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THE IMPACT OF FOREIGN DEBT ON ECONOMIC GROWTH IN SOUTH AFRICA
BY
SHAYANEWAKO V.B
A DISSERTATION SUBMITTED IN FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF COMMERCE IN ECONOMICS
DEPARTMENT OF ECONOMICS FACULTY OF MANAGEMENT AND COMMERCE
UNIVERSITY OF FORT HARE SOUTH AFRICA
NOVEMBER 2013
SUPERVISOR: PROF. R. NCWADI
i
ABSTRACT
This study analyses the economic impact between foreign debt and economic growth in South
Africa. By fitting a production function model to annual data for the period 1980-2011, the study
examines the dynamic effect of debt service, capital stock and labour force on the economic
growth of the country. By following Cunningham (1993), it has identified the long-run and short-
run causal relationships among the included variables. The results indicate that the debt servicing
burden has a negative effect on the productivity of labour and capital, and thereby affect
economic growth adversely. The results also illustrate that the debt service ratio tends to
negatively affect GDP and the rate of economic growth in the long-run, which, in turn, reduces
the ability of the country to service its debt. Similarly, the estimated error correction term shows
the existence of a significant long-run causal relationship among the specified variables. Overall,
the results suggest the existence of short-run and long-run causal relationships running from debt
service to GDP.
Key words: Foreign debt, foreign debt service, debt overhang, gross fixed capital formation
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DECLARATION AND COPYRIGHT
I, the undersigned Shayanewako V.B (student number 200808243) hereby declare that this
dissertation is my own original work and that all reference sources have been accurately cited
and acknowledged, and this work has not been submitted and will not be presented at any other
University for the award of a similar or any other degree.
Signature ……………………………………………
Date ……../……. /……….
iii
ACKNOWLEDGEMENTS
First and foremost, praise be to the “Lion Tribe of Judah”, the one and only living Jesus Christ
who made this work fruitful.
Cicero once wrote that ―gratitude is not only the greatest of virtues, but the parent of all the
others.‖ Inspired by his sentiment, I am compelled to dedicate this section to everyone who
contributed to the success of my work. I must admit I am a little embarrassed by the litany of
names I have to make mention of; then again, it would be a grave injustice if I did not
acknowledge other people‘s contribution.
My leading token of thanks goes to the academics that had a direct input in my work. The
fondest of these thanks go to my supervisor Prof. R. Ncwadi, you always had that teasing
question that prompted me to search for my full potential. You have afforded me unexampled
experience in writing and critical analysis and for this am grateful.
Next, I wish to thank Prof. Burton and Mutopo Shepherd of University of Western Sydney
Australia for their guidance in the data analysis exercise. Worth of mention, for constructive
overall analysis of variance decompositions and causality tests, is Kundai Marufu (cousin and
friend) from Taylor University in Malaysia. Also, I wish to thank Makonye Benson (brother and
friend) from University of Johannesburg, whose work and support was monumentally
inspirational and was willing to decipher any queries I had. Jahbreeze (cousin) I give thanks. In
addition, Mr Chris (cousin), your econometric input cannot go unmentioned together with
encouraging and caring colleagues, namely, Mr Magombedze Andrew and Mr Bhaikwa
Innocent. To my younger brothers Bervin and Enock at Midlands State University, your support
was prodigious. To Mablangwe,-―ndiyabonga standwa sam‖.
To understand the importance of the other category of contributors, permit me to draw from the
muses of another philosopher called Albert Schweitzer. With admirable honesty, he writes that
―At times our own light goes out and is rekindled by a spark from another person. Each of us has
a cause to think with deep gratitude of those who have lighted the flame within us‖. My flame
was lit by my parents, Mrs P. Shayanewako and Mr & Mrs Maswera; and family, Nelia, Yvonne,
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Simba and Schollastic. To Kambuzuma family, my cousins I am very grateful for your
unwavering support.
To all of you I am grateful.
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DEDICATION
To my loving and caring mum Mrs. Pelagia Shayanewako and to my late loving dad Mr. J.C
Shayanewako — I wish you could be witnessing this achievement and success.
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Table of Contents
ABSTRACT ....................................................................................................................................................... i
DECLARATION AND COPYRIGHT ................................................................................................................... ii
ACKNOWLEDGEMENTS ................................................................................................................................ iii
DEDICATION .................................................................................................................................................. v
Table of Contents ......................................................................................................................................... vi
LIST OF ACRONYMS AND ABBREVIATIONS ................................................................................................... x
LIST OF FIGURES .......................................................................................................................................... xii
LIST OF TABLES ............................................................................................................................................xiii
CHAPTER ONE ............................................................................................................................................... 1
INTRODUCTION ............................................................................................................................................. 1
1.1 Introduction .................................................................................................................................. 1
1.2 Statement of the problem ............................................................................................................ 2
1.3 Objectives of the study ................................................................................................................. 4
1.4 Hypothesis of the study ................................................................................................................ 4
1.5 Significance of the study ............................................................................................................... 4
1.6 Research methodology ................................................................................................................. 5
1.7 Organisation of the study ............................................................................................................. 5
1.8 Concluding remarks ...................................................................................................................... 5
CHAPTER TWO .............................................................................................................................................. 6
TREND OF AND MACROECONOMIC FACTORS RELATED TO FOREIGN DEBT IN SOUTH AFRICA (1980-2011)
...................................................................................................................................................................... 6
2.1 Introduction .................................................................................................................................. 6
2.2 Overview and evolution of South African foreign debt ................................................................ 6
2.1.1 Debt standstill of 1985 and subsequent restructuring ......................................................... 8
vii
2.2.1.1 The first interim debt arrangement .................................................................................. 9
2.2.1.2 A second interim debt arrangement ............................................................................... 10
2.2.1.3 A third interim debt arrangement .................................................................................. 10
2.2.1.4 The final debt arrangement ............................................................................................ 10
2.3 Foreign debt in South Africa ....................................................................................................... 11
2.4 Foreign debt service .................................................................................................................... 13
2.5 Trade liberalisation ..................................................................................................................... 14
2.6 Foreign direct investment flows into South Africa ..................................................................... 17
2.7 Gross fixed capital formation ...................................................................................................... 19
2.8 Economic growth in South Africa ................................................................................................ 20
2.9 Concluding remarks .................................................................................................................... 22
CHAPTER THREE .......................................................................................................................................... 23
DEBT-GROWTH RELATIONSHIP: THEORETICAL AND EMPIRICAL REVIEW .................................................. 23
3.1 Literature review ......................................................................................................................... 23
3.2 Theoretical Literature ................................................................................................................. 23
3.2.2.1 Theoretical model ........................................................................................................... 26
3.2.3 Exogenous versus endogenous growth theories ................................................................ 28
3.3 Empirical literature on the debt-growth relationship ................................................................ 30
3.3.1 Developed countries ........................................................................................................... 30
3.3.2 Developing countries .......................................................................................................... 31
3.3.3 Evidence from South Africa ............................................................................................. 32
3.4 Conclusion ................................................................................................................................... 33
CHAPTER FOUR ........................................................................................................................................... 35
METHODOLOGY .......................................................................................................................................... 35
4.1 Introduction ................................................................................................................................ 35
viii
4.2 Empirical model and definition of variables ............................................................................... 35
4.2.1.1 Definition of explanatory variables ................................................................................. 36
4.2.1.2 Gross fixed capital formation .......................................................................................... 36
4.2.1.3 Gross domestic product .................................................................................................. 36
4.2.1.4 Trade openness ............................................................................................................... 37
4.2.1.5 Foreign debt service ........................................................................................................ 37
4.2.1.6 Foreign debt .................................................................................................................... 37
4.2.1.7 Foreign direct investment ............................................................................................... 37
4.3 Expected priori ............................................................................................................................ 38
4.4 Data sources ................................................................................................................................ 38
4.5 Estimation techniques ................................................................................................................ 38
4.6 Stationarity .................................................................................................................................. 39
4.6.1 Augmented Dickey-Fuller test ............................................................................................. 40
4.6.2 Phillips Peron (PP) test ........................................................................................................ 40
4.7 Cointegration .............................................................................................................................. 41
4.7.1 Johansen cointegration test ................................................................................................ 42
4.8 The vector error correction model (VECM) ................................................................................ 43
4.8.1 Impulse response ................................................................................................................ 43
4.8.2 Variance decomposition ............................................................................................................ 44
4.9 Granger causality test ................................................................................................................. 45
4.10 Diagnostic checks ........................................................................................................................ 46
4.10.1Stability test .............................................................................................................................. 46
4.10.2 Heteroskedasticity .............................................................................................................. 46
4.10.3 Residual normality test ....................................................................................................... 46
4.10.4 Autocorrelation LM tests .................................................................................................... 47
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4.11 Conclusion ................................................................................................................................... 47
CHAPTER FIVE ............................................................................................................................................. 48
EMPIRICAL FINDINGS: PRESENTATION AND ANALYSIS ............................................................................... 48
5.1 Introduction ................................................................................................................................. 48
5.2 The test for stationarity .............................................................................................................. 48
5.3 Cointegration tests ...................................................................................................................... 54
5.4 The long-run relationship ............................................................................................................ 58
5.5 The short-run relationship ........................................................................................................... 63
5.5.1 Granger causality ................................................................................................................ 63
5.5.2 Diagnostic checks ................................................................................................................ 66
5.5.3 Impulse response analysis .................................................................................................. 68
5.5.4 Variance decomposition analysis ........................................................................................ 70
5.6 Concluding remarks .................................................................................................................... 76
CHAPTER SIX ................................................................................................................................................ 78
CONCLUSIONS, POLICY RECOMMENDATIONS AND LIMITATIONS ............................................................. 78
6.1 Summary of the study and conclusions ...................................................................................... 78
6.2 Policy implications and recommendations ................................................................................. 80
6.4 Recommendations for future research ....................................................................................... 81
6.4 Limitations of the study .............................................................................................................. 82
REFERENCES ................................................................................................................................................ 83
x
LIST OF ACRONYMS AND ABBREVIATIONS
2SLS Two-stage-least squares
ADF Augmented Dickey-Fuller
AIC Akaike information criterion
BOP Balance of payments
Bund German Government Bond
DTI Department of Trade and Industry
ECM Error correction model
ECT Error correction term
EIU Economist Intelligence Unit
EMTN Euro-medium term note
FDI Foreign direct investment
FEAA Foreign exchange adjustment account
FECA Forward exchange contracts adjustment account
FIML Full information maximum likelihood
GDP Gross domestic product
GFECRA Gold and foreign exchange contingency reserve account
GFSM Government Finance Statistics Manual
GNP Gross national product
GPAA Gold price adjustment account
HIPCs Highly indebted poor countries
xi
HQC Hannan-Quinn criterion
IIF International Institute of Finance
IMF International Monetary Fund
IRF Impulse response function
LDCs Less-developed countries
LM Langrange multiplier
MVM Mariano and Villanueva model
NEF National Economic Forum
NOFP Net open forward position
PIC Public investment commissioners
PP Phillips Perron
SA South Africa
SARB South African Reserve Bank
SC Schwarz criterion
SEC Security and Exchange Commission
SSA Sub-Saharan Africa
TNB Transnational Banks
USA United States of America
VAR Vector autoregression
VECM Vector error correction codel
WDI World Bank Development Indicators
xii
LIST OF FIGURES
Figure 2.1 Trends in stocks of foreign debt in S.A……………… ………………….12
Figure 2.2 Foreign debt service in S.A…………………………… …………………14
Figure 2.3 Ratio of foreign debt to export earnings in S.A……… ……………16
Figure 2.4 FDI net inflows in S.A………………………………………………….. 17
Figure 2.5 Gross fixed capital formation in S.A……………………………………. 19
Figure 2.6 Economic growth in S.A……………………………………………… …21
Figure 5.1a Trends in variables for 1980-2011……………………………………… .49
Figure 5.1b Trends in differenced variables for 1980-2011………………………… 50
Figure 5.2 Foreign debt ―Laffer‖ curve for S.A………………………………… ….62
Figure 5.3 Cointegration graph for the estimated equation…………………… ……63
Figure 5.4 Results for stability test………………………………………… ……….67
Figure 5.5 Impulse response of the variables of interest………………… ………….69
xiii
LIST OF TABLES
Table 5.1 Results of Phillips Perron and augmented Dickey Fuller tests…..51
Table 5.2 Pairwise correlation matrix……………………………………….52
Table 5.3 The lag lengths chosen by different information criterion………55
Table 5.4 Johansen cointegration rank test results………………………….56
Table 5.5 VECM results before normalisation………………………………59
Table 5.6 Granger causality test……………………………………………..64
Table 5.7 Variance decomposition………………………………………….72
1
CHAPTER ONE
INTRODUCTION
1.1 Introduction
The debt crisis during the early 1980s severely affected the economic performance of many low-
income developing countries and debt-relief initiatives were taken to reduce the deleterious
impact of high foreign indebtedness on the growth of indebted countries (Cohen, 1992).
Actually, the repayment of foreign debt depletes already scarce capital resources, including
common government shares and grabs the opportunities from growth orientations such as
profitable investments, export production support, human capital and infrastructure expenditures.
Additionally, as argued by Lucas (1988), as the ratio of foreign debt to GDP increases, the
marginal real cost of foreign borrowing (which is the sum of the risk-free interest rate and a risk
premium) accordingly increases. This would lead to liquidity and solvency problems, which may
even result in a financial crisis (Sachs, (1988). As a result, the over-surge of foreign debt leads to
investment slowdown and reduced economic growth rate. In relation to this, the notion of ―debt
overhang‖ introduced in the literature review section is thus helpful in understanding the
negative impacts of a high debt burden on investment incentive and production to justify debt
relief.
However, with other conditional variables constant, the borrowing resource (external debt) as
one of the main supplements that fill the financing gap should yield at least an interim period
development for many developing countries, which are plagued by the lack of domestic savings
and high current account deficits (Otani, 1988). The effect of foreign debt on growth may also
result from the efficiency of external debt management, whether the debt funds are channelled
into growth promotion orientations with effective usages that can make the positive effect of debt
on growth more than the negative effect.
Heavy external debt does not necessarily imply a slow economic growth. It is a country‘s
inability to meet its debt obligations compounded by the lack of information on the nature,
structure and magnitude of external debt that causes slow economic growth (Warner, 1992).
Countries may have heavy external debt along with relatively higher level of exports that can
2
help them to sustain their level of external debt. But external debt, if not sustainable, imposes
higher risk to the economic prosperity, as its servicing which is also an indicator of higher
current account deficit, may lead to debt overhang in a country (Krugman, 1988). For any
economy, debt either private or publicly guaranteed, which also includes the contingent
liabilities, plays a crucial role towards overall economic progress. Developing economies
typically have limited sources to raise revenues as a result of low domestic savings and
weakening current account balances coupled with low foreign investment (Indermit, 2005). If
they fail to utilise their debt productively, mobilise investment and create new employment
opportunities they will eventually get stuck with the dilemma of a lower revenue base which will
affect their spending capacity, thereby leading to higher debt servicing (Iyoha, (2000). Inability
to service debt on time not only makes it harder for the developing countries to get aid at
concessional rates with less conditions from the donor agencies but it also increases the country
risk. That not only reduces the overall level of foreign direct investment but forces a country to
rely on domestic borrowing. The higher domestic borrowing increases the domestic interest rate
which leads to crowding out; consequently, this further slows down the economic growth.
1.2 Statement of the problem
The fundamental preliminary point for understanding the macroeconomic effect of government
fiscal deficits is the economy‘s aggregate resource or saving-investment constraint. The saving-
investment constraint shows how conventional public deficits are financed by surpluses from the
private sector and the rest of the world.
One of the greatest problems facing many Sub-Saharan African countries in general and South
Africa in particular is the magnitude of their foreign indebtedness (Romar, 1986). Loan capital
was readily available to South Africa during the 1970s, and both the public and private sectors
borrowed heavily, often in the form of trade credits. However, in the early 1980s, according to
the IMF (1995), foreign investments declined relative to the value of foreign loans needed to
finance economic growth. Equity finance declined as a proportion of foreign debt from 60% in
the 1970s, to less than 30% in 1984. South Africa‘s loan increased from 40% to 70% of foreign
debt. Its total indebtedness increased steadily as loans were acquired from the IMF, whenever the
foreign bankers turn down its request for loan. In addition, indebtedness was stabilised through
3
gold swaps. The debt problem became endemic in 1984, as about two-thirds of its outstanding
loans had a maturity of one year or less. The public sector was responsible for 16% of South
Africa‘s foreign debt; 44% of South Africa‘s foreign liabilities were incurred by the banking
sector; the remaining 40% were private liabilities from Alexander Hamilton‘s funding plan in the
United States of America (World Bank, 1988).
The impact of credit freeze and refusal to roll credit over on South Africa led to a drop in the
value of rand (South African currency) and temporary closure of the financial and foreign-
exchange market (IMF, 1999). Liabilities not affected by the freeze include trade credits, credits
guaranteed by the Paris Club, member governments, and loans from IMF and central banks. Also
compounding South Africa‘s debt problem was the large proportion of debt that was
denominated in hard non-dollar currencies. Since then, South Africa‘s foreign debt has been high
and continued to follow a predictable upward trend. Foreign debt could be regarded as high when
the return on borrowed capital is not sufficient to service the principal loan and interest charged
resulting in accumulation of debts.
According to the SARB (2010), Real GDP of South Africa registered a growth of 3.1% in 2008
compared to 5.1% in the previous year, due to sharp deterioration in consumer and business
confidence. The creation of business confidence is a very important component of foreign debt
as such business confidence is reflected through the sovereign risk spread of a country. The
sovereign risk spread is the difference between the yields on foreign debt compared to a risk-free
benchmark rate such as the United States of America‘s Treasury bond rates. This risk spread
affects domestic interest rates directly via the Fisher Open equation (Karagol, 2002). This has
implications for the domestic economy through the liquidity constraint effect, which shows how
the economy is affected by the level of interest payable on the level of debt incurred by
government, private firms and households. The sovereign risk spread is determined by relating
the factors that cause episodes of financial distress in emerging markets as a result of foreign
debt. The causes of episodes of financial distress are, amongst others, supply and demand forces
as well as external factors.
4
1.3 Objectives of the study
The main objective of this research is to examine the impact of foreign debt on economic growth
in South Africa. The specific objectives of this study are as follows:
To provide trends of external debt and economic growth in South Africa during the period
1980 to 2011
To econometrically determine the impact of external debt on economic growth in South
Africa during the period 1980 to 2011
To use a Laffer curve phenomenon to establish the optimal level of foreign debt relative to
gross domestic product (GDP) in South Africa.
To provide policy recommendations based on the research findings.
1.4 Hypothesis of the study
H0: Foreign debt has a negative impact on economic growth in South Africa.
HA: Foreign debt does not have a negative impact on economic growth in South Africa.
1.5 Significance of the study
Policy makers around the world have been increasingly concerned that high foreign indebtedness
in many developing countries is limiting growth and development. In a study done by Pattillo,
Poirson and Ricci (2004) it was found that at low levels, debt has positive effects on growth, but
above particular thresholds or turning points, additional debt begins to have a negative impact on
growth, hence the core concern of this study is the growth path of indebted developing countries,
in particular, how foreign debt affects growth in South Africa.
Deriving thresholds for optimal foreign debt in relation to gross domestic product is an important
issue for the South African economy. The results of this study will provide policy makers in all
levels of government as well as the private sector with a source of information based on
scientific research project.
5
1.6 Research methodology
This research is based on a quantitative research methodology. In this regard, the study uses an
econometric model based on a VECM estimation technique on a time series data from 1980 to
2011. In order to determine the optimal level of foreign debt in relation to economic growth the
study uses a Laffer curve. In contrast to the Laffer curve used in respect of tax (which shows the
relation between the tax rates and the amount of tax revenue for the government), the Laffer
curve in respect of foreign debt is a curve showing the relationship between foreign debt and
economic growth over a period of time. Detailed information on research methodology is
outlined in chapter four of this dissertation.
1.7 Organisation of the study
This research study consists of six chapters.
Chapter one presents the introduction and background, objectives of the study, significance as
well as research methodology issues. Chapter two discusses trend of and macroeconomic factors
related to foreign debt in South Africa. Chapter three provides an overview of the trends in
foreign debt and economic growth in South Africa. Chapter four presents research methodology.
Chapter five outlines an empirical analysis and research findings. Chapter six presents summary
of the main findings, conclusions and recommendations.
1.8 Concluding remarks
Having outlined the introduction and background to the study, the next chapter presents an
overview of foreign debt and economic growth trends in South Africa during the period 1980 to
2011.
6
CHAPTER TWO
TREND OF AND MACROECONOMIC FACTORS RELATED TO FOREIGN DEBT IN
SOUTH AFRICA (1980-2011)
2.1 Introduction
Most Sub-Saharan Africa (SSA) countries have accumulated large debt stocks since the early
1970s which, compounded by structural weaknesses, may have posed obstacles for growth and
development. Nakatani and Herara (2007) assert that foreign debt may not be a solution but a
problem itself in developing countries. This debt burden problem has generally been observed to
lead to a debt overhang and crowding out effect in various nations. Large debt accumulation of
the developing nations acts as a deterrent to growth process since benefits obtained from growth
are constrained by huge debt serving requirements as well as creating a disincentive effect for
investment (especially private). With large investment/savings gaps in South Africa it seems
plausible that external borrowing can influence growth positively if well utilised or negatively as
the debt becomes a burden.
The aim of this chapter is to outline evolution of foreign debt and its trends in South Africa. The
chapter is divided into three sections. The first section presents an evolution of debt in South
Africa. This is followed by a discussion on the trends of foreign debt stocks and debt service.
The second section provides an overview of gross fixed capital formation, foreign direct
investment and trade openness in South Africa. The third section presents the trends on
economic growth in South Africa. Concluding remarks are provided towards the end of the
chapter.
2.2 Overview and evolution of South African foreign debt
The Republic of South Africa, as a British colony, borrowed in Britain in sterling during the 19th
and early 20th
centuries. In the pre-war period, governments borrowed predominantly to finance
infrastructure expenditures. Bordo (2002) maintains that foreign borrowing had subsequent
advantages over domestic borrowing:
Domestic borrowing was more expensive as interest rates were lower in Britain.
7
The commonwealth ties may have been significant as colonial debt was often admissible to
be held by trusts in the United Kingdom.
Debt proceeds were often spent on capital goods to be imported from Britain and loans
were repaid with export earnings to Britain thereby reducing the cost of currency
mismatches.
The maturity of the loans tended to be quite long (10 to 25 years).
The onset of World War1 essentially closed the London capital market and the gold convertible
currency was suspended. This forced the government to create a domestic bond market and raise
funds domestically. Harris (1988) asserts that the growth rate of public debt of South Africa
during this period was less than that of other British dominions as a result of the shift to the
domestic markets as a source of raising finance to fill in the current account and fiscal deficits.
The share of sterling debt relative to total debt fell from 90% in 1910 to 70% in 1920 and 60% of
foreign debt in 1930 (Lessard and Williamson, 1987). This trend was maintained during World
War ӏӏ and the Bretton Woods period where South Africa continued to increase domestic
issuances while decreasing sterling debt. During the post-Bretton Woods period, a number of
significant developments took place in the international debt markets. The Euro Bond market
was introduced in 1963 and, following the introduction of an interest equalisation tax in the USA
designed to stem capital outflows, subsequently raised the cost of foreign borrowing in the USA
(Bordo, 2002).
Public foreign debt in South Africa stayed low as a percentage of total public debt after 1970 and
continued to do so until 1994 (Gidlow, 2004). A significant percentage of South Africa‘s total
gross debt payable to non-residents has been denominated in domestic currency since 1994 and
foreign debt has been diversified in a number of currencies. With regard to debt in South Africa,
the following distinct phases can be identified:
Debt standstill of 1985 and subsequent restructuring;
Establishment of credibility after the first democratic election in 1994; and
Elimination of the net open forward position (NOFP) from 1998 to 2003.
8
2.1.1 Debt standstill of 1985 and subsequent restructuring
According to the World Bank (2000), South Africa‘s foreign debt increased during the first half
of the 1980s from $16.9 billion in 1980 to $24.3 billion at the end of 1984. The rand value of the
foreign debt increased from R12.6 billion in 1980 to R48.2 billion in 1984 as a result of a
depreciation in the rand. The IMF (2000) contends that South Africa‘s total external debt stock
was to the tune of $23.7 billion in 1985, of which about $15 billion was short-term debt,
repayable within 15 months. In the early 1980s, the proportion of the debt that was short term
(maturing in less than one year) rose rapidly. Short-term debt increased from 49% of total debt in
1980 to 72% in 1985 (World Bank, 2000). Factors contributing to this development included
transnational banks‘ (TNBs) fears after the Mexico default in 1982, uncertainty about South
Africa‘s economic and political future, the growing sanctions movement, and the willingness of
South African borrowers to accept relatively unfavourable terms. The high levels reached by
South African interest rates drove borrowers abroad for short-term loans, largely in the form of
interbank loans. Politically sensitive lenders, particularly those in the United States where the
sanctions movement was growing, found that ―lending to South Africa through the interbank
market provided a near perfect disguise, since transactions through the interbank market are
never published‖ (Collier and Dollar, 2001).
Although the total foreign debt in 1985 was not excessive by international standards, the build-
up of short-term debt, combined with political unrest and social developments in South Africa
and increasing pressure by international banks on South Africa, created a major liquidity crisis
and caused further downward pressure on the exchange rate of the rand.
On 15 August 1985, the South African president at that time, PW Botha, delivered his infamous
Rubicon speech refusing to abolish the apartheid system. The financial instability following this
speech resulted in the South African authorities announcing a three-day closure of the South
African foreign exchange markets and the stock exchange. In September 1985, the then Minister
of Finance, Barend du Plessis, announced that the financial rand mechanism, which had been
abolished in 1983, was to be reintroduced, and the refusal of key international banks to renew
their credit lines to South Africa led to a declaration on 1 September 1985 of a standstill on
South African foreign debt repayments (Mecer, 1988).
9
Of the total South African outstanding foreign debt of $23.7 billion on 31 August 1985, $13.6
billion was defined as ‗affected‘ debt meaning that this debt had to be written off or completely
cancelled. The balance, $10.1 billion was not affected by the standstill (IMF, 2001). The
standstill did not apply to payments for normal current transactions and specifically excluded
payments on certain categories of foreign debt, namely: (і) foreign bond debt issuances, (іі) debts
payable to international organisations, (ііі) debts guaranteed by foreign governments or their
export agencies, (іv) foreign debt commitments of the South African Reserve Bank , (v)
outstanding amounts due from domestic importers to foreign suppliers for goods and services
rendered after the beginning of 1985 in South Africa, and (vі) new loans granted by non-
residents to residents after the beginning of September 1985.
In December 1985, the standstill on affected debt was extended to the end of March 1986 to
allow for a negotiated agreement with creditor banks. This led to four distinct interim debt
arrangements. These four distinct interim debt arrangements are discussed in the following sub-
sections:
2.2.1.1 The first interim debt arrangement
This was agreed upon in March 1986 and provided for the extension of the standstill on
repayment of affected debt, which amounted to $13.6 billion, until June 1987 but provided for
the continued payment of interest on the affected debt at an approved rate and also for the
repayment of 5% of the principal of certain affected debt that matured before June 1987. Such
repayments amounted to about $500 million (National Treasury, 2004).
Under the terms of the first interim debt arrangement, foreign creditors with maturing debt
agreed with the South African debtors to roll over or extend the loan, sell the existing debt,
arrange the assumption of the debt by another South African debtor or have the loan amount,
upon maturity, deposited into a special restricted account with the Public Investment
Commissioners (PIC), a non-banking financial institution entrusted with the administration of a
wide range of public sector funds.
The arrangement also afforded creditors the option to convert affected debt into three-year
medium-term loans, subject to approval by the exchange control authorities, and any debt so
10
converted would cease to be affected debt. According to Stals (1998), by August 1991, all loans
approved under this option ($649 million) had been repaid.
2.2.1.2 A second interim debt arrangement
According to the International Institute of Finance (IIF) (2006), the second debt arrangement was
entered into in March 1987 extending the standstill on affected debt of $13.9 billion to June 1990
and made provision for the periodic repayment of up to 13% of the declining balance of affected
debt with maturity dates up to June 1990. Total repayments during this period amounted to about
$1.5 billion (IMF, 2000). An option to convert affected debt into non-affected long-term loans,
repayable over nine and a half years was also included. More than $4 billion was converted. The
second interim debt arrangement enabled South Africa to permit affected debt to be utilised for
investment domestically.
2.2.1.3 A third interim debt arrangement
This debt arrangement took place in October 1989 and extended the standstill on affected debt of
$7.3 billion to December 1993 and made provision for the periodic repayment of up to 20.5% of
the declining balance of affected debt with maturity dates up to December 1993 (IMF, 1997).
This arrangement provided an option to convert short-term affected debt into long-term non-
affected loans of at least ten years‘ maturity. The third interim debt arrangement also enabled
South Africa to permit affected debt to be utilised for domestic investment.
In September 1993 an agreement was reached with South Africa‘s foreign creditor banks for a
final rescheduling of the remaining amount of $4.4 billion of affected debt (IMF, 1998). In
accordance with this final debt arrangement, the remaining balance was repaid to creditors in full
in semi-annual instalments from February 1994 to August 2001.
2.2.1.4 The final debt arrangement
The final debt arrangement permitted the conversion of short-term affected debt into non-
affected debt provided that the new debt had a maturity of at least eight and a half years and was
repayable in not fewer than ten equal semi-annual instalments, the first of which was payable no
earlier than four years from the date of conversion. It also permitted the conversion during 1994
11
of affected debt owed by the PIC into no-affected dollar denominated notes issued by the PIC,
payable in one amount on maturity of nine years from the date of issuance. The final debt
arrangement also enabled affected debt to be utilised for investment in South Africa
(Chacholiades, 2004).
Affected debt was further reduced by conversion into longer-term non-affected debt and by debt-
for-equity swaps. Between August 1985 and December 2003, non-affected debt denominated in
foreign currency increased from $10.1 billion to $27.3 billion (IMF, 2000). South Africa repaid
the affected debt in full in August 2001 and the debt standstill regulations were repealed in
November 2001.
2.3 Foreign debt in South Africa
South Africa‘s foreign debt rose since 1970, slowly at first, then dramatically during the early
1980s, before reaching a halt in 1985. In 1980, the total foreign debt of South
Africa was $16 890 million, as illustrated by Figure 2.1, which, in rand terms, was 20.3% of the
country‘s GDP. By 1984, foreign debt reached a peak of $24 298 million which in rands was
45.7% of the GDP (reflecting in part the decline of the rand against the dollar) (IMF, 2000). In
1985, though the dollar value of South Africa‘s debt declined slightly, the continued depreciation
of the rand took the debt up to 50% of its GDP at about the time that South Africa stopped
payment on short-term debts. According to Edwards (1999), by June 1986, principal of nearly
$500 million in short-term and $1.5 million in longer-term debt debts had been repaid. However,
in mid-1989, in spite of the repayment of some of the principal, the debt reached a peak at about
$26 million. This was higher than might have been expected because some of South Africa‘s
debt was held in hard non-dollar currencies such as Swiss francs and Deutsche marks, which
appreciated against the dollar after 1985 (World Bank, 2000).
The trend of and macroeconomic factors related to foreign debt in South Africa is illustrated in
Figure 2.1 below:
12
Figure 2.1: Trend in foreign debt in South Africa from 1980 to 2011
Source: International Financial Statistics (IMF), (2011)
Figure 2.1 shows further that South Africa‘s total outstanding foreign debt increased from US$ 3
523 million at the end of 2006 to US$4 610 million at the end of 2007. The ratio of rand-
denominated debt to total debt increased considerably in 2007, passing from 39.7% to 40.2%.
In 2009, South Africa experienced its first recession since 1992, as the economy declined by
1.5% and the stock of foreign debt was US$4.32 million. The recession indicated that the South
African economy is very susceptible to shocks in external demand as foreign debt plummeted by
1.08% in 2010 and debt increased in 2011 from US$4.5 million in 2010 to US$5 million (IMF,
2011).
The data shown in Figure 2.1 above shows that South Africa borrowed profoundly after the
apartheid regime as foreign loans showed a decreasing trend since 1997 to the year 2000. This
could be as a result of participation in the global financial market posing a promising
macroeconomic base in South Africa consenting international lenders to extent credit into South
Africa.
13
2.4 Foreign debt service
Goldreich (2010) contends that debt service is defined as the sum of principal repayments and
interest actually paid in foreign currency, goods or services on long term debt, interest paid on
short-term debt, and repayments (repurchases and charges) to debtor nations or institutions.
According to the IMF (2000), principal of nearly US$500 million in short-term and
US$1.5 million in longer-term debts had been repaid by June 1986. South Africa reduced its total
disclosed foreign debt to less than US$20 million in
early 1992, down from nearly US$24 million in 1985 (SARB, 2002). A key problem in repaying
its loans was the large, but undisclosed, portion of South Africa's debt that was denominated in
hard non-dollar currencies, but appreciated in dollar terms as the dollar weakened. South Africa
nonetheless repaid between US$1.7 million and US$1.9 million of debt by 1990, and some
foreign bankers were increasingly willing to refinance maturing South
African credits. For example, US$300 million of US$900 million bearer bonds in Deutsche
marks and Swiss francs were rolled over or replaced in 1990 (IMF, 2001).
The trends in foreign debt service in South Africa are presented in Figure 2.2 below. The
government repaid about US$500 million in foreign debt in February 1994. At that time, South
Africa was considered an under-borrower by conventional financial criteria, with a foreign
debt/export ratio of about 60% and a foreign debt/GDP ratio of 15.1% (SARB, 2000)
.
14
Figure 2.2: Foreign debt service in South Africa for 1980 to 201
0
20
40
60
80
100
120
140
160
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Fo
reig
n d
ebt
serv
ice
(US
$,
mil
lions)
Source: StatsSA (2011)
The value of interest payments on total external debt in South Africa averaged US$2.6 million
reaching a peak of $5.5 billion in 1994 and a record low of US$1.2 million in 2002 (Figure 2.2).
Over the past 16 years, interest payments have fluctuated between $2.3 million in 2010 and $899
million in 2003 reaching a record low of $1.5 million in 2011. Foreign debt service during the
period 1980 to 2011 indicate that South Africa‘s repayments on debt progressively improved
since 1992 indicating that South Africa has been able to repay its interest on foreign loans. This
could be attributable to high public debt affordability and good external liquidity of the South
African economy parallel to a stable, a predictable economic policy framework and an
effervescent trade openness engagement.
2.5 Trade liberalisation
According to Jonsson and Subramanian (2001), trade liberalisation gained momentum in the
early 1990s mainly through a tripartite discussion of the National Economic Forum (NEF)
involving government, labour, and organised businesses. This led South Africa to the adoption of
a dual approach to trade liberalisation during the 1990s where it engaged in (і) unilateral trade
liberalisation and (іі) multilateral trade liberalisation in the context of the Uruguay Round of
trade negotiations. Roberts (2000) asserts that during the period between 1990 and 1994,
15
unilateral trade liberalisation took place through the elimination of the remaining import
licensing procedures, reduction of tariffs from around 28% to 16% while completely eliminating
the import surcharges. The sum of all charges on imports was reduced from 34% to 16%.
Another phase of unilateral trade liberalisation was from 1994 to 1998. During this period, South
Africa announced a schedule of unilateral trade liberalisation in which average tariffs in
manufacturing declined from 16% to 10% (Calderon, 2003). Within the period 1995 to 2002,
South Africa experienced multilateral trade liberalisation. This was done in the Uruguay round
context where South Africa made a tariff offer to phase out all tariffs over a five-year period.
The trade liberalisation programme pursued by South Africa since 1994 resulted in a more open
economy. The SARB (2010) affirms that total exports and imports of goods and services
amounted to 31% and 35% of GDP in 2007, respectively. Between 1994 and 2002, import
penetration and export orientation ratios improved. According to the analysis given in Figure 2.3,
export earnings of goods and services expressed as a percentage of total foreign debt stock
fluctuated around 80% from 1994 to 2011. This could be as a result of increased exports from
US$28.5 million in 1994 to US$117.7 million in 2011, showing a significant engagement of
openness to trade by South Africa by expanding export volumes.
However, South Africa has not experienced any significant gains from exports albeit that the
economy is open through trade liberalisation. Moreso, South Africa has not experienced any
significant surge of foreign direct investment. The ratio of foreign debt to export earnings in
South Africa is portrayed in Figure 2.3 below:
16
Figure 2.3: Ratio of foreign debt to export earnings in South Africa for 1980 to 2011
Source: DTI (2011)
Exports suffered from relatively slow growth of productivity in the tradable goods since 1980 to
1984. The ratio of foreign debt to export earnings gathered momentum since 1982 reaching its
peak in 1994. From 1994 to 2004 South Africa witnessed a favourable trend of ratio of export
earnings to foreign debt. This could be as a result of high debt servicing and a decline in
accumulation of foreign loans during this period parallel to a favourable current account balance
(SARB, 2011).
South Africa was hit by a trade deficit since 2004 because of rising demand for imports, fuelled
by high consumer spending and fixed capital investment, while exports lagged. The current
account balance progressively deteriorated since 2003, reflecting the movements in trade
balance. In 2006, the deficit reached 6.5% of GDP, and widened further in 2007 to an estimated
6.9% of GDP (SARB, 2011). Nevertheless, capital inflows financed a record 37% of gross
investment in the third quarter of 2007 (SARB, 2010).
Foreign capital has been forthcoming to finance this deficit, due to strong domestic performance
and high commodity prices stimulating the global demand for South African bonds and equities.
In fact, capital inflows more than offset the current account deficit so the overall balance of
17
payments remained in surplus. FDI inflows are discussed in the following section.
2.6 Foreign direct investment flows into South Africa
Since the mid-1990s, capital account liberalisation and broader economic reform in South Africa
contributed to transformation of the balance of payments. For the most of the period since 1994,
net capital inflows into South Africa helped to alleviate key structural constraint of low domestic
savings. FDI inflows into South Africa declined from US$26.5 billion in 1992 to US$16.9 billion
in 2002, a level comparable to the peak of US$15.3 billion achieved in 2008, prior to the onset of
the global financial crisis (World Bank, 2011).
South Africa experienced low volumes of FDI flows followed by a declining trend of FDI net
inflows from the year 1994 to 2002. The decline in the ratio of FDI net inflows as a percentage
of GDP was attributable to the major foreign debt crisis that hit South Africa in 1996 which led
to the precipitous drop of the value of the rand as South Africa failed to secure loans from banks.
FDI inflows during 2003 to 2011 in South Africa increased manifold as compared to during mid-
1983 to 1985 (Figure 2.4). This is attributable to measures introduced by the government to
liberalise provisions relating to FDI in 1991 to lure investors from every corner of the world. The
FDI flows into South Africa rebounded from $1.23 billion in 2010 to $5.81 billion in 2011,
making South Africa the second-biggest FDI destination on the continent in 2011 (Figure 2.4)
18
Figure 2.4: FDI net inflows into South Africa for 1980 to 2011
Source: QUOIN Institute (Pty) Limited and SARB (2011)
Figure 2.4 clearly illustrates that South Africa received a declining or very little inward FDI from
1993 to 2002, and fluctuated below US$10 billion. This is attributed to low volumes of FDI in
South Africa as investors had little confidence in the first democratic government (Business Map
Report, 2002). Clark and Borgran (2003) argue that during the apartheid era, South African
economic policies were not conducive to FDIs as there was extensive state intervention in the
economy.
After 2002 there was a gradual increase from around US$1.3 billion to US$3.5 billion in 2003 in
the volume of FDI. This was as a result of the political democratisation and subsequent openness
of the economy. In 2004, there was a marked increase in FDI inflow as it increased from R3.5
billion to US$17.6 billion. The FDI recorded a huge increase of 196% in 2007 from US$40.104
billion in 2009 to US$41.184 billion in 2010-2011 financial years (Figure 2.4).
Clearly, FDIs increased over time since the political democratisation process of 1994, but
relative to the size of South Africa‘s economy and other similar developing countries classified
as emerging markets, FDI performance is still below expected levels. The FDI seems to be
mostly in mergers and acquisitions, and not Greenfield investment (a form of foreign direct
investment where a parent company starts a new venture in a foreign country by constructing
19
new operational facilities from the ground up). This suggests that South Africa receives
relatively low levels of FDI.
2.7 Gross fixed capital formation
One of the main variables which determine economic growth in any country is investment in
physical capital stock in the economy. Both classical and endogenous growth theories postulate
that investment in physical capital is a primary source of growth. According to Levine and
Renelt (1992), capital formation is the single and most robust variable used in empirical cross
sectional growth studies. In essence, theory points out that savings have a very strong
relationship with investments. Lewis (2002) reflects that South Africa‘s domestic savings are
generally low by international standards when compared to other middle income countries.
Savings have been declining steadily over the past years, from an average of 22% of GDP during
the 1980s to only 14% during 1998. Lewis (2002) further points out that a notable change in the
composition of savings has also been experienced over the years. Government savings have been
responsible for much of the overall decline, reflected by the swing from positive rates in the
early 1980s to negative rates in the early 1990s. Private savings were fairly stable, but declined
during the latter part of the 1990s when household savings dropped from around 4% of GDP in
1992 to almost zero in 1999 (SARB, 2011).
Figure 2.5 reveals that gross fixed capital formation (GFCF) has been on an upward trend since
as early as the 1980s and became much steeper as early as 2005. Capital accumulation has
improved steadily since the election of the democratic government in 1994. The continuous
upward trend in capital formation reached a peak in 2007 to the tune of R284 364 billion
followed with a decline in 2008 as a result of the financial crisis that depreciated the value of the
rand. Gross fixed capital formation from 2008 to 2011 showed a declining trend as savings and
the current account balance debilitated.
20
Figure 2.5: Gross fixed capital formation in South Africa for 1980 to 2011
Source: SARB (2011)
Since South Africa is experiencing a short-fall in savings relative to the investment needs of the
economy, the country remains dependent on capital inflows in order to finance its physical
capital formation. According to Fedderke and Liu (2010), capital inflows respond positively to
higher domestic returns on assets, and negatively to risk and higher returns on foreign assets.
Political risk plays a crucial role in determining capital flows in South Africa. An improved rate
of return on assets and reduced risk on assets will therefore increase capital inflows into South
Africa.
2.8 Economic growth in South Africa
Growth in South Africa was relatively low, fluctuating between US$50 and US$100 billion
(Figure 2.6). Despite a slight recovery in 1984, continued political uncertainty and growing
social unrest were responsible for this erratic pattern. The country was hit by a series of droughts
in the 1980s, which seriously affected agricultural output (SARB, 2011). Further erratic changes
in gold prices led to a series of booms and busts, reducing the average real GDP growth to 0.33%
21
during 1982-1986. By the mid-1980s, there was a small recovery of the economy averaging
2.09%, although the economy was distorted by apartheid policies which were designed to
exclude many of South Africa‘s citizens from significant participation in the nation‘s wealth.
Figure 2.6 illustrates that the weak recovery of the economy during the period 1995-2000 might
have been due to the decrease in FDI inflows and rising foreign debt service which result in
capital flights. Thus, from 1990-1992, a small recovery in 1993, due to the dismantling of
apartheid policy, resulted in GDP growth averaging 0.32 during 1991 to 1994 (SARB, 2011).
Figure 2.6 elucidates that the period 2002-2007 witnessed a steady increasing trend of growth,
with GDP reaching a peak in 2007, coupled with a sluggish growth in 2008. Economic growth
increased soon after the financial crisis in the late 2009 showing an increasing trend to 2011.
During the period 2002 to 2007 growth in South Africa showed increasing trends while foreign
debt was relatively low coupled with low debt servicing. This augments the economic theory that
points to a negative relationship between huge foreign debt stocks and economic growth.
Figure 2.6: Economic growth in South Africa for 1980 to 2011
Source: QUOIN Institute (Pty) Limited and SARB (2011)
The GDP in South Africa, as depicted in Figure 2.6, as per GDP value, was $363 billion as of
2010, reaching a peak of US$47 236 billion in December 2011. The trend analysis of growth,
measured in terms of GDP, in South Africa indicate that growth has been remarkably improving
22
since 1994, however, as the GDP to foreign debt ratio increases, growth falls. This is as a result
of capital flight and debt overhang effects as a result of debt servicing in an economy
characterised by low domestic savings, persistent deficit in the balance of payments and high
fiscal deficits.
2.9 Concluding remarks
It is evident in this chapter that since 1994 the economy has witnessed positive economic growth
rates. This has been a result of increased productivity and emergence of other dominating factors
apart from the traditional mining and manufacturing sectors. The discussion of the trends in
foreign debt and debt service has also indicated that South Africa‘s foreign debt history is strong
and remarkably high as the country tries to fill in the investment gap and supplement the low
domestic savings. South Africa has been able to service its debt and large volumes of FDI
inflows had been positively high due to a stable political and social structure as opposed to the
past. A strong macroeconomic policy framework has helped to improve foreign borrowing over
the past two decades, but for South Africa, for example, the 2008/09 downturn highlighted the
limitations of the domestic-demand-led growth path, which calls for the need of foreign
borrowing, which has characterised South Africa in recent years. This supports the need to
investigate the economic impact of foreign debt in South Africa.
23
CHAPTER THREE
DEBT-GROWTH RELATIONSHIP: THEORETICAL AND EMPIRICAL REVIEW
3.1 Literature review
This chapter discusses theoretical and empirical literature underpinning the study. The first part
of the discussion presents theoretical links between foreign debt and economic growth. The
theoretical literature presented is a critical analysis of the endogenous and the exogenous growth
theories. Endogenous growth theorists do not share the same perspective with exogenous growth
theorists on how growth is achieved. The second part of this chapter will illustrate how foreign
debt affects growth via the debt servicing effect. The analysis is expected to give a much defined
picture on how foreign debt affects economic growth in South Africa. Lastly in this chapter, a
conclusion on the theoretical and empirical findings will be presented.
3.2 Theoretical Literature
Assuming full employment, market clearance, and perfect competition, the Harrod-Domar model
shows that economic growth is based directly on capital accumulation. According to this model,
if debt can raise capital accumulation, growth will be achieved. In relation to this, Arthur Lewis
(1954, p.141) states:
The theoretical problem in the theory of economic development is to understand the
process by which a community which was previously saving and investing 4 or 5 per cent
of its national income or less, converts itself into an economy where voluntary saving is
running at about 12 to 15 per cent of national income or more. This is the central problem
because the central fact of economic development is rapid capital accumulation (including
knowledge and skills with capital).
Based on this statement, increasing domestic savings rates would ensure growth because the
capital accumulation is at the center of development. On the other side, it is shortage of capital,
not of labour or technology, which prevents industrial growth. However, empirical evidence in
developing countries since the 1980s seems to be showing opposite consequence to what is being
suggested by theory in that massive foreign debt accumulated is not accompanied by an increase
24
in per capita income as a result of the debt over hang theory. The debt over hang theory implies
the draining out of a countries‘ limited resources and restriction on its financial resources for
domestic need of development due to the repayment of debt in the form of principal and interest
payments which is cumulatively known as ―debt service payments‖. Benedict Clements (2003)
suggested that foreign borrowing has a positive impact on investment and growth of a country up
to a threshold level but external debt service can potentially affect the growth as most of the
funds will go in the repayment of the debt rather in the investments.
This section presents various growth theories in order to ascertain determinants of economic
growth given the foreign debt in the country.
3.2.1 Exogenous growth theories
The neoclassical growth model by Solow-Swan (1956) identifies two possible variation sources
of output per worker: differences in capital per worker and differences in the effectiveness of
labour, with an assumption that technical change and savings are exogenous and the technology
process is labour-augmenting or Harrod-neutral (if knowledge or effectiveness of labor denoted
by A enters in the output growth expressed as Y = F (K, AL). This technology progress is Harrod-
neutral, where Y represents output, K means capital and L is labour. Solow‘s model is based on
the premise that there will be an effect on the level of savings but the accumulation of physical
capital cannot account for either the vast growth over time or the vast geographic differences in
output per person. This implies that the long-term driving force of growth is the exogenous
technology change or the effectiveness of labour. This position also rests on the following
assumptions:
Fundamental forces like resources, preferences, and technology lead to Pareto-efficient
outcomes, and
Institutions do not influence the choice of the equilibrium.
In the context of this study it is assumed that foreign debt affects the technology change
indirectly through capital accumulation. In this way, foreign debt should have growth effects in
the long run (Mariano and Villanueva, 2005).
25
3.2.2 Endogenous growth theories
Cohen (in Cohen and Sachs (1986) and Cohen, (1991, 1992)) presents an endogenous growth
model where capital accumulation is the sole force driving growth. In this instance growth
begins to fall to a lower level due to debt servicing. Nevertheless, growth will still be higher than
it would have been without international borrowing and lending (financial autarky) (Cohen,
1992). Furthermore, repaying foreign debt does not in any way crowd out investment because
lenders are more patient and value growth more than the debtor country itself. This result,
however, depends on the ability of lenders to implement an optimal rescheduling policy. If they
are not able to commit to this policy over the life of the lending relationship, a debt overhang
scenario will occur and investment and growth in the later stages will be even lower than in
financial autarky.
Conlisk (1967) modified the neoclassical growth model to make the technical change
endogenous within a closed-economy model. The asymptotic equilibrium growth rate of the
modified model is found to be positively related to the value of saving rate; while the former
neoclassical growth models emphasise that the equilibrium growth rate is not affected by the
saving rate in the long run.
The endogenous growth theorists believe that the sources of economic growth are endogenous.
Endogenous growth theorists have constructed a model in order to analytically illustrate the
mechanisms by which savings can affect economic growth. Among them, Greenwood and
Jovanovic (1990) and Pagano (1993) presented a model in which both capital accumulation and
growth are endogenously determined. In order to understand their model, let us consider the
simplest endogenous growth model of the production function proposed by Pagano (1993), in
which the output (Yt) is produced during the period (t) by one factor of production, which is
capital (Kt):
Yt = f (Kt)
According to Pagano (1993), it is assumed that the population is stationary, and that the economy
only produces one good which can be consumed or invested. The rate of growth, according to
Pagano (1993), is equal to the product of the marginal productivity of capital, the rate of savings
and the proportion of savings channelled to investment minus the depreciation of capital. Pagano
26
(1993), states that there is an incentive for capital-scarce countries to borrow and invest since the
marginal product of capital is above the world interest rate.
From the endogenous growth model, it can be concluded that both savings and productivity of
capital positively affect long-term growth. Endogenous growth theorists stress the need for
government and private sector institutions and markets to nurture innovation through
international borrowing, and provide incentives for individuals to be inventive (Pagano, 1993).
Following the endogenous growth theory, this research has its groundwork underpinned by the
Mariano and Villanueva (2005) model as discussed in the following section.
3.2.2.1 Theoretical model
Belonging to the class of ―endogenous growth‖ models with the Romer (1986), Lucas (1988),
Grossman and Helpman (1990) models, Villanueva (1994) incorporated the effect of learning
through experience on the steady-state growth rate of output. The model is a variant of Conlisk‘s
(1967) endogenous-technical change growth model and Arrow‘s (1962) ―learning by doing‖
model. The key relationships postulated by the models are that technical change improves with
the capital stock per capita and learning experience plays a critical role in raising labour
productivity over time. The presence of learning experience makes the growth equilibrium
endogenous.
Contrary to the conclusion of Solow-Swan model, in this model, growth equilibrium cannot only
be influenced systematically by the changes of private rates of saving, depreciation and
population growth. Furthermore, it can also be positively affected by policies regarding trade
openness (the ratio of foreign trade to GDP), fiscal deficits, expenditures on human resource
development and net investment through learning coefficient. In this way, Villanueva‘s (1994)
work expands Conlisk‘s (1967) model to incorporate learning, and changes the status of the
economy from closed to open.
Based on the endogenous growth model Mariano and Villanueva (2005) incorporated external
debt as an added factor to explain indebted countries‘ growth and extend the horizon from
Solow‘s close economy to open for global capital market. Upon strict assumptions, their model
gives direct expression of the joint interaction dynamics of external debt, capital accumulation
27
and growth: the steady-state ratio of external debt to GDP is constant at the output growth
equilibrium level when the expected net marginal product of capital matches the marginal cost of
funds. Although constant in the long-run, the steady-state external debt ratio is diversified with
differences in the economy‘s propensity to save, marginal cost of funds, depreciation rate,
working population growth rates and other exogenous parameters like risk-premium.
However, in general, the existence, uniqueness, and stability of the steady state equilibrium are
not guaranteed. Moreover, Mariano and Villanueva (2005) used a Cobb-Douglas production
function and conclude that the extended model‘s equilibrium is locally stable in the
neighbourhood of the steady state. Another significance of the model is that one is to estimate
the optimal domestic saving rate which maximises the real consumption per unit of effective
labour in the long run as consumption is taken as welfare indication and also the reduced models
give long-run steady states‘ expressions of external debt. Another significant point the Mariano
and Villanueva model makes is that the estimation for the steady-state ratio of net external debt
to GDP is associated with the optimal outcome in the long run.
The Mariano and Villanueva model used the following standard production function model to
investigate the relationship between economic growth and foreign debt:
Y = (K, DPN, DMS) (1)
where Y, K, DPN, and DMS represent GDP, capital stock, depreciation of capital stock and
domestic saving respectively.
Neoclassical theorists could not refute the fact that the kind of convergence predicted by their
theory is not occurring. When neoclassical economists go beyond the fundamentals of resources,
technology, and preferences, they focus almost exclusively on government arguing that
government performance become impediments to prevent markets from working smoothly.
Departing from the strong assumptions of neoclassical theory, the Mariano and Villanueva
model argues that the fundamentals in neoclassical theory are not the only determinants of
economic growth.
28
3.2.3 Exogenous versus endogenous growth theories
The debates between Solow‘s exogenous growth theory and Romer‘s endogenous growth theory
represent the controversy in the huge field of research on convergence. According to the Solow
models, one can derive both absolute and relative convergence. Absolute convergence implies
that the rate of return on capital is lower in countries with more capital per worker. Thus, there
are incentives for capital to flow from rich countries to poor countries. Relative convergence
results from the differences in the technology and saving rates of various countries. With the
assumption that the labor-augmenting technical change is exogenous, Solow‘s model emphasises
the capital accumulation as the source of conditional convergence, whereas Romer (1986) and
Lucas (1988) deem a combination of physical and human capital as the principal engine of
growth. Romer models make a difference in technology change across countries and overtime as
the source of convergence. From Romer and Barro (1992), convergence is also conditional on
the different structural characteristics of an economy, such as its social preferences, technologies,
rate of population and government policy. Different structural characteristics imply different
steady-state relative per capita incomes. Beginning with Baumol (1986), these theories have been
subjected to extensive empirical testing. Much of the theoretical and empirical literature is
summarised in Sala-i-Martin (1995), Quah (1996) and Jones (1997).
Kumar and Russell (2002) constructed the measurements for the world production frontier and
relative country‘s efficiency level to give the static comparison of the growth rates of economies.
Their empirical research finds that the world growth represented by productivity distributions in
period 1960-1990 change from uni-modal into bimodal showing bipolarisation character. By
building-up models for tripartite decomposition of convergence, they suggest three components
of productivity change: technological efficiency change shifting the world production frontier, its
catch-up movements towards or away from the frontier and capital accumulation. Their
systematic empirical demonstrations and analysis on the Kernel distribution imply that
worldwide technology frontier is moving up with substantial evidence of technology catch-up.
But the technological change is decidedly not neutral, hence benefiting the richer countries more
than the poor. They conclude that technology catch-up not seems to be a strong force and
technology efficiency change do somewhat more than technology catch-up but not much, capital
accumulation contributed the most to both the growth and bipolar international divergence.
29
Neoclassical theorists could not refute the fact that the kind of convergence predicted by their
theory is not occurring. When neoclassical economists go beyond the fundamentals of resources,
technology, and preferences, they focus almost exclusively on government. They argue that
government performance becomes impediments to prevent markets from working smoothly.
Departing from the strong assumptions of neoclassical theory and different to Romer‘s
endogenous growth theory, Hoff and Stiglitz (2000) argue that the fundamentals in neoclassical
theory are not the only deep determinants of economic outcomes. They focus on four:
institutions, the distribution of wealth, history, and ‗ecology‘; which mean the behaviours of
other agents rather than the government in the economy that have spill-over effects. Hoff and
Stiglitz (2000) emphasise that information and enforcement problems impose limits on economic
possibilities that are just as real as the limits of technology. Non-market institutions arise in
response to those limits and influence outcomes. They contend that wealth distribution affects
contracts, incentives, and outcome while history influences a society‘s technology, skill base,
and institutions. It is not necessarily true that the impact of past events erodes over time. At the
same time, they summarise that ―ecological economics‖, more generally, modern development
economics rejects the very notion of ‗equilibrium‘ that underlies in traditional theories. In
contrast, they tend to be influenced more by biological than physical models. The latter
emphasise the forces pulling toward equilibrium-and with similar forces working in all
economies, all should be pulled toward the same equilibrium; while the latter focus more on
evolutionary processes, complex systems, and chance events that may cause systems to diverge.
The convergence in this analysis is expressed as the different adjustment speeds towards the long
run equilibrium with different assumptions on foreign debt. The faster the adjustment speeds, the
more hope developing countries could catch up, or become closer to the growth level of
developed ones. Nonetheless, based on endogenous growth models the decisive role of
government in the realisation of the effect of foreign debt on growth is emphasised. Only under
the strong assumption that government has properly channelled the foreign debt funds into
growth orientation directions with effective usages, expected growth adjustment speed can be
reached. In contrast, if the tests do not support the strong positive relation of foreign debt to
growth, this may imply that mismanagement of foreign debt exist.
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3.3 Empirical literature on the debt-growth relationship
This section reviews relevant empirical studies that give detail regarding the impact of foreign
debt on economic growth. Literature from developed countries, developing countries as well as
that from South Africa is taken into account. Empirical literature will help to distinguish the
imaginary scenarios entrenched in theories by revealing results from the real experiences. The
various policies implemented and the results shall be matched to determine the economic impact
of foreign debt in the South African economy.
3.3.1 Developed countries
There is an abundance of empirical evidence that supports the impact of foreign debt on
economic growth. Savvides (1992), using the vector error correction model (VECM), examined
the relationship between foreign debt and economic growth in Greece for the period 1975-1990,
and claimed that developed debtor nations who were unable to pay their external debts would
have any debt payment to be negatively linked to economic performance. Their finding suggests
that economic benefits that accrue to the debtor nation in terms of increments in output or
exports are minimised due to debt servicing requirements.
Cunningham (1993) suggests that there exists a linear relationship between external debt and
economic growth in developed countries. However, Feder (2000) found the relationship between
foreign debt and economic growth to be nonlinear. Patillo (2002) supports the view of a
nonlinear relationship between external debt and growth rate of GDP for Germany over a period
of 29 years, beginning from 1969. The study concludes that the relationship between external
debt and economic growth is nonlinear in the form of an inverted U shaped curve. By
implication, at low levels of external debt, growth is affected positively but at higher levels of
total debt, the relationship becomes negative. Pattillo (2002) was able to determine the exact
turning point which was put at 35-40% of debt to GDP ratio and between 160-170% for debt
export ratio.
However, Schclarek (2004) conducted a similar study like that of Patillo (2002) but using nine
developed and 24 industrial countries with datasets obtained from World Bank Development
Indicators (WDI) dataset. For developed countries, the study found lower levels of external debt
to be related to higher growth rates. Notwithstanding, the study did not find existence of an
31
inverted U shape relationship between total external debt and economic growth as claimed by
Patillo (2002). In the case of industrial countries, the study found no significant relationship
between total government debt and economic growth. Adegbite (2008) was also unable to find
any significant nonlinear relationship between external debt and economic growth for Japan.
3.3.2 Developing countries
Savvides (1992) while trying to measure the impact of debt overhang on the country‘s economic
performance, used a two stage limited dependent variable model (2SLDV) procedure by cross
section time series data from 43 less developed countries (LDCs) encountering debt problems.
The study concludes that debt overhang and decreasing foreign capital flows have a significant
negative effect on investment rates.
The empirical findings of Afxentiou and Serletis (1996) using the VECM, for developing
countries, shows that there exists a negative relationship between indebtedness and national
productivity from 1980-1990. This was attributed to excess debt accumulation from 1970-1980
when foreign loans were taken to cushion the shock from oil price increases in early 1970. In the
same vein, earlier findings of Geiger (1990) also assert this using some highly indebted South
American countries. The result of the study showed existence of a statistically significant inverse
relationship between debt and economic growth from 1974 to 1986.
Using the 2 stage-least-squares (2SLS) technique, Fosu (1996) examined the degree to which
debt had a negative impact on economic growth in Sub-Saharan African countries. The result
confirmed that debt directly and negatively affects growth by reducing productivity and, on
average, a high debt country experiences almost 1% of reduction in GDP growth rate annually.
His findings seemed to be consistent with the ‗direct effect of debt hypothesis‘ which
theoretically states that for countries facing large repayment, debt outstanding and servicing will
directly and negatively impede growth even if it does not affect investment. Fosu (1999), using
the vector error correctional model (VECM), reaffirmed his earlier findings that foreign debt
directly affects Sub-Saharan African countries negatively. Further evidence from his work also
showed a weak negative effect of debt on investment levels. On the contrary, there have been
few studies like Cohen (1993) who for a large dataset of developing countries found no
32
implicative evidence of a negative effect of debt on economic growth for the period 1965-1989.
The degree to which foreign debt affects an economy varies by country. Chowdhury (1994)
investigated the extent of external debt impact on GDP and vice versa using a system of
simultaneous regressions. The study employed panel data for the period 1970-1988 on selected
Asian and Pacific countries which include Bangladesh, Indonesia, Malaysia, Philippines, South
Korea, Sri Lanka and Thailand. Results obtained from the standard simultaneous equation model
showed that foreign debt (private and public) had only small effects on gross national product
(GNP). Hence, by his findings, it could be summarised that external debt has no significant effect
on economic growth.
Metwally and Tamaschke (1994) investigated the interaction between debt servicing, capital
inflows and growth for three North African countries (Algeria, Egypt, and Morocco) for the
period of 1975-1992. Using the VAR model and the Two 2 stage least square (2SLS) methods,
they examined simultaneous models. Their result suggests that there was a two-way relationship
between debt servicing and growth. Furthermore, they discovered that debt servicing affected
economic growth negatively. High growth rate was also found to accelerate capital inflow which
again enhances economic growth. This was observed to have a positive effect on productivity as
it leads to reduction in overdependence on foreign loans as well as reducing the adverse effects
of debt servicing on an economy.
3.3.3 Evidence from South Africa
The South African literature on the impact of foreign debt on economic growth is limited. The
South African government, which seems to be the major client for work on the country‘s position
in international financial markets, continues to adhere to neo-liberal solutions (a liberal social
economic thinking and for it to end everyone would have to become far left liberal - people do
not have to be a democrat to be neo liberal) with regard to debt, which imply that their thinking
on foreign debt is in line with mainstream neo-liberal thinking. This may be a reason for the
paucity of South African economic literature on this important subject. The government and
SARB accepted the mainstream argument that financial liberalisation is good for the economy
(SARB, 2004). As a result, the government is pushing ahead with plans to acquire stocks of debt
33
on the international markets despite their tacit acknowledgement that there has been large capital
flight in the form of a foreign exchange amnesty (SARB, 2004).
Patillo (2002) found that between 1970 and 1996, total capital flight from South Africa was in
the neighbourhood of $87 billion (at 1996 value), which implies that for every dollar of foreign
borrowing, approximately 40 cents flowed out in the form of capital flight in the same year. Had
this money circulated in the country, debt burdens would have been reduced considerably.
The initial studies on this topic focused on time series analysis, but later, many studies used
panel data and sophisticated econometric techniques to deal with various data management and
empirical issues. Among those pioneering studies, Geiger (1990) used the lag distributional
model to assess the impact of foreign debt on economic growth in South Africa over a period of
12 years (1974-1986), and found a statistically significant inverse relationship between the debt
burden and economic growth.
Chowdhury (1994) attempted to resolve the controversy of cause and effect relationship between
foreign debt and growth, by conducting granger causality tests for South Africa over a period of
1970-88. He found that both public and private foreign debt has a relatively very small impact on
GNP and both have opposite signs. He found that any increase in GNP leads to a higher level of
external debt, but overall foreign debt does not have any negative impact on economic growth. A
study by Johannes Petrus Schoeman (2008), which analyses (through simple correlation
statistics) the developments of public (gross central government) debt and the long-term real
GDP growth rate in South Africa for the period 1980-2009, finds that: (1) the relationship
between government debt and long-term growth is weak for debt/GDP ratios below a threshold
of 90% of GDP; (2) above 90%, the median growth rate falls by one percent and the average by
considerably more. A similar change in the behaviour of GDP growth in relation to the debt is
also found by Kumar and Woo (2010).
3.4 Conclusion
It is evident from the theoretical review that reasonable levels of debt are expected to have a
positive effect on growth. According to traditional neoclassical models growth is achieved by
allowing for capital mobility, or increasing the ability of a country to borrow and lend. There is
34
an incentive for capital-scarce countries to borrow and invest since the marginal product of
capital is above the world interest rate. Some endogenous growth models have similar
implications. Eaton (1993) for example, extends the Uzawa-Lucas model and shows that an
increase in the cost of foreign capital that lowers external borrowing leads to lower long-run
growth.
However, one wonders why large levels of accumulated debt stocks lead to lower growth. The
debt overhang theory clearly points out that if there is some likelihood that in the future debt will
be larger than the country‘s repayment ability, then expected debt service will be an increasing
function of the country‘s output level. The returns from investing in the country therefore face a
high marginal tax by the external creditors, and new domestic and foreign investment is
discouraged. Turning to empirics, the empirical literature has revealed mixed views on the
impact of foreign debt on economic growth in both developing and developed nations. Robust
empirical literature exists pointing to a negative relationship between external debt and economic
growth in developing countries, particularly in South Africa This provides a rationale for this
study in order to determine how foreign debt economically impacts growth in South Africa.
35
CHAPTER FOUR
METHODOLOGY
4.1 Introduction
Chapter three conducted a review of numerous literatures as regards the economic impact of
foreign debt on growth in South Africa. In spite of the contentious findings on the relationship
between external debt and output growth, most findings, in general, support a negative effect of
foreign debt accumulation on economic growth particularly in developing countries. Many of
these studies have employed different empirical approaches to uncover the relationship for
different time frames. In this chapter, an empirical framework based on theoretical underpinnings
is presented to test the economic effect of foreign debt on growth in South Africa. The variables
used for the study will be defined and the sources for such data shall be outlined. The underlying
hypothesis will be interrogated through econometric analysis, whose various elements will be
expounded.
4.2 Empirical model and definition of variables
To exterminate the limitations of the neoclassical growth model, discussed in chapter three, this
study adopts the production function of the Mariano and Villanueva model (2005) by adding
other variables such as gross fixed capital formation (GFCF), trade openness (TO), foreign direct
investment (FDI) and foreign debt (FRD) and foreign debt service (FDS) such that the
production function takes the form:
GDP = ƒ (GFCF, TO, FDI, FRD, FDS) (2)
Guided by the MV model (2005), the model to be estimated will take the form:
GDPt = α0 + β1GFCFt + β2FRDt + β3FDIt + β4FDSt + β5TOt + μt (3)
To obtain elasticity coefficients and remove the effect of outliers, the variables have to be
transformed to logarithms. The econometric model is therefore specified as:
ƖGDPt = α0 + β1ƖGFCFt + β2ƖFRDt + β3ƖFDIt + β4ƖFDSt + β5ƖTOt + μt (4)
36
Where α0 is the constant term and β1 to β5 are coefficients of the explanatory variables. μ is the
stochastic error term. The subscript t refers to time period in equation (4) above and;
FDI = Foreign Direct Investment
FDS = Foreign Debt Service
FRD = Foreign Debt
GDP = Gross Domestic Product
GFCF = Gross Fixed Capital Formation
TO = Trade Openness.
4.2.1.1 Definition of explanatory variables
The explanatory variables, foreign debt, the gross fixed capital formation, gross domestic
product, trade-openness, foreign direct investment and foreign debt service are explained below:
4.2.1.2 Gross fixed capital formation
According to Were (2001) gross fixed capital formation is the net value obtained after
subtracting disposals from the total fixed asset acquisitions during that particular accounting
period. These fixed assets should have been acquired for productive purposes and they include
the purchases of machinery and the construction of factories. In most cases the factors of
production considered for any meaningful economic progress are capital and labour and these
two are used in predetermined proportions relative to each other. Cohen (1992) argues that
capital accumulation should go along with a certain proportion of growth hence an increase in
capital should be positively related to economic growth.
4.2.1.3 Gross domestic product
It refers to the market value of all final goods and services. It is the immediate measure of the
economy‘s wellbeing (Sachs, 1989). In Zurich, IMF chief Christine Lagarde mentioned that
―growth is a means to an end-enriching human life, ennobling human dignity, engendering
human potential and developing progress. And for that growth needed should spread its gains far
37
and wide‖ (The Sunday Times, 27 May 2012). Economic theory generally postulates that GDP is
negatively related to foreign debt. GDP measures income generated within a territory and is
equivalent to total consumer, investment and government spending plus net exports.
4.2.1.4 Trade openness
This is a measure of trade liberalisation used for this particular study. It is the ratio of exports
plus imports to GDP. Ognumuyiwa (2011) points out that this ratio is the most basic measure of
trade intensity. Openness in trade refers to the degrees to which countries or economies permit or
have trade with other countries or economies. The trading activities include import and export,
foreign direct investment (FDI), borrowing and lending, and repatriation of funds abroad. Trade
openness enables avenues to obtain funds from other countries, and also invest its surplus funds
in other countries.
4.2.1.5 Foreign debt service
Foreign debt service refers to the series of payments of interest and principal required on a debt
over a given period of time (Krugman, 1988). It is known that increase in foreign debt volumes,
according to Rockerbie, (1994), is invariably accompanied by a concomitant increase in debt-
service requirement, which has unfavourable implications for economic growth and thereby for a
country‘s ability to settle the principal debt and debt service obligations.
4.2.1.6 Foreign debt
It is that part of the total debt in a country that is owed to creditors outside the country. The
debtors can be the government, corporations or private households. The debt includes money
owed to private commercial banks, other governments, or international financial institutions such
as the IMF and World Bank. IMF (2008) hold that foreign debt is the outstanding amount of
those actual current, and not contingent, liabilities that require payment(s) of principal and/or
interest by the debtor at some point(s) in the future and that are owed to non-residents by
residents of an economy.
4.2.1.7 Foreign direct investment
Indermit (2005) proposes that foreign direct investment is defined as direct investment in
38
business operations in a foreign country and has the potential to generate employment, raise
productivity, and transfer managerial skills and technology. As part of the national accounts of a
country FDI refers to the net inflows of investment to acquire a lasting management interest
(10% or more of voting stock) in an enterprise operating in an economy other than that of the
investor (IMF, 2008). It is the sum of equity capital, other long-term capital, and short-term
capital as shown in the balance of payments.
4.3 Expected priori
The coefficient of FRD is expected to be negative since foreign debt, if not sustainable, imposes
higher risk to the economic prosperity, as its servicing which is also an indicator of higher
current account deficit, may lead to debt overhang in a country. Developing countries,
particularly South Africa, typically have limited sources to raise revenues and eventually get
trapped with the dilemma of lower revenue base which affect their spending capacity leading to
higher debt servicing; therefore, the coefficient of FRDS is expected to be negative. The
coefficients of FDI, GFCF and TO are expected to be positive. GFCF is a component of the
expenditure on GDP, and thus shows how much of the new value added in the economy is
invested rather than consumed thereby promoting economic growth.
4.4 Data sources
The data set spans over the period 1980 to 2011. The data used in this study was obtained from
the joint publications of the SARB, DTI and the Economist Intelligence Unit (EIU). These are
easily accessible as annual bulletins. Various publications of the SARS as well as StatsSA‘s
publications of foreign debt and GDP surveys were also taken into consideration. Annual data is
used in the analytical framework.
4.5 Estimation techniques
Time series data is being used hence it is essential to first investigate the unit root properties of
the data series. In this process, the variables are subjected to the Augmented Dickey-Fuller
(ADF) unit root test and the Phillips-Perron (PP) unit root tests. If there is unit root it implies that
there is nonstationarity hence stationarity will only be achieved by using the first differences of
the data series. To determine whether there is a long-run relationship between variables,
39
Johansen (1988) and Johansen and Juselius (1990) cointegration techniques will then be used.
The existence of any cointegrating equation will lead to vector error correction modelling
(VECM) which disaggregates and ascertains the long run and short run effects of the variables in
the model. Evidence of cointegration also implies that there is likely to be some interdependence
or causality among the variables; hence, the Granger causality test will be performed and this
will be a vector autoregression (VAR) in first difference form.
4.6 Stationarity
Classical regression models deal with relationships between stationary variables; however, most
of the economic indicators usually follow a non-stationary trend. Brooks (2002) defines a
stationary series as one with a constant mean, constant variance and constant auto covariance for
each given lag. According to Gujarati (2003), time series data is generated by a stochastic or
random process. The stochastic process is said to be stationary if the mean as well as the variance
are constant over time and the value of the covariance between two time periods depends only on
the lag between the two time periods and not on the actual time the covariance is computed.
For the ADF test, the Akaike information criterion (AIC) and Schwarz criterion (SC) are
employed for selection of the appropriate lag length of the model. Selection of the appropriate
lag length is necessary to ‗whiten the residual‘ (Asteriou et al., 2007). When the lag length
selected to run the test is small then there are possibilities of choosing the wrong model whereas
if the lags are too many then the power of the test may be weakened. In the case of mixed results
from the two criteria then the Hannan-Quinn criterion (HQC) is employed as a check as well as
scrutinising the model estimates to determine which model minimises the AIC and SC values.
Unlike the ADF tests, PP unit root tests allow for auto correlated residuals and hence, it is
employed for further check of order of integration of variables.
Since we use annual time series data, we run our unit root tests automatically for individual
series up to a maximum of two lags after which the model with the best lag length is tested for
unit root. The null hypothesis of the unit root test states that the variable is non-stationary and
hence, contains unit root. This null is rejected if the calculated t-statistic is greater, in absolute
terms, than the critical values of the test statistics.
40
4.6.1 Augmented Dickey-Fuller test
A valid Dickey-Fuller test is obtained only if ut is assumed not to be auto correlated, but would
be so if there was autocorrelation in the dependent variable of the regression ∆Yt. In that case, the
test would be oversized, meaning that the true size of the test would be higher than the nominal
size used. The solution to this shortfall is to use the ADF (Dickey and Fuller, 1984). The ADF
augments the test by using lags to the dependent variable where:
∆Tt = β1 + β2 + ψYt-1 + ∑p
i=1 ῼ i∆yt-1 + μt (5)
The lags of ∆Yt now absorb any dynamic structure present in the dependent variable, to ensure
that ut is not auto correlated. The Dickey-Fuller test, as with any other unit root tests, has its own
weaknesses. Gujarati (2003) states that most tests of the Dickey-Fuller type have low power, that
is, they tend to fail to reject the null of unit root more frequently than is warranted. Power
depends on the time span of data more than mere size of the sample. The Dickey-Fuller test is
weaker in its ability to detect a false null hypothesis so the use of both the Dickey-Fuller and
ADF tests can actually give more conclusive results.
The study employs the ADF unit root test to determine the order of integration of the series and
follows the methodology of Johansen (1988) and Johansen and Juselius (1990) in testing the
cointegration among the variables included in the study. Long-run and short-run dynamics are
captured by adding an error correction term to the VECM specification. On the one hand, the
VECM reveals the direction of causality; on the other hand, it distinguishes between the short-
run and long-run Granger causality.
4.6.2 Phillips Peron (PP) test
The PP test proposes an alternative (nonparametric) method of correcting for serial correlation
and heteroskedasticity (HAC) in unit root testing. Basically, it uses the standard DF or ADF test,
but modifies the t-ratio so that the serial correlation does not affect the asymptotic distribution of
the test statistic.
ADF and PP tests have very low power against 1(0) alternatives that are close to being 1(1) (near
nonstationary processes or highly persistent processes). To get maximum power against very
persistent alternatives, it is preferable to use the Elliot, Rothenberg, and Stock (1996) test.
41
4.7 Cointegration
The existence of long-run equilibrium (stationary) relationships among economic variables is
referred to in the literature as cointegration. The Johansen procedure examines the question of
cointegration and provides not only an estimation methodology but also explicit procedures for
testing for the number of cointegrating vectors as well as for restrictions suggested by economic
theory in a multivariate setting. If the economic variables are found to be cointegrated, we can
proceed to utilise the VAR representation in deriving the impulse response functions and the
forecast-error decompositions. The basis of these impulse responses (triggered by
monetary/fiscal innovations or shocks) and error decompositions here is the Johansen technique,
which precisely looks at a VECM.
The most common application of cointegration is to test the existence of long-run relationships.
One argument sometimes made is that cointegration is about long-run economic relationships,
and one needs really long time series (not in the number of observations but in time span) to use
cointegration technique. Maddala and Kim (1999), stress that this is not a meaningful argument
for two reasons:
1. If the variables are nonstationary, then existence of a long-run equilibrium economic
relationship implies cointegration. But not all cointegrating relationships need have
meaning in the sense of long-run economic relationships. Cointegrating relationships does
not need to have any economic interpretation.
2. How long the long run is depends on the speed of adjustment of the particular markets
considered.
Among the several ways of testing for cointegration, there is the Engle-Granger approach which
is residual based and the Johansen and Julius technique which is based on maximum likelihood
estimation on a VAR system. The Johansen (1988, 1991) and Johansen and Juselius (1990)
estimation techniques have distinct advantages over Engle and Granger (1987) single equation
method because of their estimating values of cointegrating vectors and their optimal statistical
inference properties. Johansen‘s techniques use the full information maximum likelihood (FIML)
to estimate the cointegrating vectors, and to test for the order of cointegrating vectors and linear
relationships in a multivariate model. The VAR based cointegration tests using the methodology
42
developed by Johansen (1991, 1995) shall be used in this study as it has several advantages over
other cointegration based techniques.
4.7.1 Johansen cointegration test
The Johansen‘s test for cointegration based on VAR framework makes use of a maximum
likelihood procedure in estimating and determining the rank of cointegrating vectors.
Algebraically, if we assume that a vector of p variables, GDPt = (FRD1t, FDI2t, GFCF3t, FDS4t,
TO5t), generated by a VAR of order k then this can be written as:
GDPt = β0 + ∏1FRDt-1 + ∏2FDSt-2 + ∏3GFCFt-3 + ∏4FDSt-4 + ∏5TOt-5 + µt
(6)
where GDPt is an nx1 vector of I(1) variables, β0 is an nx1 constant vector, Πi is an nxn matrix
of unknown parameters to be estimated (with i=1,2,3,4,5) and µt is an nx1 independent and
Identically distributed (i.i.d) vector of error terms assumed to be white noise. To use Johansen
test, the VAR model must be transformed into a VECM which is specified as equation (7) below:
∆GDPt = Г1∆FRDt-1 + Γ2∆FDIt-2 + Γ3∆GFCFt-3 + Γ4∆FDSt-4 + Γ5∆TOt-5 + Гk-1GDPt-k-1 +
ΠGDPt-k + µt (7)
Where ∆GDPt is now I(0)
The square matrix which is to be estimated (Π) is also known as the long run coefficient or
impact matrix. The rank of this matrix indicates the number of cointegrating vectors in the
system. If rank (Π) = r then we reject the null of no cointegration if 0 < r ≤ P. If this is the case,
then the P variables have a long run relationship with r cointegrating vectors. Furthermore, Π=
αβ‘ where α measures the average speed of adjustment and β is the matrix containing
cointegrating vectors.
The Johansen‘s method suggests use of two test statistic measures to determine the number of
cointegrating vectors in the VAR system. These measures include the trace statistic (λtrace) and
the maximum eigenvalue statistic (λmax). The λtrace is a joint test with the null that the number of
43
cointegrating vectors is either less than or equal to rank (r) whereas the alternative states that the
ranks are more. Separate tests are performed by λmax on individual eigenvalues with the null that
the rank of cointegrating vectors is r against an alternative of r +1. Where r = 0 then there is no
cointegration.
4.8 The vector error correction model (VECM)
Once cointegration has been established, then all variables are integrated of the same order and
hence, the possibility of having a spurious regression is null. The Engel and Granger (1987)
show that if variables are cointegrated then there must be an error correction representation. For
the VAR case, we get a VECM which acts as a special type of restrictive VAR and shows
changes in the dependent variable as a function of long run speed of adjustment parameters,
captured by the error correction term (ECT), and short-run dynamics. Since VEC models are
specified in first difference, this means that all variables in the system are stationary. Hence,
there is data consistency such that the conventional t-statistic can be employed to analyse the
model parameters.
An error correction model in a single line equation will be employed to capture the short run
direct of our regressors on the responding variable (dependent variable). Though it does not
produce a system of equations as presented in VECM, its celebrated merit lies in the fact that it
allows for examination of individual equations and permits control of the equation by adding
desirable variables (at contemporaneous and lagged terms) that is specific to each single line
equation. For this reason, the advantages of the VECM are not obvious especially when it has
only one cointegrating vector.
4.8.1 Impulse response
Structural VAR embeds economic theory within time series models, providing a convenient and
powerful framework for policy analysis. Impulse response function (IRF) tracks the impact of
any variable on others in the system. It is an essential tool in empirical causal analysis and policy
effectiveness analysis. Innovation accounting and impulse response analysis in a VAR
framework were pioneered by Sims (1980, 1981) and others as an alternative to classical macro
econometric analyses (Lutkepohl, 1993:58). Impulse response analyses trace out the
44
responsiveness of the dependent variables in a VAR to shocks from each of the variables
(Brooks, 2002:341). A unit shock is applied to the error term for each variable in each equation
separately and the effects on the VAR system are observed over time (Brooks, 2002:341).
According to Brooks (2002:341), if the system is stable, the shocks should gradually die away. If
the series are not stationary at level, impulse responses are computed from the VECM because
the forecast error variances estimates will be consistent and the predictions asymptotically
optimal (Aziakpono, 2006a: 21).
The generalised impulse response analysis is used in this study. The approach fully takes into
account historical patterns of correlations amongst the different shocks (Lutkepohl, 1993).
Furthermore, Lutkepohl (1993) explains that the technique does not require orthogonalisation of
shocks and is invariant to the ordering of the variables in the restricted VAR. The IRF in this
study are formulated as:
Let GDPt be a k-dimensional vector series generated by:
GDPt = A1FRDt-1 + A2FDIt-2 + A3FDSt-3 + A4GFCFt-4 + A5TOt-5 + μt
= Φ(B)μt = ∑i=0 Φiμt-1 (8)
I = (I – A1B – A2B - ……… - ApBp) Φ(B) (9)
Where cov(μt) = ∑, Φi is the MA coefficients measuring the impulse response. More specifically,
Φjk,i represents the response of variable j to an unit impulse in variable k occurring ith
period ago.
IRF are used to evaluate the effectiveness of a policy change, say decreasing foreign debt stocks
via debt servicing.
As ∑ is usually non-diagonal, it is impossible to shock one variable with other variables fixed.
Some kind of transformation is needed, with the most popular one being the Cholesky
decomposition.
4.8.2 Variance decomposition
Variance decomposition analysis provides the proportion of movements in the dependent
variables that are due to its own shocks, against shocks to other variables (Brooks, 2002). Brooks
45
(2002) points out that variance decomposition determines how much of the s-step ahead forecast
error variance of a given variable is explained by innovations to each explanatory variable.
Empirical research has often shown that own series shocks explain most of the error variance of
the series in a VAR (Lutkepohl, 1993 and Brooks, 2002). Brooks (2002) suggests that ordering
of variables is important in impulse response analysis and variance decompositions which can be
solved by using financial theory. Sims (1981) cited in Lutkepohl (1993) suggests that the
problem can be solved by trying different orderings and analysing the sensitivity of results when
ordering is changed. However, according to Lutkepohl (1993), ordering is less of an issue when
residuals are not contemporaneously correlated.
This study adopts the Cholesky decomposition framework for variance decomposition analysis
derived as follows:
Let P be a lower triangular matrix such that ∑ = PP’ then equation (26) can be rewritten as:
Yt = ΘiԜt-i
where Θ = ФiP,wt = P−1
Ut, and E(wtw’t ) = I. Let D be a diagonal matrix with same diagonals
with P and W = PD−1
, ʎ = DD’. After some manipulations, we obtain:
GDPt = β0GDPt + β1FRDt-1 + β2FDSt-2 + β3TOt-3 + β4FDIt-4 + β5GFCFt-5 + νt
Where β0 = Ik – W-1
, W = PD-1
, βi = W-1
Ai. Obviously, β0 is a lower triangular matrix with 0
diagonals. In other words, Cholesky decomposition imposes a recursive causal structure from the
top variables to the bottom variables but not the other way round.
4.9 Granger causality test
Brooks (2002), states that the Granger causality test has been widely used in Economics. The
standard Granger causality test examines whether past changes in one variable, x, help to explain
current and past changes in another variable, y. Where this is true, then the conclusion is that x
Granger causes y. The above experiment is repeated with x and y interchanged to determine
whether causality runs in the other direction. Four possible outcomes are possible:
1. Unidirectional causality: x Granger causes y, but not vice versa.
46
2. Unidirectional causality: y Granger causes x, but not vice versa.
3. Bi-directional causality: x Granger causes y and y Granger causes x.
4. Independence: neither variable Granger causes the other.
The application of the standard Granger test requires that the variables be stationary. Most
economic variables are non-stationary in level forms. Thus, differenced stationary variables are
used in conducting the standard Granger causality test.
4.10 Diagnostic checks
This stage is crucial in the analysis of the impact of foreign debt on economic growth in South
Africa because it validates the parameter estimation outcomes achieved by the estimated model.
Diagnostic checks help to test the stochastic properties of the model such as residual
autocorrelation, heteroskedasticity and normality. These tests shall briefly be explained below.
4.10.1Stability test
The AR Roots Graph shows the inverse roots of the characteristic AR polynomial. If all roots
have modulus less than one and lie inside the unit circle then it implies that the estimated VAR is
stable (stationary). In the case that the VAR is not stable, certain results such as impulse response
standard errors will not be valid (Brooks, 2002).
4.10.2 Heteroskedasticity
According to Brooks (2002), there are a number of formal statistical tests for heteroskedasticity.
One such popular test is the White‘s (1980) general test for heteroskedasticity. The null
hypothesis for the White test is homoskedasticity and if we fail to reject the null hypothesis then
we have homoskedasticity. If we reject the null hypothesis, then we have heteroskedasticity.
4.10.3 Residual normality test
One of the most commonly applied tests for normality is the Jarque-Bera test. It uses the
property of a normally distributed random variable that the entire distribution is characterised by
the first two moments — the mean and the variance. The Jarque-Bera test statistic asymptotically
47
follows a X2 under the null hypothesis that the distribution of the series is symmetric. The null
hypothesis of normality would be rejected if the residuals from the model are either significantly
skewed or leptokurtic/ platykurtic (or both) (Brooks, 2002).
4.10.4 Autocorrelation LM tests
Lagrange Multiplier (LM) test centers on the value of the R2 for the auxiliary regression. If one
or more coefficients in an equation are statistically significant, then the value of R2 for that
equation will be relatively significant, while if none of the variables is significant, R2
will be
relatively low. The LM test operates by obtaining R2 from the auxiliary regression and
multiplying it by the number of observations, T. It can be shown as:
TR2 ≈ χ 2 (m) (10)
where m is the number of regressors in the auxiliary regression (excluding the constant term),
equivalent to the number of restrictions that would have to be placed under the F tests approach
(Brooks, 2002).
4.11 Conclusion
This chapter elucidated the theoretical model and empirical model as well as the definition of
variables. The estimation method employed in the study, that is, the Johansen cointegration and
VECM framework was presented. This was followed by the discussion of impulse response and
variance decomposition analysis. This chapter concluded with exploration and discussion of the
diagnostic checks. The following chapter deals with the actual estimation of the impact of
foreign debt on growth in South Africa using the above mentioned techniques.
48
CHAPTER FIVE
EMPIRICAL FINDINGS: PRESENTATION AND ANALYSIS
5.1 Introduction
The previous chapter set the analytical framework and reviewed the model estimation techniques
to be used in this study. This chapter augments the analysis by applying that framework and the
analytical techniques proposed on annual time series South African data covering the period
1980 to 2011. In this chapter, the questions underpinning the research problem shall be
addressed. Analysis shall be conducted in accordance with the methodology presented in the
previous chapter. The unit root tests shall be conducted first, followed by the cointegration tests.
This will lead to the formulation of the VECM on which orthogonalised impulse response and
variance decomposition analysis, granger causality and diagnostic checks will be executed. This
chapter concludes with the empirical findings to determine the impact of foreign debt on
economic growth in South Africa.
5.2 The test for stationarity
The first step of the Johansen methodology is to determine the order of integration of the series.
In this study, one informal test for stationarity and two formal tests are employed. One of the
most popular informal tests for stationarity is the graphical analysis of the series. A visual plot of
the series is usually the first step in the analysis of any time series before pursuing any formal
tests. This preliminary examination of the data is important as it allows the detection of any data
capturing errors, and structural breaks and gives an idea of the trends and stationarity of the data
set.
The first impression that we get from the plots in Figure 5.1a is that the variables trade openness
(TO), gross domestic product (GDP), gross fixed capital formation (GFCF), foreign direct
investment (FDI), foreign debt service (FDS), and foreign debt (FRD) clearly reveal trendy
behaviour in which there is considerable growth over time. GFCF, TO, GDP, FDI and FDS seem
to be trending upward and FRD trending downward, albeit with fluctuations. The data in levels is
clearly non-stationary. FDI do not show any trend, but also show huge fluctuations over time. All
49
the other variables in Figure 5.1a have a time variant mean and variance suggesting that they are
not stationary in their levels.
Figure 5.1a: Trends in variables for 1980-2011
Source: Author‘s own graph — generated using eViews 7 software
Figure 5.1b shows that all the differenced variables seem to be fluctuating around the zero mean
hence the variables are likely to be integrated of order one I(1).
50
Figure 5.1b: Trends in differenced variables for 1980-2011
51
Source: Author‘s own graph — generated using eViews 7 software
Based on the above analysis alone, there is uncertainty about the stationarity status of the
variables, especially those that do not follow a clear trend such as GDP, FDI and FDS in this
study. Therefore, what is required here is some kind of formal hypothesis testing procedure.
The two formal tests that are employed in this study are the ADF and the PP tests, discussed in
Chapter Five. These tests were applied to the data under different deterministic trend
assumptions, but those that included a constant and no trend produced robust results. The option
with no trend and no intercept produced ‗explosive‘ results, while the option with both a trend
and intercept made test statistics less significant.
52
Table 5.1: Results for Phillips Perron (PP) and augmented Dickey-Fuller tests
Order of
Integration
Variable
Phillips Perron (PP) test
Intercept Intercept No Intercept
& Trend &Trend
Augmented-Dickey Fuller(ADF) test
Intercept Intercept & Trend No Intercept &
Trend
Level LGDP 0.791854 -1.2585 -0.1823 0.977817 -0.272857 1.912049
1st Diff DGDP -2.960411* -4.2846** -2.642** -3.661661** -4.284580** -2.641672**
Level LTO -2.298438 -2.6936 -0.6169 -2.202356 -2.769133 -0.602703
1st Diff DTO -2.619160*** -4.285** -2.642** -2.960411* -3.215267* -2.341512*
Level LGFCF
1.806360 -1.0586 -4.0857
4.373990 -1.974314 5.057641
1st Diff DGFCF -2.619160* -4.3746** -2.641** -3.724070** -4.296729* -2.660720**
Level LFRD -1.350704 -3.1945 1.041 -1.350704 -3.051861 -1.100890
1st Diff DFRD -3.661661 -3.2164* -2.733** -2.619160** -4.284580** -2.139012*
Level LFDS -1.312583 -1.8496 -0.8052 -1.456190 -1.862829 -0.860826
1st Diff DFDS -2.765411* -4.2941** -2.941** -3.670170** -4.284580** -2.644302**
Level LFDI -1.110314 -1.2585 -0.1823 -0.762862 -1.103608 -0.018669
1st Diff DFDI -3.671661** -4.2845** -2.822** -2.960411* -4.284580 ** -2.641672**
1% **
5%*
10%***
Critical
Values
-3.662 -4.285 -
2.642
-2.960 -3.563 -
1.952
-2.619 -3.215 -
1.610
-1.350704 -4.284580 -2.641672
-2.960411 -3.562882 -1.952066
-2.619160 -3.215267 -1.610400
** represents a stationary variable at 1% level of significance
*** represents a stationary variable 10% level of significance
* represents a stationary variable at 5% level of significance
L represents Logarithms of variables
53
D indicates that the variable has been differenced
The ADF and the PP tests shown in table 5.1 are both founded on a null hypothesis of unit root.
The null hypothesis is to be rejected in the event that the test statistic has a greater absolute value
compared to the critical values at 1%, 5% and 10% levels of significance. Rejection of the null
hypothesis basically means that the alternative hypothesis of stationarity is not rejected thus
indicating the absence of unit root.
The ADF is a much stricter test of the two and it tests variables in intercepts, intercepts and
trends as well as when there is neither intercept nor trend. For variables in levels the test in
intercepts revealed that none of the variables are stationary with exception to TO and GFCF. All
differenced variables are stationary at 1% significance level except for GFCF, TO and FDI
which are stationary only at 5% significance level. When the intercept and trend are considered,
none of the variables are stationary in levels except FRD and TO being stationary at 1%
significance level. For the test under no intercept and no trend, all variables in levels are non-
stationary except for GFCF which is stationary at 1% significance level. When first differenced,
the rest of the variables are stationary at 1% significance level.
The PP test has revealed that TO is the only stationary variable at levels for all variables in levels
tested in intercepts. GDP and FDS become stationary at 5% significance level whereas GFCF
and TO at 10% significance level and FDI at 1% significance level after first differencing. When
the trend and intercept is considered, under the PP test, all variables are non-stationary at levels
except for TO and FRD. All variables became stationary after first differencing at 1%
significance level. Regarding no intercept and trend, only GFCF is stationary at levels. All other
variables come to be stationary after first differencing at 1% significance level.
All the methods used to assess stationarity have considerably revealed that the data series are
non-stationary in levels and stationary when differenced. The results for the ADF tests in Table
5.1a show that most of the series are non-stationary in levels, since their test statistics are all
smaller than the MacKinnon 1% critical value of -3.485. Since the variables are stationary when
first differenced, a conclusion that the series are integrated of order I(1) can therefore be made.
54
5.3 Cointegration tests
Having established that the variables are integrated of the same order, cointegration tests are
performed to determine the existence of any long-run equilibrium relationships. One lag was
selected using the lag length selection criteria based on the SC. This is due to the fact that the
SIC imposes a harsher penalty for including an increasingly large number of regressors (Gujarati,
2004). If variables are cointegrated, it means that the linear combination of the variables is
stationary even though the individual variables will be non-stationary. The cointegration
approach is advantageous in that it allows one to ascertain the long-run and short-run
relationships between variables within a single combined framework. To test for cointegration,
the Johansen‘s maximum likelihood approach developed by (Johansen, 1988; Johansen and
Juselius, 1990) is used.
Prior to cointegration test, to achieve a parsimonious equation, a pairwise correlation matrix is
carried out to guide the variable selection exercise. Table 5.2 presents the pairwise correlations
of the variables of the full econometric model.
Table 5.2: Pairwise correlation matrix
GDP TO FDI FRD FDS GFCF
GDP 1 0.438594730 0.856983122 0.303999194 0.715757385 0.812461082
TO 0.438594730 1 0.303254401 0.410433151 0.726944589 0.553357478
FDI 0.856983122 0.303254401 1 0.099472466 0.645935175 0.523837021
FRD 0.303999194 0.410433151 0.099472466 1 0.183234176 0.689156603
FDS 0.715757385 0.726944589 0.645935175 0.183234176 1 0.546598493
GFCF 0.812461082 0.553357478 0.523837021 0.689156603 0.546598493 1
By looking at the pairwise correlations in Table 5.2, the following observations can be made:
FDI, FDS, and GFCF are the only variables that are highly correlated with the GDP.
55
FRD and TO have low correlations with the GDP, confirming the results from stationarity
tests.
As mentioned in Chapter four, the Johansen cointegration technique requires us to specify the lag
order and the deterministic trend assumption for the VAR. Since the unit root tests accepted the
inclusion of a constant but no trend, we choose case 3 in E-views which excludes a trend but
includes a constant. As for the choice of the lag order for the VAR, the information criteria
approach, augmented by theoretical priors, is used as a guide in selecting the lag order. Table 5.3
shows the lag lengths chosen by different information criteria.
Table 5.3: The lag lengths chosen by different information criteria.
Lag LogL LR FPE AIC SC HQ
0 -3600.180 NA 4.1e+100 248.7021 248.9850 248.7907
1 -3452.266 224.4210 1.94e+97 240.9839 242.9641* 241.6041
2 -3405.902 51.16088* 1.35e+97 240.2691 243.9467 241.4209
3 -3333.906 49.65222 3.53e+96* 237.7866* 243.1615 239.4700*
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
Since the series are annual, the selection is drawn from a maximum of three lags in order to
allow for adjustment in the model and the attainment of well-behaved residuals. As shown in
Table 5.3, the LR select two lags; SC choose one lag, while the FPE, AIC and the HQ choose
56
three lags for the VAR. Thus, the information criteria approach produces conflicting results and
no conclusion can be reached on this approach alone, as expected. Brooks (2002) attributes this
problem to a small sample bias. To reach a conclusion, we consider the performance of the
model under the three suggested lag orders. Lag length 1 did not produce good diagnostic check
results, while third lag resulted in well-behaved residuals. The Johansen cointegration test is
therefore conducted under the assumption of no trend but a constant in the series and three lags
for the VAR.
Table 5.4 shows the cointegration test results for the specified model based on trace and
maximum eigenvalue statistics.
57
Table 5.4: Johansen cointegration rank test results
Unrestricted Cointegration Rank Test (Trace)
Hypothesised Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.684349 102.2214 95.75366 0.0166
At most 1 0.605983 67.62787 69.81889 0.0738
At most 2 0.456304 39.68702 47.85613 0.2339 At most 3 0.316219 21.40605 29.79707 0.3328 At most 4 0.279640 10.00254 15.49471 0.2805 At most 5 0.005399 0.162405 3.841466 0.6869
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesised Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None 0.684349 34.59355 40.07757 0.1823 At most 1 0.605983 27.94085 33.87687 0.2162
At most 2 0.456304 18.28097 27.58434 0.4719 At most 3 0.316219 11.40351 21.13162 0.6070
At most 4 0.279640 9.840138 14.26460 0.2226 At most 5 0.005399 0.162405 3.841466 0.6869
Max-eigenvalue test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
The top part of Table 5.4 presents the Johansen cointegration test based on the trace test, while
the bottom part presents the results of this test based on the maximum eigenvalue test. Beginning
with the trace test, the null hypothesis of no cointegrating vectors is rejected, since the test
58
statistic of about 102.2214 considerably exceeds the critical value (of 95). In the same vein, the
null hypothesis that there are at most 1 cointegrating vectors cannot be rejected since the test
static of approximately 67.62787 is now less than the 5% critical value of about 69.81889. The
trace test, therefore, indicates one cointegrating relationships (vectors) at the 5% level of
significance. The maximum eigenvalue form of the Johansen test also rejects the null hypothesis
of no cointegration, and hence indicates no cointegration at the 0.05 level. The maximum
eigenvalue test, therefore, suggests that there is no cointegrating relationship in the model.
The trace test is more robust than the maximum eigenvalue form of the Johansen test. However,
there is need to use the results of each test and let a priori theoretical knowledge guide us in
selecting the cointegration rank. We estimated VECMs restricted on none and one cointegrating
vectors separately, as chosen by the maximum eigenvalue and trace test, respectively. Results
from the estimations confirm Luintel and Khan‘s (1999) finding that the trace test is more robust
than the maximum eigenvalue test, since zero cointegrating relationship chosen by the maximum
eigenvalue test does not produce economically meaningful results. Therefore, there is one
cointegrating relationship in the model. Furthermore, there are cointegrating relationships
between I(0) and I(1) variables, thus corroborating Harris‘ (1995) finding that variables
integrated of different orders may be cointegrated. What remains is to identify whether the
cointegrating vector represent the true cointegrating relationship.
5.4 The long-run relationship
The number of cointegrating relationships obtained in the previous step, the number of lags and
the deterministic trend assumption used in the cointegration test are all used to specify a VECM.
This VECM enables one to distinguish between the long- and short-run impacts of foreign debt
on growth. However, before interpreting the results from the VECM, there is need to identify the
true cointegrating relationship that has been suggested in the preceding section. Table 5.5
presents the results from the estimated VECM without any restrictions (except for those
automatically imposed by E-views).
59
Table 5.5: VECM results before normalisation
Standard errors in ( ) & t-statistics in [ ]
Cointegrating Eq: CointEq1
GDP(-1)
FDI(-1)
GFCF(-1)
FRD(-1)
FDS(-1)
TO(-1)
C
1.000000
-6.403003
(0.36527)
[-17.5296]
-0.126234
(0.09065)
[-1.39248]
3.250444
(0.83841)
[3.87694]
-0.074972
(0.06445)
[-1.16324]
-1.59E+11
(5.0E+10)
[-3.17492]
7.49E+10
60
Error Correction: D(GDP) D(FDI) D(GFCF) D(FRD) D(FDS) D(TO)
CointEq1 -0.438319 0.091801 0.326695 -0.036730 -0.253109 1.64E-12
(0.56398) (0.05377) (0.17718) (0.03807) (0.40870) (7.5E-13)
[-0.77719] [ 1.70716] [ 1.84386] [-0.96492] [-0.61931] [ 2.17763]
Following Arestis and Demetriades (1999) in normalising the vector on the variable for which a
clear evidence of error correction was found between foreign debt and growth. An assessment of
the coefficients of the error correction terms (cointEq1 at the bottom of Table 5.5) for the
identified vector shows that FRD has a significant coefficient, with a t-value of -0.96492 and has
a negative sign together with FDS with -.0.253109 and -0.61931 correct coefficient and t-value
respectively. Foreign debt is negative in the short run and positive in the long run implying usage
of borrowed funds to enhance growth.
The results above confirm the debt overhang theory in South Africa which is believed to create a
disincentive effect which inversely affects growth (shown by a negative coefficient value of
FDS). The major argument of debt overhang theory is that productive capacity is limited with
indebtedness. This is seen to serve as disincentives to investment (especially private) as a result
of expectation about the consequential economic policies (like increased taxation) required to
service debts.
The theoretical literature has shown that debt over hang theory implies the draining out of a
countries‘ limited resources and restriction on its financial resources for domestic need of
development due to the repayment of debt in the form of principal and interest payments.
61
Benedict Clements (2003) suggested that foreign borrowing has a positive impact on investment
and growth of a country up to a threshold level but external debt service can potentially affect the
growth as most of the funds will go in the repayment of the debt rather in the investments.
Results also show that debt service tends to affect negatively GDP and thereby the rate of
economic growth in the long-run, which, in turn, reduces the ability of the country to service its
debt. Similarly, the estimated error correction term shows the existence of a significant long-run
causal relationship among the specified variables. Overall, the results point to the existence of
short-run and long-run causal relationship running from debt service to GDP.
However, for South Africa, theory also suggests that at ‗reasonable‘ levels of foreign debt, the
country can enhance economic growth. This is because at early stages of development small
stocks of capital are likely to provide investment opportunities with rates of return higher than
those in industrial countries (RSA, 2011). Figure 5.2 shows the results of a numerical simulation
of the cointegrating equation above for different levels of foreign debt. It is evident from Figure
5.2 that economic growth induced by foreign loans in South Africa increases up to point A. An
increase beyond point A leads to a decrease in the level of GDP (debt overhang).
62
Figure 5.2: Foreign debt “Laffer” curve for South Africa
Source: Calculations based on data from SARB (2011)
It can be seen that the optimum point for accumulating foreign debt is approximately 35.8%.
This is consistent with the studies done by Cohen (1993) who found the level at 35.5%,
Clements, Bhattacharya and Nquyen (2003), 36% and Hansen (2004), 35.2%. By drawing a 45-
degree line, the tangential point represents the maximum point of zero default risk (point C).
The conclusion of this study is supported by the plot in Figure 5.3 showing the first vector in the
cointegration space, which appears to be stationary.
0.0E+00
50.000
100.000
200.000
300.000
400.000
500.000
0.00 10.5 15.4 20.1 20.4 23.2 25.5. 30.8 35.8 40.7 45.6 50.5 60.4 66.6 88.8 133.3
C
A B
Foreign Debt % of GDP
GDP
63
Figure 5.3: Cointegration graph for the estimated equation
The cointegration depiction shown in Figure 5.3 confirms stationarity and cointegration of the
estimated variables within the model. The identification of cointegation leads to the use of an
error correction model (ECM).
5.5 The short-run relationship
This analysis is intended to capture the short-run economic impact of foreign debt on growth. In
this section, diagnostic checks, granger causality, impulse response analysis and variance
decomposition tests are performed. The results for the VECM on the short run effects, in terms
of granger causality, of foreign debt are presented in Table 5.6.
5.5.1 Granger causality
The Granger causality test in this section is used to examine pair-wise causality in the six
variable VAR system. Madalla (1998) indicates that if two variables are cointegrated, there must
be at least one direction causality between investigated variables. The objective is to investigate
whether observations of a variable like foreign debt and debt service is potentially useful for
64
anticipating future movements in GDP. Table 5.5 illustrates the results for the pairwise Granger
causality test in which particular attention shall be paid to the effect of foreign debt specifically
to GDP. The null hypothesis in each particular case is that the specified variable does not
Granger causes the other.
The results show very little evidence of lead--lag interactions between the series. None of the
results shows any causality that is significant at the 5% level with exception of TO, although
there is causality from the FRD (with a probability of 0.576) to GDP and from FDS to the GDP
that is almost significant at the 10% level with 0.449875 chi-square value and the highest
probability of 0.7986, but no causality in the opposite direction and no causality between the FDI
and the GFCF in either direction. These results might be interpreted as suggesting that
information is incorporated slightly more quickly in the foreign debt service than in the foreign
debt or foreign direct investment and gross investment.
65
Table 5.6: Granger causality test
Dependent variable: GDP
Excluded Chi-sq df prob
TO 1.572167 2 0.4556 FRD 1.115066 2 0.5726 FDS 0.449875 2 0.7986
GFCF 1.161701 2 0.5594 FDI 0.478209 2 0.7873 All 12.98789 10 0.2243
Dependent variable: TO
Excluded Chi-sq df Prob. GDP 0.382082 2 0.8261 FRD 1.476862 2 0.4779 FDS 2.614841 2 0.2705
GFCF 0.074183 2 0.9636 FDI 0.540540 2 0.7632 All 10.95555 10 0.3610
Dependent variable: FRD
Excluded Chi-sq df Prob.
GDP 20.07087 2 0.0000 TO 6.064866 2 0.0482
FDS 13.58721 2 0.0011 GFCF 4.286843 2 0.1173 FDI 2.570963 2 0.2765 All 47.32339 10 0.0000
Dependent variable: FDS
Excluded Chi-sq df Prob. GDP 0.642532 2 0.7252 TO 0.439712 2 0.8026
FRD 0.225555 2 0.8933 GFCF 0.079405 2 0.9611 FDI 0.022926 2 0.9886 All 7.298582 10 0.6970
Dependent variable:
GFCF
Excluded Chi-sq df Prob. GDP 1.487684 2 0.4753 TO 2.959751 2 0.2277
FRD 3.454041 2 0.1778 FDS 1.825403 2 0.4014 FDI 0.858631 2 0.6510 All 19.73268 10 0.0319
Dependent variable: FDI
Excluded Chi-sq df Prob. GDP 48.01792 2 0.0000 TO 18.46128 2 0.0001
FRD 9.518855 2 0.0086 FDS 12.73916 2 0.0017
GFCF 12.82930 2 0.0016 All 103.5382 10 0.0000
Since the null hypothesis that FRD does not Granger cause GDP or even the reverse cannot be
66
rejected this implies that there is statistically insignificant causality between foreign debt and
growth in the long run. These results are very much in line with the VECM assessment which
also highlighted the statistical inverse relationship between foreign debt and growth in the long
run in South Africa. These results are of economic relevance due to the fact that, they help to
reflect the channels that policy action can take to bring remedial action.
5.5.2 Diagnostic checks
Diagnostic checks are crucial in this analysis, because if there is a problem in the residuals from
the estimation of a model, it is an indication that the model is not efficient, such that parameter
estimates from such a model may be biased. Of importance in this analysis are the residual
diagnostic checks for serial correlation, normality and heteroskedasticity. As mentioned in
Chapter four, the three tests are based on the null hypothesis that there is no serial correlation,
there is normality and there is no heteroskedasticity problem for the LM, Jarque-Bera and White
heteroskedasticity tests, respectively.
In this section, the VAR model is subjected to diagnostic checks. The fitness of the model will be
tested in four main ways. Firstly it is tested for stability using the AR roots graph, and then serial
correlation is tested using the LM test, followed by the White test for hetersoskedasticity and
lastly the Jarque-Bera normality test.
67
Figure 5.4: Results for stability test
The AR Roots Graph in Figure 5.4 shows that the inverse roots of the characteristic AR
polynomial have modulus which is less than one and lie inside the unit circle. This implies that
the estimated VAR is stable (stationary).
The problem of serial correlation arises when a variable has relationships with itself in a manner
that the value of such a variable in past periods will have an effect on its future values. The test
for serial correlation produced an LM statistic of 13.579 with a probability of 0.969. The null
hypothesis of no serial correlation cannot be rejected.
The presence of heteroskedasticity means the model will be having some misspecifications hence
conclusive results cannot be derived from such a model. The White test with no cross terms
produced a CH-sq of 186.989 at a probability of 0.345. The null hypothesis of no
heteroskedasticity or no misspecification will thus not be rejected. The model does not suffer
from any misspecifications hence can be relied on.
The null hypothesis for the Jarque-Bera test states that there is a normal distribution. The results
obtained for this particular test show a Jarque-Bera statistic of 5.936616 with a probability of
0.139693. The null hypothesis of normal distribution is rejected at 1% and 5% significance levels
68
but not at 10%. In this case, probability is greater therefore we fail to reject the null hypothesis of
a normal distribution.
The diagnostic checks have all revealed the suitability of the model. The AR roots graph showed
that the VAR model is stable, there is no serial correlation, there is also no misspecification and
the errors are normally distributed. The outcomes of this research can therefore be relied on.
Valid conclusions on the economic impact of foreign debt on economic growth in South Africa
can be deduced and effective policies can safely be formulated.
5.5.3 Impulse response analysis
The impulse response analysis, together with variance decomposition (to be covered in the next
section), reveals a wealth of information on dynamic effects that is missing in both static studies
and those dynamic studies that do not employ these techniques. Figure 5.5 presents the results
from the impulse response analysis performed on the VECM regression (1).
Since this study focuses on the economic impact of foreign debt on growth only the responses of
foreign debt, debt service and other variables impacting growth are reported in Figure 5.5.
69
Figure 5.5: Impulse responses of the variables of interest
70
These impulse response functions show the dynamic response to a one-period standard deviation
shock to the innovations of the system and also indicate the directions and persistence of the
response to each of the shocks over a 32-annual period. For the most part, the impulse response
functions have the expected pattern and confirm the results from the short-run relationship
analysis. Shocks to GDP are not significantly different from zero and are transitory with
exception of TO and FDS which show a slight deviation from significance but persistent, while
shocks to FRD variable are significant, but only TO is persistent. A one-period standard
deviation shock to GFCF, TO, FDS and FDI marginally appreciates growth but the impact dies
off quickly, on average. A shock to FDS has a marginal depreciation effect on economic growth
but is also transitory.
A one period standard deviation shock to FRD inversely affect growth by more than 2%, but also
gradually levels off in about two-and-half years.
Variables that have persistent and significant effects on the growth rate are the domestic
investment and foreign direct investment. The response of GDP to a one-period shock to FRD is
a continued decrease in growth. This result implies that increase in foreign loans, in the long run,
has a negative impact on growth.
Lastly, a shock to FDI has a lasting positive impact on growth. A one-period shock to the trade
openness and gross fixed investment increases growth rate by over 2% in a year‘s time and will
widen the gap. Thus, only debt service and foreign debt have a significant negative (deviating
more persistently from zero) impact on growth in the long run. However, all the other variables,
with the exception of TO, have only a transitory effect on economic growth.
5.5.4 Variance decomposition analysis
As mentioned in Chapter four, variance decomposition analysis provides a means of determining
the relative importance of shocks in explaining variations in the variables of interest. In the
context of this study, it therefore provides a way of determining the relative importance of
shocks to each of the determinants of growth in explaining variations and economic impact of
foreign debt on growth. The results of the variance decomposition analysis are presented in
Table 5.7 and these show the proportion of the forecast error variance in growth explained by its
71
own innovations and innovations in its explanatory variables.
Table 5.7: Variance decomposition
VD GDP
Period S.E. GDP FRD FDI FDS GFCF TO
1 3.53E+10 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000
2 5.09E+10 92.33745 0.023940 2.822169 0.518130 4.294238 0.004073
3 6.07E+10 87.69644 0.273506 3.425588 3.746299 3.025811 1.832356
4 6.55E+10 83.82110 0.258900 3.875769 5.439441 3.001689 3.603096
5 6.94E+10 78.79481 0.232040 4.213315 7.784128 4.375335 4.600375
6 7.27E+10 74.01774 0.222258 4.454823 10.90207 5.937379 4.465724
7 7.63E+10 68.73955 0.226153 4.632135 15.36191 6.947644 4.092608
8 8.09E+10 62.62693 0.226978 4.700093 20.77483 7.280431 4.390734
9 8.64E+10 56.16114 0.229111 4.641244 26.10301 7.174958 5.690544
10 9.25E+10 49.98404 0.249715 4.478724 30.52502 6.854210 7.908296
72
VD FRD
Period S.E. GDP FRD FDI FDS GFCF TO
1 2.67E+09 5.468421 94.53158 0.000000 0.000000 0.000000 0.000000
2 4.06E+09 24.85266 47.19396 0.843026 26.47541 0.046754 0.588189
3 4.82E+09 27.31683 33.83889 0.618598 31.74020 1.401023 5.084458
4 5.50E+09 26.77741 26.18348 0.493015 25.17350 1.127314 20.24528
5 6.07E+09 23.96542 21.43515 0.669985 20.92934 0.938516 32.06159
6 6.49E+09 21.62601 19.20263 0.946301 18.54996 0.824362 38.85074
7 6.76E+09 19.92014 18.57614 1.016884 17.23492 0.759711 42.49220
8 6.97E+09 18.92085 18.30425 0.981904 16.24164 0.875759 44.67559
9 7.15E+09 18.34192 17.83708 0.935497 15.43275 1.445888 46.00687
10 7.29E+09 17.94312 17.34483 0.905352 14.86983 2.088220 46.84865
VD FDI
Period S.E. GDP FRD FDI FDS GFCF TO
73
1 3.15E+09 19.92717 0.859899 79.21293 0.000000 0.000000 0.000000
2 8.20E+09 82.21520 0.159416 14.82353 1.558082 0.093250 1.150527
3 1.08E+10 82.63707 0.382935 10.70071 1.121916 0.190175 4.967197
4 1.22E+10 80.90521 0.316363 9.552674 0.905967 0.432938 7.886852
5 1.30E+10 77.04632 0.360024 9.287702 1.893789 0.421016 10.99115
6 1.36E+10 71.90628 0.483742 8.958530 3.904772 1.665486 13.08119
7 1.41E+10 67.72041 0.503177 8.740834 5.999638 3.648106 13.38784
8 1.45E+10 64.25170 0.473751 8.596087 8.793687 5.192688 12.69209
9 1.51E+10 60.47944 0.445221 8.402185 12.82066 5.950923 11.90157
10 1.58E+10 56.02814 0.414879 8.101800 17.71786 6.143379 11.59394
VD FDS
Period S.E. GDP FRD FDI FDS GFCF TO
1 2.64E+10 11.55052 16.53716 5.524860 66.38747 0.000000 0.000000
2 3.58E+10 17.16115 14.61753 4.802837 61.24389 1.528683 0.645914
3 3.99E+10 17.70123 12.40290 5.608749 60.64179 2.375539 1.269798
4 4.27E+10 15.80355 10.84876 5.861480 62.13137 3.603367 1.751466
74
5 4.50E+10 14.33236 9.798241 5.853201 64.07966 4.339365 1.597170
6 4.72E+10 13.48419 8.951471 5.660252 65.36483 4.633806 1.905451
7 4.94E+10 12.96145 8.213595 5.315237 65.28721 4.638159 3.584347
8 5.15E+10 12.46072 7.539456 4.922232 64.06214 4.509315 6.506137
9 5.36E+10 11.89217 6.986951 4.559289 62.21907 4.312045 10.03048
10 5.55E+10 11.27433 6.600534 4.252786 60.13481 4.068823 13.66872
VD GFCF
Period S.E. GDP FRD FDI FDS GFCF TO
1 1.05E+10 0.864644 7.670738 2.651227 2.059586 86.75380 0.000000
2 1.63E+10 2.509322 11.82252 1.102316 4.103159 77.37591 3.086771
3 2.21E+10 16.27072 12.12562 1.321847 9.448181 50.60787 10.22577
4 2.94E+10 25.76020 9.049296 2.230777 17.67367 28.85876 16.42730
5 3.71E+10 27.72975 6.136207 2.800636 26.11879 18.17181 19.04281
6 4.36E+10 26.51551 4.556366 2.997779 31.84112 13.25380 20.83543
7 4.89E+10 24.51558 3.750914 2.973176 35.02148 10.93326 22.80559
8 5.39E+10 22.39498 3.402212 2.833402 36.58769 9.690011 25.09171
75
9 5.88E+10 20.41556 3.312207 2.657613 37.33968 8.700110 27.57484
10 6.41E+10 18.70397 3.285266 2.489629 37.69451 7.692652 30.13397
VD TO
Period S.E. GDP FRD FDI FDS GFCF TO
1 0.051624 0.152786 0.126377 1.227351 19.00116 51.37207 28.12025
2 0.060428 1.236638 2.490785 0.996490 14.62463 51.42714 29.22432
3 0.062684 1.152664 3.368952 2.187720 17.77795 48.09439 27.41832
4 0.067413 1.132862 3.155373 2.822402 26.07066 42.14522 24.67348
5 0.072666 1.568164 3.905302 2.725513 31.74285 36.38643 23.67175
6 0.076350 2.788651 4.144980 2.487758 33.23379 33.13890 24.20593
7 0.078915 4.043239 3.886815 2.346024 32.71909 31.69970 25.30513
8 0.080945 4.676063 3.907908 2.261170 31.82527 30.52870 26.80089
9 0.082725 4.740972 4.101794 2.173612 31.08009 29.24800 28.65554
10 0.084459 4.602124 4.139304 2.085664 30.37799 28.15910 30.63583
Cholesky Ordering: GDP FRD FDI FDS GFCF TO
76
While the information criteria suggested that lag order 3 for the VAR was sufficient, the variance
decompositions for 32 years compounded into 10 periods for each variable will be allowed in
order to ascertain the effects when the variables are allowed to affect the economic growth for a
relatively longer time. With regard to economic growth, GDP is explained by its own
innovations (shocks), as suggested in Brooks (2002). With respect to the ahead forecast error
variance under S.E., GDP itself explains about 87 per cent of its variation, while all its regressors
explain only the remaining 13%. Of this 13%, foreign debt amounts to approximately 5.5%,
domestic investment about 3.7% and debt service 2.5%, while the remaining variable do not
significantly contribute to the variation in growth.
However, after a period of two years, GDP explains about 77% of its own variation, while its
explanatory variables explain the remaining 23%. The influence of foreign debt increases
substantially to about 8.5%, explaining the largest component of the 23% variation in growth that
is explained by its regressors. This result is in line with Joyce and Kamas‘s (2003) finding on
their study of the impact of external debt on growth in Argentina, Colombia and Mexico. FDI
accounts for about 8% and GFCF about 5%, all with their impacts increasing over time. Thus,
foreign debt accounts for the largest component of the variation in the economic growth rate
followed by debt service, foreign direct investment and openness. Shocks to the other variables
continued to explain an insignificant proportion of the variation in the real exchange rate
(exchange rate as dependent variable as selected econometrically with the E-views for impulse
response). These results, therefore, are similar to those from the impulse response analysis in that
only foreign debt, debt service have a significant inverse impact on growth in the long run.
5.6 Concluding remarks
This chapter analysed the economic impact of foreign debt on economic growth. The chapter
analysed the time series properties of the data employing both informal and formal tests for
stationarity. It was discovered that the variables were not integrated of the same order. The
Johansen cointegration tests provided evidence that there is one cointegrating equation between
GDP and its regressors which were included in the model. This finding, therefore, indicates that
the GDP is subject to permanent changes as a result of changes in its fundamentals. Evidence of
cointegration allowed the estimation of VECMs, which simultaneously provided the parameter
77
estimates for both the long and short run relationships. The variables that have a long run
negative relationship with economic growth are foreign debt and debt service. An increase in the
trade openness, foreign direct investment and gross fixed capital formation all positively increase
growth in South Africa. These results, therefore, corroborate the theoretical framework to a large
extent.
Another interesting finding is that the optimum point for accumulating foreign debt in South
Africa is approximately 35.8% of GDP. Beyond this level, acquisitions of debt stocks result in
debt overhang effect as confirmed by the VECM results. An additional interesting parameter in
VECMs is the speed of adjustment coefficient which, in this study, measures the speed of
adjustment in economic growth following a shock in the system. The estimate of this parameter
found in this study indicates that about 33% of the variation in gross domestic product from its
equilibrium level is corrected within a year. This speed of adjustment is slightly higher than
those from previous studies on South Africa, but does not present a surprise given the relative
importance of macroeconomic policies endeavoured upon since apartheid in South Africa. The
short-run dynamics from the VECMs suggested that only foreign direct investment, openness
and gross capital formation significantly affects the growth in the short run by positively
increasing it.
However, a better picture of the short-run dynamics emerged from the impulse response and
variance decomposition analyses. The latter tests provided evidence that the foreign debt service
and foreign debt have a significant negative impact on economic growth in the long run.
However, only shocks to openness, foreign direct investment and gross fixed capital formation
have persistent effects on economic growth. Another interesting result which emerged from this
analysis, which is supported by previous research is that among other explanatory variables in
the model, foreign debt, debt service and openness explain the largest proportion of the variation
in the real exchange.
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CHAPTER SIX
CONCLUSIONS, POLICY RECOMMENDATIONS AND LIMITATIONS
6.1 Summary of the study and conclusions
To fill the lacuna of the previous studies this study analysed the impact of foreign debt on
economic growth in South Africa. In order to achieve the objective of this study, this research
employed the Johansen testing approach to cointegration to examine the presence of
cointegrating relationships. For policymaking, this study applied the Granger causality test
within the VECM framework to verify the direction of causality between GDP, foreign debt,
debt service, trade openness, foreign direct investment and gross capital formation. The
economic effects of foreign loans were reviewed in this study and are well documented in the
literature for both developed and developing economies. The variables included in this model as
potential factors influencing growth include the degree of openness, foreign direct investment,
gross fixed capital formation, foreign debt and debt service.
In order to determine the long- and short-run impact of foreign debt on economic growth, the
Johansen cointegration and error correction methodology was preferred to the other techniques,
because of its several advantages over those alternative techniques. In the application of this
methodology, the study analysed the time series properties of the data employing both informal
and formal tests for stationarity. The variables were not integrated of the same order, as they
clearly revealed trendy behaviour in which there is considerable growth over time before
differencing. Five variables seemed to be trending upward whilst one trending downward, albeit
with fluctuations. All the methods used to assess stationarity have considerably revealed that the
data series are non-stationary in levels and stationary when differenced. The maximum
eigenvalue test, therefore, suggests that there is no cointegrating relationship in the model.
However, the Johansen cointegration test concludes that there are cointegrating relationships
between I(0) and I(1) variables included in the model. Therefore, this finding indicates that the
growth rate is subject to permanent changes as a result of changes in its fundamentals.
Another thought-provoking finding of this study is the derived optimal level of debt in South
Africa. It can be concluded that the optimum point for accumulating foreign debt is
approximately 35.8% of GDP beyond which the negative impacts of foreign debt become
79
noticeable with any further acquisition of debt. It follows from these findings that the real
exchange rate is largely a function of real variables in the long run. So, these results, for the most
part, corroborate both with the theoretical predictions and findings from previous research.
An interesting parameter in VECMs, concluded upon in this study, is the speed of adjustment
coefficient which, in this study, measures the speed of adjustment in gross domestic product
following a shock in the system. The estimate of this parameter found in this study indicates that
about 33% of the variation in the growth rate from its equilibrium level is corrected within a
year. This speed of adjustment is slightly higher than those from previous studies on South
Africa, but does not present a surprise given the relative significance and viability of
macroeconomic policies in place in South Africa since the apartheid era. The short-run dynamics
from the VECMs suggested that only foreign debt and debt service does not significantly affect
the growth rate in the short run by shrinking it.
Granger causality indicates that debt service is an important factor of GDP. The existence of
causality in debt service and GDP relationship may be due to the fact that borrowed resources are
misallocated or wasted on consumption. The negative effects on productivity will haunt the
economy as it agonises over debt servicing in the future.
However, a better picture of the short run dynamics emerged from the impulse response and
variance decomposition analyses. This study presents the first application of these techniques on
the impact of foreign debt on growth in South Africa and they provide a wealth of dynamic
effects that are often lacking in those studies that do not apply these techniques. The impulse
response analysis provided evidence that the other variables, except foreign debt and debt
service, have a significant positive impact on the growth rate in the short run. However, a shock
to FDS has a marginal reduction effect on economic growth but is also transitory and has
persistent effects on growth. It is also concluded in this study that a one-period standard
deviation shock to FRD inversely affect growth by more than 2%, but also gradually levels off in
about two-and-a-half years.
Results from the variance decompositions of the impact of debt on growth are largely similar to
those from the impulse response analysis and reveal that the fundamentals explain some, but not
80
all, of the variations of growth rate. GDP itself explains about 87% of its variation, while all its
regressors explain only the remaining 13%. Of this 13%, foreign debt amounts to 5.5%, domestic
investment about 3.7% and debt service 2.5%, while the remaining variable do not significantly
contribute to the variation in growth. The most interesting result which emerged from this
analysis and which is supported by previous research is that after a period of two years, GDP
consists of 77% of its own variation, while its explanatory variables account for the remaining
23%. The impact of foreign debt increases substantially to about 8.5%, explaining the largest
component of the 23% variation in growth that is explained by its regressors. Thus, foreign debt
and debt repayments fluctuations in twelve-monthly data are predominantly equilibrium
responses to economic growth in the form of an increasing function in the short run than in the
long run.
Trends of and macroeconomic factors in South Africa showed remarkable increases during the
1980s as government borrowed heavily. During periods of high government borrowing growth
tended to be decreasing due to debt repayments. Economic growth in South Africa, as shown by
the foreign debt Laffer curve, tend to increase at low volumes of foreign debt until an optimal
point when growth tend to decrease as debt servicing increases.
6.2 Policy implications and recommendations
Taken together, the results of this study have a number of policy implications. At the outset, the
results of this study can be used as a guideline for national liquidity management. The foreign
debt of the country as a whole contributes towards the optimum point. Thus, should the private
sector expand foreign debt, and this pushes the sustainable level close or over the optimum point,
the government can counteract this position by active foreign debt management. This framework
is also applicable in managing the potential foreign debt of public entities with large foreign debt
requirements, such as Eskom.
After the apartheid regime, the period 2000-2011 was the period of high debt/GDP percentage
but slow and (or) slow GDP growth rates in South Africa. This situation is not unconnected with
wasteful expenditures and high level of financial indiscipline on the part of South African
government at this time. This led to high debt overhang during the period and fall in foreign
81
investment and growth. For debt to promote growth in South Africa and other highly indebted
countries fiscal discipline and high sense of responsibility in handling public funds should be the
watchword of these countries‘ leaders.
Furthermore, it should also be mentioned that when foreign debt exceeds the optimum point
(35.5% of GDP), economic growth is still higher than the current level of South Africa (26% of
GDP), and this can then push tolerable level of foreign debt higher to approximately 50% of
GDP. At this level, it would have reduced all the benefit gained from acquisition of foreign debt
in the first place. Thus, a potential higher level can be pursued as long as the growth exceeds the
negative impact of additional foreign debt.
There is need to improve the competitiveness of the economy in order to develop macro
imbalances and to help mobilise the domestic resources in order to reduce the economy‘s
dependence on external debt. Also, provision of a favourable macroeconomic environment is
needed to reduce mismanagement so as to promote economic growth. Further, it is pertinent that
a viable monitoring system be put in place that can ensure proper and systematic utilisation of
the external borrowings for the liquid developmental projects. There is also a need for reducing
the external debt, as it will contribute to economic growth by boosting capital accumulation and
productivity improvement.
6.4 Recommendations for future research
The results of this study show that foreign debt has a positive effect on economic growth, up to
an optimum point. This makes the impact of foreign debt on the economy asymmetric. It should
be questioned how these results will be applicable to highly indebted poor countries in the light
of debt relief. Does it imply that when such a country receives debt relief it can start
accumulating foreign debt again, and will this have a negative impact on economic growth?
Also applicable to highly indebted poor countries: will the regression results or a new regression
altogether be meaningful if a country already exceeds the optimum point as derived in this
study? In other words, will it be possible to derive an optimum point for foreign debt if such debt
has already negatively impacted on GDP growth?
This study shows that foreign debt indeed makes a country vulnerable to a change in investor
82
sentiment. The reason has been used by the World Bank, the IMF and many other development
institutions to promote the development of the domestic capital market as an alternative to
foreign borrowing. The question should be asked: at what point does the accumulation of
domestic debt make the growth show negative implications for growth?
Finally, econometric aspects used in this work, regarding specification of the estimated model,
variables used, tests and so on, do not constitute the final and definitive work. Further research
can be pursued as new data become available.
6.4 Limitations of the study
At the outset, although the results are generally robust, the Johansen procedure was found to be
sensitive to lag length chosen. Various authors including Gonzalo (1994), Hawtrey (1997) and
Yuhn (1996) have all also reported similar sensitivity. The other limitation is on the
measurement of trade liberalisation where the measure can either be qualitative or quantitative.
In other studies, trade liberalisation was considered as a dummy for example in Jimoh (2006)
who studied the effects of trade liberalisation on real exchange rates in Nigeria. In a study by
Dutta and Ahmed (2004) who executed a cointegration analysis of trade liberalisation and
industrial growth in Pakistan, exports and imports ratio to GDP were used to measure trade
liberalisation. This study took a measure similar to that of Teweldemedhin and Van Schalkwyk
(2010) who measured trade openness as the ratio of real imports and real exports to real GDP.
Such a measure was also confirmed by Yanikkaya (2003). Therefore, the impact of trade
liberalisation policies is quite complicated to ascertain due to the different methods of
measurement involved. Apart from these shortfalls, everything else was conducive for the
execution of an acceptable study.
83
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APPENDIX: DATA
Source: DTI (2011)