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Stylized Facts Before IMF-Supported Macroeconomic Adjustment

Author(s):
International Monetary Fund. Research Dept.
Published Date:
January 1996
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In the four decades following the introduction in 1952 by the International Monetary Fund (IMF) of the first facility recognizable as a standby arrangement, more than 800 regular IMF arrangements spanning 132 member countries have been approved. The use of IMF resources has changed substantially over the decades. What started as balance of payments support for industrial countries (the 1952 stand-by arrangement was with Belgium) gradually became destined to serve developing countries.1 After a timid beginning in the 1950s, developing countries were receiving the majority of the increasing number of IMF arrangements in the 1960s.

The collapse of the Bretton Woods system in the 1970s temporarily interrupted the increasing trend in the use of IMF resources, but after the onset of the debt crisis in developing countries more than 250 financial arrangements were approved in the 1980s, committing in excess of SDR 60 billion of the IMF’s resources. This vigorous pace in the number of arrangements and amount of resources committed has continued in the 1990s with the restructuring of the centrally planned economies of Eastern Europe and the former Soviet Union into market-based systems.

As the IMF has adapted to the world’s changing circumstances, so have the modalities of its financial assistance. After the historic introduction of the stand-by arrangement in 1952, what would later become known as the compensatory and contingency financing facility (CCFF) was established in 1963 to provide financial assistance to members experiencing exogenous shortfalls in export earnings. In 1974 the extended Fund facility (EFF) was introduced to allow member countries to adopt a medium-term horizon in solving their balance of payments adjustment problems; in 1986 the structural adjustment facility (SAF) was established to provide financial assistance on concessional terms to low-income members, and one year later this concessional assistance was expanded through the enhanced structural adjustment facility (ESAF). In 1992 the systemic transformation facility (STF) was introduced to assist member countries in the transition toward a market-based system.2

After four decades of IMF financial arrangements, a conventional wisdom has emerged in circles of academics and practitioners alike regarding the economic situation of those countries that turn to the IMF. This conventional wisdom was initially influenced by the experience and the analytical methods that prevailed during the Bretton Woods period, when a system of fixed-but-adjustable par values provided the setting for the analysis, and then the conventional wisdom gradually incorporated new developments in both theoretical and applied economics. According to the conventional wisdom, there is the presumption of a dramatic deterioration in the economic conditions in the period prior to an IMF-supported macroeconomic adjustment program. Since a “balance of payments need” is a prerequisite for IMF financial assistance, it is not surprising that the requesting country faces critical conditions in the balance of payments. Another piece of the conventional wisdom is that the macroeconomic disequilibria prior to the approval of a financial arrangement can be characterized by high or rising inflation, real exchange rate appreciation, and low or declining growth in GDP.3 However, in spite of four decades of IMF arrangements, the evidence gathered to support or refute this conventional wisdom is scant and, at best, mixed.

In fact, the bulk of the analysis in the literature that studies IMF-supported programs has focused on the macroeconomic effects of the adjustment programs, and much less research has been done on the characteristics of countries that enter into IMF-supported programs. It would be no exaggeration to claim that despite the long experience with IMF-supported programs over the decades, much more is known about the ex post effects of IMF-supported programs than about the factors that lead up to the adoption of programs in the first place.4 The sparse literature that has dealt with the period before the adoption of an adjustment program has mostly focused on the “demand for” IMF arrangements.5 This study attempts to bridge this gap in the literature.

This paper studies initial conditions or stylized facts before macroeconomic adjustment supported by IMF financial arrangements is undertaken. It presents evidence from 104 IMF arrangements in 74 non-oil developing countries during the period 1973–91, and thus complements previous work by Knight and Santaella (1994) on the determinants of IMF arrangements. The paper also documents differences and similarities across decades in these stylized facts before the inception of IMF-supported macroeconomic adjustment. The choice of sample was made for both practical and conceptual reasons. On the practical side, data limitations precluded extending the sample to more countries or capturing earlier IMF arrangements. Conceptually, the time period under consideration corresponds to the post-Bretton Woods system, a period that has not been studied sufficiently and, given that the conventional wisdom was mostly forged during the Bretton Woods period, it has spurred many academic and policy making controversies.6 Nevertheless, the sample used in this investigation is quite representative since it covers a significant number of financial arrangements approved during this period. The methodological approach followed here is very basic, relying heavily on the use of simple descriptive statistics, and is similar to the empirical analysis of inflation and devaluation episodes of Harberger (1981 and 1988) and Edwards (1989), among others. This approach avoids the need to impose behavioral assumptions to analyze the empirical evidence, which contrasts with the approach of Knight and Santaella (1994), where explicit demand and supply of IMF arrangements were postulated in order to analyze the empirical determinants of IMF arrangements.

By documenting the initial macroeconomic conditions of IMF-supported macroeconomic adjustment, this paper addresses some important questions.7 First, better knowledge of the initial conditions would enhance the design of macroeconomic policies geared to tackle the macroeconomic disequilibria of IMF member countries. Second, a complete characterization of the stylized facts before adoption of IMF-supported macroeconomic adjustment would also improve the ex post evaluation of IMF programs. Solid evidence about the initial macroeconomic conditions would enhance the robustness of the results of a “before-after” type of evaluation, and would also document observable differences between program and nonprogram episodes in order to undertake a “with-without” type of evaluation.8 Third, such work would shed some light on the controversial subject of the origins of the macroeconomic imbalances that give rise to IMF-supported programs. At the risk of oversimplifying the issues, one can look at two extreme positions. One group of observers argues that macroeconomic imbalances before IMF-supported programs generally originate in the external conditions faced by the country in question, such as deterioration in the terms of trade, a slowdown of demand for the country’s exports, or adverse conditions in international financial markets. At the opposite end of the spectrum, another group of observers contends that imbalances are endogenous to the country and are brought about mostly by the government pursuing unsustainable macroeconomic policies. Reality, however, is rarely either black or white, and the case studies by Killick and Malik (1992) show that in most cases a combination of both external and internal factors have led to IMF arrangements.

The paper is organized as follows. Section I gathers the stylized facts that lead a country to seek a macroeconomic adjustment program supported by IMF financial arrangements. Empirical regularities are presented for a series of macroeconomic indicators in the three-year period preceding the approval of an “initial” IMF financial arrangement. As explained below, the analysis focuses only on the first of a possible sequence of IMF arrangements since this “initial” arrangement is taken to represent the beginning of the macroeconomic adjustment process. To obtain a clear idea of the extent of economic deterioration before the inception of IMF-supported adjustment programs, a control group of nonprogram observations is included as a benchmark. The comparison with a control group will allow explicit and precise identification of how the economic situation in program countries differs from the situation associated with normal economic conditions. Section II contains the statistical analysis and presents a series of nonparametric tests that are computed to study to what extent countries initiating a program are “significantly different” from the control group. This analysis is complemented by the use of discriminant analysis to ascertain which types of variables are most useful in distinguishing program episodes from non-program observations. Section III returns to the historical dimension of the paper by comparing the preprogram characteristics of countries undergoing macroeconomic adjustment supported by an IMF arrangement in the 1970s with those in the 1980s. Finally, Section IV concludes by summarizing the interpretation of the evidence.

I. The Three-Year Period Preceding IMF-Supported Macroeconomic Adjustment

This section examines the behavior of a selected group of macroeconomic variables in the three-year period before the initiation of macroeconomic adjustment supported by IMF financial arrangements. The evidence presented below shows a particularly feeble economic situation in member countries one year before the inception of initial IMF financial arrangements: weak balance of payments, low output growth rates, tough external conditions, and expansive financial policies. The economic situation is not as precarious three years before the outset of IMF-supported adjustment programs. Important deteriorations over this three-year period occur in the overall balance of payments, stock of international reserves, external current account, output growth, terms of trade, external indebtedness, fiscal stance, and credit expansion. These stylized facts conform only partially to the conventional wisdom.

The sample of IMF financial arrangements was obtained from a total population of 91 non-oil developing countries during the period 1973–91. Encompassing the four types of IMF financial arrangements that contain conditionality elements (stand-by arrangements in the upper credit tranches, EFF, SAF, and ESAF), 324 arrangements were approved by the IMF in support of adjustment programs for this population (Table 1). The vast majority of these arrangements were consecutive, with an initial arrangement followed by “successor” arrangements. These consecutive strings of arrangements can really be thought of as a single macroeconomic adjustment program. Since this paper focuses only on the initial conditions before the adjustment programs are undertaken, only the initial arrangement of a sequence was considered in the sample of program episodes. Inclusion of “successor” arrangements in the characterization of macroeconomic conditions before programs would have distorted the picture because they were already preceded by some macroeconomic adjustment. To capture only the outset arrangements, an arbitrary selection criterion was defined: arrangements would be included in the sample of program episodes if the member country had not been involved in any other arrangement during the previous three years. This criterion yielded 104 program episodes in 74 countries and for each of those programs annual data were covered for the three-year period preceding the outset arrangement.9

To assess the distinctive features of the program episodes, the behavior of the main economic variables is compared with a control group of observations. In studies of the ex post effects of IMF-supported programs, a control group is typically used to obtain a counterfactual; namely, assuming that the nonprogram countries share the same exogenous environment as the program countries, the control group provides an approximation of what would have happened in the absence of a program.10 The objective of a control group in this study is rather different: it is to capture the macroeconomic performance that predominates under “normal circumstances” in countries that do not enter into an IMF-supported program over an extended period of time. The control group thus includes countries that do not enter into IMF arrangements either because their economic conditions do not warrant one or because, in spite of having the need, they do not feel inclined to enter into one (Knight and Santaella (1994)). Given the obvious difficulty of disentangling these two types of countries in the control group, no attempt will be made here to separate them. However, countries that do not enter into an arrangement because they do not need one are likely to dominate the characteristics of the whole group, since it would be hard for a country in need of an arrangement to avoid a program indefinitely. This feature of the control group would make it a legitimate indicator of macroeconomic performance in “normal” or “noncritical” circumstances.

Table 1.Program Episodes and Control Group Observations
1973197419751976197719781979198019811982198319841985198619871988198919901991
AfghanistanUCT
AlgeriaUCT
ArgentinaUCTUCTUCTUCTUCTUCTUCT
Bahrain
BangladeshUCTUCTEFFUCTUCTSAFESAF
BarhadosUCT
BeninSAF
BoliviaUCTUCTUCT.SAFESAF
Botswana
BrazilEFFUCT
Burkina FasoSAF
BurundiUCT.SAFESAF
CameroonUCTUCT
Central African RepublicUCTUCTUCTUCTUCTUCT.SAF
ChadSAF
ChileUCTUCTUCTEFF
China. People’s Republic of
Colombia
CongoUCTUCTUCT
Costa RicaUCTEFFUCTUCTUCTUCTUCT
Cote d’IvoireEFFUCTUCTUCTUCTUCTUCT
CyprusUCT
Dominican RepublicEFFUCTUCT
EcuadorUCTUCTUCTUCTUCTUCT
EgyptUCTEFFUCTUCT
El salvadorUCTUCT
EthiopiaUCT
Fiji
GabonUCTEFFUCTUCTUCT
Gambia, TheUCTUCTUCT.SAFESAF
GhanaUCTUCTUCTUCTEFF.SAFESAf
GuatemalaUCTUCT
GuineaUCTUCTUCT.SAFESAF
GayanaUCTEFFEFFUCT.SAF
HaitiUCTUCTEFFUCTUCTSAFUCT
HondurasEFFUCTUCT
IndiaEFFUCT
IndonesiaUCT
IsraelUCTUCT
JamaicaUCTUCTEFFEFFEFFUCTUCTUCTUCTUCTUCT
JordanUCT
KenyaEFFUCTUCTUCTUCTUCTUCT.SAFESAF
KoreaUCTUCTUCTUCTUCT
Lehanon
LesothoSAFESAF
LiberiaUCTUCTUCTUCTUCT
MadagascarUCTUCTUCTUCTUCTUCTSAFUCTESAF
MalawiUCTUCTUCTEFFUCT.ESAF
Malaysia
MaliUCTUCTUCTUCT.SAF
Malta
MauritaniaUCTUCTUCTUCT.SAFUCTESAF
MauritiusUCTUCTUCTUCTUCT
MexicoEFFEFFUCTEFF
MoroccoEFFEFFUCTUCTUCTUCTUCTUCT
MyanmarUCTUCTUCTUCT
NepalUCTSAF
NiearaguaUCTUCT
NigerUCTUCTUCTUCT.SAFESAF
NigeriaUCTUCTUCT
PakistanUCTUCTUCTEFFEFFUCT.SAF
PanamaUCTUCTUCTUCTUCTUCT
Paraguay
PeruUCTUCTUCTEFFUCT
PhilippinesUCTEFFUCTUCTUCTUCTUCTEFFUCT
RomaniaUCTUCTUCTUCT
RwandaSAF
SenegalEFFUCTUCTUCTUCTUCT.SAFUCTESAF
Sierra LeoneUCTEFFUCTUCT.SAF
Singapore
SomaliaUCTUCTUCTUCTUCT.SAF
South AfricaUCTUCT
Sri LankaUCTUCTEFFUCTSAFESAF
SudanUCTUCTEFFUCTUCTUCT
Swaziland
Syrian Arab Republic
TanzaniaUCTUCTUCTSAFESAF
ThailandUCTUCTUCT
TogoUCTUCTUCTUCTUCTUCT.SAFESAF
Trinidad and TubugoUCTUCT
TunisiaUCTEFF
TurkeyUCTUCTUCTUCTUCT
UgandaUCTUCTUCTUCTSAFESAF
UruguayUCTUCTUCTUCT
VenezuelaEFF
Western SamoaUCTUCTUCT
Yemen Arah Republic
Yemen, P.D, Republic
YugoslaviaUCTUCTUCTUCTUCTUCT
ZaïreUCTUCTEFFUCTUCTUCTUCT.SAFUCT
ZambiaUCTUCTEFFUCTUCTUCT
Key: UCT; Stand-by arrangement in upper credit tranches; EFF: extended Fund facility; SAF: structural adjustment facility; ESAF: enhanced structural adjustment facility. Black cells indicate program observations and shaded cells indicate control group observations.
Key: UCT; Stand-by arrangement in upper credit tranches; EFF: extended Fund facility; SAF: structural adjustment facility; ESAF: enhanced structural adjustment facility. Black cells indicate program observations and shaded cells indicate control group observations.

Specifically, in this study the control group observations were selected from the same 91-country population during 1973–91 as the program country observations were selected from. The control group observations were defined as those country-year observations that did not have an IMF-supported program in place for at least the next three consecutive years. Three years without an IMF financial arrangement is an arbitrary but reasonable selection criterion that yields a sample of 693 observations spanning 84 different countries (see Table 1). In sum, of 1,729 potential observations in the population, 40 percent were classified as control group observations and 6 percent as program episodes; each of the latter comprises a period of three years prior to the adoption of an IMF financial arrangement.

In comparing the evolution of economic developments during the period preceding the adjustment program for program episodes with those for the control group, a distinction is made between the behavior of macroeconomic targets or outcome variables, external sector indicators, and domestic policy variables. This distinction is useful in dealing with the origins of the macroeconomic disequilibria that lead to an adjustment program. As a first step, the characteristics of the two sample groups are described, using some basic descriptive statistics. Next, in Section II, a formal statistical analysis is performed to test for the apparent differences in macroeconomic indicators between the two groups.

Macroeconomic Outcomes

Table 2 shows the evolution of the sample distribution of some macroeconomic indicators—the overall balance of payments, the current account, the stock of international reserves, the inflation rate, the growth of per capita GDP, the domestic investment rate, and the real effective exchange rate— over the three-year period before the inception of IMF-supported macroeconomic adjustment. Table 2 compares the distributions of these variables in the third, second, and first year before the initiation of the outset arrangement— labeled “t–3,” “t–2,” and “t– 1”—with the control group’s sample distribution. Figure 1 displays this evolution over time for the median program episodes against the first, second, and third quartiles of the control group.

The stylized facts that emerge from this evidence are promising. As expected, the most consistent pattern that can be inferred is that IMF-supported macroeconomic adjustment programs are preceded by a sharp deterioration in the country’s external accounts. Three years before the program, the median overall balance of payments is –0.4 percent of GDP, compared with a balance of 0.6 percent of GDP for the control group. The balance of payments of program episodes then worsens sharply to reach –2.4 percent of GDP in the year prior to the program, a larger deficit than the first quartile of the control group.

Table 2.Macroeconomic Outcomes in Program and Control Groups: Distribution of Samples
Program episodesControl
t – 3t – 2t – 1group
Balance of payments (percentage of GDP)
First quartile–2.6–3.3–5.5–1.6
Median–0.4–1.1–2.40.6
Third quartile1.50.4–0.53.2
Mean1.7–2.5–4.70.5
Standard deviation7.57.510.88.5
N104104104693
Current account (percentage of GDP)
First quartile–7.4–8.2–9.5–7.3
Median–3.4–5.1–6.1–3.2
Third quartile–0.8–2.2–3.30.5
Mean–5.5–6.5–8.0–3.9
Standard deviation8.57.38.19.4
N104104104693
International reserves (months of imports)
First quartile1.51.10.82.0
Median2.12.21.54.1
Third quartile4.03.52.76.8
Mean3.12.51.85.2
Standard deviation2.32.11.94.7
N103104104693
Consumer prices (percentage change)
First quartile6.17.36.66.3
Median10.812.412.011.4
Third quartile22.022.722.717.5
Mean157.178.0209.720.1
Standard deviation1,402.2481.41,357.357.8
N104104104693
GDP per capita (percentage change)
First quartile–2.5–2.6–4.0–1.1
Median1.30.7–0.42.2
Third quartile6.03.63.55.4
Mean1.70.2–0.51.9
Standard deviation6.54.86.67.2
N104104104693
Investment (percentage of GDP)
First quartile15.916.115.517.4
Median23.123.123.523.0
Third quartile28.329.028.830.2
Mean23.523.223.324.6
Standard deviation10.610.010.010.2
N100100100635
Real effective exchange ratea
(percentage change)
First quartile–4.1–4.1–5.9–4.5
Median0.91.20.90.6
Third quartile6.56.56.06.1
Mean5.04.16.62.6
Standard deviation23.725.950.130.6
N102102102671

A minus sign (–) denotes a depreciation.

Sources: World Economic Outlook database; and author’s calculations.

A minus sign (–) denotes a depreciation.

Sources: World Economic Outlook database; and author’s calculations.

The behavior of the balance of payments is almost mirrored by the evolution of the stock of international reserves: three years before the program the median stock of international reserves is 2.7 months of imports—in contrast to a median of 4.1 months of imports for the control group—and it falls to only 1.5 months of imports by the year prior to the adjustment program, below the 2 months of imports of the first quartile of the control group.

The external current account also shows a sharp deterioration before the inception of IMF-supported macroeconomic adjustment. It starts from a level similar to that observed in the control group: the median current account deficit in the program episodes is 3.4 percent of GDP three years before the arrangement, compared with the 3.2 percent for the control group. However, the median current account deficit in the program group falls to 6.1 percent of GDP one year before the outset arrangement.

Another important feature of the program episodes is that the dispersion of the stock of international reserves is substantially smaller (and centered around a lower level) for the program episodes than for the control group. Clearly, program episodes start from a weak balance of payments position that deteriorates markedly to almost unsustainable levels of foreign exchange reserves before the initial arrangement’s approval.

Figure 1.Economic Indicators Before Adjustment

Source: Table 2.

aA minus sign (–) indicates a depreciation.

The comparative behavior of the other macroeconomic outcome variables for program episodes and for the control group is less dissimilar. In fact, the behavior of inflation seems to be at variance with the conventional wisdom of accelerating inflation before the approval of an IMF arrangement. The median inflation rate is lower initially in program episodes than in the control group, although the difference is fairly small (10.8 percent per annum three years before the program versus 11.4 percent in the control group). Interestingly enough, the median inflation rate in the program episodes rises somewhat as the adjustment draws nearer but does not accelerate markedly: it reaches only 12.0 percent one year before the outset arrangement. The dispersion of inflation is, nevertheless, substantially higher in the program episodes than in the control group. In fact, high inflation program observations—such as those corresponding to the arrangements in the aftermath of hyperinflations in Bolivia and Nicaragua—drive the average inflation rate to well above the median inflation for this group, a feature that also appears in the growth rates of other nominal variables as described below.

The behavior of the real exchange rate is broadly compatible with the conventional wisdom of an exchange rate appreciation before IMF-supported macroeconomic adjustment is undertaken. The median real exchange rate of program episodes appreciates continuously as the program gets closer: about 1 percentage point per annum in each of the three years prior to the program, compared with only 0.6 percent per annum for the control group. However, looking at a central tendency figure can be misleading in this case, since program episodes also exhibit a more volatile real exchange rate than the control group, a volatility that rises as the outset arrangement approaches. This is evidenced by the larger dispersion one year before the adjustment’s inception. In fact, one year before the program almost half (46 percent) of the episodes in the program group were depreciating the real exchange rate, a close proportion to that observed in the control group (47 percent).

Also in line with the conventional wisdom, the median GDP per capita growth rate is substantially lower in the period prior to the programs than in the control group, and it deteriorates steadily from 1.3 percent in t— 3 to –0.4 percent in t— 1 (compared to a median growth of GDP per capita of 2.2 percent a year in the control group). Another interesting observation is that program episodes exhibit a fairly constant median rate of domestic investment of about 23 percent of GDP during the three-year period, much like that observed in the control group.

External Indicators

This subsection focuses on the following variables for the external sector: the terms of trade, growth of export markets, and external debt indicators. As can be seen from Table 3 and Figure 2, the median terms of trade deteriorates over time for the program group, falling a cumulative 5.8 percent over the three years prior to the arrangement—with the largest deterioration occurring one year before the outset arrangement—while for the control group there is a modest improvement in the terms of trade (0.6 percent per annum). Interestingly enough, both samples exhibit roughly the same degree of terms of trade variability according to the interquartile range and the standard deviation. The median growth rate of export markets is slightly higher for the program episodes three years before the program than for the control group (3.8 percent compared with 3.5 percent a year), but it deteriorates somewhat over time for the program group (3.3 percent one year before the outset arrangement).

Table 3.External Indicators in Program and Control Groups: Distribution of Samples
Program episodesControl
t – 3t – 2t – 1group
Terms of trade (percentage change)
First quartile–8.0–11.3–9.8–6.9
Median–0.3–1.5–3.90.6
Third quartile11.76.64.29.3
Mean1.3–1.7–2.13.4
Standard deviation19.517.318.220.8
N104104104693
Export markets (percentage change)
First quartile2.52.52.22.3
Median3.83.53.33.5
Third quartile4.54.44.34.7
Mean3.63.43.13.5
Standard deviation1.41.61.71.8
N103103103649
External debt service (percentage of exports)
First quartile8.49.111.14.5
Median17.318.519.410.1
Third quartile29.436.031.119.0
Mean21.626.425.114.1
Standard deviation17.431.221.413.4
N104104103693
External debt (percentage of GDP)
First quartile19.621.323.612.4
Median34.838.443.124.8
Third quartile51.754.459.441.4
Mean48.753.062.233.6
Standard deviation84.392.7121.635.6
N104103103693
Sources: World Economic Outlook database; and author’s calculations.
Sources: World Economic Outlook database; and author’s calculations.

Figure 2.External Environment Before Adjustment

Source: Table 3.

Finally, both the external debt service and the total external debt ratios show sharp differences between the program episodes and the control group.11 Although these two variables are not entirely exogenous because they largely reflect policy decisions undertaken in previous periods, external debt service has an important exogenous component: the interest rate prevailing in international capital markets. The median external debt-service ratio for program episodes (17.3 percent of exports) is already close to that for the third quartile of the control group three years before the program. This ratio grows steadily over time, reaching 19.4 percent of exports in the year prior to the arrangement. Similarly, total external debt is substantially higher for program episodes than for the control group. The median debt-to-GDP ratio starts at 34.8 percent and rises over time to reach 43.1 percent of GDP one year before the outset arrangement, above the third quartile of the control group. Also evident from the data is the higher dispersion of these two variables for the program group than for the control group, reflecting the inclusion in the program episodes of some countries that at some point exhibited acute external debt problems, such as Brazil, Ecuador, Mexico, and Nicaragua, to name a few.

Policy Variables

The next piece of evidence deals with the behavior of economic policy variables—monetary, fiscal, and nominal exchange rate indicators. The overall picture that emerges is that program countries differ substantially from the control group with respect to their fiscal, credit, and exchange rate policy stances, but not so much with respect to the growth of their broad monetary aggregates.

The three fiscal policy indicators (the fiscal balance, the flow of net government borrowing from the banking system, and the rate of expansion of domestic credit to the government) show that the fiscal performance of program countries is substantially weaker than that prevailing in the control group. For example, the median fiscal deficit in program countries three years before the program is 5.1 percent of GDP, compared with 3.4 percent in the control group, while the median rate of growth of domestic credit to the government in program countries in the same period (t–3) is 22.7 percent a year compared with 15.9 percent in the control group (Table 4 and Figure 3). Moreover, the fiscal policy stance deteriorates markedly during the three-year period prior to the approval of the financial arrangement: the median fiscal deficit reaches a startling 6.9 percent of GDP and the median rate of growth of domestic credit to government surpasses 42 percent one year before the inception of the adjustment program.

The behavior of monetary policy in the program episodes is somewhat surprising. On the one hand, the growth rate of total domestic credit is initially only slightly higher in program episodes three years before the program than in the control group—22.0 percent a year versus 20.4 percent—and rises over time reaching 25.1 percent one year before the outset arrangement. On the other hand, a different picture is depicted by the growth rate of the broad money supply, which remains roughly constant during the three-year period prior to the adjustment program and just below the median growth rate of the control group (19.7 percent per annum). These two facts are consistent with the steady decline in international reserves documented above. In addition, the fact that the growth rate of credit to the government in program episodes is higher on average than the growth rate of total domestic credit implies that the government’s share of credit is growing over time.

Table 4.Macroeconomic Policy in Program and Control Groups: of Samples Distribution
Program episodesControl
t – 3t – 2t – 1group
Flow of government borrowing (percentage of GDP)
First quartile0.90.71.8–0.6
Median2.73.13.61.2
Third quartile5.76.87.84.2
Mean4.05.65.03.0
Standard deviation6.412.66.010.3
N767779572
Fiscal balance (percentage of GDP)
First quartile–9.5–9.7–12.3–8.6
Median–5.1–5.7–6.9–3.4
Third quartile–2.9–3.0–4.6–0.8
Mean–7.1–7.3–8.6–5.9
Standard deviation7.47.27.29.6
N104104104693
Domestic credit (percentage change)
First quartile14.817.315.610.1
Median22.026.325.120.4
Third quartile30.744.039.738.2
Mean204.961.3397.933.4
Standard deviation1,805.8272.52,737.2150.3
N103103104684
Broad money supply (percentage change)
First quartile11.49.411.212.2
Median18.718.818.919.7
Third quartile31.427.127.529.9
Mean155.463.5178.827.2
Standard deviation1,214.9299.91,081.844.1
N104104104693
Domestic credit to the government (percentage change)
First quartile8.212.412.7–11.6
Median22.728.742.415.9
Third quartile52.464.798.247.9
Mean167.767.91928.2–19.6
Standard deviation1,204.0236.022.886.02,121.2
N102102103674
Nominal effective exchange ratea (percentage change)
First quartile–6.5–7.3–10.1–1.8
Median–0.9–1.0–1.2–0.1
Third quartile2.82.9243.8
Mean–3.2–4.1–7.9–2.1
Standard deviation19.421.721.714.4
N102102102671

A minus sign (–) indicates a depreciation.

Sources: World Economic Outlook database; and author’s calculations.

A minus sign (–) indicates a depreciation.

Sources: World Economic Outlook database; and author’s calculations.

Finally, program episodes differ from the control group in the pattern displayed by exchange rate policy. Program countries depreciate more than the control group: the median annual percentage change in the nominal effective exchange rate is -0.9 percent three years before the program, against –0.1 percent for the control group. As time goes by, program countries allow some acceleration in the rate of depreciation, and by the year prior to the outset arrangement the median nominal effective depreciation rate is –1.2 percent a year. Not only do exchange rates depreciate more during program episodes, but they also exhibit greater dispersion, a feature also observed for the real exchange rate.

II. Statistical Analysis

The previous section provided a broad overview of a set of selected macroeconomic indicators distinguishing between program episodes and a control group. In this section a formal statistical analysis is carried out, with a twofold purpose. First, a series of nonparametric tests is performed in order to determine whether the apparent differences between program episodes and control group observations are statistically significant. Second, a discriminant analysis is presented to ascertain which variables are the most useful indicators in distinguishing program and nonprogram observations.

Figure 3.Economic Policy Before Adjustment

Source: Table 4.

aA minus sign (–) indicates a depreciation.

The evidence presented below supports some of the patterns reported earlier in this paper and also provides new insights. It shows that macroeconomic performance deteriorates markedly over time before the inception of an adjustment program supported by IMF financial assistance. In particular, three years before the outset arrangement, program episodes and the control group are only statistically different in a handful of their macroeconomic characteristics. However, one year before the program, the statistical differences become widespread and affect most of the macroeconomic indicators, with the exception of the rate of inflation, investment, real effective depreciation, the growth of export markets, and the expansion of a broad money aggregate. Moreover, the discriminant analyses suggest that some indicators—such as the stock of international reserves, the overall balance of payments, and the flow of credit to the government—appear to be quite powerful in detecting the incidence of IMF-supported programs.

Nonparametric Tests

Univariate nonparametric tests indicate in what precise sense program episodes are different from the control group observations. The nonparametric statistics used test the equality of medians (Wilcoxon rank-sum test), the equality of distributions (Kolmogorov-Smirnov test), and the equality of populations (Kruskal-Wallis test) of the two samples.12 It must be recalled that nonparametric statistical tests require fewer and weaker assumptions than parametric tests. In particular, nonparametric tests are better equipped to deal with samples made up of observations from different populations. Moreover, they are also less distorted by problems of measurement error. The traditional disadvantage of nonparametric tests—i.e., the “waste” of information reflected in a low power-efficiency ratio—can be overcome with sufficiently large samples, such as those used in this study.

The results of the nonparametric tests for the comparison of three years before the inception of macroeconomic adjustment supported by IMF arrangements and the control group are presented in Table 5. With respect to the macroeconomic outcome variables, the evidence is revealing. Three years before the outset arrangement, the Wilcoxon rank-sum test cannot reject equality of medians between program episodes and the control group for all indicators, except for the overall balance of payments and the stock of international reserves, cases in which program episodes displayed smaller medians. In addition to these two variables, the Kolmogorov-Smirnov test suggests that the current account three years before a program may also exhibit a different distribution from that of the control group. As time goes by, nearly all the nonparametric statistics increase continuously, indicating deepening differences in macroeconomic outcomes between the program episodes and the control group as the adoption of macroeconomic adjustment gets closer. One year before the inception of IMF-supported adjustment, many of the differences described in the previous section become statistically significant. In the case of the overall balance of payments, the balance in the external current account, the stock of international reserves, and the growth rate of per capita income, the Wilcoxon rank-sum test now rejects the null hypothesis that the program episode median is equal to that of the control group median in favor of the alternative of a smaller median for the program episodes; in the case of the inflation rate, investment, and the real effective depreciation rate, the null hypothesis of equality of medians is not rejected. The Kolmogorov-Smirnov and the Kruskal-Wallis tests confirm these results because they also reject, respectively, the null hypotheses of equality of sample distribution functions and equality of populations for the same macroeconomic outcome indicators.

The nonparametric tests also corroborate the stylized facts detected for the external sector indicators (terms of trade, export markets, and external debt variables). Three years before the program, the null hypotheses of equality of medians, equality of distributions, and equality of populations are all strongly rejected for the debt indicators. In the case of the set of external sector variables, differences between the program episodes and the control group also tend to increase as the program approaches, and one year before the program the behavior of the terms of trade and (only marginally) of export markets is also significantly different in program episodes from that in the control group.

Results that emerge from the nonparametric tests performed on policy variables are even more interesting. They confirm the observed differences between program and nonprogram observations in terms of fiscal, credit, and exchange rate policies, as well as the similarities with respect to monetary expansion. Three years before the outset arrangement, the stance of fiscal policy is weaker in program episodes than in the control group: the Wilcoxon rank-sum test rejects the equality of medians for the flow of net government borrowing, the fiscal balance, and the growth of domestic credit to the government, while the Kolmogorov-Smirnov and the KruskalWallis tests also reject equality of distributions and populations. However, the expansion of credit and the broad money supply, as well as the nominal effective depreciation rate three years before the program, are statistically similar to those of the control group. As adjustment gets closer, in addition to the dissimilarity of fiscal policy, differences also appear in the expansion of domestic credit and nominal effective depreciation, but not in the growth of the money supply. The null hypotheses of equality of medians, equality of distributions, and equality of populations between the program episodes and the control group are now strongly rejected for the growth of total domestic credit and for the rate of nominal effective depreciation. However, in the case of the growth of the money supply, it is clear that the groups are almost indistinguishable throughout the three-year period before a program: none of the null hypotheses is rejected at conventional significance levels.

Table 5.Macroeconomic Differences Between Program Episodes and the Control Group: Nonparametric Statistical Analysis
Wileoxon rank-sum (Z): equality of mediansKolmogorov -Smirnov (D): equality of distributionKruskal-Wallis(H): equality of populations
t – 3t – 2t – 1t – 3t – 2t – 1t – 3t – 2t – 1
Macroeconomic Outcomes
Balance of payments (percentage of GDP)–3.32***–5.16***–8.25***0.19***0.29***0.43***11.03***26.63***68.13***
Current account (percentage of GDP)–1.39–3.30***–5.29***0.15**0.20***0.30***1.9410.86***27.98***
International reserves (months of imports)–4.56***–6.37***–8.54***0.28***0.35***0 46***20.82***40.52***72.99***
Consumer prices (percentage change)0.291.82*1.530.120.120.120.093.31*2.34
GDP per capit (percentage change)–0.49–3.16***–3.94***0.090.18***0.23***0.249.96***15.53***
Investment (percentage of GDP)–0.72–0.85–0.980.060.070.120.520.720.96
Real effective exchange rate (percentage change)0.790.82–0.230.080.080.070.620.680.05
External Indicators
Terms of trade (percentage change)–0.73–1.94*–3.35***0.070.110.23***0.543.75*11.22***
Export markets (percentage change)0.59–0.18–1.71*0.110.100.16**0.350.032.93*
External debt service (percentage of exports)4.86***5.99***6.12***0.24***0.31***0.28***23.60***35.92***37.48***
External debt (percentage of GDP)3.51***4.40***5.77***0.19***0.23***0.27***12.32***19.39***33.33***
Macroeconomic Policy
Flow of government borrowing (percentage of GDP)3.47***3.87***5.85***0.24***0.24***0.39***12.01***14.97***34.25***
Fiscal balance (percentage of GDP)–3.14***–3.42***–5.22***0.21***0.23***0.36***9.88***11.72***27.28***
Domestic credit (percentage change)0.642.90***2.26**0.15**0.20***0.15**0.418.40***5.13**
Broad money supply (percentage change)–0.25–0.96–0.770.070.100.090.060.920.59
Domestic credit to the government (percentage change)2.16**3.28***5.02***0.15**0.23***0.25***4.66**10.78***25.20***
Nominal effective exchange rate (percentage change)–1.10–1.14–2.23**0.13*0.100.131.211.294.96**
Note: Asterisks indicate whether the null hypothesis is rejected at the 10 percent (*), 5 percent (**), or 1 percent (***) confidence level.
Note: Asterisks indicate whether the null hypothesis is rejected at the 10 percent (*), 5 percent (**), or 1 percent (***) confidence level.

In sum, the nonparametric tests verify that the macroeconomic initial conditions of program episodes are significantly different in many respects from the empirical regularities observed in the control group. The program and control groups differ three years before the inception of IMF-supported programs in certain external sector indicators (overall balance of payments and external debt) and fiscal policy stance, and these differences are accentuated in most cases as the outset arrangement approximates. Over this preadjustment period, program episodes begin to differentiate themselves from the control group on other indicators—namely, output growth, terms of trade, credit expansion, and nominal depreciation. In the end, program episodes exhibit weaker balance of payments, growth, external conditions, and fiscal and credit policies than the control group; they are also characterized by a higher degree of external indebtedness, while their nominal effective exchange rates are more depreciated than those of the control group. According to the sample in this study, no statistical differences are evident between the two groups with respect to the rate of inflation, investment, real effective depreciation, and the growth rate of broad money.

Discriminant Analyses

Having observed that program episodes and the control group differ in many of their macroeconomic characteristics, an interesting question is to determine which of these indicators are able to discriminate better between the two groups. Such evidence will provide further insights on the controversial issue of the origins of macroeconomic imbalances that lead to an IMF-supported adjustment program, that is, whether macroeconomic outcome characteristics, external environments, or domestic macroeconomic policies are important in differentiating program episodes. In fact, the discriminant analysis can also be viewed as a first-pass signal detection exercise in the identification of IMF-supported programs: discriminant functions yield illustrative “rules-of-thumb” that pick programs when a given variable exceeds an estimated threshold.

Before describing the results some preliminaries are necessary. In the discriminant analysis the two samples P1 and P2 from the program and nonprogram observations are merged. The space defined by the vector of macroeconomic indicators x is then bisected into regions R1 and R2. Each observation i is allocated to either of these regions according to whether their macroeconomic indicators xi, when evaluated with a given discriminant function f(xi), falls short of or above a cutoff point c*. In other words:

The classification rule is as follows: observation i is allocated to P1 if xi falls in R1 or to P2 if it falls in R2. For simplicity just a linear function is used, so the discriminant function f(xi) becomes d’xi and, under standard assumptions, the cutoff c* is 1/2d(x¯1+x¯2),, where the vector of parameters d is obtained by maximizing the between-group sample variance relative to the within-group sample variance, and

is the sample mean of Pj,j = 1,2.13 Observations are partitioned for each of the indicators discussed above in a series of univariate discriminant analyses.

In evaluating the discriminating power of each variable, three statistics commonly used in the signal detection literature are considered: the model sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve.14 Sensitivity is defined as the fraction of program episodes that are correctly classified by the classification rule (true positives). Similarly, specificity is defined as the fraction of nonprogram observations that are correctly classified (true negatives). A model thus discriminates perfectly between the two groups in the sample if it yields unitary sensitivity and specificity rates. Except for the case of a perfectly discriminating model, a trade-off exists between sensitivity and specificity: varying the cutoff point improves the classification in one criterion but worsens it in the other. This trade-off is similar to the one encountered in statistical inference between Type I and Type II errors. A way to capture in a single statistic the trade-off between the sensitivity and specificity criteria is to use the ROC curve, which graphs sensitivity versus (1 —specificity) as the cutoff point is varied in a logit model—with the cutoff of the univariate linear discriminant function being a special case.15 The area below the ROC curve provides a summary indicator of the discriminating power of a model, being equal to unity for a perfectly discriminating model and 0.5 for a model with no discriminating power. When an economic indicator yields a higher area under the ROC locus, it implies that higher sensitivity can be achieved for the same specificity, which can be interpreted as a stronger signal and, thus, as an economic indicator that is more powerful in the detection of programs.

The results of the discriminant analysis for macroeconomic outcomes, external indicators, and policy variables are presented in Table 6. Due to space limitations and consistency of the results with evidence reported in previous sections, only results pertaining to one year before the program vis-a-vis the control group are presented. Given the evidence presented earlier, the results are not surprising. With respect to the macroeconomic outcome indicators, the stock of international reserves turned out to be the most powerful variable in sorting out program and nonprogram observations. The linear discriminant model implied a cutoff of international reserves equal to 3.54 months of imports, below which observations were classified as program episodes and above which they were classified as nonprogram observations. Bisecting the sample in that way, the univariate model based on international reserves has a remarkable sensitivity, picking up almost 85 percent of the program episodes. Despite its somewhat low specificity, the stock of international reserves has a strong discriminating power, as shown by the large area under the ROC curve (0.76).

Other macroeconomic outcomes do not fare as well as the stock of international reserves in sorting out the two groups from the sample. On the one hand, the rest of the linear discriminant models have lower ability to recognize program episodes (i.e., they all have lower sensitivity) than the model based on international reserves, with the growth rate of GDP per capita a distant second with a sensitivity rate of 58.7 percent. On the other hand, all models based on macroeconomic outcome indicators (except for investment) pick better nonprogram observations (i.e., higher specificity) than the model based on international reserves. The particularly low sensitivity of the inflation rate—matched by a very high specificity—reflects the fact that the linear discriminant function is heavily influenced by outliers, as evidenced by the high cutoff inflation rate. A summary assessment of the discriminating power of these macroeconomic indicators based on the area under the ROC curve shows that possibly only the overall deficit in the balance of payments is as useful as the stock of international reserves in sorting out the two groups. To illustrate this point, consider the following example: reducing the cutoff deficit for the balance of payments by 1 percentage point of GDP from the estimated deficit of 2.13 percent of GDP in order to capture more programs improves the sensitivity rate by 10.6 percent, with an associated sacrifice of 7.5 percentage points in the specificity count. Except for international reserves and the balance of payments, no substantial discriminating gains are obtained by varying the cutoff point of other macroeconomic outcome variables: to obtain comparable sensitivity rates with models based on other indicators, a substantial offsetting loss in the specificity rate must be tolerated.

Table 6.Macroeconomic Characteristics of Program Episodes in (t – 1) and the Control Group: Univariate Discriminant Analysis
VariableLinear cutoffaModel sensitivitybModel specificitycArea under ROCd
Macroeconomic Outcomes
Balance of payments (percentage of GDP)–2.1352.8878.790.7507
Current account (percentage of GDP)–5.9750.9669.260.6607
International reserves (months of imports)3.5484.6257.000.7595
Consumer prices (percentage change)114.895.7797.840.5464
GDP per capita (percentage change)0.6858.6561.470.6197
Investment (percentage of GDP)23.9552.0044.880.5304
Real effective exchange rate (percentage change)4.6231.3769.600.4930
External Indicators
Terms of trade (percentage change)0.6770.1949.640.6017
Export markets (percentage change)3.3350.4957.010.5524
External debt service (percentage of exports)19.5749.5175.610.6868
External debt (percentage of GDP)47.9240.7880.520.6761
Macroeconomic Policy
Flow of government borrowing (percentage of GDP)4,0048.1074.130.7029
Fiscal balance (percentage of GDP)–7.2548.0875.150.6587
Domestic credit (percentage change)215.673.8597.810.5688
Broad money supply (percentage change)103.006.7397.550.4767
Domestic credit to the government (percentage change)954.280.9798.810.6534
Nominal effective exchange rate (percentage change)–5.0134.3175.410.5684

Threshold derived from the linear discriminant function that bisects sample into the two different groups.

Percentage of program observations classified as such by the linear discriminant function (true positives).

Percentage of control group observations classified as such by the linear discriminant function (true negatives).

Area under the receiver operating characteristic (ROC) curve—the graph denned by varying the cutoff point in a logistic regression in the sensitivity versus (1 – specificity) plane.

Threshold derived from the linear discriminant function that bisects sample into the two different groups.

Percentage of program observations classified as such by the linear discriminant function (true positives).

Percentage of control group observations classified as such by the linear discriminant function (true negatives).

Area under the receiver operating characteristic (ROC) curve—the graph denned by varying the cutoff point in a logistic regression in the sensitivity versus (1 – specificity) plane.

With respect to the external indicators, a linear discriminant function based on the terms of trade yields both the most sensitive and the least specific classification, the growth of export markets fares poorly on both counts, and the external debt variables are not very sensitive but fare better on the specificity count. It is interesting, however, that the external debt variables fare better as potential discriminators when varying the cutoff point in a logistic univariate regression, as illustrated by the slightly higher areas under their respective ROC curves than for other external indicators.

Finally, regarding the models based on the policy variables, the evidence also confirms some of the previous results. Although neither of the linear discriminant functions seems to yield classifications that are very sensitive, models based on fiscal policy indicators perform better than those based on monetary, credit, and exchange rate indicators. Models based on money and credit aggregates are highly specific and, as was the case of the inflation rate, seem to be affected by the influence of outlier observations. Considering the summary indicator of discriminating abilities, the area under the ROC curves suggests that fiscal policy indicators—especially the flow of net government borrowing—are more capable of sorting out the two groups in the sample than are monetary, credit, and exchange rate policy indicators.

Some of the cutoff points estimated with the linear discriminant function are of interest in their own right. They provide a ready estimate of the threshold value for different macroeconomic indicators that separates program episodes from the control group. As noted earlier, observations with a stock of international reserves of less than 3.54 months of imports are classified by the univariate linear discriminant function as program episodes. Also illustrative are the results that when a deficit in excess of 2.13 percent of GDP in the balance of payments, or 5.97 percent of GDP in the current account, is recorded, the observations are classified as program episodes; an external debt service exceeding 19.57 percent of exports, or an external debt above 47.92 percent of GDP, or fiscal deficits in excess of 7.25 percent of GDP, or a flow of net government borrowing exceeding 4 percent of GDP also correspond to program episodes according to the univariate linear discriminant functions.

Overall, this exploratory investigation based on a univariate discriminant analysis suggests that the stock of international reserves, the overall balance of payments, and the flow of credit to the government are the most promising indicators of whether an observation belongs to the program or the nonprogram group. In principle, it should be possible to improve the discriminating power of the univariate methods presented here by using more general multivariate models, such as a broader discriminant function, or multivariate logistic or probit regressions as is done in Knight and Santaella (1994). However, application of such methods lies beyond the basic stylized facts sought in this investigation.

The results of this statistical analysis can provide some insights on the origins of macroeconomic disequilibria before IMF-supported adjustment programs. As would be expected, the results seem supportive of a middle-ground view; disequilibria observed prior to programs are attributable to both domestic and external factors rather than to one or the other. The fact that indicators of the fiscal stance are quite important in differentiating program episodes from the control group points to the relevance of domestic factors. At the same time, evidence from the external debt variables, which are the result of policy decisions in previous periods as well as of movements in the interest rate prevailing in international capital markets, together with the behavior of the terms of trade, points to the importance of external factors in macroeconomic disequilibria before IMF-supported adjustment.

III. Programs in the 1970s Versus Programs in the 1980s

Perhaps one of the most interesting issues concerning the empirical regularities before the inception of IMF-supported programs is the question of whether the stylized facts described in the preceding sections have been relatively stable over time. The evidence presented in this section indicates that in fact some particularly stark features have differentiated initial conditions of IMF-supported programs in the 1970s from those in the 1980s. This section compares macroeconomic outcomes, external indicators, and macroeconomic policy variables prior to 48 IMF arrangements during 1973–80 with those prior to the remaining 56 arrangements in our sample during 1981–91 (Tables 79).

The first interesting difference between the initial conditions of IMF-supported macroeconomic adjustment is that, despite the initial similarity of the median deficits in the external current account three years before the adjustment onset, capital flows were supportive of higher current account deficits during 1973–80 (a median deficit of 7.5 percent of GDP in t–1) than during 1981–91 (a median deficit of 5.1 percent of GDP in t–1). Furthermore, programs during 1981–91 registered substantially larger deficits in the overall balance of payments before the inception of the financial arrangements. In fact, balance of payments deficits twice as large during 1981–91 as during 1973–80 were reflected in lower stocks of international reserves.

Table 7.Macroeconomic Outcomes in Program Episodes in the 1970s and l980s: Distribution of Samples
1973–801981–91
t – 3t – 2t – 1t – 3t – 2t – 1
Balance of payments (percentage of GDP)
First quartile–1.4–2.4–5.1–3.6–3.8–5.7
Median0.2–0.3–1.6–1.3–2.0–3.1
Third quartile3.11.0–0.20.70.2–0.8
Mean0.1–1.1–3.0–3.1–3.8–6.2
Standard deviation4.74.74.59.09.114.0
N484848565656
Current account (percentage of GDP)
First quartile–7.5–10.0–13.8–7.1–6.9–8.4
Median–3.4–6.2–7.5–3.4–4.5–5.1
Third quartile–0.8–2.4–3.6–1.0–1.6–3.3
Mean–5.0–7.1–9.1–5.8–6.0–7.2
Standard deviation6.06.68.59.97.97.8
N484848565656
International reserves (months of imports)
First quartile2.01.71.11.00.80.6
Median3.02.51.62.11.91.2
Third quartile4.23.82.83.93.32.5
Mean3.42.82.12.82.31.7
Standard deviation2.01.71.52.62.32.2
N484848555656
Consumer prices (percentage change)
First quartile5.87.98.76.85.75.7
Median10.411.012.511.414.112.0
Third quartile17.018.723.026.828.021.0
Mean15.617.228.3278.3130.1365.2
Standard deviation16.819.050.51,910.4654.01.842.5
N484848565656
GDP per capita (percentage change)
First quartile–1.8–1.0–2.0–3.3–3.0–4.3
Median2.71.61.11.10.2–1.3
Third quartile7.23.95.43.62.91.9
Mean3.01.00.50.6–0.4–1.4
Standard deviation6.94.57.26.05.15.8
N484848565656
Investment (percentage of GDP)
First quartile17.317.317.315.715.314.7
Median23.324.725.922.922.720.2
Third quartile32.532.333.027.227.127.2
Mean24.925.325.722.421.521.4
Standard deviation11.711.511.09.58.48.7
N454545555555
Real effective exchange rate (percentage change)a
First quartile–3.5–3.5–4.1–4.3–4.8–7.9
Median1.81.81.40.40.8–0.7
Third quartile6.46.48.09.09.04.7
Mean4.74.73.55.33.59.3
Standard deviation23.023.017.024.528.567.2
N484848545454

A minus sign (–) denotes a depreciation.

Sources: World Economic Outlook database; and author’s calculations.

A minus sign (–) denotes a depreciation.

Sources: World Economic Outlook database; and author’s calculations.

This weakening of the member countries’ external positions during the 1980s appears to be closely related to the external debt crisis and, to a lesser extent, to the lower rate of growth of the world economy. The differences in the external debt figures before the adoption of IMF-supported adjustment programs in 1981–91 vis-a-vis those in the earlier period are glaring. The median external debt in the immediate year preceding an IMF financial arrangement climbed from 32.8 percent of GDP in 1973–80 to a staggering 49 percent of GDP in 1981–91. The increase in external debt service from the first period to the second was substantial but not as marked as the increase in the stock of debt—most likely due to the accumulation of arrears—and was basically concentrated in programs in the higher end of the distribution of external debt service.

Developing countries undertaking adjustment programs in 1981–91 faced lower growth in export markets than they did in 1973–80 (the median growth rate of export markets declined from 4.2 percent to 2.6 percent one year before the outset arrangement). On the other hand, although there was some deterioration in the terms of trade before the inception of IMF-supported adjustment programs in 1981–91 relative to 1973–80, this deterioration appears small.

Macroeconomic adjustment to these adverse conditions on the external front is evident from, on the one hand, the evolution of macroeconomic outcomes (output, investment, the real exchange rate, and, to a lesser extent, the inflation rate) and, on the other hand, the stance of macroeconomic policies. Dramatic differences across time are observed in the growth of GDP per capita and the investment rate. The median growth rate of GDP per capita just before an IMF-supported program declined from 1.1 percent in 1973–80 to –1.3 percent in 1981–91, while the median investment-to-GDP ratio fell from 26 percent to 20 percent.

The changing external conditions, chief among which was the debt crisis afflicting developing countries in the 1980s, also had their impact on real exchange rates. While the median real effective exchange rate appreciated slightly before an IMF program in 1973–80, it depreciated during 1981–91. The real exchange rate also became more volatile in the latter decade. These findings reconcile the conventional wisdom’s perception of a real appreciation before an IMF program with the behavior of real exchange rates prior to the debt crisis and the associated large increase in use of IMF resources. Of course, this result also shows that the earlier results for the whole sample period 1973–91 are driven by the appreciation before program episodes in the subperiod 1973–80.

Table 8.External indicators in Program Episodes in the 1970s and 1980s: Distribution of Samples
1973–801981–91
t – 3t – 2t – 1t – 3t – 2t – 1
Terms of trade (percentage change)
First quartile–6.1–13.9–11.0–10.9–9.8–9.1
Median1.0–2.1–3.8–2.6–1.5–4.2
Third quartile17.310.53.25.83.85.8
Mean5:7–1.1–3.7–2.5–2.2–0.6
Standard deviation20.521.813.618.012.421.4
N484848565656
Export markets (percentage change)
First quartile3.53.43.42.31.91.6
Median4.34.44.23.33.32.6
Third quartile5.15.14.63.93.63.5
Mean4.14.03.93.11.02.4
Standard deviation1.51.81.61.11.11.5
N484848555555
External debt service (percentage of exports)
First quartile5.97.911.210.812.010.6
Median15.916.816.618.920.321.5
Third quartile25.726.428.032.039.139.6
Mean19.522.123.623.530.126.3
Standard deviation17.620.720.017.237.822.5
N484847565656
External debt (percentage of GDP)
First quartile16.418.322.822.224.026.7
Median31.832.332.839.944.649.0
Third quartile41.746.651.755.258.770.0
Mean32.636.239.462.467.181.3
Standard deviation20.523.824.1112.0122.6161.6
N484747565656
Table 9.Macroeconomic Policy in Program Episodes in the 1970s and 1980s, Distribution of Samples
1973–801981–91
t – 3t – 2t – 1t – 3t – 2t – 1
Flow of government borrowing (percentage of GDP)
First quartile0.61.02.11.00.61.8
Median2.84.17.12.82.73.2
Third quartile5.76.610.25.77.94.9
Mean3.64.56.64.36.43.8
Standard deviation4.36.05.17.715.86.4
N333334434445
Fiscal balance (percentage of GDP)
First quartile–10.8–9.4–13.5–8.4–10.1–10.0
Median–5.0–5.3–8.2–5.3–5.9–6.4
Third quartile–2.9–3.2–5.1–2.6–2.5–4.2
Mean–7.0–7.2–8.9–7.2–7.3–8.3
Standard deviation6.17.16.28.47.48.0
N484848565656
Domestic credit (percentage change)
First quartile17.818.819.312.013.513.6
Median23.728.130.019.223.919.0
Third quartile32.245.053.830.141.633.3
Mean28.232.653.0353.285.5693.6
Standard deviation21.020.288.92,449.2368.83,719.0
N474748565656
Broad money supply (percentage change)
First quartile15.915.514.110.37.58.8
Median22.220.822.316.315.714.1
Third quartile32.029.933.027.126.425.0
Mean51.125.235.3244.796.3301.8
Standard deviation181.219.165.71.648.7407.21.467.9
N484848565656
Domestic credit to the government (percentage change)
First quartile–0.818.315.98.92.48.0
Median23.737.459.021.120.331.9
Third quartile59.665.5101.851.563.372.7
Mean63.699.184.4255.141.13,475.6
Standard deviation145.1318.6124.71,629.0126.831,080.5
N474747565556
Nominal effective exchange rate (percentage change)a
First quartile4.1–4.1–8.1–13.7–14.111.6
Median–0.4–0.4–0.5–1.0–1.2–2.8
Third quartile2.62.61.92.96.43.5
Mean–0.1–0.1–4.6–5.9–7.7–10.9
Standard deviation21.121.117.317.521.824.8
N484848545454

A minus sign (–) indicates a depreciation.

Sources: World Economic Outlook database; and author’s calculations.

A minus sign (–) indicates a depreciation.

Sources: World Economic Outlook database; and author’s calculations.

Curiously enough, programs in the lower half of the distribution of the inflation rate appear to exhibit fairly similar levels of price increases across the two decades—perhaps even a slightly higher inflation rate in 1973–80 than in 1981–91. However, programs in the higher end of the distribution experienced higher inflation rates before programs during 1981–91 than during 1973–80. This is evidenced in the mean inflation rate one year before the adjustment, which increased from 28.3 percent in 1973–80 to a striking 365.2 percent in 1981–91, as well as in the substantially higher inflation dispersion.

The overall impression from the evolution of macroeconomic policies before an IMF financial arrangement is that member countries followed somewhat stricter financial policies in 1981–91 than they did in the previous years. With respect to fiscal and budgetary policies, the behavior of the flow of government borrowing, the fiscal balance, and the expansion of domestic credit to the government indicate a tighter fiscal stance in the 1980s than in the 1970s. For instance, the median fiscal balance one year before an IMF-supported program declined from a high deficit of 8.1 percent of GDP in 1973–80 to 6.4 percent of GDP in 1981–91. A similar conclusion can be reached with respect to monetary and credit policies. The median rates of growth of domestic credit and broad money just before the outset financial arrangement declined from 30 percent and 22 percent in 1973–80, respectively, to 19 percent and 14 percent in 1981–91. However, it is noteworthy that some program episodes at the higher end of the distribution—as was the case with the inflation rate—did experience higher expansion of monetary aggregates in 1981–91 than before, as shown by the evolution of the mean rates of growth.

Finally, program episodes during 1981–91 were preceded by higher rates of nominal effective depreciation than in 1973–80. The median nominal effective exchange rate depreciation rose from 0.5 percent in 1973–80 before the adjustment to 2.8 percent in 1981–91.

IV. Summary and Conclusions

This paper has examined the basic empirical regularities that distinguish macroeconomic performance before the approval of an IMF financial arrangement from the performance of countries that do not enter into a financial arrangement and are therefore assumed to have “normal” or “sustainable” macroeconomic conditions. This permits one to document the stylized facts that precede the adoption of a macroeconomic adjustment program, stylized facts that the conventional wisdom tends to infer from casual observation without any rigorous statistical analysis. Determining the characteristics of program episodes before the approval of an IMF arrangement would also provide some answers about the sources of the macroeconomic disequilibria that trigger the need for an IMF financial arrangement and the adjustment program that it supports.

Using a sample of 104 IMF-supported programs during the period 1973–91, important differences were found between program episodes and the control group in macroeconomic characteristics. The starting macroeconomic conditions prevailing in program episodes are significantly different from those observed in the control group. Program episodes exhibit weaker balance of payments, output growth, external conditions, and fiscal and credit policies than the control group; they are also characterized by a higher degree of external indebtedness and their exchange rates are more depreciated in nominal terms than those of the control group. Only in the case of the rate of inflation, investment, real effective depreciation, and growth rate of broad money do the two groups appear to be statistically similar. The statistical differences are accentuated as the inception of adjustment gets closer. The discriminant analyses suggest that the stock of international reserves, the overall balance of payments, and the flow of net government borrowing are some of the most promising detection indicators of whether an observation belongs to the program group or to the control group.

On the basis of the evidence presented here, one can put together a story about macroeconomic developments in the typical country that enters into an IMF-supported program. Countries that enter into an IMF-supported arrangement seem to follow more expansionary financial policies characterized by relatively large fiscal deficits and substantial growth of domestic credit. These policies are reflected early on in a weak balance of payments. According to the sample in this study, the median country copes with such seemingly unsustainable policies by increasing public sector liabilities both with domestic and foreign creditors, trying to keep “tight” money growth, and depreciating the nominal effective exchange rate. This policy stance seems to avoid an explosive rise in inflation in the short run, but it does not seem to be sufficient to revert the external accounts. Furthermore, these policies appear to crowd out the private sector in the credit market, constrain the rate of investment, and slow down the growth of output. In addition to these conditions in the domestic economy, the median program episode in the sample is also affected more severely by external shocks than is the control group: terms of trade and conditions in international capital markets behave relatively more adversely for program episodes. Any negative impact coming from this external environment accelerates the unraveling of the precarious conditions originating from domestic developments and precipitates a balance of payments crisis. It must be recognized, however, that the “story” is just that; in reality, actual developments leading to the adoption of an IMF-supported program would vary significantly from case to case as shown by the high variability displayed by program episodes.

The stylized facts have changed over time. Comparing the period before IMF programs in 1973–80 with programs in 1981–91, initial positions in the latter period were characterized by tougher external conditions, namely, the developing countries’ debt crisis and the sluggish expansion of export markets. This situation was reflected in weaker balance of payments, lower output growth and investment, more depreciated real exchange rates, and somewhat tighter macroeconomic policies before the adoption of IMF arrangements in the period 1981–91 than in the period 1973–80.

Characterization of these macroeconomic stylized facts in the period prior to the inception of IMF financial arrangements should have two important ramifications. First, it should improve the design of macroeconomic policy that is oriented toward eliminating economic disequilibria that give rise to the need for IMF arrangements. Second, by providing additional benchmarks, these stylized facts should also improve the ex post evaluation of IMF programs. It should be clear from the results presented here that in order to evaluate IMF-supported programs using a “with-without” approach, considerable care should be taken to control for the large differences between program and nonprogram countries.

APPENDIX

With two subsamples P1 and P2 of sizes n1 and n2, let T be the sum of the ranks when a combined sample of n = n1 + n2 observations is ordered in ascending order of magnitude. T is called the Wilcoxon rank-sum statistic, which can be approximated by a standard normal distribution

where the expected value of T is

and the standard deviation is

For the Kolmogorov-Smirnov test, let F(x) and G(x) be the empirical distributions for the sample to compare. Defining the following statistics:

and

then the combined statistic is

which can be shown to have the following asymptotic P-value:

For the Kruskal-Wallis test with two samples, let T1 and T2 be the sum of the ranks for each of the subsamples. Define the following statistic

which has a sampling distribution that is approximately X2 with one degree of freedom.

In the discriminant analysis, for k explanatory variables in vector x, a linear function d’x is found such that it provides the best discrimination between two groups P1 and P2. Define

The vector of parameters d is selected by maximizing the variance between groups relative to the variance within groups

which gives

The means of the discriminant function in the two samples are

Given an observation xi one computes the discriminant function evaluated at that point,

and assigns it to P1 if yi is closer to y¯1 than to y¯2; that is, if yi>12(y¯1+y¯2) then ‘, is assigned to Pi and otherwise yi is assigned to P2.

Define the sensitivity rate as the fraction of predicted program observations given that they were actually program observations, P(program I program); and the specificity rate as the fraction of predicted control group observations conditional on belonging to the control group, P(control I control). Then the area under the ROC curve is

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*

Julio A. Santaella is an Economist in the Research Department. He holds a Ph.D. from the University of California, Los Angeles. Comments from Patrick Conway, Nadeem Haque, Mohsin Khan, Donald Mathieson, Miguel Savastano, Peter Wickham, and other colleagues are gratefully acknowledged. Brooks Dana Calvo and Ravina Malkani provided helpful research assistance.

1

In fact, the last time major industrial countries received financial assistance was in 1977 when the stand-by arrangements for Italy and the United Kingdom were approved.

2

Other special facilities that have been used by member countries include the buffer stock financing facility and the temporary oil facilities in the mid-1970s. For a historical analysis of the use of IMF resources and the IMF’s experience with balance of payments adjustment, see Horsefield (1969), Horsefield and de Vries (1969), de Vries (1976, 1985, and 1987), and James (1995).

3

For instance, Buira (1983) and Bird (1990) describe the situation before the adoption of an adjustment program as characterized by imbalances in an economy that are usually reflected in the prevalence of inflation and balance of payments deficits. See also Sachs (1989) and Edwards and Santaella (1993) for more elaborate depictions of the conventional wisdom.

4

Recent papers on the macroeconomic effects of adjustment programs supported by IMF arrangements are Khan (1990), Killick, Malik, and Manuel (1992), Killick and Malik (1992), Schadler and others (1993), Conway (1994), Schadler and others (1995), and Bagci and Perraudin (1995).

6

For a study of IMF-supported adjustment programs during the Bretton Woods period, see the analysis of 48 devaluation episodes by Edwards and Santaella (1993).

7

Recent papers that describe initial conditions before IMF programs are Schadler and others (1993) for SAF and ESAF arrangements during 1986–92, and Schadler and others (1995) for stand-by and EFF arrangements during 1988–92.

8

On the evaluation of macroeconomic programs, see Goldstein and Montiel (1986) and Khan (1990).

9

The initial conditions of the larger sample of 324 arrangements are documented in Santaella (1995).

10

For instance, Gylfason (1987) attempted to select a control group that confronted balance of payments difficulties similar to those faced by IMF-supported programs. For a methodological discussion, see Goldstein and Montiel (1986) and Khan (1990).

11

Scheduled debt-service payments—on which data have been publicly available since 1994 through the IMF Staff Country Reports—would be a better measure of the burden of external indebtedness than are actual debt-service payments. However, actual debt-service payments are used in this paper because the lack of consistent time series on scheduled payments during 1973–91 comparable across 91 countries prevented their use.

12

Definitions of these tests can be found in the Appendix.

13

See Maddala (1983), Amemiya (1985), and the Appendix.

15

In the logit model the predicted probability of a positive outcome for observation i is

An observation i is classified as positive if pi ≥ p*, and otherwise is classified as negative. The usual assumption is that p* = ½, but in the ROC curve the cutoff p* is varied to yield different classifications. For the relation between discriminant and logit analysis, see Cox and Snell (1989).

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