation, and implementation stages. Such disaggregated information on IMF efforts would permit researchers analyzing IMF-supported programs to ascertain the effectiveness with which the IMF allocates its resources in addressing the needs of borrowing countries.

Appendix. Detailed Definitions and Data Sources

Program Implementation

An interruption occurs if an SBA program review was delayed by more than three months or not completed at all; if a program review for ESAF/PRGF programs was delayed by more than six months or not completed at all; if there was an interval of more than six months between two subsequent years of a multiyear arrangement; or if at least one of the annual arrangements was not approved. Exceptions are programs that were canceled and replaced by another program, in which case noncompleted reviews and nonapproved annual arrangements are not counted as interruptions.

An irreversible interruption occurs if either the last scheduled program review was not completed (all programs) or all scheduled reviews were completed but the subsequent annual arrangement was not approved (ESAF/PRGF arrangements).

The Macroeconomic Implementation Index for a given macroeconomic performance criterion is equal to 100 percent if the macroeconomic performance criterion was met or met after modification; it is equal to 0 if the macroeconomic performance criterion was not met, not met after modification, waived, or waived after modification. The Macroeconomic Implementation Index for a program then is computed as the average of Macroeconomic Implementation Indices across all macroeconomic performance criteria for this program.

The Structural Implementation Index for a given structural condition is equal to 100 percent if the structural condition was met or met with small delay for structural benchmarks; it is equal to 50 percent if the structural condition was partially met or delayed for performance criteria; and it is equal to 0 if the structural condition was not met. The Structural Implementation Index for a program then is computed as the average of Structural Implementation Indices across all structural conditions for this program.

The Average Overall Implementation Index for a given program is the average of Macroeconomic and Structural Implementation indices over all conditions in this program.

Political Indicators

Ethnic fractionalization measures the probability that two randomly selected people in a country belong to different ethno-linguistic groups. (In regressions, this variable was scaled to range between zero and 100.) (See Easterly and Levine, 1997.)

The political instability index is computed based on the index of internal conflict provided by the ICRG on a scale from zero to 12. Higher values of the index correspond to more internal political instability. We replaced the value of this variable by its maximum score (12) if there was a change of chief executive in the course of an IMF-supported program.

The executive index of electoral competitiveness is a dummy variable that is equal to one if the executive index of electoral competitiveness is equal to seven and 0 otherwise. The executive index of electoral competitiveness is from the Database of Political Institutions of the World Bank. It ranges from one to seven, with higher values corresponding to more competitive elections.

Time in power is the number of years a chief executive has been in power by the approval year of the program. We assigned zero to this variable if there was a change of chief executive in the course of the program (Database of Political Institutions and CIA World Factbook for the most recent years).

The strength of special interests is computed as the maximum share of seats in the parliament held by parties representing special interests (Database of Political Institutions, World Bank). Four special interest groups are identified: religious, nationalistic, regional, and rural.

The index of political cohesion is defined as follows: “For presidential systems, it is zero if different parties are in control of the executive and legislature (if multiple pro-presidential parties, they must not control the legislature); it is one if the same party is in control of the executive and legislature (if there are multiple pro-presidential parties, they must together control the legislature).”

For parliamentary systems, the index is zero for a minority government, one for a coalition government with three or more parties, two for a coalition government with two parties, and three for a one-party majority government (Database of Political Institutions, World Bank).

Bureaucracy quality (ICRG) measures the quality of a country’s bureaucracy on a four-point scale. There was a change in scale for this variable, from a six-point to a four-point scale, in August 1997. We rescaled the older series to be measured on a four-point basis. We interact this variable with the dummy indicating that there was a change of chief executive (Database of Political Institutions and CIA World Factbook for the most recent years).

IMF-Related Variables

IMF effort is the dollar cost of IMF programs computed based on its Budget Reporting System (BRS) data on hours spent by staff on program implementation (it includes program preparation and supervision) and estimated average salaries of the staff by grade. Alternative measures of IMF effort were dollar costs of resident representatives (provided by the Office of Budget and Planning (OBP)), number of missions, and number of mission days (both were provided by the Policy Development and Review Department (PDR)).

Number of conditions per program year is the total number of conditions (structural and quantitative) divided by the actual duration of the program (the IMF’s Monitoring of IMF Arrangements (MONA) database).

Share of quantitative PCs waived is the number of quantitative performance criteria waived over the course of the program divided by the total number of quantitative performance criteria for this program, in percentage points (MONA).

Share of structural conditions is the number of structural conditions divided by the total number of conditions, in percentage points (MONA).

Loan size as percentage of quota is the total committed amount including augmentations divided by the country’s quota at the IMF (International Financial Statistics (IFS)).

Debt to the IMF as percentage of IMF quota is actual holdings as percentage of quota from IFS.

Program approval year from MONA.

Expected program duration is the number of years the program was scheduled to last (MONA).

IMF quota is from the IMF’s International Financial Statistics database.

Economic Conditions and Policies

Terms of trade shock is the average growth rate of dollar export prices multiplied by the initial share of exports in GDP minus average growth rate of dollar import prices multiplied by the initial share of imports in GDP over the course of the program, from IFS.

The following variables are from IFS: central government balance, GDP, reserves minus gold, CPI inflation, and imports.

The following variables are from the IMF’s World Economic Outlook (WEO) database: current account balance; initial population.

Finally, GDP per capita is from the World Bank’s World Development Indicators (WDI).

Other

The average share of bilateral aid given by the Group of Seven (G-7) to the country before the program start was computed as the average of the shares of gross official transfers that each of the G-7 countries allocated to a particular country one year prior to the approval year of the IMF program for that country (Organization for Economic Cooperation and Development (OECD)).

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1This is a revised version of IMF Working Paper 03/08. For useful comments and discussion, we would like to thank the participants in the Second Annual IMF Research Conference, Washington, November 29–30, 2001. We are especially indebted to Patrick Conway, our rapporteur at the conference; Graham Bird; Jim Boughton; Peter Clark; Charles Engel; Judy Gold; Arthur Goldberger; Mohsin Khan; Tony Killick; and Sunil Sharma. Tim Lane encouraged us to undertake this project and made invaluable comments and suggestions on earlier drafts. We alone, however, are responsible for any remaining errors.
2See IMF (2001a, 2001b), Bredenkamp and Schadler (1999), IMF (1998), and Boughton and Mourmouras (2004).
3See Mayer and Mourmouras (2002) and Drazen (2002).
4Prior actions are conditions that must be implemented before the IMF can approve or continue disbursements of its loans.
5The Monitoring of IMF Arrangements (MONA) database is maintained by the IMF’s Policy Development and Review Department. MONA was started in 1992 and is missing 18 programs approved in that year.
6The ESAF was restructured and renamed the Poverty Reduction and Growth Facility (PRGF) in 1999.
7An alternative to this approach is to construct a comprehensive indicator of implementation to reflect the extent to which programs reach their broad objectives. Dollar and Svensson (2000) used such a definition, based on the independent (but subjective) judgments of the World Bank’s Operations Evaluation Department. No such measure was available to us—the IMF’s Independent Evaluation Office (IEO) was set up only in 2001.
8Exceptions are programs that were canceled and replaced by other programs, in which case noncompleted reviews and nonap-proved annual arrangements are not counted as interruptions. The appendix explains in detail the definitions of program implementation and the political and other variables used in the econometric work.
9Changes in MONA submissions instituted in 2002 have corrected this weakness.
10The only exception was the reversible-interruption indicator, which is not significantly correlated with the structural implementation index. Since the reversible-interruption dummy captures “small” policy slippages that were subsequently corrected, we decided not to include this measure in our econometric analysis.
11Uninterrupted programs started with significantly higher inflation as measured one year before the approval year.
12The authoritative survey of the empirical literature is Haque and Khan (1998). See also Schadler and others (1995a, 1995b); Conway (1994, 1998); and Joyce (2002).
13Studying fully the relationship between success in IMF programs and improvement in macroeconomic performance requires a more elaborate econometric framework. In particular, one needs to take into account the dynamic structure of participation in IMF programs. Conway (2000) presents such a framework.
14Hence, this test is also related to the theory of veto players. See Drazen (2002) and Tsebelis (2001).
15When we included the quality of bureaucracy itself in the regression, the coefficient on that term was not statistically significant.
16For all IMF effort variables, we had to make a decision on how to attribute the data on hours/missions available by countries and months to specific programs. We used approval dates and actual end dates of programs. Recognizing that we might be losing a significant part of IMF effort invested in program preparation, we also constructed alternative measures of these variables, taking into account IMF effort in the country three and six months before program approval. Econometric results for alternative measures were essentially the same and are not reported here but are available from the authors upon request.
17Tailoring programs to members’ circumstances is a key principle underlying the IMF’s 2002 conditionality guidelines (IMF, 2002). On flexibility in the design of IMF-supported programs, see also Mussa and Savastano (1999); and Boughton and Mourmouras (2004).
18This is unfortunate, as economies in transition are good “candidates” for testing the negative impact of special interest groups on the implementation of IMF-supported programs. Rentseeking behavior and state capture in transition economies are well documented in the literature: see Hellman and Kaufmann (2001); Åslund (1999); Odling-Smee (2001); Havrylyshyn and Odling-Smee (2000); and the discussion in the conference version of this paper available at http://www.imf.org/external/pubs/ft/staffp/2001/00-00/pdf/aiwmgaam.pdf.
19The MIMIC model is a special case of covariance structure model (LISREL), which is a generalization of the factor analysis model.
20Note that since the question is narrowly focused on a set of countries that each had an IMF-supported program, there is no issue of selection bias.
21The reason for testing the hypothesis about the importance of IMF effort only in this model is that computing standard errors using the delta method with more than one endogenous variable in the MIMIC model is cumbersome.
22We estimate IV regressions on each of the implementation measures separately using Amemiya’s generalized-least-squares (GLS) IV probit/tobit estimators.
23Table 5 (column 1) presents regressions of our implementation measures on the political economy variables used by Dollar and Svensson (2000). These coefficients are insignificant, both individually and jointly.
24The turning point varies between 44 and 55 on a 0–100 scale (Tables 57). This is close to the range estimate (44–49) obtained by Dollar and Svensson (2000) in their study of World Bank programs.
25In this model, δ=1 only in the equation relating the probability of successful implementation and irreversible interruption dummy while allowing the other two δs to vary.
26It will be recalled that Dollar and Svensson (2000) concluded that the implementation of Bank-supported programs improves in countries with democratically elected governments.
27The null hypothesis that the coefficients on all of these variables are jointly insignificant could not be rejected at the 5 percent significance level. Likelihood-ratio test statistics for this test going from the tobit regression in column 2 of Table 5 to the tobit regression in column 3 of Table 5 was 6.98 with p-value equal to 0.43.
28We also tried the number of structural conditions per program year as an alternative measure of the extent of structural conditionality. We do not report the results of this estimation, as the results were essentially the same. Interestingly, the coefficient on the number of structural conditions turned negative in many cases, although still insignificant.
29IMF-related variables are included in these regressions taking into account the limitations of our small sample size. See the notes to Tables 8 through 10 for the description of the shorter IV sets used in the regressions of the implementation measures separately.
30We also included the share of prior actions and conditions for completion of review in the total number of conditions in our work. We tried two regressions, one ignoring the endogeneity of prior actions and a second one in which we instrumented for this variable. In both cases, the coefficient on prior actions was insignificant and is not reported. It appears that more careful study on prior actions is needed in order to analyze their impact on program implementation. One consideration is the lack of information on programs that are not approved or of program reviews that are not completed as a result of failing to meet prior actions. (MONA does not provide this information.) In our view, it is unlikely that this result will change even if the selection bias is properly accounted for.
31It will be recalled that this linkage was not present when IMF effort was proxied by the estimated cost of IMF-supported programs.

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