Boon or burden? The effect of private sector debt on the risk of sovereign default in developing countries.
Celasun, Oya ; Harms, Philipp
I. INTRODUCTION
The past two decades witnessed a strong increase in private foreign
borrowing in emerging markets and developing countries. Although in 1990
the private sector accounted for a mere 16% of all external loans
disbursed to developing countries, this share has increased to 77% in
2006 (Figure 1A). Likewise, in 2006, the private sector's
non-publicly guaranteed debt liabilities accounted for 44% of developing
countries' total external debt--up from a mere 5% in 1990 (Figure
1B). (1) A key question in light of the ongoing global financial turmoil
is what the boom in private sector external indebtedness will imply for
sovereign risk in developing countries. In this paper, we empirically
investigate the record of the past three decades to throw some light on
this issue.
On theoretical grounds, there are arguments both in favor and
against a stabilizing effect of private-sector borrowing on sovereign
risk. A critical view of private-sector exposure is based on the notion
that large-scale private borrowing creates vulnerabilities that may
eventually lead to a sovereign default. A "sudden stop" may
force the public sector to assume at least part of the private debt, and
this may eventually cause debt-service difficulties for the government.
Following this logic, both public and private external debt pose a
threat to sovereign creditworthiness. The opposite argument that
private-sector borrowing does not harm government creditworthiness can
be made by invoking the idea that the private sector is exposed to
greater competitive pressure, which raises the incentives to use the
borrowed funds productively. More importantly, a potentially stabilizing
role of private-sector borrowing can also be linked to the
distributional consequences of sovereign defaults: Agents who are
reliant on foreign credit are particularly vulnerable to the disruptions
that come along with sovereign default. A larger share of the private
sector in total external debt--a proxy of the relative size and stake of
agents that would be hurt by sovereign default--would thus raise the
political costs of default.
Given the competing theoretical arguments, the role of the private
sector for sovereign creditworthiness is an empirical question. A first
impression of how these magnitudes may be related is provided in Figure
2, which plots the Institutional Investor's measure of country
creditworthiness (IICCR) against the level of external debt relative to
GNI (Figure 2A) and the share of private long-term external debt in
countries' total long-term external debt (Figure 2B). (2) Not
surprisingly, the correlation between debt and creditworthiness is
negative (-0.37). By contrast, the correlation between private-sector
share and the IICCR is positive (0.54). (3) Although this picture
obviously does not prove a causal relationship, it suggests that
relative private-sector exposure and perceived creditworthiness might be
positively related.
[FIGURE 1 OMITTED]
[FIGURE 2 OMITTED]
Further evidence that private and public debt are likely to have
very different effects on the risk of sovereign default is provided in
Table 1. The entries in this table are cross-country averages of various
debt-related variables just before the onset of 5-yr periods in which a
sovereign default did or did not take place. (4) The first column of
Table 1 shows that initial debt relative to GNI is, on average, much
higher before a default period than before a non-default period.
Conversely, column 2 indicates that the average share of the private
sector in total external debt is much higher before non-default periods
than before default periods.
A first look at the data thus seems to point into the direction
that a higher share of private debt in total external debt is associated
with higher perceived creditworthiness--as reflected by the
Institutional Investor's country credit ratings--and with a lower
likelihood of sovereign default. The aim of this paper is to subject
this hypothesis to closer scrutiny: Does the composition of external
debt still matter if we account for other determinants of sovereign risk
and the potential endogeneity of international borrowing and lending? Is
this relationship driven by a particular group of countries or limited
to a specific time interval? Our findings suggest that there is indeed a
case to be made that a high share of the private sector in
countries' external debt is more of a boon than a burden: an
exogenous increase of this share reduces sovereign risk.
The rest of the paper is structured as follows: The next section
offers a review of the relevant literature and highlights our own
contribution. Section III introduces our empirical specification, the
data we use, and comments on the results. Section IV summarizes and
concludes. Detailed information on data definitions and sources are
given in the data appendix.
II. REVIEW OFTHE LITERATURE
One reason why a larger share of the private sector in total
external debt may improve a government's creditworthiness is that
private sector debtors are particularly vulnerable to the frictions
associated with sovereign default, and that the costs of default
therefore increase in private-sector exposure. Although there is a rich
literature on the causes and consequences of sovereign risk, (5) there
are few studies that explicitly consider the distributional effects of
debt crises and agents' conflicting interests with respect to
sovereign default. Notable exceptions are Tomz (2002, 2004), Arteta and
Hale (2008) as well as IMF (2002). (6) Tomz (2002) analyzes the shift in
popular attitude that preceded the Argentine default of 2001. In
particular, he documents how the sentiment of workers increasingly
turned against compliance with international repayment obligations. Tomz
(2004) presents the results of a survey which relates agents'
attitude toward debt repayment to their professional and educational
background. Not surprisingly, agents for whom access to international
capital markets is important advocate debt repayment while public
employees and individuals who are dependent on public welfare payments
appreciate the relaxed budget constraint that comes along with sovereign
default. The empirical findings of Arteta and Hale (2008) point into the
same direction: A sovereign default substantially worsens firms'
access to international credit markets and thus hurts those who are most
reliant on foreign credit. IMF (2002) analyzes the distributional
consequences of four recent default episodes. In some of these cases,
default was associated with a sharp depreciation of the domestic
currency. This depreciation "... eroded the balance sheets of
banks, particularly those with significant open foreign exchange
positions" (IMF 2002, 15). By contrast, "... others,
particularly low-leveraged firms, reaped benefits from the
depreciation" (IMF 2002, 16). These observations single out private
agents with a large exposure to international capital markets as a group
whose wealth and income is particularly affected by the
government's default decision and suggests a distributional
conflict between those individuals on the one hand and workers,
non-leveraged firms, and public sector employees on the other hand. In
Celasun and Harms (2007), we present a simple model that formalizes this
notion by juxtaposing "workers" and "entrepreneurs":
while workers are predominantly interested in low taxes and therefore
support default, entrepreneurs who borrow abroad to finance their
investments suffer a capital loss in case of default and thus advocate
repayment. In this model, a larger "entrepreneurial
class"--that is, a greater volume of private external
borrowing--increases the political costs of default and therefore raises
sovereign creditworthiness.
The alternative view that large private sector external debt is a
source of financial risk for the public sector is based on the notion
that private-sector overborrowing eventually harms sovereign
creditworthiness through more or less explicit bailout guarantees. (7)
In particular, Reinhart and Reinhart (2008) document that sovereign
defaults in middle- and low-income countries in 1960-2007 were more
often than not preceded by surges in overall capital inflows. They state
that "on the basis of the historical track record, it is plausible
to expect a higher chance of a sovereign default after a [capital flow]
bonanza even in cases where government debt is not increasing. This is
because the government sooner or later has usually ended up guaranteeing
private sector debts." These arguments suggest that private
borrowing is more of a burden than a boon to sovereign creditworthiness
since private agents frequently succumb to overborrowing, and a high
level of private-sector debt may threaten government solvency even in
cases of healthy public finances. (8)
To determine which of the theoretical effects sketched above is
dominant we estimate how the share of the private sector in total
external debt affects countries' perceived creditworthiness and the
likelihood of sovereign default. (9) Both the empirical studies on the
determinants of sovereign defaults (Detragiache and Spilimbergo 2001;
Manasse, Roubini, and Schimmelpfennig 2003; Manasse and Roubini 2005)
and the literature on sovereign ratings (Cantor and Packer 1996; Haque
et al. 1996; Harms and Rauber 2006; Mellios and Paget-Blanc 2006; Borio
and Packer 2004; Afonso, Gomes, and Rother 2007) support the notion that
high external debt is an important cause of debt crises. However, to the
best of our knowledge, none of these contributions considers the
potentially different impact of private versus public debt. (10)
III. EMPIRICAL ANALYSIS
A. Creditworthiness and Defaults
The goal of this paper is to investigate whether an increasing
share of the private sector in external debt affects developing
countries' creditworthiness and the likelihood of sovereign
default. We proceed in two steps: in a first set of regressions, we
estimate the impact of private sector exposure on a widely used
indicator of creditworthiness, namely, the Institutional Investor's
country credit rating. In a later section, we then estimate whether the
share of private debt has an effect on the occurrence of actual
defaults.
Our data set covers 65 developing countries and emerging markets
for the years 1980-2005. The unit of time measurement we adopt is 5 yr,
and the variables used in our regressions will be either 5-yr averages
(1981-85, 1986-1990, ..., 2001-2005), or initial values preceding the
respective 5-yr periods (1980, 1985, ..., 2000). We are interested in
the following question: How does a change of the private-sector share in
total external debt affect average creditworthiness and the likelihood
of sovereign default in the subsequent 5 yr? Our choice of 5-yr averages
is based on the notion that many of the theoretical mechanisms sketched
above are likely to have a discernible effect on creditworthiness only
at a low frequency. In addition, our dynamic structure has the virtues
of simplicity and transparency: using annual data would require a more
sophisticated dynamic specification and would possibly lead to
coefficients that are difficult to interpret. Moreover, it would be much
harder to address issues like unobserved heterogeneity and endogeneity.
Although focusing on actual defaults seems straightforward at first
glance, it comes with a number of serious difficulties: First, there is
no generally accepted definition of sovereign default. In our analysis,
we rely on the definition of the rating agency Standard and Poor'
s, which characterizes sovereign defaults as "... the failure to
meet a principal or interest payment on the due date (or within the
specified grace period) contained in the original terms of the debt
issue" (Standard and Poor's 2006). Although this approach has
the advantage of applying a straightforward and transparent criterion,
it does not consider the size of arrears, nor does it capture those
latent debt crises whose occurrence was prevented by foreign rescue
operations and concessions. (11) A further problem with exclusively
focusing on actual default episodes is that governments'
creditworthiness frequently recovers while they are still negotiating
the terms on which to repay existing arrears. During these periods, they
are technically "in default," but the likelihood to deny
repayment in the future may be much lower than suggested by their
default status.
Therefore, as a first step, we use the IICCR which is likely to
represent a more delicate and informative seismograph of investors'
assessment whether current loans will be repaid in the future. The IICCR
ranks countries on a scale from 0 to 100, with a lower rating reflecting
a higher likelihood that borrowers in this country will default on their
debt. The ratings are "... based on information provided by senior
economists and sovereign risk analysts at leading global banks and money
management and securities firms" (Institutional Investor 2002, 170)
and have been published twice per year since 1979. (12) Although it does
not exclusively refer to the likelihood of government default, we
conjecture that sovereign risk makes up for a large share of
"country creditworthiness." Our conjecture is confirmed by
comparing the Institutional Investor's indicator to ratings which
more explicitly focus on government creditworthiness, but cover a
smaller number of countries and years. (13)
The performance of credit ratings in predicting financial crises
has frequently been criticized in the recent past. However, Reinhart
(2002) documents that ratings do a fairly good job in predicting
sovereign defaults. This notion is confirmed by the numbers in Table 2,
which gives the results of regressing the variable SOVDEFAULT on the
Institutional Investor's measure of creditworthiness. SOVDEFAULT is
a binary variable which is one if Standard and Poor's rated a
government to be in default at least once during a 5-yr period (1981-85,
1986-90, etc.) and zero otherwise. [IICCR.sup.ini] is the value of IICCR
in the year preceding that period (1980, 1985, etc.). The regression is
based on a pro-bit model and includes both regional dummies and time
dummies. The Institutional Investor credit rating has a significant
relationship with the likelihood of default. Evaluated at the mean,
raising [IICCR.sup.ini] by 1 point reduces the likelihood of default by
about 1 percentage point. In terms of goodness of fit, the regression
performs reasonably well: Approximately 81% of default episodes and 72%
of the episodes without default are correctly predicted. Column (2.2) of
Table 2 demonstrates that [IICCR.sup.ini] is still significant if we
include the lagged value of SOVDEFAULT: Hence, it is a good predictor of
future defaults even if we control for the possibility that past
defaults both raise the probability of future defaults and reduce
current creditworthiness.
B. Private Debt and Creditworthiness: Data and Model Specification
To investigate how a larger share of the private sector in total
external debt affects perceived creditworthiness, we estimate variants
of the following equation:
(1) [IICR.sup.av.sub.it] = [[beta].sub.1]
[PRIVSHARE.sup.ini.sub.it] + [[beta].sub.2] [DEBT.sup.ini.sub.it]
+ [K.summation over (k=1)] [[gamma].sub.k] [x.sub.k, it] +
[[xi].sub.t] + [[epsilon].sub.t]
where [IICR.sup.av.sub.it] is the Institutional Investor's
average measure of country creditworthiness for country i in period t,
and [PRIVSHARE.sup.ini.sub.it] is the initial percentage share of
country i's long-term private external debt in its total long-term
external debt. (14)
The variable [DEBT.sup.ini] is the initial level of external
debt--short-term and long-term--relative to GNI. (15) Note that by using
the values of PRIVSHARE and DEBT observed at the end of the previous
5-yr period we are reducing the potential for reverse causality, that is
of creditworthiness affecting private and public borrowing.
Our choice of control variables [X.sub.k.it] largely follows the
studies of Haque et al. (1996) as well as Harms and Rauber (2006).
First, we use the lagged 5-yr average of the IICCR as a regressor
([IICCR.sup.av](-1)). A dynamic specification is suggested by Haque et
al. (1996, 718) who find that "there is considerable persistence in
the ratings, so that a country tends to retain its rating over time
unless significant adverse or positive developments occur."
Moreover, by controlling for lagged [IICCR.sup.av], we further reduce
the potential endogeneity of the debt-variables: If a positive
correlation between [PRIVSHARE.sup.ini] and [IICCR.sup.av] were only
driven by the high persistence of credit ratings and the fact that
[PRIVSHARE.sup.ini] reacts to ratings of the past, the correlation
should disappear once lagged creditworthiness is explicitly taken into
account.
A correlation between initial private sector debt and average
creditworthiness could, of course, also reflect the expectation of more
favorable economic and political conditions in the future: It is quite
plausible that private sector borrowing expands more than
proportionately in anticipation of a boom, and that such an upswing is
also reflected by a rising measure of creditworthiness. To account for
this possibility, we introduce two proxies for "economic
prospects": the average growth rate of real per capita GDP in the
preceding 5-yr period ([GROWTH.sup.av](-1)) and the average growth rate
of the main trading partners' GDP ([TPGROWTH.sup.av]) in the
current period. The advantage of using trading partners' growth is
that this variable--while being significantly correlated with domestic
growth--is unlikely to be endogenous with respect to [IICCR.sup.av].
(16) We also include the 5-yr average of an index of government
stability ([GOVSTABILITY.sup.av]), compiled by the International Country
Risk Guide, which captures the extent of political risk during a given
time period, and which is likely to affect both creditworthiness and
private borrowing.
To account for the possibility that the share of the private sector
in total external debt merely reflects the level of economic
development, we include the logarithm of real per capita income (in
international dollars) at the end of the previous 5-yr period
([INCOMEPC.sup.ini]). Moreover, we use measures of financial and
macroeconomic stability which are likely to affect both private
borrowing and creditworthiness: the initial volume of reserves as a
share of imports ([RESERVES.sup.ini]) and the log of the average
inflation rate in the preceding 5-yr period ([INFLA.sup.av] (-1)). We
also include the initial degree of trade openness ([OPEN.sup.ini]),
measured as the ratio of exports and imports to GNI. Because more open
economies are more vulnerable to the declines in foreign trade
identified by Rose (2005) their willingness to default should be lower.
At the same time, countries that are more open are likely to be more
vulnerable to external shocks and may thus face a higher risk of
default.
Finally, we use dummies for East Asia, Eastern Europe and Central
Asia, South Asia, Latin America and Sub-Saharan Africa to account for
regional differences, as well as time dummies [[xi].sub.t] to capture
time-variant factors--changes in world interest rates or investor
sentiment--that influence all countries' creditworthiness.
C. Private Debt and Creditworthiness: Results
Private Debt and Creditworthiness: Benchmark Regressions. Column
(3.1) of Table 3 shows the results of estimating Equation (1) by
ordinary least square (OLS). (17) All control variables have the
expected sign. The coefficient on [DEBT.sup.ini] is highly significant,
confirming the notion that a large level of external debt reduces
creditworthiness. (18) Most importantly for our analysis, the share of
private sector debt, as reflected by [PRIVSHARE.sup.ini], has a
significantly positive coefficient: Although the coefficient is rather
small in absolute terms--raising [PRIVSHARE.sup.ini] by one standard
deviation increases [IICCR.sup.av] by 0.12 standard deviations--this
result implies that, ceteris paribus, countries with a higher share of
private debt in total external debt tend to have higher
creditworthiness. (19)
Column (3.2) of Table 3 includes the second lag of [IICCR.sup.av]
as an additional regressor: If the serial correlation of
creditworthiness goes beyond one period and if private debt is slow to
react to changes in credit rankings, omission of this variable could
lead to biased estimates. However, this does not seem to be the case:
Although the second lag of [IICCR.sup.av] has a significantly negative
coefficient, the estimated coefficient of [PRIVSHARE.sup.ini] is almost
unaffected.
The results so far suggest that a higher share of the private
sector in total external debt is significantly associated with a higher
level of creditworthiness. However, the significantly positive
coefficients could just indicate that countries with a more developed
financial sector have a larger share of private external debt and run a
lower risk of sovereign default. To account for this possibility, we
include the initial value of domestic credit to the private sector
relative to GDP ([DOMCREDIT.sup.ini]) as a measure of financial depth.
Column (3.3) demonstrates that, although this variable has a positive
sign, it is not significant and its inclusion has almost no effect on
the coefficient of [PRIVSHARE.sup.ini].
Column (3.4) of Table 3 reports the results of replacing
[GOVSTABILITY.sup.av] with another measure of the investment climate.
The variable [GOVERNANCE.sup.av] is also based on the International
Country Risk Guide's assessments, but refers to different
criteria--namely, the control of corruption, the quality of the
bureaucracy, and the rule of law. Although this modification slightly
lowers the size of the coefficient of [PRIVSHARE.sup.ini], the effect is
not substantial, and the variable remains significant.
We also considered another disaggregation of external debt which
possibly affects country creditworthiness and which might be correlated
with private-sector exposure: Column (3.5) in Table 3 reports the result
of including the variable [STDEBT.sup.ini], which reflects the share of
short-term debt in total external debt. It turns out that this variable
has no significant independent effect on perceived creditworthiness, and
that its inclusion does not influence the coefficient of
[PRIVSHARE.sup.ini]. (20)
Private Debt and Creditworthiness: Accounting for Unobserved
Heterogeneity and Endogeneity. There is, of course, a high probability
that the regressors we have included do not capture all sources of
cross-country heterogeneity. The positive coefficient of
[PRIVSHARE.sup.ini] may thus merely reflect the influence of other
country-specific factors which affect both private borrowing and
creditworthiness. Moreover, we have not yet come to terms with the
possible endogeneity of [PRIVSHARE.sup.ini]: Although we have argued
above that regressing the 5-yr average of [IICCR.sup.av] on the initial
share of private debt in total external liabilities should reduce the
potential for reverse causality--especially, when the lagged measure of
creditworthiness is included--there are more sophisticated ways to deal
with this issue.
In this subsection, we first follow the approach of Arellano and
Bond (1991) and estimate the parameters of interest by differencing
Equation (1) and by using lagged levels of the regressors as
instruments. (21) The results in column (4.1) of Table 4 are based on a
specification that uses up to four lags of the regressors as
instruments. To avoid the overfitting that comes along with an excessive
number of moment conditions and that results in biased estimates and
uninformative diagnostic statistics, we impose the condition that the
coefficients are uniform across the time periods in the first stage.
(22) Moreover, we adopt a two-step approach that uses an optimal
weighting matrix to aggregate the individual moment conditions. Standard
errors are computed using the finite sample correction suggested by
Windmeijer (2005). The p-values referring to Hansen's J-test of
overidentifying restrictions (Hansen 1982) and to the (m2-) test of no
second-order autocorrelation in the differenced residuals (Arellano and
Bond 1991) are given at the bottom of the table. As the results in
column (4.1) show, most coefficients change when we move from pooled OLS
to the "Difference-GMM estimator" of Arellano and Bond (1991),
suggesting that unobserved heterogeneity and endogeneity may indeed have
influenced the results presented in Table 3. Nevertheless, our finding
that the share of the private sector in total external debt raises
countries' creditworthiness is strengthened rather than weakened.
Column (4.2) shows the results of applying the estimator developed
by Arellano and Bover (1995) and Blundell and Bond (1998) to Equation
(1). This approach simultaneously estimates the first-differenced
version of the regression equation--using lagged levels of the
righth-and-side variables--and the original equation in levels using
lagged differences as instruments. The first advantage of this
"Systems-GMM" estimator is that it exploits the information
contained in the first period--a property that is of particular merit in
our case where the number of periods is small. Moreover, it mitigates
the weak-instruments problem that arises if the time series involved are
very persistent. Column (4.2) in Table 4 demonstrates that, with this
specification, the coefficient of [PRIVSHARE.sup.ini] is significantly
positive.
So far, we have followed the standard approach of using lags of all
regressors as instruments. Although the J-test gives no warning signs,
our results may be biased if the right-hand side variables are
endogenous. We are particularly concerned about [PRIVSHARE.sup.ini] and
[DEBT.sup.ini] and therefore remove these variables from the list of
instruments. Column (4.3) in Table 4 presents the results of following
this approach when the "Difference-GMM" estimator is used,
column (4.4) refers to the "Systems-GMM" estimator. In both
cases the coefficient of [PRIVSHARE.sup.ini] remains significant.
The last columns of Table 4 present the results of treating the
problems of unobserved heterogeneity and potential endogeneity of
[PRIVSHARE.sup.ini] separately: First, we estimated our model with fixed
effects. To account for the bias inherent in dynamic panel estimation
(Nickell 1981), we applied the bias-correction suggested by Kiviet
(1995) and Bruno (2005). (23) As indicated by column (4.5), using this
"corrected LSDV (LSDVC)" estimator barely changes the
coefficient of [PRIVSHARE.sup.ini] as compared to the pooled OLS results
reported in Table 3. Finally, we accounted for the potential endogeneity
of [PRIVSHARE.sup.ini] by instrumenting this variable with potential
determinants of private-sector borrowing: the (lagged) quality of
financial sector regulation ([CREDREG.sup.av](-1)), which is based on an
index compiled by the Fraser Institute (Fraser Institute 2006), the
distance from the equator ([LATITUDE.sup.av]) and the lagged average of
the Freedom House index of civil liberties ([REPRESS.sup.av] (-1)) as
proxies for the quality of governance (Freedom House 2006), the initial
number of telephone main lines per 1,000 people ([TELEPHONES.sup.ini])
as a proxy for the quality of a country's infrastructure, as well
as a lagged index of de jure exchange rate flexbility ([ERFLEX.sup.av]
(-1)) adopted from Harms and Kretschmann (2009). The Kleibergen-Paap
rk-statistic for underidentification in the presence of heteroskedastic
disturbances and Hansen's J-test of overidentifying restrictions
indicate that these instruments perform reasonably well in terms of
relevance and exogeneity (see Baum, Schaffer, and Stillman 2007).
Moreover, instrumenting for ([PRIVSHARE.sup.ini]) does not strongly
affect its estimated coefficient. The notion that the coefficient of
initial private sector debt is not driven by reverse causality is also
supported by an explicit test for endogeneity which prevents us from
rejecting the hypothesis that ([PRIVSHARE.sup.ini]) is exogenous. (24)
We conclude from the results presented in this subsection that
accounting for unobserved heterogeneity and potential endogeneity has a
bearing on the size of the estimated parameters--without, however,
affecting our key finding that a higher share of the private sector in
total external debt raises a country's creditworthiness.
Private Debt and Creditworthiness: Varying Samples and Alternative
Specifications. This section reports the results of estimating Equation
(1) using various subsets of the original sample and of further varying
the original specification. It is apparent from Figure 2B that in a
large number of countries, all external borrowing is performed by the
government. We explored whether dropping the observations for which
[PRIVSHARE.sup.ini] is zero changes the coefficient of this regressor.
Column (5.1) of Table 5 demonstrates that it does not. We then
restricted our attention to countries for whom IICCR exceeded the value
of 25. Reinhart, Savastano, and Rogoff (2003) identity this value as a
threshold below which countries do not really have access to
international capital markets. Column (5.2) demonstrates that the size
of the coefficient of [PRIVSHARE.sup.ini] is slightly reduced in this
case, but the significantly positive estimated effect is not affected.
Finally, we checked whether our result depended on the simultaneous
decline of creditworthiness and private foreign borrowing observed
during the 1980s. As column (5.3) of Table 5 reports, excluding the
observations from the "lost decade" reduces the sample by
almost one third, but barely affects the coefficient of
[PRIVSHARE.sup.ini]. (25)
To check whether our results merely reflect the fact that a lower
level of public debt is good for government creditworthiness, we
replaced the regressor [DEBT.sup.ini] by the initial level of long-term
public (and publicly guaranteed) debt relative to GNI
([PUBDEBT.sup.ini]). Although this variable has the expected negative
effect, the coefficient of [PRIVSHARE.sup.ini] in column (5.4) indicates
that the role of a larger private-sector share goes beyond just
signaling the positive implications of lower government debt. In column
(5.5), we explore the separate effects of long-term private, and public
debt relative to GNI ([PRIVDEBT.sup.ini] and [PUBDEBT.sup.ini]).
Although public debt has a significantly negative effect, the
coefficient of [PRIVDEBT.sup.ini] is positive, but not significant. This
could indicate that private debt per se is irrelevant for a
government's creditworthiness. However, when comparing this result
to our previous findings, we need to take into account that the
specification in Equation (1) suggests a positive, but nonlinear
influence of [PRIVDEBT.sup.ini] on [IICCR.sup.av]. Hence, imposing
linearity may result in the large standard errors of column (5.5).
Moreover, although explicitly accounting for endogeneity did not lead to
very different results in column (4.6) of Table 4, the coefficient of
[PRIVDEBT.sup.ini] increases considerably if we run a IV-regression
using the set of instruments mentioned above (see column [5.6] of Table
5), and a formal test barely prevents us from rejecting the
null-hypothesis that this regressor is exogenous. We conclude that a
specification which considers private debt and public debt in isolation
clearly rejects the notion that private-sector exposure hurts government
creditworthiness. However, it does not really support the role of a
stabilizing effect, either. Given the many channels through which
private borrowing may influence sovereign creditworthiness this is not
surprising. (26)
D. Private Debt and Sovereign Default: Results
Benchmark Results. So far, we have used the Institutional
Investor's measure of creditworthiness as a dependent variable.
Although we found strong evidence that the perceived likelihood of
sovereign default is reduced by a larger share of the private sector in
total external debt, this does not necessarily prove that
governments' decisions on default versus repayment are actually
affected by private sector exposure. To explore whether this is indeed
the case we now use the dummy SOVDEFAULT as the dependent variable. As
described in Section III.A., this variable is one if Standard and
Poor's rated a government as being in default--that is failing to
meet its repayment obligations--for at least 1 yr in a 5-yr interval,
and zero otherwise. Except for the lagged indicator of creditworthiness,
we are using the same set of covariates as in the previous subsections.
Our first regression uses the probit estimator to identify the
determinants of sovereign defaults. The results are reported in column
(6.1) of Table 6. With the exception of [INCOMPEC.sup.ini], the
coefficients of the control variables have the expected sign--although
only lagged growth and the initial debt to GNI ratio are statistically
significant. [PRIVSHARE.sup.ini] clearly has a negative relationship
with the likelihood of default. At the bottom of Table 6, we report the
partial effect of this variable: evaluated at the sample mean, an
increase of [PRIVSHARE.sup.ini] by roughly 1 percentage point ceteris
paribus reduces the likelihood of default by 1 percentage point. As
column (6.2) in Table 6 shows, using the logit estimator instead of
probit yields a marginal effect of almost identical size. In terms of
goodness of fit, both approaches do reasonably well: The
pseudo-[R.sup.2] of McFadden (1974) is approximately 0.31 in both cases,
and the percent correctly predicted is 75%. (27)
There might, however, be a problem with taking these results at
face value: As the previous sections have indicated, sovereign
creditworthihess is quite persistent--even if we focus on 5-yr averages.
This is likely to apply a fortiori to actual defaults: after the initial
denial of full repayment, it usually takes several years until an
agreement with creditors is reached. During this period the country is
rated as a defaulter, which, in turn, is likely to affect private
borrowing (see Arteta and Hale [2008]). Hence, the negative coefficient
of [PRIVSHARE.sup.ini] may just capture the persistence of defaults,
combined with the negative effect of past defaults on private external
borrowing. Columns (6.3) and (6.4) in Table 6 indicate that this
conjecture is at least partly correct: The coefficient of the first lag
of SOVDEFAULT is significantly positive, and the influence of
[PRIVSHARE.sup.ini] decreases once we include the first lag of the
dependent variable. (28)
However, as the partial effects at the bottom of Table 6 reveal,
this change is quite limited: Once we include the lagged dependent
variable, it takes an increase of [PRIVSHARE.sup.ini] by approximately
1.3 percentage points to reduce the likelihood of default by 1
percentage point. The entries in the bottom rows indicate that including
the lagged dependent variable improves the fit, but not by very much:
the pseudo-[R.sup.2] moves from 0.31 to 0.36 while the percent correctly
predicted increases from 75 to 81 and 82, respectively.
Accounting for Unobserved Heterogeneity. As in the previous
subsections, we need to be concerned about unobserved heterogeneity: the
likelihood of default may depend on country-specific characteristics
which we have not explicitly accounted for in our regression equation.
Although introducing country-specific effects is the straightforward
solution to this problem in a linear regression model, things are a bit
more complicated when it comes to discrete-choice models. The probit
estimator, in particular, suffers from the incidental parameters
problem--that is, it is not possible to consistently estimate the
coefficients of the covariates using maximum likelihood without
estimating the country-specific effects. This, in turn, fails if the
number of time periods is finite. (29) There are several remedies to
this problem: Under the assumption that the individual effects are not
correlated with the covariates, the random effects probit estimator
yields consistent estimates (Wooldridge 2002a). Column (7.1) in Table 7
shows the results of adopting this approach. Column (7.2) reports the
coefficients we obtain when estimating the random effects model using
logit: interestingly, the partial effects for [PRIVSHARE.sup.ini]
(-0.014 and -0.015) do not stray too far from the values we received
from the pooled regression in Table 6. (30) Column (7.3) reports the
results from applying the fixed effects logit estimator. If the
distribution of the underlying error term is assumed to be logistic,
consistent estimation of the relevant parameters is possible even if
unobserved heterogeneity is treated by means of fixed effects. However,
this advantage comes at a cost: Because the fixed effects cannot be
estimated, it is impossible to compute partial effects. Hence, the
magnitude of the negative coefficient of [PRIVSHARE.sup.ini] in column
(7.3) cannot be readily compared with our previous results. To better
understand the role of unobserved heterogeneity, we finally specified
the model as a simple linear equation including fixed effects. (31)
Interestingly, the coefficient of [PRIVSHARE.sup.ini] in column (7.4) is
very close to the partial effects we reported in Table 6.
The last two columns in Table 7 return to the issue of whether the
significantly negative effect of [PRIVSHARE.sup.ini] merely reflects the
persistence of sovereign defaults. To explore this issue, we include the
lagged value of SOVDEFAULT as an additional regressor, adopting two
alternative approaches: We first follow Wooldridge (2002b) who suggests
to estimate a random effects (logit or probit) model conditioning on
initial observations. The results of this strategy are presented in
column (7.5) of Table 7: Although the lagged dependent variable is
significant, the coefficient of [PRIVSHARE.sup.ini] is still
significantly negative, and the estimated marginal effect barely differs
from the one displayed at the bottom of column (7.1). However, this
estimator is biased unless the other regressors are strictly exogenous.
Hence, as a robustness check, we also estimated the linear probability
model (LPM) with a lagged dependent variable and fixed effects. As
column (7.6) demonstrates, the lagged dependent variable has no impact
on the other coefficients in this case. Of course, given the caveats
with respect to this estimator, we do not want to overrate this result.
The main finding we take away from these estimations is that the
significantly negative effect of [PRIVSHARE.sup.ini] on sovereign
defaults does not seem to be an artifact of neglecting unobserved
heterogeneity and the persistence of defaults.
IV. SUMMARY AND CONCLUSIONS
Although external debt figures among the usual suspects when it
comes to explaining sovereign risk, little attention has been devoted to
the potentially different effects of private and public external debt.
The main contribution of our paper is to emphasize that this difference
is substantial: in the time period covered by our sample, a higher share
of the private sector in total external debt raised country
creditworthiness and reduced the likelihood of default for a given stock
of external debt. Our results thus offer one potential explanation for
the observation of Reinhart, Savastano, and Rogoff (2003) that countries
with similar levels of external debt may exhibit vast differences in
their creditworthiness and their propensity to default: "debt
intolerance" may more heavily afflict countries where the
government accounts for most of the external borrowing.
A battery of robustness tests indicate that our results are not
driven by unobserved heterogeneity--that is, creditworthiness and
private sector exposure being driven by country-specific unobserved
parameters--and that they do not just reflect the reverse impact of
creditworthiness on private borrowing. Our findings suggest that, for a
given country, an exogenous increase in the private-sector share of
foreign debt improves a government's creditworthiness. An
explanation as to why countries exhibit such large differences in the
composition of their external debt goes beyond the scope of this paper,
but offers a promising subject for further research.
Note, finally, that our analysis focused on the factors that
determine the likelihood of sovereign default rather than on financial
crises in general. The historical record suggests that, in the past, the
stabilizing effect of private-sector debt on government creditworthiness
at least compensated (if not dominated) the destabilizing effect.
Whether these forces will shape governments' decisions in the
crisis that started in 2007 is an open question that future research
will be able to answer.
ABBREVIATIONS
IICCR: Institutional Investor's Measure of Country
Creditworthiness
LPM: Linear Probability Model
OLS: Ordinary Least Square
doi: 10.1111/j.1465-7295.2010.00267.x
APPENDIX
A. Definitions and Sources
[CREDREG.sup.av]: Five-year average of the Fraser Institute's
index of credit market regulation, ranging from 0 (minimal regulation)
to 10 (maximal regulation). Source: Fraser Institute (2006).
[DEBT.sup.ini]: Total external debt relative to GNI (in percent).
Sources: World Bank (2007a) and World Bank (2007b).
[DOMCREDIT.sup.ini]: Domestic credit to private sector relative to
GDP (in percent). Source: World Bank (2007a).
[ERFLEX.sup.av](-1): Average index of de jure exchange rate
flexibility in the preceding 5-yr period. The index is based on the
IMF's Annual Report on Exchange Arrangements and Exchange
Restrictions and ranges from 1 to 4, where 1 represents a peg, 2
represents limited flexibility, 3 represents a managed float, and 4
represents a pure float. Source: Harms and Kretschmann (2009).
[GOVERNANCE.sup.av]: Simple average of indices measuring
bureaucratic quality, corruption, and the rule of law, from the
International Country Risk Guide. Source: Political Risk Services
(2007).
[GOVSTABILITY.sup.av]: Index of government stability from the
International Country Risk Guide. Source: Political Risk Services
(2007).
[GROWTH.sup.av](-1): Average growth rate of real per capita income
in the preceding 5-yr period. Source: Penn World Table 6.2 (Heston,
Summers, and Aten [2006]).
[IICCR.sup.av]: Five-year average of the country credit ratings
published in the Institutional Investor magazine every March and
September since 1980. Source: Institutional Investor magazine.
[INCOMEPC.sup.ini]: Log of initial value of real per capita GDP in
constant PPP-adjusted dollars. Source: World Bank (2007a).
[INFLA.sup.av](-1): Log of the average growth rate of the consumer
price index in the preceding 5-yr period. Source: World Bank (2007a).
[OPEN.sup.ini]: Initial value of the ratio (exports + imports)/ GNI
(in percent). Source: World Bank (2007a).
LATITUDE: Squared latitude. Source: World Bank (2001).
[PRIVDEBT.sup.ini]: Initial private nonguaranteed long-term
external debt relative to GNI (in percent). "Private nonguaranteed
debt outstanding and disbursed (LDOD) is an external obligation of a
private debtor that is not guaranteed for repayment by a public entity.
Long-term debt outstanding and disbursed (LDOD) is the total outstanding
long-term debt at year end. Long-term external debt is defined as debt
that has an original or extended maturity of more than one year and that
is owed to nonresidents and repayable in foreign currency, goods, or
services." Source: World Bank (2007b).
[PRIVSHARE.sup.ini]: Initial share of private nonguaranteed
long-term external debt in total long-term external debt (in percent).
Source: World Bank (2007b).
[PUBDEBT.sup.ini]: Initial public and publicly guaranteed long-term
external debt relative to GNI (in percent). Source: World Bank (2007b).
[REPRESS.sup.av]: Five-year average of the Freedom House index of
civil liberties, ranging from 1 (maximal rights) to 7 (minimal rights).
Source: Freedom House (2006).
[RESERVES.sup.ini]: Initial value of the ratio (international
reserves)/(imports of goods and services) in percent. Source: World Bank
(2007a).
SOVDEFAULT: Dummy variable which equals one if Standard and
Poor's rates a government to be in default at least once during a
5-yr period and zero otherwise. A default is characterized by "the
failure to meet a principal or interest payment on the due date (or
within the specified grace period) contained in the original terms of
the debt issue." Source: Standard and Poor's (2007).
[STDEBT.sup.ini]: Ratio of short-term debt to total external debt
(in percent). "Short-term external debt is defined as debt that has
an original maturity of one year or less. Available data permit no
distinction between public and private nonguaranteed short-term
debt." Source: World Bank (2007b).
[TELEPHONES.sup.ini]: Number of telephone main lines per 1,000
people. Source: World Bank (2007a).
[TPGROWTH.sup.av]: Five-year average of the growth rate of a
weighted average of trading partners' GDP. Sources: World Bank
(2007a) and IMF (2006).
B. Countries
Algeria, Argentina, Bangladesh, Benin, Bolivia, Botswana, Brazil,
Bulgaria, Cameroon, Chile, China, Colombia, Congo Rep., Costa Rica, Cote
d'Ivoire, Democratic Republic of Congo, Dominican Republic,
Ecuador, Egypt Arab Rep., El Salvador, Estonia, Gabon, Ghana, Haiti,
Honduras, Hungary, India, Indonesia, Jamaica, Jordan, Kenya, Latvia,
Lithuania, Malawi, Malaysia, Mali, Mauritius, Mexico, Morocco,
Nicaragua, Nigeria, Oman, Pakistan, Panama, Papua New Guinea, Paraguay,
Peru, Philippines, Poland, Romania, Russian Federation, Senegal, Sierra
Leone, South Africa, Sri Lanka, Syrian Arab Republic, Tanzania,
Thailand, Togo, Trinidad and Tobago, Tunisia, Turkey, Uganda, Ukraine,
Uruguay, Venezuela RB, Zambia, Zimbabwe.
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(1.) Figure 1 and the following figures refer to long-term debt,
which comprises instruments that have an original or extended maturity
of more than 1 yr (World Bank 2007b). Comprehensive data on private
versus public external debt and borrowing is available only for
long-term debt instruments.
(2.) For the IICCR, the data points refer to 5-yr averages between
1980 and 2005, while the debt-variables are measured at the beginning of
these 5-yr periods.
(3.) In both cases, extreme observations seem to play a prominent
role. If we remove the top 5% of the debt variable
observations--limiting our attention to countries with
debt-to-GNI-ratios below 200% and a share of the private sector below
40%, respectively--the correlation in Figure 2A becomes -0.43, while the
correlation in Figure 2B decreases to 0.46.
(4.) Here and in what follows we adopt the definition of the rating
agency Standard and Poor's which identifies a sovereign default as
the "... failure to meet a principal or interest payment on the due
date (or within the specified grace period) contained in the original
terms of the debt issue" (Standard and Poor's 2006).
(5.) See, for example, Eaton and Gersovitz (1981), Bulow and Rogoff
(1989), Cole and Kehoe (1997), Manasse, Roubini, and Scbimmelpfennig
(2003), Rose (2005), Borensztein and Panizza (2007). Eaton and Fernandez
(1995) as well as Sturzenegger and Zettelmeyer (2006) offer excellent
surveys of this discussion.
(6.) Chang (2006) analyzes the interdependence between capital
flows and distributional conflict, but focuses on income taxation. Chang
(2007) analyzes the political economy of default from a
representative-agent perspective.
(7.) See, for example, Larrain and Velasco (1990) for an account of
private debt nationalizations and the Chilean external debt
restructuring during the 1982 crisis.
(8.) Corsetti, Pesenti, and Roubini (1998) identify excessive
foreign borrowing by the private sector as one of the key causes of the
Asian currency crises of 1997-1998. Indonesia, one of the countries hit
hardest by the crisis, restructured its foreign currency bank debt in
1998-99 and was thus classified by Standards and Poor's as being in
sovereign default status at that time.
(9.) Jeske (2006) also explores the different roles of private and
public debtors; however, he does not consider potential interactions
between the two components of external debt.
(10.) A notable exception is Frankel and Rose (1996) who explore
inter alia how the share of the public sector in total external debt
affects the occurrence of currency crises. Interestingly, a higher share
of the government raises the likelihood of a currency crash in the
subsequent year. By contrast, the effect of the public sector-share on
currency crises in the same period is not significant.
(11.) Given these considerations, Manasse, Roubini, and
Schimmelpfennig (2003) augment the Standard and Poor's data with
information on concessional IMF loans, whereas Beim and Calomiris (2001)
differentiate between outright repudiation and minor, pre-announced
defaults. Rose (2005) identifies sovereign defaults with the onset of
Paris-Club negotiations, Arteta and Hale (2008) combine information
about renegotiations with those of Standard and Poor's and the
Economist Intelligence Unit.
(12.) As reported by Haque et al. (1996), the individual criteria
used by banks to assess default risk are not specified.
(13.) The IICCR has been widely used in empirical work on sovereign
creditworthiness, given its coverage of a large number of countries and
years. The rank correlation between the IICCR and the sovereign ratings
published by Moody's in the 1990s is 0.92. The rank-correlation
with the sovereign ratings of Fitch ratings is 0.85.
(14.) A detailed description of data definitions and sources is
provided in the data appendix. The variable [PRIVSHARE.sup.ini] is based
on long-term debt because the public versus private sector decomposition
is not available for short-term debt. As we will show later, the focus
on long-term debt does not appear to be consequential for our empirical
results.
(15.) For the time being, we do not distinguish between different
sources of loans. That is, [DEBT.sup.ini] comprises both loans from
official sources and loans from private investors. As we will show
later, this aggregation is not crucial for our results.
(16.) Including the growth rate of domestic output did not alter
the qualitative results we report in subsequent sections.
(17.) The standard errors presented in square brackets are based on
a covariance matrix that is robust with respect to heteroskedasticity
and serial correlation of cluster-specific disturbances.
(18.) This result is because of dropping the observation for
Nicaragua in 1990, which is characterized by an excessively high level
of external debt (1087 percent of GNI). Including this data point
substantially increases the standard error of [DEBT.sup.ini] without,
however, changing the qualitative results with respect to the other
regressors.
(19.) This result does not hinge on our indiscriminate treatment of
private and official creditors. If we constrain our attention to debt
owed to private agents and institutions-thus netting out official
loans--we obtain a somewhat smaller, but significantly positive
coefficient.
(20.) To further explore whether the maturity structure of external
debt was important for our results we ran our benchmark regression under
the two alternative assumptions that all short-term external debt was
either private or public. It turned out that the modified values of
[PRIVSHARE.sup.ini] still had a significantly positive impact on
[IICCR.sup.av].
(21.) Bond (2002) and Wooldridge (2002a) offer excellent surveys on
dynamic panel estimation. We used the xtabond2 module by Roodman (2006)
to implement the difference-GMM estimator.
(22.) We do this by using the collapse option of the xtabond2
routine in Stata.
(23.) To compute these results, we used the xtlsdvc routine
developed for Stata by Giovanni Bruno.
(24.) To test for endogeneity, we used both the
WuDurbin-Hausman-test and the Difference-in-Sargan test implemented in
Stata by Baum, Schaffer, and Stillman (2003) and Baum. Schaffer, and
Stillman (2007).
(25.) Using the Difference-GMM estimator of Arellano and Bond
(1991) and dropping those observations for which [PRIVSHARE.sup.ini] was
zero or those in which creditworthiness did not exceed a minimum
threshold did not alter our key result--nor did restricting attention to
the years after 1990. These results are available upon request.
(26.) When we replaced external public debt ([PUBDEBT.sup.ini]) by
total government debt (relative to GNI), the latter variable had no
significant effect. The same result occured when we used both external
and total public debt as regressors. These results are available upon
request.
OYA CELASUN and PHILIPP HARMS *
* We are indebted to two anonymous referees for helpful comments.
The views expressed in this paper are those of the authors and do not
necessarily represent those of the International Monetary Fund, its
Board of Executive Directors, or the governments the latter represent.
Celasun: International Monetary Fund (IMF)--Research Department, 700
19th Street NW, Washington, DC 20431. Phone (202) 623 4274, Fax (202)
589 4274, E-mail OCelasun@imf.org
Harms: RWTH Aachen University, Faculty of Business and Economics,
Templergraben 64/III, 52062 Aachen, Germany. Phone +49(0)241 8096203,
Fax +49(0)241 8092649, E-mail harms@rwth-aachen.de
TABLE 1
Debt Variables before Defaults
Initial External Initial Prov. Ext.
Debt/GNI Debt/Ext. Debt
Default in t 108.51 5.02
No default in t 55.33 10.59
Source: World Bank (2007b) and Standard and Poor's
(2007).
TABLE 2
Institutional Investor's Country Credit Rating
(IICCR) and the Likelihood of Sovereign
Default
(2.1) Probit (2.2) Probit
[IICCR.sup.ini] -0.032 *** -0.017 **
[0.007] [0.008]
East Asia and Pacific 0.623 0.483
[0.439] [0.454]
South Asia -0.325 0.048
[0.678] [0.675]
Europe and Central Asia 0.368 0.265
[0.388] [0.405]
Sub-Saharan Africa 0.917 *** 0.874 **
[0.328] [0.344]
Latin American and the 0.899 *** 0.766 **
Caribbean
[0.314] [0.330]
OIL 0.437 0.352
[0.270] [0.281]
1986-90 -0.086 -0.243
[0.302] [0.319]
1991-95 -0.546 * -0.903 ***
[0.301] [0.324]
1996-00 -1.039 *** -1.363 ***
[0.286] [0.306]
2001-05 -1.463 *** -1.684 ***
[0.290] [0.306]
SOVDEFR ULT(-1) 1.032
[0.2211
Constant 0.953 ** 0.245
[0.4591 [0.506]
Marginal effect of IICCR -0.013 *** -0.007 **
[0.003] [0.0031
Observations 281 281
Pseudo [R.sup.2] 0.23 0.29
Percent correctly 0.77 0.76
predicted
Notes: Standard errors in parentheses are based on a
robust covariance matrix.
***, **, * denote significance levels of 1%, 5%, and
10%. The data sample is an unbalanced panel, comprising
5-yr averages or initial values between 1980 and 2005.
The dependent variable is SOVDEFAULT, which is a
binary variable indicating if Standard and Poor's rated a
government as being in default at least once during a 5-yr
period.
TABLE 3
The Effect of PRIVSHARE on IICCR-Pooled OLS
(3.1) OLS (3.2) OLS
[IICCR.sup.av](-1) 0.553 *** 0.771 ***
[0.040] [0.061]
[PRIVSHARE.sup.ini] 0.115 *** 0.123 ***
[0.030] [0.030]
[DEBT.sup.ini] -0.022 *** -0.019 ***
[0.007] [0.007]
[TPGROWTH.sup.av] 2.695 * 1.544
[1.370] [1.356]
[GROWTH.sup.av](-1) 0.713 *** 0.595 ***
[0.114] [0.152]
[GOVSTABILITY.sup.av] 1.253 *** 1.477 ***
[0.312] [0.322]
[INCOMEPC.sup.ini] 1.553 2.343 **
[1.044] [1.059]
[RESERVES.sup.ini] 0.042 ** 0.057 **
[0.021] [0.024]
[INFLA.sup.av] (-1) -0.365 -0.079
[0.452] [0.558]
[OPEN.sup.ini] 0.027 ** 0.036 **
[0.013] [0.017]
East Asia and Pacific -0.404 -0.269
[1.524] [1.777]
South Asia 0.392 1.453
[1.978] [2.083]
Europe and Central Asia 4.568 ** 3.655 *
[1.793] [1.849]
Sub-Saharan Africa -1.791 -1.07
[1.456] [1.539]
Latin America and the Caribbean -2.273 ** -1.744
[1.119] [1.080]
OIL -0.919 -0.553
[1.345] [1.831]
[IICCR.sup.av](-2) -0.243
[0.053]
[DOMCREDIT.sup.ini]
[GOVERNANCE.sup.av]
[STDEBT.sup.ini]
Constant -17.490 ** -18.257 **
[8.585] [8.041]
R-Squared (adj.) 0.88 0.89
Number of observations 257 207
(3.3) OLS (3.4) OLS
[IICCR.sup.av](-1) 0.534 *** 0.526 ***
[0.044] [0.047]
[PRIVSHARE.sup.ini] 0.115 *** 0.093 ***
[0.030] [0.029]
[DEBT.sup.ini] -0.023 *** -0.026 ***
[0.007] [0.007]
[TPGROWTH.sup.av] 2.667 * 2.829 **
[1.398] [1.409]
[GROWTH.sup.av](-1) 0.718 *** 0.705 ***
[0.115] [0.119]
[GOVSTABILITY.sup.av] 1.237 ***
[0.310]
[INCOMEPC.sup.ini] 1.372 1.391
[0.969] [1.110]
[RESERVES.sup.ini] 0.041 * 0.041 *
[0.021] [0.022]
[INFLA.sup.av] (-1) -0.281 -0.363
[0.468] [0.452]
[OPEN.sup.ini] 0.022 * 0.029 **
[0.013] [0.014]
East Asia and Pacific -0.499 -0.817
[1.492] [1.704]
South Asia 0.467 -1.057
[2.025] [2.067]
Europe and Central Asia 5.354 *** 2.498
[1.718] [1.726]
Sub-Saharan Africa -1.702 -3.187 *
[1.344] [1.664]
Latin America and the Caribbean -2.176 * -3.818 ***
[1.128] [1.222]
OIL -0.57 0.473
[1.506] [1.550]
[IICCR.sup.av](-2)
[DOMCREDIT.sup.ini] 0.026
[0.025]
[GOVERNANCE.sup.av] 2.036 ***
[0.690]
[STDEBT.sup.ini]
Constant -16.031 ** -8.54
[7.991] [9.418]
R-Squared (adj.) 0.88 0.88
Number of observations 255 257
(3.5) OLS
[IICCR.sup.av](-1) 0.548 ***
[0.040]
[PRIVSHARE.sup.ini] 0.114 ***
[0.032]
[DEBT.sup.ini] -0.023 ***
[0.007]
[TPGROWTH.sup.av] 2.569 *
[1.417]
[GROWTH.sup.av](-1) 0.706 ***
[0.114]
[GOVSTABILITY.sup.av] 1.259
[0.313]
[INCOMEPC.sup.ini] 1.431
[1.003]
[RESERVES.sup.ini] 0.043 *
[0.021]
[INFLA.sup.av] (-1) -0.355
[0.453]
[OPEN.sup.ini] 0.027 **
[0.013]
East Asia and Pacific -0.508
[1.564]
South Asia 0.418
[2.010]
Europe and Central Asia 4.431 **
[1.874]
Sub-Saharan Africa -2.012
[1.437]
Latin America and the Caribbean -2.407 **
[1.162]
OIL -0.879
[1.382]
[IICCR.sup.av](-2)
[DOMCREDIT.sup.ini]
[GOVERNANCE.sup.av]
[STDEBT.sup.ini] 0.029
[0.043]
Constant -16.225 *
[8.529]
R-Squared (adj.) 0.88
Number of observations 257
Notes: Standard errors in parentheses are based on a
robust covariance matrix.
***, **, *denote significance levels of 1%, 5%, and 10%. The data
sample is an unbalanced panel, comprising 5-yr averages or initial
values between 1980 and 2005. The dependent variable is
Institutional Investor's average country credit rating for the
5-yr period [IICCR.sup.av]. All regressions include time dummies;
their coefficients are available upon request.
TABLE 4
The Effect of PRIVSHARE on IICCR-GMM and Fixed Effects Estimation
(4.1) (4.2)
Diff-GMM Sys-GMM
[IICCR.sup.av](-1) 0.392 *** 0.484 ***
[0.109] [0.057]
[PRIVSHARE.sup.ini] 0.213 ** 0.216 ***
[0.101] [0.055]
[DEBT.sup.ini] -0.013 -0.003
[0.017] [0.008]
[TPGROWTH.sup.av] 4.312 ** 3.078 *
[1.766] [1.750]
[GROWTH.sup.av](-1) 0.705 *** 0.880 ***
[0.2311 [0.143]
[GOVSTABILITY.sup.av] 1.393 * 1.234 ***
[0.724] [0.417]
[INCOMEPC.sup.ini] 7.063 -0.061
[5.550] [3.195]
[RESERVES.sup.ini] 0.066 * 0.051 *
[0.033] [0.028]
[INFLA.sup.av] (-1) -1.259 -0.484
[0.760] [0.757]
[OPEN.sup.ini] -0.042 0.014
[0.061] [0.030]
Constant -1.595
[25.857]
R-Squared (adj.)
Number of observations 193 257
Hansen's J-stat. (p-value) 0.26 0.40
AB m2-stat.(p-value) 0.95 0.88
Kleibergen-Paap rk-stat
(p-value)
(4.3) Diff-GMM (4.4) Sys-GMM
(red. instr.) (red. instr.)
[IICCR.sup.av](-1) 0.351 *** 0.470 ***
[0.099] [0.077]
[PRIVSHARE.sup.ini] 0.364 *** 0.329 ***
[0.093] [0.087]
[DEBT.sup.ini] -0.017 -0.014
[0.028] [0.020]
[TPGROWTH.sup.av] 4.893 ** 2.422
[1.978] [2.185]
[GROWTH.sup.av](-1) 0.573 *** 0.717 ***
[0.200] [0.177]
[GOVSTABILITY.sup.av] 1.843 *** 1.731 ***
[0.590] [0.414]
[INCOMEPC.sup.ini] 3.479 -0.276
[5.026] [4.448]
[RESERVES.sup.ini] 0.059 * 0.053 *
[0.032] [0.027]
[INFLA.sup.av] (-1) -1.041 -0.706
[0.784] [0.683]
[OPEN.sup.ini] -0.07 0.012
[0.062] [0.039]
Constant -3.108
[35.771]
R-Squared (adj.)
Number of observations 193 257
Hansen's J-stat. (p-value) 0.40 0.48
AB m2-stat.(p-value) 0.72 0.56
Kleibergen-Paap rk-stat
(p-value)
(4.5) Corrected (4.6)
Fixed Effects Pooled IV
[IICCR.sup.av](-1) 0.653 *** 0.521
[0.086] [0.048]
[PRIVSHARE.sup.ini] 0.099 * 0.159 **
[0.051] [0.071]
[DEBT.sup.ini] -0.041 *** -0.020 **
[0.014] [0.008]
[TPGROWTH.sup.av] 3.902 ** 3.102 **
[1.585] [1.409]
[GROWTH.sup.av](-1) 0.717 *** 0.701
[0.208] [0.115]
[GOVSTABILITY.sup.av] 1.193 *** 1.502
[0.426] [0.302]
[INCOMEPC.sup.ini] -5.559 * 1.486
[3.194] [1.063]
[RESERVES.sup.ini] 0.065 ** 0.042 **
[0.026] [0.019]
[INFLA.sup.av] (-1) -0.657 -0.851 *
[0.478] [0.461]
[OPEN.sup.ini] 0.061 * 0.014
[0.033] [0.012]
Constant -16.357 **
[7.698]
R-Squared (adj.) 0.87
Number of observations 221 242
Hansen's J-stat. (p-value) 0.23
AB m2-stat.(p-value)
Kleibergen-Paap rk-stat 0.02
(p-value)
Notes: Standard errors in parentheses.
***, **, * denote significance levels of 1%, 5%, and 10%. The data
sample is an unbalanced panel, comprising 5-yr averages or initial
values between 1980 and 2005. The dependent variable is
Institutional Investor's average country credit rating for the 5-yr
period ([IICCR.sup.av]). All regressions include time dummies;
their coefficients are available upon request. Estimates presented
in columns (4.1)-(4.4) are based on two-step standard errors with
the Windmeijer (2005) finite sample correction. For columns (4.3)
and (4.4), we did not use lagged values of [PRIVSHARE.sup.ini] and
[DEBT.sup.ini] as instruments. For the corrected fixed effects
estimation in column (4.5), the Arellano and Bond (1991)
difference-GMM estimator was used to initialize the
bias-correction. Estimates presented in column (4.6) are based on
robust standard errors clustered by country.
TABLE 5
The Effect of PRIVSHARE on 1ICCR-Varying Samples and Specifications
(5.1) OLS (5.2) OLS (5.3) OLS
PRIVSHARE > 0 IICCR > 25 No 1980s
[IICCR.sup.av](-1) 0.554 *** 0.484 *** 0.592 ***
[0.047] [0.057] [0.054]
[PRIVSHARE.sup.ini] 0.103 *** 0.100 *** 0.120 ***
[0.035] [0.033] [0.027]
[PUBDEBT.sup.ini]
[PRIVDEBT.sup.ini]
[DEBT.sup.ini] -0.028 ** -0.064 *** -0.012 *
[0.013] [0.021] [0.006]
[TPGROWTH.sup.av] 3.651 ** 4.249 *** 0.160
[1.420] [1.446] [1.529]
[GROWTH.sup.av](-1) 0.705 *** 0.804 *** 0.729 ***
[0.140] [0.160] [0.143]
[GOVSTABILITY.sup.av] 1.378 *** 1.290 *** 1.188 ***
[0.384] [0.463] [0.340]
[INCOMEPC.sup.ini] 1.224 2.150 * 2.077 *
[1.276] [1.089] [1.102]
[RESERVES.sup.ini] 0.045 * 0.050 *** 0.057 *
[0.023] [0.018] [0.030]
[INFLA.sup.av](-1) -0.812 -2.170 *** -0.033
[0.623] [0.505] [0.595]
[OPEN.sup.ini] 0.030 * 0.010 0.034 **
[0.015] [0.012] [0.017]
Constant -9.766 -18.135 ** -14.301
[9.047] [8.137] [8.642]
R-Squared (adj.) 0.86 0.81 0.89
Number of 208 160 176
observations
Hansen's J-stat.
(p-value)
Kleibergen-Paap
rk-stat (p-value)
(5.6)
(5.4) OLS (5.5) OLS Pooled IV
[IICCR.sup.av](-1) 0.551 *** 0.590 *** 0.536 ***
[0.040] [0.039] [0.061]
[PRIVSHARE.sup.ini] 0.106 ***
[0.031]
[PUBDEBT.sup.ini] -0.027 *** -0.032 *** -0.031 ***
[0.009] [0.009] [0.010]
[PRIVDEBT.sup.ini] 0.022 0.256
[0.073] [0.247]
[DEBT.sup.ini]
[TPGROWTH.sup.av] 2.708 * 1.923 2.667 *
[1.364] [1.325] [1.583]
[GROWTH.sup.av](-1) 0.724 *** 0.736 *** 0.837 ***
[0.113] [0.121] [0.111]
[GOVSTABILITY.sup.av] 1.276 *** 1.173 *** 1.592 ***
[0.317] [0.329] [0.398]
[INCOMEPC.sup.ini] 1.522 1.856 1.577
[1.056] [1.149] [1.156]
[RESERVES.sup.ini] 0.042 ** 0.044 ** 0.032 *
[0.021] [0.021] [0.019]
[INFLA.sup.av](-1) -0.363 -0.280 -0.424
[0.454] [0.466] [0.424]
[OPEN.sup.ini] 0.026 * 0.031 ** 0.025 *
[0.013] [0.014] [0.014]
Constant -17.271 * -17.187 * -18.042 *
[8.730] [9.305] [9.195]
R-Squared (adj.) 0.88 0.87 0.86
Number of 257 257 242
observations
Hansen's J-stat. 0.11
(p-value)
Kleibergen-Paap 0.03
rk-stat (p-value)
Notes: See notes on Table 3.
TABLE 6
The Effect of PRIVSHARE on SOVDEFAULT
(6.1) Probit (6.2) Logit
[PRIVSHARE.sup.ini] -0.023 ** -0.040 **
[0.009] [0.016]
[DEBT.sup.ini] 0.011 ** 0.020 **
[0.004] [0.008]
[TPGROWTH.sup.av] -0.322 -0.774
[0.501] [0.945]
[GROWTH.sup.av](-I) -0.078 * -0.152 *
[0.044] [0.083]
[GOVSTABILITY.sup.av] -0.173 -0.321
[0.108] [0.196]
[INCOMEPC.sup.ini] 0.492 * 0.902 *
[0.286] [0.482]
[RESERVES.sup.ini] -0.003 -0.006
[0.004] [0.006]
[INFLA.sup.av] (-1) -0.012 -0.002
[0.104] [0.184]
[OPEN.sup.ini] -0.004 -0.006
[0.004] [0.008]
East Asia and Pacific 1.199 *** 2.055 ***
[0.398] [0.658]
South Asia 0.684 1.103
[0.473] [0.853]
Europe and Central Asia 0.066 -0.051
[0.520] [0.864]
Sub-Saharan Africa 1.280 *** 2.078 ***
[0.470] [0.789]
Latin America and the Caribbean 1.172 *** 1.953 ***
[0.407] [0.718]
OIL 0.421 * 0.713
[0.255] [0.452]
SO VDEFAULT(- I)
Marginal effect of PRIVSHARE -0.009 ** -0.010 **
[0.004] [0.004]
Observations 229 229
Pseudo [R.sup.2] 0.31 0.31
Percent correctly predicted 75.55 75.55
(6.3) Probit (6.4) Logit
with LDV with LDV
[PRIVSHARE.sup.ini] -0.018 ** -0.032 **
[0.009] [0.015]
[DEBT.sup.ini] 0.009 ** 0.016 **
[0.004] [0.008]
[TPGROWTH.sup.av] -0.235 -0.539
[0.500] [0.987]
[GROWTH.sup.av](-I) -0.07 -0.13
[0.045] [0.096]
[GOVSTABILITY.sup.av] -0.195 * -0.356 *
[0.103] [0.182]
[INCOMEPC.sup.ini] 0.462 * 0.867 *
[0.267] [0.477]
[RESERVES.sup.ini] -0.003 -0.005
[0.004] [0.006]
[INFLA.sup.av] (-1) -0.014 -0.008
[0.099] [0.182]
[OPEN.sup.ini] -0.003 -0.004
[0.004] [0.007]
East Asia and Pacific 1.019 *** 1.715 ***
[0.335] [0.578]
South Asia 0.749 * 1.11
[0.433] [0.841]
Europe and Central Asia -0.132 -0.351
[0.416] [0.693]
Sub-Saharan Africa 1.002 ** 1.730 **
[0.391] [0.680]
Latin America and the Caribbean 0.822 ** 1.369 **
[0.368] [0.663]
OIL 0.419 * 0.727 *
[0.218] [0.418]
SO VDEFAULT(- I) 0.936 *** 1.617
[0.222] [0.403]
Marginal effect of PRIVSHARE -0.0071 ** -0.0079 **
[0.0036] [0.0037]
Observations 229 229
Pseudo [R.sup.2] 0.36 0.36
Percent correctly predicted 81.22 82.53
Notes: Standard errors in parentheses.
***, **, * denote significance levels of 1%, 5%, and 10%. The data
sample is an unbalanced panel, comprising 5-yr averages or initial
values between 1980 and 2005. The dependent variable is SOVDEFAULT,
which is a binary variable indicating if Standard and Poor's rated
a government as being in default at least once during a 5-yr
period. All regressions include time dummies; their coefficients
are available upon request. Robust standard errors clustered by
country are reported for all regressions.
TABLE 7
The Effect of PRIVSHARE on SOVDEFAULT--Probit and Logit
with Fixed and Random Effects
(7.1) (7.2) (7.3)
Probit RE Logit RE Logit FE
[PRIVSHARE.sup.ini] -0.035 ** -0.060 ** -0.123 **
[0.016] [0.028] [0.056]
[DEBT.sup.ini] 0.012 *** 0.021 *** 0.031 **
[0.004] [0.007] [0.013]
[TPGROWTH.sup.av] -0.438 -0.804 -1.064
[0.462] [0.833] [1.068]
[GROWTH.sup.av](-1) -0.093 ** -0.169 ** -0.346 **
[0.041] [0.073] [0.136]
[GOVSTABILITY.sup.av] -0.257 ** -0.446 ** -0.588 **
[0.122] [0.210] [0.261]
[INCOMEPC.sup.ini] 0.692 * 1.201 ** 6.591 **
[0.357] [0.609] [2.580]
[RESERVES.sup.ini] -0.004 -0.006 -0.006
[0.005] [0.009] [0.017]
[INFLA.sup.av] (- 1) 0.047 0.074 0.2
[0.131] [0.223] [0.362]
[OPEN.sup.ini] -0.004 -0.006 -0.028
[0.006] [0.010] [0.023]
East Asia and Pacific 1.549 * 2.615 *
[0.906] [1.536]
South Asia 1.032 1.487
[1.163] [2.059]
Europe and Central Asia 0.007 -0.04
[0.791] [1.336]
Sub-Saharan Africa 1.621 ** 2.698 **
[0.699] [1.183]
Latin America and the Caribbean 1.369 ** 2.298 **
[0.631] [1.077]
OIL 0.54 0.908
[0.472] [0.802]
SOVDEFAULT(-1)
Marginal effect of PRIVSHARE -0.0140 ** -0.0148 **
[0.0064] [0.007]
R-Squared (adj.)
Percent correctly predicted 74.24 74.24
Number of observations 229 229 167
(7.4) (7.5) (7.6)
LPM FE Probit RE LPM FE
[PRIVSHARE.sup.ini] -0.009 ** -0.036 ** -0.009 **
[0.004] [0.016] [0.004]
[DEBT.sup.ini] 0.002 * 0.006 * 0.002 *
[0.001] [0.003] [0.001]
[TPGROWTH.sup.av] -0.138 -0.349 -0.137
[0.159] [0.441] [0.154]
[GROWTH.sup.av](-1) -0.029 *** -0.083 ** -0.028 ***
[0.010] [0.039] [0.011]
[GOVSTABILITY.sup.av] -0.067 * -0.17 -0.067 *
[0.035] [0.127] [0.035]
[INCOMEPC.sup.ini] 0.539 ** 0.524 * 0.539 **
[0.243] [0.283] [0.243]
[RESERVES.sup.ini] -0.001 0.002 -0.001
[0.001] [0.006] [0.001]
[INFLA.sup.av] (- 1) 0.042 -0.004 0.042
[0.034] [0.117] [0.034]
[OPEN.sup.ini] -0.001 0 -0.001
[0.002] [0.005] [0.002]
East Asia and Pacific 1.502 **
[0.748]
South Asia 1.214
[0.849]
Europe and Central Asia -0.314
[0.674]
Sub-Saharan Africa 1.190 **
[0.556]
Latin America and the Caribbean 0.754
[0.519]
OIL 0.377
[0.368]
SOVDEFAULT(-1) 1.063 *** 0.003
[0.256] [0.066]
Marginal effect of PRIVSHARE -0.0142 **
[0.0065]
R-Squared (adj.) 0.31 0.31
Percent correctly predicted 57.21 82.29 57.64
Number of observations 229 192 229
Notes: See notes on Table 6.
