Dynamics of external debts among heavily indebted poor countries (HIPCs): a panel data approach.
Anoruo, Emmanuel ; Dimkpah, Young ; Ahmad, Yusuf 等
ABSTRACT
This study uses panel data for 29 Heavily Indebted Poor Countries (HIPCs) from 1984 to 2000 to examine the dynamic relationships between
growth of external debts with other determinant variables (exchange
rate, interest payment on debt, and non-interest current account
balance) and control variables such as governance indicators. The fixed-
and random-effect models were used to investigate these relationships.
First, the results show that high interest payments have adverse effects
on the growth of external debts. Second, real exchange rates have
positive influence on growth of external debts. Third, corruption is
found to distort economic growth and reduces the efficiency of the
public sector. Finally, stability index contributes negatively to the
growth of external debts. Therefore, given these dynamic relationships;
this study suggests that there are strong correlation between growth of
external debts and exchange rate policy, interest payments and some
governance indicators. This evidence may partially explain the explosive
external debt position of the HIPCs.
INTRODUCTION
The world witnessed unprecedented explosion in public debt
throughout the 1980s and the 1990s in developing countries, especially
those of the Heavily Indebted Poor Countries (HIPCs). Most of the public
debt holdings of developing countries are external debts. In most of
these countries the share of debt in gross domestic product grew over
time (see Figures 1 and 2). In fact, most of these countries face
external debt that is more than two times the size of their gross
domestic product. Due to scarce foreign exchange in most of these
countries, efforts to service the debt consumed large shares of their
government revenues. As the debt burden increases, these countries must
allocate a greater portion of their revenues to service external debts,
resulting in higher taxes, more borrowing, and eventually debt default.
The severe difficulties that most HIPCs faced in servicing their
external debts resulted in the persistent accumulation of arrears, which
are unpaid debt service obligations. Despite several repeated attempts
at rescheduling, many HIPCs have not been able to meet their debt
service obligations fully and on time for several years.
The worldwide economic growth slowdown has resulted in an increase
in the level of debt burden, especially in the 1980s and 1990s
(Easterly, 2001). This is partly because these countries have low
incomes and their economies tend to grow slower than those of the higher
income countries. Slower economic growth poses problems in expanding
exports and delays progress in debt restructuring. This, in turn,
impedes flows of capital to HIPCs. The massive external debts of these
countries have reduced the inflows of foreign direct investment,
employment, and growth of their economies and therefore have become a
stumbling block to sustainable development.
In light of the above, this paper attempts to address the primary
questions including: What are the factors behind the growth of external
debts of the HIPCs? What are the consequences of massive external debts
for these countries? What are the correlating effects within the factors
themselves, so as to determine future patterns?
LITERATURE REVIEW
Over the years, the issue of public debt has occupied primary
importance in both local and international arenas. Claessen, et al
(1997) argued that HIPCs are characterized not only by high debt
relative to income, but also by relatively poor economic performance.
The reason is the combination effect of the large inflows of
concessional finance despite the emerging debt burden and low growth
rates of output and exports. In addition, the poor economic performance
in these countries could be attributed to adverse terms of trade development, civil and political unrest, weak macroeconomic management,
and inefficient allocation of resources.
The most recent article by Easterly (2001) confirmed that the
slowing down of economic growth in the past decades since 1975 can be
attributed to the increases in the burden of public debts in most middle
income countries and HIPCs. In the case of HIPCs, Easterly (2001) found
that the public debt burden was worse than other lower income countries
due to their slow economic growth after 1975 compared to their
counterparts as a result of their weak policies. Elbadawi, Ndulu, and
Ndungu (1997) found that while current debt inflows enhanced economic
growth, past debt accumulation, which was viewed as a proxy for debt
overhang had a negative impact on economic growth. They argued that if
the accumulation of past external debts reached a certain critical
level, it would actually discourage investment and retard economic
growth. The authors also confirmed that the liquidity constraints caused
by rising external debt servicing payments reduced exports and thus
hampered economic growth. Ajayi (1997) analyzed the correlation between
external debt and capital flight in the HIPCs and found that the
building up of excessive external debts would encourage capital flight.
Capital flight, he argued, resulted in increased demand for external
debts to fill the domestic investment needs. In addition, capital flight
reduces growth, erodes the tax base, worsens income distribution, and
reduces debt-servicing capacity by diverting domestic savings from
investment.
de Larosiere (1984) found that the growth of external debt in most
developing countries are attributable to their fiscal imbalances. In
order to finance the deficit, these countries needed to increase taxes
while at the same time reduce the non-interest expenditures. The two
policies, however, are uncommon for these countries both because they
are politically difficult to implement.
MODEL AND METHODOLOGY
In order to determine the factors that cause the growth of external
debts, consider the following expression:
GEDBT = [alpha] + [beta]1NICA + [beta]2IPED + [beta]3RER + [mu] (1)
where,
GEDBT = growth of external debt to GDP
NICA = non-interest current accounts
IPED = interest payments on external debts
RER = real exchange rates
[mu] = the error term
In equation (1) the growth of external debts is regressed on
non-interest current accounts, interest payments on external debts,
exchange rates. The expected signs of the explanatory variables in
relation to their effects on growth of external debts are presented in
Table 1.
Control Variables
In addition to corruption index, this study uses governance
indicators such as, internal conflict index, government instability
index, and bureaucracy quality index as control variables. These control
variables are included in this study because they are likely to affect
the growth of external debts. For example, corruption has implications
for the composition of government expenditures. Corrupt government
officials try to channel public funds to finance their personal
ventures. Such diversion of resources public funds to personal use
negates economic growth. The coefficients of corruption index and
bureaucracy quality is expected to be positive, while the coefficients
of internal conflict and government stability are predicted to be
negative. Equation (1) can be rewritten to include the governance
indicators as follows:
GEDBT = [alpha] + [beta]1NICA + [beta]2IPED + [beta]3RER +
[beta]4COR + [mu] (2)
GEDBT = [alpha] + [beta]1NICA + [beta]2IPED + [beta]3RER +
[beta]4COR + [beta]5GI + [mu] (2a)
where,
GI = Governance indicators
COR = Corruption index
In equation (2) the growth of external debts is regressed on
non-interest current accounts, interest payments on external debts,
exchange rates and corruption index. In equation (2a) growth in external
debts is regressed on non-interest current accounts, interest payments
on external debts, real exchange rate, corruption index, and governance
indicators. The expected effects of these variables on the growth of
external debts are as follows:
Equations (1) through (2a) are estimated via the fixed- and
random-effect. Fixed-effects model is used in order to allow the
countries to have different intercepts that may be correlated with the
regressors. The models are based on the following equation:
Yit = [chi]'it [gamma]it + [mu]it] (3)
where Y represents the dependent variable (Growth of External
Debt), [chi]' is a vector of explanatory variables, i stands for
the countries in the sample (i= 1, 2, 3, 4,...., 29), t is the period
under investigation (t = 1984, 1985, 1986, 1987, 1988, 1989, 1990,
1991,...... 2000) and [[mu].sub.it] is the error term. From equation (3)
we derive the fixed effects model in terms of the notations used in the
study as follows:
GEDBTit = [[beta].sub.1]NICASit + [[beta].sub.2]IPEDit +
[[beta].sub.3][RER.sub.it] + [[beta].sub.4][GI.sub.it] + [alpha]i +
[delta]i + [mu]it (4)
where GEDBT represents growth of external debt, NICA stands for
non-interest current account balance, IPED, RER, and GI as explained,
while [mu] is the error term. In equation (4), [[alpha].sub.i] captures
unobserved country-specific effects assumed fixed over time. The
year-effects represented by [[delta].sub.i] are included to account for
shocks that are common to all countries in the sample, such as rapid
population, slow economic growth, and imperfect capital markets.
From equation (3), we again generate the random effects model as
follows:
GEDBTit = [[beta].sub.1]NICAit [gamma]i +
[[beta].sub.2][IPED.sub.it][gamma]I] +
[[beta].sub.3][RER.sub.it][gamma]i + [[beta].sub.4][GI.sub.it][gamma]i +
[delta]i + [mu]it, [gamma]I = [bar.[gamma]] + [??]i (5)
where [mu] is the error term, [??]i stands for random country
effect while [bar.[gamma]] represents the mean of the coefficient
vector. Under the random effects model, the slope coefficients are
allowed to vary randomly across countries
Most of the previous country-studies applied the standard OLS procedure to examine the determinant of the growth of external public
debts. These studies assumed that the omitted variables are independent
of the explanatory variables and are independently, identically
distributed. This assumption however leads to biased inferences
especially when country-specific features such as policy changes. Hsiao
(1986) points out that the OLS procedure yields biased and inconsistent
estimates when the omitted country-specific variables are correlated
with the explanatory variables.
The panel data approach provides avenues through which the
country-specific characteristics (whether observed or unobserved) can be
incorporated into cross-country studies to avoid biases resulting from
the omission of relevant variables. The fixed-effect procedure yields
unbiased and consistent estimates when the omitted country-specific
variables are correlated with the explanatory variables. One of the
shortcomings of the fixed-effects framework is that it assumes that
differences across countries represent shift in the regression equation.
This assumption implies that the fixed-effects model is appropriate when
the entire population rather than the sample is investigated. However
the random-effects model is applied when a sample rather the population
is considered. The random-effects model is not without flaws. It yields
biased regression estimates if the omitted country-specific variables
are correlated with the explanatory variables. This study considers both
the fixed-effect and random-effect procedures given the weaknesses
associated with each of the models. Furthermore, our sample (25
countries) is large enough to warrant the application of both
approaches.
DATA AND EMPIRICAL RESULTS
The data on external debts, non-interest current account, and
interest payments on external debts were taken from the Global
Development Finance published by the World Bank. The governance
indicators were obtained from the International Country Risk Guide. The
exchange rate data were obtained from the International Financial
Statistics 2003 CD Rom version published by International Monetary Fund
(IMF). The list of HIPCs consists of 29 countries including Angola,
Burkina Faso, Cameroon, Congo, Democratic Republic, Congo Republic, Cote
D Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Guyana, Honduras, Kenya,
Liberia, Madagascar, Malawi, Mali, Mozambique, Nicaragua, Niger,
Senegal, Sierra Leone, Sudan, Tanzania, Togo, Uganda, Vietnam, and
Zambia. The range of the study is from 1984 to 2000.
Table 2 displays the summary statistics for bureaucracy quality
index (BQI), corruption index (COR), growth of external debt (GEDBT),
real exchange rate (RER), government stability index GSI), internal
conflict index (ICI), interest payment ratio of GDP (IPED), and current
account deficits (NICA). The mean values for BQI, COR, GEDBT, RER, GSI,
ICI, IPED, and NICA are 1.16, 2.58, 144.73, 1201.13, 6.22, 6.68, 0.10,
and -0.32, respectively. The maximum and minimum values show
cross-country variability among the variables used in the study. The
standard deviations indicate that exchange rates fluctuated the most for
the period under investigation.
Table 3 presents the bivariate correlations between growth of
external debts, exchange rates, interest payments on external debts,
non-interest current account, and governance indicators. From Table 3,
it can be seen that growth of external debts are negatively correlated
with non-interest current account, interest payments, corruption index,
internal conflict index, government stability index, and bureaucracy
quality index. However, growth of external debts and real exchange are
positively correlated. Furthermore, the results show that non-interest
current account, interest payments, corruption index, internal conflict
index, government stability index, and bureaucracy index are positively
correlated with each other.
Table 4 displays the results from the fixed and random effect
models in conjunction with the associated test statistics. The
explanatory variables are exchange rates, non-interest current accounts,
interest payments on external debts, several governance indicators, and
corruption index. All the t-statistics are given in parenthesis. The
results from the fixed and random effect models for without governance
indicators and corruption index are presented in column A of Table 4.
The results from both the fixed and random effect models reveal that
interest payments have significantly negative effect on the growth of
external debts. Exchange rates are found to have significant influence
on growth of external debts. Column B of Table 4 presents the results
from both the fixed and random effect models with corruption index
included as an additional explanatory variable. Again, the results
reveal that interest payments have negative influence on growth of
external debts. Exchange rates are found to affect growth of external
debts positively. Interestingly, the results reveal that corruption
engenders growth of external debts for the sample countries for the time
period under investigation. The regression coefficient on corruption
index is statistically significant at the 10 percent level. The
regression coefficients on exchange rates and interest payments on
external debts in Columns A and B are statistically significant at the 1
and 5 percent levels, respectively. The exchange rates and interest
payments on external debts have the expected signs. The magnitudes of
all significant variables are reasonable. The finding that interest
payments have negative influence on growth of external debts suggests
that high interest payments discourage foreign loans. However, it is
important to point out that contrary to the conventional wisdom, some of
the HIPCs have been able to increase their external debts through debt
rescheduling and restructuring, irrespective of the level of interest
payments.
The results from the fixed- and random-effect models with all
governance indicators are presented in Column C of Table 4. The results
from both procedures indicate that interest payments have significant
adverse effects on the growth of external debts at the 5 percent
significance level. However, non-interest current account deficits have
no significant effects on growth of external debt for 29 HIPCs from 1984
to 2000. Interest payments, exchange rates and government stability
index have significant effects on growth of external debts. Government
stability index contributed negatively to the growth of external debt
and is statistically significant at the 10 percent level. This result
indicates that instability is associated with external debt problems. In
other words, the less stable a country is, the more it encounters debt
problems.
What is striking about these results is that when only corruption
index was included in the model, it turned out the corruption played a
positive role in determining the growth of external debts. It is
important to point out that our prediction relative to effect of
corruption on the growth of external debts was confirmed since the
regression coefficient on corruption index has the expected sign. It is
logical to say that corruption distorts economic growth and reduces the
efficiency of government. Inefficiency in the public sector spurred by
corruption leads to increase in demand for foreign loans at higher
interest rates. In this particular study, we can conclude that as a
country experiences less and less corruption, fewer funds are needed to
supplement the loss of money as result of corruptions. On the contrary,
one can also argue that activities associated with corruption can result
in lack of trust in the eyes of international community. Lack of trust
will reduce the inflow of foreign capital. Therefore, the less corrupt a
country is, the easier it is for it to obtain external funds. The
acquisition of additional foreign loans will increase the country's
external debts to GDP ratio. Both arguments are quite valid depending on
the credibility of the particular country under study.
It is interesting to note that the contributions of interest
payment and exchange rate variables and signs remained relatively the
same for with and without governance indicators as can be seen in
Columns A through C of Table 4. Only non-interest current accounts
balance really did not contribute to the growth of external debts. The
regression sign on non-interest current accounts did not change in all
three estimations and it is statistically insignificant in all of the
cases. We have to be mindful of the fact that there has been an upward
trend in the current account deficits for the HIPCs as a result of
fluctuations in commodity prices. Fluctuations in commodity prices
adversely affect the extent to which the HIPCs depend on foreign
capital. However, given that the regression coefficient on non-interest
current accounts is statistical insignificant in all cases, its
contribution to the growth of external debts can be described at best,
as marginal.
The regression coefficients on exchange rates and interest payments
on external debts turned out as expected and statistically significant
in all of the cases. This relationship is quite robust and obvious since
most of the HIPCs followed aggressive exchange rate policies, which led
to increases in devaluation of local currencies in terms of purchasing
power parity. Inefficient exchange rate policies pursued by most of the
HIPCs caused the ratio of debt to GDP to increase as a result of capital
loss.
The results from the fixed- and random-effect models with all
governance indicators are presented in Column C of Table 4. The results
reveal that most of the governance indicators have insignificant effects
on growth of external debts. In short, only government stability index
significantly contributed to the growth of external debts. As expected,
the regression coefficient on government stability index is negative.
This result implies that the more stable a country is, the less it
relies on foreign loans. Interestingly, all of the regression
coefficients on the governance indicators exhibited the expected signs.
Internal conflict and bureaucracy quality indexes appear not to have
implications for growth of external debts, as they are statistically
insignificant at the conventional levels. However, it is important to
point out that most of the countries in this classification (HIPCs) are
plagued with internal conflicts. Most of these countries divert
substantial resources and political attention from economic, financial,
and social programs to internal conflicts. The reallocation of resources in favor of internal conflicts has a negative effect on economic
development, as important parts of the productive infrastructure are
either neglected or destroyed due to internal strife.
CONCLUSIONS AND POLICY RECOMMENDATIONS
High external debts can erode confidence in economic reforms and
thus diminish the sustainability of what might be an otherwise sound
economic reform strategy. Massive external debts can have indirect
negative consequences on governments in terms of public support insofar as debts are perceived to contribute to poor growth and poor policies.
This paper attempted to ascertain the determinants of the growth of
external debts. The study uses panel data for 29 HIPCs from 1984 to
2000. The fixed- and random-effect models were used to investigate the
relationships between growth of external debts, exchange rate, interest
payments on external debts, and non-interest current account balance.
The governance indicators including internal conflict index, government
instability index, and bureaucracy quality index were used as control
variables. The main results of the paper are interesting and intuitive.
Interest payments on debts and real exchange rates have significant
effects on growth of external debts. Exchange rates exhibited the lowest
regression coefficient but has the highest significant level amongst all
explanatory variables. Surprisingly, non-interest current account
balance, which was expected to be significant along with interest
payments and exchange rates, proved otherwise. In terms of control
variables, only one (i.e. government stability index) out of the four
governance indicators has implications for growth of external debts.
In all, the results suggest that there are strong correlation
between growth of external debts to GDP ratio, exchange rates, interest
payments on external debts and some of the governance indicators
namely--corruption and government stability indexes. The results have
important implications for the HIPCs, especially as they struggle to map
out strategies to curtail their reliance on foreign capital and to avoid
further debt-overhangs. The HIPCs should formulate strategies that will
enable them to curtail their external debt burdens. The results of this
study show that the increases in foreign debt burdens can be attributed
to high debt service costs. The inability of the HIPCs to curb their
external debt burdens can be blamed on exchange rate misalignments,
rather than, the size of their current account deficits. The aggressive
exchange rate policies pursued by the HIPCs actually weakened their
currencies in terms of purchasing power parity. The implied capital loss
due to worsening purchasing power parity exacerbates the external debt
burdens of these countries. The HIPCs should confront the issue
pertaining to corruption in their economies. They should develop
policies designed to curtail wide spread corruptions in these countries.
Above all, stable institutions and governance measures should be
strongly encouraged, as these will enable the HIPCs to alleviate their
external debt burdens.
It should be noted, however, that the economic and political
situations of these countries make them a non-typical sub-sample.
Further research is therefore necessary to provide a more definitive
assessment of the relationship between growth of external debts and some
of the variables used in this study.
ACKNOWLEDGMENTS
The authors wish to express their gratitude towards two anonymous
referees of this journal and the participants at the Eastern Economic
Association conference (Washington D.C.) 2004, for their valuable
comments and suggestions. The usual disclaimers apply.
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Emmanuel Anoruo, Coppin State University Young Dimkpah, Virginia
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Table 1: Expected Signs of the Explanatory Variables
BQI COR RER GSI ICI IPED NICA
Equation (1) + - -
Equation (2) + + - -
Equation (2a) - + + - + - -
BQI = Bureaucracy quality index, COR = Corruption index, GEDBT
= Growth of external debts/GDP, RER = Real exchange rates, GSI
= Government stability index, ICI = Internal conflict index,
IP = Interest payments on external debts/GDP, and NICA = Non-interest
current account balance.
Table 2: Summary Statistics
BQI COR GEDBT RER
Mean 1.16 2.58 144.73 1201.13
Median 1.00 3.00 113.00 364.84
Maximum 3.00 5.00 1064.00 22332.50
Minimum 0.00 0.00 0.00 0.00
Std. Dev. 0.89 1.13 115.48 3244.07
Skewness 0.50 -0.38 3.26 4.30
Kurtosis 2.59 2.87 19.23 21.62
Jarque-Bera 23.54 11.98 6285.07 8638.11
Probability 0.00 0.00 0.00 0.00
Observations 493.00 493.00 493.00 493.00
GSI ICI IPED NICA
Mean 6.22 6.68 0.10 -0.32
Median 6.00 7.00 0.05 -0.23
Maximum 11.00 12.00 0.79 3.27
Minimum 1.00 0.00 0.00 -2.59
Std. Dev. 2.44 2.57 0.13 0.40
Skewness 0.31 -0.22 2.70 -0.11
Kurtosis 2.41 2.59 11.91 20.28
Jarque-Bera 15.08 7.46 2229.43 6137.60
Probability 0.00 0.02 0.00 0.00
Observations 49300.00 493.00 493.00 493.00
BQI = Bureaucracy quality index, COR = Corruption index,
GEDBT = Growth of external debts/GDP, RER = Real exchange
rates, GSI = Government stability index, ICI = Internal
conflict index, IP = Interest payments on external debts/GDP, and
NICA = Non-interest current account balance
Table 3: Correlation Matrix
BQI COR GEDBT RER
BQI 1.00
COR 0.33 1.00
GED BT -0.07 0.13 1.00
RER 0.08 0.15 0.05 1.00
GSI 0.15 0.09 -0.12 0.20
ICI 0.27 0.20 -0.10 0.25
IPED 0.38 0.06 -0.08 0.06
NICA -0.01 -0.03 -0.07 -0.17
GSI ICI IPED NICA
BQI
COR
GED BT
RER
GSI 1.00
ICI 0.37 1.00
IPED 0.07 0.27 1.00
NICA -0.03 0.06 -0.09 1.00
BQI = Bureaucracy quality index, COR = Corruption index,
GEDBT = Growth of external debts/GDP, RER = Real exchange
rates, GSI = Government stability index, ICI = Internal
conflict index, IP = Interest payments on external debts/GDP,
and NICA = Non-interest current account balance
Table 4: Dependent Variable: Growth of External Debts
Without Governance
Indicators
Independent Variables Fixed Random
Effects Effects
Constant 149.85 *** 148.81 ***
(19.66) (7.67)
Interest Payments on -139.17 ** -127.56 **
External Debts
(2.29) (2.25)
Exchange Rates 0.01 *** 0.01 ***
(4.30) (4.16)
Non-Interest Current -3.07 -3.96
Account Balance
(0.31) (0.40)
Internal Conflict Index -- --
Corruption Index -- --
Government Stability Index -- --
Bureaucracy Quality Index -- --
Adjusted R-Square 0.043 0.043
Number of Observations 493 493
With Corruption only
Independent Variables Fixed Random
Effects Effects
Constant 128.00 *** 126.57 ***
(9.09) (5.61)
Interest Payments on -132.60 ** -122.30 **
External Debts
(2.18) (2.16)
Exchange Rates 0.01 *** 0.01 ***
(4.30) (4.15)
Non-Interest Current -2.83 -3.66
Account Balance
(0.29) (0.38)
Internal Conflict Index -- --
Corruption Index 8.24 * 8.48 *
(1.84) (1.94)
Government Stability Index -- --
(2.61) (2.62)
Bureaucracy Quality Index -- --
Adjusted R-Square 0.050 0.050
Number of Observations 493 493
With All Governance Indicators
Independent Variables Fixed Random
Effects Effects
Constant 171.37 *** 168.99 ***
(9.02) (6.51)
Interest Payments on -146.53 ** -130.93 **
External Debts
(2.18) (2.10)
Exchange Rates 0.01 *** 0.01 ***
(5.02) (4.87)
Non-Interest Current -3.37 -4.16
Account Balance
(0.34) (0.43)
Internal Conflict Index -2.51 -2.52
(1.29) (1.32)
Corruption Index 5.22 * 5.82 *
(1.90) (1.98)
Government Stability Index -4.02 ** -4.00 **
Bureaucracy Quality Index 4.98 4.08
(0.88) (0.73)
Adjusted R-Square 0.08 0.08
Number of Observations 493 493
Note: Absolute value of robust t -statistics are in parentheses;
* significant at 10%; ** significant at 5%; and *** significant at 1%