Evaluating the effect of IMF lending to low-income countries
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Journal of Development Economics
Vol. 61 Ž2000. 495–526
www.elsevier.comrlocatereconbase
Evaluating the effect of IMF lending to
low-income countries
Louis Dicks-Mireaux ) , Mauro Mecagni, Susan Schadler
International Monetary Fund, Policy DeÕelopment and ReÕiew Department, 700 19th Street, NW,
Washington, DC 20431, USA
Abstract
The purpose of this paper is twofold: to apply to a group of low-income borrowers from
the IMF, the most commonly used technique for measuring the independent effects on
economic developments of IMF support; and to develop a minimum set of diagnostic tests
for determining whether necessary conditions for using the methodology exist. The
modified control-group methodology is used to measure the effect of IMF support on three
key variables — output growth, inflation, and the external debtrservice ratio. The sample
comprises adjustment programs begun during 1986–1991 supported by the IMF’s Enhanced
Structural Adjustment Facility ŽESAF.. The distinguishing feature of the modified control-
group approach is the estimation of a policy counterfactual — policies that would have
been followed in the absence of IMF support against which to compare actual policies and
resulting outcomes. Using this approach for the ESAF, the sample reveals statistically
significant beneficial effects of IMF support on output growth and the debtrservice ratio
but no effects on inflation. Diagnostic tests of these results, rarely if ever reported in the
literature, are shown to be critical in interpreting the validity of the results of assessments of
adjustment lending. For this sample, at least, the diagnostic tests cast doubt on the
reliability of estimates of the effects of IMF-supported programs using panel data in a
modified control-group model. The most obvious and manageable modifications to the
)
Corresponding author. Tel.: q1-202-623-5699.
E-mail address: ldicksmireaux@imf.org ŽL. Dicks-Mireaux..
0304-3878r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 0 4 - 3 8 7 8 Ž 0 0 . 0 0 0 6 6 - 3496 L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
model do not overcome its basic weaknesses. q 2000 Elsevier Science B.V. All rights
reserved.
JEL classification: E65; F33
Keywords: International Monetary Fund; Conditionality; Stabilization programs
1. Introduction
Evaluations of macroeconomic programs supported by international financial
institutions ŽIFIs. do not all address the same question. Some look at the design of
such programs to see if they represent ‘‘best practices’’ for correcting countries’
macroeconomic problems. Others examine whether programs are effectively im-
plemented. Another question that has attracted attention recently is whether IFI
support has significant independent effects; i.e., does it bring about developments
significantly different from those that would have occurred in the absence of
support from the IFI in question? This is a difficult question to address because it
requires the construction of a counterfactual indicating what policies and outcomes
would have been in the absence of IFI support. The independent effects are then
calculated as the difference between outcomes that would have occurred in the
absence of IFI support and actual outcomes.
Since the mid-1980s, several papers have considered how to construct a
counterfactual for such exercises and how to address other problems in identifying
independent effects of IFI-supported programs. In particular, differentiating the
effects of the counterfactual policies from exogenous developments, initial condi-
tions and IFI support. The methodology that has been most widely applied was
developed by Goldstein and Montiel Ž1986. by adapting techniques from the
literature on labor training evaluation. Essentially, this technique, referred to as the
General Evaluation Estimator ŽGEE. or modified control group, involves using
policy reaction functions estimated for countries that did not have support from a
particular IFI to approximate the counterfactual for countries that did have IFI
backing for their program. 1 The GEE is a potentially powerful technique,
although, as Goldstein and Montiel point out, it entails many restrictive assump-
tions; e.g., it must be possible to characterize macroeconomic policy choices in a
relatively simple reaction function based on quantifiable data, and it must be
credible that the reaction functions estimated for countries that do not receive IFI
support describe the counterfactual for countries that do receive such support.
The purpose of this paper is both to apply the GEE methodology to data for
low-income countries eligible for the IMF’s Enhanced Structural Adjustment
1
Applications of the GEE can be found in Greene Ž1989., Khan Ž1990., Faini et al. Ž1991., Corbo
and Rojas Ž1992. and Conway Ž1994..L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526 497
Facility ŽESAF. 2 and to examine in greater detail than previous studies have done
the conditions that must be met for the GEE to give robust results. To do this, we
calculate the effects of ESAF-supported programs during the first 6 years of the
facility’s existence Ž1986–1991. on three macroeconomic variables that are typi-
cally the main objectives of the programs: output growth, inflation and, a key
indicator of progress toward external viability, the external debtrservice ratio. We
then perform several diagnostic tests to answer the question, ‘‘Are the restrictive
assumptions underlying the standard GEE consistent with the sample?’’. Three
main issues are addressed in these tests: does the single, relatively simple
macroeconomic model used in applications of the GEE capture the interaction
between macroeconomic policies and outcomes for a large number of countries
over time; can a robust policy reaction function be estimated for periods when, and
in countries where, IMF support is not in place; is it possible to address sample
selection bias that is likely to characterize applications of the GEE to date?
The results, like others for different data sets, point to significant positive
effects of IMF-supported programs on growth and the debtrservice ratio. The
diagnostic tests, however, cast doubt on the appropriateness of the restrictive
assumptions underlying the GEE and accordingly about the reliability of the
results. This finding raises questions about whether there are inherent problems in
estimating GEE models with panel data. At a minimum, it strongly indicates that
future applications of the GEE on other data sets need to incorporate standard
diagnostic tests to ascertain whether the GEE methodology is valid for the sample
under study.
2. Specification of the model
The GEE is geared toward answering the question, ‘‘Did the involvement of
the IMF through a lending arrangement significantly improve the outcomes for
important macroeconomic variables relative to what they would have been in the
absence of ESAF support?’’. To answer this question, the macroeconomic out-
comes or target variables in countries are described as a function of: Ži. policies
that would have been observed in the absence of an IMF-supported program; Žii.
2
The ESAF is the window through which the IMF can make highly concessional long-term loans
available to low-income countries. This facility succeeded the Structural Adjustment Facility ŽSAF.
which had similar characteristics but with somewhat less rigorous conditions on economic policies than
the ESAF. During the period covered in this study, 74 countries were eligible for loans through the
ESAF.498 L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
exogenous external factors; Žiii. the existence or otherwise of an IMF-supported
program; and Živ. unobservable random shocks:
yi j s b o j q b jk x i k q a jh wi h q b jIMFd i q e i j , Ž 1.
where yi j is the jth target variable in country i, x i k is a k-element vector of
policy variables that would be observed in country i in the absence of IMF
support, wi h is an h-element vector of exogenous external variables for each
country i, d i is a dummy variable equal to 1 if an IMF program is in effect and
zero otherwise, and e i j is a zero mean, fixed variance, serially uncorrelated error.
For the jth target variable, b jk and a jh are kxl and hxl vectors, respectively, of
fixed parameters. After postulating a rule for policies in the absence of an
IMF-supported program Ž x i k ., the model is estimated using pooled cross-section
and time-series data drawn from countries and periods in which IMF support was
in place and those in which IMF support was absent. The aim is to get consistent
estimates for b jIMF, the ‘‘independent effect’’ of IMF support on each target
variable. If these are statistically significant at a reasonable confidence level, IMF
support is found to have significant effects.
Policies adopted in the absence of an IMF-supported program Ž x i k . are directly
observable only for nonprogram periods, and thus, a key element of the GEE is the
construction of a counterfactual for policies during programs. In Goldstein and
Montiel Ž1986. and subsequent empirical applications, this counterfactual is based
upon a policy reaction function that links changes in policy instruments to the
deviation of the observed lagged value for each target from its desired value, yidj .3
Specifically, the policy reaction function is described by:
D x i k sg k j yidj y yi jŽy1. q hi k , Ž 2.
where yi j is a j-element vector of target variables, hi k is a zero mean, fixed
variance, serially uncorrelated error term assumed to be uncorrelated with e i j , and
D is the first difference operator.4 The kxj parameter matrix g k j indicates the
extent to which policy instruments are adjusted in response to disequilibria in the
target variables.
3
In the empirical exercise, we also experimented with other reaction functions. One, derived
explicitly from an optimizing exercise, produced a final reduced form equation in which past policies
do not enter Žsee Eq. 3.. A second included lagged values of exogenous influences wi hŽy 1. , which
would be important, e.g., if lagged changes in export or import prices affected the current fiscal
position. Both of these performed less well than Eq. 2 in estimation and are not, therefore, reported
here. See Dicks-Mireaux et al. Ž1995. for a fuller discussion of selecting a reaction function.
4
The lack of mutual correlation between hi k and e i j implies that changes in policy instruments
Ž D x i k . are not influenced by contemporaneous exogenous shocks to the target variables.L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526 499
Substituting Eq. 2 into Eq. 1 and subsuming yidj in the constant Ži.e., assuming
yidj is invariant across countries and over time., we obtain:
D yi j s b jo y Ž b jkg k j q 1 . yi jŽy1. q b jk x i kŽy1. q a jh wi h q b jIMFd i
q Ž e i j q b jk hi k . . Ž 3.
Eq. 3 constitutes the basic GEE reduced form model as applied in most earlier
studies.5 Its operational usefulness depends on the validity of several restrictive
assumptions, discussed in the remainder of this section.6
First, an important question for the empirical application is whether individual
country behavior can be sensibly aggregated in a uniform model that is stable
across countries and over time. Specifically, differing institutional characteristics
Že.g., the degree of policy discipline inherent in specific exchange rate arrange-
ments or the relationship with a major donor., changing political conditions, or
varying severities of economic distress are likely to result in countries formulating
policies with respect to different or changing objective functions, or subject to
different or changing constraints. Another question is whether it is appropriate to
assume that the policy reaction function of a program country, had it not received
IMF support, is identical to that of a nonprogram country that did not seek IMF
support. For example, the counterfactual for a country receiving IMF support may
involve the imposition of trade or exchange restrictions, while countries that do
not seek IMF support may constrain themselves to ‘‘IMF-type’’ policies; i.e.,
avoiding the use of trade or exchange restrictions.
Second, the constant additive term b jIMFd i is meant to capture four separate
channels through which IMF support could affect macroeconomic targets: Ži.
changes in the state of confidence in the economy; Žii. changes in the desired
value of targets, e.g., through structural reforms aimed at raising the rate of
potential growth; Žiii. policies different from what they would have been in the
absence of a program; and Živ. changes in the effectiveness of any given stance of
policies. While an additive term in a reduced form equation like Eq. 3 can capture
the first two of these channels, a more complex specification requiring the explicit
estimation of the policy reaction functions is needed to capture the third and fourth
channels of influence.7 The intuition is straightforward. Both the third and fourth
channels involve effects that are proportional to the size of the difference between
actual and counterfactual policies: strictly, therefore, they require the different
sizes of effects of actual and counterfactual policies to be measured directly. In
sum, a simple invariant additive term in the GEE may not do full justice to the
5
A potential source of bias in Eq. 3 because of nonzero correlation between the error terms and
explanatory variables is ruled out by assuming a stochastic structure whereby shocks are transitory.
6
Some of these restrictive assumptions characterize other, especially cross-section, estimators.
7
See Dicks-Mireaux et al. Ž1995. and Goldstein and Montiel Ž1986. for a discussion of the
specification needed to accurately capture these channels and the operational constraints in doing so.500 L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
range of potential program effects. In practice, the possibility of a more informa-
tive decomposition of program effects that allows variation across countries
requires a large sample and the ability to identify empirically a stable policy
reaction function as examined in Section 4.
Third, Eq. 3 contains only the most simple dynamics. The principal dynamic
influence comes from the lagged target variable term in the policy reaction
equation — policies are assumed to adjust to deviations of actual from desired
values of the target variables in the preceding period. Lags in the effects of
policies on target variables or inertial effects in the targets themselves are typically
not considered.8
Indeed, in Eqs. 1–3, the stochastic terms e i j and hi k are assumed to be serially
uncorrelated: all shocks are assumed to be transitory and to cause one-period
changes in target variables that are fully reversed in the following period. This
restrictive assumption requires that each target variable be stationary, but it also
rules out a wide range of stationary stochastic processes for which the impact of
temporary shocks persists over time.9 If, in fact, significant inertia exists; impos-
ing full one-period reversion to mean will understate the positive effects of an
IMF-supported program after a negative shock. Empirically, this issue could not
be explored for ESAF countries because of the short sample time span. While a
more general dynamic form can be easily specified, in practice, the data set for
ESAF countries is not long enough to apply it meaningfully.10
3. Estimation procedures
3.1. The sample
The model, as specified in Eq. 3, was estimated with data from 1986 to 1991
for 61 of the 74 countries ŽTable 1. eligible to use ESAF resources as of 1992
Žexclusions are noted below.. Nineteen of these countries had ESAF arrangements
at some time during the sample period.11 The sample was restricted to ESAF-eligi-
ble countries, rather than a larger set of developing countries: including only
8
Inertial effects may arise for a variety of reasons, such as backward-looking indexation, slowly
adjusting expectations, staggered contracts, and transaction costs.
9
In the absence of stationarity, the concept of reversion to mean is not well-defined because the
mean of the stochastic process is not time-invariant, and the series will tend to move continuously away
from a given level as a result of past and current shocks. See Harvey Ž1981. and Priestley Ž1981..
10
See Dicks-Mireaux et al. Ž1995. for a fuller discussion of the dynamic properties of the model and
the full dynamic specification.
11
For these countries, program years are those when either a SAF or ESAF arrangement was in
place. SAF arrangements typically had less stringent conditionality than ESAF arrangements. For most
countries that had ESAF arrangements, however, prior SAF arrangements were used to establish
commitment to adjustment and were close in nature to ESAF-supported programs.L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526 501
low-income countries reduced the scope for parameter instability owing to differ-
ences in structural and institutional conditions between low-income countries and
other developing countries.
Even the sample restricted to ESAF-eligible countries is quite diverse. The
program countries are dominated by heavily indebted African countries with a
narrow range of exports and relatively simple market structures. The nonprogram
countries are more diverse, including, in about equal proportions, indebted African
countries and small Caribbean or Pacific island countries with close institutional
and financial links to particular industrial countries. There are also a few South
and Southeast Asian countries.
For estimation, a number of data points are dropped from the full sample of
ESAF-eligible countries.12 Some observations are excluded because of inadequate
data owing to civil strife wAfghanistan Ž1990–1991. and Angola, Liberia, and
Nicaragua Žall years.x; major discontinuities which could not be corrected wDjibouti,
Sao Tome and Principe, and Zaire Žall years.x; extreme isolation wAlbania,
Cambodia and Mongolia Žall years.x; or political discontinuities wDemocratic
Republic of Yemen Ž1990–1991. and Yemen Arab Republic Ž1989–1991.x. Years
in which countries had a SAF arrangement Žexcept when followed immediately by
an ESAF arrangement., standby, or extended arrangement are omitted because
they were considered invalid as nonprogram counterfactuals; this excludes Cote
d’Ivoire, Philippines and, together with lack of data in 1991, Somalia, entirely
from the sample.13
Even for the limited sample covered, the quality of data is poor. In many
instances, the accuracy of the measures of macroeconomic variables, such as GDP,
is likely to very weak. Also, ad hoc correction for breaks in the series was
frequently needed. These fundamental weaknesses qualify the inferences or judge-
ments that can be drawn from the data.
3.2. Definitions: targets, policies, exogenous influences and period of IMF support
This study considers three target variables Ž yi j . that reflect the objectives of the
ESAF: Ži. the growth rate of real GDP; Žii. consumer price inflation; and Žiii. one
12
As a result, the structure of the panel data is incomplete. Techniques for analyzing panel data in
which missing observations are random or regular Žor ‘‘rotating’’. are not applicable because the
sample data exclusions do not conform to either of these patterns. Instead, for estimation, the panel data
are handled as a pooling of annual observations, with the number of program and nonprogram years
varying from country to country.
13
In principle, years when countries had a SAF, standby, or extended arrangement could have been
included as neither ESAF years nor nonprogram years, but rather as additional categories. In this case,
the selection of the facility through which a country borrowed Žwith its attendant conditionality. would
have had to be modeled as the outcome of strategic negotiations; see, e.g., Knight and Santaella Ž1994..
The correction for sample selection bias Ždiscussed below. would also have had to be differentiated for
each facility.502 L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
Table 1
Sample of countries and programrnonprogram years a
Nonprogram Nonprogram years SAFrESAF Program years Nonprogram
countries program countries years
Afghanistan 1986r1987– Bangladesh 1986r1987–
1988r1989 1991r1992
Benin 1986–1988 Bolivia 1987–1991
Bhutan 1986–1991 Burundi 1986–1989, 1990
1991
Burkina Faso 1986–1991 Gambia, The 1986r1987– 1991r1992
1990r1991
Cape Verde 1986–1991 Ghana 1987–1991
Central African 1991 Guinea 1987–1991 1986
Republic
Chad 1986, 1991 Guyana 1990–1991 1986–1989
China 1988–1991 Kenya 1988–1991 1986–1987
Comoros 1986–1990 Lesotho 1988r1989– 1986r1987–
1991r1992 1987r1988
Dominica 1990–1991 Madagascar 1987–1991
Dominican 1986–1990 Malawi 1988–1991 1986–1987
Republic
Egypt 1989r1990 Mauritania 1986–1990 1991
Equatorial 1987–1988, Mozambique 1987–1991 1986
Guinea 1990–1991
Ethiopia 1986–1991 Niger 1987–1991
Grenada 1986–1991 Senegal 1986r1987–
1991r1992
Guinea 1986, 1988, Sri Lanka 1988–1991 1986–1987
Bissau 1991
Haiti 1985r1986, Tanzania 1987r1988– 1986r1987
1987r1988– 1991r1992
1988r1989,
1990r1991
Honduras 1986–1989 Togo 1988–1990 1991
India 1986r1987– Uganda 1987r1988– 1986r1987
1989r1990 1991r1992
Kiribati 1986–1991
Lao, PDR 1986–1988
Maldives 1986–1991
Mali 1987, 1991 Number of countries: 19
Myanmar 1986–1991 Number of annual program observations: 88
Nepal 1990r1991 Number of annual nonprogram observationsb : 20
Nigeria 1986, 1988,
1990
Pakistan 1986r1987–
1987r1988
Rwanda 1986–1990
St. Kitts and Nevis 1986–1991
St. Lucia 1986r1987–
1991r1992L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526 503
Table 1 Ž continued .
Nonprogram Nonprogram SAFrESAF Program years Nonprogram
countries years program countries years
St. Vincent 1986–1991
Sierra Leone 1987r1988–
1990r1991
Solomon Islands 1986–1991
Sudan 1986–1991
Tonga 1986–1991
Vanuatu 1986–1991
Vietnam 1986–1991
Western Samoa 1986–1991
Yemen, AR 1986–1988
Yemen, PDR 1986–1989
Zambia 1988–1991
Zimbabwe 1986–1991
Number of countries: 42
Number of annual nonprogram observations:b 183
a
Fiscal years tr t q1 were considered to correspond to calendar year t if the fiscal year started on
or before July 1st.
b
Includes observations in which a SAF, ESAF, SBA or EFF arrangement was not in place.
of the key measures by which the IMF measures progress toward external viability
—the ratio of external debtrservice to exports. The last target is preferred to other
indicators of the external position, such as the current account, the overall balance
of payments, or the level of international reserves for several reasons.14 Most
countries entered ESAF arrangements with large debt overhangs, and reducing the
debtrservice ratio to manageable levels was the primary external objective of the
program. For other external variables, however, even the direction of targeted and
actual changes varied depending on initial conditions and prospects for attracting
concessional inflows. Moreover, for nonprogram periods, developments in these
other variables sometimes reflected the imposition of formal or informal trade and
exchange restrictions rather than changes in the viability of the external position.
Increases in reserves, probably the next most general indication of external sector
developments, were at times associated with the accumulation of arrears. Also, a
reserves target did not exist for many countries ŽCFA and ECCB members. that
did not directly own international reserves.
14
The debtrservice ratio is not, however, an infallible indicator of progress toward external viability.
For example, changes in this ratio may reflect only a temporary change in export prices.504 L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
Three policy instruments Ž x i k . are considered: Ži. the deficit of the central
government in relation to GDP; Žii. the growth of net domestic assets of the
banking system ŽNDA.;15 and Žiii. the change in the nominal effective exchange
rate ŽNEER.. Ideally, the vector of policy instruments would also include indica-
tors of structural reforms and conditions and institutional arrangements Žsuch as
flexible or fixed exchange rate regimes.. However, these variables cannot easily be
objectively quantified or reduced to an index. The external environment indicators
Ž wi h . comprise changes in the terms of trade and the growth of export markets.16
Whenever possible, the data were taken from documents for the Executive Board
of the IMF.
Several questions arise in defining the variable denoting the presence of an
IMF-supported program Ž d i .. First, the distinction between program and nonpro-
gram years is blurred when IMF support starts in the middle of the year. For this
study, any year in which a SAFrESAF-supported program was in effect for 6
months or more was considered a program year. Even this rule, however, does not
clearly delineate the period during which IMF support influenced policies and
outcomes. Usually, substantive negotiations and policy actions occurred in antici-
pation of IMF support in the year preceding the formal program. In some cases,
the IMF influenced policies even after an ESAF arrangement. For example, The
Gambia’s SAFrESAF arrangements stretched from FY1986 ŽJuly–June. to
FY1990, but even in FY1991, the IMF monitored macroeconomic developments
vis a` vis quantified targets agreed with the authorities.
A second question is whether the influence of the IMF should be measured only
through a 0–1 dummy Žone when the arrangement is in effect, zero when it is not.
or also through the proportion of purchases made relative to total access under the
arrangement as a measure of the completeness of implementation of agreed
policies. Purchases are likely to be an imperfect indicator of implementation,
however, because purchases in SAFrESAF-supported programs are scheduled at
6-month frequencies and waivers may be granted to permit purchases even when
implementation slips. This study uses a binary, one-zero index of IMF involve-
ment for the dummy variable Ž d i .: a period when a country has an agreed program
with the IMF but fails to implement it or meet the targets is treated as a program
year in which the effect of the IMF is low. Conway Ž1994. used both these
approaches as well as the proportion of the year covered by the program as proxies
15
Although the controllable monetary policy instrument of the authorities is net domestic assets of
the central bank, data on a comparable basis across countries were not available.
16
In principle, only one of market price and volume indicators should be used. In practice, the
suitability of each varies among the countries: the terms of trade are relevant for small primary
producers, but world market growth is relevant for large primary producers and countries with
differentiated exports.L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526 505
for participation, but obtained similar estimates of the effects of IMF-supported
programs for all three proxies.
A third point to recognize is that estimates of IMF-supported programs will
likely include the effects of parallel World Bank programs. As distinct from the
IMF’s standby and extended facilities, SAFrESAF arrangements require explicit
collaboration among country authorities, the World Bank and the IMF.
4. Results
4.1. The generalized eÕaluation estimator
Estimation results were obtained for the basic GEE Eq. 3. Several modifica-
tions of the basic specification were also estimated but produced worse results:
apart from those involving richer specifications of the reaction function,17 two
other efforts to reduce the restrictiveness of the basic GEE model were tried: Ži.
country and time dummies were introduced to help account for some of the
cross-country differences in economic structures and time-specific exogenous
developments not captured in the terms of trade and market growth variables; and
Žii. a correction procedure developed by Heckman Ž1979. Žexplained in detail in
Appendix A. was tried to correct for possible sample selection bias.
Regression estimates based on pooled time-series, cross-country data are prone
to heteroschedasticity, and even after the inclusion of country-specific and time
dummies in the estimated equations, regression residuals display this characteris-
tic.18 Without information on the form of heteroschedasticity, however, the
primary form of one weighting scheme over another is unknown. Therefore, the
reported t-statistics were computed from heteroschedastic-consistent estimates of
the standard errors based on White’s variance–covariance estimator that provides
consistent estimates even when the exact form of heteroschedasticity is not
known.19
Table 2 presents the preferred estimates from these exercises. These estimates
exclude the time dummies mentioned above because their coefficients were
generally not significant and had little effect on Žor worsened. the fit of the
equations. The impact of IMF-supported programs is found to be sizeable and
statistically significant with respect to growth Žat the 5% level. and the external
debtrservice ratio Žat the 10% level., but not inflation. On average, growth rates
are found to be more than 1 percentage point per annum higher during program
17
See footnote 3 and Dicks-Mireaux et al. Ž1995..
18
Statistically significant values of the Breusch–Pagan test for heteroschedasticity ŽBreusch and
Pagan, 1979. were observed in the estimated GEE equation for inflation Žat the 5% significance level.
and the external debtrservice ratio Žat the 1% level..
19
See White Ž1980..506 L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
Table 2
Estimates of the GEE a
Target variable Real GDP growth rate Inflation rate External debtr
service ratio
Constant y6.619 Žy1.71. 10.248 Ž1.08. 22.258UU Ž3.98.
Lagged y1.107UU Žy17.96. y0.764U Žy2.18. 0.022 Ž0.09.
real GDP
growth rate
Lagged 0.0005 Ž0.13. y0.687UU Žy4.76. 0.027 Ž1.09.
inflation rate
Lagged 0.013 Ž0.74. 0.106Ž1.14. y0.376UU Žy3.09.
external debtr
service ratio
Lagged y0.042 Žy1.37. y0.467 Žy1.31. 0.097 Ž0.76.
fiscal balancer
GDP
Lagged 0.004 Ž1.82. y0.088 Žy1.47. y0.020 Žy1.78.
net domestic
asset growth
Lagged y0.009 Žy1.03. 0.436U Ž2.12. 0.058 Ž1.05.
percentage
change in NEER
Current 0.002 Ž0.21. y0.104 Žy0.78. y0.104UU Ž3.44.
percentage
change in terms
of trade
Current 0.090 Ž1.78. 0.293 Ž1.26. y0.059 Žy0.30.
export market
growth
IMF program 1.374U Ž2.18. y3.330 Žy0.35. y5.552 Žy1.75.
dummy
R2 0.537 0.398 0.069
SEE 3.259 29.612 15.734
Number of 291 291 291
observations
Breusch– 1.35 10.83U 23.71UU
Pagan test for
heteroschedasticity
Jarque–Bera 26.57UU 28,231.00UU 7086.90UU
test for
normality of
residuals
a
The regression estimates were obtained using an ordinary least squares procedure, with country-
specific dummies included in the specification. Standard errors and t-statistics of coefficients are
computed using White’s heteroschedasticity-consistent variance–covariance estimator. The figures in
parentheses are t-statistics; R 2 is the adjusted coefficient of determination; SEE is the standard error of
the regression. A single asterisk indicates statistical significance at the 5% level; two asterisks indicate
statistical significance at the 1% level.L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526 507
periods than they would have been in the absence of an IMF-supported program.
Debtrservice ratios are found on average to be more than 5 percentage points
lower during program periods than they would have been in the absence of an
IMF-supported program.20
On the other right-side variables, very few coefficients are significant at the 5%
or 1% confidence level. The exceptions are own lagged levels of the target
variables, which enter with coefficients significantly different from zero at the 1%
level; the lagged level of the real GDP growth rate and the change in the nominal
effective exchange rate Žwhich has an implausible positive sign. in the inflation
equation; and the terms of trade in the debtrservice ratio equation.21 The lagged
fiscal balance and lagged net domestic asset growth are not found to have a
significant impact on the outcome of any target variable.22
4.2. EÕaluating the underlying assumptions of the GEE
We now turn to examining the robustness of the results of the GEE estimates.
Specifically, we go beyond the standard presentation of GEE results, which
focuses on the estimates of b jIMF only, to examine whether the regression
estimates are stable and unbiased — evidence that would support the underlying
assumptions of the model. The tests reported, which should be standard in all
presentations of GEE applications, cut to the core issue of this paper — are
estimates of the effects of ESAF programs using panel data in the GEE framework
meaningful?
4.2.1. The policy reaction function
The first window on this issue is the policy reaction function. Identifying the
coefficients and measuring the standard errors in the policy reaction function from
20
This effect on the debtrservice ratio is not attributable to the link between Paris and London Club
agreements and IMF support, because the debtrservice ratio is measured before debt relief. The effect
of stock of debt reduction operations associated with IMF-supported programs would be reflected in
measures of the debtrservice ratio before debt relief in years subsequent to the debt reduction.
However, in the sample of program countries considered in this study, no stock of debt operations
linked to IMF-supported programs was undertaken.
21
It is possible that this link reflects the presence of export prices in both the terms of trade and
debtrservice ratio. However, in light of the many other variables affecting these ratios, this seems
unlikely.
22
The residuals of most of the estimated equations fail to pass the Jarque–Bera test for normality.
The t-tests should, therefore, be interpreted cautiously as they may be sensitive to nonnormality in a
fashion that is determined by the numerical value of the regressors. This cautionary note applies also to
other regression diagnostic tests Žsee Jarque and Bera, 1987..508 L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
the reduced form GEE are not possible nor is it necessary for obtaining estimates
of the b jIMF terms.23 Nevertheless, obtaining estimates by directly estimating the
counterfactual policy reaction functions with data from the Žobservable. nonpro-
gram periods only, provides a means of evaluating the validity of the reaction
functions for the sample over which they can be estimated. Direct estimates of the
policy reaction function also allow one to test and correct for sample selection bias
arising when unobservable factors that influence countries’ decisions to receive
IMF support also influence their policy reactions.24
Regression estimates of the policy reaction functions ŽEq. 2. consistent with the
basic GEE ŽEq. 3. are reported in Table 3.25 These estimates are poor in several
respects. The R 2 statistics are negative or very close to zero; t-statistics for
individual coefficients are insignificant Žexcept on the debtrservice ratio in the
equation for the nominal effective exchange rate.; F-tests cannot reject the null
joint hypothesis of zero slopes; and the regression residuals exhibit statistically
significant heteroschedasticity and nonnormality. In short, these estimates provide
a weak basis for deriving estimates of the unobservable counterfactual policies for
program periods.
The inverse Mills ratio ŽIMR. was included as a regressor in the policy reaction
functions to correct for possible sample selection bias, following a two-step
procedure proposed by Heckman Ž1979..26 Appendix A describes in detail and
reports estimates of this procedure. However, the estimated coefficients of the
IMR are statistically insignificant, suggesting that sample selection bias is not
present.
Sample selection bias may arise in estimates of the reduced form GEE ŽEq. 3.
even when it is not present in the reaction function. This would occur if the choice
of having a program depends on expectations of better performance in the target
variable. To test for this, the IMR was included in the estimated reduced form
23
The g k j parameters cannot be identified from the single equation estimates because the number of
structural parameters exceeds the number of reduced form coefficients. However, by pooling the
parameter estimates from the three equations, the g k j parameters can be identified if policy instru-
ments and macroeconomic targets are equal in number. When the number of instruments is less than
that of targets, the g k j parameters cannot be identified, and when they exceed the number of targets,
multiple solutions exist. It is not possible to measure the standard errors of the estimates.
24
Goldstein and Montiel Ž1986., Greene Ž1989. and Khan Ž1990. do not attempt to correct for this
potential source of bias, but several studies of World Bank adjustment lending do Žsee World Bank,
1990; Faini et al., 1991; Corbo and Rojas, 1992..
25
Country and time dummies were introduced in the policy reaction function, but most had
coefficients insignificantly different from zero and had little effect on, or worsened, the overall fit of
the equation Žreduced the R 2 . and therefore were not retained.
26
The IMR is a monotone decreasing function Žranging from 0 to `. of the probability that an
observation is selected into the sample of nonprogram countries.L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526 509
Table 3
Estimates of the policy reaction functiona
Policy variable D Fiscal balancer D Net domestic D Percentage change
GDP asset growth in NEER
Constant 1.643 Ž0.67. y2.209 Žy0.11. y2.607 Žy0.35.
Lagged real GDP growth rate 0.024 Ž0.19. y1.090 Žy1.11. y0.204 Žy0.69.
Lagged inflation rate 0.006 Ž1.02. y0.081 Žy0.12. 0.017 Ž0.29.
Lagged external debtr y0.0007 Žy0.04. y0.097 Žy0.40. y0.152U Žy2.22.
service ratio
IMRb y3.911 Žy0.65. 16.271 Ž0.24. 13.070 Ž0.76.
R2 y0.013 y0.016 0.019
SEE 7.064 106.339 23.239
Number of observations 203 203 203
F-statistics Žzero slopes. 0.36 0.22 1.96
Breusch–Pagan test for 0.70 62.27UU 21.88UU
heteroschedasticity
Jarque–Bera test for 4875.06UU 17,986.30UU 495.36UU
normality ofresiduals
a
The regression estimates were obtained from the sample of nonprogram observations using an
ordinary least squares procedure. Standard errors and t-statistics of coefficients are computed using
White’s heteroschedasticity-consistent variance–covariance estimator. The figures in parentheses are
t-statistics; R 2 is the adjusted coefficient of determination; SEE is the standard error of the regression.
A single asterisk indicates statistical significance of the 5% level; two asterisks indicate statistical
significance at the 1% level.
b
Values of the IMR were computed using the estimated probit equation reported in Table 8.
GEE. The estimated coefficients of the IMR were statistically insignificant, again
suggesting the absence of sample selection bias Žsee also Appendix A..27
4.2.2. Significance and stability of the estimates
A second window on the robustness of the GEE estimates is to test for the
significance and stability of the parameter estimates. The question at issue is
whether the GEE results reported here or in other studies are robust to changes in
the size and composition of the sample.
There are many ways to evaluate the regression estimates, and the approach
taken here is not intended to be exhaustive. As measured by the R 2 statistics, the
overall fit of the estimated equations is modest Žalmost nil for the external
27
The IMR was highly collinear with the indicator of the presence of an IMF-supported program Ž d i .
in Eq. 3, and the statistically significant impact of a program on growth and the external debtrservice
ratio was statistically insignificant. The coefficient estimates of the other regressors in the GEE were
not significantly altered by the inclusion of the IMR. An alternative procedure using the predicted
probability of undertaking a program as an instrument for d i in Eq. 3 did not significantly alter the
coefficient of the other regressors. These results are not reported here, but are available from the
authors.510 L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
debtrservice ratio.. This, together with the evidence of heteroschedastic residuals
Ževen after the inclusion of country and time dummies. and the large number of
coefficients insignificantly different from zero or with counterintuitive signs,
suggests the possibility of biased coefficient estimates due to misspecification. For
instance, one potentially important omission is structural conditions and reforms
that figured prominently in ESAF-supported programs. A second concern is the
possibility of heterogeneity bias ŽHsiao, 1986..28 A third concern is the risk of
invalid inferential statements, even in large samples ŽJarque and Bera, 1987. given
that the regression residuals fail to pass the Jarque–Bera test for normality.
The reliability of the parameter estimates is also revealed by the stability
properties of the model. Many GEE applications investigate changes in the
effectiveness of IFI support as measured by the b jIMF coefficients between two
sub-periods of the sample used; Greene Ž1989. and Corbo and Rojas Ž1992. also
report changes in estimates of the other coefficients of the reduced form GEE. Yet,
in each study, the evidence of changes in point estimates of the b jIMF and other
coefficients Žat times statistically significant. is not seen as an indication of
instability in the underlying model.
The best approach to exploring instability would be to estimate a varying
parameters model in which estimated parameters are allowed to vary across
countries 29 and over time and therefore, also between program and nonprogram
periods.30 This general approach would nest tests of stability and uniformity
restrictions within a less restrictive framework, e.g., taking into account het-
eroschedastic errors in estimation and testing of coefficient variation; this property
is particularly appealing in pooled cross-section, time-series data. However, such
an approach requires a data set considerably larger than that available in this
study: to explore inter-country instability, the number of data points for each
country must exceed the number of regressors; and to explore instability between
program and nonprogram periods, the number of data points for each country in
each regime Žprogram and nonprogram. must exceed the number of regressors.31
For our sample Žwhich is small and has an unequal number of annual
observations for each country., the stability of the individual parameters b jk and
28
The key problem in heterogeneity bias is that the imposition of identical parameters leads to an
averaging of coefficients that differ greatly across countries Žor time. and therefore produces nonsensi-
cal results. In effect, the assumption of a ‘‘representative country’’ that can be described by an average
is not valid.
29
See Swamy Ž1970..
30
See Hsiao Ž1986..
31
Extending the sample, however, would have required dropping several countries owing to the lack
of consistent data for earlier periods. Also, the number of additional useable observations Ži.e., years in
which countries did not have a standby or extended arrangement in place. would have been a rather
small proportion of the total.L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526 511
b jIMF has to be examined by recursive regression methods.32 Two types of
recursive exercises were done: in the first, the reduced form GEE was estimated
recursively, starting with a baseline sample Žcomprising all nonprogram observa-
tions plus one program observation. and then adding in program observations one
at a time Žreported as ‘‘program recursions’’.; 33 in the second, the recursive
procedure was carried out by starting with the full sample and then subtracting
nonprogram observations one-by-one Žreported as ‘‘nonprogram recursions’’.. The
share of estimates in the recursions that differ significantly from the pooled sample
estimates or from zero indicates the sensitivity of the estimated parameters to
variations in the sample.34
For the estimates of the effectiveness of IMF support — the b jIMF coefficients
— four of the six recursive exercises Žtwo for each of the three equations; Figs.
1–6. produce numerous estimates of b jIMF that are significantly different from the
whole sample estimates. In all the recursive exercises, estimates were frequently
not significantly different from zero. Thus, the finding of significant effects of
IMF support on growth and the debtrservice ratio cannot be considered robust to
variations in the sample.
For the coefficients on the lagged policy variables, the recursive exercises
suggest that the full sample estimates of policy effects are relatively more robust
ŽTables 4 and 5.. The general pattern is for lagged policy variables to have little
effect on target variables.
The stability of the policy reaction functions parameters Ž g k j . was tested in a
simplified recursive exercise over the sample of nonprogram observations. The
recursions began with an initial sample of 25 observations, and observations were
added one by one.35 The results, reported in Tables 6 and 7, indicate that the
coefficient estimates from the full sample of nonprogram observations are gener-
ally robust across recursions, and Žexcept for the lagged external debtrservice
ratio in the equation, the nominal effective exchange rate policy. are insignifi-
32
Stability is assessed in terms of the point estimates and standard errors of individual parameters.
An alternative approach would be to conduct Chow or Wald tests for the joint stability of the
coefficients of interest on the recursive estimates of the equations. However, this was not possible
because the fitted equation does not meet the requirement of both tests that the set of regressors
remains constant over recursive estimates: the country dummies, which entered with statistically
significant coefficients, change across the recursions.
33
The additional program country observation was required to avoid singularity in the presence of
the IMF dummy variable Ž d i ..
34
These recursive procedures consider only a small subset Žarbitrarily chosen. of all subsamples that
could be drawn from the data set. Thus, the range of coefficient estimates reported in Tables 4–7 does
not necessarily encompass global maximum and minimum values for all permutations of the sample.
35
The size of the initial block of observations was chosen so as to start with a reasonable number of
degrees of freedom Ž20..512 L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526 Fig. 1. Recursive point estimates and confidence intervals for IMF program dummy variable Žreal GDP growth rates.. cantly different from zero. Sign reversals are not widespread and most of the estimates are neither significantly different from zero nor from the full sample estimate.
L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
Fig. 2. Recursive point estimates and confidence intervals for IMF program dummy variable Žreal GDP growth rates..
513514 L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526 Fig. 3. Recursive point estimates and confidence intervals for IMF program dummy variable Žrate of inflation.. 4.2.3. Dynamics and initial conditions The empirical results call into question the adequacy of the simple dynamic specification of the GEE commonly used in the literature. Specifically, the product
L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
Fig. 4. Recursive point estimates and confidence intervals for IMF program dummy variable Žrate of inflation..
515516 L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526 Fig. 5. Recursive point estimates and confidence intervals for IMF program dummy variable Žexternal debtrservice ratio.. of independent estimates of g k j from the policy reaction function for nonprogram periods and estimates of b jk from the GEE reduced form for each equation is close to zero. If this is true, the coefficient on the lagged target variables in the
L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
Fig. 6. Recursive point estimates and confidence intervals for IMF program dummy variable Žexternal debtrservice ratio..
517518 L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
Table 4
Share of statistically significant t-statistics at the 5% level in recursive estimates of the GEE a
Target Lagged fiscal balancer Lagged NDA growth, Lagged NEER percent
variable GDP, b 1 b2 Change, b 3
H o : b1 s 0 H o : b1 s H o : b2 s 0 H o : b2 s H o : b3 s 0 H o : b3 s
full sample full sample full sample
value value value
D Real GDP growth rate
Share in total 0.0 0.0 0.0 0.0 0.0 0.0
program year
recursionsb
Share in total 45.0 0.0 47.0 0.0 0.0 4.5
nonprogram year
recursions c
D Inflation rate
Share in total 0.0 0.0 0.0 0.0 3.4 0.0
program year
recursionsb
Share in total 0.0 0.0 10.9 13.9 85.6 0.0
nonprogram year
recursions c
D External debtr serÕice ratio
Share in total 0.0 36.8 0.0 0.0 0.0 0.0
program year
recursionsb
Share in total 54.0 49.5 57.4 0.0 0.0 2.5
nonprogram year
recursions c
a
Excluding full sample estimates, the total number of recursive estimates was equal to 87 for
program year observations, and to 202 for nonprogram year observations.
b
Recursive procedure starts with the baseline sample Žall nonprogram observations plus one
program observation. and adds program observations one by one.
c
Recursive procedure starts with the entire sample and subtracts out one by one nonprogram
observations.
reduced form equation yŽ b jkg k j q 1.4 should be close to y1. But the data
strongly reject this hypothesis, except in the growth equation.36 This finding,
36
The null hypothesis of an estimated coefficient equal to y1 on the lagged dependent variable is
rejected at the 10% level for real GDP growth, 5% level for the inflation rate, and 1% level for the
debtrservice ratio.Table 5
Generalized evaluation estimates of policy parameters Ž b . a
Target variable Policy variable
L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
Lagged fiscal balancerGDP Lagged net domestic asset growth Lagged NEER Žpercentage change.
b 1 y2 b1 b 1 q2 b 2 y2 b2 b 2 q2 b 3 y2 b3 b 3 q2
standard Ž t-statistics. standard standard Ž t-statistics. standard standard Ž t-statistics. standard
errors errors errors errors errors errors
D Real GDP growth rate
Full sample y0.103 y0.042 Žy1.37. 0.019 0.0004 0.004 Ž1.82. 0.009 y0.027 y0.009 Ž1.03. 0.009
estimates
Recursive
estimates
Ø Minimum y0.411 y0.180 Ž1.55. 0.051 y0.001 0.003 Ž1.46. 0.006 y0.055 y0.022 Žy1.30. 0.012
Ø Maximum y0.094 y0.033 Žy1.08. 0.028 y0.004 0.013 Ž1.51. 0.030 y0.005 0.010 Ž1.32. 0.026
D Inflation rate
Full sample y1.181 y0.467 Žy1.31. 0.247 y0.207 y0.088 Žy1.47. 0.031 0.025 0.436U Ž2.12. 0.846
estimates
Recursive
estimates
Ø Minimum y6.101 y2.592 Žy1.48. 0.917 y0.216 y0.116U Žy2.31. y0.015 y0.190 0.170 Ž0.95. 0.530
Ø Maximum y0.954 1.358 Ž1.17. 3.669 y0.032 0.162 Ž1.67. 0.356 0.193 0.873U Ž2.567. 1.553
D External debtr serÕice ratio
Full sample y0.157 0.097 Ž0.76. 0.351 y0.043 y0.020 Žy1.78. 0.002 y0.052 0.058 Ž1.05. 0.168
estimates
Recursive
estimates
Ø Minimum y0.291 y0.116 Žy1.33. y0.068 y0.041UU Žy2.99. y0.174 y0.057 Žy0.97. 0.060
Ø Maximum 1.095 1.790 Ž5.149.UU 2.486 y0.067 0.004 Ž0.11. 0.076 y0.018 0.128 Ž1.76. 0.275
a
Standard errors and t-statistics are computed using White’s heteroschedasticity-consistent variance–covariance estimator. A single asterisk indicates
519
statistical significance at the 5% level; two asterisks indicate statistical significance at the 1% level.Table 6
520
Sensitivity of estimates of policy reaction function parameters Žg . a
Target variable Policy variable
D Fiscal balancerGDP D Net domestic asset growth D NEER Žpercentage change .
gy2 g gq2 gy2 g gq2 gy2 g gq2
standard Ž t-statistics. standard standard Ž t-statistics. standard standard Ž t-statistics. standard
errors errors errors errors errors errors
Lagged GDP growth rate
Full sample y0.222 0.0024 Ž0.19 . 0.269 y3.063 y1.090 Žy1.11 . 0.882 y0.795 y0.204 Žy0.69 . 0.388
estimates
Recursive
estimates b
Ø Minimum y0.712 y0.159 Žy0.57 . 0.394 y2.793 y1.181 Žy1.46 . 0.431 y1.861 y0.685 Žy1.16 . 0.491
Ø Maximum y0.205 0.156 Ž0.86 . 0.516 y1.184 0.149 Ž0.22 . 1.482 y0.355 0.510 Ž1.18 . 1.375
Lagged inflation rate
Full sample y0.005 0.006 Ž1.02 . 0.016 y1.474 y0.081 Žy0.12 . 1.312 y0.097 y0.017 Ž0.29 . 0.131
estimates
Recursive
estimates b
Ø Minimum y1.196 y0.448 Žy1.20 . 0.300 y2.636 y1.398U Žy2.26 . y0.160 y0.768 y0.439UU Žy2.67 . y0.110
Ø Maximum y0.148 0.104 Ž0.82 . 0.356 y0.851 0.504 Ž0.74 . 1.861 0.192 0.511UU Ž3.20. 0.830
Lagged external debt r serÕice ratio
Full sample y0.041 y0.0007 Žy0.04 . 0.039 y0.584 y0.097 Žy0.40 . 0.389 y0.289 y0.152 U Žy2.22 . y0.015
estimates
Recursive
estimates b
Ø Minimum y0.102 y0.029 Žy0.79 . y1.300 y0.691U Žy2.27 . y0.708 y0.397 U Žy2.55 . y0.085
Ø Maximum y0.127 0.075 Ž0.74 . 0.277 y0.222 0.277 Ž1.11 . 0.775 y0.065 0.211 Ž1.53 . 0.487
a
L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526
Standard errors and t-statistics are completed using White’s heteroschedasticity-consistent variance–covariance estimator. A single asterisk indicates statistical
significance at the 5% level; two asterisks indicate statistical significance at the 1% level.
b
Recursive least squares estimates obtained by adding observations one-by-one to an initial sample of 25 nonprogram years Žcorresponding to a minimum of 20
degrees of freedom ..L. Dicks-Mireaux et al.r Journal of DeÕelopment Economics 61 (2000) 495–526 521
Table 7
Share of statistically significant t-statistics at the 5% level in recursive estimates of the policy reaction
functiona Žin %.
Policy D Fiscal balancer D Net domestic D NEER percentage
variable GDP assets growth change
H o: g s0 H o: g s H o: g s0 H o: g s H o: g s0 H o: g s
full sample full sample full sample
value value value
Lagged GDP 0.0 Ž0.0. 0.0 Ž0.0. 0.0 Ž0.0. 0.0 Ž3.4. 0.0 Ž0.0. 0.0 Ž0.6.
growth rate
Lagged 39.3 Ž57.9. 0.0 Ž38.8. 0.6 Ž3.4. 9.0 Ž9.6. 3.4 Ž7.3. 2.8 Ž7.3.
inflation rate
Lagged 0.0 Ž0.0. 0.0 Ž0.0. 9.0 Ž14.0. 0.0 Ž7.9. 70.8 Ž73.6. 5.6 Ž14.0.
external debtr
service ratio
a
Excluding full nonprogram sample estimates, the total number of recursive estimates was equal to
178 for nonprogram years observations. Shares of statistically significant t-statistics at the 10% level
are reported in parentheses.
together with the fact that the reduced form coefficients for the lagged target
variables are significantly different from zero, calls into question the GEE
assumption that all shocks to target variables are fully reversed within one period.
Indeed, if the reaction function parameters cannot account for the statistical
significance of the lagged target variable, partial inertial effects in the target
variables may explain why initial conditions do influence subsequent macroeco-
nomic performance. However, the logical consequence of interpreting the coeffi-
cients of the lagged target variable as reflecting inertial effects is that all other
coefficient estimates should also be interpreted in light of a richer dynamic
specification. In this case, the b jIMF estimates cannot be interpreted as the full
contemporaneous effect of IMF support in a given period.
5. Conclusions
With respect to the central objectives of this paper — to use the GEE
framework to identify the independent effects of ESAF support during 1986–1991
on key macroeconomic variables and to assess whether the assumptions underly-
ing the GEE are applicable to the ESAF-eligible countries — conclusions can be
summarized as follows. For output growth and the debtrservice ratio, sizable
beneficial effects that are statistically significantly different from zero are identi-
fied.37 The effects on inflation are not significantly different from zero. These
37
For the external debtrservice ratio, statistical significance was at the 10% level.You can also read
