While there is a large literature on the macroeconomic consequences of IMF-supported programs, country samples have varied significantly across studies, and very few papers have focused exclusively on LICs. However, regardless of the country sample, the main challenge of these studies has always been the treatment of the endogeneity/self-selection issues related to the participation in IMF-supported programs, and the identification of relevant macroeconomic outcomes of such programs. Initial economic conditions differ systematically between program and nonprogram countries. Countries that approach the IMF often do so because they are already facing economic difficulties. Structural vulnerabilities such as commodity dependence or poor governance may also lead to longer-term use of Fund facilities, and may result in increased exposure to shocks and a decreased ability to implement appropriate macroeconomic policies in the face of these shocks.
If econometric estimations of the impact of IMF-supported programs ignore these systematic differences between program and nonprogram countries, the estimated effect of the Fund’s engagement on growth and other macroeconomic indicators will likely be biased. In order to disentangle the different factors—and, hence, isolate the specific contribution of IMF engagement—many studies begin by attempting to assess the determinants of countries’ participation in IMF-supported programs.1 While early research emphasized the economic determinants, the low predictive capacity of these models increasingly led researchers to include political variables that would affect the “supply” side of programs.2 Evidence on the significance of these factors is again mixed.3 Steinwand and Stone (2008, p. 129) conclude that “the variety of models used to explain participation in IMF programs and the plethora of contradictory results they produce indicates that existing models are far from definitive. This unfinished business is the strongest reason to urge caution in rushing to judgment about the effects of IMF lending.”
Recently, two studies analyzed the determinants of LIC participation in IMF-supported programs. Bird and Rowlands (2009) report significant differences between the specifications for low- and middle-income countries, though results for the LIC specification are weak.4 Bal Gündüz (2009) focuses on a specific subset of IMF arrangements with LICs that addresses policy and exogenous shocks and reports various economic variables as being statistically significant. While the literature has primarily focused on explaining the participation in IMF-supported programs in any given year, a few studies look into factors behind the prolonged use of Fund resources.5 Overall, this limited literature suggests that repeated use is peculiar to LICs and explained by both economic and structural variables.
The large empirical literature has reached some consensus that IMF-supported programs are associated with significant improvement in the balance of payments and have some effect on inflation, but results are mixed regarding the impact on growth (Table 3.1). A few observations are noteworthy: (1) most of the previous research examines only nonconcessional programs; (2) only a few studies explore the impact of prolonged engagement on long-term growth;6 (3) although the literature widely acknowledges that whether IMF-supported programs are fully implemented or not is a key issue in properly assessing their impact, most studies do not take into account compliance with programs;7 and (4) correcting for selection bias has become a standard component of the analysis only more recently, with most studies having applied either the Heckman two-stage methodology or instrumental variable (IV) regressions.8
Summary of Literature on the Impact of IMF-Supported Programs, 2000–12
Countries with low propensity scores show improvement, while for those with high propensity scores inequality deteriorates.
Significant only for SAF/ESAF, positive but nonsignificant in mixed sample.
This study applies Heckman correction to growth equation, however, the inverse Mills ratio (IMR) turns nonsignificant. The author notes that his participation equation is not strong. Therefore, it is difficult to know whether the nonsignificance of the IMR is because a stable participation equation is not identified or participation is random.
Finds significant negative effect for prolonged users only, while the impact on growth is nonsignificant for temporary users.
Results from IV regressions are very close to zero.
Easterly (2005) notes that his instruments are weak.
They report a significantly positive impact from the interaction of IMF and World Bank programs.
Based on descriptive comparison vis-à-vis the control group constructed by the PSM, therefore, significance level is not reported.
Positive effect is reported for years following the initiation of programs.
The findings are reversed for the period 2000–09 with IMF programs leading to lower poverty and lower inequality.
Summary of Literature on the Impact of IMF-Supported Programs, 2000–12
| Outcome variables | GDP growth | Inflation | Fiscal deficit | Current account balance | Monetary growth | Gini coefficient | Education spending | Health spending | Poverty | Period | Countries | Type of programs | Selection correction | Method | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Bordo and Schwartz (2000) | − | + | + | 1973–1998 | 14 emerging markets | SBA EFF | No | ||||||||
| Dicks-Mireaux, Mecagni, and Schadler (2000) | +* | − | 1986–1991 | 61 LICs | ESAF | No | |||||||||
| Garuda (2000) | +*/–* a | 1975–1991 | 39 countries | Mixed | Yes | PSM | |||||||||
| Przeworski and Vreeland (2000) | −* | 1970–1990 | 79 countries | Mixed | Yes | Heckman | |||||||||
| Evrensel (2002) | +* b | − | +* | +/– | 1971–1997 | 91 countries | Mixed & by program | No | |||||||
| Hardoy (2003) | − | 1970–1990 | 109 developing countries | Mixed | Yes | PSM PSM (DID) | |||||||||
| Hutchison (2003) | −* | 1975–1997 | 67 countries | SBA EFF | Yes | Heckmanc | |||||||||
| Vreeland (2003) | −* | 1961–1993 | 110 countries | Mixed | Yes | Heckman | |||||||||
| Dreher and Vaubel (2004) | −* | −* | 1975–1997 | 94 countries | Mixed | No | |||||||||
| Hutchison (2004) | + | 1975–1997 | 25 emerging markets | SBA EFF | Yes | PSM Heckman | |||||||||
| Independent Evaluation Office (2002) | −*/– d | 1975–1999 | 82 prolonged versus temporary users | Mixed & by program | Yes | IV | |||||||||
| Barro and Lee (2005) | −* | 1975–1999 | 130 countries | SBA EFF | Yes | IV | |||||||||
| Butkiewicz and Yanikkaya (2005) | −* | 1970–1999 | 100 developing countries | Mixed | Yes | IV | |||||||||
| Easterly (2005) | 0 e | − | − | 1980–1999 | 20 repeated users | Mixed | Yes | IV f | |||||||
| Atoyan and Conway (2006) | +/– | −* | +* | 1993–2002 | 95 developing countries | Mixed | Yes | PSM Heckman | |||||||
| Dreher (2006) | −* | 1970–2000 | 98 developing countries | SBA EFF | Yes | IV | |||||||||
| Nooruddin and Simmons (2006) | −* | −* | 1980–2000 | 98 countries | Mixed | Yes | |||||||||
| Eichengreen, Gupta, and Mody (2008) | −* | 1980–2003 | 24 countries with sudden stops | SBA EFF | Yes | Heckman | |||||||||
| Marchesi and Sirtori (2011) | − * g | 1982–2005 | 128 developing countries | Mixed | Yes | IV | |||||||||
| IMF (2012d)h | −i | − | + | 0 | 2002–2011 | 44 MICs | SBA EFF | Yes | PSM | ||||||
| Clements, Gupta, and Nozaki (2013) | +* | +* | 1985–2009 | LICs and MICs | Mixed & by program | Yes | |||||||||
| Oberdabernig (2013) | + * j | + * j | 1982–2009 | LICs and MICs | Mixed | Yes | IV |
Countries with low propensity scores show improvement, while for those with high propensity scores inequality deteriorates.
Significant only for SAF/ESAF, positive but nonsignificant in mixed sample.
This study applies Heckman correction to growth equation, however, the inverse Mills ratio (IMR) turns nonsignificant. The author notes that his participation equation is not strong. Therefore, it is difficult to know whether the nonsignificance of the IMR is because a stable participation equation is not identified or participation is random.
Finds significant negative effect for prolonged users only, while the impact on growth is nonsignificant for temporary users.
Results from IV regressions are very close to zero.
Easterly (2005) notes that his instruments are weak.
They report a significantly positive impact from the interaction of IMF and World Bank programs.
Based on descriptive comparison vis-à-vis the control group constructed by the PSM, therefore, significance level is not reported.
Positive effect is reported for years following the initiation of programs.
The findings are reversed for the period 2000–09 with IMF programs leading to lower poverty and lower inequality.
Summary of Literature on the Impact of IMF-Supported Programs, 2000–12
| Outcome variables | GDP growth | Inflation | Fiscal deficit | Current account balance | Monetary growth | Gini coefficient | Education spending | Health spending | Poverty | Period | Countries | Type of programs | Selection correction | Method | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Bordo and Schwartz (2000) | − | + | + | 1973–1998 | 14 emerging markets | SBA EFF | No | ||||||||
| Dicks-Mireaux, Mecagni, and Schadler (2000) | +* | − | 1986–1991 | 61 LICs | ESAF | No | |||||||||
| Garuda (2000) | +*/–* a | 1975–1991 | 39 countries | Mixed | Yes | PSM | |||||||||
| Przeworski and Vreeland (2000) | −* | 1970–1990 | 79 countries | Mixed | Yes | Heckman | |||||||||
| Evrensel (2002) | +* b | − | +* | +/– | 1971–1997 | 91 countries | Mixed & by program | No | |||||||
| Hardoy (2003) | − | 1970–1990 | 109 developing countries | Mixed | Yes | PSM PSM (DID) | |||||||||
| Hutchison (2003) | −* | 1975–1997 | 67 countries | SBA EFF | Yes | Heckmanc | |||||||||
| Vreeland (2003) | −* | 1961–1993 | 110 countries | Mixed | Yes | Heckman | |||||||||
| Dreher and Vaubel (2004) | −* | −* | 1975–1997 | 94 countries | Mixed | No | |||||||||
| Hutchison (2004) | + | 1975–1997 | 25 emerging markets | SBA EFF | Yes | PSM Heckman | |||||||||
| Independent Evaluation Office (2002) | −*/– d | 1975–1999 | 82 prolonged versus temporary users | Mixed & by program | Yes | IV | |||||||||
| Barro and Lee (2005) | −* | 1975–1999 | 130 countries | SBA EFF | Yes | IV | |||||||||
| Butkiewicz and Yanikkaya (2005) | −* | 1970–1999 | 100 developing countries | Mixed | Yes | IV | |||||||||
| Easterly (2005) | 0 e | − | − | 1980–1999 | 20 repeated users | Mixed | Yes | IV f | |||||||
| Atoyan and Conway (2006) | +/– | −* | +* | 1993–2002 | 95 developing countries | Mixed | Yes | PSM Heckman | |||||||
| Dreher (2006) | −* | 1970–2000 | 98 developing countries | SBA EFF | Yes | IV | |||||||||
| Nooruddin and Simmons (2006) | −* | −* | 1980–2000 | 98 countries | Mixed | Yes | |||||||||
| Eichengreen, Gupta, and Mody (2008) | −* | 1980–2003 | 24 countries with sudden stops | SBA EFF | Yes | Heckman | |||||||||
| Marchesi and Sirtori (2011) | − * g | 1982–2005 | 128 developing countries | Mixed | Yes | IV | |||||||||
| IMF (2012d)h | −i | − | + | 0 | 2002–2011 | 44 MICs | SBA EFF | Yes | PSM | ||||||
| Clements, Gupta, and Nozaki (2013) | +* | +* | 1985–2009 | LICs and MICs | Mixed & by program | Yes | |||||||||
| Oberdabernig (2013) | + * j | + * j | 1982–2009 | LICs and MICs | Mixed | Yes | IV |
Countries with low propensity scores show improvement, while for those with high propensity scores inequality deteriorates.
Significant only for SAF/ESAF, positive but nonsignificant in mixed sample.
This study applies Heckman correction to growth equation, however, the inverse Mills ratio (IMR) turns nonsignificant. The author notes that his participation equation is not strong. Therefore, it is difficult to know whether the nonsignificance of the IMR is because a stable participation equation is not identified or participation is random.
Finds significant negative effect for prolonged users only, while the impact on growth is nonsignificant for temporary users.
Results from IV regressions are very close to zero.
Easterly (2005) notes that his instruments are weak.
They report a significantly positive impact from the interaction of IMF and World Bank programs.
Based on descriptive comparison vis-à-vis the control group constructed by the PSM, therefore, significance level is not reported.
Positive effect is reported for years following the initiation of programs.
The findings are reversed for the period 2000–09 with IMF programs leading to lower poverty and lower inequality.
The use in the earlier literature of the Heckman two-step approach and the IV strategy faced the principal challenge of identifying exclusion restrictions, namely, variables that appear in the selection equation but not necessarily in the structural model (for example, a low level of reserves would increase both the likelihood of IMF-supported programs and the need for adjustment, resulting in a negative impact on growth should an adverse exogenous shock materialize).9 The more recent literature builds on techniques borrowed from the microeconometric impact evaluation literature. Under this approach, each IMF-supported program-country observation is matched to a counterfactual nonprogram-country observation with a similar predicted probability of having a program, and their macroeconomic outcomes are then compared. Using this technique, Atoyan and Conway (2006) found little statistical support that IMF-supported programs contemporaneously improve real economic growth in participating countries (though the programs did improve the fiscal and current account balances), but found stronger evidence of an improvement in economic growth in years following a program.
The analysis in this paper contributes to the existing empirical literature on the impact of IMF-supported programs in at least four ways:
It focuses exclusively on LICs, motivated by the fact that they present unique characteristics that set them apart from other countries as discussed in Chapter 2.
It studies two homogenous and complementary subsets of IMF-supported programs with LICs (medium-term prolonged support versus short-term episodic support) not examined by the earlier research. This level of disaggregation significantly improves the identification of economic and structural factors in participation models, which is the key step to correct for selection bias, but also makes it possible to distinguish between short-run effects and effects of prolonged use of IMF facilities for LICs.
Based on a sample covering nearly three decades and ending in 2010, it examines a wide range of macroeconomic and social outcomes using the Propensity Score Matching (PSM) methodology, previously implemented only by a handful of studies for a few outcome indicators and for a mixed sample of countries, to correct for selection bias.
Finally, the paper investigates a few potential channels of transmission through which longer-term IMF engagement can affect long-term growth and distinguishes between effects due to IMF financing versus the role of its policy advice and capacity development.
Impact of Longer-Term IMF Engagement in Low-Income Countries
As noted in Chapter 2, macroeconomic conditions have improved substantially over the last two decades for most LICs, regardless of whether they were engaged with the IMF.10 On average, LICs experienced significant long-term increases in real GDP per capita growth, government balances, reserves, current account balances, FDI, exports, institutional quality, and social spending, while also achieving noticeable reductions in economic volatility, inflation, external debt, inequality, and poverty (Figure A2.1). This finding holds across country sizes (small versus nonsmall economies), geographical groupings (coastal versus landlocked), institutional capacity (as measured by the World Bank’s Country Policy and Institutional Assessment—CPIA), and per capita income (Figure A2.2).
LICs with extensive IMF-supported program engagement have experienced, on average, a comparatively strong improvement in longer-term economic performance. Looking at the past three decades, countries with extensive program engagement faced comparatively weaker initial economic conditions in the 1980s, and experienced on average larger increases in real GDP per capita growth, government balance, exports, FDI, and social spending than countries without such extensive engagement.11 LICs with longer-term program engagement also achieved a more marked reduction in economic volatility, inflation, and external debt. This stylized fact was first reported in IMF (2009) and continues to hold after updating the data to include the most recent years covering the global financial crisis. The strong economic improvement among extensive program users has largely eliminated the performance gap that existed relative to other LICs around the time when the ESAF was created in 1987. Figures 3.1 and A2.3 show a similar result when looking at the change in decadal averages of economic indicators and splitting the country sample into LICs with longer-term engagement (at least five years within the later decade) and those without such engagement.
Changes in Average Decadal GDP Per Capita Growth and Poverty Gaps, 1986–2010
(In percent)
Source: IMF staff calculations using World Bank data.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. Poverty gap is defined as the mean shortfall from the poverty line (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. A more negative change in the figure implies a bigger reduction in the poverty gap.Changes in Average Decadal GDP Per Capita Growth and Poverty Gaps, 1986–2010
(In percent)
Source: IMF staff calculations using World Bank data.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. Poverty gap is defined as the mean shortfall from the poverty line (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. A more negative change in the figure implies a bigger reduction in the poverty gap.Changes in Average Decadal GDP Per Capita Growth and Poverty Gaps, 1986–2010
(In percent)
Source: IMF staff calculations using World Bank data.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. Poverty gap is defined as the mean shortfall from the poverty line (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. A more negative change in the figure implies a bigger reduction in the poverty gap.Econometric Analysis
The analysis that follows investigates to what extent the positive association of longer-term IMF engagement and economic performance holds up when controlling for other factors and addressing the sample selection bias. The following two questions are addressed:
The analysis uses a panel data set of 75 LICs and decadal averages spanning the period 1986–2010. Given the focus on longer-term engagement, we worked with decadal averages where periods share a 50 percent overlap with each other.12 We also worked with yearly rolling decadal averages, but considered them suboptimal given the serial correlation generated by the repetition of the bulk of the observations. For any given 10-year period, longer-term IMF engagement is captured by a dummy variable that takes the value of 1 if a country has had five or more years of IMF-supported programs in the10-year period and 0 otherwise. The qualifying programs are all Fund financial arrangements available to LICs, primarily the ECF and its predecessors (PRGF, ESAF, and 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.13
The Propensity Score Matching Approach
To control for selection bias, a PSM selection equation is specified to estimate the determinants of longer-term IMF engagement. The independent variables are chosen broadly in line with the approach in the literature of including both demand and supply factors determining IMF support, with the aim of identifying a parsimonious set of variables that achieves a relatively good fit based on the historical data series. Longer-term IMF engagement is assumed to be determined by a country’s initial macroeconomic buffers and its structural characteristics, as well as external demand conditions during the period, but also by the role of Fund quotas in determining the country’s available financing. 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 landlocked countries, political connectedness, natural resource rents, and institutional characteristics, with the latter stemming from the more recent empirical focus on political and institutional influences on Fund agreements. 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 Fund quota. Annex 1 presents additional discussion and estimation results.
The results of the PSM suggest that longer-term IMF engagement has been associated with improved macroeconomic and socio-institutional outcomes. Table 3.2 presents the PSM results for dependent variables measured in changes in order to capture relative differences between countries with and without longer-term Fund engagement in their macroeconomic outcomes. The PSM estimations are run using four different matching approaches (nearest-neighbor matching, five-nearest-neighbor matching, radius matching, and Kernel matching). The results include the following:
Impact of Longer-Term IMF Engagement on Economic Performance
Impact of Longer-Term IMF Engagement on Economic Performance
| Nearest-neighbor matching | Five-nearest-neighbor matching | Radius matching | Kernel matching | |
|---|---|---|---|---|
| GDP per capita growth | 3.38 *** | 3.41*** | 3.30*** | 3.50*** |
| (0.80) | (0.73) | (0.75) | (0.68) | |
| GDP per capita growth volatility | −1.85 | −2.33** | −2.02*** | −2.45*** |
| (1.33) | (0.99) | (0.75) | (0.90) | |
| Inflation | −10.57* | −10.46** | –10.75*** | –9.60** |
| (5.60) | (4.21) | (3.98) | (4.22) | |
| Tax revenue | 0.47 | 0.92 | 1.23* | 0.93 |
| (0.90) | (0.73) | (0.69) | (0.76) | |
| Government balance | 3.58*** | 3.52*** | 1.59 | 3.38*** |
| (1.16) | (1.02) | (1.15) | (0.95) | |
| Reserve coverage | 0.18 | 0.20 | 0.32 | 0.15 |
| (0.45) | (0.35) | (0.30) | (0.33) | |
| Current account | –0.21 | 0.02 | 0.10 | –0.08 |
| (1.44) | (1.08) | (1.02) | (1.04) | |
| FDI | 1.76*** | 1.86*** | 1.06*** | 1.85*** |
| (0.39) | (0.39) | (0.38) | (0.35) | |
| External debt | 50.23 | –17.53 | –4.19 | –25.65 |
| (38.77) | (27.18) | (22.63) | (19.90) | |
| CPIA | 0.26 | 0.31* | 0.14 | 0.33 |
| (0.23) | (0.19) | (0.15) | (0.18) | |
| Social spending | 1.07 | 1.00* | 1.06*** | 0.91 |
| (0.67) | (0.52) | (0.34) | (0.57) | |
| Education spending | 0.78 | 0.80* | 1.01*** | 0.80* |
| (0.58) | (0.44) | (0.27) | (0.49) | |
| Health spending | 0.29 | 0.20 | 0.05 | 0.12 |
| (0.27) | (0.24) | (0.16) | (0.23) | |
| Aid | –0.45 | 0.17 | –0.26 | –0.19 |
| (1.72) | (1.45) | (1.45) | (1.32) | |
| Poverty gap | –3.90 | –3.98* | –3.83** | –3.89 |
| (3.00) | (2.39) | (1.89) | (2.59) | |
| Poverty rate | –2.16 | –2.98 | –2.51 | –2.49 |
| (3.18) | (2.66) | (2.43) | (2.81) | |
| Gini | –4.94* | –3.67* | –3.84*** | –3.58* |
| (2.56) | (2.14) | (1.38) | (2.09) |
Impact of Longer-Term IMF Engagement on Economic Performance
| Nearest-neighbor matching | Five-nearest-neighbor matching | Radius matching | Kernel matching | |
|---|---|---|---|---|
| GDP per capita growth | 3.38 *** | 3.41*** | 3.30*** | 3.50*** |
| (0.80) | (0.73) | (0.75) | (0.68) | |
| GDP per capita growth volatility | −1.85 | −2.33** | −2.02*** | −2.45*** |
| (1.33) | (0.99) | (0.75) | (0.90) | |
| Inflation | −10.57* | −10.46** | –10.75*** | –9.60** |
| (5.60) | (4.21) | (3.98) | (4.22) | |
| Tax revenue | 0.47 | 0.92 | 1.23* | 0.93 |
| (0.90) | (0.73) | (0.69) | (0.76) | |
| Government balance | 3.58*** | 3.52*** | 1.59 | 3.38*** |
| (1.16) | (1.02) | (1.15) | (0.95) | |
| Reserve coverage | 0.18 | 0.20 | 0.32 | 0.15 |
| (0.45) | (0.35) | (0.30) | (0.33) | |
| Current account | –0.21 | 0.02 | 0.10 | –0.08 |
| (1.44) | (1.08) | (1.02) | (1.04) | |
| FDI | 1.76*** | 1.86*** | 1.06*** | 1.85*** |
| (0.39) | (0.39) | (0.38) | (0.35) | |
| External debt | 50.23 | –17.53 | –4.19 | –25.65 |
| (38.77) | (27.18) | (22.63) | (19.90) | |
| CPIA | 0.26 | 0.31* | 0.14 | 0.33 |
| (0.23) | (0.19) | (0.15) | (0.18) | |
| Social spending | 1.07 | 1.00* | 1.06*** | 0.91 |
| (0.67) | (0.52) | (0.34) | (0.57) | |
| Education spending | 0.78 | 0.80* | 1.01*** | 0.80* |
| (0.58) | (0.44) | (0.27) | (0.49) | |
| Health spending | 0.29 | 0.20 | 0.05 | 0.12 |
| (0.27) | (0.24) | (0.16) | (0.23) | |
| Aid | –0.45 | 0.17 | –0.26 | –0.19 |
| (1.72) | (1.45) | (1.45) | (1.32) | |
| Poverty gap | –3.90 | –3.98* | –3.83** | –3.89 |
| (3.00) | (2.39) | (1.89) | (2.59) | |
| Poverty rate | –2.16 | –2.98 | –2.51 | –2.49 |
| (3.18) | (2.66) | (2.43) | (2.81) | |
| Gini | –4.94* | –3.67* | –3.84*** | –3.58* |
| (2.56) | (2.14) | (1.38) | (2.09) |
Longer-term IMF engagement leads to significantly higher long-term real per capita GDP. The panel growth regressions that follow below attempt to identify some of the channels through which longer-term engagement may lead to such outcomes.
Longer-term IMF users have significantly higher reductions in growth volatility and inflation, corroborating the role of continued Fund engagement in restoring or fostering macroeconomic stability.
Improvements in the government balance are significantly larger for longer-term users.
Longer-term IMF engagement is associated with significantly larger increases in FDI.
The poverty gap decreased more for countries with longer-term IMF engagement. Declines in poverty rates are also larger for countries with longer-term IMF engagement, but they are not statistically significant. Data availability for poverty gaps and poverty rates is limited, especially for earlier years, leading to a significantly smaller regression sample and possibly less variation in the data.
Longer-term IMF engagement is associated with significantly greater reductions in income inequality. Like the poverty data, data on income inequality are limited for the earlier years. However, the regression coefficients are consistently significant across matching techniques.
Changes in social spending, in particular education spending, are larger for countries with longer-term IMF engagement. They are also positive but not statistically significant for health spending.
Changes in reserve coverage, tax revenue, and CPIA are larger for countries with longer-term IMF engagement but are not significantly different from the control group (with the exception of the CPIA under one estimation). The relationship between longer-term IMF engagement and changes in aid, external debt, and the current account is not conclusive under the four estimation techniques.
At first sight some of these findings may seem surprising. For reserves, one possible explanation could be that the presence of an IMF-supported program implies that countries are often able to adjust less, since the availability of Fund financing serves to some extent as insurance when a balance of payments need arises. Therefore, countries may have less need to accumulate reserves, especially considering the high opportunity cost of doing so in countries where development needs are vast. Furthermore, oftentimes LICs requesting IMF financial support face protracted balance of payments needs and the necessity to undertake major structural reforms, so the role of IMF-supported programs may not necessarily be to boost reserves. This is also suggested by the first-stage regression, which links long-term Fund engagement with initial levels of reserves. As for aid and debt, it is quite plausible that the effect of IMF engagement is weakened when measured as an average over the decade but may be nevertheless significant at the start of an IMF-supported program, or at the point when debt relief is granted in the context of the program.14
Panel Growth Regression
Panel regressions are used as a complementary approach to estimate the impact of longer-term IMF engagement on growth and to identify the associated transmission channels. The starting point for the growth specification follows a large strand of empirical growth literature that seeks to link economic growth performance to economic as well as institutional variables in a panel data set context. The analysis uses a generalized method of moments procedure that addresses endogeneity and controls for unobserved country-specific factors in order to estimate the growth effect of IMF engagement as well as other policy and nonpolicy variables. Under an initial regression specification, a model is estimated where we include as explanatory variables certain growth determinants that have received attention in the literature, but exclude variables that are likely to be under the direct influence of IMF-supported programs. In subsequent regressions, we augment our specification by including explanatory variables that are likely to be influenced by IMF engagement as identified in the PSM analysis above, and we study the change in magnitude of the coefficients associated with the longer-term IMF engagement dummy variable along with the changes in their statistical significance.15 A variable will be considered a likely transmission channel if the coefficient associated with the Fund dummy variable decreases in size and/or significance relative to the benchmark model. All regressions also control for the endogeneity of longer-term IMF engagement through the inverse Mills ratio estimated in the first-stage PSM regression.16
The panel growth regressions corroborate the PSM findings that longer-term IMF engagement appears to support higher real per capita GDP growth. They also help to identify the transmission channels through which this impact is achieved (Table 3.3). The findings include the following:17
Determinants of Long-Term Real Per Capita GDP Growth
Determinants of Long-Term Real Per Capita GDP Growth
| Long-term IMF engagement | 3.52* | 3.35 | 3.18* | 3.53* |
| (2.08) | (2.13) | (1.92) | (2.15) | |
| Inflation | –0.003 | |||
| (0.02) | ||||
| Growth volatility | –0.001 | |||
| (0.12) | ||||
| IMF disbursements | –0.19 | |||
| (1.12) | ||||
| Initial GDP per capita | 0.97 | 0.79 | 0.49 | 0.92 |
| (2.42) | (2.57) | (2.19) | (2.75) | |
| Trade openness | –0.02 | –0.021 | –0.01 | –0.18 |
| (0.04) | (0.04) | (0.04) | (0.04) | |
| Education | 0.14** | 0.13** | 0.13** | 0.14** |
| (0.06) | (0.05) | (0.06) | (0.07) | |
| Change in terms of trade | 0.04 | 0.02 | 0.04 | 0.04 |
| (0.05) | (0.05) | (0.05) | (0.06) | |
| Inverse Mills ratio | –1.80 | –1.58 | –1.56 | –1.80 |
| (1.23) | (1.27) | (1.14) | (1.29) | |
| Constant | –11.78 | –9.55 | –8.60 | –11.48 |
| (14.23) | (15.44) | (13.59) | (16.39) | |
| Observations/countries | 181/54 | 178/54 | 181/54 | 181/54 |
| Arellano-Bond test for AR(1) in first differences | 0.16 | 0.29 | 0.20 | 0.20 |
| Arellano-Bond test for AR(2) in first differences | 0.56 | 0.36 | 0.43 | 0.56 |
| Sargan test of overidentifying restrictions | 1.00 | 0.99 | 0.99 | 1.00 |
| Hansen test of overidentifying restrictions | 0.38 | 0.41 | 0.29 | 0.34 |
Determinants of Long-Term Real Per Capita GDP Growth
| Long-term IMF engagement | 3.52* | 3.35 | 3.18* | 3.53* |
| (2.08) | (2.13) | (1.92) | (2.15) | |
| Inflation | –0.003 | |||
| (0.02) | ||||
| Growth volatility | –0.001 | |||
| (0.12) | ||||
| IMF disbursements | –0.19 | |||
| (1.12) | ||||
| Initial GDP per capita | 0.97 | 0.79 | 0.49 | 0.92 |
| (2.42) | (2.57) | (2.19) | (2.75) | |
| Trade openness | –0.02 | –0.021 | –0.01 | –0.18 |
| (0.04) | (0.04) | (0.04) | (0.04) | |
| Education | 0.14** | 0.13** | 0.13** | 0.14** |
| (0.06) | (0.05) | (0.06) | (0.07) | |
| Change in terms of trade | 0.04 | 0.02 | 0.04 | 0.04 |
| (0.05) | (0.05) | (0.05) | (0.06) | |
| Inverse Mills ratio | –1.80 | –1.58 | –1.56 | –1.80 |
| (1.23) | (1.27) | (1.14) | (1.29) | |
| Constant | –11.78 | –9.55 | –8.60 | –11.48 |
| (14.23) | (15.44) | (13.59) | (16.39) | |
| Observations/countries | 181/54 | 178/54 | 181/54 | 181/54 |
| Arellano-Bond test for AR(1) in first differences | 0.16 | 0.29 | 0.20 | 0.20 |
| Arellano-Bond test for AR(2) in first differences | 0.56 | 0.36 | 0.43 | 0.56 |
| Sargan test of overidentifying restrictions | 1.00 | 0.99 | 0.99 | 1.00 |
| Hansen test of overidentifying restrictions | 0.38 | 0.41 | 0.29 | 0.34 |
The regression results confirm the PSM finding above that longer-term IMF engagement appears to have a positive impact on long-term real per capita GDP growth.
Based on the different specifications of the panel regression, it appears that real per capita GDP growth volatility is a significant transmission channel of the IMF’s longer-term impact on growth.
When both longer-term IMF engagement and the size of net Fund disbursements in the decade are controlled for, only the longer-term IMF engagement dummy is significant. This suggests that, for longer-term growth performance, it is the IMF’s policy support that matters rather than the overall level of financing provided in this context.18
Impact of Short-Term IMF Financing in Low-Income Countries
This section explores the short-term macroeconomic effects of IMF financial support to LICs experiencing immediate balance of payments needs as a result of policy slippages or external shocks.19 The nature of Fund support evaluated in this section differs from the more extensive program support provided through successive medium-term arrangements, as discussed above. Here we focus on short-term IMF financial support, either through augmentations of access under existing medium-term financial arrangements or through short-term or emergency financing instruments. Such support would often be called for when a country faces a pressing balance of payments problem that requires a combination of macroeconomic adjustment and external financing. The IMF’s engagement in these cases would typically involve understandings on short-term macroeconomic adjustment accompanied by Fund financing, which could potentially have catalytic effects inducing additional bilateral and multilateral financing.
Sample selection bias is an even greater methodological challenge when the short-term impact of IMF-supported programs is studied. If countries that are experiencing balance of payments crises owing to policy slippages or exogenous shocks are more likely to participate in IMF-supported programs, failing to correct for selection bias could lead to a flawed conclusion that programs “cause” these crises along with adverse effects on macroeconomic outcomes. As in the previous section, this section employs the PSM methodology. In the first stage, the annual probability of participating in IMF-supported programs is estimated conditional on observable economic conditions and country characteristics. The second stage uses these probabilities, or propensity scores, to match program countries to nonprogram countries, and thereby construct a statistical comparison, or control, group (see Annex 1 for details).
Empirical Analysis
The probability of participation in IMF-supported programs that address policy or exogenous shocks increases with the deterioration in the preshock macroeconomic conditions and the magnitude of the adverse external shocks. The selection model for LIC participation in IMF-supported programs draws on Bal Gündüz (2009). This study finds that lower reserve coverage, a deterioration in the current account balance, weaker real GDP growth, increased macroeconomic instability (evident in higher fiscal deficits, inflation, and exchange market pressures), and adverse terms of trade shocks would increase the likelihood of Fund financing. Moreover, global conditions, including changes in real oil and non-oil commodity prices and world trade, are also significant determinants of participation in IMF-supported programs which could potentially create cycles in demand for Fund financing as a result of adverse global shocks. Finally, persistent differences in the debt service burden and resource inflows among LICs seem to be significantly associated with unobserved country heterogeneity.
While growth is estimated to be 0.9 percent higher than the control group for the full sample, the impact rises to 1¼ to 1¾ percent and becomes significant only for countries with high propensity scores, which indicate immediate balance of payments problems brought about by existing macroeconomic imbalances or external shocks (Table 3.4).20 Furthermore, comparative changes in growth are positive but turn out to be significant only for those with high propensity scores.
Results: Impact of Short-Term IMF Engagement by Propensity Score Matching
All variables except for health and education spending and change in the real effective exchange rate (REER), for which data are more limited.
Results: Impact of Short-Term IMF Engagement by Propensity Score Matching
| All LICs | LICs with weaker fundamentals | ||
|---|---|---|---|
| PS > 0.5 | PS > 0.7 | ||
| Macroeconomic outcomes | |||
| Real GDP growth (%) | 0.88 | 1.24* | 1.76** |
| (0.56) | (0.66) | (0.75) | |
| Inflation (%) | –12.09 *** | –14.30*** | –15.83*** |
| (4.11) | (4.95) | (5.76) | |
| Reserve coverage (in months of imports) | 0.77*** | 0.99*** | 0.81*** |
| (0.15) | (0.16) | (0.16) | |
| Current account balance plus FDI (% of GDP) | 2.26** | 2.98*** | 3.97*** |
| (0.98) | (1.14) | (1.32) | |
| Government balance (% of GDP) | 2.23*** | 2.50*** | 2.89*** |
| (0.63) | (0.77) | (0.88) | |
| Change in real health spending per capita (%) | 8.38* | 8.59 | 13.42 |
| (5.02) | (6.74) | (8.41) | |
| Change in real education spending per capita (%) | 0.85 | –1.85 | –3.45 |
| (3.16) | (4.07) | (5.25) | |
| Change in REER | –3.44 | –3.40 | –5.28 |
| (5.8) | (6.59) | (7.66) | |
| ODA commitments (% of GDP) | 2.19*** | 1.78** | 1.92*** |
| (0.61) | (0.7) | (0.75) | |
| ODA disbursements (% of GDP) | 1.55*** | 1.31** | 1.36** |
| (0.52) | (0.57) | (0.62) | |
| Change in macroeconomic outcomes (X(t)–X(t–1)) | |||
| Real GDP growth (%) | 0.74 | 1.41 | 2.21** |
| (0.75) | (0.87) | (1) | |
| Inflation (%) | –6.45 | –10.47* | –13.96** |
| (4.6) | (5.33) | (6.22) | |
| Reserve coverage (in months of imports) | 0.71*** | 0.93*** | 0.79*** |
| (0.1) | (0.11) | (0.12) | |
| Current account balance plus FDI (% of GDP) | 1.44** | 2.03** | 2.43** |
| (0.71) | (0.84) | (0.97) | |
| Government balance (% of GDP) | 0.78* | 1.06* | 1.41** |
| (0.46) | (0.55) | (0.59) | |
| Change in ODA commitments (% of GDP) | –0.24 | –0.98 | –1.04 |
| (0.72) | (0.87) | (1.01) | |
| Change in ODA disbursements (% of GDP) | –0.15 | –0.57 | –0.73 |
| (0.42) | (0.49) | (0.57) | |
| Number of observations1 | 633 | 349 | 293 |
All variables except for health and education spending and change in the real effective exchange rate (REER), for which data are more limited.
Results: Impact of Short-Term IMF Engagement by Propensity Score Matching
| All LICs | LICs with weaker fundamentals | ||
|---|---|---|---|
| PS > 0.5 | PS > 0.7 | ||
| Macroeconomic outcomes | |||
| Real GDP growth (%) | 0.88 | 1.24* | 1.76** |
| (0.56) | (0.66) | (0.75) | |
| Inflation (%) | –12.09 *** | –14.30*** | –15.83*** |
| (4.11) | (4.95) | (5.76) | |
| Reserve coverage (in months of imports) | 0.77*** | 0.99*** | 0.81*** |
| (0.15) | (0.16) | (0.16) | |
| Current account balance plus FDI (% of GDP) | 2.26** | 2.98*** | 3.97*** |
| (0.98) | (1.14) | (1.32) | |
| Government balance (% of GDP) | 2.23*** | 2.50*** | 2.89*** |
| (0.63) | (0.77) | (0.88) | |
| Change in real health spending per capita (%) | 8.38* | 8.59 | 13.42 |
| (5.02) | (6.74) | (8.41) | |
| Change in real education spending per capita (%) | 0.85 | –1.85 | –3.45 |
| (3.16) | (4.07) | (5.25) | |
| Change in REER | –3.44 | –3.40 | –5.28 |
| (5.8) | (6.59) | (7.66) | |
| ODA commitments (% of GDP) | 2.19*** | 1.78** | 1.92*** |
| (0.61) | (0.7) | (0.75) | |
| ODA disbursements (% of GDP) | 1.55*** | 1.31** | 1.36** |
| (0.52) | (0.57) | (0.62) | |
| Change in macroeconomic outcomes (X(t)–X(t–1)) | |||
| Real GDP growth (%) | 0.74 | 1.41 | 2.21** |
| (0.75) | (0.87) | (1) | |
| Inflation (%) | –6.45 | –10.47* | –13.96** |
| (4.6) | (5.33) | (6.22) | |
| Reserve coverage (in months of imports) | 0.71*** | 0.93*** | 0.79*** |
| (0.1) | (0.11) | (0.12) | |
| Current account balance plus FDI (% of GDP) | 1.44** | 2.03** | 2.43** |
| (0.71) | (0.84) | (0.97) | |
| Government balance (% of GDP) | 0.78* | 1.06* | 1.41** |
| (0.46) | (0.55) | (0.59) | |
| Change in ODA commitments (% of GDP) | –0.24 | –0.98 | –1.04 |
| (0.72) | (0.87) | (1.01) | |
| Change in ODA disbursements (% of GDP) | –0.15 | –0.57 | –0.73 |
| (0.42) | (0.49) | (0.57) | |
| Number of observations1 | 633 | 349 | 293 |
All variables except for health and education spending and change in the real effective exchange rate (REER), for which data are more limited.
Overall, program countries attain significantly higher current account balances, and reserve coverage, as well as lower inflation and fiscal deficits compared to their control groups. Moreover, reflecting the stabilization achieved under IMF-supported programs, these variables tend to post significant improvements during the program, with the impact especially pronounced for the high-propensity group. Although program countries tend to have more depreciated real exchange rates, differences with the control groups are not significant. Changes in real health and education spending per capita are not statistically different from those of the control group.
The estimated positive impact on growth could be attributed to IMF financing (along with its potential catalytic effects) easing the burden of the short-term adjustment as well as restoration of macroeconomic stability, especially for countries experiencing significant levels of instability prior to the program. Both commitments and disbursements of official development assistance (ODA) are significantly higher for the program group. Lower differences in disbursements than those of commitments compared to the control group may suggest room for improving the utilization and predictability of ODA for program countries. However, contrary to the presumed catalytic role of IMF-supported programs, no significant change in ODA is detected. One explanation could be that some countries with high propensity scores could avoid or delay requesting Fund assistance thanks to an ad hoc increase in ODA flows, weakening the estimated catalytic impact for this group. Another explanation could be that ODA provided as budget support may be more responsive to IMF-supported programs than the project support. Additional exploration of this issue is left for further research.
Channels
The empirical work presented above suggests that IMF program support has benefited LICs’ economies in two distinct ways:
Longer-term policy support appears to have helped LICs gradually build macroeconomic buffers. Longer-term program support by the IMF is positively associated with higher long-term growth rates, less growth volatility, more rapid reduction in poverty and inequality, higher government balances, greater social spending, higher FDI, and lower inflation. Noticeably, this result does not seem to depend on the amount of Fund financing provided over the longer term.
Short-term liquidity support to LICs has likely played an important role in mitigating the impact of shocks. Short-term IMF financial support in the context of shocks and policy slippages is positively associated with higher short-term growth, current account balances, and reserve coverage, as well as lower inflation and fiscal deficits compared to their control groups, with the impact especially pronounced for countries with high propensity scores, which indicate immediate balance of payments problems brought about by existing macroeconomic imbalances or external shocks.
What may explain these empirical results?
Longer-Term Engagement
The IMF has had program engagement with almost half of all LICs for more than half of the last 25 years. Many commentators initially saw the emergence of this longer-term engagement as a sign of failure, believing that it must reflect flaws in either the design or the implementation of IMF-supported programs that resulted in a dependence on Fund financing.21 However, the results presented above suggest that, rather than being symptomatic of failure, the prolonged engagement may have led to sustained, if gradual, improvements in the macroeconomic performance of most program countries. The fact that the amount of IMF financing is not in itself significant may point to the importance of nonlending aspects of programs in delivering improved macroeconomic results.
There are several nonfinancial channels through which IMF-supported programs may have an impact:
Ensuring a consistent macroeconomic framework. An IMF-supported program can help a country in developing a comprehensive and coherent macroeconomic framework. This important coordinating role ensures that monetary and fiscal targets are mutually consistent and compatible with macroeconomic stability. Countries with repeated IMF-supported programs were therefore less likely to have imbalances manifested in the form of rapid inflation or unsustainable fiscal and current account deficits that could undermine growth.
Policy advice and conditionality. With the IMF undertaking missions to these countries several times a year, countries with programs tend to have a close engagement with the Fund on key policy issues. The Fund’s advice goes beyond the core area of ensuring macroeconomic and financial stability. It also covers fiscal issues such as strengthening revenue efforts to reduce reliance on external borrowing, public financial management and natural resource revenue management, and the orientation of spending toward pro-poor and pro-growth projects. Significant attention is also paid to monetary issues such as central bank operations and monetary policy, and broader structural reforms aimed at supporting the country’s development strategy. In this way the IMF may have provided a useful independent source of advice to country authorities that could have resulted in improved outcomes.
Debt relief. The IMF has played a major role in supporting the debt relief process that eased the burden of many LICs—first in its role at the Paris Club, and later through participation in and support of the HIPC Initiative and MDRI. By making an IMF-supported program a precondition for debt relief, the international community undoubtedly increased the demand for engagement with the Fund. To some extent, the reduced debt burden that resulted from the debt relief process may also have supported better economic outcomes.
Capacity building. While all member countries are able to benefit from IMF technical assistance (see below), those with programs have traditionally been the most intensive users. The deep engagement implied by IMF-supported programs can bring to light particular weaknesses that the Fund can then help advise on how to solve.
Catalytic role. There is some evidence that an IMF-supported program can encourage additional aid flows from other donors.22
The relative importance of these factors has varied from country to country and over time. In the case of Senegal, for example, the Fund’s most intensive engagement came in the aftermath of the 1994 devaluation of the CFA franc, as the authorities sought to restore the conditions for growth and external balance (Box 3.1). In Mozambique, under somewhat different circumstances, the Fund’s initial value added was in providing a framework for macroeconomic stabilization and supporting market-oriented structural reforms in a post-conflict situation where the state had played a dominant role in the economy (Box 3.2). In more recent years, as the initial imbalances in both countries were addressed, IMF support has become more focused on public financial management, scaling up infrastructure investment, and health and education spending.
IMF Engagement in Senegal
In the early 1990s, unfavorable trade developments combined with weaknesses in fiscal and financial policies led to a deterioration in the macroeconomic situation in a number of countries in the CFA franc zone, which was pegged to the French franc. With the fiscal and balance of payments situation worsening, the countries of the CFA franc zone decided to seek to restore competitiveness by devaluing the exchange rate on January 12, 1994, from CFA francs 50 to CFA francs 100 per French franc. This decision also reflected the advice of the IMF and other donors, which had pointed out the increasing costs of relying on internal adjustment alone.
Following the 1994 devaluation, the government of Senegal undertook a series of adjustment and economic reform programs aimed at restoring the conditions for strong, sustainable economic growth and ensuring domestic and external financial viability. These programs were based on strict management of domestic demand to bring inflation and the government deficit quickly under control, and on implementation of wide-ranging structural reforms aimed at liberalizing the economy, reducing the size of the public sector, and fostering private sector development. The programs also involved implementation of ambitious plans to improve the provision of health and education services and, more generally, to raise the living standards of the most disadvantaged social groups. In this effort to thoroughly restructure its economy, Senegal received substantial assistance from the international community, particularly the IMF (Figure 3.1.1), World Bank, European Union, and several bilateral partners. The implementation of the reform program was coupled with a deepening of the democratic process, decentralization, and the transfer of responsibilities to local governments.
IMF Arrangements in Senegal
(GNI per capita in current U.S. dollars)
Source: IMF staff calculations.Note: ESAF = Enhanced Structural Adjustment Facility; ESF = Exogenous Shocks Facility; GNI = gross national income; PRGF = Poverty Reduction and Growth Facility; PSI = Policy Support Instrument; SBA = Stand-By Arrangement.IMF Arrangements in Senegal
(GNI per capita in current U.S. dollars)
Source: IMF staff calculations.Note: ESAF = Enhanced Structural Adjustment Facility; ESF = Exogenous Shocks Facility; GNI = gross national income; PRGF = Poverty Reduction and Growth Facility; PSI = Policy Support Instrument; SBA = Stand-By Arrangement.IMF Arrangements in Senegal
(GNI per capita in current U.S. dollars)
Source: IMF staff calculations.Note: ESAF = Enhanced Structural Adjustment Facility; ESF = Exogenous Shocks Facility; GNI = gross national income; PRGF = Poverty Reduction and Growth Facility; PSI = Policy Support Instrument; SBA = Stand-By Arrangement.IMF Engagement in Mozambique
Twelve years after independence in 1975, a legacy of war, socialist experiments, and weak institutions had left Mozambique ranked as the world’s poorest economy. Tax revenue and export earnings had collapsed, the debt burden had spiraled out of control, and physical and human capital had collapsed. A development strategy focused on large state farms and import-substitution had failed to deliver growth, and had fostered inefficiencies and rent-seeking that compounded the unfavorable external environment, exacerbated the ongoing conflict, and led to faltering agricultural and industrial production. With the central planning system increasingly questioned, the authorities sought support from the IMF and World Bank on economic reforms (Figure 3.2.1).
IMF Arrangements in Mozambique
(GNI per capita in current U.S. dollars)
Source: IMF staff calculations.Note: ECF = Extended Credit Facility; ESF = Exogenous Shocks Facility; GNI = gross national income; PSI = Policy Support Instrument.IMF Arrangements in Mozambique
(GNI per capita in current U.S. dollars)
Source: IMF staff calculations.Note: ECF = Extended Credit Facility; ESF = Exogenous Shocks Facility; GNI = gross national income; PSI = Policy Support Instrument.IMF Arrangements in Mozambique
(GNI per capita in current U.S. dollars)
Source: IMF staff calculations.Note: ECF = Extended Credit Facility; ESF = Exogenous Shocks Facility; GNI = gross national income; PSI = Policy Support Instrument.Early IMF programs focused on reestablishing a system of market prices as a precondition for economic stabilization and output recovery. Measures included adjustments of the official exchange rate and a progressive reduction in the number of commodities subject to price control, tariff reforms, and the phasing out of import licensing schemes and export retention schemes. Subsidies on consumer items were replaced by food distribution as well as income supplements for the urban poor. Fiscal measures focused on improving revenue mobilization and containing recurrent expenditure.
The move to a market-based economy in 1987 led to a sustained economic recovery. Real GDP growth reached an average of about 7 percent from 1987 to 1991, and inflation declined from more than 160 percent in 1987 to below 35 percent in 1991. Growth increased following the cease-fire in 1992, driven by large industrial projects (“megaprojects”) financed by foreign direct investment. Real GDP grew by an average of 8 percent between 1993 and 2012. However, while poverty rates have declined, growth has become less pro-poor, as it has relied on the role of megaprojects, which have had a limited effect on generating employment and fiscal revenues. Reflecting Mozambique’s strong policy track record of reform, the country received over $6 billion in debt relief under the HIPC Initiative (2001) and MDRI (2006). It is also one of the largest recipients of aid and has benefited from Fund advice on management of scaled-up aid and, more recently, on the management of natural resources.
Recent IMF programs have placed a greater emphasis on poverty reduction. The structural objectives include strengthening government revenue, enhancing the efficiency and transparency of government operations, and improving bank supervision and financial sector intermediation. Mozambique is working closely with the Fund to strike a balance between addressing pressing infrastructure bottlenecks and taking into account the risks entailed in a rapid expansion of external debt.
Moreover, to the extent that IMF engagement across the membership can deliver some of these benefits, the results presented above may understate the impact of Fund support. The IMF carries out bilateral surveillance of the entire membership through the annual Article IV process, which can be particularly useful in the case of LICs, where alternative sources of comprehensive macroeconomic information are often scarce. These discussions can also provide an important opportunity for the authorities to assess the coherence of their domestic and external policies. A survey by the IMF’s Independent Evaluation Office (2012) reported that three-quarters of LIC authorities found that the Fund had been “effective” or “very effective” in contributing to the development of policy frameworks. The published Article IV reports are widely used by the donor and investor community as an input in their aid and investment decisions.23
Similarly, the impact of the IMF’s work on capacity-building is not limited to program countries, since the Fund provides technical assistance in its areas of core expertise to the entire membership. On average, around 40 percent of IMF technical assistance is provided to LICs. IMF Resident Representatives can also provide frequent and timely policy advice. In countries where aid is an important source of finance for the budget and the balance of payments, the Resident Representative often plays a significant role in working with the authorities and the donor community in support of aid effectiveness and harmonization. While most Resident Representatives are in program countries, with a presence in over 80 countries, many countries without or between programs also have Resident Representatives.
Short-Term IMF Financial Support
The impact of short-term financial support is somewhat easier to discern. For example, the global SDR allocation along with the IMF’s sharp increase in financial assistance in 2009—doubling access and increasing commitments to roughly four times the historical average—helped relax countries’ liquidity constraints at the height of the global financial crisis, which allowed them to preserve or even increase spending.24,25 The combination of stronger pre-crisis buffers and crisis financing allowed most LICs to mount a countercyclical fiscal policy response in 2009—a first for these countries, which in past crises tended to cut spending and tighten the fiscal stance (sometimes also running arrears).26 This domestic response facilitated a rapid economic recovery in LICs, which in past crises had lagged behind the rest of the world.
See Bird (2007) and Steinwand and Stone (2008) for comprehensive surveys.
Andersen, Hansen, and Markussen (2006), Barro and Lee (2005), Oatley and Yackee (2004), Dreher, Sturm, and Vreeland (2006), Dreher and Jensen (2007), Stone (2002, 2004), and Presbitero and Zazzaro (2012) explore the impact of various political variables, including the size of governments, quotas at the IMF, various instruments for U.S. and European influence, and the number of veto players.
Although some individual political factors appear to be significant, they do not significantly improve the predictive power of models (Bird and Rowlands, 2001).
Only three variables are significantly related to participation in IMF arrangements in LICs—previous Fund arrangements, high inflation, and the rescheduling of debt—and explanatory power vis-à-vis middle-income countries is weaker.
Bird, Hussain, and Joyce (2004), Conway (2007), Joyce (2005), and Independent Evaluation Office (2002).
Mercer-Blackman and Unigovskaya (2000) report a positive association between growth and compliance in transition economies. Dreher (2006) finds that increased compliance reduces the negative impact of programs on growth.
For early studies Ul Haque and Khan (1998) conclude that those that do not correct for selection bias (before-after and with-without approaches) yield less-favorable results compared to studies using the Generalized Evaluation Estimator (GEE). 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.
Barro and Lee (2005) is a prominent example of this literature strand.
The country sample comprises up to 75 of the 78 countries that were eligible to receive the IMF’s concessional support as of January 1, 2010. Timor-Leste, Somalia, and Tonga were excluded because of severe lack of data.
While the current account for LICs with longer-term IMF engagement seems to have deteriorated over the last two decades, the current account adjusted for FDI has significantly improved, pointing to the likely high import content of FDI.
This allows for a larger set of observations and also reduces the possible bias from arbitrarily selecting a 10-year period. The periods from which decadal averages are generated are: 1986–95, 1991–2000, 1996–2005, and 2001–10.
This is captured by examining cases in which there was a delay of more than six months in completing a review owing to noncompliance with macroeconomic performance criteria. The program interruptions series is taken from Bal Gündüz (2009) and updated by the authors of this study for the period from 2008–11.
Some evidence of this is provided in the subsequent section on the impact of short-term IMF engagement, which corroborates the catalytic role of the Fund. Regarding debt, alternative participation equations do show that countries that received HIPC/MDRI debt relief face a higher probability of Fund engagement. However, even after controlling for the HIPC/MDRI status of countries in the sample, the second-stage regression results remain broadly the same. Regarding the nonsignificant impact of longer-term IMF engagement on debt, multiple events can be taking place within the span of a decade (which is our unit of measurement). For example, Fund programs may have catalyzed other donor financing on more concessional terms, thus reducing the burden on countries’ budgets but increasing the debt-to-GDP ratios. An additional explanation includes the fact that debt relief operations may have allowed many LICs to scale up a significant amount of financing for their large capital spending projects (though in principle under sound debt sustainability analyses) aimed at fostering long-term growth and strengthening countries’ capacity to repay.
The variables used to augment the growth regressions must have turned out to be significant in the PSM analysis in conjunction with being common determinants of growth in the literature.
The inverse Mills ratio is the ratio of the probability density function to the cumulative density function of a distribution.
Mumssen and others (2013) report robustness of these results. The results presented so far are broadly consistent under four robustness checks entailing (1) changing the periods of analysis; (2) running alternative participation equations; (3) not correcting for IMF program implementation; and (4) including additional controls for the impact analysis, namely, donor aid and HIPC/MDRI. Furthermore, the authors conducted Rosenbaum’s analysis for sensitivity to hidden bias and found that the results are less sensitive to hidden bias for all outcome variables compared to relevant thresholds.
The findings remain the same when gross IMF disbursements are controlled for to take into account the fact that over a 10-year period an IMF loan is typically repaid in full.
The set of arrangements include those addressing an immediate balance of payments need arising from policy or exogenous shocks. SBAs, SAF/ESAF/PRGF/ECF augmentations, and Compensatory Financing Facility (CFF), ESF, SCF, and RCF arrangements are included in this set. The sample period covers 1980–2010. More details are provided in Annex 1.
Mumssen and others (2013) report extensively on the robustness of these results to four sensitivity analyses: (1) relaxing the adjustment made for the implementation record of programs; (2) setting the sample to 1980–99 to improve the comparability of results to earlier research; (3) conditioning matching on propensity score and official development assistance disbursements to explore the Fund impact at similar levels of assistance; and (4) conditioning matching on propensity score and lagged GDP growth to examine whether the positive growth impact is driven by a cyclical recovery in program countries having very weak growth prior to the program. Results turn out to be robust to these tests. Further, they conduct Rosenbaum’s sensitivity analysis to hidden bias and find that results are less sensitive to hidden bias for all outcome variables for countries with high propensity scores.
See Independent Evaluation Office (2002) for such a discussion.
Bird and Rowlands (2007) explore the extent to which the IMF has had a catalytic effect on official development assistance and the potential channels through which catalysis might work. They find strong evidence of a positive association between participation in IMF-supported programs and ODA and suggest that this may have more to do with conditionality than with the provision of IMF resources.
In recent years, the IMF’s surveillance has become increasingly transparent. In 2011, 98 percent of member countries agreed to the publication of a Public Information Notice, which provides information on the IMF Executive Board’s assessment of the member’s macroeconomic and financial situations; and 91 percent published the Article IV consultation staff report (stand-alone or combined with an assessment of an IMF-supported program or other related matter) that was the basis of the board’s assessment.
This came in addition to the Fund’s response to the food and fuel price shocks of 2008, when various new programs and augmentations were approved and the ESF was modified to better support eligible members.
See IMF (2010). For a detailed analysis of program design and objectives, and outcomes, see IMF (2012c).
