Preliminary and Incomplete
Comments Welcome
When Is External Debt Sustainable?
Aart Kraay and Vikram Nehru
The World Bank
Abstract: We empirically examine the determinants of `debt distress", which we define
as periods in which countries resort to exceptional finance in any of three forms: (i)
significant arrears on external debt, (ii) Paris Club resecheduling, and (iii) non-
concessional IMF lending. Using probit regressions, we find that three factors explain a
substantial fraction of the cross-country and time-series variation in the incidence of debt
distress: the debt burden, the quality of policies and institutions, and shocks. We show
that these results are robust to a variety of alternative specifications, and we show that our
core specifications have substantial out-of-sample predictive power. We also explore the
quantitative implications of these results for the lending strategies of official creditors.
World Bank Policy Research Working Paper 3200, February 2004
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
exchange of ideas about development issues. An objective of the series is to get the findings out quickly,
even if the presentations are less than fully polished. The papers carry the names of the authors and should
be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely
those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors,
or the countries they represent. Policy Research Working Papers are available online at
http://econ.worldbank.org.
________________________________________
1818 H Street N.W., Washington, DC 20433, akraay@worldbank.org,
vnehru@worldbank.org. We would like to thank Nancy Birdsall, Christina Daseking,
Gershon Feder, Alan Gelb, Indermit Gill, Rex Ghosh, Nicholas Hope, Sona Varma, and
seminar participants at the Center for Global Development for very helpful comments,
Carmen Reinhart for kindly sharing historical data on default episodes, and Sunyoung
Lee for superb research assistance.
1. Introduction
This paper analyzes the probability of debt distress in developing countries and
examines the implications of these results for the lending policies of official creditors and
the borrowing strategies of low income debtor countries. We define debt distress
episodes as periods in which countries resort to exceptional financing in any one of three
ways: (i) incur substantial arrears on their external debt, (ii) receive debt relief from the
Paris Club of creditors, and (iii) receive non-concessional balance of payments support
from the International Monetary Fund. We find that three factors--the debt burden, the
quality of institutions and policies, and shocks that affect real GDP growth--are highly
significant predictors of debt distress.
Three features of this paper distinguish it from much of the large empirical
literature on debt sustainability: first, we are interested in understanding the determinants
of debt distress among very low-income countries that have been at the center of recent
debt relief efforts such as the Highly-Indebted Poor Countries (HIPC) initiative. Much
of the existing empirical literature focuses on middle-income emerging market
economies. In many low-income countries, however, sovereign external borrowing is
mostly, if not entirely, from official concessional sources. Unlike in middle and high
income countries, few market indicators are available to signal risks of future sovereign
debt default by low income countries. Barely any of the debt of these countries is traded
in secondary markets, and rates of interest on new loans from official bilateral and
multilateral creditors are highly subsidized and have little connection to risks of non-
repayment. Developing an empirical model of the determinants of debt servicing
difficulties in low-income countries can usefully inform the lending strategies of bilateral
and multilateral concessional lenders.
Second, in this paper we find that non-financial variables are key determinants of
debt distress, especially the quality of policies and institutions. The idea that policies and
institutions matter for debt sustainability is not novel. But it has received relatively little
attention in the empirical literature so far. A notable recent exception is the paper by
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Reinhart, Rogoff, and Savastano (2003), who document the importance of countries'
history of non-repayment and macroeconomic instability in driving market perceptions of
the likelihood of default. Our evidence complements theirs by showing that not only
does the history of non-repayment and weak policy matter for the likelihood of debt
distress, but contemporaneous policies and institutions also matter. This opens the
possibility that improvements in the policy and institutional environment in the medium
term can lower the likelihood of debt distress for any given level of the debt burden.
Moreover, we find that the contemporaneous effect of improvements in policies and
institutions on the probability of debt distress is quantitatively large, and is roughly of the
same order of magnitude as reductions in debt burdens.
Our third contribution is to consider explicitly the implications of our findings for
the lending strategies of multilateral concessional creditors such as the World Bank and
the IMF. In these institutions, notions of debt sustainability have focused almost
exclusively on simple projections of debt burden indicators. For example, debt relief
under the HIPC initiative is calibrated to ensure that countries emerge from the HIPC
process with a present value of debt to exports of 150 percent. We find, however, that
applying a common single debt burden indicator to assess debt sustainability in a large
group of countries may not be very appropriate because institutions and policies, as well
as shocks, also matter for the likelihood of debt distress. In this respect our findings are
not dissimilar from those of Reinhart, Rogoff and Savastano (2003), who find that market
perceptions of the probability of default in emerging markets are influenced by the
history of non-repayment and macroeconomic instability as well as by the size of the debt
burden. Our estimates also allow us to summarize striking tradeoffs between debt
indicators, policies, and shocks for a given probability of debt distress: for example,
countries at the 75th percentile of our measure of policies and institutions can have a
present value of debt to exports that is three times higher than countries at the 25th
percentile of this indicator, without increasing the probability of debt distress. These
tradeoffs suggest that the targeted level of "sustainable" debt of a country should vary
with the quality of its policies and institutions, as well as with the shocks that the country
experiences.
2
A fundamental premise underlying our work is that avoiding debt distress,
especially in low-income countries, is desirable. We think this premise is justifiable, for
three reasons:
· First, resolving debt distress is costly. For example, costs associated so far with
the debt reduction of the poorest countries of the world under the original and
enhanced (Heavily Indebted Poor Countries) HIPC Initiative is estimated at $50
billion. Moreover, the costs of excessive debt extend beyond the costs of debt
reduction alone. Excessive debt can also undercut support for policy reforms by
political and civil society groups in debtor countries if they perceive that benefits
from reforms will be directed to high debt service rather than deliver needed
public services to the poor. The pressure to meet external debt service payments
may also tempt debtor country governments to seek short-term solutions at the
expense of fundamental, longer-term reforms. Creditors, as well, may be tempted
to allocate resources according to resource needs rather than policy performance.1
· Second, non-repayment of loans to multilateral lenders can have perverse
distributional effects among borrowing countries. Absent new resources from
donors, the failure to repay concessional loans reduces the ability of development
banks to provide new loans to other developing countries. Moreover, to the
extent that new lending is intended for countries with sound policies and
institutions, but countries with poor policies and institutions are more likely to fail
to service their past debts, this can result in a transfer of resources from countries
with good policies to countries with bad policies. The amounts at stake are non-
trivial. Consider for example the World Bank-administered International
Development Association (IDA), which provides very substantial resources to the
world's poorest countries. As of 2003, IDA's portfolio consists of highly
concessional loans with a face value of roughly $110 billion.2 During the 2003
1 Birdsall, Claessens, and Diwan (2002).
2The figures in this paragraph are taken from World Bank (2003).
3
fiscal year, it disbursed $7 billion in new loans, of which only $1.4 billion was
financed by repayments on existing loans, with most of the balance coming from
infusions from rich countries. However, given the long grace periods in IDA
lending, this flow of repayments is anticipated to increase sharply in the future,
averaging $2.3 billion per year over 2003-2008, $3.3 billion per year over the next
five years, and $4.2 billion in the five years after that. Holding constant future
donor infusions into IDA, it is clear that any disruption in this flow of future
repayment resulting from episodes of debt distress will have significant
implications for IDA's ability to provide new lending to the poorest countries.
· And third, in Monterrey, Doha, and Johannesburg, the global community
endorsed a set of development objectives known as the Millennium Development
Goals (MDGs). A key ingredient in this consensus is the accepted need for a
significant increase--some estimates suggest a doubling--in official development
assistance to poor countries. This highlights the additional challenge of ensuring
that this additional financing is available on terms that are consistent with long
term debt sustainability in low income countries. Financing the MDGs on
inappropriate terms could lead to the re-emergence of debt problems in these
countries and would undermine the very development goals that they are trying to
achieve.
The remainder of this paper is structured as follows. In the next section, we
provide a brief review of the existing empirical literature on determinants of debt default.
We then describe in detail our methodology for identifying debt distress episodes in
Section 3. Section 4 contains our main results, where we show that just three factors go a
long ways toward explaining the risk of future debt distress: the public external debt
burden of the country (traditionally measured as the present value of debt to exports or
the debt service to exports ratio); the quality of policy performance of the government
(as measured by the World Bank's Country Policy and Institutions Assessment (CPIA)
index); and the experience of shocks (measured as real GDP growth). The results are
robust to alternative model specifications and alternative measures of the key variables.
4
Moreover, an out-of-sample forecasting exercise suggests that up to 84 percent of debt
distress events can be correctly predicted in advance.
Section 5 concludes with a discussion of the policy implications of our results. In
brief, we argue that while the policy performance of low income countries should dictate
the allocation of official development assistance, the terms of such resource flows,
including the share of grants, should be calibrated with some reference to the risk of debt
distress. Since the risk of debt distress depends not only on debt burdens, but also on
policies and institutions, and shocks, the share of grants will need to vary significantly
across countries. If adopted, such an approach will have important implications for the
way in which loans and grants are delivered to low income countries by the entire
international creditor and donor community. This is particularly important in light of the
very substantial resource flows to the world's poorest countries that have been advocated
in order to meet the Millenium Development Goals.
2. Relation to Existing Literature
The debt crisis of the early 1980s prompted a surge of empirical work to identify
the factors contributing to debt servicing difficulties. Of these, the paper of McFadden et.
al. (1985) is most closely related to ours. They construct an indicator of debt servicing
difficulties based on arrears, rescheduling, and IMF support much like the one we use
here, for 93 countries over the period 1971-1982. They find that the debt burden, the
level of per capita income, real GDP growth, and liquidity measures such as non-gold
reserves are significant predictors of debt distress, while real exchange rate changes are
not. We find broadly similar results in our sample covering the following two decades,
with some exceptions that are noted below. They also investigate the importance of state
dependence and country effects and conclude that both matter, while in our updated
sample we do not find comparable evidence of state dependence. Other papers in this
early literature include Cline (1984), who focuses primarily on financial variables as
determinants of debt servicing difficulties, and Berg and Sachs (1988) who in contrast
emphasize "deep" structural factors such as income inequality (which they argue proxies
5
for political pressures for excessive borrowing) and a lack of trade openness as
determinants of debt servicing difficulties among middle-income countries. In addition,
Lloyd-Ellis et. al. (1990) model both the probability of debt reschedulings and their
magnitude, again emphasizing financial variables. Interestingly, none of these papers
focus on direct measures of the quality of policies and institutions.
Another strand of the literature on debt sustainability attempted to find a
discontinuity in the relationship between debt burden indicators (usually the external
debt-to-export ratio) and the incidence of default or market-based indicators of risk (such
as the premium over benchmark interest rates on debt securities traded in the secondary
market), for example, Underwood (1991) and Cohen (1996) These papers found that
above a threshold range of about 200-250 percent of the present value of debt-to-export
ratio, the likelihood of debt default climbed rapidly. This range then became the
benchmark adopted by the original HIPC Initiative in 1996, and was subsequently
lowered in 1999 under the Enhanced HIPC framework.
Several more recent papers are also related to our current work. Aylward and
Thorne (1998) empirically investigate countries' repayment performance vis-a-vis the
IMF, emphasizing the importance of countries' repayment histories and IMF-specific
financial variables in predicting the likelihood of arrears to the IMF. Detragiache and
Spilimbergo (2001) study the importance of liquidity factors such as short-term debt, debt
service, and the level of international reserves in predicting debt crises, and find that all
three are significant (although the effect of short-term debt primarily reflects endogeneity
problems -- countries with imminent debt crises can only borrow short-term). While the
sample of countries covered in these two papers includes many low-income countries as
we do, neither paper focuses on the quality of institutions and policies.
We have already mentioned the paper by Reinhart, Rogoff, and Savastano (2003),
which looks at historical determinants of "debt intolerance", a term used to describe the
extreme duress which many emerging markets experience at debt levels that seem quite
manageable by industrial country standards. Their key finding most relevant to our work
6
is that the Institutional Investor magazine's sovereign risk ratings can be explained by a
very small number of variables measuring the country's repayment history, its external
debt burden, and its history of macroeconomic stability. However, there are three key
differences between our paper and this one. First, their dependent variable, the
Institutional Investor rating, measures perceptions of the probability of debt distress,
whereas we attempt to explain the incidence of actual episodes of debt distress.3 Second,
their sample consists mostly of middle- and upper-income countries. Third, as we will
show in more detail below, we find that contemporaneous policy, and only to a lesser
extent a history of bad policy and non-repayment, matters for the incidence of debt
distress.
Finally, Manasse, Roubini and Schimmelpfennig (2003) is the recent paper most
closely related to the analysis contained in this paper. They define a country being in a
debt crisis if it is classified as being in default by Standard & Poor's or if it has access to
non-concessional IMF financing in excess of 100 percent of quota. They then use logit
and binary recursive tree analysis to identify macroeconomic variables reflecting
solvency and liquidity factors that predict a debt crisis episode one year in advance.
Once again, the key difference with the analysis contained in this paper is that Manasse
et. al. restrict their analysis to a sample of emerging market developing countries for
which such data is available (especially the Standard & Poor's data), whereas a special
focus of this paper is the factors affecting debt distress in low income countries. Several
of their key results, however, are broadly consistent with ours. They find that debt
burden indicators and GDP growth, as well as a somewhat different set of measures of
policies and institutions, significantly influence the likelihood of debt crises.
3. Empirical Framework
3.1 Identifying Debt Distress Episodes
3As documented in Reinhart et. al. (2003), country risk ratings such as these are only imperfect predictors
of actual default episodes.
7
We define episodes of debt distress as periods in which any one or more of the
three following conditions hold: (a) the sum of interest and principal arrears is large
relative to the stock of debt outstanding, (b) a country receives debt relief in the form of
rescheduling and/or debt reduction from the Paris Club of bilateral creditors, or (c) the
country receives substantial balance of payments support from the IMF under its non-
concessional Standby Arrangements or Extended Fund Facilities (SBA/EFF). The first
condition is the most basic measure of debt distress: the inability to service external
obligations resulting in an accumulation of arrears. But countries that are unable to
service their external debt need not necessarily fall into arrears; they can also obtain
balance of payments support from the IMF and, in addition, seek debt rescheduling or
debt reduction from the Paris Club. The Paris Club assesses the "extraordinary external
financing needs" of countries based on balance of payments projections set within a
macroeconomic framework agreed as part of an IMF program, and takes into account all
other sources of external financing available to the country. This paper does not define
debt reductions under the HIPC Initiative as a separate indicator of debt distress, because
all debt relief under the Initiative requires parallel debt reduction by the Paris Club on
Naples terms (which involves a reduction in the debt of Paris Club members by 67
percent in present value terms).
Data on arrears and debt are taken from the World Bank's Global Development
Finance (GDF) publication. The arrears data consist of arrears to all creditors (official
and private), and refers only to arrears on long-term debt outstanding. We obtain data on
commitments under SBA/EFF programs from the electronic version of the IMF's
International Financial Statistics. Finally, we have compiled a comprehensive list of
Paris Club relief episodes from data provided to us by the Paris Club. Our sample of
countries consists of all low and midde-income countries reporting debt data in the GDF.
As will be clearer below, we will select our explanatory variables for debt distress
episodes to ensure that as many low-income countries as possible remain in our sample.
To implement our rule for identifying debt distress eposides, we need to identify
thresholds for "large" values of arrears and "substantial" levels of IMF support. Our
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initial threshold for arrears is 5 percent of total debt outstanding, and for IMF programs
we look only at those for which committments are greater than 50 percent of the
country's IMF quota. While any threshold for defining debt distress episodes would be
somewhat arbitrary, it is worth noting that these values are quite high relative to the
experience of the typical developing country. Pooling all country-years for all
developing countries since 1970, the median value of arrears as a fraction of debt
outstanding is 0.4 percent, and we are choosing a threshold that is roughly ten times
greater. Similarly pooling all country-year observations, the median value of IMF
committments relative to quota is zero, reflecting the fact that less than half the country-
years in the sample indicate the existence of an IMF program including access to non-
concessional IMF facilities. When such programs are in place, the median committment
is 52 percent of quota. This means that our threshold identifies only the top half (in
terms of committments relative to quota) of all SBA/EFF programs. Note that we do not
include access to the Poverty Reduction and Growth Facility (PRGF) of the IMF as a
debt distress indicator, since, in many cases, financing from this facility is no longer to
meet temprorary payments imbalances but has become a source of long term
development finance.4 Finally, we include access to the Paris Club for debt rescheduling
or debt reduction as an indicator of debt distress. The Paris Club itself designates its
support as extraordinary financing once all other sources of financing are taken into
account within the macroeconomic framework of an IMF-supported program. The
rationale is that without such extraordinary financing, countries would not be in a
position to service their debt without endangering implementation of the IMF program.
In this way -- using the existence of significant arrears, Paris Club arrangements, or resort
to non-concessional IMF facilities -- we can be certain that we are identifying severe
cases of debt servicing difficulties. In any case, we have verified that our findings are
generally robust to reasonable variations in these thresholds.
As a complement to debt distress episodes, we also define non-distress episodes,
or "normal times", to use as a control group in the analysis that follows. We define these
4 See a report by the IMF's Independent Evaluation Office on "The Prolonged Use of IMF Resources."
9
"normal times" as non-overlapping periods of five consecutive years in which none of
our three indicators of debt distress are observed.
Figure 1 illustrates how we identify normal and debt distress episodes, for the
case of Kenya (top panel), and Thailand (bottom panel). In each panel, we show
SBA/EFF committments (solid black line), arrears (dashed line), and Paris Club relief
(gray line). During the 1970s and 1980s, Kenya received balance of payments support in
excess of 50 percent of its quota for a total of ten years, while during the 1990s it had
four years in which arrears were more than 5 percent of debt outstanding. Finally, it also
received substantial Paris Club relief in 1994, and again in 2000. Since Paris Club relief
is typically based on three-year balance of payments projections by the IMF, we count
the year of Paris club relief as well as the two subsequent years as the period indicating
debt distress for each case. This means that in total, between 1970 and 2000, Kenya
experienced 17 years of debt distress. In contrast, it managed only one five-year period
of normal times, beginning in 1970, in which there were no arrears, debt relief, or IMF
support. Thailand's experience, shown in the bottom panel of Figure 1, is quite different.
It has neither had arrears nor has it approached the Paris Club. We only identify a total of
six years of debt distress signalled by STBY/EFF programs, in the early 1980s and in the
late 1990s during the East Asian financial crisis.
Finally, it is worth noting that in both Kenya and Thailand (and many other
countries), there are cases where the debt distress episodes we identify are quite short,
and in the case of Kenya, are immediately preceded by other distress episodes. In order
to be sure that we are identifying severe episodes of prolonged debt distress, we begin by
eliminating all seemingly temporary distress episodes that are less than three years long.
In addition, since we want to measure all of our predictors of debt distress in the years
prior to the outbreak of the distress episode, we eliminate all distress episodes that are
preceded by periods of distress in any of the three previous years. This procedure
identifies a total of 94 episodes of debt distress and 286 normal times episodes over the
period 1970-2001. In our regression analysis which follows, we will work with a subset
10
of 57 distress episodes and 227 normal times episodes for which data on our core
explanatory variables is available.
These 57 distress episodes are listed in Table 1, which also reports the average
during the distress episode of arrears, Paris Club relief, and SBA/EFF support. This list
contains many familiar episodes, including many Latin American countries during the
debt crisis of the 1980s. Thailand and Indonesia during the more recent East Asian
financial crisis. There are also many lengthy episodes of debt distress in Sub-Saharan
Africa.5 Figure 2 shows the incidence of debt distress over time, plotting the number of
distress events beginning in each year since 1970. While many distress episodes began in
the late 1970s and early 1980s, there has been a fairly steady incidence of new debt
distress episodes since then, varying between one and three episodes per year.6
Table 2 provides some basic descriptive statistics for our sample of episodes. A
striking feature of debt distress episodes is that they are long. In our sample, the median
distress episode lasts fully 11 years. The longest distress episode is for the Central
African Republic, which has been continuously in debt distress according to our
definition since 1971, primarily because of high arrears. Comparing the top and bottom
panel of Table 2 reveals very sharp differences in the indicators of debt distress in
distress episodes relative to normal times. In distress episodes, average arrears are nearly
10 percent of debt outstanding, while average arrears in normal times episodes are less
than one-half of one percent. During distress episodes, SBA/EFF support averages 83
percent of quota, while during normal times it is only 3 percent of quota. While by
construction Paris Club relief is zero in normal times, it averages 1.7 percent of debt
outstanding during distress episodes. Taken together, these figures suggest that our
procedure identifies quite severe episodes of often prolonged debt distress. Finally, Table
2 shows that real GDP growth is substantially lower during debt distress episodes,
5One anomalous observation is Vietnam, which we identify as being in continuous debt distress since the
late 1980s. This reflects continuous high levels of arrears relative to non-bilateral, non-Paris Club
creditors, much of which is ruble-denominated. In the vast majority of our episodes of debt distress based
on arrears primarily vis-a-vis multilateral and bilateral Paris Club creditors.
6Since our sample is restricted to distress events lasting at least three years, and our time period ends in
2001, we do not show any new distress events beginning in 1999-2001.
11
averaging 2.9 percent per year during periods of distress and 5.2 percent per year during
normal times.
3.2 Modelling the Probability of Debt Distress
We will model the probability of debt distress using the following probit
specification:7
(1) P[yct = 1] = (' Xct )
where yct is an indicator value taking on the value of one for debt distress episodes, and
zero for normal times episodes, each beginning in country c at time t; (.) denotes the
normal distribution function; Xct denotes a vector of determinants of debt distress; and
is a vector of parameters to be estimated. Our sample consists of an unbalanced and
irregularly spaced sample of observations of distress and normal times. In our core
specification, we will consider a very parsimonious set of potential determinants of debt
distress. In our regressions, we measure each of these variables in the first year of normal
times episodes, and in the year prior to the beginning of each distress episode in order to
reduce the potential endogeneity bias arising from the fact that our predictors of debt
distress might worsen during the distress episode itself.
We consider three groups of explantory variables. The first consists of traditional
debt burden indicators. In our core specifications, we will focus on two of these: the
present value of debt service obligations relative to contemporaneous exports, and total
debt service obligations as a fraction of exports. The debt service data is obtained from
7Since our interest is primarily in the incidence of distress episodes, rather than their precise timing, we
rely on this very simple probit specification. Collins (2003) shows how the timing of currency crises can
be modeled explicitly as the first-passage-time of a latent variable to a threshold, of which the simple probit
specification here is a special case. Manasse, Roubini and Schimmelpfenning (2003) suggest that binary
recursive tree analysis better captures the nonlinearities in the relationship between debt crises and their
determinants, in a sample of middle-income countries. We have not yet investigated whether similar
nonlinearities are important in our sample.
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the World Bank's Global Development Finance publication, and is available since 1970.
We rely on a new dataset on the present value of debt constructed by Dikhanov (2003).
He applies currency-, maturity-, and time-specific market interest rates to the flow of debt
service obligations on a loan-by-loan basis, using data from the World Bank's Debtor
Reporting System database to arrive at a historical series of present value of debt for all
developing countries since 1980. We also consider a number of other indicators,
including the face value of debt relative to exports, debt service relative to current
government revenues, and debt service relative to non-gold reserves.
Our second group of explanatory variables is intended to measure the quality of
policies and institutions in the country. In our core specifications we will rely on the
World Bank's Country Policy and Institutional Assessment (CPIA) ratings, which are
available on an annual basis since 1977, although with some methodological changes
over time. In some of our regressions, we extend this variable back to 1970 by imputing
missing values with the fitted values of a simple OLS regression of the CPIA on the
logarithm of one plus the inflation rate. Since the CPIA is not publicly available, we also
confirm that our results hold if we use a publicly-available measure of institutional
quality constructed by Kaufmann, Kraay and Mastruzzi (2002). This variable measures
property rights enforcement or the rule of law, which we treat as time-invariant and so
use only a cross-section of data for 2002.
Our third set of variables measures shocks that countries experience. Our most
comprehensive measure of shocks is simply real GDP growth in constant local currency
units, which reflects the confluence of all domestic and external shocks the country
experiences. We also attempt, although with limited success, to identify the proximate
causes of these shocks, by separately looking at real exchange rate movements and
fluctuations in the terms of trade. In particular, we construct the growth rate of the real
exchange rate relative to the US dollar using changes in the nominal exchange rate and
GDP deflators. Positive values of this variable correspond to real depreciations. We
measure the income effect of terms of trade changes as the current local currency share of
exports in GDP times the growth rate of the local currency export deflator, minus the
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share of imports in GDP times the growth rate of the import deflator. All data for these
variables is taken from the World Bank's World Development Indicators database.
Table 3 reports the pairwise correlations among our determinants of debt distress.
Not surprisingly, the present value of debt relative to exports, and debt service relative to
exports, are quite strongly correlated at 0.45 in our sample. All of the remaining pairwise
correlations are quite small, and with the exception of the correlation between policy and
the present value of debt at 0.26, are less than 0.20 in absolute value. This gives us
some indication that there is sufficient independent variation across our different
determinants of debt distress that we can identify the partial effects of most of them.
Figure 3 illustrates the simple bivariate relationship between each of our
explanatory variables and the distress indicator variable. In each panel, we divide the
sample of observations by deciles of the explanatory variable of interest. We then
compute the mean value of the explanatory variable by deciles, and plot it against the
mean of the distress indicator variable by decile. Thus, for example, in the first panel of
Figure 3, the mean value of the present value of debt to exports in the top decile of this
variable is just above 4, and the proportion of distress observations in this decile is just
above one-half.
The first part of Figure 3 shows that there is a strong positive correlation between
a variety of debt burden indicators and the incidence of debt distress. An interesting
feature of these graphs, however, is that the maximum value on the vertical axis rarely
exceeds 0.5. This means that, even when traditional debt indicators are extremely high
(in the upper deciles), there are many cases where debt distress does not occur. For
example, in the case of the present value of debt relative to exports, only nine of 17
episodes in the top decile of this variable correspond to distress. Among the non-distress
episodes in the top decile are Bangladesh, Burundi and Morocco, which all had a present
value of debt of more than three times exports for most of the late 1980s and early 1990s
yet did not experience debt distress. It is also interesting to note that there is little
14
visually-obvious evidence of non-linearities or threshold effects in the relationship
between debt burdens and the probability of debt distress in our sample of events.
The second part of Figure 3 shows the relationship between debt distress and our
measures of policies and shocks. There is a very strong negative relationship between
measures of the quality of policies and institutions, and the incidence of distress. Fully
60 percent of the observations in the lowest decile according to the CPIA rankings
correspond to distress episodes. To put this figure in context, recall that the
unconditional probability of distress in our sample is just above 20 percent. This means
that countries in the lowest decile according to policy experience debt distress at a rate
three times the average for all countries. Turning to shocks, we see a strong negative
relationship between GDP growth and distress. However, there is relatively little
evidence of a significant simple correlation between two possible sources of shocks, real
depreciations and terms of trade movements, and distress episodes.
4. Results
4.1 Core Specifications
Table 4 reports our core specifications. In the first seven columns, we restrict
attention to a sample of 163 episodes during the 1980s and 1990s in which we have data
on all four of our main variables of interest: present value of debt, debt service, CPIA
ratings, and real GDP growth. In the first four columns, we show simple univariate
probit regressions with each of these variables in turn. Not surprisingly, measures of debt
burden, of the quality of policy, and of shocks are all highly significant determinants of
debt distress. In terms of explanatory power, total debt service does best, with a pseudo-
R-squared of 18 percent. However, the other variables, especially policy, all have quite
respectable explanatory power as well.
In columns (5) and (6), we consider the partial effects of the debt, policy and
shock variables, for the two debt indicators. We continue to find that all three variables
15
are highly significant and with the expected signs. Strikingly, even the magnitude of the
estimated coefficients changes very little relative to the univariate specifications,
consistent with the quite low correlations among our different determinants of debt
distress. The explanatory power of these specifications is also substantially higher than
in the univariate regressions, with pseudo-R-squareds of 23 percent (35 percent) for
regressions with the present value of debt (debt service) measures. In column (7) we
enter both the present value of debt, and total debt service relative to exports. We find
that total debt service remains strongly significant, while the present value measure loses
significance. One possible interpretation of this is that the debt service measure is a
better proxy for liquidity problems, as opposed to the present value measure which
captures solvency, and that distress episodes are more likely to be precipitated by
liquidity problems.
In the last column, we show the same regression as in column (5), but in a larger
sample extending back to the 1970s, where we have data on debt service but not the
present value of debt. Again our results are very consistent, with debt burden, policy, and
shocks all strongly predicting debt distress. The only difference with the previous sample
covering the 1980s and 1990s is that the magnitude of the coefficient on growth is
somewhat smaller, but still is highly significant.
It is also important to note that the magnitude of the estimated effects of debt,
policy and shocks on debt distress is practically quite significant as well. For example,
based on the regression in column (6), the probability of debt distress for a country at the
25th percentile of debt service is 7 percent, but rises to 27 percent for a country at the 75th
percentile (holding constant policy and growth at their average values). Similarly, the
probability of distress for a country at the 25th percentile of policy is 26 percent, which is
much higher than the probability of distress of 9 percent for a country with good policy at
the 75th percentile of this measure. Interestingly, the magnitude of this effect is almost
identical to the magnitude of the debt service effect. This suggests that quantitatively,
improvements in policy are as important as reductions in debt for reducing the probability
of debt distress. In the case of growth, the magnitude of the effect is somewhat smaller,
16
but still non-trivial. The probability of distress for a country at the 25 percentile of
growth is 20 percent, as opposed to 8 percent for a country at the 75th percentile.
Since our ultimate interest is in predicting debt distress episodes based on a
parsimonious set of variables, it is useful to also examine the out-of-sample predictive
power of each of these specifications. We first re-estimate each regression using data
through 1989. We then use the use the estimated coefficients, together with the observed
right-hand-side variables to predict the outcome of each of our observations in the 1990s.
In particular, we predict that a debt distress episode will occur if the predicted probability
conditional on the observed data included in each regression is greater than the
unconditional probability of distress in the pre-1990 sample, which is 0.23 in the sample
of 163 observations in first seven columns, and slightly lower at 0.20 in the last column.
The first row reports the fraction of all episodes that are correctly predicted by each
model, which ranges from 0.43 (for the model with growth only) to 0.84 (for the model
with debt service, CPIA, and growth). One striking feature of the univariate regressions
is that the out-of-sample predictive power of the CPIA is just as good as the predictive
power of debt service, and considerably better than the predictive power of the present
value measure. This suggests that the quality of policy is as good or better a predictor of
debt distress as traditional debt burden indicators.
The remaining two rows disaggregate the correct predictions. The second row
shows the fraction of the 10 distress episodes during the 1990s that are correctly
predicted, while the third row reports the fraction of normal time episodes during the
1990s incorrectly predicted as distress events, i.e. false alarms. In most specifications, 70
percent or more of distress episodes are correctly predicted (with the exception of the
regression with growth only, for which only 60 percent are correctly predicted. There is
a lot of variation across specification in the likelihood of false alarms. In our best
specification from a predictive point of few (column (6)), only 13 percent of normal
times episodes are incorrectly signalled as distress. However, predictions based only on
the present value of debt or growth have quite high probabilities of false alarms, at 55
percent and 63 percent of normal times episodes, respectively.
17
Overall, these results suggest that a considerable fraction of distress events can be
correctly predicted using a very small set of just three explanatory variables: debt
burdens, policy, and shocks. Moreover, the predictive power of the combined
specification is considerably better than any univariate prediction, suggesting that it is
important to take all three factors into account when assessing the likelihood of debt
distress. Before we turn to the policy and operational implications of this finding in the
last section of this paper, we first subject this basic specification to a number of
robustness checks.
4.2 Robustness of Core Specification: Alternative Measures of Debt, Policy and
Shocks
First we examine how robust our results are to alternative measures of debt
burden, policies, and shocks. Table 5 reports results using three alternative debt burden
indicators: the nominal value of debt relative to exports, debt service relative to current
government revenues, and debt service relative to non-gold reserves. The first three
columns correspond to the sample covering the 1980s and 1990s, and the second three
columns correspond to the larger sample including the 1970s. In all cases we find that all
three alternative debt indicators are significant, although again we see that the flow
measures of debt service are much more significant than the stock measure of the face
value of debt. Interestingly, however, comparing the first column of Table 5 with the
fifth column of Table 4, we see that the present value measure of debt stocks is a better
predictor of debt distress than simply the face value of debt. In all specifications in Table
5, we continue to find that policies and institutions as proxied by the CPIA are a highly
significant determinant of debt distress, and in most cases growth also remains
significant, although its significance is weaker than in our core specifications in the
previous table.
In Table 6 we consider alternative measures of policies and shocks. In the first
two columns we use the time-invariant measure of institutional quality from Kaufmann,
18
Kraay and Mastruzzi (2003). This measure of rule of law is significant at the 90 percent
level in the smaller sample, and at the 99 percent level in the larger sample including the
1970s. The somewhat weaker explanatory power of this measure relative to the CPIA is
probably due to the fact that it captures institutional quality in 2002, rather than at the
beginning of each episode, and there is at least some variation over time in institutional
quality. Importantly, however, we continue to find that the debt burden and shock
measures from our core specification remain highly significant, and with only modest
changes in their magnitudes. In the remaining four columns of Table 6 we look at two
potential sources of shocks to GDP growth: real depreciations, and changes in the terms
of trade. We find very little evidence of the significance of either of these direct
measures of shocks in predicting debt distress, with only terms of trade growth being
marginally significant in one specification. We do however continue to find that debt
burden and policy are highly significant.
4.3 Robustness of Core Specification: Level of Development
We now investigate the role of cross-country differences in the level of
development in driving our results. In the first two columns of Table 7 we add log real
per capita GDP at PPP to our basic specifications with the present value of debt, and with
debt service. We do this to check whether debt burdens and policies, both of which are
strongly correlated with per capita income, are not simply serving as proxies for the level
of development. We find that per capita income is statistically insignificant, and
moreover, the magnitude and significance of our core variables of interest change little
with the addition of this variable.
The remaining colums of Table 7 investigate how the determinants of debt
distress differ in the richer and poorer halves of our developing-country sample. We
return to the core specifications in columns (5) and (8) of Table 4, and divide our sample
in half at the median level of per capita income. We then re-estimate our core
specifications in these two subgroups. Since our main interest here is how the magnitude
of the estimated effects of debt, policies and shocks differ in the two samples, we report
19
the estimated marginal effects instead of the slope coefficients in columns (3)-(6) of
Table 7, together with the absolute value of the t-statistics associated with the underlying
coefficients.
Our results show interesting similarities and differences in the two subsamples.
In the both the low-income and high-income sample, we continue to find that debt
burdens and policy matter for debt distress, although the statistical significance is
somewhat weaker in some specifications. Interestingly, however, the magnitudes of the
estimated effects of debt burdens and policy are quite different. In the low-income
sample, the marginal effect of improvements in policy on the likelihood of debt distress is
twice as large as in the high-income sample. Conversely, the marginal effect of higher
debt burdens is almost three times as large in the high-income sample as in the low-
income sample. Although we find little evidence that these differential effects are
statistically significant, qualitatively at least this suggests a relatively greater role for
policies and institutions as captured by the CPIA in the poorest developing countries,
while a greater role for more traditional financial indicators of debt burden in richer
developing countries.
Finally, our evidence on the importance of shocks as proxied by real GDP growth
remains, but is somewhat weaker in the two subsamples than in the full sample.
However this, together with our failure to find strong evidence of statistically significant
differential effects in richer and poorer developing countries, may simply reflect our
much smaller sample size in the two subgroups. Overall, however, these results suggest
to us that our main findings are not driven exclusively by richer or poorer developing
countries, nor simply by differences between these two groups of countries. Rather, our
results suggest that debt burdens, policies and shocks matter for debt distress both in low-
income and middle-income developing countries.
4.4 Robustness of Core Specification: Role of History
20
We finally ask whether the significance of policy in predicting debt distress
reflects the effect of current policies and institutions on debt distress, or whether it simply
proxies for a history of bad policies and institutions that permanently raises the
probability of debt distress for a country. This question is motivated by the finding of
Reinhart, Rogoff, and Savastano (2003) that a country's history of default and bad policy
is a robust predictor of investors' perceptions of the likelihood of sovereign default. We
check for this in several ways.
First, we add to our core specifications a variable capturing a country's history of
bad policy in the pre-sample period. In particular, for the regression with the present
value of debt relative to exports during the 1980s and 1990s, we add a variable measuring
the fraction of years between 1960 and 1979 that the country's inflation rate was greater
than 40 percent. For the regression with debt service covering the 1970s as well, we
construct the same inflation history variable, but only for the 1960s. We then include this
variable in the regression to control for a country's history of bad policy. In column (1)
we find that the country's inflation history during the 1960s and 1970s is a significant
predictor of debt distress during the 1980s and 1990s. However, the coefficient on
contemporaneous policy remains highly significant and the magnitude of the effect is
only slightly smaller in absolute value relative to the core specification. In the larger
sample of observations in column (2), we do not find any evidence of a significant effect
of inflation history, while contemporaneous policy remains significant. Together, these
results suggest that while the history of policy does matter to some extent, it does not
appear to dominate the effect of contemporaneous policy on the probability of debt
distress.
Second, we attempt to disentangle the effects of cross-country differences in long-
run average policy performance from within-country fluctuations over time in policy
performance. Because of the intrinsic nonlinearity of the probit specification, we cannot
isolate the between- and within-country variation in the data with a simple differencing or
averaging transformation. Instead, we separate our measure of policy into a country-
average of policy, and a deviation of contemporaneous policy from that average, and we
21
enter both terms into the regressions separately. If the significance of policy in our basic
specification reflects only the fact that distress is more likely to occur in countries with
persistently poor policies, then we should find that average policy is significant but the
deviation from average is not. In columns (3) and (4) we find that both average policy
and the deviation of policy from its time average are significant predictors of debt
distress. This casts doubt on the hypothesis that our policy variable matters only because
it is picking up long-run differences across countries in the quality of policy. Moreover,
in column (3) we find that we cannot reject the null hypothesis that the coefficient on
average policy and on the policy deviation are equal, justifying the inclusion of simply
the level of policy as we do in our core specification. In column (4) in our larger sample
including the 1970s, we do find a statistically significant difference between the effects of
average policy and its deviation, but we still do find a significant effect of both.
For completeness, in the next two columns of Table 8 we perform the same
decomposition exercise, but now for real GDP growth. The rationale for this robustness
check is the same as before. It is possible that our growth variable is simply picking up
the difference between fast-growing and slow-growing countries, and the probability of
distress is higher in the latter. In this case, however, we find no evidence that our growth
variable is picking up these persistent cross-country differences. We find that the
deviation of real GDP growth from average is highly significant, while averages over
time of real GDP growth are not significant determinants of debt distress.
Third, we directly investigate the role of a country's history of default on its
external obligations as a predictor of debt distress. In particular, we use the database of
default episodes compiled by Reinhart, Rogoff and Savastano (2003) to identify the
fraction of years between independence (or 1824, whichever is later) and 1980 in which a
country was in default on its external borrowing.8 We then add this variable to our core
specification covering distress episodes in the 1980s and 1990s. The results of these
regressions are reported in columns (7) and (8) of Table 8. In column (7) we report a
8Since the Reinhart et. al. dataset is a comprehensive list of external debt default episodes, we set this
variable to zero for those countries not appearing in their dataset.
22
univariate regression on just the default history variable, and confirm the spirit of
Reinhart et. al. finding that this variable is a good predictor of debt distress. The default
history variable enters significantly at the 5 percent level, although its explanatory power
is quite modest relative to the other univariate specifications in Table 4. More interesting
for our purposes is the final column of Table 8, where we now add the default history
variable to our core specification. The significance of the default history variable drops
to the 10 percent level. Importantly however, the significance and magnitude of our core
determinants of debt distress, debt burdens, policy and shocks, are virtually unaffected
the the inclusion of this variable. In addition, the incremental explanatory power of the
default history variable is quite modest, with the pseudo-R-squared increasing from 0.23
in the core specification in Table 4, to 0.26 here. We conclude from this that, while
default histories clearly matter to some extent for the incidence of debt distress, it is also
clear that history is not destiny contemporaneous values of debt burdens, institutions
and policy, and shocks matter much more for predicting the incidence of debt distress.
All of the robustness checks discussed so far in this subsection have to do with the
importance of time-invariant country-specific determinants of debt distress that may be
correlated with our core set of explanatory variables. As a fourth and final robustness
check for the importance of such factors, we estimate the following dynamic probit
specification with unobserved country-specific effects:
(2) P[yct = 1] = ' Xct + yc
( )
,t-+ µi
where yc,t- denotes the value of the distress indicator in the episode immediately prior to
the one occurring at time t in country c; is a parameter capturing the persistence of
distress, and µi is an unobserved country-specific time-invariant effect capturing
persistent country characteristics that influence the probability of debt distress. As noted
above, this country-specific effect cannot be eliminated by a differencing transformation
common in linear panel data models. Moreover, since we have a lagged dependent
variable, we are faced with the familiar initial conditions problem: loosely, we cannot
23
ignore the fact that by construction, the lagged dependent variable is correlated with the
unobserved individual effect.
We estimate this model by applying the initial conditions correction suggested by
Wooldridge (2002). He proposes modelling the individual effect as a linear function of
the initial observation on the dependent variable for each country, as well as time
averages of all of the right-hand-side variables. He also shows that this specification can
be simply estimated using standard random-effects probit software, as long as the list of
explanatory variables is augmented with the initial value of the dependent variable and
time averages of all of the right-hand-side variables for each country.
The results of this specification can be found in Table 9.9 The first four rows of
this table contain the main coefficients of interest, on the lagged dependent variable and
our three usual explanatory variables of interest. As before, we continue to find that debt
indicators, policy, and growth remain significant predictors of the probability of debt
distress. Although the point estimates of the coefficients differ somewhat relative to
previous specifications and in some cases the significance is somewhat weaker, the
results for these variables remain quite consistent with those discussed earlier.
Interestingly, we find no evidence that debt distress in the previous period significantly
raises the probability of distress in the next period, after debt burdens, policy and growth
have been controlled for. Taken together, these results suggest that unobserved time-
invariant country characteristics are not responsible for our main results, and that the
observed persistence of debt distress over time is mostly due to the persistence of debt
burdens, policies, and shocks rather than a recent history of distress itself.10
9Since we have an irregularly spaced panel, there is a risk of misspecification in treating the coefficient on
the lagged dependent variable as equal across all observations regardless of the spacing between them. In
principle we could allow this coefficient to vary across observations with some non-linear parametric
function of the lag length, at the cost of considerably complicating the estimation. Instead, we adopt the
shortcut of dropping those observations for which the lag until the previous observation is greater than 10
years.
10This last result contrasts with the finding of McFadden et. al. (1985), who do find evidence for state-
dependence.
24
5. Policy Implications and Conclusions
We have showed that the likelihood of debt distress can be substantially explained
using three factors: debt burdens, policy, and shocks. We conclude by considering the
implications of these findings for the lending policies of official creditors.
The first of these implications is quite straightforward: there is substantial value-
added in assessing the quality of policies and the severity of shocks in addition to debt
burden indicators when assessing the probability of debt distress of individual countries.
The regression results clearly show that policy performance and the severity of shocks
matter a lot in predicting which countries are likely to suffer debt distress. From a
predictive standpoint, there are non-trivial differences between the predicted probability
of distress based on debt burdens alone, and the probability of distress based on the fully
specified multivariate regression equation. We illustrate this in Figure 4, where we graph
the present value of debt relative to exports in 2001 for all countries, against the predicted
probability of debt distress using the most recently available data for 1999-2001 and the
regression in the fifth column of Table 4. We indicate the HIPC threshold of 150 percent
debt/exports as a vertical line, and the historical unconditional probability of debt distress
in our sample of 0.23 as a horizontal line. As Figure 4 illustrates, there are many
countries which may have low debt burdens relative to these benchmarks, but still have
high probabilities of debt distress owing to either to poor policy performance or recent
poor growth performance. Similarly, there are some countries that are able to bear
significantly higher debt burdens without a higher-than-average distress probability
because of the quality of their policies and the robustness of their economic growth.
Our second policy implication follows closely from the first: using a single debt
threshold for all low income countries to identify countries that are vulnerable to debt
distress ignores the importance policies and shocks. Country-specific debt thresholds
reflecting policies and shocks are more appropriate. In Figure 5, we illustrate how these
other factors influence the "sustainable" debt level of a country. In the top panel, we
consider a hypothetical country with a growth rate of 3.6 percent equal to the mean of our
25
sample. Then, for the indicated value of policy on the horizontal axis, we compute the
level of the present value of debt relative to exports that would be consistent with a
predicted probability of debt distress equal to 10 percent, 25 percent, and 40 percent, and
we graph these (truncating negative values at zero).11 Consider first the line
corresponding to a distress probability of 25 percent, which is equal to the historical
unconditional average rate of debt distress in our sample. It tells us that a country with
average growth, and poor policy (corresponding to a CPIA score of 3 which is roughly
the first quartile of our sample), would be able to tolerate a present value of debt to
exports of less than 100 percent. In contrast, a country with good policy (corresponding
to a CPIA score of 4.2 which is the fourth quartile of our sample), would be able to
tolerate a debt level nearly three times higher with the same distress probability. Of
course, for lower (higher) debt distress probabilities, these lines shift down (up),
corresponding to lower (higher) levels of debt for any level of policy.
The bottom panel of this Figure 5 does the same exercise, but instead holding
policy constant at its mean value, and varying growth from the bottom to the top quartile.
Again, we see sharp differences in the level of debt that are consistent with a given
distress probability. For countries with low growth and average policy, a debt level of
200 percent of exports is consistent with a debt distress probability of 25 percent, while
countries with high growth can tolerate a 300 percent debt/export ratio with the same
distress probability. Of course, the precise magnitudes of the effects of differences in
debt and policy on these implied debt levels depends on all of the estimated coefficients
in the regressions on which these estimates are based, and these are subject to margins of
error and vary across specifications. Thus, these figures can only give us a sense of the
rough order of magnitude of effects of policies and shocks on the level of debt consistent
with a given distress probability.
Our third and final policy implication is also straightforward: the risk of debt
distress should be taken into account when deciding the terms and modalities of resource
11These implied debt levels are obtained by solving p=(0+1xDebt+2xPolicy+3xGrowth) for debt,
where p is the desired probability of debt distress.
26
transfers to low income countries. The objective here is to ensure that resources are
provided in such a way as to keep debt distress probabilities at manageable levels, while
at the same time ensuring that aid is targetted to countries with good policies and
institutions. Clearly, this will involve altering the concessionality of aid by using a
mixture of grants and loans, with the mix tailored to the risk of debt distress in each
country. This point is especially important in light of the much larger flows of
development finance that have been advocated if countries are to meet the Millenium
Development Goals. It has been estimated that an additional $50 billion per year in
development finance will be required to reach these targets, which would represent
approximately a doubling of current flows to developing countries.
If the additional ODA flows envisioned in light of the MDGs are provided
primarily as (even concessional) loans, the vast majority of recipient countries will
experience very sharp increases in their predicted debt distress probabilities, and are
likely to experience serious difficulties repaying these loans in the future. The empirical
results in this paper provide rough-and-ready estimates of the scale of this problem,
which we illustrate with the following counterfactual exercise. We first take the most
recently-available data on low-income countries' debt burdens, policies, and growth
rates, and use it to compute current estimated probabilities of debt distress, as in Figure 4.
We then assume that an additional $100 billion in new lending is allocated across low-
income countries in proportion to these countries' IDA borrowing during the second half
of the 1990s.12 We then compute the new present value of debt relative to exports,
assuming that this new lending has the same average concessionality as each country's
stock of past borrowing. Finally, we calculate the new probability of debt distress,
assuming that policies and growth are unchanged.
Figure 6 summarizes the results of this calculation. The horizontal axis reports
the current predicted distress probabilities, while the vertical axis reports the predicted
12Since IDA lending is already allocated across countries according to a formula that explicitly recognizes
good policy and high poverty, this seems like a reasonable basis to allocate new lending in our hypothetical
experiment.
27
distress probabilities reflecting the additional $100 billion in lending. Several
observations on this calculation are in order.
· This additional lending would represent a very substantial increase in the debt
burden of many low-income countries. Although the $100 billion in additional
lending represents only about 15 percent of the total stock of external debt of
these countries, this understates the increase in debt burden of a "typical" low
income country because China accounts for nearly one-quarter of the external
debt of this group of countries but borrows relatively little from IDA because of
its access to commercial finance. In contrast, the average increase across
countries in the face value of debt is nearly 40 percent of exports, and the average
increase in the present value of debt, given past patterns of concessionality, is 25
percent of exports.
· The counterpart of the increase in debt burdens is a substantial increase in the
probability of debt distress for many low-income countries. This can be seen
from Figure 5, where for each country the vertical distance to the 45-degree line
indicates the increase in distress probability associated with this increased
concessional lending. Consider Uganda, with its good policy and fairly low
income levels: it receives a substantial fraction of new lending, increasing its
present value of debt from 1.7 times exports to nearly 3 times exports. Given
current policy and growth, this would increase the predicted probability of debt
distress from 13 percent to 21 percent. More dramatically, consider Ethiopia,
which in our scenario would see the present value of debt increase from 3 times
exports to 5 times, and the debt distress probability increase from 30 percent to 50
percent. Note also that countries with very high current debt distress probabilities
will see little increase in this probability, but this is simply because their initial
levels are already so high. The increases in distress probabilities for countries
with low initial probabilities is also small. This largely reflects the fact that many
of these countries have higher per capita incomes and have borrowed relatively
little from IDA in the past, and so receive little additional lending in our
hypothetical scenario.
28
· It could be argued that these calculations overstate the effects of additional
lending on debt distress in low income countries because they ignore likely future
export growth which would contribute to lower debt burdens relative to exports.
However, many of the countries receiving new lending in our hypothetical
scenario have had sharply negative export growth over the past five years, so if
past trends continue we would be understating the increase in debt burdens in
these countries. In fact, for the group of IDA borrowers as a whole, the
unweighted average growth rate of nominal dollar exports between 1997 and
2001 was only 2.8 percent per year. Moreover, our hypothetical scenario results
on average in a greater proportionate increase in the present value of debt in
countries where past export growth was low or negative. Thus, if we were to
adjust downward the increase in the debt to export ratio by historical cumulative
export growth over the past five years, we find that the unweighted average
percentage increase in this debt burden indicator is even higher than when we
ignore future export growth (65 percent versus 40 percent).
This calculation highlights the importance of rethinking the average terms of
resource flows to low-income countries if sharp increases in debt servicing difficulties are
to be avoided. These calculations show that, if financing the MDGs using external aid is
to be taken seriously, then a greater role for grants will be required, and, for countries
with a given quality of policies, that the share of grants will need to be significantly
higher where debt distress probabilities are high, and lower where distress probabilities
are low. At the same time, however, grants should not supplant loans one-for-one as the
probability of debt distress increases, for two reasons. First, replacing loans with grants
equal to the face value would represent a vastly larger resource transfer than is currently
envisioned by donors, and obtaining the necessary financing would be difficult. Second,
such a scheme would implicitly "reward" countries implementing weak policies with
more grants, and greater overall resource transfers, undermining efforts to target aid to
countries with good policies.
29
One possible scheme for calibrating the share of grants without exacerbating
moral hazard problems would be the following three-step process. First, the total amount
of new lending can be converted into its grant-equivalent from the donors' perspective,
by taking the face value of the new lending and subtracting the present value of future
debt service. Second, this grant-equivalent could be allocated across countries following
some kind of aid allocation rule that recognizes the importance of "needs" (i.e. the
prevalence of poverty), and "aid effectiveness" (i.e. a function of the quality of policies
and institutions of the recipient country). Third, for countries below a specified distress
probability (in other words, where the capacity for servicing debt in the future is
considered relatively good), this grant equivalent could be "grossed-up" into a much
larger amount of concessional lending with the same grant equivalent. Such a scheme
would have a number of advantages:
· Most obviously, by calibrating the amount of new lending to the probability of
distress, it avoids the large and likely unsustainable increases in debt burdens
that would follow from large-scale across-the-board new lending to low-
income countries.
· This scheme also ensures that resources are targetted to countries with high
poverty and good policies, and moreover provides and additional reward for
good policy. This is because countries would prefer to be able to "gross-up"
as much of their grant-equivalent allocation as possible into lending, and
improvements in policy can create additional "headroom" for new borrowing
by lowering the probability of debt distress.
· This scheme also would not require any new commitments by donors to
finance new grants, over and above the implicit commitment to new transfers
in grant equivalent terms implicit in donors commitments to lending at
existing rates of concessionality. This is because donors would be committing
to the same transfer to a country whether they provide only the grant element,
or they convert this grant element into loan with the same grant equivalent. If
anything, the resource transfer from the perspective of the donor might be
even smaller, to the extent that calibrating the fraction of loans to the
30
probability of debt distress results in higher actual repayment rates in the
future.
In summary, we have shown in this paper that the risk of debt distress depends
significantly on a small set of factors: debt burdens, policies and institutions, and shocks.
We have shown that this finding is robust to several robustness checks, and that our
empirical model does a reasonable job of predicting future debt distress. While at some
level these results should not be too surprising, they do have profound implications for
how resource transfers to low-income countries could be financed. Our results indicate
that the probability of debt distress is already high in many low-income countries, and is
likely to increase sharply if the large-scale development finance required to meet the
Millenium Development Goals is provided in the form of concessional lending at historic
levels of concessionality. We have also proposed a simple scheme of financing resource
transfers to low-income countries in a way that controls the probability of debt distress,
provides good incentives to borrowers, and does not involve additional donor
commitments to finance large-scale new grants.
31
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32
Wooldridge, Jeffrey (2002). "Simple Solutions to the Initial Conditions Problem in
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33
Figure 1: Identifying Debt Distress Events
Kenya
250% 10%
SBA/EFF
9%
Committments Arrears/DOD
(Left Axis) (Right Axis)
200% 8%
Paris Club
Quota 7%
of Relief/DOD
(Right Axis)
150% 6%
Percent
as
5%
Percent
ttments
100% 4%
Commi
EFF 3%
SBA/
50% 2%
1%
0% 0%
1970 1975 1980 1985 1990 1995 2000
=
Thailand
600%
500%
a
Quot
of 400%
Percent
as
s
300% SBA/EFF
ent
mt Committments
mmitoC
200%
EFF/A
SB
100%
0%
1970 1975 1980 1985 1990 1995 2000
34
Figure 2: Incidence of Debt Distress Events
12
10
Events 8
6
Distress
of
4
Number
2
0
1970 1975 1980 1985 1990 1995 2000
.
35
Figure 3: Correlates of Debt Distress -- Debt Indicators
Present Value of Debt/Exports Total Debt Service/Exports
1 1
s s
0.8 0.8
ress
Episode Episode 0.6
of Distress 0.6
of Dist
in in
0.4 oni 0.4
y = 0.11x + 0.05 y = 0.88x + 0.04
R2 = 0.81
Ending0.2 R2 = 0.60 Ending 0.2
Proportion Proport
0 0
0.0 2.0 4.0 6.0 0.0 0.2 0.4 0.6
Average of RHS Variable by Decile Average of RHS Variable by Decile
Face Value of Debt/Exports Total Debt Service/Current Revenue
1
1 s
0.8
0.8
Episode
0.6 0.6
of Distress
in
0.4 y = 0.06x + 0.11 0.4
y = 0.65x + 0.08
0.2 R2 = 0.44 Ending0.2 R2 = 0.86
0 Proportion
0.0 2.0 4.0 6.0 8.0 0
0.0 0.2 0.4 0.6 0.8
Average of RHS Variable by Decile
Average of RHS Variable by Decile
Total Debt Service/Non-Gold
1 Reserves
s
0.8
Episode 0.6
of Distress
in
0.4 y = 0.11x + 0.07
R2 = 0.87
Ending 0.2
Proportion
0
0.0 2.0 4.0 6.0
Average of RHS Variable by Decile
36
Figure 3 Cont'd: Correlates of Debt Distress Policies and Shocks
Overall CPIA Score Real GDP Growth
1 1
s s
0.8 y = -0.16x + 0.80 0.8
R2 = 0.59
Episode 0.6 Episode
Distress 0.6
of Distress of
in in y= -1.40x + 0.26
0.4 0.4 R2 = 0.64
Ending 0.2 Ending 0.2
Proportion Proportion
0 0
0.0 2.0 4.0 6.0 -0.1 0.0 0.1 0.2
Average of RHS Variable by Decile Average of RHS Variable by Decile
Real Exchange Rate Depreciation Terms of Trade Growth
1 1
s s
0.8 0.8
y= 0.08x + 0.22
Episode 0.6 y = -0.14x + 0.20 Episode
Distress 0.6 R2 = 0.00
of Distress of
in R2 = 0.03 in
0.4 0.4
Ending 0.2 Ending 0.2
Proportion Proportion
0 0
-0.4 -0.2 0.0 0.2 0.4 -0.2 -0.1 0.0 0.1 0.2
Average of RHS Variable by Decile Average of RHS Variable by Decile
37
Figure 4: Debt Levels and Debt Distress Probabilities
1 Low Income Countries
BDI
0.9
SLE
Data 0.8
GNB
SDN
STP
CAF
Current 0.7
on ZWE
SLB
0.6 HTI
Based
COM
0.5 AGO TGO NER
Distress ZMB
of
0.4 CIV RWA
UZBNGA TCD
LAO MWI
COG
ERI PNG GIN
GUY KEN ETH
Probability 0.3 CMR
GMB
KGZ MDG
BFA MRT
MNGGEOMDA
TJK BOL KHM IDN
BEN
PAK
MLI
GHA
0.2 YEM
LSO HND
Predicted BGD SEN
IND
NPL
TZAARM UGA
UKR
VNM
0.1 GNQ AZE MOZ
BTN
0
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Present Value of Debt/Exports, 2001
38
Figure 5: Policies, Growth, and Debt Distress Probabilities
6
P[Distress]=0.25
5
P[Distress]=0.1
Indicated P[Distress]=0.4
4
with
Probability 3
Consistent
Distress2
1
Debt/Exports
PV
0
3 3.2 3.4 3.6 3.8 4 4.2 4.4
CPIA Rating
.
6
P[Distress]=0.25
P[Distress]=0.1
Distress 5
P[Distress]=0.4
Indicated 4
with
3
Probability
Consistent 2
1
Debt/Exports
PV
0
0.03 0.035 0.04 0.045 0.05 0.055 0.06 0.065 0.07
Real GDP Growth
39
Figure 5: Increase in Debt Distress Probabilities Due to New Concessional Lending
1 BDI
SLE
0.9 GNB
0.8 CAF
STP
0.7 ZMB
Borrowing RWA NER ZWE
HTI
SLB
New 0.6 COM
TCD TGO
With MWI
0.5 ETH AGO
ERI
CIV
Distress LAO
KGZ
0.4
Debt MRTKEN
MDG
NGA
of BFA GINCOG
GEO GMBGUY
CMR
DJIDMA
0.3 MLIKHM
BEN
TJK
MDA
BOL
GHA IDN
MNGPAK
SEN
ARM YEM
LSO
HND
Probability0.2 UGABGDEGY
MKD
BIHLCA
TZA
NPL
CPVLKA
AZEGRD
MOZ
ALBIND
VNM
WSM
VCT
0.1 BTN
MDV
CHN
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Probability of Debt Distress Before New Borrowing
40
Table 1: Distress Events
Average During Episode of:
Start Year End Year Arrears/DOD Paris Club Relief/DOD SBA/EFF Commitments/Quota
ALB 1992 2002 0.05 0.03 0.06
ARG 1983 1996 0.08 0.01 1.13
BEN 1983 1999 0.12 0.03 0.00
BFA 1987 1999 0.05 0.01 0.00
BGD 1979 1982 0.00 0.00 2.53
BRA 1983 1986 0.01 0.01 2.90
CAF 1971 2000 0.11 0.01 0.10
CHL 1983 1990 0.00 0.00 1.34
CIV 1981 1997 0.10 0.02 1.03
CMR 1987 2000 0.10 0.05 0.22
COG 1985 2000 0.23 0.06 0.21
COM 1987 2000 0.17 0.00 0.00
CPV 1988 2000 0.10 0.00 0.00
CRI 1980 1996 0.08 0.01 0.64
DOM 1983 2000 0.12 0.01 0.48
DZA 1994 1998 0.00 0.10 1.08
ECU 1983 1997 0.14 0.01 0.39
EGY 1977 1981 0.02 0.00 1.92
EGY 1984 1996 0.09 0.07 0.28
ETH 1991 2000 0.33 0.01 0.00
GHA 1996 1999 0.01 0.00 0.00
GNB 1981 2000 0.21 0.02 0.00
GUY 1978 2001 0.13 0.04 0.45
HND 1979 2001 0.06 0.01 0.26
HTI 1978 1981 0.00 0.00 1.24
IDN 1997 2002 0.03 0.01 2.82
IND 1981 1984 0.00 0.00 2.70
JAM 1977 2000 0.05 0.01 1.25
JOR 1989 2001 0.06 0.04 1.02
KEN 1975 1978 0.00 0.00 1.40
KEN 1992 1997 0.04 0.01 0.00
KHM 1989 2000 0.31 0.01 0.00
LBR 1980 2000 0.46 0.01 0.23
MAR 1980 1995 0.02 0.02 0.92
MDG 1980 2002 0.15 0.03 0.29
MEX 1983 1993 0.00 0.01 1.84
MWI 1979 1986 0.00 0.01 1.89
NER 1983 1991 0.02 0.02 0.21
NGA 1986 2002 0.23 0.08 0.13
NIC 1983 2000 0.29 0.01 0.06
PAK 1980 1984 0.00 0.01 1.82
PAK 1995 2001 0.00 0.02 0.56
PER 1977 1980 0.00 0.00 1.20
PHL 1976 1979 0.00 0.00 1.28
PRY 1986 1995 0.11 0.00 0.00
RWA 1994 2000 0.07 0.01 0.00
SDN 1977 2000 0.45 0.01 0.51
SEN 1980 2002 0.01 0.03 0.32
SLV 1990 1993 0.01 0.02 0.24
SOM 1981 2000 0.38 0.00 0.29
THA 1997 2000 0.00 0.00 4.26
TTO 1988 1993 0.03 0.03 0.22
TUN 1986 1992 0.00 0.00 1.17
TUR 1978 1985 0.00 0.02 2.33
URY 1983 1987 0.00 0.00 1.53
VNM 1988 2000 0.35 0.00 0.05
ZAR 1977 2000 0.25 0.03 0.40
41
Table 2: Summary Statistics
Normal Times
N mean min max p25 p50 p75
Length of Episode 227 5.000 5.000 5.000 5.000 5.000 5.000
Average During Episode of:
Arrears/DOD 227 0.004 0.000 0.041 0.000 0.000 0.004
Paris Club/DOD 227 0.000 0.000 0.000 0.000 0.000 0.000
SBAEFF/Quota 227 0.033 0.000 0.298 0.000 0.000 0.050
GDP Growth 227 0.052 -0.039 0.199 0.033 0.049 0.070
Value at Beginning of Episode of:
PV Debt/Exports 126 1.024 0.029 9.920 0.343 0.722 1.263
Debt Service/Exports 227 0.151 0.000 0.626 0.057 0.115 0.219
Debt Service/Revenues 144 0.208 0.007 0.811 0.084 0.171 0.278
Debt Service/Reserves 221 0.754 0.000 7.886 0.158 0.393 0.961
CPIA 227 3.660 2.111 6.000 3.391 3.485 3.795
Real GDP Growth 227 0.051 -0.149 0.301 0.026 0.051 0.078
Real Depreciation 224 0.001 -0.453 0.482 -0.049 -0.002 0.045
TOT Growth 199 -0.008 -0.352 0.234 -0.026 -0.002 0.013
Distress Events
N mean min max p25 p50 p75
Length of Episode 57 11.281 3.000 29.000 5.000 11.000 16.000
Average During Episode of:
Arrears/DOD 57 0.099 0.000 0.460 0.003 0.053 0.130
Paris Club/DOD 57 0.017 0.000 0.100 0.003 0.011 0.022
SBAEFF/Quota 57 0.828 0.000 4.262 0.100 0.404 1.246
GDP Growth 57 0.029 -0.025 0.087 0.014 0.032 0.043
Value at Beginning of Episode of:
PV Debt/Exports 37 2.120 0.015 8.170 1.153 1.940 2.423
Debt Service/Exports 57 0.313 0.000 0.968 0.135 0.268 0.395
Debt Service/Revenues 41 0.347 0.015 1.400 0.184 0.271 0.485
Debt Service/Reserves 47 2.589 0.103 9.374 0.582 1.744 3.268
CPIA 57 3.127 1.000 5.125 2.500 3.222 3.500
Real GDP Growth 57 0.018 -0.277 0.167 -0.010 0.026 0.066
Real Depreciation 54 -0.008 -0.456 0.516 -0.071 -0.015 0.064
TOT Growth 51 -0.018 -0.627 0.123 -0.018 -0.004 0.006
42
Table 3: Correlation Among Distress Variables
PV Debt/ Debt Service/ CPIA Real GDP Real TOT
Exports Exports Growth Depreciation Growth
PV Debt/Exports 1
163
Debt Service/Exports 0.454 1
163 284
CPIA -0.2599 -0.0254 1
163 284 284
Real GDP Growth -0.1631 -0.1575 0.1006 1
163 284 284 284
Real Depreciation 0.0754 0.2056 0.0411 0.0209 1
158 278 278 278 278
TOT Growth -0.0176 0.0404 0.1 0.1525 -0.0085 1
142 250 250 250 248 250
43
Table 4: Determinants of Debt Distress
(1) (2) (3) (4) (5) (6) (7) (8)
PV Debt/Exports 0.317 0.228 0.064
(0.079)*** (0.086)*** (0.100)
Total Debt Service/Exports 3.615 4.011 3.750 3.341
(0.720)*** (0.992)*** (0.991)*** (0.063)***
CPIA Rating -0.627 -0.514 -0.652 -0.625 -.640
(0.144)*** (0.153)*** (0.159)*** (0.164)*** (0.144)***
Real GDP Growth -7.178 -6.670 -5.075 -5.009 -3.358
(1.993)*** (2.303)*** (2.475)** (2.483)** (1.684)**
Constant -1.206 -1.580 1.438 -0.539 0.915 0.765 0.630 0.801
0.162*** (0.205)*** (0.506)*** (0.126)*** (0.587) (0.556) (0.596) (0.502)
# Observations 163 163 163 163 163 163 163 284
Pseudo R-Squared 0.087 0.184 0.127 0.085 0.234 0.348 0.350 0.242
Out-of-Sample Predictive
Power
Correct Predictions 0.51 0.67 0.67 0.43 0.70 0.84 0.84 0.69
Distress Events 0.90 0.80 0.60 0.80 0.70 0.70 0.70 0.90
False Alarms 0.55 0.35 0.32 0.63 0.30 0.13 0.13 0.34
44
Table 5: Robustness of Basic Result: Alternative Debt Measures
(1) (2) (3) (4) (5) (6)
Face Value Debt/Exports 0.11 0.11
(0.06)* (0.06)*
Debt Service/Revenues 3.18 3.01
(0.89)*** (0.70)***
Debt Service/Reserves 0.35 0.33
(0.08)*** (0.06)***
CPIA Rating -0.54*** -0.64 -0.52 -0.54 -0.75 -0.52
(0.15) (0.19)*** (0.18)*** (0.15)*** (0.17)*** (0.16)***
Real GDP Growth -6.77 -3.27 -4.48 -6.77 -0.22 -3.31
(2.27)*** (3.02) (2.72)* (2.27)*** (2.10) (1.90)*
Constant 1.12 0.64 0.66 1.12 1.09 0.58
(0.58) (0.63) (0.63) (0.58) (0.56) (0.57)
# Observations 163 121 153 163 185 268
Pseudo R-Squared 0.21 0.26 0.30 0.21 0.19 0.23
45
Table 6: Robustness of Basic Result: Alternative Policy and Shock Measures
(1) (2) (3) (4) (5) (6)
PV Debt/Exports 0.25 0.27 0.56
(0.08)*** (0.09)*** (0.14)***
Total Debt Service/Exports 3.16 3.90 3.51
(0.58)*** (0.67)*** (0.64)***
CPIA Rating -0.42 -0.45 -0.56 -0.54
(0.16)*** (0.18)*** (0.15)*** (0.15)***
KK Rule of Law Index -0.36 -0.42
(0.19)* (0.15)***
Real GDP Growth -6.97 -3.77
(2.19)*** (1.55)**
Real Depreciation -0.28 -1.47
(0.85) (0.80)*
Terms of Trade Growth 0.06 -1.21
(1.40) (1.28)
Constant -1.02 -1.52 0.29 0.11 0.25 0.26
_CONS (0.18)*** (0.19)*** (0.61) (0.69) (0.53) (0.56)
# Observations 162 283 158 142 278 250
Pseudo R-Squared 0.18 0.19 0.15 0.23 0.22 0.22
46
Table 7: Robustness Checks: Role of Level of Development
(1) (2) (3)* (4)* (5)* (6)*
Low-Income Sample High-Income Sample
PV Debt/Exports 0.63 0.043 0.200
(0.16)*** (1.52) (3.84)***
Total Debt Service/Exports 3.69 0.787 0.772
(0.69)*** (2.75)*** (4.39)***
CPIA Rating -0.50 -0.51 -0.162 -0.241 -0.076 -0.058
(0.18)*** (0.16)*** (2.48)** (4.17)*** (1.77)* (1.37)
GDP Growth -5.68 -3.88 -1.386 -0.291 -1.413 -1.511
(2.67)** (1.82)** (1.49) (-0.53) (1.76)* (2.47)**
Log(Real Per Capita GDP) 0.30 -0.22
(0.21) (0.15)
Constant -2.04 1.96*
(1.79) (1.13)
# Observations 144 258 81 139 82 145
Pseudo R-Squared 0.31 0.26 0.18 0.21 0.46 0.33
* These columns report estimated marginal effects. Absolute value of t-statistics associated with the underlying slope coefficients are reported in
parentheses.
47
Table 8: Robustness Checks: Role of History
(1) (2) (3) (4) (5) (6) (7) (8)
PV Debt/Exports 0.54 0.23 0.23 0.212
(0.15)*** (0.09)** (0.09)** (0.087)**
Total Debt Service/Exports 3.25 3.57 3.33
(0.66)*** (0.65)*** (0.63)***
CPIA Rating -0.46 -0.59 -0.54 -0.62 -0.515
(0.18)** (0.17)*** (0.16)*** (0.15)*** (0.155)***
GDP Growth -7.01 -2.76 -6.58 -3.35 -6.496
(2.79)** (1.91) (2.32)*** (1.65)** (2.305)***
Inflation History 2.08 -0.17
(1.04)** (0.71)
Average CPIA -0.43 -0.95
(0.28)* (0.22)***
CPIA Deviation -0.57 -0.49
(0.22)*** (0.16)***
Average GDP Growth -0.32 -6.99
(7.31) (6.12)
GDP Growth Deviation -7.71 -3.02
(2.63)*** (1.76)*
Fraction of years in default 2.306 1.929
prior to 1980 (0.905)** (1.021)*
Constant 0.21 0.61 0.62 1.80 0.72 0.90 -0.864 0.839
(0.70) (0.62) (1.03) (0.73) (0.63) (0.53) (0.120)*** (0.594)
# Observations 149 250 163 284 163 284 163 163
Pseudo R-Squared 0.31 0.22 0.23 0.26 0.24 0.24 0.04 0.26
P[Average = Deviation] 0.73 0.06 0.36 0.54
48
Table 9: Dynamic Fixed-Effects Probit Specification
(1) (2)
Present Value of Debt/Exports 0.816
(0.335)**
Total Debt Service/Exports 6.208
(1.728)***
CPIA Rating -0.697 -0.506
(0.408)* (0.227)**
Real GDP Growth -12.878 -4.726
(4.377)*** (2.873)*
Lagged Distress Indicator -0.707 -0.861
(0.786) (0.634)
Initial Value of Dependent Variable -0.119 0.669
(0.747) (0.662)
Average Present Value of Debt/Exports -0.742
(0.377)**
Average Total Debt Service/Exports -2.942
(1.869)
Average CPIA Rating 0.018 -0.503
(0.425) 0.441
Average Real GDP Growth 18.095 8.961
(8.004)** (5.494)
Constant 1.024 1.876
(1.027) (1.180)
Number of Observations 140 200
Number of Countries 68 85
Average Observations Per Country 2.1 2.4
49