Dreher and Jensen IMF Independence Actor or Agent

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105

[Journal of Law and Economics, vol. 50 (February 2007)]

2007 by The University of Chicago. All rights reserved. 0022-2186/2007/5001-0004$10.00

Independent Actor or Agent?An Empirical Analysis of the Impact

of U.S. Interests on InternationalMonetary Fund Conditions

Axel Dreher ETH Zurich

Nathan M. Jensen Washington University in St. Louis

Abstract

In this paper, we analyze whether International Monetary Fund (IMF) condi-

tionality is exclusively designed to be in line with observable economic indicators

or whether it is partly driven by the IMFs major shareholder, the United States.

A panel data analysis of 206 letters of intent from 38 countries, submitted

during the period April 1997 through February 2003, revealed that the number

of conditions on an IMF loan depended on a borrowing countrys voting pattern

in the UN General Assembly. Closer allies of the United States (and other Group

of 7 [G7] countries) received IMF loans with fewer conditions, especially prior

to elections. These results are relevant to current public policy debates on the

role and process of setting IMF loan conditions and provide broader insight

into the influence of the United States and other G7 countries on international

institutions.

1. Introduction

There is a growing debate on the purpose, role, and impact of internationalinstitutions. Institutions of global governance, such as the United Nations andthe International Criminal Court, are struggling to find their place on the worldstage. Other international institutions designed to govern the global political

economy, such as the World Trade Organization, the World Bank, and the In-ternational Monetary Fund (IMF), have been the subject of protests and criticismby scholars, activists, and politicians.

Some of the criticism leveled at international institutions has been for theirperceived failure to solve global political and economic problems. The institution

We thank Thomas Barnebeck Andersen, Graham Bird, Andy Mertha, Andy Sobel, Finn Tarp, andthe two anonymous referees for their comments and insights. We also thank Gyung-Ho Jeong, Jong-Hee Park, and Natsuki Yamada for their valuable research assistance. Funding for data collectionwas provided by the Weidenbaum Center on the Economy, Government, and Public Policy.


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106 The Journal of LAW&ECONOMICS

that has come under some of the most intense fire is the IMF.1 In the wake of

the Asian financial crisis, scholars from both outside and inside the IMF issued

scathing criticisms of both the organizations inability to help avoid financial

crisis and its overly draconian policy prescriptions. Critics viewed the IMF as

either too passive or too active in reacting to the crisis. Others attributed part

of the blame to the IMFs major shareholders, specifically the United States. This

is the main focus of our paper.

We argue that the IMF is responsive to domestic politics in the country

receiving its loan. The literature on political business cycles shows that politicians

have the incentives (and usually the means) to expand monetary and fiscal policy

in the period prior to elections. We believe that, during these periods, U.S.

influence on IMF policy would be the most obvious. For countries that are not

strongly allied with the United States, the IMF would restrain fiscal and monetarypolicy expansion by setting tight conditions on loans. For countries that are

allied with the United States, the IMF would be more lenient, rewarding incum-

bent politicians with loose conditions and the opportunity to manipulate the

economy for electoral gain.

In this paper, we focus on how the IMF sets conditions for borrower countries.

In an empirical analysis of 38 countries during the period April 1997 through

February 2003, we found that political factorsnamely, the borrowers rela-

tionship with the United States (and the other Group of 7 [G7] countries)

were important determinants of the number of conditions that the IMF imposed.

The paper proceeds as follows. We start by discussing our theory of the

relationship between donor interest and IMF conditions and postulating our

hypotheses. We then present our data, methods, and results. The final sectionsummarizes our conclusions.

2. Theory

With the introduction of structural adjustment loans in 1986, IMF conditions

became more numerous and intrusive,2 and the conception of the IMF as an

agent of the most powerful stakeholders strengthened in the popular press. The

IMF is seen as an agent of U.S. foreign policy, promoting the interests of the

1 For some of the harshest criticism on the link between International Monetary Fund (IMF)agreements and lower levels of gross domestic product growth, see Przeworski and Vreeland (2000),Vreeland (2003), and Dreher (2006). For a review of the recent literature, see Stone (2002) and the

discussion between Meltzer (2006) and Krueger (2006). Jensen (2004, 2006) has found that IMFloans have a negative impact on the inflow of foreign direct investment, and Boockmann and Dreher(2003) have shown that neither IMF credits nor IMF conditionality promote economic freedom increditor countries.

2 The development of IMF conditionality and the specific conditions involved are summarized inDreher (2002). Dreher and Vaubel (2004a) also document the increase in the number of conditions.Dreher (2004a) provides a public choice perspective on the development of IMF (and World Bank)conditionality. See also Gould (2003).


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International Monetary Fund Conditions 107

United States behind the veil of an international institution (Crane 2000, p. D2). 3

This power is exercised through a system of weighted voting based on the size

of a countrys capital contribution, in contrast to the one-country, one-vote

system used by the UN General Assembly. With a voting share greater than 17

percent, the United States is able to veto all important decisions. Even official

UN documents lament this aspect of the institutional framework of the IMF

(United Nations 2002, p. 112).

A number of academic works have stressed the disproportionate influence of

U.S. foreign policy on international organizations. Vaubel, Dreher, and Soylu

(2005) have shown that the staff of international organizations expands if the

financing share of the largest contributor (usually the United States) declines

and if the ideological orientation of the U.S. president shifts to the left.

Gould (2003) has claimed that IMF conditions are partly driven by privatebanks attaching their loans to those of the IMF. Others assert that the U.S.

government drives IMF policies (Frey 1997, p. 121; Goldstein 2000, p. 67). Oatley

(2003) presents the examples of the United States pressing the IMF to extend

credits to Argentina during the 1980s and to Mexico in 1982 and 1985. 4 The

U.S. Congress even passed several legislative mandates instructing the American

executive director to enforce American interests (General Accounting Office

2001). It has been stated that no managing director . . . can make a major

decision without clearance from the U.S. (Swedberg 1986, p. 379).

Thacker (1999) and Barro and Lee (2005) report that access to IMF programs

is skewed toward countries supportive of U.S. foreign policy. Oatley (2003) has

found that closer allies of the United States have received larger loans (especially

after the end of the Cold War). The empirical analysis of Dreher and Sturm

(2005) showed that, after receiving an IMF loan, countries are more likely to

vote with the United States (and other G7 countries) in the UN General As-

sembly.5 Dreher, Sturm, and Vreeland (2006) report temporary members of the

UN Security Council to receive more IMF programs. According to these results,

the United States uses its influence in the IMF to enforce its own political agenda.

In order to further test this proposition, we analyzed whether political relations

with the United States influence IMF conditionality.

Other scholars have argued that the IMF can behave as an independent actor

in the international system. In this view, changes in an institution would reflect

3 For an interesting discussion of leadership selection in the IMF, see Kahler (2001). Vaubel (2006)provides an excellent survey on principal-agent problems in international organizations.

4 Other examples are credits granted to Russia in 1992 and 1996, when the U.S. government exerted

strong pressure on the IMF to lend despite missed targets (Goricki 1999, p. 223), and Pakistanreceiving low-conditionality credits from the IMF after joining the recent U.S.-led alliance againstAfghanistan.

5 Dreher and Sturm (2005) also tested whether bilateral aid is robustly related to voting in theUN General Assembly. According to their results, no robust relationship exists. However, with regardto the UN Security Council, Kuziemko and Werker (2006) showed that rotating members receivemore aid from the United States during their tenure than before, especially during years with keydiplomatic events.


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the institutions drive for greater power (Vaubel 1996). International institutionsmay be created by nation-states, but, once they are built, they develop their own

sets of preferences. As an example of this dynamic, Vaubel (1991) showed thatthe IMF tries to obtain quota increases by hurry-up lending at the time ofregular quota reviews. Also, the growth of IMF staff does not seem to be relatedto the need for balance-of-payments credits, as defined by the IMF, but seemsto grow because a larger staff is in the bureaucracys own interest (Vaubel 1996).

We argue that examination of the functions of the IMFspecifically, how theIMF sets conditionscan help answer the following question: does the IMF setconditions on the basis of economic fundamentals, or does the IMF set policiesin accordance with the interests of its principle stakeholders, particularly theUnited States? We believe that answering this question is important for under-

standing the role and impact of the IMF, as well as for intellectual debates onthe role of international institutions in the global economy.The purpose of this paper is not a comprehensive test of the competing theories

on the role, functions, and operations of international institutions. Rather, wewant to situate the debate on the functioning of the IMF within the literatureon international institutions. As an institution, does the IMF function to solvefinancial crises, reacting to domestic economic conditions, policy, or humani-tarian demands? Many studies have tried to answer these questions by focusingon large-sample analyses of IMF program conclusions or of the amounts ofcredit drawn. These studies have found that IMF lending is influenced by a

borrower countrys debt service, its international reserves, and its economicgrowth and that political variables, such as government stability, the quality ofbureaucracy, and the extent of political opposition, are robust predictors of IMF

lending.6

Since amount of credit is only one of the IMFs two major policy instruments,similar patterns may prevail with respect to its other instrument, conditionality.We therefore theorize that the IMF, functioning as the lender of last resort tocountries in financial crisis and acting as an international organization designedto solve problems associated with financial crises, will set the number of con-ditions on loans according to observable economic indicators in the borrower

country.7 Thus, we set our first hypothesis as follows:

Hypothesis 1. The IMF will set conditions on the basis of domestic economicconditions, including the growth rate of real gross domestic product (GDP), the

6 Sturm, Berger, and de Haan (2005) provide an overview of the more recent literature on thistopic. See also Dreher (2004b) and Dreher and Vaubel (2004b).

7

Clearly, the IMF might also consider domestic political conditions when the number of conditionsto be included in a program is decided. To the extent that, for example, commitment to reformimplies fewer conditions, our results might be biased by this omission. However, Martin and Bargawi(2005) report little difference in the number of conditions included in programs with good performersversus those with bad performers. Sturm, Berger, and de Haan (2005) showed that IMF lending isdetermined mainly by economic variables. Dreher (2003) found that the only domestic politicalvariable related to IMF program interruptions refers to national elections. We employed this variablein our empirical analysis below.


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governments consumption, the budget deficit, the rate of monetary expansion,and the current account balance.

Alternatively, the IMF may set conditions on the basis of political relationshipsbetween the largest actor in the international system (namely, the United States)and the recipient country. Countries with closer alliances to the United Stateswill be rewarded IMF loans with looser conditions, whereas the IMF will imposestricter conditions on nonallied countries.

Hypothesis 2. The IMF will specify fewer conditions for countries that areclosely allied with the United States.

A second set of hypotheses takes into account the incentives of political leaders

in democracies to engage in political business cycles prior to elections. Incumbent

politicians attempt to manipulate economic policy to change expectations offuture economic performance, increasing their probability of reelection.8 Weargue that insights from the literature on political business cycles with regardto incentives for fiscal and monetary policy expansion prior to elections haveimportant implications for IMF conditionality. International Monetary Fundconditions usually prescribe austerity measures that conflict with incumbentsincentives for fiscal and monetary policy expansion. Politicians have an incentiveto inflate the economy, while the IMF is attempting to tighten monetary andfiscal policy, which results in short-term contraction of the economy. On theother hand, IMF money can be used to finance the desired expansion (Vaubel

1991).According to results reported by Dreher (2005), participation in IMF Standby

and Extended Fund Facility arrangements does improve economic policy. How-ever, several empirical studies have shown that the pattern of IMF involvementis different at election times: the conclusion of IMF arrangements is significantlyless likely immediately prior to elections (Dreher 2004b), although net creditsare significantly larger (Dreher and Vaubel 2004b). Moreover, breakdowns ofIMF programs are more likely at those times (Dreher 2003). Program conclusionis more likely after elections (Przeworski and Vreeland 2000; Vreeland 2003). Ithas even been shown that the IMF can help incumbents win elections (Dreher

2004b) or stay in power (Smith and Vreeland 2003).We believe that different incentives in the period prior to elections also provide

some insights into the functioning of IMF conditionality. If the IMF is attemptingto limit the impact of financial crisis and adverse policy, its conditions wouldbe expected to become stricter in the periods prior to democratic elections.

Politicians have an incentive to expand the economy during this period, and theIMF will specify a more complete contract to limit the opportunities for ex-pansion. There is a second reason why more conditions prior to elections wouldbe expected. Given the uncertain outcome of an election, the IMF may well want

8 The classic works are Nordhaus (1975) and Tufte (1978). See Alesina, Roubini, and Cohen (1999)and Franzese (2002) for an excellent overview of the literature.


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to bind whoever will come to power. Although the IMF might know the interestsof the incumbent and, therefore, might have less need to make those terms

explicit that it assumes the incumbent will follow anyway, no such implicitcontract or understanding would exist with the newly elected government. Sincenew political leaders often claim a mandate for change immediately followingelections, the IMF might want to specify a more complete contract in order toguard against reforms not in its interest.

Hypothesis 3. The IMF will set more conditions during the period priorto democratic elections.

An alternative hypothesis is that, if the IMF reflects the power of the hegemoniccountry (namely, the United States), it will act strategically during the period

prior to democratic elections. For governments that are not allied with the UnitedStates, the IMF will impose strict conditions. For governments closely allied withthe United States, the IMF will impose looser conditions in order to allow theincumbent to have some degree of discretion over monetary and fiscal policyauthority. In short, the IMF will be careful not to threaten the political survivalof incumbents closely allied with the United States.

Hypothesis 4. For countries closely allied with the United States, the IMFwill set fewer conditions during the period prior to democratic elections.

3. Data and Methods

Since it is difficult to objectively measure and compare the intrusiveness andstringency of particular conditions, we set the dependent variable for our em-

pirical analysis as the number of IMF conditions. The number of conditions hasbeen the focus of heated debate. For example, in 1999 the U.S. Congress threat-ened to refuse ratification of a quota increase if the IMF did not reduce thestringency and number of its policy conditions.

The number of conditions has been used as a proxy for stringency in previousstudies. Mosley (1991) used this measure to study the tightness of World Bankstructural adjustment loans. Gould (2003), Dreher (2004a), and Dreher and

Vaubel (2004a) used the number of IMF conditions to analyze the determinantsand causes of conditionality. Ivanova et al. (2005) used them to measure theextent of conditionality. The IMF (2001) also has used similar data in empiricalanalyses. Our data on the number of IMF conditions are from a report by Dreherand Vaubel (2004a), who analyzed 206 IMF letters of intent from 38 countries,between April 1997 and February 2003. However, after inclusion of the relevantcovariates for our regression analyses, the sample was reduced to a maximumof 139 letters of intent from 29 countries.9

9 The following countries and number of letters of intent (in parentheses) were included in thestudy (countries shown in italics were included in the sample but were not included in the regressionanalyses): Albania (5), Argentina (3), Armenia (5), Azerbaijan (3), Belarus(1), Benin (5), Bolivia


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A typical loan agreement includes very detailed descriptions of the policies

that the borrowing government promises to implement over the time of the

arrangement. For older IMF programs, whether these statements were subject

to IMF evaluation or were included by the borrowing government to express

its policy objectives is very difficult to judge; most recent arrangements have

provided tables classifying conditions into performance criteria and structural

benchmarks. Information on prior actions, however, is not always available to

the public. For those cases in which it was not obvious whether prior actions

were not included in a program or prior actions were not attached to the loan

agreement, the specific country period was omitted from the disaggregated anal-

ysis, and the number of prior actions was set to zero in the analysis of the total

number of conditions. Since an objective method of weighting the different types

of conditions was not possible, we used the unweighted sum. However, we alsoprovide a separate analysis for each type of condition.

Of the countries included in our sample, 23 received loans under the IMFs

Standby Arrangement and Extended Fund Facility and 18 countries received

Poverty Reduction and Growth Facility (PRGF) loans. For each country, all letters

of intent that were publicly available were analyzed, starting with the first letter

posted on the IMFs Web page in October 1997. The subsequent data are quarterly

and refer to periods when an IMF arrangement was active.10

Whereas the total number of conditions of each type could be counted ob-

jectively, the classification of conditions according to category was sometimes

critical. Clearly, a ceiling on monetary growth is a monetary condition, whereas

a ceiling on government expenditure is a fiscal condition. However, the classi-

fication of some conditions was less obvious. In those critical cases, the conditions

were not classified as monetary or public sector (thus, they were included onlyin the analysis of the total number of conditions). In Table 1, we summarize

the number of conditions by type and sector. As can be seen, the country

programs analyzed included more than 22 conditions on average.

To empirically estimate how relations between the United States and the coun-

try signing an IMF agreement affect conditionality, we included a variable on

voting in the UN General Assembly.11 We operationalized our variable Voting

with United States as the percentage of UN General Assembly votes in which

the recipient country and the United States either both voted yes or both voted

no on a given issue.12 Since some quarters had more UN votes than others,

(4), Brazil (6), Bulgaria (3), Burkina Faso (8), Cambodia (3), Cameroon (3), Cape Verde (2), CentralAfrican Republic (3), Colombia (5), Croatia (5), Estonia (3), Georgia (3), Ghana (4), Guinea(5),

Indonesia (11), Jordan (4), Kazakhstan(1), Korea (7), Latvia (4), Mali (5), Moldova (2), Nicaragua(5),Pakistan(1), Panama (8), Peru (6), Russia(2), Rwanda (6),Sao Tome and Principe(5), Uganda(5), Uruguay (6), and Zimbabwe(1).

10 Notice that, since all countries in our sample were under active programs, we did not havesample selection bias.

11 UN General Assembly voting records are publicly available through the official UN Web site(http://unbisnet.un.org).

12 Abstentions and absences were omitted.


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Table 1

International Monetary Fund Conditionality: Summary Statistics

Condition Mean Median Minimum Maximum SD

All 22.22 18.5 5 102 14.95Performance criteria 8.96 8 3 21 3.09Structural benchmarks 10.70 7 0 94 14.57Prior actions 2.56 0 0 39 4.70Monetary sector:

Performance criteria 2.78 3 0 12 1.47Structural benchmarks 4.11 1 0 75 9.90Prior actions .39 0 0 9 1.04

Total 7.28 5 0 79 10.31Public sector:

Performance criteria 2.15 2 0 10 1.89Structural benchmarks 2.46 2 0 20 3.15

Prior actions 1.02 0 0 15 2.09Total 5.63 5 0 23 4.31

Source. Dreher and Vaubel (2004a).Note. Statistics are based on 206 letters of intent from 38 countries between April 1997 and February2003.

we smoothed the time series by using a quarterly moving average. To test forthe robustness of our results, we also changed the construction of the UN votingvariable, starting with a zero value for each new government. The estimatedresults were qualitatively similar to the results reported below.

We also included a dummy variable for democratic elections within the next6 months. We coded cases of legislative and presidential elections from a numberof sources. In the empirical analysis, we used the dummy variable Election, which

included legislative and presidential elections.13 Our estimation sample included15 elections.14

We used a number of economic control variables. Most of these control var-iables are from the IMFs International Financial Statistics indicators. All ad-ditional variables, with their means and standard deviations and their precisedefinitions and data sources, are listed in the Appendix.

Our estimates were based on pooled time-series, cross-sectional regression

analyses. Since our data were strongly skewed to the right, we estimated themodel by using Poisson regression analysis. However, the data displayed signsof overdispersion, and the relevant tests revealed that not all our dependentvariables followed the Poisson distribution. We therefore replicated all regressionsby using ordinary least squares and negative binomial regressions. The basicresults, however, were robust to the method of estimation.

13 We also tested legislative and presidential elections independently. Our empirical results remainedunchanged.

14 Over the sample period, there was one election in Albania, Armenia, Benin, the Central AfricanRepublic, Colombia, Estonia, Ghana, Latvia, Mali, Moldova, Nicaragua, Uganda, and Uruguay. Therewere two elections in Peru. On average, voting coincidence was slightly higher for countries withelections (.26, compared to .25).


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Since some (quarterly) data were not available for all countries or periods,the panel data were unbalanced, and the number of observations depended on

the choice of explanatory variables. To account for time-invariant unobservableheterogeneity that potentially correlated with the regressors, we used countrydummies. We also included a dummy for each quarter of a year (fixed timeeffects).15 All quantitative variables were set to lag by 1 quarter, to avoid simul-taneity.

4. Results

Column 1 of Table 2 replicates the analysis of Dreher and Vaubel (2004a). 16

The IMF programs included significantly more conditions when the borrowers

real GDP was low and the real per capita GDP growth in Organisation forEconomic Co-operation and Development countries was high. If the IMF staffis interested in enforcing as many conditions as possible, they negotiate more-stringent programs with countries in a weak bargaining position. The possibilityto enforce its own agenda in negotiations with the IMF is weaker when a gov-ernment is in more need of IMF loans. Moreover, a countrys power to negotiateis influenced by other countries willingness to support the potential borrower(Bird and Rowlands 2003). Both a countrys own (direct) influence on the IMFand the support from other countries rise with its GDP, since countries with ahigher GDP are more important in the world economy. Moreover, their quota

with the IMF is higher, which results in a higher level of voting rights. Thus,countries with a lower GDP must accept more conditions. The IMF staff mayenforce more conditions during recessions. However, the staff also might be

inclined to lend more freely, since they feel that external circumstances, notdomestic misgovernment, has led a country into crisis. The IMF might evendeliberately vary its conditionality countercyclically. The latter effects were foundto dominate in our analysis.

The London Interbank Offer Rate (LIBOR) on 3-month credits to U.S. banksincreased the number of conditions, since the interest rate subsidy provided by

15 The Hausman test rejects the random-effects specification in favor of fixed effects at the 1 percentlevel of significance. Clearly, within-groups variation is small, compared to between-groups variation(with a standard deviation of .01, versus .1). The time dummies were significant at the 1 percentlevel. We had to omit two time dummies, to identify the variables OECD (Organisation for EconomicCo-operation and Development) Growth Rate and LIBOR (London Interbank Offer Rate). The initialregressions included a time trend. However, the trend term was shown to be completely insignificant;thus, we do not report the results below. The other results were unchanged by the inclusion of thetrend variable.

16

We omitted variables related to the World Bank, since they might be endogenous to (the numberof) IMF conditions. Similar covariates have been used by Dreher (2004a). The exceptions are realgross domestic product growth, LIBOR, the governments budget deficit, and changes in internationalreserves. In addition, Dreher used principal arrears, U.S. military grants and loans, public and publiclyguaranteed bilateral and commercial debt, an index measuring democracy, and an index measuringeconomic freedom. We did not employ these variables in our analysis, since they are not availableon a quarterly basis. The same is true for other domestic political variables, such as governmenteffectiveness or quality of bureaucracy.


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Table 2

Total Number of International Monetary Fund (IMF) Conditions

Variable (1) (2) (3) (4) (5) (6)

Voting with United States (t 1) 23.74** (4.30) 8.62* (2.34) 26.86** (4.76) 9.75** (2.61) 26.50** (Election within next 6 months .44** (2.97) .15 (1.80) .19 Voting with United States # election

variable .9 2 Real GDP (t 1) .03** (4.19) .02** (3.84) .01** (3.00) .02** (3.70) .01** (2.93) .02** (Real GDP growth (t 1) .004 (.60) .003 (.40) .001 (.13) .001 Real per capita GDP growth in OECD

countries (t 1) .59** (5.07) .58** (4.95) .50** (4.92) .60** (5.15) .48** (4.72) .60** (LIBOR (t 1) .12* (2.06) .22** (3.39) .13** (2.85) .24** (3.71) .14** (3.07) .23** (Government consumption

(% GDP; t 1) .0 1 ( .3 7) .04 (1.12) .04 (1.17) .04 (Government budget deficit

(% GDP; t

1)

.0 1 ( .9 3)

.002 (.35)

.01 (1.00)

.0 1 Monetary expansion (%; t 1) .01** (4.53) .01** (4.08) .01** (4.80) .01** (3.53) .01** (4.96) .01** (Change in international reserves (t 1) .002 (.69) .003 (.98) .002 (.96) .002 Current account balance (% GDP; t 1) .004 (.50) .01 (1.18) .01 (1.27) .01 (New net IMF credit (% quota; t 1) .001 (1.71) .001 (1.57) .001 (1.94) .001 (Log likelihood 254.25 244.93 398.01 240.26 396.35 240.11Number of countries 19 19 29 19 29 19 Number of observations 92 92 139 92 139 92

Note. Values are based on a Poisson regression analysis of quarterly panel data, April 1997 to February 2003. Fixed country and time dummy vz-statistics are in parentheses. GDP p gross domestic product; OECDp Organisation for Economic Co-operation and Development; LIBORp LRate.Significant at the 10% level.*Significant at the 5% level.**Significant at the 1% level.


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the IMF was higher when world interest rates were higher and demand for IMFcredits rose. The coefficient for LIBOR is significant at the 5 percent level. At

the 10 percent level of significance, net IMF credits disbursed reduced the numberof conditions. This stands in contrast to the IMFs claim that higher loan ratescoincide with tougher conditionality.

Finally, the results show that a high rate of monetary expansion leads tosignificantly more conditions, which would be expected from a normative per-spective.17 Table 2 also shows that a countrys real GDP growth, governmentconsumption, the governments budget deficit, the change in international re-serves, and the current account balance do not significantly influence the numberof conditions.

The results in the following columns of Table 2 reflect variables added to

directly test our hypotheses. We report the results of two regressions for eachspecification. The first included all the variables in column 1, and the secondused only those variables that were significant at the 5 percent level (at least) inthis regression. This increased our number of observations from 92 to 139.

The results in columns 2 and 3 include the variable measuring voting com-pliance in the UN General Assembly (with a lag of 1 quarter). At the 1 percentand 5 percent levels of significance, respectively, the number of conditions waslower for closer allies of the United States. According to estimates for the largersample, reported in column 3, an increase in the voting index from zero to onereduced the number of conditions by almost nine. An increase in the voting

index from the 10th to the 90th percentile reduced the number of conditionsby approximately two. This provides strong evidence in favor of hypothesis 2.Thus, our results for IMF lending are similar to those of Thacker (1999) and

Oatley (2003), reported above. Our findings are in contrast to those of Gould(2003), who reported that the United States has not driven changes in IMFpolicy.18

For the results in columns 4 and 5, we included a dummy variable for electionswithin the next 6 months, and the results in columns 6 and 7 included theinteraction of the election variable with the voting variable. Most important forour analysis, the coefficient for the voting variable remained significant (at the

5 percent level at least) in all regressions. When the interaction term was excluded,the election variable had a negative and significant effect on the number ofconditions. This result contradicts hypothesis 3. One possible explanation isprovided by Dreher (2003), whose results show that, in democratic countries,fewer IMF programs break down prior to elections. He attributes this to a general

17

Note that this does not seem to reflect the impact of high inflation but that of bad economicpolicy. When the rate of inflation is included, instead of (or in addition to) monetary expansion,its coefficient is insignificant.

18 Using the variable employed by Gould (2003) to proxy U.S. interest in a countrythat is, U.S.loans and grantsDreher (2004a) also found no influence. However, U.S. loans and grants areprobably an inferior proxy, since the United States often tries to press the IMF to lend to exactlythose allies that the United States cannot, for political reasons, lend to by itself (Dreher and Sturm2005).


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tendency of IMF staff to take into account the incumbent politicians concerns

at election time. After all, the number and stringency of conditions are the

outcome of a bargaining process, and the IMF, which is eager to lend, is probably

prepared to endorse fewer conditions if it feels that this is necessary in order to

reach an agreement. Another possible explanation is that IMF participation is a

joint decision between the IMF and the borrower. Prior to democratic elections,

countries only sign IMF agreements with loose conditions and refuse loans with

tighter conditions. Finally, softer conditionality in programs for closer allies of

the United States might dominate the tougher conditions for non-allies if no

interaction term is included in the regression. With the inclusion of the inter-

action term (columns 6 and 7), the coefficient for the election variable becomes

individually insignificant. In any case, owing to the small sample and few number

of elections, the results are based on only 15 elections.In the larger sample of column 7, the interaction term was significant at the

10 percent level and showed the expected outcome: prior to elections, programs

included fewer conditions the more often a country voted in line with the United

States in the UN General Assembly. For the results in columns 6 and 7, the

voting and election variables were both significant at the 1 percent level and

reflected the outcomes implied by our political hypotheses. According to the

estimates in column 7, an increase in the voting index from zero to one directly

reduced the number of conditions by 9.56 and by an additional 1.47 when the

election effect was included. An increase in the voting index from the 10th to

the 90th percentile reduced the number of conditions before elections by 2.5.

As the coefficient of the dummy variable shows, IMF programs include .21 more

conditions during election periods.19

Table 3 shows how IMF policies varied by sector. Voting with the United Statespredominantly affected conditions in the public sector. For all three specifica-

tions, the voting variable was significant at the 1 percent level, whereas it was

not a significant determinant of conditions in the monetary sector. Again, the

evidence supports the election hypothesis. When both the election variable and

the interaction term were included, closer allies of the United States had to accept

significantly fewer monetary and public sector conditions prior to elections.

According to the coefficients of column 6, an increase in voting coincidence by

10 percentage points reduced the number of public sector conditions by almost

three. Prior to elections, the same increase in voting compliance further reduced

the number of conditions by .3. An increase in the voting index from the 10th

to the 90th percentile reduced the number of conditions before elections by

about seven. As the results in column 3 show, countries not allied with theUnited States had to accept more conditions in the monetary sector prior to

elections.

19 Note that the coefficient of the voting variable for the larger sample was substantially smallerthan that for the smaller sample. This does not reflect the impact of the additional control variablesbut the difference in countries included.


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Table 3

Total Number of International Monetary Fund Conditions, by Sector

Variable

Monetary Conditions Public Sector Cond

(1) (2) (3) (4) (5)

Voting with United States (t 1) 3.11 (.44) 2.17 (.30) 1.02 (.14) 28.88** (3.53) 29.52** (3.5Election within next 6 months .13 (.78) .94* (1.99) .07 (.4Voting with United States #

election variable 3.23 (1.81) Real GDP (t 1) .01 (1.77) .01 (1.78) .01 (1.79) .02 (1.25) .02 (1.2Real per capita GDP growth

in OECD countries (t 1) .61** (2.69) .63** (2.77) .65** (2.85) .28 (1.53) .28 (1.4LIBOR (t 1) .16 (1.58) .15 (1.46) .14 (1.34) .06 (.69) .07 (.7Monetary expansion (%; t 1) .01* (2.50) .01 (1.08) .01* (2.54) .01* (1.97) .01* (2.0Log likelihood 224.41 146.94 222.71 232.09 231.98

Note. Values are based on a Poisson regression analysis of quarterly panel data, April 1997 to February 2003. Fixed country and timeincluded; z-statistics are in parentheses. For each column, the number of countries is 29, and the number of observations is 139. Gproduct; OECD p Organisation for Economic Co-operation and Development; LIBORp London Interbank Offer Rate.Significant at the 10% level.*Significant at the 5% level.**Significant at the 1% level.


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Table 4

Total Number of International Monetary Fund Conditions, by Type of Condition

VariablePerformance

CriteriaStructural

BenchmarksPrior

Actions

Voting with United States (t 1) 1.05 (.16) 14.96 (1.63) 52.37* (2.25)Election within next 6 months .39 (1.25) 2.26 (1.91) 1.24 (1.59)Voting with United States #

election variable 1.51 (1.19) 11.52* (2.45) 2.72 (.78)Real GDP (t 1) .001 (.21) .06** (4.46) .08 (1.45)Real per capita GDP growth

in OECD countries (t 1) .09 (.59) 1.35** (6.16) .0002 (.00)LIBOR (t 1) .03 (.47) .15 (1.66) .61** (2.86)Monetary expansion (%; t 1) .0001 (.05) .01** (3.64) .01 (1.05)Log likelihood 216.22 218.84 124.16Number of countries 29 29 20

Number of observations 139 139 85

Note. Values are based on a Poisson regression analysis of quarterly panel data, April 1997 to February2003. Fixed country and time dummy variables are included;z-statistics are in parentheses. GDP p grossdomestic product; OECDpOrganisation for Economic Co-operation and Development; LIBORpLondonInterbank Offer Rate.Significant at the 10% level.*Significant at the 5% level.**Significant at the 1% level.

Table 4 shows disaggregated results by type of condition. However, we reportresults only for the larger sample, with all voting and election variables included.

Performance criteria were not influenced by either UN General Assembly votesor elections. The effects of our political variables were confined to structuralbenchmarks and prior actions. Since some performance criteria were included

in almost all programs, the results for these conditions were less variable whencompared with those for structural benchmarks and prior actions. Thus, theomission of typical performance criteria is more difficult to explain. However,an analysis of whether performance criteria are less demanding for allies of theUnited States would be interesting. Unfortunately, we lacked the data for suchan analysis.

As the results in Table 4 show, the number of structural benchmarks during

election periods was significantly higher for countries not voting in line withthe United States in the UN General Assembly and lower for closer allies of theUnited States. Programs included significantly more structural benchmarks priorto elections and fewer structural benchmarks and prior actions for closer U.S.allies. The next section further expands on these results.

5. Further Tests

In the regressions reported above, the UN voting variable lagged by 1 quarter.To test for robustness, we replicated all regressions with a contemporaneous

voting variable, and the results were basically unchanged. We also included UNvoting behavior up to 3 quarters into the future, to test whether countries demand


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loans with low conditionality and then reward the United States with votingcompliance after these agreements have been negotiated. These leads, however,

were individually and jointly insignificant, indicating a lack of support for thisvariant of the tests (results not reported in tables).20

We could argue that fixed country effects eliminated cross-country informationthat might be an important source of information about the relation betweenUN voting patterns and conditionality. We replicated our analysis with the fixedcountry effects excluded and found the coefficient for the UN voting variableto be insignificant (results not reported in tables). This result was similar to theresults reported by Thacker (1999), which indicates that the absolute positionof countries did not affect their relationship with the IMF as much as the changein those positions.

Finally, we questioned whether the results for the United States could bereplicated for other major shareholders of the IMF. The United States is con-sidered to be the most important source of external pressure on the IMF, andmost of the literature has focused on this relationship. Nevertheless, some studieshave included data for other G7 countries as well.21 Thus, Table 5 replicates ourfull specification for all other G7 countries. The final column of Table 5 testedwhether the inclusion of variables controlling for voting with these countriesaffected our results for the United States.

As shown in columns 16 of Table 5, the results for the United States heldfor the other G7 countries as well. In general, closer allies of all G7 countries

were given significantly fewer conditions, particularly at election time. The elec-tion dummy variable was significant at the 10 percent level in three regressions,with the expected sign. For all six of the additional countries, the voting variable

was significant at the 1 percent level. The coefficients were also quantitativelysimilar. With all else remaining constant, voting with Japan resulted in thesmallest discount in the number of conditions, whereas voting with Canadaresulted in the highest discount: thus, the reduction in the number of IMFconditions was between 5.12 (Japan) and 6.20 (Canada) after an increase invoting compliance from zero to one. An increase in the voting index from the10th to the 90th percentile amounted to a change in conditions from 1.1 to 1.4.

Given the high correlation in voting behavior between the United States and theother G7 countries (correlation of .74.82), these results were not a surprise.The G7 countries frequently vote together in the UN General Assembly (seeDreher and Sturm 2005). Column 7 of Table 5 therefore includes results withall voting variables at the same time, despite the high correlation among them.However, the interaction between the voting variable and the election variable

20 In the specification of Table 2, column 7, the coefficient for the first (second, third) lead is16.11 (8.83, 2.09).

21 For example, Barro and Lee (2005) employed data for the United States, France, Germany, andthe United Kingdom to construct their voting variable. Dreher and Sturm (2005) used data for allGroup of 7 countries to examine the impact of IMF and World Bank loans on voting patterns inthe UN General Assembly.


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was only included for the United States, since the analysis did not include enoughelection periods to reliably identify seven interaction terms.

The results in column 7 show that, when all voting variables were includedjointly, three of them remained significant at conventional levels. Voting withthe United States and Germany still reduced the number of conditions includedin IMF programs, whereas voting with the United Kingdom increased the numberof conditions. Both the election variables and the interaction of the election termwith the U.S. voting variable were completely insignificant. After controlling foralso voting with the other G7 countries, the impact of voting with the UnitedStates slightly increased. Countries that switched from complete noncomplianceto full compliance obtained IMF programs with a discount of more than 13conditions. However, given the high correlation among the voting variables, the

results of the final regression have to be interpreted with caution.

6. Summary

International institutions, such as the IMF, play an important role in thefunctioning of the global economy and, in some cases, have an enormous impacton nation-states. Understanding how these international institutions functionhas important academic and public policy ramifications.

In this paper, we analyzed whether IMF conditionality is driven by its majorshareholder, the United States. Our empirical results revealed that the numberof conditions depended on a borrowing countrys voting pattern in the UNGeneral Assembly. Countries that voted with the United States in the UN GeneralAssembly systematically received IMF loans with fewer conditions.

Our empirical results for the relationship between IMF programs and dem-

ocratic elections were equally straightforward. We found evidence that countriesreceive fewer conditions prior to elections when they are closer allies of theUnited States.

These results show that the IMF does not function simply as an institutionof multilateralism. Although domestic economic conditions are an importantdeterminant of the number of IMF conditions that a country faces, the UnitedStates and the other G7 countries remain dominant players in influencing IMFpolicy.


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Appendix

Table A1

Descriptive Statistics and Data Sources

Variable Data Source Mean SD

Voting in the United Nations http://unbisnet.un.org .36 .13Dummy for elections Journal of Democracy

(19972004); Day (2002);http://www. electionworld.org .08 .27

Real GDP ($billions) IMF (2003) 18.74 53.83GDP growth rate IMF (2003) 1.65 12.00Real per capita GDP growth in

OECD countries OECD (2003) .61 .38LIBOR IMF (2003) 4.53 1.84

Government consumption (%GDP) IMF (2003) 15.18 5.60

Government budget deficit (%GDP) IMF (2003) 11.72 148.84

Monetary expansion (%) IMF (2003) 19.81 27.54Change in international reserves

(%) IMF (2003) 3.85 20.76Current account balance (% GDP) IMF (2003) 45.37 599.82Change in IMF liabilities (% quota) IMF (2003) .39 55.51

Note. GDPp gross domestic product; IMFp International Monetary Fund; OECD p Organisation forEconomic Co-operation and Development; LIBORp London Interbank Offer Rate.

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