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Political Institutions and Debt Crises

Article in Public Choice · February 2009

DOI: 10.1007/s11127-008-9364-0 · Source: RePEc

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Public Choice (2009) 138: 387–408
DOI 10.1007/s11127-008-9364-0

Political institutions and debt crises

Caroline Van Rijckeghem · Beatrice Weder

Received: 2 October 2006 / Accepted: 13 August 2008 / Published online: 8 September 2008
© Springer Science+Business Media, LLC 2008

Abstract This paper shows that political institutions matter in explaining defaults on ex-
ternal and domestic debt obligations. We explore a large number of political and macro-
economic variables using a non-parametric technique to predict safety from default. The
advantage of this technique is that it is able to identify patterns in the data that are not cap-
tured in standard probit analysis. We find that political factors matter, and do so in different
ways for democratic and non-democratic regimes, and for domestic and external debt. In
democracies, a parliamentary system or sufficient checks and balances almost guarantee the
absence of default on external debt when economic fundamentals or liquidity are sufficiently
strong. In dictatorships, high stability and tenure play a similar role for default on domestic
debt.

Keywords Sovereign debt crises · Political institutions · Early warning systems

JEL Classification D27 · F30 · F34

1 Introduction

Defaults on sovereign debt have been observed at very different debt levels. Some countries,
such as Belgium, have tolerated more than 100% of GDP of debt, whereas countries such
as Argentina have repeatedly defaulted at much lower levels. Of course, there are important
economic conditions that make debt more tolerable in one country than in others. But it
is also highly likely that political conditions matter in government decisions to default and
that it is, frequently, the combination of economic and political conditions that explains
differences in behavior. In fact, the literature on sovereign debt has long emphasized that

C. Van Rijckeghem
Sabanci University, Istanbul, Turkey
e-mail: cvanrijckeghem@superonline.com

B. Weder ()
University of Mainz and CEPR, Mainz, Germany
e-mail: Beatrice.Weder@uni-mainz.de
388 Public Choice (2009) 138: 387–408

“willingness to pay” in addition to “ability to pay” plays an important role in debt crises
(Bulow and Rogoff 1989). It takes only a small step to conclude that this “willingness” will
in turn be influenced by politics, i.e., by the distribution of interests and the institutions and
power structures that mediate them. Also, one of most striking regularities in debt crises is
that countries with histories of default have a higher propensity to resort to defaults again
(Reinhart et al. 2003). Again, such “serial defaults” are observed at very different debt levels,
which might also suggest that the cause is underlying political institutions. In spite of this,
the empirical literature on sovereign defaults has, with few exceptions, ignored the role of
politics.
This paper uses a flexible estimation technique to unearth political conditions that help
countries steer clear of sovereign default. The technique allows one to test hypotheses such
as that a political system with many veto players is less prone to default but only at certain
levels of liquidity and/or real growth. It provides estimates of “safe zones”—thresholds for
debt above which default does not occur conditional on specific political institutions. The
strength of the technique we deploy is to identify such interactions and thresholds across a
great number of variables. The purpose of the paper, therefore, is to take a new and sweeping
look at the possible ways in which politics could interact with economic factors in providing
safety from default.
We take our cue on relevant variables from the theoretical literature that focuses ex-
plicitly on the internal politics of fiscal adjustment or default. For instance, Alesina and
Drazen (1991) suggest that a more polarized government may lead to a game of attrition
and delay stabilization. Thus, a system characterized by polarization amongst veto play-
ers should have a higher propensity to default. Alternatively, a high level of political con-
straints and many veto players will restrain the executive. For instance, Kohlscheen (2005)
argues that the probability of default is less under a parliamentary system than a presi-
dential system, since the former imposes more constraints on the executive. When look-
ing to the broader political economy literature, one can identify a large number of addi-
tional political variables that should bear on economic outcomes.1 Prominent among these
are elections, political instability (including large disruptions such as wars), the length of
tenure of the chief executive and corruption. Alesina et al. (1997), for instance, discuss
different theories of electoral cycles and find evidence of policy changes in the immedi-
ate aftermath of elections. For authoritarian systems, the role of tenure is important since
governments with a short horizon will tend to make opportunistic decisions knowing that
negative consequences will be pushed into the future (e.g., Olson 1993; Clague et al. 1996;
Tullock 1987).
To date, the empirical literature on sovereign defaults is sparse. Kohlscheen (2005) stud-
ies democracies only and shows that the probability of default on external debt is lower in
parliamentary democracies, when there are a large number of veto players, in coalition gov-
ernments, and when the tenure of the government is long. Similarly, Saiegh (2005) finds a
lower probability of default on external debt in coalition governments. Manasse et al. (2003),

1 Amador (2003) and Alichi (2005) consider whether democracies offer greater commitment to debt repay-
ment than non-democracies. Chang (2003) models political revolts to bring about default. Saiegh (2005)
considers the role of coalition governments versus two-party contests. Outside the default literature, Roubini
and Sachs (1989), Persson et al. (2000), and Keefer (2002b) have examined fiscal consequences of the po-
litical structure. Other related literature includes papers on partisan politics (Persson and Svensson 1989;
Tabellini and Alesina 1990; Cusack 1997; Allers et al. 2001). Bussiere and Mulder (2000) in a study of cur-
rency crises rather than debt crises find that currency crises tend not to occur before elections and are instead
bunched after elections.
Public Choice (2009) 138: 387–408 389

henceforth MRS, find that in years with presidential elections, the probability of a debt cri-
sis increases. Manasse and Roubini (2005) find that countries with presidential elections in
less than five years have a high probability of default when international capital markets are
tight.
In comparison with the existing empirical literature this paper studies the role of polit-
ical institutions more comprehensively. We analyze both democratic and non-democratic
regimes, whereas the literature either focuses on democratic regimes or does not differenti-
ate across regimes; we investigate the role of a large number of political characteristics; we
study both domestic and foreign debt crises (the literature has ignored domestic debt crises)
and we explore the role of political variables in conjunction with each other as well as with
macro-economic variables.
An innovation in this paper is the approach used to predict safety from default. The lit-
erature thus far uses mostly probit analysis to analyze the probability of default, notable
exceptions being MRS and Manasse and Roubini (2005), who use CART (Classification
and Regression Tree Analysis). We use the non-parametric technique developed by Osband
and Van Rijckeghem (2000) to study currency crises, which is similar to but has certain ad-
vantages over CART. Essentially, we search for powerful conditions that can classify obser-
vations into two groups. In one “safe” group, defaults are historically never observed. The
other group includes observations with and without default. Powerful conditions—which
we call strong filters—are those that classify large numbers of observations within the first
group. To give an example, defaults were never observed in parliamentary democracies with
debt-service to exports ratios less than 19%, and close to 200 observations out of 745 tran-
quil observations satisfy the criteria, making this a powerful filter. Compared to probit, this
technique (1) efficiently explores non-linearities, which is important if different ingredients
are necessary in combination to steer clear of default (e.g., a low debt-service in conjunction
with a favorable political situation), and (2) helps uncover the overall role of politics in debt
default, including its effect through debt levels and real growth. In regression analysis, by
contrast, some political characteristics might be eliminated from regressions that control for
debt, fiscal deficit and growth, even if it is those political characteristics that account for low
debt or favorable debt dynamics.
The remainder of the paper is organized as follows. Section 2 introduces the data used
in the empirical analysis, Sect. 3 discusses the empirical strategy, and Sect. 4 presents the
results. Section 5 concludes.

2 Data

For our main sample, we consider the low and middle-income countries for which we have
annual data from 1974 to 2000. The sample includes 73 countries (see Table A.1). For tran-
sition countries, we include observations only from 1992 onwards. We focus on the middle
and low-income countries to avoid raising doubts as to whether our results are driven by
industrial countries, which have not experienced default in recent years. We use the 1974–
2000 data to study defaults during 1975–2001 and reserve the last two years of data for out
of sample testing (we use 2001 data to predict safety in 2002 and 2002 data to predict safety
in 2003). As we aimed at both avoiding endogeneity bias and at developing a system even-
tually usable as an early warning system, we used lagged variables whenever practicable. In
practice, this means we looked at the characteristics of “pre-debt crisis” observations (pre-
crisis observations for short), or observations the year before default. As is common in the
literature, we also exclude all default-observations that follow an initial entry into default.
390 Public Choice (2009) 138: 387–408

For instance, if a sovereign was in default during 1995–2000, we exclude observations for
1996–2000 from the data. Both political and economic variables are systematically affected
by default, so including these observations would bias the results.
Our sample contains 1915 democratic observations and 1652 non-democratic observa-
tions. A country is classified as democratic if the average of the Gastil Indices on political
rights and on civil rights is below 5. Years in default other than the year of entry into default
are not included in our sample. The default rates according to regime and type of debt—
external versus domestic—are given in Table 1.
We investigate the determinants of defaults on (1) external debt, as defined by Standard
and Poor’s (S&P) (2003), and (2) domestic debt, as defined by S&P or a large increase in
domestic credit to the government (more than 25% of current period total domestic credit).
We used S&P (2003) for these variables, supplemented with data on domestic credit to
the government from International Financial Statistics (IMF). S&P defines a default as (1)
an instance where debt service is not paid as scheduled within the grace period; or (2) an
exchange offer with terms less favorable than the original. The data cover local and for-
eign currency bonds and bank loans, and thus exclude debt to official creditors. Default
or restructuring of debts to official creditors are likely to be motivated by different fac-
tors than those to commercial creditors, may in fact be initiated by creditors, and can be
expected to have fewer negative repercussions, so the fact that the S&P data exclude ar-
rears/restructuring on debt to official creditors is a desirable feature for our purposes. An-
other advantage of the S&P data is that they refer exclusively to defaults on sovereign debt.
Data on arrears/restructuring of external debt provided in the Global Development Finance
(World Bank), used in many other studies, instead include arrears by commercial entities,
not just the sovereign.
Our economic variables on the fiscal side include total external debt, public external
debt, and the overall budget balance (all scaled by GDP), debt-service/exports, government
revenue/gnp, and inflation, based on IMF and World Bank published sources. There is some
evidence that the sizes of external debt and debt-service/exports affect the probability of
default (Detragiache and Spilimbergo 2001; MRS 2003; Kraay and Nehru 2006), but at the
same time it has been noted that default may occur at very low debt levels and not occur at
very high debt levels (Reinhart et al. 2003; IMF 2003). Studies have tried but failed to find
evidence that the budget balance affects the probability of default (Hemming et al. 2003;
MRS 2003). Several studies have found a role for inflation (e.g., MRS 2003). Studies have
also found a role for openness (the ratio of exports plus imports over GDP) (Detragiache
and Spilimbergo 2001; MRS 2003; Sy and Pescatori 2004). This variable can be conceived
of as a measure of ease of debt-servicing, but also as a political variable indicating the sizes
of interest groups which would oppose default, if default would imperil trade relations or
trade credits.
We also include a number of variables to proxy for adverse shocks: real GDP growth
and currency (see Reinhart 2002) and banking crises. For banking crises, we use the data
of Glick and Hutchison (2002), updated with the more recent systemic and non-systemic
banking crises identified by Caprio and Klingebiel (2003). For currency crises, we use a
binary variable that is defined as one when the real exchange rate (end-of-period) depreciates
by at least 25% in a year.
As measures of liquidity we use reserves/short-term debt and reserves over M2, variables
which numerous earlier studies confirm as being important to the likelihood of default on
external debt. To proxy for international liquidity conditions we include the US treasury bill
rate and the London Inter-Bank Offer Rate (LIBOR).
Public Choice (2009) 138: 387–408 391

We use a large number of political variables. Our main data source is Database of Polit-
ical Institutions (DPI) (Beck et al. 2001; Keefer 2002a) but we also supplemented with data
from other sources (see Tables A.2 and A.3).
One group of variables (“constraints”) measures the extent to which government is con-
strained by the presence of veto players and thus aims to measure the feasibility of policy
change. A veto player is a president, prime minister, or government party with veto power
over policy. This group includes the variables “system,” “checks and balances,” and “politi-
cal constraints” (PolconIII and PolconV). The variable “system” classifies systems into pres-
idential and parliamentary systems. When there are both a prime minister and a president,
the system is classified as presidential if the president has veto power over legislation or can
appoint and dismiss the prime minister and/or other ministers, dissolve parliament and call
for new elections. We also checked an alternative definition of presidential/parliamentary
systems according to Persson and Tabellini (2000) that is based on the existence of a vote of
confidence by the legislature. Under both definitions, parliamentary systems as a rule pro-
vide a greater constraint on the chief executive. Checks and balances (“checks”) is a variable
that records the number of veto players in the executive branch of government, adjusted for
the alignment of government parties with the opposition. Political constraints (PolconIII
and PolconV) are similar variables intended to capture the difficulty of effecting change in
policies. PolconIII measures the alignment of preferences across executive and legislative
branches of government (the executive and each house of parliament is considered a “veto
point”, which is not to be confused with the concept of veto player). PolconV is derived
the same way as PolconIII but includes two additional “veto points” (the judiciary and sub-
federal entities) across which alignment is calculated (see Henisz 2000, 2002). In addition to
these constraints on the government we also consider constraints on fiscal policy execution
emanating from a federal structure of government. When revenues and/or expenditures are
devolved to the local level, it is more difficult to carry out fiscal adjustment. We use the vari-
able “State,” which measures whether the provincial executive and/or legislature are directly
locally elected.
As an indicator of polarization we used “polarization”, which is defined as the absolute
value of the greatest difference in orientation (left, center, right) between the chief execu-
tive’s party, the three largest government parties and the largest opposition party.
As indicators of elections we track (1) recent elections and (2) planned elections. We
would like to establish whether countries are safe when there has not been a recent election
and when no election is on the horizon. For recent elections we use an indicator variable de-
noting whether a parliamentary or presidential election was held the current or the previous
year (noparel, noprel) and variables indicating years since parliamentary and presidential
elections were held (ysparel, ysprel). For planned elections we used the remaining years in
the current term of the chief executive (yrcurnt2).
For length of tenure of the executive and parliament variables we use: longest length
of tenure of a veto player (tenlong); shortest length of tenure of a veto player (tenshort);
the length of tenure of the chief executive (yrsoffc); and the remaining term of the chief
executive (yrcurnt). We group these variables along with other variables measuring political
stability, namely the share of veto players who drop from government and opposition (stabs)
and “war” (including internal strife) from Nils et al. (2002).
A final political variable is “corruption”. The political indicators are not highly corre-
lated among themselves. Only very few indicators have correlations of 0.4 or above. Tenure
variables tend to be highly correlated with one other. So are checks and balances, political
constraints, and “system.”
392 Public Choice (2009) 138: 387–408

3 Empirical methodology

Given our desire to identify complementary factors that help countries avoid defaults, we
use the non-parametric technique of Osband and Van Rijckeghem (2000) to search for con-
ditions under which debt crises are never observed. This method is similar to the popular
CART methodology, which aims to classify observations into two groups (e.g., default-
ers versus non-defaulters). Like CART, its flexibility is its main strength, allowing one to
uncover patterns in the data that probit analysis cannot do easily. The method handles non-
linearities readily, which is important since we a priori expect important complementarities
(voters may or may not favor default depending on the level of economic variables, such as
debt service and growth). We also expect thresholds above which there is no marginal effect
from improving fundamentals (e.g., a decrease in debt/GDP ratio is not expected to have
much impact on the probability of default if debt/GDP already is low). Such patterns are
not easily uncovered by probit analysis when a large number of independent variables are
involved. Furthermore, in probit analysis, the general to specific modeling strategy would
normally eliminate variables that are insignificant on account of being correlated with other
independent variables. Thus, some political characteristics might be eliminated from regres-
sions that control for debt, fiscal deficit and growth, even if it is those political character-
istics that account for low debt or favorable debt dynamics. In classification analysis, these
political factors will show up strongly. Our analysis also allows us to use more data, since
observations are not dropped when observations are missing on just one characteristic, as
in probit. The only requirement is for (pre-) default-years not to have missing data. Our
analysis is also robust to outliers in a way that probit is not, since extreme values have no
effect on whether a condition such as x > a holds. Finally, the analysis handles categori-
cal independent variables (a variable with a range consisting of several categories such as a
corruption index) without the need to introduce a large number of dummy variables, which
would otherwise be necessary to allow for a different impact of a move in e.g., a corruption
index from a value of 1 to 2 to a value of 5 to 6. It is also not affected by heterogeneity in
the relationship between default and independent variables across countries.
Classification methods such as CART allow for unequal misclassification costs, e.g., they
allow one to put a heavy weight on avoiding certain types of classification errors. In our
case, given our interest in identifying conditions under which default is highly unlikely, we
in effect put a very large weight on avoiding classifying default observations as safe. We
conduct a search over a large number of combinations of conditions (42 × 42), such as
“external debt/GDP less than x and fiscal balance larger than sample average” to identify
those conditions for which no debt crises are ever observed.
We restrict the second leg of each “and” condition to be a comparison with the sample
mean in order to restrict the number of searches. Note that this also means we consider
combinations such as “inflation < x and inflation < sample average”, so that we are by the
same token considering filters that contain effectively only one condition. When the effect in
theory could go either way, as in the case of the number of veto players or tenure, negative
values of the variables are also considered.
In practice, we search for the highest value of a variable x where a pre-crisis is observed
(i.e., the year before a default), conditional on the second leg of the condition. We call the
highest value of such a variable a threshold for safety. For example, we search for the largest
real GDP growth rate ever observed one year ahead of default, conditional on external debt
being less than sample average. This largest GDP growth rate is 9%, so we set the threshold
for safety at 9% of GDP. For any value of x higher than the threshold of x, defaults were,
by construction, never observed historically the next year. We label the conditions we find
through this procedure filters.
Public Choice (2009) 138: 387–408 393

It can be seen intuitively that the filter in our example is not very “useful” since there
are only few countries and years among tranquil observations that fulfill the condition of
real GDP growth over 9%. In fact there are only 63 tranquil observations that fulfill this
condition; that is the filter provides a “pass” of safety only for 63 of the tranquil observations.
Therefore the next step is to determine a minimum of observations that a filter can extract
from the data in order to be a useful filter. A useful filter should also have a small error out
of sample, that is, it should have a small chance of mislabeling an observation as safe.
It is easy to show formally that the confidence we can have in any one filter depends
on the number of extractions (S) of tranquil observations by that filter. With a sufficiently
large number of sufficiently uncorrelated and un-trended observations, as proven in Osband
and Van Rijckeghem (2000), environments identified as safe bear about a 1/S% risk of a
sovereign debt default. Thus, in order to limit the risk of misclassification to about 1%, we
would want to consider filters with at least 100 extractions or passes. In that case, for each
100 passes, one pass on average will be faulty and associated with a debt crisis. The ability
to calculate the classification error analytically is an advantage of our method, compared to
CART.
The proof (from Osband and Van Rijckeghem) relies on non-parametric statistics. Denote
the probability of mislabeling an observation as safe from default as Pr(pre-crisis|pass).
Note that this probability can be re-written as a function of Pr(pass|precrisis) using Bayes’
rule
Pr(pass/precrisis) · Pr(precrisis)
Pr(precrisis/pass) = . (1)
Pr(pass)
In turn, the probability Pr(pass/pre-crisis) that a filter will give a “pass” to a pre-crisis
observation equals the probability that fundamentals of that pre-crisis observation exceed
the threshold set in all C previous crises. Based on non-parametric statistics in the case of
untrended fundamentals, this is equivalent to the probability of being the best performer in
a sample of C + 1 observations. Filters will thus give a “pass” to a pre-crisis observation
with a probability 1/(C + 1). Thus, Pr(pass|pre-crisis) = 1/(C + 1). Continuing our above
example, 9% growth is the maximum growth observed in pre-crisis, of which there were 39
in our data. The next crisis will be the 40th crisis, and there will be a 1/40 probability that
growth will exceed 9% in the year before that crisis.
Next note that Pr(pre-crisis) = C/(C + T ) and Pr(pass) = S/(C + T ), assuming that the
sample is representative of the whole. Substituting in the expression above, we obtain

Pr(pre-crisis|pass) = [C/(C + 1)]/S ∼ 1/S. (2)

For large C, this is just slightly less than 1/S, the inverse of the number of passes.
To avoid confusion, it has to be clarified that the safety check’s out-of-sample classi-
fication error of the type Pr(pre-crisis|pass) is in fact not gauged by 1/S, but by 1/S in
comparison to the probability of crisis. This is because a system that simply labeled all ob-
servations as safe would have an error equal to the probability of crisis. An error smaller
than this probability of crisis is therefore an improvement.
We use the cutoff of 150 extractions in our work in order to be confident that our error
will be less than 1%. The in-sample probability of entry into default is 3.6–5.6% (depending
on the sample, since we distinguish according to the degree of democracy and the nature of
debt—see Table 1); thus a system that simply labeled all observations as safe would have an
error of about 3.6–5.6%. A filter with an error of less than 1% is therefore an improvement.
Note for comparison that a filter based on irrelevant variables would extract only 19 obser-
vations in the case of democracies and external debt (Osband and Van Rijckeghem 2000),
compared to the 150 we require.
394 Public Choice (2009) 138: 387–408

It should be highlighted that with larger datasets, the system will normally have a lower
error rate, since S, the number of extractions, will normally be larger. Let S, C, and T dou-
ble as the number of observations double. Then in equation (1) Pr(pass) and Pr(pre-crisis)
remain unchanged, but Pr(pass|precrisis) declines and as a result so does Pr(precrisis|pass).
Intuitively, with more data and more crises, there will be a smaller chance that the next
pre-crisis observation will exceed the threshold value of fundamentals in the previous pre-
crisis observations. Again continuing our previous example, if we had double the sample
and therefore double the number of crises, or 78 crises, there would only be a 1/79 chance
of growth in the next pre-crisis exceeding fundamentals in all earlier pre-crises.

4 Results

We start by looking at the pattern of defaults across debt categories and regime types. Then
we discuss the findings on strong filters for every type of regime and debt.

4.1 Probabilities of default

Table 1 shows the probability of default for democratic and non-democratic countries and for
foreign and domestic debt. Under our definition of domestic debt default, the incidence of
defaults on external debt is higher than for domestic defaults in both democracies and non-
democracies, consistent with the view that foreign debtors tend to be penalized more than
domestic debt holders because of governments’ concerns for its voter base. An interesting
feature to note is that the default rate on domestic debt is lower in non-democratic regimes
(3.6 versus 4.4 for democracies). This goes against the concept of a “democratic advantage,”
i.e., the idea that accountability to voters confers a commitment advantage on domestic debt
to democracies (North and Weingast 1989; Schultz and Weingast 2003). Defaults on foreign
and domestic debt and in democracies and non-democracies could have different causal
factors. We therefore chose not to pool regime types but conduct the empirical analysis that
follows by regime and debt types separately.
The number of observations (country/years) is similar in all four cases. Therefore we are
comparing groups of roughly similar size.

Table 1 Probability of default by regime and debt type

Number of defaults Total observations Probability of default

(in percent)

Democracies
Foreign debt 39 784 5.0
Domestic debt 41 925 4.4

Non democratic countries

Foreign debt 35 622 5.6
Domestic debt 28 779 3.6

Total observations exclude default years subsequent to the year of entry into default
Public Choice (2009) 138: 387–408 395

4.2 Strong filters

We now present the strong filters that predict safety from debt crises in the next year. The
next four tables present the list of all strong filters by regime and debt type, sorted by purely
macroeconomic conditions and by political conditions.
One finding applies across regime and debt types: as far as political and institutional
conditions are concerned, they matter only in conjunction with economic conditions (that is,
in none of our filters are both legs political conditions). The only exception is that political
factors do enter in combination with openness, which can be considered a political variable
(as it affects interest groups) or an economic variable (as it is a measure of the ability to
service external debt).
Table 2 shows the strong filters for safety from debt crises on foreign debt in democracies.
The entries in the table are read as follows: The first filter says that no defaults on foreign
debt have been observed in democracies with a parliamentary system and a sufficiently high
level of reserves, specifically with a threshold of M2 over reserves that is smaller than 9.1.
This filter alone yields 242 extractions, that is, it identifies 30% of all tranquil observa-
tions.
Overall, the results on political factors support the hypothesis that political constraints
(parliamentary democracies and systems with a large number of veto players) in conjunction
with favorable economic conditions help ensure safety from default. It is important to under-
score the early warning nature of these results: the results state only that countries satisfying

Table 2 Democracies and foreign debt: strong filters for safety from default in the next year

Extractions

Political conditions
Broad money/reserves < 9.1 and parliamentary system 242
Short term debt/reserves < 0.8 and parliamentary system 193
Political constraints (PCV) > 0.5 and openness > average 188
Short term debt/reserves < 0.4 and veto players leaving government < average 187
US T-bill rate < 5 and political constraints (PCIII) > average 186
Debt service/exports (%) < 19 and parliamentary system 182
Inflation < 11 and parliamentary system 181
Total debt/exports < 1.1 and parliamentary system 159

Macroeconomic conditions
Short term debt/reserves < 1 and real growth rate > average 308
Real growth rate > 3.5 and debt service/exports < average 290
Real growth rate > 4.3 and total debt/exports < average 274
Total debt/exports < 1.3 and real growth rate > average 229
Debt service/exports (%) < 14 and real growth rate > average 213
US T-bill rate < 5 and total external debt/gdp < average 189
US T-bill rate < 5 and overall fiscal balance/gdp > average 187
Broad money/reserves < 3.7 and official external debt/total > average 181
Short term debt/reserves < 0.55 and overall fiscal balance/gdp > average 157
Short term debt/reserves < 0.47 and official external debt/total > average 155

Strong filters are filters yielding 150 or more extractions

396 Public Choice (2009) 138: 387–408

certain conditions are unlikely to experience a crisis within one year (they are unlikely to get
into trouble quickly), not that they are unlikely to experience a crisis ever. In fact, many of
our strong filters show that political constraints are not helpful when countries are already
experiencing a loss in confidence, as captured by, e.g., short-term debt to reserves.
The strongest evidence relates to the form of the political system, and indicates that par-
liamentary systems confer a debt servicing advantage. Five strong filters involve the pres-
ence of a parliamentary system. The combination of a parliamentary system with a high
level of liquidity (M2/reserves < 9 or short term debt/reserves < 0.8), a low level of infla-
tion (below 11%) or a low level of debt service or debt to exports all indicate safety from
default in the following year. This finding is consistent with the evidence in Kohlscheen
(2005), who finds that parliamentary systems have lower propensities to default than presi-
dential ones when controlling for economic fundamentals, and with our own probit analysis
(results are available upon request). However, the filter methodology points to the impor-
tance of the interaction between economic and political conditions. An executive faced with
a strong parliament will be more likely to service debt, but this only is true as long as debt
service is not too high and/or liquidity conditions are not binding. Conversely, a presidential
system is not safe from default a year later, even when economic conditions are good. This
could reflect the fact that conditions can change/deteriorate quickly in a presidential system,
because it is by nature less constrained.
Before continuing on to the other variables, it is worthwhile asking why a parliamen-
tary system may be helpful in steering a country clear of default. A cursory examination
shows that parliamentary systems perform better on almost all fundamentals affecting debt
dynamics (growth, debt/GDP), liquidity and debt servicing capacity (STD/R, openness), as
well as less standard variables (currency crisis, war, corruption, democracy) (Table A.4).
The fact that there are so many variables that perform better under parliamentary regimes
suggests that there is a major underlying driver such as uncertainty over property rights.
Greater uncertainty may arise in two ways under presidential regimes: (1) unchecked power
and less secure property rights when the presidential party is in control of parliament (2)
overly conflictual relations when the presidential party is not in control of parliament or the
cabinet.
Two strong filters involve the number of veto players. Countries with many veto players
and which are simultaneously open appear to steer clear of default. This finding fits the view
that countries pay their debts because of concerns over international trade, as argued in the
default literature. This is discussed empirically by Rose (2005), who finds that Paris Club
renegotiations are associated with a decline in trade of 8% a year. Following this interpreta-
tion, openness has an influence on the chance of default through its effect on the distribution
of interests: the larger fraction of the population whose livelihood depends on trade in con-
junction with a sufficient number of veto players, which ensures representation of various
interest groups, the lower the chance of default. A second condition links high political con-
straints with low interest rates on US treasury bills (US T-bill rate < 5%). This suggests that
defaults are not to be expected when international liquidity conditions are good, when there
are sufficient political constraints, which can be interpreted as a willingness to service debt.
We also found the expected impact for high government revenues and fiscal centralization
in conjunction with the number of veto players (“PolconV”), but the number of extractions
was approximately only 120, below our cutoff of 150 for a strong filter.
In general the results point to the benefits of a system with strong checks and balances and
a large number of veto players. We did not find a strong filter involving the absence of polar-
ization. Thus, the results appear to run counter to the predictions of “delayed-stabilization”,
whereby one would expect a larger number of veto players to make agreement on how to
Public Choice (2009) 138: 387–408 397

share the burden of stabilization more difficult. What we have found regarding the impor-
tance of a sufficient number of veto players is however somewhat more subtle, because of the
required complementary conditions (high international liquidity, an open economy). We do
not suggest, for example, that a country like Argentina with a large number of veto players
at the time of its default, was relatively safe (since it did not meet any of the complementary
conditions).
Political stability, measured by a low percentage of veto players dropping out of gov-
ernment or opposition, works in conjunction with a sufficient level of reserves relative to
short-term debt.
None of the strong filters for foreign debt involves elections, though there is one filter
involving elections with 145 extractions, just below our chosen threshold. That filter indi-
cates that when short-term debt/reserves is less than 0.4, countries whose last parliamentary
election was at least one year ago never experienced default. These results are consistent
with the idea that large changes are implemented in the immediate aftermath of elections, as
proposed in some models of political business cycles (see Alesina et al. 1997).
In terms of the macroeconomic variables we find a long list of conditions that include
both internal and external conditions.
Some of the strongest results are obtained for liquidity measures. We find that no coun-
tries that had short-term debt equal to reserves, thus following the so-called Guidotti-
Greenspan rule, and also had growth higher than 3.4% ever defaulted. Growth is a very
important variable, as it enters in six strong filters. This can be interpreted against the role
that growth plays in debt sustainability analysis, but could also be interpreted in a political
sense. Pettis (2001), for example, has argued that low growth leads to dwindling support
for market openness and reform. No defaults on external debt have been observed histor-
ically in democratic regimes delivering growth rates exceeding 3.5% to 4% that also had
moderate-average levels of debt or debt service (in relation to their exports). Levels of re-
serves less than short-term debt are sufficient for safety if fiscal conditions are not binding
(levels of about half short-term debt if the fiscal balance is larger than the sample average
of −4.4% of GDP). Such a fiscal balance is also sufficient for safety if international inter-
est rates are sufficiently low. This suggests that the fiscal balance only becomes binding at
moderate short-term debt to reserve ratios or under reasonably tight international liquidity
conditions. This might be the reason why the existing literature, which did not allow for
complementarities, has not found a role for fiscal variables.
External conditions matter in that when US treasury rates are low, countries tend to be
safe from default. This result neatly fits with the pro-cyclical nature of lending to emerging
markets, which has been the focus of the literature of late. In years that mature market
interest rates are low, a push for yields occurs, with market appetite for debt instruments of
countries that would be defaulters in other years. The level of debt, and as already noted,
political constraints and the fiscal balance, are the complementary conditions.
Table 3 shows the results for domestic debt in democratic regimes. The most striking
fact in this table is that inflation less than 7.2%, without any complementary condition, is
sufficient for safety from debt crisis in democracies. Adding political constraints to this in-
flation constraint does not help increase safety. Thus, political considerations (at least those
considered in this paper) cannot help ensure safety from default on domestic obligations in
democratic regimes. It may seem paradoxical that political considerations (notably political
regime and constraints) are not effective in ensuring safety from default on domestic debt,
but are effective for external debt. This may be because a default on domestic debt, which
most often results from monetary financing of a deficit, does not necessarily involve the vote
of confidence by the legislature.
398 Public Choice (2009) 138: 387–408

Table 3 Democracies and domestic debt: strong filters for safety from default in the next year

Extractions

Political conditions
...
Macroeconomic Conditions
Inflation < 7.24 292
Inflation < 7.68 and real growth rate > average 261

Strong filters are filters yielding 150 or more extractions

Table 4 Non-democracies and external debt: strong filters for safety from default in the next year

Extractions

Political conditions
...
Macroeconomic conditions
Short term debt/reserves < 1.8 and US T-bill rate < average 250
Short term debt/reserves < 1.8 and LIBOR < average 205
Real growth rate > 5.8 and broad money/reserves < average 198
Real growth rate > 4.544 and US T-bill rate < average 171
Broad money/reserves < 4.317 and US T-bill rate < average 158
Short term debt/reserves < 0.3 156
Short term debt/reserves < 0.335 and broad money/reserves < average 151

Strong filters are filters yielding 150 or more extractions

Table 4 presents the results for non-democratic regimes and external debt. The finding
here is as in Table 3. We could not identify a single strong filter for safety that would involve
a political factor. Thus, political constraints do not ensure safety from default on external
debt in non-democratic regimes.
The relevant considerations for these regimes are economic constraints and external fac-
tors. Four strong filters involve the level of international interest rates. As above, this in-
dicates that push factors on international capital markets play a role in explaining the in-
cidence of default. Another strong condition is the level of short-term debt relative to re-
serves, which in fact enters even by itself. No defaults have been observed when reserves
were about three times higher than short-term debt. In combination with low international
interest rates reserves need only be about half short-term debt to provide safety from de-
fault.
Table 5 shows the results for domestic debt in non-democratic regimes. Recall from above
that this is the constellation where we find the lowest probability of default. Not surprisingly,
in non-democratic regimes veto players do not appear in strong filters. Interestingly, elec-
toral considerations do play a role, indicating that the level of regime support is important
even in non-democratic settings. A low level of polarization, in combination with low infla-
tion, is another condition for no default. Polarization—the difference in orientation between
the party of the chief executive and other parties on the left-center-right axis—is the variable
Public Choice (2009) 138: 387–408 399

Table 5 Non-democracies and domestic debt: strong filters for safety from default in the next year

Extractions

Political conditions
Inflation < 8.2 and polarization in government < average 281
Inflation < 8.2 and no recent elections* 243
Inflation < 8.2 and peace 242
Total debt/exports < 2.6 and longest tenure of a veto player > average 197
Total debt/exports < 2.6 and years in office of chief executive > average 192

Macroeconomic conditions
Inflation < 7.6 and total debt/exports < average 201
Total external debt/gdp < 64.9 and official external debt/total external debt > average 199
Inflation < 5.3 190
Inflation < 7.6 and total external debt/gdp < average 168

Strong filters are filters yielding 150 or more extractions

* Recent elections refer to elections the prior or same year as the default data

that most closely fits the concept of polarization of interests in Alesina and Drazen’s war of
attrition, lending some support to this theory in non-democratic settings. A long tenure of
the chief executive in combination with not too high a level of external debt or the absence
of a war also ensures safety from default. All three conditions can be interpreted as pointing
to the importance of government stability. They also show the important role of complemen-
tarities: when economic conditions are not favorable (as when inflation or external debt is
relatively high) political conditions do not ensure safety from default. This is as expected.
Facing a high debt, for example, even a dictator with long expected tenure may find it bene-
ficial to default.
With so many conditions apparently guaranteeing safety from default with only a 1%
error, the question arises whether it is sufficient to meet just one of the conditions for safety.
The 1% error refers however to the error when one condition is met by itself, without knowl-
edge of the results on the other tests. If a country passes one test but fails many others, the
probability of crisis will be much higher than 1%, while if it meets many other tests the
probability of crisis will be lower.

5 Tests of our approach

We conclude by providing two tests of the power of our approach. First, we check the range
that debt variables can adopt in the group of observations labeled as safe. For this exercise
we take the example of the filter “broad money/reserves < 9.1 and parliamentary system”
in democratic regimes. The results are shown in Table 6.
We find that external debt ranged up to 98% of GDP and four times exports in safe
country-years (those with broad money/reserves < 9.1 and parliamentary system), a very
wide range. Thus the range of debt that can nevertheless be identified as safe under certain
economic and political conditions is considerable. This testifies to the power of the filter
approach in identifying conditions for debt sustainability that would not be identified with
simple rules.
400 Public Choice (2009) 138: 387–408

Table 6 Range of debt variables in episodes labeled safe based on “broad money/reserves < 9.1 and parlia-
mentary system”. Comparison between pre-crisis and tranquil observations

Total external debt/GDP Total external debt/exports

Pre-crisis Tranquil If broad Pre-crisis Tranquil If broad
money/reserves money/reserves
<9.1 and <9.1 and
parliamentary parliamentary
system system

Mean 62.04 46.43 41.20 2.30 1.63 1.00

Maximum 187.33 230.15 98.0 6.51 16.86 3.98
Minimum 18.60 0.49 4.0 0.54 0.06 0.07
Std. dev. 36.80 32.77 20.7 1.10 1.54 0.66

Observations 38.00 669.00 221.00 38.00 654.00 219.00

Table 7 Prediction for countries that experienced default in 2002 or 2003 (out-of-sample test using one-year
lagged data)

Gabon Moldova Nigeria Paraguay Uruguay

2002 2002 2002 2003 2003

Parliamentary system and M2Y/R < 9.1 Not safe Not safe Not safe Not safe Not safe
STD/R < 1 and real growth > 3.4% Not safe Not safe Not safe Not safe Not safe
US treasury rate < 5 and debt/gdp < 57.4% Not safe Not safe Not safe Safe Not safe

Second, we test the out-of sample performance of three relatively uncorrelated filters for
external debt in democracies: (1) a parliamentary system with broad money/reserves of less
than 9; (2) short-term debt/reserves of less than one and real growth above 3.4%; and (3) US
treasury bill rate less than 5% and debt/GDP less than 57%. These filters individually each
have over 200 extractions and are uncorrelated in the sense that they each extract a large
number of observations that the others do not (see Osband and Van Rijckeghem 2000 for
further details on the methodology). We check whether any of the default countries during
2002–03 satisfied these filters (since 2003 no new sovereigns entered default). Defaults on
external debt in democracies during that period occurred in Gabon (2002), Moldova (2002),
Nigeria (2002), Paraguay (2003), and Uruguay (2003).2 We find that the three filters per-
formed well, with an error rate of about 1% (one error for about 100 passes), compared to a
frequency of crisis of 5%. Thus, we conclude that these filters may serve as an early warning
system for debt default (Table 7).

2 S&P classifies Indonesia as a default because of a restructuring of syndicated bank credits at terms less
favorable than the original in April 2002. These terms were required by the Paris Club of official creditors in
April 2001. For this reason, it is inaccurate to characterize Indonesia as entering default in 2002 for purpose
of our study. It is also not characterized as a default in other studies. See Sturzenegger and Zettelmeyer (2006)
who provide an in-depth discussion of the last wave of debt defaults. Including Indonesia our error rate would
be two percent rather than one percent.
Public Choice (2009) 138: 387–408 401

6 Conclusions

The main findings are as follows. First, we find that the factors that safeguard countries
from default vary substantially across democracies and non-democracies and across debt
types. Second, we find strong evidence that political institutions help countries steer clear of
default, but only in conjunction with (1) strong international liquidity or (2) favorable eco-
nomic fundamentals, such as high foreign exchange reserves, a low debt-service to exports
ratio and high growth, or (3) sufficient openness. There is thus no simple political panacea
that ensures safety from default.
We find inter alia that in democracies political constraints (a parliamentary regime, a
large number of checks and balances) are helpful in avoiding default but only when the
economic situation is supportive.
Further exploration showed that the advantage of parliamentary regimes in democracies
stems from better performance on a wide range of factors affecting debt-dynamics (low
debt-ratios, high growth and openness, low corruption, few currency crises), as well as a
lower probability of default holding constant these factors. The purpose of this paper was
to uncover empirical patterns, rather than to present possible explanations for these results.
However, we speculate that the underlying factor in the better performance of parliamentary
regimes may be greater control of uncertainty and better commitment technology. Presiden-
tial regimes may produce more uncertainty through (1) unchecked power and less secure
property rights when the presidential party is in control of parliament and/or (2) overly con-
flictual relations when the presidential party is not in control of parliament or the cabinet.
Certainly, these issues would merit further research.
Another empirical regularity we uncover is an absence of default on domestic debt in
non-democratic regimes characterized by less polarization, long tenure, and stability (ab-
sence of recent elections and war) in conjunction with macroeconomic stability (low infla-
tion).
We also confirm the role of certain economic variables (liquidity, growth, inflation, and
openness) found in the literature. And we find—in contrast to the existing literature—a role
for the fiscal balance, once we allow for certain complementary conditions. We find evidence
that small fiscal deficits ensure “safety” provided international interest rates are low. The
literature thus far has not been able to uncover a role for the fiscal deficit, and our results here
attest to the power of an approach such as ours that incorporates complementary conditions.
A further finding is that the non-parametric approach is powerful in the sense that we
are able to accurately predict safety from defaults even at very high levels of debt and that
our classification system performs well as an early warning for the latest set of sovereign
defaults.
Our approach can also contribute to the debate in international institutions about appro-
priate “debt thresholds,” or maximum recommended debt levels. In the past, debt sustain-
ability analysis was mostly based on economic considerations, and was frequently distilled
into simple rules of thumb. For instance, in the context of the initiative to alleviate the debt
burden of highly indebted poor countries (Highly Indebted Poor Country Initiative, HIPIC),
the threshold for indebtedness was set at debt/exports larger than 150% (or in certain cases
250% of fiscal revenues). Our analysis lends support to suggestions of, e.g., Kraay and
Nehru (2006) that “smart thresholds” for debt should be related to the quality of policies
and institutions.
Acknowledgements We would like to thank Reuven Glick, Emanuel Kohlscheen, and Alex Schim-
melpfennig for generously sharing their data. We thank our anonymous referees and participants at seminars
at the IMF, Koc University, Lacea, Sabanci University and the University of Mainz, and Roland Beck in
particular, for helpful comments.
402 Public Choice (2009) 138: 387–408

Appendix

Table A.1 Countries included in

the sample Afghanistan Lithuania
Algeria Madagascar
Argentina Malawi
Bangladesh Malaysia
Bolivia Mali
Botswana Mauritius
Brazil Mexico
Burkina Faso Moldova
Burundi Morocco
Cameroon Niger
Chile Nigeria
Colombia Oman
Costa Rica P. N. Guinea
Cote d’Ivoire Pakistan
Cyprus Panama
Czech Rep. Paraguay
Dom. Rep. Peru
Ecuador Philippines
Egypt Poland
El Salvador PRC
Estonia Romania
Ethiopia Russia
Gabon S. Africa
Guatemala Senegal
Haiti Sierra Leone
Honduras Slovakia
Hungary Sri Lanka
India Sudan
Indonesia Thailand
Jamaica Trinidad-Tobago
Jordan Tunisia
Kazakhstan Turkey
Kenya Ukraine
Korea Uruguay
Latvia Venezuela
Lebanon Zambia
Lesotho Zimbabwe
Public Choice (2009) 138: 387–408 403

Table A.2 Summary statistics for democracies

Variable Definition Source Mean Median Max. Min. Std. dev.

BCRIS Systemic and Glick and 0.25 0.00 1.00 0.00 0.43
borderline banking Hutchison +
crises Caprio and
Klingebiel

CAGNP Current account/GNP MRS −3.54 −3.46 33.88 −39.49 5.94

CCRIS Currency crisis Own 0.09 0.00 1.00 0.00 0.28

calculations

CHECKS Checks and balances DPI 3.23 3.00 18.00 1.00 1.83

CORRUPT Control of corruption ICRG merged 2.61 3.00 6.00 0.00 1.08
with TI

DEBTLOCAL Default on local S&P, own 0.13 0.00 1.00 0.00 0.34
currency debt calculations

DEBTSP Default on foreign S&P 0.26 0.00 1.00 0.00 0.44

currency debt

DEMOCRACY Average of 2 indeces Gastil 2.93 3.00 4.50 1.00 1.03

DNONOIL Non-oil producers MRS 0.92 1.00 1.00 0.00 0.26

DSX Debt service/ GDF 21.27 18.67 181.60 0.02 15.19

exports (%)

GX Export growth (%) GDF 9.40 8.13 204.05 −34.53 16.91

INF Inflation (%) IFS 75.89 11.41 11749.63 −68.00 518.04

LIBOR Libor (%) MRS 9.44 7.63 17.06 5.38 3.50

M2YR M2/reserves IFS 9.90 4.15 348.01 0.00 24.50

OBY Overall fiscal MRS −4.43 −3.71 19.42 −48.78 5.67

balance/GDP (%)

ODTD Official debt/total GDF 46.26 44.05 99.93 0.00 26.82

debt (%)

OPEN (imports + exports)/ MRS 78.33 67.31 436.35 11.94 45.53

GDP (%)

PAREL Parliamentary MRS 0.25 0.00 1.00 0.00 0.43

elections

PCIII Political constraints Wharton U. 0.31 0.37 0.65 0.00 0.19

(polcon III)

PCV Political constraints Wharton U. 0.43 0.43 0.86 0.00 0.28

(polcon V)

PEACE No war Nils et al. 0.77 1.00 1.00 0.00 0.42

PEDY Public external MRS 40.72 32.08 177.82 1.39 31.52
debt (%)
404 Public Choice (2009) 138: 387–408

Table A.2 (Continued)

Variable Definition Source Mean Median Max. Min. Std. dev.

POLARIZ Polarization of DPI 0.43 0.00 2.00 0.00 0.77

government

PREL Presidential elections MRS 0.12 0.00 1.00 0.00 0.32

REVY Government MRS 22.9 21.3 76.1 7.1 10.2

revenue/GNP

RGRWT Real gdp growth MRS 3.33 4.09 67.7 −57.0 6.38

STABS Percent of veto DPI 0.16 0.00 1.00 0.00 0.32

players who leave
government

STATE Ranges from 0–2 DPI 0.8 1 2 0 0.8

depending on whether
Provincial executive
and/or legislature are
locally elected

STDR Short-term GDF 3.79 0.84 1176.6 0.008 39.16

debt/reserves

STTD Short-term GDF 15.29 12.24 83.36 0.09 11.81

debt/total debt (%)

SYSTEM2 Parliamentary system DPI 0.31 0.00 1.00 0.00 0.46

TDEG Total external GDF 57.54 47.84 249.28 0.49 39.59

debt/GDP (%)

TDEX Total external GDF 2.06 1.64 16.86 0.06 1.77

debt/exports (%)

TENLONG Longest tenure of DPI 6.84 4.00 46.00 1.00 6.93

a veto player

TENSHORT Shortest tenure of DPI 3.32 2.00 39.00 1.00 3.38

a veto player

UST 3-months US MRS 6.35 5.51 14.08 3.02 2.42

Treasury bill rate

YRCURNT2 Remaining terms of DPI 3.13 3 7 1 1.43

chief executive

YRSOFFC Years in office of DPI 5.28 3.00 46.00 1.00 6.54

chief executive

YSPAE Years since MRS 1.93 2.00 15.00 0.00 2.09

parliamentary
elections

YSPRE Years since MRS 2.16 2.00 15.00 0.00 2.29

presidential elections
Public Choice (2009) 138: 387–408 405

Table A.3 Summary statistics for non-democratic regimes

Variable Definition Source Mean Median Max. Min. Std. dev.

BCRIS Banking crisis Glick and 0.2 0 1 0 0.4

Hutchison

CAGNP Current account/GNP MRS −3.8 −3.6 19.5 −33.5 6.1

CCRIS Currency crisis Own 0.09 0 1 0 0.3

calculation

CHECKS Checks and balances DPI 1.4 1 8 1 0.9

CORRUPT Control of corruption ICRG 2. 2 5 −0.2 1.1

merged
with TI

DEBTLOCAL Default on local S&P, own 0.052 0 1 0 0.22

currency debt calculation

DEBTSP Default on external S&P 0.26 0 1 0 0.44

debt

DEMOCRACY Average of 2 indeces Gastil 5.5 5.5 7 4.5 0.7

DNONOIL Non-oil producers MRS 0.9 1 1 0 0.3

DSX Debt service/exports GDF 20.9 18.6 77.8 0.2 13.5

GX Export growth GDF 8.0 7.0 187.1 −60.1 20.2

INF Inflation IFS 28.7 10.3 1879.9 −14.9 121.1

LIBOR Libor MRS 10.4 10.6 17.1 5.4 3.5

OBY Overall fiscal (%) MRS −5.9 −4.9 12.8 −57.7 7.0
balance/GDP

ODTD Official debt/total GDF 54.0 54.8 100 4.5 25.1

debt (%)

OPEN (imports + exports) MRS 70.2 58.8 410.0 10.3 46.1

/GDP

PAREL Parliamentary MRS 0.10 0 1 0 0.30

elections

PCIII Political constraints DPI 0.05 0 0.58 0 0.13

(polcon III)

PCV Political constraints DPI 0.08 0 0.78 0 0.20

(polcon V)

PEACE No war Nils et al. (2002) 0.75 1 1 0 0.43

PEDY Public external MRS 47.1 37.2 266.1 0.9 34.2

debt (%)
406 Public Choice (2009) 138: 387–408

Table A.3 (Continued)

Variable Definition Source Mean Median Max. Min. Std. dev.

POLARIZ Polarization of MRS 0.036 0 2 0 0.25

government

PREL Presidential elections MRS 0.052 0 1 0 0.22

REVY Government revenue/GNP MRS 21.5 19.3 48.4 7.1 8.6

RGRWT Real gdp growth MRS 3.44 4.07 44.5 −42.2 6.70

STABS percent of veto players DPI 0.10 0 1 0 0.27

who leave government

STATE Ranges from 0–2 DPI 0.4 0 2 0 0.6

depending on whether
Provincial executive
and/or legislature are
locally elected

STDR Short-term debt/reserves GDF 21.0 1.0 2268.1 0.0004 128.9

STTD Short-term debt/total debt GDF 14.2 12.1 83.4 0.013 11.6

SYSTEM2 Parliamentary system DPI 0.083 0 1 0 0.28

TDEG Total external GDF 66.2 52.4 415.9 0.13 49.0

debt/GDP (%)

TDEX Total external GDF 3.1 2.1 36.5 0.006 3.9

debt/exports (%)

TENLONG Longest tenure of DPI 11.1 9 45 1 8.9

a veto player

TENSHORT Shortest tenure of DPI 7.9 5 37 1 7.5

a veto player

UST 3-month US Treasury MRS 6.9 5.8 14.1 3.0 2.7

bill rate

YRCURNT2 Remaining terms of DPI 3.1 3 7 1 1.5

chief executive

YRSOFFC Years in office DPI 10.6 8 45 1 9.0

of chief executive

YSPAE Years since MRS 4.0 3 22 0 4.1

parliamentary elections

YSPRE Years since presidential MRS 2.9 2 13 0 2.8

elections
Public Choice (2009) 138: 387–408 407

Table A.4 Averages by regime (democracies only)

Variable Mean Mean P-value

presidential parliamentary difference
regime regime = 0*

Entry into external default (probability) 0.07 0.02 0

External debt/GDP 52.5 40.4 0
Public external debt/GDP 37.4 26.6 0
Debt service/exports 20.9 16.1 0
Short-term debt/reserves 1.7 0.9 0
Real GDP growth 3.1 4.3 0
Control of Corruption (ICRG index) 2.5 3.0 0
Current Acount/GDP −3.7 −2.7 0.05
Currency Crisis (probability) 0.08 0.04 0.02
Checks and balances (DPI index) 2.6 4.3 0
Openness (imports + exports)/GDP) 72.0 95.3 0
Absence of Democracy (Gastil index) 3.2 2.7 0
War (dummy variable) 0.11 0.04 0
Institutional Investor Rating (score) 36.9 42.4 0
GDP per capita (95USD, PPP) 4694 4171 0.02
Fiscal balance/GDP −4.6 −4.1 0.36
Official debt/total debt 47.2 45.3 0.34

* Difference in means-test allows for different variances in the two groups

Variable definitions are given in Table A.3

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