Abstract

This paper aims to assess the economic impact of the IMF’s support through its facilities for low-income countries. It relies on two complementary econometric analyses: the first investigates the longer-term impact of IMF engagement—primarily through successive medium-term programs under the Extended Credit Facility and its predecessors (and more recently the Policy Support Instrument)—on economic growth and a range of other indicators and socioeconomic outcomes; the second focuses on the role of IMF shock-related financing—through augmentations of Extended Credit Facility arrangements and short-term and emergency financing instruments—on short-term macroeconomic performance.

Annex 1: Propensity Score Matching Methodology

Addressing Selection Bias—Alternative Approaches

The literature on the impact of IMF-supported programs has used various approaches to address sample selection bias, with the aim of constructing a credible counterfactual. One strategy is the “before-after” approach, which assumes that all the conditions that can affect a country’s performance are the same before a program is in place as they are after, hence any change in performance can be attributed to the IMF-supported program (Ghosh and others, 2005). This method suffers from biases associated with changes in the economic structure of the country or shocks between the two periods that are unrelated to the decision to participate in a program. Another approach is to use instrumental variables that are correlated with treatment selection but are not directly correlated with the outcome variable. The identification of appropriate and truly exogenous instruments is a major challenge for this approach. The Generalized Evaluation Estimator (GEE) uses policy reaction functions for nonprogram countries to approximate the counterfactual (Goldstein and Montiel, 1986). However, Dicks-Mireaux, Mecagni, and Schadler (2000) largely discredit the validity of the GEE owing to many restrictive assumptions necessary to define the counterfactual based on policy reaction functions.1 Yet another approach is Heckman’s selection correction model, which reduces the sample selection problem to an omitted-variable problem. In the first stage, a probit model is used to predict the probability of IMF-supported program engagement and in the second stage, the inverse Mills ratio, a transformation of the predicted individual probabilities from the first stage, is included as a regressor.2 The latter term drops out only if the correlation between unobserved determinants of program participation and unobserved determinants of the outcome variable is 0. Heckman-type selection models are appropriate only when at least one explanatory variable influences selection but not the outcome of interest, which is known as an exclusion restriction. A final method, used in this paper, is discussed below.

Methodology

The econometric analyses in this annex use the PSM approach to control for selection bias. This is a relatively new and innovative class of statistical methods for impact evaluation. It involves a statistical comparison of country groups based on two steps:

  • First, the probability of participating in IMF-supported programs is estimated conditional on observable economic conditions and country characteristics (selection model);3

  • Second, these probabilities, or propensity scores, are used to match program countries to nonprogram countries, and thereby construct a statistical control group.

The matching based on the likelihood of participation in IMF-supported programs assures similarity of initial macroeconomic conditions and country characteristics in the comparison, or control, group. The control group provides in effect a proxy for the counterfactual, that is, for macroeconomic outcomes if program countries had not had a program. The effects of the IMF-supported program are then calculated as the mean difference in a range of macroeconomic outcomes across these two groups.

The results from this approach should be interpreted with caution, as PSM is useful when only observed pretreatment characteristics are believed to affect program participation. Two necessary assumptions for identification of the program effects are conditional independence and the presence of a common support. Conditional independence, also called confoundedness, implies that program participation is based entirely on observed preshock characteristics of LICs. If unobserved characteristics determine program participation, conditional independence will be violated, and PSM would not be an appropriate method. Using a rich set of preprogram data to estimate the probability of participation in IMF-supported programs helps support the conditional independence assumption. In other words, a well-specified and comprehensive selection model explaining the participation in IMF-supported programs is the key to properly assessing the impact of those programs. The second condition—presence of a common support—ensures that treatment observations have comparison observations “nearby” in the propensity score distribution.

In the analyses of this paper, IMF engagement is taken as a treatment status, analogous to the program evaluation literature in microeconomic studies.4 Countries that have engagement with the Fund are called the treatment group, whereas the remaining others in the sample are called the control group. The average treatment effect of IMF engagement on the treated group (ATT) is given by

ATT=E[Yi1|Di=1]E[Yi0|Di=1],(1)

where D is the dummy variable identifying LICs with IMF engagement in a given window period (annual for short-term engagement, and decadal for longer-term engagement), Yi0 | Di = 1 is the value of the macroeconomic outcome that would have been observed if a LIC with IMF engagement had not experienced such an engagement, and Yi1 | Di = 1 is the outcome value observed on the same country. The key assumption needed to apply the matching method is the conditional independence assumption, which requires that, conditional on some control variables X, the outcomes be independent of the IMF engagement dummy D. Under this assumption, equation (1) can be rewritten as

ATT=E[Yi1|Di=1,Xi]E[Yi0|Di=0,Xi],(2)

where we have replaced E[Yi0|Di = 1, Xi] with E[Yi0|Di = 0, Xi], which is observable. Rosenbaum and Rubin (1983) propose to match the treated units and control units on their propensity scores (which represent here the probabilities of being long-term IMF program countries for the longer-term engagement or participating in annual IMF-supported programs addressing immediate balance of payments needs for the short-term engagement) conditional on X, estimated by simple probit or logit models. A further assumption needed to apply propensity score matching is the common support assumption (p(Xi) < 1), which requires the existence of some comparable control units for each treated unit. When propensity score matching is used, the ATT now can be estimated as

ATT=E[Yi1|Di=1,p(Xi)]E[Yi0|Di=0,p(Xi)](3)

The strategy then consists of computing the differences in the outcomes (Yi) for observations with similar propensity scores (the probability of engaging with the IMF). Various methods have been proposed in the literature to match observations. In this study, we present results using various matching techniques (nearest-neighbor, radius, and Kernel matching). The nearest-neighbor matching estimator sorts all records by the estimated propensity score and then searches forward and backward for the closest control units. In this study we make use of the three, four, and five nearest neighbors. Radius matching uses all comparison observations within a predefined distance around the propensity score, while Kernel matching entails a weighted average of the outcome of all nontreated units, where the weights are related to their proximity to the treated unit.

Specification of the Selection Model

Despite the vast literature on determinants of IMF arrangements, existing models are far from definitive. Bird (2007) argues that the empirical evidence so far may imply that important determining variables may still have been omitted, or there is no one overall explanation for IMF arrangements. Consistent with this view, the econometric analysis in this paper focuses on the subgroup of LICs and distinguishes longer-term engagement from short-term financing, thereby creating more homogenous samples that allow for a more robust identification of the determinants of participation in IMF-supported programs.

Selection Model for Longer-Term IMF Engagement

The selection model estimated is a pooled probit regression. The dependent variable is a dummy variable identifying longer-term IMF engagement. The dummy variable takes the value of 1 if a country has had five or more years of IMF-supported programs in a 10-year period and 0 otherwise. The qualifying programs are all Fund financial arrangements available to LICs, primarily the ECF and its predecessors (PRGF, ESAF, SAF), but also the SBA, ESF-HAC, and SCF, as well as the nonfinancial PSI. Program years have been purged of episodes when there were prolonged program interruptions.5 Given the focus on longer-term engagement, the analysis is based on decadal averages in which periods share a 50 percent overlap with each other in order to increase the number of observations.6 Longer-term IMF engagement is determined by a country’s initial macroeconomic buffers, structural characteristics, and external demand conditions and the size of its Fund quota. The independent variables are chosen broadly in line with the literature’s approach of including both demand and supply factors, with the aim of identifying a parsimonious set of variables that achieves a relatively good fit based on the historical data series. Initial macroeconomic buffers are proxied by the reserve coverage and the ratio of foreign aid to GDP at the beginning of each decade. Structural characteristics are proxied by a dummy variable identifying countries’ geographic characteristics as well as resource rents in the economy. Institutional characteristics are captured by variables related to political connectedness and polity.7,8 Trading partners’ real GDP growth captures external demand conditions that are entirely exogenous to LICs. Finally, countries’ access to IMF resources is proxied by their IMF quota. The empirical findings (Table A1.1) indicate that countries with higher initial reserves and lower aid have exhibited a lower propensity for longer-term IMF engagement. Moreover, the probability of longer-term Fund engagement tends to increase with lower trading partner economic growth in the decade. Finally, landlocked and resource-poor countries have had a higher propensity for longer-term IMF engagement, while a larger quota and a lower political connectedness have implied a lower probability of longer-term engagement.9

Table A1.1.

Determinants of Longer-Term IMF Engagement

article image
Source: IMF staff calculations.Note: Robust standard errors in parentheses. A country is considered to have longer-term engagement in a given decade if in five or more years it had a financial arrangement or a Policy Support Instrument in place, for at least six months in each of these years. *10 percent significance; **5 percent significance; ***1 percent significance.

Selection Model for the Short-Term IMF Engagement

The selection model adopted in this study draws on Bal Gündüz (2009).10 This is the only study looking into determinants of LIC participation in IMF arrangements addressing immediate balance of payments needs in response to domestic policy and/or external shocks. Examining this more homogenous subset of Fund arrangements significantly improves the specification of the selection model, which is key to counter selection bias to properly assess the impact of IMF-supported programs.

The dependent variable is a panel dummy variable, taking the value of 1 if a new IMF arrangement is approved, and 0 otherwise, indicating a normal episode. The set of arrangements include those addressing an immediate balance of payments need arising from policy and/or exogenous shocks. SBA, SAF/ESAF/PRGF/ECF augmentations, ESF, SCF, RCF, and CFF are included in this set. The following refinements are made to this basic set: (1) precautionary SBA/SCF and SBA/PRGF/ECF augmentations addressing natural disasters are excluded,11 and (2) some SAF/ESAF/PRGF/ECF arrangements are added if they address immediate balance of payments needs arising from policy shocks. In order to systematically determine the latter cases, this study relied heavily on program interruptions preceding SAF/ESAF/PRGF/ECF arrangements. For first-time SAF/ESAF/PRGF arrangements, narratives from IMF staff reports are used to identify programs that envisaged a drastic shift in macroeconomic policies to address an immediate financing gap. Normal episodes are identified as the initial year of two successive years with no IMF financing for shocks when the member is eligible to access Fund resources. Several refinements are made to normal episodes to identify cases where supply constraints are binding:12

  • The effects of various economic variables on the probability of a LIC requesting Fund financing in response to shocks are assessed by estimating a binary response model for panel data. The general specification for panel probit models is given by

    yit=1if Fund financing is requestedyit=0normal episodesP(yit=1|xit,ci)=Φ(xirβ+ci)i=1,,nandt=1,,T,(4)

    where y is the observed outcome, Φ is the cumulative normal density function, Xit is the 1 × k vector of explanatory variables, and β is the k × 1 vector of coefficients associated with Xit. Different estimators are constructed depending on their assumptions for the panel heterogeneity, that is, how they treat ci.13 The estimations are carried out step by step under different estimators, and a correlated random effects probit model is preferred based on the econometric tests for the significance of both the individual specific effect and the sample average for covariates.

  • Bal Gündüz (2009) finds that a number of economic variables are significantly associated with increased probability of Fund financing, including reserve coverage, the ratio of the current account balance to GDP, real GDP growth, the macroeconomic stability indicator, and terms of trade shocks (Table A1.2).14 Moreover, Bal Gündüz reports that adverse global shocks to the change in real oil and non-oil commodity prices, and the cyclical component of world trade, increase participation in Fund arrangements. Therefore, the demand for Fund resources by LICs is likely to be cyclical in response to global conditions, with its intensity depending on the magnitude and persistence of adverse external shocks.

Table A1.2.

Demand for IMF Financing in Response to Policy and/or Exogeneous Shocks

article image
Source: Bal Gündüz (2009).Note: Demand for IMF financing in response to policy and/or exogenous shocks excluding natural disasters is estimated by a correlated random effects probit model. *Significant at 10 percent, **at 5 percent, ***at 1 percent; t-statistics in parentheses. Country-specific averages are calculated as the sample average of variables for each country. FDI = foreign direct investment; LR = likelihood ratio test.

The CFA franc zone consists of 14 countries in sub-Saharan Africa, each affiliated with one of two monetary unions maintaining the same currency, the CFA Franc.

The ultimate objective is to distinguish the short-term impact of IMF-supported programs when a country has an immediate external financing need. The treatment variable is identified mostly symmetrically to the one used in the selection equation. A panel dummy variable taking the value of 1 for the approval of IMF-supported programs with LICs addressing immediate balance of payments needs, and 0 for nonprogram episodes, is constructed as the treatment variable.15 Refinements to the program and nonprogram episodes are made similar to those for the dependent variable in the selection equation. Within the set of program countries, a higher propensity score will identify the IMF-supported programs addressing a clear financing need. Severe state failure events are excluded from both program and nonprogram sets as the macroeconomic outcomes in these episodes will be frail, independent of the impact of IMF-supported programs.16 Furthermore, in order to take account of program implementation, years of program interruptions are excluded from the sample.

1

They report that the counterfactual policy reaction function does not have any significant explanatory power for the sample of nonprogram observations.

2

The inverse Mills ratio is the ratio of the probability density function to the cumulative density function of a distribution evaluated at observed covariates of each observation using the estimated coefficients of the probit regression.

3

This paper estimates two selection models: the first equation looks into factors determining the longer-term engagement in IMF programs, while the second equation explains determinants of annual participation in IMF programs addressing immediate balance of payments needs.

4

The use of the PSM technique in the macroeconomic literature has been popularized by recent empirical papers focusing on the effects of the inflation-targeting arrangement on macroeconomic performances (Lin and Ye, 2007, 2009; Lin, 2010), on the effects of fiscal rules on fiscal behavior in developing countries (Tapsoba, 2012), and on the economic effects of foreign capital flows (Chari, Chen, and Dominguez, 2012).

5

Following the approach introduced by Mecagni (1999), a delay of more than six months in completing a review owing to noncompliance with macroeconomic performance criteria is taken as an interruption. The program interruption series is taken from Bal Gündüz (2009) and updated for the period from 2008 to 2011. Bal Gündüz used the Ivanova and others (2003) data set, identifying interruptions for the whole program as an input, and extended it to identify specific years of interruptions and the Mecagni (1999) data set, which identified program interruptions for the SAF/ESAF.

6

The periods from which decadal averages are generated are 1986–95, 1991–2000, 1996–2005, and 2001–10.

7

Recent empirical studies have highlighted the role of institutional characteristics in explaining IMF agreements. See Bird and Rowlands (2001), Butkiewicz and Yanikkaya (2005), and Stone (2004).

8

Political connectedness is a composite indicator capturing the presence of foreign embassies in a country, a country’s membership in international organizations, participation in UN Security Council missions, and ratification of international treaties.

9

A country’s quota as a share of GDP is interpreted here as the size of the available financing relative to the country’s economic needs. The more “related” the quota is to the size of the economy, the less need for a country to become a long-term user of IMF resources.

10

Before Bal Gündüz (2009), only Bird and Rowlands (2009) looked into determinants of IMF arrangements with LICs, albeit without much success in improving the model specification. Only three variables turned out to be significant: the presence of previous Fund arrangements, high inflation, and the rescheduling of debt in the current year.

11

The exclusion was based on the lack of immediate balance of payments need for precautionary SBAs and the different nature of the shock for SBAs and PRGF augmentations addressing natural disasters.

12

Members with overdue obligations to the IMF are ineligible to use Fund resources, so observations with arrears to the Fund are excluded from normal episodes. Also excluded are observations with Fund financing for natural disasters through the ENDA or PRGF augmentations, program interruptions or breakup of negotiations for a program, the Staff-Monitored Program, the EPCA, and three years leading up to EPCAs. Finally, episodes during which members incurred arrears to other bilateral and multilateral creditors and did not have adjustment programs that would garner Fund support and rescheduling by their major creditors are excluded from normal episodes.

13

Pooled probit models assume independence of observations over both t and i. A random-effects probit model treats the individual specific effect, ci, as an unobserved random variable with ci|xit~IN(μc,σc2) if an overall intercept is excluded, and imposes independence of ci and xi. A fixed-effects probit model treats ci as parameters to be estimated along with β, and does not make any assumptions about the distribution of ci given xi. This can be problematic in short panels as both β and ci are inconsistently estimated owing to an incidental parameters problem. Finally, a correlated random effects model relaxes independence between ci and xi using the Chamberlain (1982)-Mundlak (1978) device under conditional normality. In this specification, the time average is often used to save on degrees of freedom.

14

In order to assess the macroeconomic policy stance based on a comprehensive set of complementary indicators, this study used a variant of the composite indicator introduced by Jaramillo and Sancak (2009). The version of this index that includes the black market premium was first used in Bal Gündüz (2009). The formula for the indicator is given by

mitotit=ln(cpiitcpiit1)σΔln(cpi)+ln(xritxrit1)σΔln(xr)resitresit1mgst1σΔres/msgt1gbalitgdpitσgbal/gdp+ln(1+blackprit)σΔln(xr),

where mitot is the macroeconomic stability index for country i at time t, cpi is the consumer price index, xr is the exchange rate of the national currency with respect to the U.S. dollar (an increase indicates a nominal depreciation), res is the stock of international reserves, mgs is imports of goods and services, gbal is the government balance, gdp is the nominal GDP, blackpr is the black market premium, and σ is the standard deviation of each variable. Weights are inverses of the standard deviation of each component for all countries over the full sample after removing the outliers. Higher levels of mitot indicate increased macroeconomic instability.

15

Some asymmetries compared to the dependent variable in the participation equation are introduced for nonprogram episodes to increase the common support for the PSM. The treatment variable includes nonprogram years followed immediately by an IMF-supported program and nonprogram episodes without IMF membership as zeros, whereas the dependent variable in the participation equation excludes these observations from the sample.

16

The severe state failure events are identified from the Political Instability Task Force data set. Four types of political crises are included in this data set: revolutionary wars, ethnic wars, adverse regime changes, and genocides and politicides. Using this data set, a severe state failure event is identified when the variable SFTPMMAX (the maximum magnitude of all events in a year) exceeds 3.9.

Annex 2: Panel Regression on the Determinants of Long-Term Growth

The impact of IMF-supported programs on per capita GDP growth also addresses the selection issue and is computed using two-way fixed-effects models for panel data. All regression specifications control for the inverse Mills ratio to address the selection bias discussed above. The starting estimation does not control for macroeconomic variables that are considered possible transmission channels of the longer-term impact of IMF-supported programs. The analysis here focuses on the effect of longer-term Fund engagement on the long-term average real GDP per capita growth rate. The specification is the following:

(git)=θ2IMFit+Zitβ+it,(1)

where g refers to the real GDP per capita growth rate, Z is the matrix of control variables that are chosen not to be related to Fund engagement, and θ2 measures the total effect of IMF-supported programs on the level of growth.

The study also assesses the strength of the transmission channels in the outcome equations by controlling for those channels and looking at the behavior of the coefficients associated with the longer-term IMF dummy. In order to assess the strength of each transmission channel of Fund engagement, equation (2) is augmented with the variables Y that were significantly affected by the IMF-supported program dummy in the PSM estimation. The specification is then

(git)=θ3IMFit+Zitβ+αYit+it.(2)

If the inclusion of a potential transmission channel variable Y lowers (in absolute terms) the magnitude and the significance of the coefficient associated with the IMF dummy, this will confirm that the variable Y is one channel through which IMF-supported programs help foster economic growth. One would then expect |θ3 |>|θ2 | along with changes in the significance of the two coefficients.

We use the generalized method of moments estimators developed for dynamic models of panel data that were introduced by Holtz-Eakin, Newey, and Rosen (1988), Arellano and Bond (1991), and Arellano and Bover (1995). These estimators are based, first, on differencing regressions or instruments to control for unobserved effects, and, second, on using previous observations of explanatory and lagged-dependent variables as instruments (which are called internal instruments).

Figure A2.1
Figure A2.1
Figure A2.1

Macroeconomic Conditions in Low-Income Countries across Decades

Source: IMF staff calculations.Note: The sample is composed of 75 low -income countries (LICs). Each value represents an unweighted average (except inflation, which shows the median) over each decade. Longer-term engagement is defined as 10 or more years of having an IMF financial arrangement or Policy Support Instrument in place during 1991–2010, for at least six months in each of these years. CPIA = Country Policy and Institutional Assessment; FDI = foreign direct investment.
Figure A2.2
Figure A2.2

Macroeconomic Conditions in Low-Income Countries across Decades and Country Groupings

Source: IMF staff calculations.Note: The sample is composed of 75 low-income countries (LICs). Each value represents an unweighted average over each decade. Longer-term engagement is defined as 10 or more years of having an IMF financial arrangement or Policy Support Instrument in place during 1991–2010, for at least six months in each of these years.
Figure A2.3
Figure A2.3
Figure A2.3
Figure A2.3

Changes in Macroeconomic Performance of Low-Income Countries

Source: IMF staff calculations.Note: The sample is composed of 75 low-income countries and four overlapping decadal period averages:1986–95; 1991–2000; 1996–2005; and 2001–10. A country is considered to have longer-term (LT) engagement in a given decade if in five or more years it had a financial arrangement or a Policy Support Instrument in place, for at least six months in each of these years. The figure shows the distribution of decadal changes across countries by quartiles. CPIA = Country Policy and Institutional Assessment; FDI = foreign direct investment.

References

  • Andersen, Thomas Barnebeck, Henrik Hansen, and Thomas Markussen, 2006, “US Politics and World Bank IDA Lending,Journal of Development Studies, Vol. 42, No. 5, pp. 77294.

    • Search Google Scholar
    • Export Citation
  • Arellano, Manuel, and Stephen Bond, 1991, “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,Review of Economic Studies, Vol. 58, No. 2, pp. 27797.

    • Search Google Scholar
    • Export Citation
  • Arellano, Manuel, and Olympia Bover, 1995, “Another Look at the Instrumental Variable Estimation of Error-Components Models,Journal of Econometrics, Vol. 68, No. 1, pp. 2951.

    • Search Google Scholar
    • Export Citation
  • Atoyan, Ruben, and Patrick Conway, 2006, “Evaluating the Impact of IMF Programs: A Comparison of Matching and Instrumental-Variable Estimators,Review of International Organizations, Vol. 1, No. 2, pp. 99124.

    • Search Google Scholar
    • Export Citation
  • Bal Gündüz, Yasemin, 2009, “Estimating Demand for IMF Financing by Low-Income Countries in Response to Shocks,IMF Working Paper No. 09/263 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Barro, Robert J., and Jong-Wha Lee, 2005, “IMF Programs: Who Is Chosen and What Are the Effects?Journal of Monetary Economics, Vol. 52, No. 7, pp. 124569.

    • Search Google Scholar
    • Export Citation
  • Bird, Graham, 2007, “The IMF: A Bird’s Eye View of Its Role and Operations,Journal of Economic Surveys, Vol. 21, No. 4, pp. 683745.

    • Search Google Scholar
    • Export Citation
  • Bird, Graham, Mumtaz Hussain, and Joseph P. Joyce, 2004, “Many Happy Returns? Recidivism and the IMF,Journal of International Money and Finance, Vol. 23, No. 2, pp. 23151.

    • Search Google Scholar
    • Export Citation
  • Bird, Graham, and Dane Rowlands, 2001, “IMF Lending: How Is It Affected by Economic, Political and Institutional Factors?Journal of Policy Reform, Vol. 4, No. 3, pp. 24370.

    • Search Google Scholar
    • Export Citation
  • Bird, Graham, and Dane Rowlands, 2007, “The IMF and the Mobilisation of Foreign Aid,Journal of Development Studies, Vol. 43, No. 5, pp. 85670.

    • Search Google Scholar
    • Export Citation
  • Bird, Graham, and Dane Rowlands, 2009, “A Disaggregated Empirical Analysis of the Determinants of IMF Arrangements: Does One Model Fit All?Journal of International Development, Vol. 21, No. 7, pp. 91531.

    • Search Google Scholar
    • Export Citation
  • Bordo, Michael D., and Anna J. Schwartz, 2000, “Measuring Real Economic Effects of Bailouts: Historical Perspectives on How Countries in Financial Distress Have Fared with and without Bailouts,Carnegie-Rochester Conference Series on Public Policy, Vol. 53, No. 1, pp. 81167.

    • Search Google Scholar
    • Export Citation
  • Boughton, James M., 2012, Tearing Down Walls: The International Monetary Fund 1990–1999 (Washington: International Monetary Fund).

  • Butkiewicz, James L., and Halit Yanikkaya, 2005, “The Effects of IMF and World Bank Lending on Long-Run Economic Growth: An Empirical Analysis,World Development, Vol. 33, No. 3, pp. 37191.

    • Search Google Scholar
    • Export Citation
  • Chamberlain, Gary, 1982, Multivariate Regression Models for Panel Data, Journal of Econometrics, Vol. 18, No. 1, pp. 546.

  • Chari, Anusha, Wenjie Chen, and Kathryn Dominguez, 2012, “Foreign Ownership and Firm Performance: Emerging Market Acquisitions in the United States,IMF Economic Review, Vol. 60, No. 1, pp. 142.

    • Search Google Scholar
    • Export Citation
  • Clements, Benedict, Sanjeev Gupta, and Masahiro Nozaki, 2013, “What Happens to Social Spending in IMF-Supported Programs?Applied Economics, Vol. 45, No. 28, pp. 402233.

    • Search Google Scholar
    • Export Citation
  • Conway, Patrick, 2007, “The Revolving Door: Duration and Recidivism in IMF Programs,Review of Economics and Statistics, Vol. 89, No. 2, pp. 20520.

    • Search Google Scholar
    • Export Citation
  • Cornea, Giovanni Andrea, Richard Jolly, and Frances Stewart (eds.), 1987, Adjustment with a Human Face: Protecting the Vulnerable and Promoting Growth, Vol. 1 (Oxford: Clarendon Press).

    • Search Google Scholar
    • Export Citation
  • Dicks-Mireaux, Louis, Mauro Mecagni, and Susan Schadler, 2000, “Evaluating the Effect of IMF Lending to Low-Income Countries,Journal of Development Economics, Vol. 61, pp. 495526.

    • Search Google Scholar
    • Export Citation
  • Dreher, Axel, 2006, “IMF and Economic Growth: The Effects of Programs, Loans, and Compliance with Conditionality,World Development, Vol. 34, No. 5, pp. 76988.

    • Search Google Scholar
    • Export Citation
  • Dreher, Axel, and Nathan M. Jensen, 2007, “Independent Actor or Agent? An Empirical Analysis of the Impact of US Interests on IMF Conditions,Journal of Law and Economics, Vol. 50, No. 1, pp. 10524.

    • Search Google Scholar
    • Export Citation
  • Dreher, Axel, Jan-Egbert Sturm, and James Raymond Vreeland, 2006, “Does Membership on the UN Security Council Influence IMF Decisions? Evidence from Panel Data,Working Paper Series No. 1808 (Munich: CESifo Group).

    • Search Google Scholar
    • Export Citation
  • Dreher, Axel, and Roland Vaubel, 2004, “Do IMF and IBRD Cause Moral Hazard and Political Business Cycles? Evidence from Panel Data,Open Economies Review, Vol. 15, No. 1, pp. 522.

    • Search Google Scholar
    • Export Citation
  • Easterly, William, 2005, “What Did Structural Adjustment Adjust? The Association of Policies and Growth with Repeated IMF and World Bank Adjustment Loans,Journal of Development Economics, Vol. 76, No. 1, pp. 122.

    • Search Google Scholar
    • Export Citation
  • Eichengreen, Barry, Poonam Gupta, and Ashoka Mody, 2008, “Sudden Stops and IMF-Supported Programs,in Financial Markets Volatility and Performance in Emerging Markets, pp. 21966 (Cambridge, Massachusetts: National Bureau of Economics Research).

    • Search Google Scholar
    • Export Citation
  • Evrensel, Ayse, 2002, “Effectiveness of IMF-Supported Stabilization Programs in Developing Countries,Journal of International Money and Finance, Vol. 21, No. 5, pp. 56587.

    • Search Google Scholar
    • Export Citation
  • Fabrizio, Stefania, 2009, “Coping with the Global Financial Crisis: Challenges Facing Low-Income Countries” (Washington: International Monetary Fund). http://www.imf.org/external/pubs/ft/dp/2010/dp1005.pdf.

    • Search Google Scholar
    • Export Citation
  • Fabrizio, Stefania, 2010, “Emerging from the Global Crisis: Macroeconomic Challenges Facing Low-Income Countries” (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2010/100510.pdf.

    • Search Google Scholar
    • Export Citation
  • Garuda, Gopal, 2000, “The Distributional Effects of IMF Programs: A Cross-Country Analysis,World Development, Vol. 28, No. 6, pp. 103151.

    • Search Google Scholar
    • Export Citation
  • Ghosh, Atish, Charalambos Christofìdes, Jun Kim, Laura Papi, Uma Ramakrishnan, Alun Thomas, and Juan Zalduendo, 2005, The Design of IMF-Supported Programs, IMF Occasional Paper No. 241 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Goldstein, Morris, and Peter Montiel, 1986, “Evaluating Fund Stabilization Programs with Multi-country Data: Some Methodological Pitfalls,IMF Staff Papers, Vol. 33, No. 2, pp. 30444.

    • Search Google Scholar
    • Export Citation
  • Hardoy, Inés, 2003, “Effect of IMF Programmes on Growth: A Reappraisal Using the Method of Matching,paper presented at the annual conference of the European Economic Association, Stockholm, August 20–24.

    • Search Google Scholar
    • Export Citation
  • Holtz-Eakin, Douglas, Whitney Newey, and Harvey S. Rosen, 1988, “Estimating Vector Autoregressions with Panel Data,Econometrica, Vol. 56, No. 6, pp. 137195.

    • Search Google Scholar
    • Export Citation
  • Hutchison, Michael M., 2003, “A Cure Worse than the Disease? Currency Crises and the Output Costs of IMF-Supported Stabilization Programs,in Managing Currency Crises in Emerging Markets, ed. M. P. Dooley and J. A. Frankel, pp. 32159 (Chicago: University of Chicago Press).

    • Search Google Scholar
    • Export Citation
  • Hutchison, Michael M., 2004, “Selection Bias and the Output Costs of IMF Programs,Working Paper Series No. 04–15, Economic Policy Research Unit, Department of Economics (Copenhagen, Denmark: University of Copenhagen).

    • Search Google Scholar
    • Export Citation
  • Independent Evaluation Office, 2002, “Evaluation of Prolonged Use of IMF Resources” (Washington: International Monetary Fund). http://www.ieo-imf.org/ieo/files/completedevaluations/092502Report.pdf.

    • Search Google Scholar
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  • Independent Evaluation Office, 2012, “The Role of the IMF as Trusted Advisor” (Washington: International Monetary Fund). http://www.ieo-imf.org/ieo/files/completedevaluations/RITA_-_Main_Report.pdf.

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    • Export Citation
  • International Monetary Fund (IMF), 1993, Economic Adjustment in Low-Income Countries: Experience under the Enhanced Structural Adjustment Facility, Occasional Paper No. 106 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 1998, The ESAF at Ten Years: Economic Adjustment and Reform in Low-Income Countries, Occasional Paper No. 156 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2009, “The Fund’s Facilities and Financing Framework for Low-Income Countries—Supplementary Information,” March 13 (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2009/031309.pdf.

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    • Export Citation
  • International Monetary Fund (IMF), 2010, “Emerging from the Global Crisis: Macroeconomic Challenges Facing Low-Income Countries,” October 5 (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2010/100510.pdf.

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    • Export Citation
  • International Monetary Fund (IMF), 2012a, “Review of Facilities for Low-Income Countries,” July 26 (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2012/072612.pdf.

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  • International Monetary Fund (IMF), 2012b, “Review of Facilities for Low-Income Countries—Supplement 1,” July 26 (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2012/072612a.pdf.

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    • Export Citation
  • International Monetary Fund (IMF), 2012c, “2011 Review of Conditionality—Overview Paper,” June 19 (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2012/061912a.pdf.

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    • Export Citation
  • International Monetary Fund (IMF), 2012d, “2011 Review of Conditionality—Background Paper: Outcomes of IMF-Supported Programs” (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2012/061812c.pdf.

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  • International Monetary Fund (IMF), 2013, “Review of Facilities for Low-Income Countries—Proposals for Implementation,” March 15 (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2013/031813.pdf.

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    • Export Citation
  • Ivanova, Anna, Wolfgang Mayer, Alex Mourmouras, and George Anayiotos, 2003, “What Determines the Implementation of IMF-Supported Programs?IMF Working Paper No. 03/8 (Washington: International Monetary Fund).

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    • Export Citation
  • Jaramillo, Laura, and Cemile Sancak, 2009, “Why Has the Grass Been Greener on One Side of Hispaniola? A Comparative Growth Analysis of the Dominican Republic and Haiti,IMF Staff Papers, Vol. 56, No. 2, pp. 32349.

    • Search Google Scholar
    • Export Citation
  • Joyce, Joseph P., 2005, “Time Past and Time Present: A Duration Analysis of IMF Program Spells,Review of International Economics, Vol. 13, No. 2, pp. 28397.

    • Search Google Scholar
    • Export Citation
  • Lin, Shu, 2010, “On the International Effects of Inflation Targeting,Review of Economics and Statistics, Vol. 92, No. 1, pp. 19599.

    • Search Google Scholar
    • Export Citation
  • Lin, Shu, and Haichun Ye, 2007, “Does Inflation Targeting Really Make a Difference? Evaluating the Treatment Effect of Inflation Targeting in Seven Industrial Countries,Journal of Monetary Economics, Vol. 54, No. 8, pp. 252133.

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    • Export Citation
  • Lin, Shu, 2009, “Does Inflation Targeting Make a Difference in Developing Countries?Journal of Development Economics, Vol. 89, No. 1, pp. 11823.

    • Search Google Scholar
    • Export Citation
  • Marchesi, Silvia, and Emanuela Sirtori, 2011, “Is Two Better than One? The Effects of IMF and World Bank Interaction on Growth,Review of International Organizations, Vol. 6, No. 3, pp. 287306.

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    • Export Citation
  • Mecagni, Mauro, 1999, “The Causes of Program Interruptions,” in Economic Adjustment and Reform in Low-Income Countries, ed. Hugh Bredenkamp and Susan Schadler (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Mercer-Blackman, Valerie, and Anna Unigovskaya, 2000, “Compliance with IMF Program Indicators and Growth in Transition Economies,IMF Working Paper No. 00/47 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Mumssen, Christian, Yasemin Bal Gündüz, Christian Ebeke, and Linda Kaltani, 2013, “IMF-Supported Programs in Low-Income Countries: Economic Impact over the Short and Long Term,IMF Working Paper (forthcoming; Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Mundlak, Yair, 1978, “On the Pooling of Time Series and Cross Section Data,Econometrica, Vol. 46, No. 1, pp. 6985.

  • Nooruddin, Irfan, and Joel W. Simmons, 2006, “The Politics of Hard Choices: IMF Programs and Government Spending,International Organization, Vol. 60, No. 4, pp. 100133.

    • Search Google Scholar
    • Export Citation
  • Oatley, Thomas, and Jason Yackee, 2004, “American Interests and IMF Lending,International Politics, Vol. 41, pp. 41529.

  • Oberdabernig, Doris A., 2013, “Revisiting the Effects of IMF Programs on Poverty and Inequality,World Development, Vol. 46, pp. 11342.

    • Search Google Scholar
    • Export Citation
  • Presbitero, Andrea F., and Alberto Zazzaro, 2012, “IMF Lending in Times of Crisis: Political Influences and Crisis Prevention,World Development, Vol. 40, No. 10, pp. 194469.

    • Search Google Scholar
    • Export Citation
  • Przeworski, Adam, and James Raymond Vreeland, 2000, “The Effect of IMF Programs on Economic Growth,Journal of Development Economics, Vol. 62, No. 2, pp. 385421.

    • Search Google Scholar
    • Export Citation
  • Rosenbaum, Paul R., and Donald B. Rubin, 1983, “The Central Role of the Propensity Score in Observational Studies for Causal Effects,Biometrika, Vol. 70, No. 1, pp. 4155.

    • Search Google Scholar
    • Export Citation
  • Steinwand, Martin, and Randall Stone, 2008, “The International Monetary Fund: A Review of the Recent Evidence,Review of International Organizations, Vol. 3, No. 2, pp. 12349.

    • Search Google Scholar
    • Export Citation
  • Stone, Randall W., 2002, Lending Credibility: The International Monetary Fund and the Post-communist Transition (Princeton, New Jersey: Princeton University Press).

    • Search Google Scholar
    • Export Citation
  • Stone, Randall W., 2004, “The Political Economy of IMF Lending in Africa,American Political Science Review, Vol. 98, No. 4, pp. 57791.

    • Search Google Scholar
    • Export Citation
  • Tapsoba, Rene, 2012, “Do National Numerical Fiscal Rules Really Shape Fiscal Behaviours in Developing Countries? A Treatment Effect Evaluation,Economic Modelling, Vol. 29, No. 4, pp. 135669.

    • Search Google Scholar
    • Export Citation
  • Ul Haque, Nadeem, and Mohsin S. Khan, 1998, “Do IMF-Supported Programs Work? A Survey of the Cross-Country Empirical Evidence,IMF Working Paper No. 98/169 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Vreeland, James Raymond, 2003, The IMF and Economic Development (Cambridge: Cambridge University Press).

  • Williamson, John, 1990, “What Washington Means by Policy Reform,” in Latin American Adjustment: How Much Has Happened? ed. John Williamson (Washington: Institute for International Economics).

    • Search Google Scholar
    • Export Citation
  • Andersen, Thomas Barnebeck, Henrik Hansen, and Thomas Markussen, 2006, “US Politics and World Bank IDA Lending,Journal of Development Studies, Vol. 42, No. 5, pp. 77294.

    • Search Google Scholar
    • Export Citation
  • Arellano, Manuel, and Stephen Bond, 1991, “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,Review of Economic Studies, Vol. 58, No. 2, pp. 27797.

    • Search Google Scholar
    • Export Citation
  • Arellano, Manuel, and Olympia Bover, 1995, “Another Look at the Instrumental Variable Estimation of Error-Components Models,Journal of Econometrics, Vol. 68, No. 1, pp. 2951.

    • Search Google Scholar
    • Export Citation
  • Atoyan, Ruben, and Patrick Conway, 2006, “Evaluating the Impact of IMF Programs: A Comparison of Matching and Instrumental-Variable Estimators,Review of International Organizations, Vol. 1, No. 2, pp. 99124.

    • Search Google Scholar
    • Export Citation
  • Bal Gündüz, Yasemin, 2009, “Estimating Demand for IMF Financing by Low-Income Countries in Response to Shocks,IMF Working Paper No. 09/263 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Barro, Robert J., and Jong-Wha Lee, 2005, “IMF Programs: Who Is Chosen and What Are the Effects?Journal of Monetary Economics, Vol. 52, No. 7, pp. 124569.

    • Search Google Scholar
    • Export Citation
  • Bird, Graham, 2007, “The IMF: A Bird’s Eye View of Its Role and Operations,Journal of Economic Surveys, Vol. 21, No. 4, pp. 683745.

    • Search Google Scholar
    • Export Citation
  • Bird, Graham, Mumtaz Hussain, and Joseph P. Joyce, 2004, “Many Happy Returns? Recidivism and the IMF,Journal of International Money and Finance, Vol. 23, No. 2, pp. 23151.

    • Search Google Scholar
    • Export Citation
  • Bird, Graham, and Dane Rowlands, 2001, “IMF Lending: How Is It Affected by Economic, Political and Institutional Factors?Journal of Policy Reform, Vol. 4, No. 3, pp. 24370.

    • Search Google Scholar
    • Export Citation
  • Bird, Graham, and Dane Rowlands, 2007, “The IMF and the Mobilisation of Foreign Aid,Journal of Development Studies, Vol. 43, No. 5, pp. 85670.

    • Search Google Scholar
    • Export Citation
  • Bird, Graham, and Dane Rowlands, 2009, “A Disaggregated Empirical Analysis of the Determinants of IMF Arrangements: Does One Model Fit All?Journal of International Development, Vol. 21, No. 7, pp. 91531.

    • Search Google Scholar
    • Export Citation
  • Bordo, Michael D., and Anna J. Schwartz, 2000, “Measuring Real Economic Effects of Bailouts: Historical Perspectives on How Countries in Financial Distress Have Fared with and without Bailouts,Carnegie-Rochester Conference Series on Public Policy, Vol. 53, No. 1, pp. 81167.

    • Search Google Scholar
    • Export Citation
  • Boughton, James M., 2012, Tearing Down Walls: The International Monetary Fund 1990–1999 (Washington: International Monetary Fund).

  • Butkiewicz, James L., and Halit Yanikkaya, 2005, “The Effects of IMF and World Bank Lending on Long-Run Economic Growth: An Empirical Analysis,World Development, Vol. 33, No. 3, pp. 37191.

    • Search Google Scholar
    • Export Citation
  • Chamberlain, Gary, 1982, Multivariate Regression Models for Panel Data, Journal of Econometrics, Vol. 18, No. 1, pp. 546.

  • Chari, Anusha, Wenjie Chen, and Kathryn Dominguez, 2012, “Foreign Ownership and Firm Performance: Emerging Market Acquisitions in the United States,IMF Economic Review, Vol. 60, No. 1, pp. 142.

    • Search Google Scholar
    • Export Citation
  • Clements, Benedict, Sanjeev Gupta, and Masahiro Nozaki, 2013, “What Happens to Social Spending in IMF-Supported Programs?Applied Economics, Vol. 45, No. 28, pp. 402233.

    • Search Google Scholar
    • Export Citation
  • Conway, Patrick, 2007, “The Revolving Door: Duration and Recidivism in IMF Programs,Review of Economics and Statistics, Vol. 89, No. 2, pp. 20520.

    • Search Google Scholar
    • Export Citation
  • Cornea, Giovanni Andrea, Richard Jolly, and Frances Stewart (eds.), 1987, Adjustment with a Human Face: Protecting the Vulnerable and Promoting Growth, Vol. 1 (Oxford: Clarendon Press).

    • Search Google Scholar
    • Export Citation
  • Dicks-Mireaux, Louis, Mauro Mecagni, and Susan Schadler, 2000, “Evaluating the Effect of IMF Lending to Low-Income Countries,Journal of Development Economics, Vol. 61, pp. 495526.

    • Search Google Scholar
    • Export Citation
  • Dreher, Axel, 2006, “IMF and Economic Growth: The Effects of Programs, Loans, and Compliance with Conditionality,World Development, Vol. 34, No. 5, pp. 76988.

    • Search Google Scholar
    • Export Citation
  • Dreher, Axel, and Nathan M. Jensen, 2007, “Independent Actor or Agent? An Empirical Analysis of the Impact of US Interests on IMF Conditions,Journal of Law and Economics, Vol. 50, No. 1, pp. 10524.

    • Search Google Scholar
    • Export Citation
  • Dreher, Axel, Jan-Egbert Sturm, and James Raymond Vreeland, 2006, “Does Membership on the UN Security Council Influence IMF Decisions? Evidence from Panel Data,Working Paper Series No. 1808 (Munich: CESifo Group).

    • Search Google Scholar
    • Export Citation
  • Dreher, Axel, and Roland Vaubel, 2004, “Do IMF and IBRD Cause Moral Hazard and Political Business Cycles? Evidence from Panel Data,Open Economies Review, Vol. 15, No. 1, pp. 522.

    • Search Google Scholar
    • Export Citation
  • Easterly, William, 2005, “What Did Structural Adjustment Adjust? The Association of Policies and Growth with Repeated IMF and World Bank Adjustment Loans,Journal of Development Economics, Vol. 76, No. 1, pp. 122.

    • Search Google Scholar
    • Export Citation
  • Eichengreen, Barry, Poonam Gupta, and Ashoka Mody, 2008, “Sudden Stops and IMF-Supported Programs,in Financial Markets Volatility and Performance in Emerging Markets, pp. 21966 (Cambridge, Massachusetts: National Bureau of Economics Research).

    • Search Google Scholar
    • Export Citation
  • Evrensel, Ayse, 2002, “Effectiveness of IMF-Supported Stabilization Programs in Developing Countries,Journal of International Money and Finance, Vol. 21, No. 5, pp. 56587.

    • Search Google Scholar
    • Export Citation
  • Fabrizio, Stefania, 2009, “Coping with the Global Financial Crisis: Challenges Facing Low-Income Countries” (Washington: International Monetary Fund). http://www.imf.org/external/pubs/ft/dp/2010/dp1005.pdf.

    • Search Google Scholar
    • Export Citation
  • Fabrizio, Stefania, 2010, “Emerging from the Global Crisis: Macroeconomic Challenges Facing Low-Income Countries” (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2010/100510.pdf.

    • Search Google Scholar
    • Export Citation
  • Garuda, Gopal, 2000, “The Distributional Effects of IMF Programs: A Cross-Country Analysis,World Development, Vol. 28, No. 6, pp. 103151.

    • Search Google Scholar
    • Export Citation
  • Ghosh, Atish, Charalambos Christofìdes, Jun Kim, Laura Papi, Uma Ramakrishnan, Alun Thomas, and Juan Zalduendo, 2005, The Design of IMF-Supported Programs, IMF Occasional Paper No. 241 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Goldstein, Morris, and Peter Montiel, 1986, “Evaluating Fund Stabilization Programs with Multi-country Data: Some Methodological Pitfalls,IMF Staff Papers, Vol. 33, No. 2, pp. 30444.

    • Search Google Scholar
    • Export Citation
  • Hardoy, Inés, 2003, “Effect of IMF Programmes on Growth: A Reappraisal Using the Method of Matching,paper presented at the annual conference of the European Economic Association, Stockholm, August 20–24.

    • Search Google Scholar
    • Export Citation
  • Holtz-Eakin, Douglas, Whitney Newey, and Harvey S. Rosen, 1988, “Estimating Vector Autoregressions with Panel Data,Econometrica, Vol. 56, No. 6, pp. 137195.

    • Search Google Scholar
    • Export Citation
  • Hutchison, Michael M., 2003, “A Cure Worse than the Disease? Currency Crises and the Output Costs of IMF-Supported Stabilization Programs,in Managing Currency Crises in Emerging Markets, ed. M. P. Dooley and J. A. Frankel, pp. 32159 (Chicago: University of Chicago Press).

    • Search Google Scholar
    • Export Citation
  • Hutchison, Michael M., 2004, “Selection Bias and the Output Costs of IMF Programs,Working Paper Series No. 04–15, Economic Policy Research Unit, Department of Economics (Copenhagen, Denmark: University of Copenhagen).

    • Search Google Scholar
    • Export Citation
  • Independent Evaluation Office, 2002, “Evaluation of Prolonged Use of IMF Resources” (Washington: International Monetary Fund). http://www.ieo-imf.org/ieo/files/completedevaluations/092502Report.pdf.

    • Search Google Scholar
    • Export Citation
  • Independent Evaluation Office, 2012, “The Role of the IMF as Trusted Advisor” (Washington: International Monetary Fund). http://www.ieo-imf.org/ieo/files/completedevaluations/RITA_-_Main_Report.pdf.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 1993, Economic Adjustment in Low-Income Countries: Experience under the Enhanced Structural Adjustment Facility, Occasional Paper No. 106 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 1998, The ESAF at Ten Years: Economic Adjustment and Reform in Low-Income Countries, Occasional Paper No. 156 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2009, “The Fund’s Facilities and Financing Framework for Low-Income Countries—Supplementary Information,” March 13 (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2009/031309.pdf.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2010, “Emerging from the Global Crisis: Macroeconomic Challenges Facing Low-Income Countries,” October 5 (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2010/100510.pdf.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2012a, “Review of Facilities for Low-Income Countries,” July 26 (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2012/072612.pdf.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2012b, “Review of Facilities for Low-Income Countries—Supplement 1,” July 26 (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2012/072612a.pdf.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2012c, “2011 Review of Conditionality—Overview Paper,” June 19 (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2012/061912a.pdf.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2012d, “2011 Review of Conditionality—Background Paper: Outcomes of IMF-Supported Programs” (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2012/061812c.pdf.

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    • Export Citation
  • International Monetary Fund (IMF), 2013, “Review of Facilities for Low-Income Countries—Proposals for Implementation,” March 15 (Washington: International Monetary Fund). http://www.imf.org/external/np/pp/eng/2013/031813.pdf.

    • Search Google Scholar
    • Export Citation
  • Ivanova, Anna, Wolfgang Mayer, Alex Mourmouras, and George Anayiotos, 2003, “What Determines the Implementation of IMF-Supported Programs?IMF Working Paper No. 03/8 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Jaramillo, Laura, and Cemile Sancak, 2009, “Why Has the Grass Been Greener on One Side of Hispaniola? A Comparative Growth Analysis of the Dominican Republic and Haiti,IMF Staff Papers, Vol. 56, No. 2, pp. 32349.

    • Search Google Scholar
    • Export Citation
  • Joyce, Joseph P., 2005, “Time Past and Time Present: A Duration Analysis of IMF Program Spells,Review of International Economics, Vol. 13, No. 2, pp. 28397.

    • Search Google Scholar
    • Export Citation
  • Lin, Shu, 2010, “On the International Effects of Inflation Targeting,Review of Economics and Statistics, Vol. 92, No. 1, pp. 19599.

    • Search Google Scholar
    • Export Citation
  • Lin, Shu, and Haichun Ye, 2007, “Does Inflation Targeting Really Make a Difference? Evaluating the Treatment Effect of Inflation Targeting in Seven Industrial Countries,Journal of Monetary Economics, Vol. 54, No. 8, pp. 252133.

    • Search Google Scholar
    • Export Citation
  • Lin, Shu, 2009, “Does Inflation Targeting Make a Difference in Developing Countries?Journal of Development Economics, Vol. 89, No. 1, pp. 11823.

    • Search Google Scholar
    • Export Citation
  • Marchesi, Silvia, and Emanuela Sirtori, 2011, “Is Two Better than One? The Effects of IMF and World Bank Interaction on Growth,Review of International Organizations, Vol. 6, No. 3, pp. 287306.

    • Search Google Scholar
    • Export Citation
  • Mecagni, Mauro, 1999, “The Causes of Program Interruptions,” in Economic Adjustment and Reform in Low-Income Countries, ed. Hugh Bredenkamp and Susan Schadler (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Mercer-Blackman, Valerie, and Anna Unigovskaya, 2000, “Compliance with IMF Program Indicators and Growth in Transition Economies,IMF Working Paper No. 00/47 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Mumssen, Christian, Yasemin Bal Gündüz, Christian Ebeke, and Linda Kaltani, 2013, “IMF-Supported Programs in Low-Income Countries: Economic Impact over the Short and Long Term,IMF Working Paper (forthcoming; Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Mundlak, Yair, 1978, “On the Pooling of Time Series and Cross Section Data,Econometrica, Vol. 46, No. 1, pp. 6985.

  • Nooruddin, Irfan, and Joel W. Simmons, 2006, “The Politics of Hard Choices: IMF Programs and Government Spending,International Organization, Vol. 60, No. 4, pp. 100133.

    • Search Google Scholar
    • Export Citation
  • Oatley, Thomas, and Jason Yackee, 2004, “American Interests and IMF Lending,International Politics, Vol. 41, pp. 41529.

  • Oberdabernig, Doris A., 2013, “Revisiting the Effects of IMF Programs on Poverty and Inequality,World Development, Vol. 46, pp. 11342.

    • Search Google Scholar
    • Export Citation
  • Presbitero, Andrea F., and Alberto Zazzaro, 2012, “IMF Lending in Times of Crisis: Political Influences and Crisis Prevention,World Development, Vol. 40, No. 10, pp. 194469.

    • Search Google Scholar
    • Export Citation
  • Przeworski, Adam, and James Raymond Vreeland, 2000, “The Effect of IMF Programs on Economic Growth,Journal of Development Economics, Vol. 62, No. 2, pp. 385421.

    • Search Google Scholar
    • Export Citation
  • Rosenbaum, Paul R., and Donald B. Rubin, 1983, “The Central Role of the Propensity Score in Observational Studies for Causal Effects,Biometrika, Vol. 70, No. 1, pp. 4155.

    • Search Google Scholar
    • Export Citation
  • Steinwand, Martin, and Randall Stone, 2008, “The International Monetary Fund: A Review of the Recent Evidence,Review of International Organizations, Vol. 3, No. 2, pp. 12349.

    • Search Google Scholar