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January 2016
The Impact of External Debt on Economic Growth:
Empirical Evidence from Highly Indebted Poor Countries*
by
Abu Siddique, Business School, The University of Western AustraliaE A Selvanathan, Griffith Business School, Griffith University, and Saroja Selvanathan**, Griffith Business School, Griffith University
Abstract During the 1970’s and 1980’s, the external debt levels of poor countries rose to a level constituting a ‘debt crisis.’ The main source of external debt was the surplus revenue generated by significant increases in the price of oil during the 1970s. Unfortunately, many of the countries failed to use the external debt wisely and prudently. When the revenue from oil sales started to decline due to low oil prices during the 1980s, heavily indebted poor countries (HIPCs) experienced difficulty servicing the debt. Using HIPCs data, this paper analyses the extent to which the external debt burden impacts on a country’s GDP.
Key words: External debt, Economic growth, highly indebted poor countries, Debt relief.
JEL Codes: O10, F34, H12, H63, O47, O55
* The authors would like to acknowledge and thank the four anonymous reviewers and the editor of this journal for their constructive comments on an earlier version of the paper.
** Corresponding author: Professor Saroja Selvanathan, Economics and Business Statistics Discipline, Griffith Business School, Griffith University, Nathan campus, 170 Kessels Road, Nathan, Queensland, 4111, Australia; email: s.selvanathan@griffith.edu.au.
The Impact of External Debt on Economic Growth:
Empirical Evidence from Highly Indebted Poor Countries
1. Introduction
External debt is an important source of finance mainly used to supplement the domestic sources
of funds for supporting development and other needs of a country. Usually, external debt is
incurred by a country which suffers from shortages of domestic savings and foreign exchange
needed to achieve its developmental and other national objectives. However, if the external debt
is not used in income-generating and productive activities, the ability of a debtor nation to repay
the debt is significantly reduced. It is often argued that this excessive debt constitutes an obstacle
to sustainable economic growth and poverty reduction (Maghyereh and Hashemite, 2003; and
Berensmann, 2004). Over the 1970’s and 1980’s, the external debt levels of ‘highly indebted
poor countries’ (HIPC)1 rose to a level constituting a ‘debt crisis.’ The bulk of this debt was
made up of public and publicly guaranteed debt (PPG). The main source of this supply of
external debt was the emergence of the Eurodollar market resulting from the surplus revenue
generated by the Organisation of Petroleum Exporting Countries (OPEC) through significant
increases in the price of oil between 1973 and 1979. Cheap ‘petrodollars’ were recycled to the
countries which needed external debt. Unfortunately, many of the countries failed to use the
external debt wisely and prudently. A number of interrelated factors contributed to the rise in
external debt including macroeconomic policy, increases in the price of a number of primary
commodities encouraging countries to borrow, low real interest rates and a favourable world
environment. Unfortunately, the favourable conditions were short-lived and, when they did
change over the 1980’s, heavily indebted countries experienced difficulty in servicing the debt
(for details, see Barro, 1989; Altvater, 1991; Abbott, 1993; Barro and Lee, 1994; Siddique, 1996;
Abrego and Ross, 2001; and Clements et al, 2003).
1 HIPC countries have 3 main common characteristics: (a) they incurred heavy debt mainly in the 1970s but the symptoms of HIPCs emerged in 1980s; (b) debt ratio of these countries is much higher than other low income or developing countries; and (c) they are poor countries with a lower economic growth (Birdsall et al., 2002).
1
In 1996, the International Monetary Fund (IMF) launched the HIPC initiative in an
attempt to reduce the external debt burden of low-income countries to sustainable levels in a
reasonably short period of time2. The HIPC initiative has generated a lot of attention and has
been hailed by many as a significant event, promising economic opportunities for debt ridden
poor countries. How much benefit did this ‘initiative’ bring for the HIPC? Literature examining
the relationship between reductions in external debt and economic growth gives mixed signals to
the policy makers in both the developed and the developing countries. It suggests that while debt
can have a positive short term economic growth impact up to a certain ‘threshold’ point (useful
for smoothing out business cycle fluctuations in unemployment and inflation) and it can be a
valuable source of finance for development/ investment in HIPCs that otherwise could not self-
finance, debt can also have a negative economic growth impact in the long term and beyond the
‘threshold’ level. Consequently, for HIPCs which have passed this ‘threshold’, the best way to
ensure the effectiveness of appropriate lending and economic reforms is to combine them with
debt relief.
Several research papers that analyses the impact of external debt on the economic well-
being of either a single or a group of countries have been published, starting from the early
1980’s (for example, see Adams et.al, 1983; Kwack, 1983; Filatove and MaHiona, 1985) and to-
date (for example, see Gomez-Puig and Sosvilla-Rivero, 2015; Hallett and Oliva, 2015; and
Timo and Florian, 2015). The main objective of this paper is to examine the influence of a
change in external debt on economic growth in the HIPC countries over the period 1970 to 2007,
using recent developments in time series and cross-sectional analysis. Standard growth
accounting process done by decomposing the sources of economic growth will be employed for
2 In order to qualify for HIPC Initiative assistance, a country must meet the following four conditions: “1. Be eligible to borrow from the World Bank’s International Development Agency, which provides interest-free loans and grants to the world’s poorest countries, and from the IMF’s Poverty Reduction and Growth Trust, which provides loans to low-income countries at subsidized rates; 2. Face an unsustainable debt burden that cannot be addressed through traditional debt relief mechanisms;3. Have established a track record of reform and sound policies through IMF- and World Bank-supported programs; and4. Have developed a Poverty Reduction Strategy Paper (PRSP) through a broad-based participatory process in the country.” (IMF, 2014).
2
this purpose. In addition, the paper will analyse the extent to which the external debt held by
heavily indebted poor countries has impacted on their economic growth.
2. The Relationship between External Debt and Economic Growth: A Brief Review
In this section, we briefly review the literature examining the impact of external debt on
economic growth based on a number of selected studies.
Using data covering 1969-1998, Pattillo et al. (2002) analyse the effect that debt burden
has on 93 developing economies. They have included, in their analysis, a set of variables
common in the growth literature, namely that of trade openness, schooling, population and
government budget. In summary, they found that for a country with average indebtedness, a
doubling of the debt ratio would reduce annual per capita growth by between a half and a full
percentage point. Furthermore, they found that the average impact of debt only becomes negative
at debt ratios above 160-170 percent of exports or 35-40 percent of Gross Domestic Product
(GDP), and that the marginal impact of debt only starts becoming negative at about half of these
levels.
Clements et al. (2003) seek to analyse the channels through which external debt impacts
upon economic growth in low-income countries. They estimated a reduced form growth equation
for 55 low-income countries using data from 1970 to 1999. Their results support the debt
overhang hypothesis (which is?) and they estimated a threshold level of debt to exports of 100-
105 percent; and a threshold level of debt to GDP of 20-25 percent.
Contrary to the above findings, Jayaraman and Lau (2009) found that higher debt levels
can promote higher economic growth. Their study involves six Pacific island countries, covering
the period 1988-2004, and is based on regressing external debt stock, exports and the budget
deficit (all as a percentage of GDP) against GDP. They found that a 1 percent increase in the
external debt stock leads to a 0.25 percent increase in national output. They also tested for
causality and found that, whilst there is no Granger causality relationship between real gross
3
domestic product (RGDP) and external debt in the long-run, there is a significant causal
relationship running from external debt to GDP in the short-run.
Hameed et al. (2008) analysed the relationship between external debt and economic
growth in Pakistan, using data for 1970-2003. They identified a long-run relationship between
the external debt and economic growth; and concluded that debt service affects GDP negatively,
most likely through its adverse impacts on capital and labour productivity. They also found
short-run and long-run negative causality that runs from debt service to GDP.
Adegbite et al. (2008) investigated the impact that Nigeria’s huge external debt stock had
on its economic growth between 1975 and 2005. They used a ‘Solow-type’ neoclassical growth
model to regress the ratio of external debt to GDP (along with several other macroeconomic and
external sector exogenous variables) against the annual GDP growth rate. Their investigation of
the debt overhang theory for Nigeria revealed that external debt contributes positively to growth
up to a certain point, after which its contribution becomes negative. They also investigated the
‘crowding out’ effect of debt servicing by regressing debt service requirements against private
investment and found that Nigeria’s large debt burden did indeed ‘crowd out’ private investment.
Fonchamnyo (2009) studied the effect of economic and social performance in 60 low-
income countries to assess the relative effectiveness of the HIPC Initiative. He divided the 60
low-income countries into four groups, based on their 2005 HIPC status; non HIPCs; pre-
decision point; decision point; and completion point HIPCs. His hypothesis was that these
countries included in the HIPC Initiative will show better improvement in economic and social
development than those countries not included. From the estimation results of an investment
function and an economic growth function, the study concludes that investment and growth have
improved in HIPCs since the institution of the HIPC Initiative; and, there is also evidence that
health care and education enrolment experienced some improvement in countries that had
reached the completion point of the HIPC Initiative. (Not shown)
Fosu (1999) studied the effect of external debt on the growth of 35 countries in sub-
Saharan Africa using World Bank data for 1980-1990. By regressing GDP growth on the growth
4
rates of labour, capital, exports, and external debt, Fosu (1999) found that net outstanding debt
has a negative effect on economic growth (for given levels of production inputs). Furthermore,
he found that growth across these sub-Saharan African nations would have been 50 percent
higher during the period of study in the absence of the debt burden; and little evidence of a
negative correlation between external debt and investment levels.
Faini and de Melo (1990) assess the success of adjustment packages to developing
countries supported by loans from the World Bank and IMF; focusing on a series of
microeconomic reforms to assure supply-side improvement whilst simultaneously pursuing sharp
real exchange rate depreciation. The authors found that high external debt burdens, in
conjunction with macroeconomic instability, impede investment in developing countries. They
argue that, for such adjustment packages to result in the levels of investment necessary for the
packages to succeed, appropriate relief of external debt is required.
Froot (1989) compared different market-based debt reduction schemes, and argued that
the optimal approach to debt relief is a package that is part debt forgiveness, and part new
lending. In particular, Froot (1989) found that debtor nations that finance buybacks using current
resources can impede incentives for new investment, and can, therefore, prolong the debt relief
process.
Fry (1989) examined the effect of foreign debt accumulation on the balance of the current
account, using data from 28 countries identified to be heavily indebted to the World Bank, in
1986. He argued that, as long as an increase in foreign debt increases investment by less than it
increases saving or reduces investment by more than it reduces saving, then the current account
will enter a state of equilibrium with a maintainable ratio of foreign debt to gross national
product. In particular, Fry identified public and publically guaranteed debt as reducing saving by
more than it reduces investment, hence worsening the current account deficit over time.
Siddique (1996) analysed the external debt problem facing 32 sub-Saharan African
nations over the period 1971 to 1990, and found the following three major interdependent factors
contributing to the accumulation of external debt, namely, the trade policy, the macroeconomic
5
policy and the external and global shocks. The study also identified cases in which
macroeconomic policy designed to achieve high economic growth enhanced the ability of debt-
stricken low-income countries to meet their debt obligations.
3. Emergence of the Debt Problem
3.1 Factors Contributing to the Emergence of the HIPC Countries
The emergence of unsustainable debt in the HIPC countries can be analysed from both the
demand and supply side. From the demand side, the group of countries which are now classified
as HIPCs needed external debt to meet their development and other needs. Most of these
countries were poor with relatively lower economic growth and lower per capita income. Hence,
national rates of savings were also very low with domestic savings being insufficient to finance
their developmental and national goals. Moreover, as most of these countries were dependent on
the exports of primary commodities, their export earnings were not enough to finance import
bills as they mostly imported capital intensive goods which were relatively more expensive to
the earnings from primary products. Hence, there arose the need for external borrowing.
Fortunately (or, unfortunately) due to the significant increase in the price of oil between
1973 and 1979, the foreign reserves of oil exporting countries dramatically increased with most
of these reserves deposited mainly with European banks. Thus, there arose a market for external
debt or borrowing from overseas sources.
During the 1970s, most of the governments of developing and poor countries heavily
borrowed money primarily to finance their industrial and infrastructure development. There was
a popular belief amongst many of the developing nations that their countries’ economic success
depended on industrial development which needed protection from overseas competition during
the initial stages of development. Thus, industrial development was pursued with the aid of the
import substitution industrialisation strategy. Unfortunately, many of these countries, especially
the sub-Saharan African countries failed to invest their borrowed funds in income generating
activities and, hence, failed to enhance their ability to repay their accumulated debt.
6
Based on the results reported in Siddique (1996), which analysed the external debt
problems facing 32 sub-Saharan African nations over the period 1971 to 1990, the following
three major interdependent factors were found to be contributing to the accumulation of external
debt.
The first is the trade policy. It is argued that governments seeking to address burgeoning
external debt should pursue trade policies that would result in significantly large export earnings
to meet additional debt obligations or to reduce the total stock of external debt in the long term.
Otherwise, the trade policy is considered inappropriate. Siddique (1996) found that
approximately 21 percent of growth in the stock of external debt among the low-income
countries in question was due to inappropriate trade policy during the period 1971 to 1979.
The second is macroeconomic policy. Unsustainable expansionary monetary policy can
result in a chronic current account deficit and fiscal imbalance leading to a build-up of external
debt. Furthermore, Krueger (1987) identified unrealistic macroeconomic policy as having the
potential to induce capital flight, forcing countries to engage in further borrowing not just to
meet existing debt obligations, but also to offset the impact of capital flight. Siddique (1996)
identified a number of countries, during the period 1971 to 1979, for which poor macroeconomic
policy was almost solely responsible for growth in net debt, as well as identifying cases in which
macroeconomic policy designed to achieve high economic growth enhanced the ability of debt-
stricken low-income countries to meet their debt obligations.
The third is external and global shocks. This accounts for the contribution to external
debt from factors beyond the direct control of policymakers in low-income countries, such as
real interest rates, the onset of anti-inflationary monetary policy resulting in recession, and other
global economic conditions.
Giersch (1985) identified the following three factors which contributed to the emergence
of HIPCs:
7
The 1973 oil price increase led to an increase in import bills of the indebted oil importing
countries which, in turn, led to a balance of payment deficit, causing the need for adjustment
of the production and consumption structures.
Interest rates remained relatively lower in the 1960s and the 1970s which prompted the
developing countries to borrow more than they could afford once interest rates went up.
The second oil shock in 1979 led to global recession in the early 1980s. Export incomes of
the indebted nations shrank considerably resulting in an increase in the need for more
borrowing for oil importing countries.
3.2 Trends and Patterns of the External Debt of the HIPCs
To analyse the movements in the overall trade and debt of the heavily indebted poor countries
(HIPCs), the data for the 40 HIPCs for the period 1970-2007 were obtained from the World
Development Indicators, World Bank. All the variables, except population, are in current $US.
For selected years, in columns 2-5 of Table 1 we present the aggregate total of Public and Public
Guaranteed Debt (PPG Debt), Service Debt, Other Debt and the Total Debt.3 As can be seen, a
large share of the total debt is made up of the PPG debt. The data shows strong growth in total
debt from 1974 onwards, peaking in 1998 and then continuing on a declining path but with a
slight increase in 2007. The last two columns of the table present the Total Merchandised
Exports (TME) and the Gross Domestic Product (GDP) aggregated over the 40 HIPCs. As can
be seen, there is significant improvement in the GDP, Merchandise Exports, PPG debt and total
debt of the HIPCs during the last four decades.
Table 1 Debt, Output and Export Statistics of HIPCs, 1970-2007, selected years ($US millions, current)
3 Consistent time series data for all 40 countries are available only up to 2007. The data period does not have any impact on the general applicability of the results and their policy implications presented in the paper.
8
YearPPG Debt Service Debt Other Debt Total Debt
Total Merchandise
Exports (TME)
Gross Domestic
Product (GDP) (1) (2) (3) (4) (5) (6) (7)1970 5403.1 240.4 558.4 6201.9 5971.8 26259.41974 12338.4 606.0 1585.0 14529.3 10909.3 44846.81978 27521.0 1568.2 7630.7 36719.9 13458.7 73668.81982 49704.4 3622.7 13293.7 66620.9 15787.0 96816.11986 80593.8 4220.2 17350.2 102164.2 16291.0 115507.41990 111657.5 4734.7 24296.1 140688.3 20133.0 133790.11994 128268.7 3870.9 30443.6 162583.2 19199.4 105721.41998 130308.8 4253.1 31034.5 165596.5 25443.0 141116.22002 124355.9 3802.3 25515.4 153673.7 29010.6 149686.82006 92973.6 3123.1 23890.9 119987.6 66584.9 265230.02007 95704.6 3879.9 24403.0 123987.5 75522.1 311411.7
To analyse the depth of the debt problem, we convert the data for the debt variables listed in
Table 1 in the form of relative measures with respect to GDP and merchandise exports. In
columns 2-3 of Table 2, we provide the ratios of PPG debt and total debt to GDP; and columns
4-5 of the table present the PPG debt and total debt to exports for selected years between 1970
and 2007. As can be seen, the ratios of the PPG debt and total debt to GDP (columns 2-3) and
PPG debt and total debt to exports (columns 4-5), all peaked in the mid-1990s, before declining
to close to their 1970 levels in 2007. Clearly, it appears that some progress has been made by the
HIPCs in reducing their debt level as a percentage of GDP over the last century.
Table 2 External Debt to GDP and Exports Ratios of HIPCs, 1970-2007, selected years
YearPPG Debt/
GDPTotal
Debt/GDPPPG
Debt/ExportsTotal
Debt/Exports (1) (2) (3) (4) (5)1970 0.206 0.236 0.905 1.0391974 0.275 0.324 1.131 1.3321978 0.374 0.498 2.045 2.7281982 0.513 0.688 3.148 4.2201986 0.698 0.884 4.947 6.2711990 0.835 1.052 5.546 6.9881994 1.213 1.538 6.681 8.4681998 0.923 1.173 5.122 6.5092002 0.831 1.027 4.287 5.2972006 0.351 0.452 1.396 1.8022007 0.307 0.398 1.267 1.642
In Table 3, we present the average annual growth rate of aggregated PPG debt, total debt,
GDP and exports in the 40 HIPC counties during four subsample periods and over the whole 9
sample period. During the sub-period 1971-1979, PPG debt, total debt, GDP and exports, all had
a positive double-digit growth; for the sub-period 1980-1989, the PPG and total debts grew at a
positive rate but below that of the 1971-1979 level, and the GDP and exports had a positive but
much smaller growth rate. For the sub-period, 1990-1999, the debts, GDP and exports, all had
experienced a lower but positive growth ratio. However, for the sub-period of 2000-2007, the
growth rates for the debts were negative but the growth rates of GDP and exports had recovered
and were almost in the double-digit level of the 1970s.
Table 3 Average Annual Growth Rates of PPG Debt, Total Debt, GDP and Exports in HIPCs, 1971-2007
Period PPG Debt Total Debt GDPMerchandise
Exports (1) (2) (3) (4) (5)1971-1979 22.15 24.15 13.49 12.701980-1989 12.06 11.55 4.85 1.951990-1999 2.22 2.35 0.49 3.042000-2007 -2.36 -2.47 11.66 15.39
1971-2007 8.74 9.10 7.25 7.76
4. Modelling the Relationship between Economic Performance and External Debt
The discussions in Sections 2 and 3 suggest that GDP is affected by variables such as capital
formation (CF), debt (DB), trade (TR) and population (P). In the remainder of the paper, we
consider the following variables for our analysis: (1) GDP per capita (GDP); (2) gross capital
formation per unit of GDP (CF); (3) total debt per unit of GDP (DB); (4) total trade per unit of
GDP (TR); and (5) population (P)4. The logic behind the selection of these variables is as
follows:
Capital as a share of GDP has a positive short and long term impact on GDP as a critical
input into the production process. Some industries, such as agriculture and manufacturing
can be quite labour intensive and as such, require proportionally less capital than other more
sophisticated technology and service-driven sectors. To be most effective, the local
workforce needs to be sufficiently educated and skilled (human capital) to properly utilise the
capital in more sophisticated sectors. Otherwise, the sectors may never become efficient and 4 We also included Education as a variable in our analysis, however, due to reasons given in footnote 4, Education was dropped from the analysis.
10
other more traditional sectors, such as agriculture, will have had resources diverted from
them needlessly;
Debt can have a positive impact on growth by supplementing insufficient local resources and
investment in profitable infrastructure, but only in the short term and up to a certain
“threshold” point, after which it affects growth negatively, driven by the lower efficiency of
investment as debt increases, as well as capital and labour productivity effects, and
“crowding out” effects;
Merchandise trade as a percentage of GDP has a positive relationship with GDP by
generating export revenues. This is because the products that can be exported internationally
(without artificial support) are, by definition, the products that the country is most suited to
produce (able to compete not just with international producers, but also with international
transport costs). Consequently, resources should be devoted to its production. For developing
countries, this is often agricultural or labour-intensive manufacturing produce;
Population has a long run positive impact on GDP, possibly due to its workforce or human
capital impacts.
Test for stationarity and cointegration of the model variables
Before estimation, we investigate the time series properties of each time series variable. We test
for the existence of a unit root in each panel data time series variable. We present here, the
results from five such panel unit root tests known as LLC (Levin-Lin-Chu) test , IPS (Im-
Pesaran-Shin) test, ADF (Augmented Dickey-Fuller) test, PP (Phillips-Perron) test and Hadri
test, developed by Levin et al (2002); Im et al (2003); Dickey and Fuller (1981); Phillips and
Perron (1988); and Hadri (2000), respectively.
The null hypothesis of all these tests is that the panel series has a unit root (or that the
time series is non-stationary), except for the Hadri test which tests the opposite hypothesis of the
series and has no unit roots (or the time series is stationary). The unit root test results are
presented in Table 4. As can be seen, the results indicate that capital formation and trade
11
variables are stationary in level form (that is, I(0)), whilst all the remaining variables are
stationary in their first differences, that is, I(1). In addition, we also use the Kao Residual co-
integration test to test for cointegration between GDP, capital formation, debt, trade and
population. The value of the test statistic to test the null hypothesis of no co-integration is -1.96
with a p-value of 0.0253, indicating that there is some support for co-integration between our
variables of interest. These results show that an Auto Regressive Distributed Lags (ARDL)
approach would be the most suitable to model the relationship between GDP and capital
formation, debt, trade and population.
12
Table 4 Panel unit root and cointegration test results
Individual EffectsGDP 5.250 (1.000) 3.196 (1.000) 68.965 (0.806) 52.346 (0.993) 14.654 (0.000) -Capital Formation -4.376 (0.000) -4.893 (0.000) 165.547 (0.000) 143.550 (0.000) 9.337 (0.000) I(0)Debt -1.707 (0.044) -0.372 (0.355) 72.866 (0.701) 58.800 (0.964) 9.505 (0.000) -Trade -1.908 (0.028) -2.367 (0.009) 123.291 (0.001) 119.453 (0.002) 12.889 (0.000) I(0)Population 8.759 (1.000) 21.705 (1.000) 69.619 (0.623) 7.771 (1.000) 24.479 (0.000) -Individual Effects + TrendGDP 4.496 (1.000) 2.648 (0.996) 70.232 (0.774) 36.920 (1.000) 8.065 (0.000) -Capital Formation -5.606 (0.000) -4.615 (0.000) 157.729 (0.000) 137.432 (0.000) 9.586 (0.000) I(0)Debt 8.601 (1.000) 9.824 (1.000) 32.036 (1.000) 15.303 (1.000) 10.245 (0.000) -Trade -2.745 (0.003) -2.681 (0.004) 131.994 (0.000) 139.284 (0.000) 9.097 (0.000) I(0)Population 5.559 (1.000) 12.868 (1.000) 154.118 (0.000) 28.564 (1.000) 19.195 (0.000) -
Individual EffectsGDP -19.023 (0.000) -20.094 (0.000) 539.616 (0.000) 549.998 (0.000) 1.413 (0.079) I(1)Capital Formation -33.096 (0.000) -31.743 (0.000) 876.490 (0.000) 1008.72 (0.000) -0.213 (0.584) -Debt -22.533 (0.000) -21.676 (0.000) 599.549 (0.000) 588.157 (0.000) 0.659 (0.255) I(1)Trade -31.008 (0.000) -31.172 (0.000) 855.570 (0.000) 974.278 (0.000) 3.247 (0.001) -Population 3.800 (1.000) 1.383 (0.917) 229.581 (0.000) 335.473 (0.000) 16.798 (0.000) I(1)Individual Effects + TrendGDP -17.206 (0.000) -17.015 (0.000) 419.852 (0.000) 442.905 (0.000) 9.829 (0.000) I(1)Capital Formation -26.041 (0.000) -22.947 (0.000) 795.185 (0.000) 2485.330 (0.000) 2.891 (0.002) -Debt -20.929 (0.000) -20.374 (0.000) 547.499 (0.000) 813.104 (0.000) 3.121 (0.001) I(1)Trade -26.202 (0.000) -27.212 (0.000) 729.441 (0.000) 2578.390 (0.000) 12.157 (0.000) -Population 8.297 (1.000) -0.778 (0.218) 214.845 (0.000) 391.952 (0.000) 5.221 (0.000) I(1)
Data-based value of the test statistic (p-value in parenthesis)Conclusion
LLC IPS ADF PP HADRI
B. First Differences
A. Levels
Modelling the Relationship between Economic Performance and External Debt
To model the relationship between external debt and the economic performance of the HIPCs,
we use the stationarity and cointegration test results presented in Table 4. The results of the table
suggests that a dynamic model such as an Auto Regressive Distributed Lag (ARDL) model
would be most suitable (see, Pesaran and Shin, 1999; and Pesaran et al, 2001) for our analysis, as
some of the variables are I(0) and others are I(1). We can write an ARDL (p,q,q,…,q) dynamic
panel specification for country i and period t of the form
t=1,2, …, T and i =1,2, …, N, (1)
There exists a cointegrating relationship between the variables, assuming a long-run relationship
of the form:
13
t=1,2, …, T and i =1,2, …, N, (2)
where T is the number of time periods; N is the number of countries; Xit is a (k1) vector of
explanatory variables for country i; i, is the country-specific fixed effect; ij’s are scalars and ij
are (k1) vectors of parameters of the model to be estimated. In addition, if the variables in
equation (2) are (1) and cointegrated, then the error term is a stationary process for each country
equation. This means that an error correction model can be formed in which short-term dynamics
can be combined with an error correction term to take care of the deviation from the long-run
equilibrium. Therefore, we can parameterise equation (2) in the form of an error correction (EC)
model given by:
(3)
where j=1,2,…,p-1; and
j=0,1,…,q.
The parameter i is the error correcting speed of adjustment term. If i =0, then there
would be no evidence for a long-run relationship. We would expect the estimated value of i to
be significantly negative as the variables are co-integrated; implying that the variables should
show a return to long-run equilibrium. The θi coefficients measure the long-run relationship
between the variables and, and measure the short-run relationship between the variables.
There are three approaches suggested in the literature (see Pesaran and Smith, 1995; and
Pesaran et al, 1997 and 1999) for the estimation of dynamic heterogeneous panel equation of the
form of equation (3) when both T and N are large.
The first approach is the ‘dynamic fixed effects’ (DFE) estimation where the time series
data for each country are pooled and only the intercept coefficients are allowed to vary across
countries and the speed of adjustment coefficient and the slope (short-run) coefficients are
treated as equal across countries. It has been shown that if the slope coefficients are in fact not
identical, then such DFE estimation will produce inconsistent estimates and potentially
14
misleading results. DFE estimation is also subject to a simultaneous equation bias due to the
endogeneity between the error term and the lagged dependent variable (see Baltagi et al, 2000).
The second is the ‘mean group’ (MG) estimation method proposed by Pesaran and Smith
(1995) with the aim of resolving the bias due to heterogeneous slopes in dynamic panels. Under
this approach, the model will be estimated separately for each country and a simple unweighted
arithmetic average of the coefficients will be calculated as the final (MG) estimates. MG
estimation allows for all intercept and slope coefficients to vary and be heterogeneous (error
variances are allowed to vary across countries) in the long-run as well as in the short-run.
The third approach is the ‘pooled mean group’ (PMG) estimation introduced by Pesaran
et al (1997; 1999) which combines both pooling and averaging. The PMG estimator, as with the
MG estimator, allows the intercept, short-run coefficients and error variances to differ across
countries, but restricts the long-run (co-integrating) coefficients to be the same across countries
as with the DFE estimation method.
One advantage of the PMG over the traditional DFE is that PMG allows for the short-run
dynamic specification to differ from country to country. In terms of selecting the preferred
estimates between MG and PMG, one could use the Hausman test to see whether there is any
significant difference between the two sets of estimates. The null hypothesis of such a hypothesis
test can be defined as the difference between the MG and PMG estimates being insignificant. If
the null hypothesis is not rejected, we conclude that there is no significant difference between the
two set of estimates and select PMG estimates as they are efficient estimates. If the null
hypothesis is rejected, then we conclude that there is significant difference between the two set
of estimates. One possible solution in this situation is to use the average of the two estimators.
5. Empirical Results
We use models (1) to (3) for our analysis by replacing Y by GDP and the matrix X consisting of
variables CF, DB, TR and P. Initial analysis indicates that an ARDL(1,1,1,1,1) dynamic panel
specification is suitable for our study.
15
Table 5 presents the three sets of alternative pooled estimates, namely, (1) MG estimates
which imposes no restrictions; (2) PMG estimates, which imposes common long-run effects;
and, (3) the DFE estimates which constrains all of the slope coefficients and error variances to be
the same. As can be seen, the signs of the coefficients are mostly the same, however, some
differences can also be noticed between the three sets of estimates; the standard errors of the
estimated long-run coefficients are also generally lower for the PMG estimates than for the MG
and DFE estimates. Across all three sets of estimates, the speed of adjustment (the error
correction term) is
Table 5 Panel ARDL Estimation Results from 40 HIPCs, 1970-20075
VariablePooled Mean Group (PMG) Mean Group (MG)
Dynamic Fixed Effect (DFE)
Error Correction term -0.039 -0.253 -0.046Standard error (0.01) (0.06) (0.02)
p-value (0.00) (0.00) (0.01)
Short-runCapital formation 59.78 39.45 -3.35
Standard error (34.54) (50.55) (35.30)p-value (0.08) (0.44) (0.92)
Debt -218.36 -180.18 -94.29Standard error (25.39) (27.06) (21.38)
p-value (0.00) (0.00) (0.00)Trade -0.96 -0.95 -0.90
Standard error (0.32) (0.30) (0.37)p-value (0.00) (0.00) (0.02)
Population ( x 106) 124.00 -107.70 -21.30Standard error (287.60) (98.40) (17.00)
p-value (0.67) (0.27) (0.21)
Long-runCapital formation 2855.324 2384.174 1882.189
Standard error (659.76) (1003.36) (835.06)p-value (0.00) (0.02) (0.02)
Debt -559.224 -393.977 -315.779Standard error (118.99) (158.13) (152.45)
p-value (0.00) (0.01) (0.04)Trade 4.778 -1.658 7.829
Standard error (2.03) (4.16) (5.63)p-value (0.02) (0.69) (0.17)
Population ( x 106) 30.400 264.900 20.900Standard error (10.20) (147.70) (18.80)
p-value (0.00) (0.07) (0.27)Constant 17.20 -25.79 3.69
Standard error (9.62) (62.15) (11.81)p-value (0.07) (0.68) (0.76)
5 We also included Education as a variable in the model and the results are presented in Table A2 in the Appendix. Since the estimated coefficient for education has the incorrect sign and is insignificant, education was dropped from the model estimation.
16
negative and statistically significant. As expected, from Econometric theory, the MG error
correction estimate presented in Table 5 indicates a much faster adjustment than the PMG or
DEF error correction estimates (-0.253 versus -0.039 and -0.046). The individual country short-
run PMG estimates are given in Table A2 of the Appendix to this paper (available upon request
from the authors). For a long-run relationship to exist, we require i to be non-zero. The results
show that all but one of the individual error correction coefficients is negative and less than one
in absolute value. This means that, for a majority of the individual countries, the hypothesis of no
long-run relationship would not be rejected (for theoretical results, see Pesaran et al, 1999).
Comparing the long-run standard errors of the three sets of estimates, we could see in the
case of PMG that imposing a long-run homogeneity reduces the standard errors of the long-run
coefficients. Furthermore, all of the estimated short-run and long-run debt coefficients are
negative and statistically significant at the 5 percent level.
We use the Hausman test to test the null hypothesis of no difference between the MG and
PMG estimators6. The value of the test statistic is 15.18 with a p-value of 0.002, indicating that
we are unable to accept the null hypothesis that the MG and PMG estimates are the same.
Capital Formation as a proportion of GDP
As expected, across the three estimation methods, in general, the capital formation variable has a
positive impact on GDP in the short run as well as in the long run. This means that higher levels
of capital formation, as a proportion of GDP, would have increased the level of GDP in the
HIPCs. At the 10 percent level of significance, the PMG estimate of capital formation variable is
statistically significant in both the short- and long-run. The long-run MG and DFE estimates for
the capital formation variable are statistically significant at the 5 percent level; but both the short
run MG and DFE estimates are statistically insignificant.
6 We use the STATA software for estimation.17
Debt as a proportion of GDP
Across the three estimation methods, the debt variable has a negative and statistically significant
influence on GDP in the short run as well as in the long run and supports prior expectations. This
means that higher levels of debt, as a proportion of GDP, would have reduced the level of GDP
in the HIPCs. At the 5 percent level of significance, all the PMG, MG and DFE coefficient
estimates are statistically significant in both the short-run and long-run.
This is an interesting result as it is in line with the ‘debt overhang’ hypothesis, which
states that a country experiences debt overhang when its stock of external debt exceeds its ability
to repay its debt. This can have negative impact on economic growth of a debt ridden country as
a large proportion of its output is used to repay debts to foreign lenders, which consequently
creates disincentive to invest (Krugman, 1988; and Sachs, 1989).
This result raises important policy questions. If this relationship is true of the past, and
we expect it to remain true into the future, then a reduction of HIPCs’ debt burden should
increase their level of GDP. This, however, does not say that taking on external debt is bad if
managed well. For example, some of the East Asian economies, especially, the ‘Tiger
Economies’ were able to reduce poverty and enhance economic growth through efficient
utilisation of foreign debts.
Total Trade as a proportion of GDP
The three sets of short-run negative estimates for the trade variable coefficient suggest that
increasing total merchandise exports as a proportion of GDP has a statistically significant
negative effect, in the short-run, on GDP in the HIPCs. The long-run PMG coefficient estimate
for the trade variable is positive and statistically significant but the long-run MG and DFE
coefficient estimates for trade variable are statistically insignificant.
This result could give rise to a change in development assistance policy. It also suggests
that the best way to promote HIPC countries’ economic performance in the long-run is not to
give more aid, but to promote more exports.
18
Population
None of the short-run population coefficients are statistically significant. However, the long-run
PMG and MG population coefficient estimates are both positive and statistically significant. This
means that, in the long-run, an increase in population would impact positively on the GDP,
which may be due to the increase in the work force or human capital.
19
6. Policy Implications
The implications of the above findings, and the review of the literature for economic policy, are
that there is still a significant role for government to play in smoothing out the short term
fluctuations of the business cycles. During recessions, it is advisable for governments to increase
spending, via ‘Fiscal Policy’, even if this means increasing debt levels. In the short term, this
will stimulate economic activity and reduce unemployment further than would occur if the entire
burden were taken on just by ‘Monetary Policy’. In the longer term, after the short term
economic benefits from government spending have been absorbed and the economy has
recovered (even started to heat up), government debt levels can be brought to more sustainable
levels via spending cuts and/or tax increases in order to reduce inflation pressures, and in
preparation for future downturns where increased spending/ debt levels may again be required.
For countries already under significant debt strain (beyond the optimal “threshold”,
which Pattillo et al. (2002) and Clements et al. (2003) put as low as 17-25 percent of GDP,
beyond which additional debt has a negative growth impact), where additional government
spending may worsen an economy’s situation without any corresponding benefit to economic
growth; implying that economic reform and investment must be coupled with debt relief. This
way, economic growth can be stimulated without the need for growth-hindering austerity. This
has significant implications for groups such as the IMF and the World Bank in terms of how they
assist HIPCs in the most effective and efficient manner ergo long-term gains with minimal short-
term pain.
It is also important for groups like the IMF and World Bank to appreciate that developing
and HIPCs often need to accumulate debt in order to supplement their inadequate domestic
funding sources. Consequently, policy advice to these countries must not unduly hinder their
ability to borrow money – the negative growth impacts of this may outweigh the benefits of the
advised reforms, especially in the short term.
20
In terms of the specific types of reforms that can stimulate growth in HIPCs, especially in the
longer term, these can include:
Export- led growth: instead of just providing foreign aid, export-oriented industries should be
supported, including agriculture and labour-intensive manufacturing (industries in which
developing countries often have an advantage), but also in more sophisticated industries such
as capital-intensive manufacturing and technology or service-based industries. Certain
developing countries may not possess the natural advantages to succeed in these latter
industries. Consequently, support for these industries must focus on improving the education
and skills of the local workforce to adequately equip them for success in these industries.
Otherwise, support for these industries may simply divert resources away from industries in
which the country already has an advantage, with no long-term benefit – the country may not
develop an advantage in these new sophisticated industries without a workforce that is
adequately skilled and educated. Argentina is an example of a country that diverted resources
away from its advantages in export-oriented agriculture in favour of import substitution,
without adequately educating and skilling its workforce for the task. Consequently, after
starting the 20th century as one of the world’s wealthiest countries, Argentina suffered
perpetual decline.
Improved macroeconomic policy, including more sustainable monetary policy that generates
more sustainable current account and fiscal balances. This, in turn, will create an
environment less likely to suffer capital flight, and, therefore, less likely to require significant
debt accumulation to support.
Capital formation also has a positive relationship with GDP. Consequently, policy should
encourage investment in capital, especially that which supports traditional export-oriented
industries, but also more sophisticated capital-, technology-, and service-intensive industries.
Investment in capital for service-intensive industries will be most effective when combined
with improved education and skilling of the workforce to suit these industries.
21
Population has a long run positive impact on GDP. Although, given the fact that many
developing or HIPCs already have large young and/ or working-age populations, rather than
ageing populations, population growth may not need to be further encouraged, especially if it
adds to pre-existing unemployment issues. Skilled immigration can be valuable to support
the more sophisticated capital-intensive industries but it is important that this immigration
only temporarily supplements, not permanently replaces, the local workforce until the local
population becomes sufficiently educated and skilled to undertake this work for themselves.
Otherwise there is a risk of significant entrenched unemployment and inequality.
7. Conclusion
22
There is a significant amount of literature that examines the impact of external debt on economic
growth in highly indebted poor countries (HIPCs). This literature was largely consistent in its
findings that debt can have a positive short term economic growth impact up to a certain point.
This is consistent with typical Keynesian recommendations that would result in greater
government spending (and potentially debt accumulation) during recessions, and reduced
spending during booms, as a way of smoothing out the phases of the business cycle in the short
term, thereby reducing the occurrence and subsequent hardships of significant fluctuations in
unemployment and inflation. Furthermore, debt was often necessary for HIPCs because their
own economic growth, incomes, and savings levels were insufficient to finance their
development/ national goals; and their generally commodity-based exports were not sufficient to
finance their more expensive capital intensive imports. In the long term however, and beyond a
certain “threshold” debt level, debt appears to have negative economic growth and current
account effects, driven by the lower efficiency of investment as debt increases, as well as capital
and labour productivity effects and “crowding out” effects. Consequently, for HIPCs, the best
way to ensure appropriate lending or investment, and economic reforms (for example, income-
generating activities, export-oriented trade policy, appropriate macroeconomic policy) to actually
have positive economic growth and social impacts is to combine the loans and reforms with debt
relief so that new investment is not impeded by debt and illiquidity. Furthermore, government
spending (and potentially debt accumulation) can play a valuable role during booms and
recessions, even if it does not increase (or even decreases) long term growth, as long as debt
levels are reduced to (or below) the “threshold” level over the long term.
23
We examined short-run and long-run relationships between external debt and economic
growth in 40 HIPCs over the period 1970-2007 with the aid of the growth accounting process. In
addition, the impacts of capital formation, trade and population growth on economic growth in
these countries was also examined. We used panel data estimation of an ARDL model. Our
analysis reveals that GDP and merchandise export growth accelerated fastest since 2000, while
debt levels have fallen since 2000. Specifically, the growth rate of debt in these countries has
been falling since 1970, and was actually largely negative since 2000. The estimation results
indicate firstly that, capital formation’s share of GDP has a positive impact on HIPC’s GDPs in
the short-run as well as in the long-run. Secondly, debt as a share of GDP has a negative
influence in the short-run as well as in the long-run. This is consistent with the debt overhang
hypothesis that a country experiences debt overhang when its stock of external debt exceeds its
ability to repay its debt, which causes a large proportion of their GDP to have to be committed to
re-paying foreign lenders, which disincentives investment. Therefore, reducing HIPC’s debt
would be good for their GDP, but debt can still be good for GDP up to a certain point, for
example, East Asia’s efficient use of foreign debt to reduce poverty and enhance economic
growth. Thirdly, in the long-run, the merchandise trade as a percentage of GDP has a positive
influence on GDP. This has implications for development assistance policy in that it should
encourage exports, rather than just delivering foreign aid. Fourthly and finally, population
increase has a positive influence on the economic growth in the HIPCs, possibly due to its
workforce/ human capital impacts.
24
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APPENDIX
Individual country unit root test and test of cointegration
First we apply the Augmented Dicky Fuller (ADF) test (Dickey and Fuller, 1979; 1981) to test
the stationarity of each of the five variables GDP, CF, DB, TR and P for each country. The null
hypothesis of the ADF test is that the time series has a unit root (or the time series is non-
stationary). Table A1 presents the unit root test results. As can be seen, the p-value for testing the
null hypothesis of the existence of a unit root is mostly less than the level of significance, α =
0.05, thus we reject the null hypothesis. This means that, almost all time series are stationary,
that is I(0), except GDP for Kyrgrz Republic and Tanzania; capital formation for Liberia; debt
for Eritrea, Madagascar and São Tomé and Príncipe; and, population for Benin and Comoros.
The individual country short-run PMG estimates and their standard errors for equation (3) are
given in Table A2.
Table A3 presents the corresponding estimation results in Table 5 with Education (education
expenditure as a proportion of GDP) included as an additional variable in equation (3). Across
the three sets of short-run estimates, the coefficient attached to the education variable is mostly
negative but statistically insignificant. The long run PMG coefficient estimate for education is
negative and statistically significant, and the long-run MG and DFE coefficient estimates of
education are insignificant. These results suggest that expenditure on education as a proportion
of GDP is detrimental to the level of GDP in the HIPCs. The direction of this relationship is
markedly different from rational expectation.
28
Table A1 Value of the ADF test statistic and its p-value (in parentheses) from the Unit root test for each variable in each country
Country1 Benin -5.841 (0.000) -7.910 (0.000) -7.954 (0.000) -6.782 (0.000) -1.832 (0.355)2 Bolivia -4.860 (0.000) -6.080 (0.000) -4.560 (0.000) -5.330 (0.000) -6.266 (0.000)3 Burkina Faso -6.830 (0.000) -7.640 (0.000) -4.890 (0.000) -7.030 (0.000) -5.688 (0.000)4 Burundi -6.950 (0.000) -8.930 (0.000) -4.500 (0.000) -5.870 (0.000) -6.255 (0.000)5 Cameroon -4.350 (0.000) -6.840 (0.000) -5.340 (0.000) -6.080 (0.000) -5.718 (0.000)6 Central African Republic -6.874 (0.000) -7.559 (0.000) -7.190 (0.000) -6.874 (0.000) -6.264 (0.000)7 Chad -5.116 (0.000) -5.004 (0.000) -4.949 (0.000) -9.090 (0.000) -3.768 (0.007)8 Comoros -4.694 (0.000) -6.827 (0.000) -4.553 (0.000) -2.715 (0.009) 0.767 (0.991)9 Congo, Dem. Rep. -6.599 (0.000) -9.103 (0.000) -5.777 (0.000) -8.223 (0.000) -6.621 (0.000)10 Congo, Rep. -5.341 (0.000) -5.313 (0.000) -5.222 (0.000) -6.472 (0.000) -6.341 (0.000)11 Cote d'Ivoire -6.240 (0.000) -5.513 (0.000) -2.953 (0.004) -4.896 (0.000) -8.735 (0.000)12 Eritrea -2.145 (0.035) -6.018 (0.000) -1.840 (0.065) -4.646 (0.000) -6.250 (0.000)13 Ethiopia -6.600 (0.000) -8.385 (0.000) -4.315 (0.000) -2.990 (0.004) -4.098 (0.000)14 Gambia, The -6.609 (0.000) -5.751 (0.000) -5.367 (0.000) -8.218 (0.000) -6.319 (0.000)15 Ghana -5.653 (0.000) -6.260 (0.000) -4.733 (0.000) -4.828 (0.000) -6.603 (0.000)16 Guinea -2.873 (0.006) -3.936 (0.001) -4.067 (0.000) -3.611 (0.001) -6.231 (0.000)17 Guinea-Bissau -8.945 (0.000) -6.977 (0.000) -5.602 (0.000) -6.946 (0.000) -6.272 (0.000)18 Guyana -4.145 (0.000) -6.102 (0.000) -5.061 (0.000) -5.586 (0.000) -6.530 (0.000)19 Haiti -7.104 (0.000) -5.320 (0.000) -5.469 (0.000) -9.381 (0.000) -6.480 (0.000)20 Honduras -4.888 (0.000) -5.599 (0.000) -4.420 (0.000) -6.240 (0.000) -6.161 (0.000)21 Kyrgyz Republic -0.509 (0.480) -3.112 (0.004) -2.305 (0.025) -3.332 (0.003) -6.288 (0.000)22 Liberia -7.494 (0.000) -0.855 (0.297) -3.180 (0.002) -6.478 (0.000) -6.037 (0.000)23 Madagascar -6.189 (0.000) -7.141 (0.000) -1.812 (0.067) -6.108 (0.000) -6.655 (0.000)24 Malawi -7.845 (0.000) -8.419 (0.000) -5.463 (0.000) -7.090 (0.000) -6.208 (0.000)25 Mali -5.392 (0.000) -9.632 (0.000) -7.156 (0.000) -7.956 (0.000) -6.278 (0.000)26 Mauritania -6.127 (0.000) -9.789 (0.000) -4.576 (0.000) -8.257 (0.000) -6.337 (0.000)27 Mozambique -4.889 (0.000) -7.221 (0.000) -4.088 (0.000) -3.167 (0.003) -6.391 (0.000)28 Nepal -6.414 (0.000) -7.499 (0.000) -3.045 (0.003) -5.870 (0.000) -6.010 (0.000)29 Nicaragua -5.893 (0.000) -7.565 (0.000) -5.241 (0.000) -6.015 (0.000) -6.253 (0.000)30 Niger -6.435 (0.000) -5.957 (0.000) -5.201 (0.000) -6.620 (0.000) -4.797 (0.000)31 Rwanda -7.253 (0.000) -9.864 (0.000) -6.996 (0.000) -9.781 (0.000) -6.204 (0.000)32 Sao Tome and Principe -4.317 (0.000) a a -1.630 (0.096) a a -6.345 (0.000)33 Senegal -6.517 (0.000) -7.163 (0.000) -8.768 (0.000) -7.537 (0.000) -6.570 (0.000)34 Sierra Leone -7.088 (0.000) -8.371 (0.000) -4.767 (0.000) -7.777 (0.000) -6.299 (0.000)35 Somalia -5.319 (0.000) -10.053 (0.000) -4.245 (0.000) -4.658 (0.000) -5.009 (0.000)36 Sudan -3.574 (0.001) -5.086 (0.000) -5.692 (0.000) -5.840 (0.000) -6.805 (0.000)37 Tanzania -1.657 (0.091) -2.797 (0.009) -3.167 (0.003) -3.119 (0.004) -6.120 (0.000)38 Togo -6.088 (0.000) -5.621 (0.000) -5.382 (0.000) -6.565 (0.000) -6.300 (0.000)39 Uganda -5.553 (0.000) -7.667 (0.000) -4.827 (0.000) -5.973 (0.000) -5.947 (0.000)40 Zambia -4.020 (0.000) -7.190 (0.000) -5.224 (0.000) -6.237 (0.000) -6.349 (0.000)
GDP Capital formation Debt PopulationTrade
a: Insufficient observations; b: near-singular; highlighted cells represent insignificant coefficient estimates at the 5 percent level.
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Table A2 ARDL Estimation Results, Individual HIPCs, 1970-2007Country1 -0.039 (0.171) 79.48 (0.520) -293.42 (0.000) -0.130 (0.876) -38.70 (0.660) 10.25 (0.604)2 -0.080 (0.055) -198.32 (0.170) -307.58 (0.000) 0.550 (0.750) 718.30 (0.203) -65.28 (0.393)3 -0.029 (0.410) -170.79 (0.216) -504.67 (0.000) 1.479 (0.250) -99.70 (0.001) 19.34 (0.405)4 -0.014 (0.041) 55.95 (0.361) -55.19 (0.002) 0.324 (0.478) -17.70 (0.426) 4.61 (0.228)5 -0.040 (0.002) -373.49 (0.203) -487.70 (0.000) -7.670 (0.000) -105.40 (0.002) 47.35 (0.021)6 -0.031 (0.055) -97.13 (0.540) -314.44 (0.000) -0.528 (0.428) -135.30 (0.396) 18.56 (0.132)7 -0.034 (0.006) -189.43 (0.000) -308.20 (0.000) -0.149 (0.556) 24.50 (0.459) -3.75 (0.613)8 -0.083 (0.002) -17.19 (0.878) -426.18 (0.000) -0.526 (0.646) 10702.00 (0.104) -88.32 (0.214)9 -0.038 (0.109) -29.56 (0.811) -70.30 (0.013) -1.074 (0.096) -24.10 (0.184) -3.19 (0.878)10 -0.009 (0.650) 199.43 (0.363) -356.23 (0.000) 1.343 (0.203) -1635.50 (0.000) 160.25 (0.000)11 -0.092 (0.011) 703.94 (0.185) -314.49 (0.000) -4.140 (0.051) 37.80 (0.810) 29.95 (0.612)12 0.006 (0.639) 41.12 (0.301) -157.08 (0.000) -0.090 (0.808) 23.50 (0.737) 16.79 (0.328)13 -0.011 (0.074) -23.13 (0.786) -43.26 (0.008) -4.326 (0.000) 2.91 (0.388) -17.29 (0.088)14 -0.048 (0.007) 39.80 (0.718) -150.24 (0.000) 0.118 (0.713) -121.70 (0.429) 3.68 (0.747)15 -0.018 (0.220) 162.45 (0.289) -258.69 (0.000) -1.031 (0.150) -63.20 (0.200) 27.13 (0.127)16 -0.071 (0.195) 231.80 (0.324) -433.11 (0.000) -1.457 (0.104) -77.80 (0.126) 13.75 (0.312)17 -0.004 (0.336) 36.54 (0.367) -43.40 (0.000) -0.660 (0.009) -249.10 (0.331) 14.57 (0.044)18 -0.020 (0.448) 14.52 (0.926) -106.77 (0.000) 0.055 (0.893) -1456.60 (0.783) 32.54 (0.091)19 -0.083 (0.014) -36.36 (0.851) -648.58 (0.000) -0.288 (0.698) -13.20 (0.728) -17.21 (0.264)20 -0.034 (0.138) 378.14 (0.085) -412.86 (0.000) -1.442 (0.197) -620.80 (0.000) 112.84 (0.000)21 -0.091 (0.023) 3.46 (0.979) -291.17 (0.000) 1.881 (0.004) 645.80 (0.020) -31.73 (0.122)22 -0.009 (0.085) 210.12 (0.554) -26.86 (0.002) -0.173 (0.655) -48.40 (0.511) 17.83 (0.008)23 -0.044 (0.030) 343.72 (0.147) -50.70 (0.157) -1.059 (0.157) -94.60 (0.077) 26.71 (0.125)24 -0.013 (0.144) -32.05 (0.418) -74.49 (0.000) -0.694 (0.007) -21.70 (0.370) 3.89 (0.669)25 -0.050 (0.095) -24.69 (0.813) -129.80 (0.001) -1.061 (0.055) -23.40 (0.041) -2.82 (0.810)26 -0.035 (0.001) -44.59 (0.109) -216.18 (0.000) -0.323 (0.265) -897.30 (0.000) 69.94 (0.000)27 -0.034 (0.030) -112.94 (0.316) -10.25 (0.605) -5.214 (0.000) 7.89 (0.746) 10.63 (0.350)28 -0.041 (0.042) -156.18 (0.078) -213.16 (0.000) 0.203 (0.716) -75.30 (0.029) 2.36 (0.822)29 -0.020 (0.038) 643.61 (0.000) -48.81 (0.000) -5.437 (0.000) -145.50 (0.467) 53.31 (0.017)30 -0.046 (0.045) 222.48 (0.057) -158.61 (0.002) -0.139 (0.862) -42.80 (0.404) 1.67 (0.926)31 -0.071 (0.002) -1.17 (0.994) -106.36 (0.000) -1.739 (0.001) -13.20 (0.301) -13.90 (0.155)32 a33 -0.053 (0.035) 84.80 (0.709) -439.30 (0.000) -2.602 (0.008) -161.90 (0.267) 40.87 (0.227)34 -0.006 (0.729) -100.90 (0.535) -104.58 (0.000) 0.342 (0.545) -49.70 (0.506) 9.69 (0.283)35 -0.020 (0.080) -67.18 (0.170) -79.38 (0.000) -0.513 (0.002) -35.80 (0.092) 10.75 (0.036)36 -0.111 (0.004) 321.06 (0.172) -173.96 (0.000) -0.413 (0.846) 104.40 (0.083) -88.28 (0.021)37 -0.027 (0.059) -116.50 (0.311) -77.71 (0.002) -0.518 (0.262) -6.34 (0.704) -16.70 (0.425)38 -0.065 (0.002) 68.20 (0.364) -186.84 (0.000) 0.055 (0.895) 96.60 (0.487) -23.27 (0.302)39 -0.005 (0.863) 260.49 (0.252) -273.89 (0.000) -1.540 (0.133) -26.20 (0.494) 18.48 (0.216)40 0.040 (0.078) 21.97 (0.888) -161.53 (0.000) 1.091 (0.295) -1226.70 (0.000) 264.85 (0.000)
Mean -0.038 59.78 -218.36 -0.961 124.00 17.201
GDP Capital formation TradeDebt Population Constant
a: Insufficient observations.
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Table A3 Panel ARDL Estimation Results (including Education) from 40 HIPCs, 1970-2007
VariablePooled Mean Group (PMG) Mean Group (MG)
Dynamic Fixed Effect (DFE)
Short-runError Correction -0.07 -0.34 -0.04
(0.02) (0.15) (0.02)(0.00) (0.03) (0.01)
Capital formation 81.88 13.94 16.82(39.87) (53.73) (41.54)
(0.04) (0.80) (0.69)Debt -233.18 -167.60 -98.63
(27.02) (39.96) (24.36)(0.00) (0.00) (0.00)
Trade -0.83 -0.94 -1.03(0.33) (0.33) (0.46)(0.01) (0.01) (0.03)
Education -5.23 -10.61 -10.29(4.53) (7.17) (7.60)(0.25) (0.14) (0.18)
Population ( x 106) 169.00 -368.10 -18.10(428.90) (261.90) (15.20)
(0.69) (0.16) (0.23)Constant 49.64 -49.36 11.92
(9.99) (141.02) (14.53)(0.00) (0.73) (0.41)
Long-runCapital formation 1016.842 1420.102 1945.135
(146.68) (442.78) (1003.79)(0.00) (0.00) (0.05)
Debt -95.197 -316.774 -460.706(13.06) (168.56) (271.40)
(0.00) (0.06) (0.09)Trade 2.183 -15.315 12.923
(0.64) (11.73) (8.60)(0.00) (0.19) (0.13)
Education -56.393 36.123 -105.000(13.01) (89.97) (95.34)
(0.00) (0.69) (0.27)
Population ( x 106) -4.890 -31.200 19.000(2.68) (313.70) (20.50)(0.07) (0.92) (0.35)
For each estimate the first row in parentheses are the standard errors and the second row provides the corresponding p-values for testing the statistical significance of the estimates.
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