THE IMPACT OF A BUDGET DEFICIT ON TRANSPORT INFRASTRUCTURE
INVESTMENT IN SOUTH AFRICA
BY
APHIWE NANTO
A DISSERTATION SUBMITTED IN FULFILMENT OF THE REQUIREMENTS
FOR THE DEGREE
MASTER OF COMMERCE
(TRANSPORT ECONOMICS)
DEPARTMENT OF ECONOMICS
FACULTY OF COMMERCE AND MANAGEMENT
UNIVERSITY OF FORT HARE
SOUTH AFRICA
SUPERVISOR: PROF. R. NCWADI
2013
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i
ABSTRACT
Persistent government budget deficits and government debt have become major concerns in
both developed and developing countries. This study investigates the impact of a budget
deficit on transport infrastructure investment in South Africa. Quarterly time series data,
covering the period 1990q1- 2009q4, was used in this project. The study tests for stationarity
using the Augmented Dickey- Fuller and Phillips Perron; it tests for cointegration using the
Johansen (1991, 1995) methodology. A vector error correction model is used as an estimation
technique. The results of this study show that a budget deficit has a negative impact on
transport infrastructure investment in South Africa.
Keywords: Budget deficit, transport infrastructure investment, VECM, South Africa.
ii
DECLARATION
I, the undersigned, Aphiwe Nanto, hereby declare that this dissertation is my own original
work and that all sources have been accurately reported, acknowledged and referenced.
Moreover, I declare that this document has not previously been submitted at any university
for a similar or any other academic qualification.
Signature …………………………
Date ………/………/………….
iii
ACKNOWLEDGEMENTS
Firstly, I thank my savior the Lord Jesus Christ for his love and strength that he has given me
to make all things possible. Secondly, I thank the National Department of Transport and the
Govan Mbeki foundation for their financial assistance; none of this would have been possible
without your support. I also express my sincere gratitude to my supervisor, Prof. R. Ncwadi,
for his unlimited advice, encouragement and guidance. Finally, I thank my family and
friends who gave me the much needed words of encouragement and advice throughout the
years, it is highly appreciated.
v
LIST OF ACRONYMS
ADF: Augmented Dickey Fuller
ARDL: Auto regression distribution lags
AIC: Akaike Information Criterion
ASGISA: Accelerated Shared Growth Initiative for South Africa
BBBEE: Broad Based Black Economic Empowerment
CGC: Classical Growth Cycles
CPI: Consumer Price Index
DF: Dickey Fuller
DTI: Department of Trade and Industry
ECM: Error Correction Model
FDI: Foreign Direct Investment
FPE: Final Prediction Error
GDP: Gross domestic Product
GEAR: Growth, Employment and Redistribution
GFSY: Government Financial Statistics Yearbook
HQ: Hannan-Quinn
IMF: International Monetary Fund
IRF: Impulse Response Functions
JB: Jarque- Bera
KPSS: Kwiatkowski Phillips Schmidt Shin
LM: Lagrange Multiplier
LR: Likelihood Ratio
MTEF: Macro Transport Infrastructure Forum
NEER: Nominal Effective Exchange Rate
NGP: New Growth Path
OECD: Organisation for Economic Co-operation and Development
OLG: Overlapping Generations
OLS: Ordinary Least Squares
PIMS: Political Information and Monitoring Services
PP: Phillips-Perron
R&D: Research and Development
RDP: Reconstruction and Development Programme
vi
RGDP: Real Gross Domestic Product
SAIIA: South African Institute of International Affairs
SARB: South African Reserve Bank
SC: Schwarz Criterion
STATSSA: Statistics South Africa
SUR: Seemingly Unrelated Regression
TII: Transport Infrastructure Investment
TVP-VAR: Time Varying Parameter -Vector Auto Regression
UNCTAD: United Nations Conference on Trade and Development
US: United States
VAR: Vector Auto regression
VECM: Vector Error Correction Model
vii
Table of Contents
ABSTRACT ............................................................................................................................................. i
DECLARATION .................................................................................................................................... ii
ACKNOWLEDGEMENTS ................................................................................................................... iii
DEDICATION ....................................................................................................................................... iv
LIST OF ACRONYMS .......................................................................................................................... v
LIST OF TABLES .................................................................................................................................. x
LIST OF FIGURES ............................................................................................................................... xi
CHAPTER ONE ................................................................................................................................... 1
INTRODUCTION ................................................................................................................................. 1
1.1 Background and Problem Statement ............................................................................................. 1
1.2 Objectives of the study .................................................................................................................. 3
1.3 Hypothesis of the study ................................................................................................................. 4
1.4 Significance of the study ............................................................................................................... 4
1.5 Organisation of the study .............................................................................................................. 4
CHAPTER TWO .................................................................................................................................. 5
LITERATURE REVIEW .................................................................................................................... 5
2.1 Introduction ................................................................................................................................... 5
2.2 Theoretical Literature .................................................................................................................... 5
2.2.1 Harrod-Domar Model ................................................................................................................ 5
2.2.2 Robert Solow Model .................................................................................................................. 6
2.2.2.1 Limitations of the neo-classical growth model ....................................................................... 8
2.2.3 Endogenous Growth Model ....................................................................................................... 9
2.2.3.1 The Lucas Endogenous Growth Model ................................................................................ 10
2.2.3.2 The Romer Model of Endogenous Growth ........................................................................... 11
2.2.3.3 Limitations of the endogenous growth model ....................................................................... 13
2.2.4 Assessment of the Theories...................................................................................................... 13
2.3 Empirical Literature .................................................................................................................... 14
2.3.1 Empirical Evidence from Developed Countries ...................................................................... 14
2.3.2 Empirical Evidence from Developing Countries ..................................................................... 22
2.3.3 Empirical Evidence from South Africa .................................................................................... 29
2.4 Conclusion .................................................................................................................................. 34
CHAPTER THREE ............................................................................................................................ 36
viii
AN OVERVIEW OF THE SOUTH AFRICAN BUDGET DEFICIT AND TRANSPORT
INFRASTRUCTURE INVESTMENT ............................................................................................. 36
3. 1 Introduction ................................................................................................................................ 36
3.2 Historical overview ..................................................................................................................... 36
3.2.1 South African Public Transport Infrastructure Investment 1980-2011 .................................... 37
3.3 Government Revenue 1980-2011 ............................................................................................... 39
3.4 Government Expenditure 1980-2011 .......................................................................................... 41
3.5 Budget Deficit/Surplus 1980-2011 ............................................................................................. 45
3.6 Foreign Direct Investment 1980-2011 ........................................................................................ 47
3.7 Real Gross Domestic Product 1980-2011 ................................................................................... 49
3.8 Conclusion .................................................................................................................................. 52
CHAPTER FOUR ............................................................................................................................... 53
RESEARCH METHODOLOGY ...................................................................................................... 53
4.1 Introduction ................................................................................................................................. 53
4.2 Model specifications ................................................................................................................... 53
4.3 Definition of the variables and data sources ............................................................................... 54
4.4 Expected Priori ............................................................................................................................ 54
4.5 Estimation Techniques ................................................................................................................ 55
4.5.1 Testing for Stationarity/Unit Root ........................................................................................... 55
4.5.2 The Augmented Dickey–Fuller test and Phillips Perron test ................................................... 56
4.5.3 Cointegration and vector error correlation modeling (VECM) ................................................ 57
4.5.4 Diagnostic Tests ....................................................................................................................... 60
4.5.4.1 Autocorrelation LM Test ...................................................................................................... 61
4.5.4.2 Heteroscedasticity test........................................................................................................... 61
4.5.4.3 Residual normality test.......................................................................................................... 61
4.5.5 Impulse response and variance decomposition ........................................................................ 61
4.5.5.1 Impulse response ................................................................................................................... 62
4.5.5.2 Variance Decomposition ....................................................................................................... 62
4.6 Conclusion .................................................................................................................................. 63
CHAPTER FIVE ................................................................................................................................ 64
PRESENTATION OF EMPIRICAL RESULTS ............................................................................. 64
5.1 Introduction ................................................................................................................................. 64
5.2 Stationarity/unit root test ............................................................................................................. 64
5.3 Cointegration............................................................................................................................... 70
ix
5.4 Vector Error Correction Model and the long run relationship .................................................... 74
5.4 Diagnostic Tests .......................................................................................................................... 76
5.5 Impulse response and Variance decomposition .......................................................................... 77
5.6 Variance Decomposition ............................................................................................................. 78
5.7 Conclusion .................................................................................................................................. 80
CHAPTER SIX ................................................................................................................................... 81
SUMMARY OF THE MAIN FINDINGS, CONCLUSIONS, IMPLICATIONS AND
RECOMMENDATIONS .................................................................................................................... 81
6.1 Summary of the study and conclusions ....................................................................................... 81
6.2 Conclusions ................................................................................................................................. 82
6.3 Recommendations ....................................................................................................................... 82
6.4 Delimitations and recommendations for future research ............................................................ 82
REFERENCES .................................................................................................................................... 83
APPENDIX .......................................................................................................................................... 92
Data used in the regression analysis ..................................................................................................... 92
x
LIST OF TABLES
Table 2.1: Summary of selected empirical literature on the budget deficit and transport
infrastructure investment……………………………………………………………………..33
Table 5.1: Unit root/Stationarity Tests……………………………………………………….68
Table 5.2: Phillips-Perron……………………………………………...…………………….70
Table 5.3: Lag Length Criteria……………………………………………………………….72
Table 5.4: Johansen cointegration rank test results………………………………………….73
Table 5.5: Results of both the Long run and Short run Relationship………………………..75
Table 5.6: Results of the Diagnostic Tests…………………………………………………...77
Table 5.7: Variance Decomposition………………………………………………………….80
xi
LIST OF FIGURES
Figure 2.1: Solow Growth Model……………………………………………………………..7
Figure 3.1: Trends in Transport Infrastructure Investment (1980-2011)..…………………...38
Figure 3.2: Trends in Government Revenue (1980-2011)…………………………………...40
Figure 3.3: Trends in Government Expenditure (1980-2011)……………………………….44
Figure 3.4: Trends in the budget deficit/surplus (1980-2011)………………………….…...46
Figure 3.5: Trends in foreign direct investment (1980-2011)………………………………..48
Figure 3.6: Trends in RGDP (1980-2011)…………………………………………………...51
Figure 5.1: Plots of all variables in logarithm form 1990q1-2009q4………………………...66
Figure 5.2: Plots of all variables after differencing 1990q1-2009q4………………………...67
Figure 5.3: Johansen Cointegration Vector…………………………………………………..74
Figure 5.4: Impulse Response………………………………………………………………..78
1
CHAPTER ONE
INTRODUCTION
1.1 Background and Problem Statement
The term ‘budget deficit’ is described as the negative budget surplus whereby government
expenditure exceeds government revenue. Budget revenue includes three important
components, which are: tax revenue, tax exempt revenue and private revenues. The most
important component of the budget revenue is tax revenue. However, budget expenditure
involves four important elements, which are: current expenditure, investment expenditure,
real expenditure and transfer payments (Mwakalikamo, 2011). Current expenditure is the
kind of expenditure related to nondurable goods and services like the payment of wages and
salaries, and it is used for short term expenses. Investment expenditure is related to
investment and capital development, such as the construction of infrastructure and purchasing
of capital goods like tractors and other machines for production (Mwakalikamo, 2011).
Transfer payment includes grants and subsidies which have an indirect impact to the GDP. If
the budget expenditure exceeds budget revenue, which are both important components of the
budget, then it is stated to be a budget deficit.
Persistent government budget deficits and computing government debt have become major
concerns in both developed and developing countries. Extensive theoretical and empirical
literature has been developed to examine the relationship between budget deficits and
macroeconomic variables (Akinbobola and Oladipo, 2011). The monetarists share the view
that fiscal deficits are harmful to an economy. While some of the increases in the deficits
have been associated with declining tax revenue, resulting from the recession, others relate to
the increase in debt service payments on public debt. The development of a budget deficit is
often traced to the Keynesian inspired expenditure-led growth theory of the 1970s (Olomola
and Olagungu, 2004). Most countries of the world adopted the theory that the government has
to spend more in order to stimulate economic growth. However, its consequences on
macroeconomic variables cannot be underestimated in most countries of the world.
The South African government recognises the importance of transport infrastructure in
economic growth in South Africa. In South Africa, the improvement in public transport
infrastructure has served to link undeveloped and developed regions, such as towns and rural
2
areas, and it has been seen as the most valuable policy tool because it acts as a stimulus
during economic downturns (Negota, 2001). The improved transport infrastructure has also
improved trade, to a large extent, since goods can now be delivered without any transport
infrastructure complications (Negota, 2001). Public transport infrastructure investment in the
form of seaports, airports, rail and roads causes South Africa to move towards a sustained and
growing development (Fourie, 2006). This enables all South Africans, especially the poor, to
enjoy greater access to economic empowerment through job creation.
The Minister of Finance, Gordhan, in his budget speech stated that South Africa’s investment
in infrastructure gives impetus to growth in the economy (National Treasury, 2012). An
improvement in economic growth contributes towards the reduction of inequality, poverty
and the creation of decent work for people, especially for those who are not skilled (National
Treasury, 2012). In this regard, the New Growth Path which is currently a South African
macroeconomic strategy creates a way of making the South African economy more
developed and equitable for sustained growth. This strategy encourages stronger investments
in infrastructure, by both the public and private sectors, in order for the country to create the
necessary employment opportunities and at the same time reduce poverty.
Given these objectives of the government to stimulate the economy towards a higher growth
path, there are two primary instruments, amongst others, that a country can use, namely:
fiscal policy and monetary policies. A monetary policy is used mainly to regulate money
supply through interest rates (Mollentze & Van der Merwe, 2010). A fiscal policy on the
other hand, deals with government revenue through tax and expenditure (Nattrass, 2000 and
Ajam and Aron, 2007).
A budget is considered a useful tool of control utilised by companies. It can help set
developmental policies in the country. Budget is a record of the earning and spending of an
organization. When the actual expenditures are in conformity with the planned expenditure,
then planning becomes useful for that unit. Budget can either be a deficit or surplus. A budget
deficit results in situations where the expenditures of the country exceed its revenues, earned
from taxes and other sources. According to Sill (2005), the expenditure of an entity, which
exceeds its earning or income, has been termed a budget deficit. In the absence of financing
from external sources the deficit is carried forward to the next financial year. The deficit can
be a result of delays in the collection of revenues i.e. sales, taxes or other sources of revenues.
Budget revenues decrease due to erosion of the tax base, while expenditures most often rise
3
due to an increase in transfers to the population such as unemployment benefits, social
welfare, etc. (Anušić , 1994). The country entered a recession in 2008 with the government
already spending more than it was taking in tax. However, in any recession, the budget deficit
increases because of the automatic stabilizers that kick in where tax receipts fall and welfare
spending increases. That deficit will not last longer; it will change as soon as the country
experiences increasing returns in the economy.
South Africa reported a government budget deficit equal to 4.80 percent of the country's
Gross Domestic Product in 2011 (National Treasury, 1995). During this period 1989 until
2011, South Africa’s Government Budget averaged -3.03 percent of GDP reaching an all-
time high of 0.90 percent of GDP in December 2007 and a record low of -7.40 percent of
GDP in December 1992. Government Budget is an itemized accounting of the payments
received by government (taxes and other fees) and the payments made by government
(purchases and transfer payments).
Given this scenario of a budget deficit, if not funded by foreign aid and/or increased taxes,
the government may not invest in infrastructure. A lack of infrastructural investment may
lead to a decline in growth as well as job opportunities. However, given these large figures of
a budget deficit on GDP, the question at hand is: does the budget deficit have an effect on
infrastructure investments? How has the budget deficit affected the infrastructure investment
over the years, both in the long and short run? Lastly, what policy recommendations could be
implemented to reduce budget deficits?
1.2 Objectives of the study
The primary objective of this study is to investigate the impact of a budget deficit on public
transport infrastructure investment in South Africa. This broad objective is explored through
the following sub objectives:
To review the trends of both public transport infrastructure investments and budget deficit
from 1990-2009.
To investigate the short run and the long run response of the public transport
infrastructure investment to changes in the budget deficit from the period 1990-2009.
To make policy conclusions and recommendations based on the findings.
4
1.3 Hypothesis of the study
H0: Government budget deficit has a significant negative relationship with transport
infrastructure investment in South Africa.
HA : Government budget deficit does not have a significant negative relationship with
transport infrastructure investment in South Africa.
1.4 Significance of the study
The relationship between the budget deficits on transport infrastructure investment has
attracted a vast amount of literature from both theoretical and empirical fronts in recent years.
Many researchers investigated the relationship between the two, but they have reached
conflicting results. There is still a debate as to whether there is a positive relationship or a
negative relationship, if any, between budget deficit and transport infrastructure investment.
Therefore, this study seeks to fill in that gap by examining the relationship between the two
in South Africa.
1.5 Organisation of the study
Following this introduction, Chapter two reviews both the theoretical and empirical literature
pertaining to the relationship between public transport infrastructure investment and budget
deficit. The chapter made use of the following theories: the Harrod-Domar growth theory;
Robert Solow’s theory and the Endogenous Growth model. The empirical evidence
conducted was from developed and developing countries as well as South Africa specifically.
Chapter three provides an overview of trends in the relationship between public transport
infrastructure investment and budget deficit in South Africa. Chapter four discusses the
methodology and sources of data used in this study. Chapter five estimates the regression
model and interprets the results. Chapter six presents a summary of the study and policy
recommendations. The last chapter points out some limitations associated with the study.
5
CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
The purpose of this chapter is to explore the various theories of infrastructure investment.
The Harrod-Domar growth theory, Robert Solow’s theory and the Endogenous growth theory
are discussed in this chapter. These theories are important in that they provide the
determinants and fundamental dynamics of infrastructure investment and economic growth.
This chapter is divided into three sections. The first section presents growth theories. The
second section deals with the empirical literature on infrastructure investment and budget
deficit from developed, developing countries and from South Africa. The last section
provides the concluding remarks of this chapter.
2.2 Theoretical Literature
This section is aimed at investigating the determinants of infrastructure investments.
Traditional theories of infrastructure investment, namely; the Harrod-Domar growth theory,
Robert Solow’s theory and Endogenous Growth Models are discussed herein.
2.2.1 Harrod-Domar Model
The Harrod-Domar model stipulates that growth depends on the quality of labour and
investment leads to capital accumulation which later affects the economic growth of a
country (Jones, 2013). This theory has the following assumptions:
1. Output is a function of capital stock i.e.
Y= f (K)
Where Y = Gross Domestic Product and K = Level of Capital Stock
2. The marginal product of capital is constant. This means that marginal and average products
of capital are equal.
3. The product of the savings rate and output equals investment.
sY= S= I
4. The change in the capital stock is equal to investment less depreciation of the capital stock.
6
ΔK= I- δ K
This theory states that in order for the country to grow it solely depends on government
expenditure on investments and savings. The development of a country includes the rate of
output growth solely the rate of infrastructure investment being made in which the
government has enough capital. The main strength of this theory is that the absence of the
economic shocks predicts economic growth in the short run.
2.2.2 Robert Solow Model
The neoclassical growth theory was developed by Robert Solow (1956), a prominent
economist of the twentieth century (Uwasu, 2006). In a nutshell, the Solow model predicted
that a country may experience growth accelerations and growth slowdowns. This model
consists of both a supply and demand side, but it focuses primarily on the supply side. With
reference to this study, this means that it focuses on government expenditures that tend to
have an effect on the public transport infrastructure investment which later affects the
economic growth of a country.
The Solow model states that an increase in the labour supply results in a larger output. This
can only happen if a lot of people take part in a country’s production (for example, if those
who are not part of the labour force start working); when the transport infrastructure takes
place, real output increases. Solow stated that a productivity increase can, for example, take
place when investments in equipment like computers and machinery reduce labour hours.
Productivity increases explain the increase in output that cannot be explained by labour and
capital (inputs), called the productivity of an input, and is affected by a lot of factors.
According to this model, the productivity of an input is affected by technological factors such
as differences in capital per worker and differences in knowledge (Burda and Wyplosz,
2001).
The theory starts with a simplified assumption that there is no technological progress in the
economy; that is, output is a function of the capital-labour ratio and is expressed as follows,
Y= f (K)………………………………………………………………………………….…..2.1
Y = output per head
K = capital per head
The Solow growth model is illustrated in Figure 2.1 below.
7
Figure 2.1 Solow Growth Model
Source: Thirwall (2002:13)
In Figure 2.1, above, Y represents output per head, K represents the capital per head and the
production function is represented by Y = f (K). As the capital rises, output rises, but output
rises less at higher levels of capital than at low levels. The production function will therefore
increase steadily at lower levels of capital but will increase at a decreasing rate at higher
levels of capital. This implies that the economy will reach a long run level of output and
capital called the steady state equilibrium. The steady state equilibrium, for the economy, is
the combination of per capita GDP (output per worker) and per capita capital (capital stock
per worker) where these economic variables are no longer changing: ∆y = 0 and ∆ k = 0.
The steady state equilibrium is shown by point A, where the output per worker is constant.
The economy stops growing due to the diminishing marginal product of capital. Each
additional machine adds to production but adds less than the previous machine. At the steady
state equilibrium, savings (sy) is equal to the required investment (n+d) k. This is because the
investment required to maintain or replace worn out equipment is equal to the savings
generated by the economy (Thirwall, 2002). At low levels of physical capital accumulation, a
high marginal productivity of capital creates an incentive to invest, thus raising the capital-
labour ratio and labour productivity. A falling marginal product of capital ensures a rise in the
8
capital-output ratio and a declining incentive to invest until a point is reached at which the
full savings (and hence investment) generated by the economy are employed in order to
supply new labour hours entering the workforce with the same capital intensity as existing
previous labour hours available for production.
The only way for the economy to grow or move from the steady state equilibrium is for the
economy to raise its savings level and maintain a lower labour force growth rate. This can
only happen if savings have risen relative to investment requirements and therefore more is
saved than required to maintain the capital per head constant. This higher savings rate implies
that there will be an incentive to invest, thus increasing the capital-labour ratio and labour
productivity. A high population growth rate leads to a decline in labour productivity
(Thirwall, 2002). A lower growth rate of the labour force allows the use of investment for the
purposes of capital deepening rather than capital widening; again, the consequence is a rising
capital-labour ratio and higher labour productivity. Both changes in the savings rate and
changes in the growth rate of the labour force result in a temporary change in the growth rate
of output as the economy moves towards a new steady state defined by the new savings rate
and labour force growth rate. In a steady state the natural growth rate of the economy would
again prevail (Fedderke & Simkins, 2006). With constant-returns-to-scale production, in the
short run, savings tend to increase the growth rate of output but they do not affect the growth
rate of output in the long run. The implication here is that a higher savings rate initially
increases output or growth but the economy will reach new steady state equilibrium in the
long run.
2.2.2.1 Limitations of the neo-classical growth model
The neo-classical growth model assumes that economies reach long run steady state
equilibrium and the only way for the economy to grow is through technological progress. It
however leaves the determinants or sources of this exogenous variable unexplained. The
model assumes that in the absence of shocks or technological change all economies will
diverge to zero growth. Rising per capita incomes are only a temporary phenomenon
resulting from a change in technology. Any increase in per capita income that cannot be
attributed to labour or capital is ascribed to what is known as the Solow residual. In his
empirical studies, Solow showed that approximately 50 percent of historical growth in
industrialised nations is attributed to the residual (Fedderke & Simkins, 2006). However, it is
impossible to analyse the determinants of technological advances because it is completely
independent (exogenous) of the decisions of economic agents. Another weakness of the
9
Solow growth model is that it fails to explain large differences in residuals across different
countries. The conception that poor countries will eventually catch up with developed
countries, if technological progress is the same, is not effective if there is no explanation of
the determinants of these technological advances (Fedderke & Simkins, 2006). In view of the
weaknesses of the Solow growth model, the new growth theory (endogenous) emerged;
hence, it is discussed in the next sub section.
2.2.3 Endogenous Growth Model
The main purpose of the endogenous growth model is to explain the existence of increasing
returns to scale and contradictory long term growth patterns. The endogenous growth theory
was formed by (Romer, 1986) and (Lucas, 1988). The endogenous growth model outlines
how human capital development as well as research and development (R&D) contribute to
long run economic growth. The endogenous growth model is based on two approaches taken
by the (Romer, 1986) and (Lucas, 1988) models. The theory assumes that there are positive
externalities associated with human capital formation (education and training, for example)
and research and development that prevent marginal product from declining (Thirwall, 2002).
The theory begins with the assumption that there are constant returns in production.
The endogenous growth theory is an extension of the Solow growth model, expressed as
follows:
Y = AK…………………………………………………………………………………..…2.2
Where Y = output
A = total factor productivity (technology)
K = physical and human capital
Equation 2.2 shows that output is proportional to capital. Total factor productivity represents
the marginal product of capital which is constant. The above formula of the endogenous
growth theory proposes that there is no decreasing marginal product of capital and
endogenises technological progress. Therefore, the production function has a constant
marginal product of capital. To prevent the decrease of marginal product of capital it was
proposed that the concept of capital be increased to include human capital (Lucas, 1988). The
concept of human capital, as outlined by Lucas (1988), is explained in the next subsection.
10
2.2.3.1 The Lucas Endogenous Growth Model
The Lucas (1986) approach of endogenous growth introduces the concept of human capital as
opposed to physical labour in the production function. Human capital refers to the knowledge
accumulation or skills gained by workers through learning by doing. The Lucas (1986) model
states that the growth rate of the economy will be determined by the rate of growth of human
capital creation. The Lucas (1986) model of endogenous growth is expressed as follows:
Y = AF (Kα, H
1-α)………………………………………………………………………….2.3
Where: Y = output
A = total factor productivity
K = capital
H= human capital
α and 1-α = output elasticities of the factor inputs. (α and 1-α=1).
Equation 2.3 above illustrates human capital as a component of the production function.
Capital not decreasing and human capital displaying positive externalities, such as education
and training, enables the economy to reach long run economic growth (Todaro and Smith,
2009). Firms and consumers invest in human capital by gaining knowledge. All inputs of the
production function can thus be accumulated. The growth of capital generates new
knowledge about production in the economy as a whole. Growth is then created by assuming
that the motivation to invest in human capital is non-decreasing in human capital (Todaro and
Smith, 2009). The Lucas model of endogenous growth suggests a production function of
human capital which has constant returns to scale in human capital but with the possibility of
increasing returns to scale. Hence, the marginal product of human capital that determines the
incentive to invest in knowledge accumulation is constant. The Lucas model assumes that
human capital relates to the skills and experience gained by the labour force. Investment in
human capital has a positive impact on the growth process. The second approach to
endogenous growth, by Romer (1986, 1990), is discussed in the ensuing subsection.
11
2.2.3.2 The Romer Model of Endogenous Growth
The second aspect of the new endogenous growth theory is in line with research and
development (R&D). The path that a country could take in order to not experience
diminishing returns in the long run would be technological progress. Spending on research
and development (R&D) is considered an investment in knowledge that translates into new
technologies as well as using the resources of physical and human capital that are already in
existence more efficiently. Romer (1986) focuses on research and development as an
important tool to knowledge accumulation. The model adds R&D to the original production
function. This is expressed as follows:
Y = A (R) f (Ri, Ki, Li)............................................................................................................2.4
Where: Y = output
A = total factor productivity
Ki = capital
Li = labour
Ri = stock of results from expenditure on R&D in firm i and where spill over from private
research efforts lead to improvements in the public stock of knowledge.
Equation 2.4, above, shows an augmented production function that includes R&D. In this
model, output is not only a function of capital and labour but also R&D efforts by firms.
Economies of scale are external to firms as technology will move across to other firms
resulting in an improvement in public knowledge. The Romer model of endogenous growth
through technological progress has characteristics of a public good, which states that it is
non-rivalry and partially-excludable. The creation of new knowledge by one firm is assumed
to have a positive external effect on the production possibilities of other firms because
knowledge cannot be perfectly patented or kept secret. With spill-over effects, knowledge
production is an inadvertent side-product of all production and investment activity, and
would take place whether firms wish to undertake it or not, as long as they are engaged in
their standard productive activity (Fedderke & Simkins, 2006). The marginal cost of using
new knowledge is assumed to be zero or close to zero. The low cost of using existing
knowledge is also assumed to lower the cost of producing new knowledge, this results in
dynamic scale economies in knowledge accumulation.
12
The effect of knowledge spill-over is to ensure that the efficiency of the labour input at the
social level improves. The consequence of this is that the production function shows
increasing returns to scale at the social level (because of constant social returns to capital).
Once social returns to scale in capital are constant, it immediately follows that the marginal
product of capital also becomes constant. Consequently, the incentive to invest does not
change with a rising capital labour ratio, since the marginal product of capital and hence the
profit rate is constant. The source of the non-declining incentive to invest in Romer’s (1986)
model arises due to knowledge spill-over, which ensures a non-declining marginal product of
capital (Fedderke & Simkins, 2006). To illustrate how a firm can internalize economies of
scale, Romer (1990) developed a new production function as illustrated below.
The production function is augmented to endogenize technological progress (Romer, 1990).
Y = f (K, L, H, A)……………………………………………………………………….…...2.5
Where: Y= output
K = capital
L = labour
H = human capital
A = stock of knowledge about technological progress
The above equation includes human capital in the production function and technology is no
longer exogenous. This is because technology occurs as a result of R&D. In conducting
R&D, human capital and knowledge of capital stock are used which make technology
endogenous to the firm. In conducting R&D, the firm obtains increasing economies of scale
due to the non-declining nature of capital stock and human capital. Long run growth depends
on the human capital devoted to research and on the effectiveness of the human capital
engaged in the research.
With R&D, there seems to be stronger consent that R&D may have a persistent effect on
growth. As R&D expenditure gets higher, the growth rates tend to be higher. To the end,
overall expenditure on R&D as a share of GDP has increased since the 1980s, in most
countries, mainly as a result of increases in R&D activity in the business sector. The
endogenous growth theory emphasizes that the long-run rate of growth is not explained by
population growth, as in the Solow model, but rather by knowledge accumulation (Foss,
1998). Romer (1986) and Lucas (1988) argue that technological progress is an effect of
13
targeted research and development. Research and development results in improvement in
technological progress which, in turn, attracts more investment and leads to increased
productivity.
2.2.3.3 Limitations of the endogenous growth model
The assumption of decreasing marginal product of capital and changing the shape of the
production function to the extent that it exhibits constant marginal product of capital, violates
economic principles. The changed assumption implies that a firm with twice as much
machinery will produce twice as much output. If doubling capital doubles output, then
doubling all factors including labour will more than double output; this suggests increasing
returns to scale. The issue here is that larger and larger firms become more efficient and there
would eventually be one firm dominating the entire economy. This possibility of this
occurring is lost and therefore, increasing returns to scale to all factors is ruled out (Thirwall,
2002). The assumption that a non-declining marginal product of capital occurs as a result of
knowledge spill-over is difficult to defend. This is because the knowledge spill-over may be
difficult to internalize but it takes time for the knowledge to move across to other sectors,
regions or countries. The public good characteristic of technology, on which the theory relies,
is therefore doubtful. Another weakness of the model is the approach of technological
advancement. Even though the development theory has proven that technology has an
explicit origin (investment in capital stock) it still remains unexplained as an internal activity
on the part of economic factors. Technology continues to happen unexpectedly as it is a by-
product of intentional activity directed not at technological change itself, but at a quite
different productive activity. The expectation is of a reward from the act of investment in
physical capital rather than from technological change (Fedderke & Simkins, 2006).
2.2.4 Assessment of the Theories
A number of growth theories have been reviewed; however, the traditional neo-classical and
endogenous growth theories become relevant for the study. The neoclassical and endogenous
growth theories use a production function based approach in identifying factors that
contribute to economic growth (transport infrastructure investment). The neoclassical growth
theory assumes that capital and labour are the fundamental determinants of economic growth.
Nevertheless, the theory predicts that an economy will reach a steady state equilibrium due to
the diminishing marginal product of capital and technology (exogenous) which is the only
source of economic growth. The weakness of the neoclassical theory is that it fails to explain
the determinants of this exogenous variable. The prediction of absolute convergence, where
14
developing countries with the same access to technology as developed countries will catch
up, is another weakness. It would be very hard for developing countries to catch up if
technological determinants are not known. The endogenous growth theory is also reviewed
due to these weaknesses.
The endogenous growth model endogenises technological progress. The theory outlines that
positive externalities, such as human capital development and R&D, prevent marginal
product from declining. Technological progresses, unlike the neoclassical theory, are
attributed to these positive externalities. Human capital development through knowledge
accumulation and skills development contributes positively to growth in output. Human
capital development results in non-declining marginal product of labour and the possibility of
increasing returns to scale in production. The endogenous growth theory also attributes
technological progress to R&D activities. The endogenous growth becomes relevant because
it attributes long run economic growth to positive externalities gained through activities such
as human capital and R&D.
2.3 Empirical Literature
2.3.1 Empirical Evidence from Developed Countries
Moudud (1998), in the Jerome Institute, used the Classical Growth Cycles model (CGC) in
the study of government spending and growth cycles. The investigation was to reveal the
different situations in which government expenditure can lead to both crowding-in and
crowding-out of output and employment. It was found that an increase in government budget
deficit lowers the savings rate, investment growth rate, output and consumption. Higher
government budget deficits stimulate the demand for bank credit, thus negatively affecting
the finance charges of firms to accumulate to their cash flows. Increases in government
deficit tend to lead to a decline in investments which result in crowding out effects.
Therefore, an expansionary government deficit lowers the bond prices, raises the interest rate
of bonds, increases the demand for consumption and, lastly, raises the demand for money. A
rise in a budget deficit leads to a crowd-out effect because it increases the interest rate which
later negatively affects investment and economic growth.
Cohen and Percoco (2003) examined the fiscal implications of infrastructure developments in
Washington. The objective of this paper was to discuss government’s fiscal management with
infrastructure investments and advance a policy proposal in that regard for the Latin
15
American Region. The empirical results showed that both developing and developed
countries have faced a budget deficit which led to a debt crisis resulting in most of the
infrastructure investment being delayed or cancelled. By doing this, legislations were passed
in order to attract new investors (such as foreigners) to support infrastructure development
programs that could no longer be implemented by the government. An increase in private
infrastructure spending is associated with higher or more public spending on infrastructure.
More infrastructure spending does eliminate poverty and contributes to the improvement of
economic performance.
Bosch and Espasa (1999), in their working paper in Barcelona, saw that transport
infrastructure is one of the direct measures used to make an impact in the growth rate and the
geographical distribution of economic activity. The study used the VAR method to see how
the changes in infrastructure investment affect economic growth; it uses marginal product
calculation to check the intensity of infrastructure investment towards GDP. The data
analyzed is from the period 1991-2008 and is taken from the Department of Economy and
Finance. In order for transport infrastructure to have a significant effect on the growth rate it
depends on the availability of public capital. Transport infrastructure is an important tool in a
country as it brings opportunities such as trade and interpersonal relations between countries.
Spending on infrastructure stimulates the U.S economy and investing in infrastructure goes
beyond improvements to the quality of roads, sewers, highways and power plants. These
investments not only generate significant economic returns but also generate an increase in
the tax revenue for the government. It was found that investment in the transport
infrastructure has the most significant effect in generating economic gains in economic
growth.
The research conducted by Copeland, Levine and Mallet (2011) has as its main objective a
discussion of policy issues associated with how infrastructure can be used as a mechanism to
benefit economic recovery. The report showed that, when the government has enough
resources to spend on infrastructure investments to stimulate a sluggish economy in the short
run it leads to positive returns on the productivity of the country. It is found that the returns
are larger than the cost with spending in infrastructure and that, with more investment, there
is an increase in productivity growth. An increase in infrastructure spending stimulates labour
demand when the labour market is underutilized because workers are hired to accept
construction projects. Higher deficits slow down economic growth in the long run because
16
the government’s borrowing of funds tends to crowd out private investments. The data used
was obtained from Sweden, during a study which explored whether there is a negative
relationship between the budget deficit and the exchange rate.
Aschauer (1989) conducted a study regarding the relationship between aggregate productivity
and the flow of government spending. He found very high estimates of the elasticity of
private output with respect to public capital: 0.35 to 0.45. He argued that having the
government spend more on military variables, meaning core infrastructure such as airports,
seaports, roads, railways and sewers, brings more productivity and growth to the economy.
Aschauer (1990) stated that under certain circumstances, public capital and private factors of
production of labour and private capital may be balancing factors of production so that an
increase in the stock of public capital increases the productivity of private factors of
production. However it thereby generates increased demand for labour and private capital
investment goods. Aschauer postulates that public capital can have both a direct and indirect
effect on private output. The direct effect occurs because public capital changes the level of
output by making private labour and capital inputs more or less productive. However, an
indirect effect occurs because an increase in public capital will have an influence on marginal
product of labour or capital. He stated that the government implementing an effective fiscal
policy is a good and correct way in which the government could manage its expenditure and
could be an efficient strategy.
Moudud (1999) investigated the effect of government spending in a growing economy. A rise
in the budget deficit increases the growth rate of output and employment. By increasing
effective demand, the rise in the budget deficit raises potential business profits, thereby
stimulating investment spending. The positive effect of the budget deficit can be augmented
by expansionary monetary policies that maintain low interest rates. This has the dual effect of
providing greater monetary stimulus from the deficit and keeping financial charges on
business debt low.
Barro (1990), in his article “Government Spending in a Simple Model of Endogenous
Growth”, explored the relationship between government spending and economic growth. It
stipulates that in order to remain with a positive growth rate of output per capita, in the long
run, there must be advanced technologies in the form of new investments being made and
new processes. This theory assumes that all resources such as labour and capital are being
17
fully utilized. It states that fiscal policy measures can have an effect on the long run of the
economy. The capital used by the government to finance infrastructure investment increases
only if the capital stock also increases. Barro (1990) identifies the existence of a positive
correlation between government spending and long-run economic growth. Barro (1990)
believed that expenditure on investment and productive activities is expected to contribute
positively to economic growth, while government consumption spending is expected to retard
growth.
Georgantopoulos and Tsamis (2011) explored the Macroeconomic Effects of Budget Deficits.
This paper examined the causal links between budget deficit (BD) and other macroeconomic
variables such as Consumer Price Index (CPI), Gross Domestic Product (GDP) and Nominal
Effective Exchange Rate (NEER) in Greece. The study employed the Cointergration test,
Granger-causality using Vector Error Correction Models (VECM) and Variance
Decomposition analysis for the period 1980-2009. Data figures are calculated by employing
data obtained from the World Development Indicators (i.e. the World Bank database) and
UNCTAD (United Nations database). The Augmented Dickey-Fuller (ADF) test has been
used to test the unit roots of the concerned time series variables. It was found that the printing
of more money due to the budget deficit resulted in inflation. Budget deficit reduces the
supply of loanable funds, driving up the interest rates, crowds out investment and causes
other currencies to appreciate the domestic currency and further deteriorate the trade deficit.
Higher interest rates attract foreign investors, who want to earn higher returns. Hence, budget
deficits raise interest rates (both domestic and foreign) causing net foreign investment to fall.
The research conducted by Rutkowski (2009) in Poland found that improvements in the
quantity and quality of public infrastructure can have a positive impact on growth in Poland;
this is in line with the theory and empirical literature on the subject. A significant effort has
been made in recent years to increase public capital spending and this has contributed
towards smoothening the economic downturn during the crisis. The study employed the
vector auto regression model on quarterly variables over the period 1999-2007. Impulse
response functions point to a positive relationship between public investment, private
investment and GDP growth.
18
Tien-Ming and Yuli (2003) used Hakkio’s (1996) model in regards to seven Asian countries
and eight Euro-currency countries over the years 1951 to 2001. The Time-Series Cross-
Section Regression was applied with the Seemingly Unrelated Regression (SUR) approach to
data from 15 countries, in investigating the relationship between fiscal deficits and exchange
rates. The empirical relationship between deficit reduction and exchange rate is unclear
because the theoretical relationship is ambiguous. Deficit reduction has different effects on
the exchange rate, with some effects leading to a stronger exchange rate and other effects
leading to a weaker exchange rate. Budget deficit reduction may affect interest rates and
exchange rates both directly and indirectly. Direct effects decrease the exchange rate, while
indirect effects increase exchange rates. Theory and evidence both warn that large budget
deficits pose real threats to macroeconomic stability and, consequently to economic growth
and development. An increase in the budget deficit will result in a reduction in investment
and an increase in the current account deficit. A public sector deficit could lead to an external
debt crisis because of foreign borrowing, while borrowing domestically could result in higher
real interest rates.
Adam and Bevan (2004) studied the relationship between fiscal deficits and the economic
growth for a panel of 45 developing countries over the period 1970–1999. The OLG model is
employed. The evidence found was that of a threshold effect at a level of the deficit around
1.5% of GDP. It was also found that there is an interactive effect between deficits and debt
stocks, with high debt stocks exacerbating the adverse consequences of high deficits. The
impact of the deficit is likely to be complex, depending on the financing mix and outstanding
debt stock. In particular, deficits may encourage growth if financed by limited seigniorage;
they are likely to discourage growth if financed by domestic debt; and to have opposite flow
and stock effects if financed by external loans at market rates.
Anušić (1994) looked at the impact of the budget deficit and inflation in Croatia. The study
made use of the Keynesian economic theory which states that the increase in budget deficit
will cause ceteris paribus, the increase in real interest rate reason being due to budget deficit
occurrence the aggregate national demand increases as well. He found that the budget deficit,
along with its potential increase and its impact on the economy can cause a decrease in real
gross investment, which is called the crowding-out effect.
19
Kneller, Bleamey and Gemmell (1999) investigated the effects of a fiscal policy on growth in
Nottingham. The outcome was that an increase in government expenditure, namely investing
in public transport infrastructure, could lead to an increase in economic growth. Using the
vector auto regression method, the study stipulated that government spending or investing in
transport infrastructure constitutes benefits such as time saving and reducing the costs of
congestions. The data used was collected from a panel of 22 OECD countries from 1970 until
1995. Government budget data come from the Government Financial Statistics Yearbook
(GFSY) and from the World Bank Tables.
Zhan (2009) shows that public investments boost aggregate demand which boosts
employment and utilizes flexibility on low income countries, especially during economic
downturns. The fiscal policy can affect the investment in public transport infrastructure
negatively in such a way that some countries are not always aware of the economic downturn
that will take place during times of recession. This results in badly designed projects being
implemented during a crisis because the country failed to plan in advance, or implement
policy goals effectively. This could be problematic for an economy because the infrastructure
investments take time to be designed and evaluated.
Chmura (2011) used qualitative research to determine the long term benefits of the
government investing in public transportation infrastructure. The increase in government
expenditure does have an economic impact that is positive because it benefits the regional
industries supporting the infrastructure being developed such as trucks and site development
and later the people employed to do the infrastructure work spend their income on goods and
services resulting in regional businesses benefiting from government expenditure; all this
encourages economic growth. The economic impact of government expenditure increases the
country’s capacity of its public transportation network which provides time savings for
businesses and residents travelling using public transport. The time saving leads to higher
productivity for the country and it halves the unemployment rate.
Spoehr, Burgan and Molloy (2012) found that government expenditure on public transport
infrastructure investment does increase productivity, competitiveness and the capacity of
business to deliver high quality services. This affects the economy positively in the short run
but, in the long run, it affects it negatively. This tends to be negative in the long run because
the country is in public debt through financing the transport infrastructure.
20
Cata (2004) investigated the relationship between investment, growth and budget deficit
ceilings and found that a budget deficit crowds out the net exports of goods and services
causing an appreciation of the exchange rate and increasing the country’s debt. This budget
deficit would also force the government to raise taxes and reduce public expenditure, thus
affecting infrastructure investment negatively.
In a report submitted by Yongding (2010) in China, about the impact of the global financial
crisis on the economy, it has been shown that government surplus or a deficit can affect
investment in that the higher the national deficit, the less money there is available for
investment. It will not necessarily be negative if the total amount of money available is still
adequate for investment. In the case of the United States, a large part of the government
deficit is offset by net imports with foreigners lending the U.S. government money. In this
case, foreign loans can be used to boost the investment which would have been reduced by
the deficit spending. Each year’s deficit adds to the cumulative deficit, the total of which
would be the outstanding government debt. If government debt becomes very large, as a
proportion of an economy’s size, investors in the government debt may begin to fear that the
government may simply print money. Such a solution to reduce debt would lower the value
of that government’s money, resulting in high inflation and high interest rates, as lenders
would demand higher returns to account for the decrease of the money. Such an event might
also lead to the government’s money being worth less in relation to money from other
countries.
Pereira and Andraz (2010) explored the economic and fiscal effects of investments in road
transport infrastructure by using the vector auto regression model VAR. They made use of
impulse responses and found that investment in transport infrastructure has been a powerful
tool to increase private investments, to create new permanent jobs and to promote long term
economic growth in all countries. Policies such as a budget deficit that would reduce
investments will result in lower long term economic growth as well as worse budgetary
conditions in the future. Pereira showed that changes in public investment in road
infrastructure in the U.S. are positively correlated with lagged changes in output and
negatively correlated with lagged changes in employment. The study used annual data from
the period 1980-1998 which was obtained from the regional accounts published by the
National Institute of Statistics.
21
Srivyal and Venkata (2004) investigated the budget deficits and other macroeconomic
variables using the cointegration approach and Variance Error Correction Models (VECM)
for the annual period 1970-2002. The study tries to reveal the effects of a budget deficit with
other macroeconomic variables such as nominal effective exchange rate, GDP, Consumer
Price Index and money supply (M3). The Phillips Perron (PP) that allows weak dependence
and heterogeneity in residuals was employed, as well as the Engle and Granger (1987) and,
lastly, the maximum-likelihood test procedure established by Johansen and Juselius (1990)
and Johansen (1991). The empirical results reveal that the variables under study are
cointegrated and there is a bi-directional causality between budget deficit and nominal
effective exchange rates. However, it has not observed any significant relationship between
budget deficit and GDP, money supply and Consumer Price Index. It is also observed that the
GDP Granger causes budget deficit whereas budget deficit does not.
Chakraborty (2002) examined the real or direct and financial crowding out of investments.
An asymmetric vector autoregressive (VAR) model was employed. Data was drawn from the
new series of National Account Statistics published by the Central Statistical Organisation,
the Handbook of Statistics on Indian Economy, Reserve Bank of India. The period of analysis
is 1970–7 to 2002-03. The empirical results found that fiscal deficit does not put upward
pressure on the interest rate and high fiscal deficit affects capital formation in the economy
both by reducing private investment through an increase in the interest rate and through
reduction in the public sector’s own investment arising out of ever-increasing consumption
expenditure.
Goyal (2004), using monthly data, argues that there is a two-way causality between fiscal
deficit and interest rates. It was outlined that interest rates did not rise in recent years in spite
of high fiscal deficits because of larger liquidity available to the system. The Reserve Bank of
India has noted that raising public sector investment to boost aggregate demand in the
economy crowds-out both private consumption and investment with no long-lasting impact
on output. On the other hand, infrastructure investment by the public sector crowds-in private
investment while public investment in manufacturing crowds-out private investment.
Schäuble (2012), the Minister of Finance, stated that Germany's 2010 federal budget shows
that the country experienced a record-high deficit of well above €50 billion. Public-sector
debt surpassed €1.7 trillion, approaching 80% of GDP. The financial crisis and the ensuing
22
recession only went so far as explaining the high levels of indebtedness. The results show that
once a government's debt burden reaches a threshold perceived to be unsustainable more debt
will only stunt, not stimulate, economic growth.
Ball and Mankiw (1995) explored the case of the United States from 1960 to 1994. They
came to the same conclusion as that of research conducted on the pattern of government
expenditures for 30 developing countries. Huge budget deficits had significantly reduced the
level of national savings and private investment. Apart from that, high budget deficits will
signal to the citizens that the government has lost control in managing its funds. It was found
that the countries that faced budget deficits have a lower growth rate in comparison to
countries that faced a budget surplus. A continuous rise in budget deficits will also lead to the
problem of bankruptcy. As a result, the investors will have less confidence to invest in a
country and it will further reduce the economic growth of a country. Apart from that, the
budget deficit can also reduce the economic growth of a country based on the perspective of
politics and the election process.
Cogito (2010) found that there were large deficits in Canada which resulted in a rapidly
growing federal government debt. There has been a fierce debate over how the federal and
provincial budget deficit affected long-term growth. The supply of available funds for
investment decreases. This will result an increase in interest rates because of scarcity of
available funds. This higher interest rate then alters the behavior of firms that participate in
the loan market. Many demanders of loanable funds are discouraged by the higher interest
rate. Fewer families buy new homes and fewer firms choose to build new factories. The fall
in the investment, because of government borrowing, is called crowding out.
2.3.2 Empirical Evidence from Developing Countries
The Organisation for Economic Co-operation Development (OECD) (2002), using the cost
benefit analysis, found that investing in public transport infrastructure causes the resources to
be allocated and used efficiently in a country. Government expenditure increasing to invest in
transport infrastructure improves the accessibility of economic activities leading to an
increase in the market size for manufacturing, tourism and competitiveness. Investment in
transport infrastructure does encourage economic development in underdeveloped regions by
generating employment and improved environmental outcomes.
23
A study by Raju and Mukherje (2010) of fiscal deficit, crowding out and the sustainability of
economic growth in India from 1980-2009 shows that there is no long run relationship
between the variables. The study applied unit root tests and cointegration techniques that
allow for endogenously determined structural breaks. It was found that there is either
crowding in or crowding out of public spending and investment and the findings are in line
with the ricardian equivalence theory that implies that it does not matter whether a
government finances its spending with debt or tax increases. The convergence in fiscal deficit
and debt has helped to accelerate the rate of investment in the economy in the medium run.
Bose, Harque and Osborn (2007) investigate the relationship between budget deficit and
economic growth for 30 developing countries, from 1970 to 1990. By using panel data
analyses, they found that the budget deficit helps the economy to grow provided that the
deficits were due to productive expenditures such as education, health and capital
expenditures. The same conclusion is derived based on the research done by Fischer. A huge
budget deficit helps Morocco and Italy to grow since the excessive spending helps to increase
the level of private consumption in the short-run. It was due to the deficits which were used
to reduce the burden of taxation from the consumers’ perspective. In the long-run, huge
budget deficits ruined the level of economic growth for these two countries since they have to
struggle in paying back all the national debts.
Ramzan, Saleem and Butt (2013) explored the impact of budget deficit on economic growth
in Pakistan. Time series data was used for the period 1980 to 2010 and the study used
regression analysis. The Pearson Correlation test was also applied to check the relationship
among independent variables. The analysis reveals that the model was a good fit. The results
showed that there is moderate correlation between budget deficit and investment.
Odhiambo, Momanyi, Frederick and Othuon (2013) investigated the relationship between
fiscal deficits and economic growth in Kenya. The study used both exploratory and causal
research designs and employed time series secondary data for a period of 38 years (1970-
2009) and was estimated using the OLS method as well as the Johansen Cointegration test.
The study also performed various econometric tests such as the Dickey Fuller (DF) and
Augmented Dickey Fuller (ADF) unit root test as well as the error correction model.
Diagnostic tests, like multicollinearity, were also performed. The study found a positive
relationship between budget deficits and economic growth, in line with the Keynesian
24
assumptions and hence recommends prudent financial management and enhanced revenue
collection by revenue authorities so as not to crowd-out private sector investment by
borrowing domestically.
Kukk (2004), in a working paper about the effect of fiscal policy on economic growth in both
the short run and the long run, found that government revenue and government expenditure
have a significant effect on GDP because public investments are positively related to growth.
With the government improving its expenditure when it is needed the best results in GDP
growth can be achieved. By raising taxes and increasing investments the government could
experience accelerated growth rates. Using the cost function modelling approach and
augmented dickey fuller test for checking the unit roots it was found that underinvestment in
transport infrastructure was largely responsible for low levels of growth rates in output.
Roy, Heuty and Letouze (2006) investigated the fiscal space for public investment in
Singapore; they found that public investment has been declining since the 1980s especially in
public investment in infrastructure due to fiscal conditions such as the country experiencing a
fiscal deficit. A fiscal deficit is an important cause for the decline of public investments and
slows down economic growth. Public investments such as infrastructure have an important
role in kick starting economic growth, reducing the unemployment rate and, in turn, reducing
poverty. They used time series data from the period 1970-2000 that was obtained from the
International Monetary Fund.
Rahman (2012) explored the relationship between budget deficit and economic growth from
Malaysia’s perspective. Four variables were used, namely: real GDP, government debt,
productive expenditures and non-productive expenditures. The ARDL approach is used to
analyse the long-run relationship between all series since it can cater for a small sample size.
By using quarterly data from 2000 to 2011, it was found that there is no long-run relationship
between budget deficit and economic growth in Malaysia. However, productive expenditure
has a positive long-run relationship with the economic growth.
Hadiwibowo (2010) finds significant relationships between fiscal policy variables such as
government expenditure and government revenue and investments. In this study, the vector
error correction model was employed to investigate fiscal policy, investments and economic
growth in Indonesia. The study uses data obtained from Statistics Indonesia, Ministry of
25
Finance, world development indicators; it uses quarterly time series data from 1969-2008.
The Augmented Dickey Fuller test is applied as well as the Phillips Perron (PP) method to
check for unit roots; the Schwarz Information Criterion to determine the lag length in the
ADF tests and, lastly, the Newey West band width selection with Bartlett kernel in PP tests
was also employed. The results indicated that government development in developing
countries is more valuable because it provides higher returns, accelerates growth and the
government should be aware of the negative effects that expenditure could bring; such as, the
misallocation of resources in unproductive expenditure which would later imply budget
deficits and higher taxes. Government expenditure and government revenue (budget deficit)
have an adverse relationship with investment whereas a budget surplus has a relatively
positive relationship with investments. Higher budget deficits affect investments negatively
because of the crowding out effect and they generate higher interest rates which mean that the
cost of capital will be high, thus discouraging local investments and encouraging investments
from abroad (FDI).
Kuştepeli (2001), in Turkey, shows that a government deficit leads to inflation which later
brings uncertainty, which negatively affects economic growth. The study used Augmented
Dickey-Fuller tests to test for unit roots for the variables, the Engle Granger and Johansen
Cointergration tests and causality tests are performed. It was assumed that a high and
persistent deficit leads to increases in inflation and the monetary base; this affected the
growth of the economy negatively.
Fatima, Ahmed and Rehman (2011) investigated the effects of a budget deficit on economic
growth and looked at the indirect impact of fiscal deficit on GDP through investment as a
share of real GDP per capita in Pakistan. It used the time series data obtained from
International Financial Statistics, Pakistan Economic Surveys and the State Bank of
Pakistan’s annual reports and considered the period 1980-2009 which covers up to 30
observations; the regression analyses were performed to check the impact of a budget deficit
on economic growth. The ordinary least squares are employed in the study and it uses the
model developed by Shojai, in 1999, in investigating the effects of a budget deficit on
economic growth and the two-stage least squares method (2-SLS) is used to estimate
simultaneous equations. The results were that a budget deficit has a negative impact on the
country’s economic growth which will cause a major decline in real investments such as
transport infrastructure investments. The country’s deficit reached its highest percentage in
26
2007-2008 by 7.3% but later decreased to 4.7% of GDP in 2008-2009. With a 1% increase in
inflation it led to a decrease in investment by 84%; this indicated that there are adverse
effects of inflation to economic growth. The fiscal deficit itself showed a negative and
significant impact on investment. Lower investments will cause lower economic growth and
it clearly showed that fiscal deficit not only affected the economic growth directly but also
indirectly through investments. Moreover, the Durbin Watson statistics in the regressions
showed that the models are free from autocorrelation problems.
Reungsri (2010) examined the impact of public infrastructure investment on economic
growth in Thailand using the Dickey-Fuller and the Augmented Dickey-Fuller tests to justify
the stationary status. In addition, the OLS method can be used in estimation and ECM as well
as the ARDL. This study concentrated on quarterly time series data from 1993:Q1 to
2006:Q4 and was obtained from the Bank of Thailand, the National Economic and Social
Development Board, the Ministry of Finance, the Revenue Department, the Excise
Department, and the Customs Department. The results showed that a country experiencing a
large deficit could have a negative impact in productivity and there will be a failure in
promoting efficient and responsive goods and services, especially when the infrastructure is
financed by the public sector. The empirical results discovered that, with the government
having sufficient public capital, economic growth can be promoted through infrastructural
investments.
Joshi (2009) explores whether the does fiscal deficit of India hurt economic growth; he states
that the fiscal deficit was a budgeted 6.8 per cent of GDP in 2009-10. With a budget deficit
this is where the problem starts; it can be financed through domestic borrowing or foreign
borrowing which could, in turn, mean a cut back in spending on critical sectors such as
infrastructure. Where excessive domestic borrowing can lead to a hardening of interest rates,
too much foreign borrowing can lead to an external debt crisis. The domestic-borrowing
programme of the government puts pressure on domestic government-bond yields; this
complicates the implementation of a soft-interest rate policy by the central bank. Further, a
high deficit is bound to crowd out private investment, inflation and exchange rate
fluctuations.
27
Khan and Khattak (2008) investigated the analysis of short-term effects of budget deficits on
macroeconomic variables such as private investment, public investment, economic growth
and unemployment using the annual data for the period 1960-2005, taken from the
International Financial Statistic (2003). ECM was used for estimation. The Augmented
Dickey-Fuller (ADF) test has been used and the Akaike information criterion is used to select
the optimum ADF lag. Stationarity of the variables was checked and the Johansen
Cointegration test was used to ascertain the Cointegration in the regressions used for analysis.
The results show that short run changes in government consumption, private investment and
public investment have a positive impact on the short-run changes in growth. If the
government gives priority to long-term private public investment policies, it can gain better
results in economic growth, poverty alleviation and unemployment retardation. The parallel
and effective running of monetary, fiscal and exchange rate policies is needed to reduce the
balance of payment deficit.
According to Al-Khedar (1996), interest rates increases in the short run due to a budget
deficit but, in long run, there is not impact explored. The study employed the VAR model by
selecting the data of G-7 countries for the period 1964-1993. The outcome shows that the
deficit negatively affects the trade balance. However, the budget deficit has a positive and
significant impact on the economic growth of the country. Hence, the money borrowed from
abroad or domestically to finance the important sectors in the economy, such as transport
infrastructure, generates some returns that would make a positive impact on the growth rate.
The researcher made use of Barro’s empirical work which explores a positive and significant
impact of budget deficit on growth. This impact is due to the positive relationship between
the budget deficit and the inflation and the fact that budget deficits have a way of crowding in
investments, especially the constructive ones.
Ghali and Al-shamsi (1997) utilized Cointegration and Grangers causality to investigate the
effects of fiscal policy on economic growth for the small oil producing economy of the
United Arab Emirates, over the period 1973-1995. This study provides evidence that
government investment has a positive effect on economic growth, whereas the effect of
government consumption is insignificant. It concluded that an increase in investment leads to
an increase in the economic growth of the country.
28
Gulcan and Bilman (2005) used the cointegration method and causality test and applied ADF,
Phillips Perron and KPSS unit root tests to investigate the stationarity of the individual time
series. The data used from Turkey was for the period 1960 to 2003 and proved that there is a
strong impact of budget deficit on the real exchange rate. The study shows that the role of the
budget deficit in maintaining the real exchange rate is crucial. It was suggested that the
government must focus to stabilizing the budget because the trade balance is significantly
affected by the real exchange rate.
Joharji and Starr (2010) analysed the impact of fiscal policy on economic growth using time-
series methods and data for 1969-2005. The VAR, VECM and the Johansen Cointegration
tests found that an increase in government spending has a positive and significant long-run
effect on the rate of growth. Government investment in infrastructure and productive capacity
has been less growth-enhancing in Saudi Arabia than programs to improve the administration
and operation of government entities and support purchasing power. They concluded that
investment in transportation and communication has a positive and strong effect on growth.
Abayomi (2011) explored the effects of government expenditure on economic development
in Nigeria, using the econometrics model with Ordinary Least Square (OLS) technique. The
paper tested for presence of stationarity between the variables using the Durbin Watson unit
root test. The results showed an absence of serial correlation and that all variables
incorporated into the model were non-stationary at their levels. The findings show that there
is a positive relationship between real GDP as against recurrent and capital expenditure. The
budget deficit that leads to excessive debt has a negative effect on economic growth since
there will be reduced investments or they will be put on hold while the government searches
for other sources of financing the deficit.
Mwakalikamo (2011) analysed the public budget deficit in Tanzania and its impact on
macroeconomic variables such as inflation, trade deficit and the exchange rate. The results
indicate that an increase in budget deficit will cause a similar increase in the current account
deficit; government budget deficit has a positive impact on the real exchange rate through the
price level and government deficit impacts the inflation rate in terms of how the deficit is
financed. This means that if the government decides to increase deficit spending then Central
Bank will be obliged to increase the money supply and such monetization will easily lead to
inflation, at least in a long run. The data showed that when budget deficit increases, domestic
29
absorption - namely consumption and investment – increases; hence, importation will expand
and cause the current trade deficit. The study used descriptive and secondary data which was
collected from public documents, field notes, downloaded documents from the internet,
reports and the library.
Easterly and Hebbel (1992) investigated the public sector deficits and macroeconomic
performance varying sample of member countries of the OECD and developing countries.
The government deficit was blamed for the large part that encouraged all the downfalls that
developing countries experienced such as over indebtedness, led to a debt crisis, higher
inflation, poor investment and growth performance. The consequences of budget deficits
depend on how they are financed because: if the deficit is financed by printing more money
then it could lead to inflation; domestic borrowing leads to a credit squeeze through higher
interest rates or when interest rates are fixed through credit allocation; the external borrowing
leads to a current account deficit and real exchange rate appreciation, or an external debt.
Anayochukwu (2012), in Nigeria, explored the effects of a fiscal deficit and inflation using
the autoregressive distributed lags (ARDL) and the granger causality test from 1970-2009.
The fiscal deficit/GDP causes inflation, however, no feedback mechanism was observed. The
results from the ARDL test confirm a significant negative relationship between growth in
fiscal deficit (% of GDP) and inflation as the above results confirm the priori expectation.
Velnampy and Achchuthan (2013) studied fiscal deficit and economic growth in Sri Lanka.
Data on the fiscal deficit and economic growth from the year 1970 to 2010 was collected for
the purpose of this study. The results revealed that there is no significant impact of fiscal
deficit on the country’s economic growth. There is also no significant relationship between
fiscal deficit and economic growth from the Sri Lankan economic perspective.
2.3.3 Empirical Evidence from South Africa
The National Treasury (1998) shows that the government increasing its expenditure to
finance the infrastructure benefits the health system. Moreover people from rural areas get
access to hospitals, education increases because more learners enroll and there is more work
productivity, as well as strengthened tourism.
30
Mabungu and Chitiga (2009), in their annual report for the Financial and Fiscal Commission,
confirm that infrastructure spending is beneficial to the South African economy because
investment and consumption increases. An increase in the capital used to finance the
infrastructure investment drives other factors to increase such as employment.
Perkins, Fedderke and Luiz (2005), in their study of infrastructure investment in long run
economic growth, examined the impact of public sector spending in infrastructure on
economic growth in South Africa. The study employed the vector error correction model
(VECM) using time series data for the period 1976 to 2002. The study reported much
stronger evidence that government expenditure might lead to output growth and more
employment in South Africa. Results from the study are realistic and compatible with
economic theory. The study found that infrastructure investment is an important determinant
of GDP growth. Railway lines, the number of goods wagons, roads, the number of vehicles in
South Africa and the electricity generated in these also play a role in determining positive
economic growth. Government spending in infrastructure investment helps the country to
develop new assets but the government also faces some challenges in financing these
investments. That is caused by the financial crisis which leads to delays in projects,
inefficient operations and the poor utilization of infrastructure assets, thus forcing the
government to cut back on its public expenditure.
Ocran (2009) used the structural vector auto-regression model, with the aid of quarterly data
covering the period 1990:1 to 2008:4, in investigating fiscal policy and economic growth in
South Africa. It was found that government expenditure has a positive impact on growth,
taxes also contributed positively to economic growth and a deficit has no impact on growth. It
was found that with the presence of consumption and investment, spending could accelerate
growth more quickly. The results showed that output responds positively to a budget deficit
and 0.003 units of impulse response function. The response is also associated with a
relatively high level of uncertainty as shown by the standard error values and that standard
errors also increase over time thus making the estimates of IRF less reliable over time.
Interest rate showed a temporary negative response towards the deficit.
31
Jooste, Liu and Naraidoo (2012) employed the structural vector error correction model, time
varying parameter vector auto regression (TVP-VAR) to capture possible asymmetries, time
variation of fiscal impulses and the dynamic stochastic general equilibrium model to unearth
the effects of fiscal policy on consumption and employment. The impulse responses indicate,
first, that increases in government expenditure have a positive impact on GDP in the short
run; second, over the long run, the impact of government expenditure on GDP is insignificant
and; third, increases in taxes decrease the GDP over the short run, while having insignificant
effects over longer horizons. Jooste et al. found that with the use of the fiscal policy in South
Africa both output and consumption have been stimulated reflecting effective expenditure
made by the government. Since a large percentage of South Africans are poor, the fiscal
policy acts as a shield to individuals as well as to companies from negative economic shocks
such as the recession.
Khamfula (2004) utilized the full-information maximum likelihood method as it allows for
connections among variables from different equations within the system in checking
macroeconomic policies, shocks and economic growth in South Africa. The Johansen
technique is employed in investigating types and channels of shocks that affect long-run
economic growth and will augment the simultaneous equation method. South Africa had an
urgent need to complement its political liberation and its openness to global trade and
investment with economic growth that would benefit all members of the population. In
fulfilling this it would basically require increasing employment, since unemployment is
concentrated, to a large extent, amongst the poor. It will require improved education and
training so as to make the workforce become more employable and productive. If there is a
good environment for households and firms to invest in the developing world, economic
growth is generally observed.
Bonga-Bonga (2011) studied budget deficits and long-term interest rates in South Africa
using the co-integrating vector autoregressive (VAR) techniques whereby co-integrating
vectors were identified based on the Fisher effect theory and the expectation hypothesis of the
term structure in order to assess the effect of systematic changes in budget deficit on the long-
term interest rate. Moreover, the generalised impulse response functions obtained from the
co-integrating VAR were used to assess the effect of the surprise change in budget deficit on
the long-term interest rate. In regards to the relationship between budget deficits and interest
rates and the crowding out effect it showed that if a positive relationship between the
32
government’s budget deficit and long-term interest rates exists, then higher deficits would
crowd out public spending and slow down economic growth. Moreover, if deficit financing
has no effect on long term interest rates, then deficit spending may instead promote economic
growth. The results of the paper showed a positive relationship between budget deficits and
the long-term interest rate.
Kumo (2012) investigated infrastructure investment, employment and economic growth in
South Africa for the period 1960-2009. The study employed the Vector Auto Regression
(VAR) model with and without structural breaks. The result indicated that there is a strong
causality between economic infrastructure investment and GDP growth that runs in both
directions; this implies that economic infrastructure investment drives long term economic
growth in South Africa while improved growth feeds back into more public infrastructure
investments. It was also found that a strong two way causal relationship exists between
economic infrastructure investment and public sector employment reflecting the role of such
investments on job creation through construction, maintenance and the actual operational
activities, while increased employment could in turn contribute to further infrastructure
investments indirectly through higher aggregate demand and economic growth.
Schoeman, Robinson and de Wet (2000) explored foreign direct investment flows and fiscal
discipline in South Africa from the period 1970-1998 using the Engel, Yoo three step
approaches and the Error Correction Model with the data obtained from the International
Financial Statistics, South African Reserve Bank, Government Statistics and World Bank.
The results show that an increase in the South African corporate tax level reduces foreign
direct investment in South Africa and that changes in the budget deficit, before borrowing
relative to the GDP, have an even greater negative impact on foreign direct investment.
Table 2.1: Summary of selected empirical literature on the budget deficit and public
transport infrastructure investment
Study Country Methodology Findings
Moudud (1998) Jerome Classical Growth
Cycles model
Increases in
government deficit
lower the savings rate,
33
investment growth rate,
output and
consumption.
Bosch and Espasa
(1999)
Barcelona Vector Auto
Regression (VAR)
Investment in the
transport infrastructure
has the most significant
effect in generating
economic gains in
economic growth.
Georgantopoulos and
Tsamis (2011)
Greece Vector Error
Correction Models
(VECM)
Budget deficit crowds
out investment and
leads to inflation.
Rahman (2012) Malaysia Autoregressive
distributed lags
(ARDL)
It was found that there
is no long-run
relationship between
budget deficit and
economic growth.
Kneller et al.(1999)
Nottingham Vector Auto
Regression (VAR) An increase in
government
expenditure could lead
to an increase in
economic growth.
Fatima et al. (2011) Pakistan Two-stage least
squares method
Budget deficit causes a
major decline in
infrastructure
investments.
Ocran (2009) South Africa Vector Auto
Regression (VAR)
Government
expenditure has a
34
positive impact on
growth through
investments.
Odhiambo et al.
(2013)
Kenya Ordinary Least
Squares
The study found a
positive relationship
between the fiscal
deficit and economic
growth.
Velnampy and
Achchuthan (2013)
Sri Lanka ANOVA There is no significant
impact of fiscal deficit
on economic growth.
Anayochukwu (2012) Nigeria Autoregressive
distributed lag
(ARDL) model and
the Granger-causality
test.
The deficit causes
inflation, however,
there is a significant
negative relationship
between economic
growth and deficit.
Source: Own Computation from various empirical studies
2.4 Conclusion
This chapter presented theoretical and empirical literature regarding the effects of a fiscal
budget deficit on transport infrastructure investments. The first part of this chapter dealt with
the relevant theoretical literature. The theories reviewed in this chapter are: Harrod-Domar
theory, Endogenous Growth theory and Solow’s theory. These theories all agree that the
government’s possession of enough resources to spend on its transport infrastructure would
tend to increase the country’s output rate, thus leading to an increase in economic growth;
however, if the government is faced with detrimental effects such as fiscal deficits then it
would tend to slow down the investments. The second part of this chapter explored empirical
studies conducted by previous researchers on fiscal deficits and infrastructure investments in
developed and developing countries as well as in South Africa. The studies reviewed
employed several quantitative and qualitative models to test the impact of fiscal deficits on
infrastructure investments. Most of the studies concluded that fiscal deficit aggregates, such
as government expenditure and tax, significantly affect infrastructure investment in both
developed and developing countries as well as in South Africa. They also suggest that there is
35
a link between capital formation, which is investment in infrastructure, and economic growth.
However, it is important to note that in South Africa a large gap exists in literature regarding
the effects of fiscal deficits on infrastructure investments.
36
CHAPTER THREE
AN OVERVIEW OF THE SOUTH AFRICAN BUDGET DEFICIT AND
TRANSPORT INFRASTRUCTURE INVESTMENT
3. 1 Introduction
This chapter reviews developments and trends in the public transport infrastructure
investment for the period 1980-2011. The review highlights how changes in the budget
deficit contribute to the public transport infrastructure investment of the country for this
period. The chapter is divided into three sections: the first section provides the historical
background to the public transport infrastructure investment; the second section provides a
detailed analysis of the various variables used in the analysis, and the last section presents
concluding remarks.
3.2 Historical overview
South Africa is a developing country that has faced a number of transport challenges
including outdated rail rolling stock prone to malfunction, ports with high costs and less
optimal productivity rates as well as a road network that is under strain as 69% of freight is
being moved by road. The country also faced struggles with inadequate infrastructure
because of under-investment (National Treasury, 2012). This lack of investment, especially
evident in transport infrastructure such as railways, roads and ports has limited the rate at
which the local economy can grow without creating delays. Infrastructure development in
Africa has lagged behind the Western Hemisphere for centuries, even trailing Latin America
in recent decades. Infrastructure funding is largely provided by South Africa’s national
government and other companies have emerged in funding infrastructure; examples of these
are Eskom and Transnet.
In the interest of the long-term development of transport infrastructure in South Africa, it was
recommended that a Macro Transport Infrastructure Forum (MTIF), consisting of
representatives of all relevant parties, be established for South Africa. This would help to
achieve the political and economic objectives of the country. The MTIF would have the
responsibility of working with the government in order to determine a strategic focus
37
regarding transport infrastructure in South Africa and to pursue the development of a macro
plan by providing adjusted infrastructural development plans for the country.
The country has the ambition of having a multi-billion rand infrastructure development in
energy, electricity in particular, and transport infrastructure such as roads, railways and ports.
The government hopes that if it increases its investments further it will unlock the country’s
growth, which is what has been happening for the past seven years. With too much
expenditure on public transport infrastructure by the government it will make South Africa
reach a growth of 7% which will halve the high unemployment rate if the infrastructure plan
is successful (National Treasury, 2012).
3.2.1 South African Public Transport Infrastructure Investment
The International Transport Forum (2012) observed that Infrastructure investments are a key
determinant of performance in the transport sector. However, the sector lacks standardised
definitions and methods for measuring investment. The increasing mix of public and private
investors and operators in the transport sector adds to the complexity of measuring
investments and outcomes. Transport infrastructure has to be maintained and the
measurement of maintenance costs and outcomes differs widely across modes and countries.
However, the roadmap to achieving this aim has been less clear and is often challenging.
During the 1980s and 90s, investments in transport infrastructure were not taken into
consideration by the government, but were later recognized because of the explicit change
from non-market related controls towards market oriented policies (International Transport
Forum, 2012). The various role players were faced with the mounting challenge of using
transport systems to overcome the barriers of the apartheid spatial legacy, reconnecting
isolated nodes and communities long disconnected from opportunity.
Infrastructure has played a significant role in Africa’s recent economic turnaround, and will
need to play an even greater role if the continent’s development targets are to be reached. It is
a major constraint in doing business, and is found to depress firm productivity by around 40
percent. For most countries, the negative impact of deficient infrastructure is at least as large
as that associated with corruption, crime, financial market and red tape constraints (World
Bank, 2013).
38
The government has courted foreign direct investment to lure investors into areas that need
infrastructure, and foreign companies often build, own and operate facilities. The government
has introduced a policy of broad-based black economic empowerment (BBBEE), which
requires foreign companies to go into partnership with local businesses, thus shifting
company ownership patterns (Venter, 2009). The trends of Transport Infrastructure
Investment for the period 1980-2011 are shown in Figure 3.1 below.
Figure 3.1 Trends in Transport Infrastructure Investment (1980-2011)
Source: Department of Trade and Industry (2012)
Figure 3.1 shows that, in 1980, there was an increase in infrastructure investment but it
steadily came to a decrease in 1983, due to the fact that it was undermined by the Apartheid
system of governance that perpetuated domestic economic and political exclusion (Kumo,
2012). In 2005, the transport infrastructure investment increased drastically because the
government spent R850 billion on transport infrastructure and was planning to spend more
with the aim of using it as a key component in increasing economic activity (Budget Review,
2010). Many agree that such accelerated and increased infrastructure investment has
cushioned the South African economy from the worst global economic downturn since the
1930s and made its rapid recovery possible (Kumo, 2012). However, it is not clear whether
the increase is due to a shift in government’s infrastructure investment policy or due to the
preparation for the 2010 FIFA World Cup. In any case, to achieve the economic growth and
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
2,000,000
80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10
Transport Infrastructure Investment
39
poverty alleviation goals set in its ASGISA agenda, the government had to ensure that
infrastructure investment would be accelerated and sustained in the long run.
Since 2006, there has been an increasing and upward trend of infrastructure investment in
South Africa. This increase was led by the power utility Eskom and transport group Transnet,
both of which were earmarked to receive 40% of the R372 billion the government set aside
for infrastructure development (National Treasury, 2012). Eskom was to spend R84 billion on
energy generation, transmission and distribution. Transnet was to spend R47 billion, R40
billion of which would be distributed to harbours, ports, railways and a petroleum pipeline.
Government spending on infrastructure in 2008 declined as a change in government and the
pressure to divert spending towards greater social expenditure mounted (Budget Review,
2010). In addition, government consumption expenditure ballooned – driven in large part by
the dramatic increase in the wage bill. This slant in expenditure towards current expenditure
and away from investment expenditure did not go unnoticed by ratings agencies. The lack of
investment caused a decrease or limit in the rate at which the economy can grow and one of
the highest increases in unemployment.
Despite government’s infrastructure drive, real growth in transport infrastructure investment
decreased in the recession year (2008-2009). However, prior to the recession, the government
realised the need for investment; hence, investments in the transport sector started to increase
in 2009 (Budget Review, 2010). South Africa has also seen large-scale infrastructural
investment in connection with the 2010 FIFA World Cup. In anticipation of millions of
soccer-loving tourists, the government spent over R40 billion to upgrade stadiums, airports,
trains and roads. Improvements in public transport, security, investment and tourism have
already been shown to benefit the people of our country. The hosting of the tournament also
resulted in job creation. South Africans demonstrated an explosion of national pride and
embraced each other, making the tournament a powerful nation-building tool.
3.3 Government Revenue 1980-2011
The South African tax revenue has been regarded as the largest source of budget revenue. If
the economy tends to be underperforming it will also perform poorly, hence, during the
economic downturn it showed a decreasing response. The Political Information and
Monitoring Services (PIMS) Budget Paper states that Revenue over-runs and a budget policy
favouring surpluses over the current medium-term expenditure period are related concepts.
40
Both embody aspects of a generally prudent and risk-averse approach taken by government to
macroeconomic management. These overruns have occurred under both the deficit and
surplus in previous years. Both revenue and expenditure convergence are firstly dependent on
the accuracy of the economic growth estimate. Revenue outcomes depend quite heavily on
growth outcomes since growth trends largely determine trends in the value of tax bases, such
as income and consumption. All else being equal, the more economic activity there is, the
more there is to be taxed, and the higher revenue collection will be for a given set of tax rates.
With the exception of 2003/04, actual tax revenues have considerably exceeded revenue
estimates. The value of revenue over-runs in the six years excluding 2003/04: R21.3 billion in
2004/05, R44.6 billion in 2005/06, R38.7 billion in 2006/07 and R14.5 billion in 2007/08
(National Treasury, 2012).
Total government revenue is budgeted to increase by a relatively substantial 11%y/y in fiscal
2013/2014, which seems high compared to a growth of only 6.1% in 2012/13; this is likely to
prove difficult to achieve given the modest GDP growth forecast (National Treasury, 2012).
The South African Revenue Service shows that it had another strong year in 2011 as it had
collected R742.7 billion in revenue which is more than the R4 billion the Finance Minister
Pravin Gordhan had budgeted for and R68 billion more than the amount collected in the
previous financial year. The government revenue is shown in Figure 3.2 below for the period
1980-2011.
Figure 3.2 Trends in Government Revenue (1980-2011)
Source: South African Reserve Bank (2012)
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10
Government Revenue
41
From Figure 3.2, it is evident that tax on income and wealth over the period 1980 to 2011
grew at an exponential rate. The minimum tax revenue to ever be collected in the period was
in the year 1980 whilst the maximum was collected in 2008, decreased in 2009 and rose
again in 2011. Between 1994 and 2002, tax revenue rose at an increasing rate. This is because
the period was considered a period of fiscal and macroeconomic consolidation, were certain
tax policy changes and policy announcements that provide reference points for future reform
in the post 2002 tax reform era were made. Over this period, personal income tax was used to
raise the bulk of tax revenue; approximately 42.6 per cent was raised in 1999-2000 (National
Treasury, 2002). The period between 2002 and 2010 was a period of fiscal stabilisation. The
government was now able to comfortably adopt and support an expansionary fiscal stance as
set out in the 2002 budget, which is characterized by strong expenditure growth and
continued tax relief in the face of adverse global conditions. Since 2002, the National
Treasury stepped up the pace of fundamental income tax reforms with the distinct purpose of
aggressively broadening the tax base, thereby affording a significant rate reduction in line
with international trends (National Treasury, 2002). This broadening of the tax base increased
the tax revenue collected in current terms over the period leading up to 2011. Tax revenue, as
a percentage of GDP, has been significantly constant which shows that the government has
been more consistent in its tax collection over the years.
Revenue fell during the recession years thus creating the fiscal space for government to
expand investment, social grants and public-sector employment. Hence, the government
required higher levels of borrowing, resulting in a marked increase in debt-service costs
(Budget Review, 2010). The decline in revenue caused the government to increase or raise
its borrowing level and thus drove the country to a budget deficit. As the economy improved,
or recovered, from the recession the government revenue showed an increase in 2010 and
will continue to recover alongside the economic growth reducing the budget deficit.
3.4 Government Expenditure 1980-2011
Government spending, also called government expenditure is the amount of money that a
government spends on the public services provided to its citizens. Government expenditure
covers spending on goods and services like defence, and the judicial and education systems.
It excludes government transfers like social security and unemployment benefits (Trading
Economics, 2013). Expenditure varies with growth because lower growth may, amongst other
42
things, mean more eligible beneficiaries for entitlement-based spending. Over the past
decade, the redistributive and pro-poor character of public spending has significantly
improved, alleviating poverty and advancing social development. There is however growing
concern within government, in that additional budget allocations do not result in proper
improvements in service delivery. Government's ability to support accelerated growth,
increase employment, and reduce poverty and inequality is limited by two factors: the quality
of spending and the composition of spending, with a shift from consumption towards capital
investment becoming increasingly necessary (Trading Economics, 2013). The narrowing of
fiscal space, in combination with the erosion of the link between budget inputs and social
outputs, implies the need for additional measures to secure the country’s fiscal footing and
improve its quality of spending. While current levels of spending can be sustained over the
medium term, expenditure cannot grow at the rate at which it did over the last decade.
Every year the government spends several millions of rand on providing a better life for the
people living in South African. Over the years leading up to 2011, the South African government
strongly increased public spending (National Treasury, 2010). From 1980 to 1993 expenditure by
the apartheid government was to boost the economic, political and social wellbeing of South
Africans. During this period, government expenditure mainly benefited white South Africans who
selectively had access to good transportation systems, education, health facilities and recreational
services. Upon regime change in 1994, government expenditure focused on undoing the
inequalities created by the preceding apartheid regime. From 1994 to 2011, services that attracted
substantial attention from the ANC-led government were education, infrastructure (capital
expenditure), social welfare, debt, housing, health, protection, as well as water and agriculture
(National Treasury, 2010).
Government expenditure comes from the government’s revenue. This includes all
expenditure by the government towards infrastructural development in the country. The
rationale behind this is that improving the country’s infrastructure increases its long term
growth potential (Du Plessis and Smit, 2007). There seems to be advantages to a highly
functional infrastructure which include an increase in investment. Capital investment by the
public sector raises the country’s future growth potential by providing the economic
infrastructure required for trade and extended economic activity. This ultimately leads to
higher economic growth and trade.
43
Capital expenditure increased from R4 002 million in 1980 to R10 685 million in 1993,
averaging R8 203 million per year over thirteen years (DTI, 2011). During this period low
government revenue collections restricted the government’s capital investment. Logically, it
is also imperative to note that the government’s developmental obligation was only focussed
on a few communities that furthered the interests of minority white community. Black South
African communities were totally neglected and disenfranchised which resulted in less
government obligation to spend on investment (Levy, 1999). Nevertheless, upon attaining
democracy in 1994, government capital expenditure grew sharply as a reflection of an
inclusive budget that did not discriminate, on any account, in terms of race or gender.
Specific attention was, however, given to the previously disadvantaged black South Africans
(National Treasury, 2007). Government investment expenditure exponentially increased from
a minimum of R11 358 million in 1994 to R80 819 million in 2011, averaging R38 382
million per year over the years (DTI, 2011). The great public investment expenditure was a
result of social infrastructure programmes such as hospital revitalisation, school building and
sanitation, as well as others (National Treasury, 2010). These activities are crucial in the
delivery of government services and have thus received significant attention. The National
Treasury (2010) also postulated that spending on capital averaged 5.8 per cent of total
expenditure between 1994 and 2011 and is expected to average 7.2 per cent between 2011
and 2013. For the period 1980 to 2011, government expenditure exponentially increased from
R4 002 million (in 1980) to R80 819 million (in 2011), averaging R 24 753 million per year
over this period. The highest capital expenditure recorded over the period is R89 437 million,
which was observed in 2008 (DTI, 2011). According to the National Treasury (2010), the
figure was a result of massive government spending towards a successful 2010 FIFA Soccer
World Cup. Government expenditure over the period 1980 to 2011 is shown by Figure 3.3.
44
Figure 3.3 Trends of government expenditure 1980-2011
Source: South African Reserve Bank (2012)
The Medium Budget Policy Statement has identified that government spending has supported
the economy through the recession and continues to do so into its recovery. The fiscal
framework adds R17.9 billion to expenditure in 2011/12, R28 billion in 2012/13 and R43.2
billion in 2013/14, resulting in average real growth of 2.7 per cent in government non-interest
spending over the next three years. Government recognises the need to improve the efficiency
of public expenditure (National Treasury, 2010).
Figure 3.3, above, shows that government expenditure has grown substantially well since 1980.
Investment expenditure was greater in the post-apartheid era than during the apartheid period.
The maximum investment expenditure recorded in the apartheid era was R11 568 million in
1989. This was relatively lower than the post-apartheid figure of R89 437 million recorded in
2008 (National Treasury, 2010). Reasons for the low expenditure during apartheid and massive
expenditures in democracy include, among others, the role played by the political parties such as
the ANC led government which assumed the major responsibility of improving economic
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10
Government expenditure
45
performance. Massive government expenditure occurred since 2006, with the maximum
investment expenditure recorded in 2008. Reasons for this hike in government investment
spending also include the goal of halving poverty and unemployment by 2014, according to the
ASGISA policy, as set out in 2004. Equally important to note is the expenditure towards
infrastructural development on the journey to host a top-class 2010 FIFA Soccer World Cup.
After winning the bid, in 2006, to host the 2010 big event, South Africa invested much in the
development of its infrastructure. This therefore demanded an increase in the year-on-year
increase in government expenditure.
3.5 Budget Deficit/Surplus 1980-2011
According to the Economic Outlook (2012), South African government debt has risen from a
low of 27.3% of GDP in 2008/09 to 41.8% of GDP in 2012/13 and is projected to rise to
43.2% of GDP in 2013/2014. This is the largest debt level, as a ratio of GDP, which South
Africa has experienced. The Economic Outlook (2012) reported that in 1999-2000 revenue
over-runs from a fairly small proposed deficit led to a budget surplus for the first time in
democratic South Africa. There was a conventional budget surplus outcome in 2009-2010
that was unanticipated; in fact, a deficit of 1.5% was proposed which ended in a slightly
larger surplus than was budgeted for (0.8% vs. 0.6%). National Treasury’s annual Budget
Review shows that the structural budget balance remains close to zero or moderately in
deficit; that is to say that cyclical factors are the main contributor to the current budget
surplus environment. The Political Information and Monitoring Services (PIMS) budget
paper stated that cyclical factors are the main contributor to the current budget surplus
environment. South Africa continues to run a large structural deficit that reflects an
underlying, longer-term imbalance in revenue and expenditure.
As shown in Figure 3.4, below, the year 1992 recorded the highest deficit in the period of study.
The two successive years, 1992 and 1993, may be identified as years of transition when the
apartheid regime experienced too much resistance in order that it provide for free and fair
elections governed by the will of every South African. Preparations for the first democratic
elections in South Africa resulted in large government spending, as reflected by the deficit
recorded in 1992 and 1993 (National Treasury, 2000). The period 1995 to 1996 recorded the next
highest successive budget deficit of almost 5 per cent of GDP per year. The budget deficit
decreased between 1997 and 1998. In 1998, the budget deficit decreased to such an extent that the
ANC-led government recorded its first ever budget surpluses of 0.4 per cent of GDP in 1999.
46
According to the National Treasury (2000), reasons for the shrinking deficits include the
government’s desire to achieve a marked redistribution of spending in favour of previously
disadvantaged communities using a sound fiscal policy framework that eliminates unsustainable
deficit spending and increasing public debt, among other issues. This was initiated to support the
sustainability of government finances and contribute to ensuring that the economic achievements
of the past years are protected from cyclical and external risks (National Treasury, 2008). The
deficit in 2009, among other reasons, emanates from the government intensifying its fixed capital
investment towards the 2010 FIFA World Cup. The Budget Deficit/Surplus from the period
1980-2011 is shown in Figure 3.4 below.
Figure 3.4 Trends in the Budget deficit/Surplus (1980-2011)
Source: South African Reserve Bank (2012)
The period beginning in the early 1980s and ending with the transition to a new constitutional
and political environment was therefore marked by increasing government expenditure and
taxation, with the fiscal deficit increasing to 6.8 per cent of GDP in 1992. If moderate deficits
are run to facilitate infrastructure expansion that promises future economic returns, the deficit
may be seen in a positive light as an enabling fiscal action. The budget deficit declined to 3.0
per cent of GDP; public debt relative to GDP declined from 49.7 per cent in 1994 to 44.4 per
cent in 1998 and average borrowing costs decreased sharply, thus providing room for
-8
-4
0
4
8
12
80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10
Deficit/Surplus
47
government to spend more on social services and infrastructure (Naraidoo and Schoeman,
2011).
Although government had achieved a substantial reduction in its budget deficit target, from
6.8 per cent of GDP in 1993 to 0.6 per cent in 2008, the scenario has, in the interim, changed
again (Budget Review, 2010). This is mainly due to the slowdown in the world economy,
which also affected the revenue base of the South African economy. However, the policy of
fiscal prudence, during the period 2003 to 2008, resulted in a substantial decline in real debt
service cost, while the real growth rate of the economy increased considerably. Nevertheless,
there still exists a gap between the real debt service cost and the real growth rate since the
former exceeds the latter. Furthermore, it appears that public debt and budget deficit
reductions have been achieved at the expense of a relative reduction in service delivery
expenditure, as is evident in the reduction in the ratio of education expenditure to GDP from
an average of 6.21 per cent during the period 1990 to 1999, to an average of 5.6 per cent
during the period 2000 to 2008; and a reduction in health expenditure relative to GDP to an
average of 2.84 percent between 2000 to 2008 from 1990 to 1999 average of 2.93 per cent
(Naraidoo and Schoeman, 2011). During the economic downturn in 2008-2009 the revenue
fell; this required a significant increase in borrowing that resulted in an increase in the budget
deficit. The 2010 Medium Term Budget Statement Policy reflected that the budget deficit is
projected to narrow from 5.3 per cent of GDP to 3.2 per cent by 2013/14, reflecting a strong
recovery in revenue and moderate growth in spending.
Fiscal management will be more challenging towards the end of the forecast period because
deeper consolidation will be needed to stem the rise in debt service costs and protect investor
confidence. The government will therefore need to make some tough choices in the face of
persistent pressure to spend more on infrastructure, social welfare and wages especially in the
run-up to the 2014 elections (National Treasury, 2012).
3.6 Foreign Direct Investment 1980-2011
FDI (Foreign Direct Investment) is defined as private capital flows into a country different to
that in which the parent firm is situated. Foreign Direct Investment has become increasingly
important in a developing country in order to attract substantial and rising amounts of inward
FDI (Khaliq and Noy, 2007). Foreign Direct Investment (FDI) is seen as an increasingly
important driver of economic growth and development, particularly within the developing
48
world. FDI is distinguished from portfolio investment by the influence that gives the investor
an effective voice in management. Foreign Direct Investment therefore provides an
opportunity for local firms to improve their productivity by learning from and competing
directly with foreign firms, thereby increasing economic growth rates. FDI plays a key role in
encouraging successful transition and many countries in the region offered various incentives
to attract FDI in the country (Kinoshita and Campos, 2002). The United Nations Conference
on Trade Development saw that inflows to South Africa have tended to fluctuate greatly in
recent years in that they have dropped by 24 per cent in 2005 to $4.6 billion. In contrast, FDI
outflows from South Africa rebounded sharply to $4.4 billion, returning the country to the
position of the largest source country of FDI in Africa. South African companies were active
in acquiring operations in industries such as mining, wholesale and healthcare in 2012.
Ernst and Young’s (2009) analysis of foreign direct investment projects shows that, over the
past decade, South Africa has witnessed an increase in inward foreign direct investment from
338 new projects in 2003 to 633 in 2010 (an increase of 87%). Despite a drop in investment
in the past couple of years, following a peak in 2008, South Africa has remained an attractive
investment destination throughout the global downturn and has, as a result, managed to
maintain its relative share of global investment flows (Ernest & Young, 2009). It was said
that South Africa had received about $1.553-billion in FDI in 2010, ranking 69th in the world
and at a level amounting to only one sixth of its peak which was recorded in the country in
2008. The trends of foreign direct investment for the period 1980-2011 are shown in Figure
3.5 below.
Figure 3.5 Trends of Foreign Direct Investment (1980-2011)
Source: South African Reserve Bank (2012)
0
100,000,000
200,000,000
300,000,000
400,000,000
500,000,000
600,000,000
700,000,000
80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10
FDI
49
As a result, African countries in the 1980s reached a level where domestic firms were
allowed to be free of small markets and low productivity. This helped to build stronger links
between investment and exporting allowing free trade and free entry into foreign markets.
In this context, the shift in composition of FDI flows towards manufacturing was also slower
than elsewhere and was usually the result of heavy protection providing a captured market,
with insufficient attention to export promotion. This deterioration damaged investment
prospects and increased the sector’s vulnerability to further shocks, but was even more
decisive in constraining investment in the primary sector, where much production was
organized through state-owned companies in urgent need of restructuring and recapitalization
(UNCTAD, 2005). The developing countries have increasingly grown to receive the flow of
foreign direct investment that create an important infrastructure, but will they have an ability
to manage and respond to international economic fluctuations (Rena and Kefela, 2011).
3.7 Real Gross Domestic Product 1980-2011
South Africa’s economic development has been dominated by colonialism and apartheid.
Economic growth was conditioned, on the one hand, by changes in commodity prices and, on
the other, by a low-productivity and low-employment approach to production that took
advantage of limited competition from imports and cheap intermediate inputs. Public
transport, roads, and housing were insufficient to absorb migrants from rural or far-flung
industrial areas. Formal sector wage bargaining reflected the complex nature of households
split between rural and urban areas, elevated dependency ratios, and the high costs of
inefficient transport systems (UNCTAD, 2004b). Monetary policy became accommodative
by the 1980s, thus resulting in consistent negative real interest rates. These policies were
made possible by rigorous exchange controls, which prevented capital from crossing the
border. In the same period, public spending rose strongly in an effort to extend social
infrastructure and increase subsidies to industry. This resulted in large budget deficits and
rising debt levels.
The South African government recognised the importance of transport, transport
infrastructure in particular, and so it launched policy strategies such as the Reconstruction
and Development Programme (RDP 1994), the Growth Employment and Redistribution
(GEAR 1996) programme and the Accelerated Shared Growth Initiative for South Africa
(Mlambo-Ngcuka, 2006). The GEAR policy strategies focused primarily on improving output
50
and employment, increasing infrastructure allocations and encouraging trade. The strategy
achieved redistribution and improvement in basic living conditions because it generated some
increases in economic growth, low inflation, high demand, high commodity prices and rising
consumption. However, it was not a success in meeting the specified the employment
projections.
The Accelerated Shared Growth Initiative for South Africa (ASGISA) was developed when
the government saw the need for greater investments in infrastructure and other areas
contributing to economic growth and ensuring that the poor and unemployed share in the
country’s economic growth (Mlambo-Ngcuka, 2006). With the ASGISA strategy being
present, the government decided to make special budget allocations towards infrastructure
development in order to achieve the targeted 6% economic growth rate. ASGISA aimed to
develop scarce skills in the country by providing effective solutions such as special training
programmes for those who were inexperienced. The policy strategy failed to do what it had
aimed for and what the government had expected out of it.
There has been a considerable improvement in economic growth which averaged at about 3
per cent during the first decade of freedom. Since 2004, growth has tended to exceed 4 per
cent per year, reaching about 5 per cent in 2005 (National Treasury, 2006). According to Du
Plessis et al. (2007), if economic growth is increased, unemployment levels will fall. The
World Bank (2009) indicated that a 2 per cent increase in growth rates will result in a
reduction in unemployment ranging from 1 to 7 per cent, depending on the country.
South Africa's economy has been completely overhauled since the advent of democracy in
1994. The GDP growth rate analyses the economic performance of South Africa. The
economy rose at a real rate of 1.2% from 1994 to 2000, according to the statistics of the
South African Reserve Bank (South African Reserve Bank, 2012). Economic growth
eventually rose in 1996, because of the executed policies (GEAR) of the government of that
period and the economy showed a steady increase in economic growth to the level of 4% in
this year. This policy intended to accelerate growth that would be associated with the right
technology, was envisaged to increase employment while enabling the labour intensive
investment to rise. This was also intended to result in a major boost in the economy, and new
jobs were to be created each. This major increase in employment meant that the economy
51
would operate at full employment, thus fully utilising all its resources. The trends in real
gross domestic product, from the year 1980 to 2011, are shown in Figure 3.6.
Figure 3.6 Trends in RGDP (1980-2011)
Source: South African Reserve Bank (2012)
After 1996, economic growth eventually declined in 1997 until an extreme decline was
evident in 1998, by 2.1%, which was caused by the accumulation of joblessness, an
accelerated rate of inflation due to the expansion of money supply, the collapse of the prices
of commodities, and a decline in foreign investment which also resulted in lower interest
rates. The economy’s growth rate declined to 0.5% (Loots, 1998). The economic growth rate
increased again after 1998 due to the tightening of monetary policy; this means that money
supply was decreased which resulted in a fall in inflation and increase in interest rates. Real
growth in GDP, over the past 10 years, has been strong, consistently above 2.5% per annum,
and reaching 5.5% in 2007.
South Africa was in a recession from 2008 to 2009; there were therefore only a few
investments made in the country because investors were reluctant to invest in a country where
there would be no returns or lower returns on their investment. This caused the economic
growth to decline since it had a negative effect on people, especially the poor (Catshile,
2010).
13.7
13.8
13.9
14.0
14.1
14.2
14.3
14.4
14.5
80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10
LOG_RGDP
52
The recession that was in place in 2008 and 2009 did not last long. The government
intervened and the country was subsequently attained its full capacity (Catshile, 2010). This
happened because it was preparing to host the 2010 FIFA World Cup so it quickly recovered
from the financial crisis and operated at full employment; there were now increased
investments taking place and consumers were no longer vulnerable to the high costs of goods
and services, while inflation was at a normal rate where the interest rates were low.
Real GDP is forecast to grow by 3.7% in 2011, as a result of higher consumer spending,
increased business investment and stronger external demand. Although growth will be too
slow to make an impact on unemployment, higher wages for those who work will support
consumer demand, as will low interest rates and subdued inflation (Ernst and Young, 2012).
Business, which has been cautious about investing in the wake of the recession, will regain
confidence in line with the wider upswing and increase the pace of investment in new
capacity. The government will also remain supportive of growth via an ongoing fiscal
stimulus, directed towards investment in infrastructure in particular.
3.8 Conclusion
This chapter provided an overview of the fiscal deficit and transport infrastructure investment
trends in South Africa, over the period 1980 to 2011. It was discovered that government tax
revenue grew exponentially between 1980 and 2011. With the country obtaining democracy
in 1994, government revenue suddenly increased in South Africa. The country experienced
increased investment, lifting of consents levelled against the apartheid regime, productivity
due to technological advancements and mobility, among other things. Government
investment increased significantly since 1980. The rationale behind the massive expenditures
in a democratic South Africa include, among others, the responsibility assumed by the ANC-
led government to create sustainable and inclusive economic development. The last part of
this chapter highlighted the policy tools implemented by the government to achieve
macroeconomic equilibrium. Several policy tools used by the government since 1994 include:
the Reconstruction and Development Programme (RDP), the Growth, Employment and
Redistribution Policy (GEAR), the Accelerated and Shared Growth Initiative for South Africa
(ASGISA) and the New Growth Path (NGP). These policy tools have helped to redistribute
wealth across races and improve the transport infrastructure in South Africa.
53
CHAPTER FOUR
RESEARCH METHODOLOGY
4.1 Introduction
This chapter presents the methodology employed in the investigation of the impact of the
budget deficit on transport infrastructure investment in South Africa. The empirical model for
the study and the relevant data issues are presented herein. The first part of the chapter
specifies the model and how estimation of the model was applied. This is followed by
specifying the data that was used, the definition of variables and outlining the expected
results. The last part of the chapter explores various tests for the model, including
stationarity, cointegration error correction and diagnostic testing.
4.2 Model specification
To determine the relationship between government budget deficit and transport infrastructure
investment in South Africa, this study uses the model specified by Hadiwibowo (2010) in his
analysis of the effects of fiscal policy on investment and economic growth in Indonesia. The
study applies the explanatory variables such as the budget deficit made up of government
expenditure and government revenue as well as foreign direct investment. Therefore, the
modified version of the model is formulated as follows:
TIIt t t ……………….………….. 4.1
Where: , β1, β2 and β3 are the coefficients and ε is the error term.
TII = Transport Infrastructure Investment
(T-G) = Budget Balance
GDP = Gross Domestic Product
FDI = Foreign Direct Investment
D = Dummy Variable
ε = error term
t = Time
54
4.3 Definition of the variables and data sources
Transport Infrastructure Investment is the gross capital formation made by the government in
the transport sector. Quarterly time series data for transport infrastructure investment, from
the first quarter of 1990 to the fourth quarter of 2009, was used in the estimation and was
sourced from the Department of Trade and Industry’s (DTI) economic database.
Gross Domestic Product (GDP) is the market value of all final goods and services produced
within the country over a given period of time. Quarterly time series data for GDP (constant
market prices) from the first quarter of 1990 to the fourth quarter of 2009 was used in the
estimation and was sourced from the South African Reserve Bank.
Budget Balance is government revenue minus the government expenditure of a country.
Quarterly time series data for budget balance from the first quarter of 1990 to the fourth
quarter of 2009 was used in the estimation and was sourced from the South African Reserve
Bank.
Foreign Direct Investment is the inflow of foreign capital into the country’s borders.
Quarterly time series data for foreign direct investment from the first quarter of 1990 to the
fourth quarter of 2009 was used in the estimation and was sourced from the South African
Reserve Bank.
4.4 Expected Priori
In estimating a regression equation, all economic variables are expected to conform to
economic theory. This research follows on literature reviewed in Chapter 2 and estimates a
relationship that conforms to economic theory. The coefficient β2 is expected to have a
positive relationship with transport infrastructure investment. When GDP increases it tends to
increase the government expenditure and leads to an increase in transport infrastructure
investment.
The coefficient β3 is expected to be positively related to transport infrastructure investment
because foreign investment supports growth in developing countries by increasing
government spending which covers public investments.
The coefficient β4 is a dummy variable taking 1 if deficit and 0 otherwise. An increase in the
deficit is expected to have a negative relationship with transport infrastructure investment.
55
4.5 Estimation Techniques
There are several techniques available for parameter estimation, ranging from classical
regression methods to cointegration based techniques. The former is based on the assumption
that all the variables included in a regression are stationary. However, most time series data is
not stationary in their levels such that estimations based on this technique will be
meaningless. Differencing the variables to mechanically turn them stationary has been a
preferred approach to dealing with this problem as it can offer very useful long run
information that may be in the data. The techniques employed to test for stationarity and co-
integration are reviewed in this section.
4.5.1 Testing for Stationarity/Unit Root
A stationary series is defined as one with a constant mean, constant variance and constant
auto covariance with each lag given (Brooks, 2003:319). A series that is not stationary is
referred to as non stationary. In addition, a series is said to be integrated and is denoted as I
(d), where d is the order of integration. The order of integration refers to the number of unit
roots in the series, or the number of differencing operations it takes to make a variable
stationary.
In the classical regression model, the focus is on the relationship between stationary
variables, but most of the variables usually follow a non stationary path. Variables that have a
linear relationship (non-stationary) can produce misleading results as they might show trends.
Stationarity refers to testing and making sure that the series are integrated of the same order.
Gujarati (2003: 806) shows that if the dependent variable is a function of a non stationary
process, the regression will produce meaningless results. In other words, the dependent
variable will follow the trend of its explanatory variables. In such a case, the results will be
spurious. In fact, the t-ratio on the slope of the coefficient would be expected not to be
significantly different from zero and the value for R2
would be very low even though the
trending variables are completely unrelated. Thus, the t-ratio will not follow the t-distribution
and the f-ratio will not follow the f-distribution. Consequently, unit root or stationarity tests
should be done on all the variables before proceeding with the tests for the cointegration and
estimation of parameters. In this study, the Augmented Dickey-Fuller and Phillips Perron unit
root tests are discussed.
56
4.5.2 The Augmented Dickey–Fuller test and Phillips Perron
The Augmented Dickey-Fuller and Phillips Perron are unit root tests for stationarity.
Variables are tested for stationarity because most economic series are not stationary in their
levels, which lead to estimations being meaningless. The ADF supplements the test by using
lags to the dependent variable. The alternative model in the ADF case can be written as:
∆yt = γ yt-1∑ ∆yt-1+1 +µt.................................................................................................. 4.2
In equation (4.2) yt is the relevant time series, ∆ is a first difference operator, t is a linear
trend and ut is the error term. The error term should satisfy the assumptions of normality,
constant error variance and independent error terms. The lags of ∆yt now infuse any dynamic
structure present in the dependent variable, to ensure that ut is not auto correlated. Equations
(4.2) represent the estimated without including a trend term and without a constant.
The equation with a constant and no trend is represented
∆yt= a0 + γ yt-1 + ∑ ∆yt-1+1 +µt........................................................................................4.3
The equation with both a trend and a constant is given by:
∆yt = a0 + γ yt-1 a2 ∑ ∆yt-1+1 +µt..................................................................................... 4.4
In these models:
= γ - (1- ∑ )
and
β = - ∑
The ADF test corrects for high-order serial correlation by adding a lagged differenced term
on the right-hand side of the equations. If the calculated statistic is less (in absolute terms)
than the MacKinnon (1991, 1996) values, which are used by the E-views 7 software, the null
hypothesis is accepted and will therefore mean that there is a unit root in the series. In other
words, it means the time series is not stationary. The opposite is true when the calculated
statistic is greater than the MacKinnon critical values. However, in this ADF equation the
coefficient of interest is γ, if γ = 0, the equation is entirely in first difference form and so has
no unit root. If the coefficients of a difference equation sum up to 1, at least one characteristic
root has unity. On the equations, if ∑ai =1, γ =0 and the system has a unit root.
57
4.5.3 Cointegration and vector error correlation modelling (VECM)
The reason for undertaking cointegration tests is to determine whether all the variables in the
unemployment model are cointegrated. According to Gujarati (2003:830), cointegration of
two or more times series suggest that there is a long-run or equilibrium relationship between
them. Cointegration exists when two or more series are linked to from an equilibrium
relationship spanning in the long run. Variables are defined as co-integrated if a linear
combination of them is stationary. A cointegrating relationship may, in other words, be seen
as a long-term or equilibrium occurrence. This is because it is possible that co-integrating
variables may deviate from their relationship in the short run, but their association would
return in the long-run. There are different ways of testing cointegration, such as: 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. However, the majority of these
techniques have numerous problems when applied to multivariate models. The Johansen
technique will be used in this study because it has emerged as a powerful and popular
technique in determining cointegration. The purpose of this cointegration test is to determine
whether the variables in the growth model are cointegrated or not. The Johansen (1991, 1995)
technique has become an essential tool in the estimation of models that involve time series
data. This approach is preferred as it captures the underlying time series properties of the
data and is a systems equation test that provides estimates of all cointegrating relationships
that may exist within a vector of non stationary variables or a mixture of stationary and non
stationary variables (Harris, 1995: 80). The Johansen technique has several advantages over
other cointegration based techniques. This technique is preferred in this study as it will allows
estimating a dynamic error correction specification, which provides estimates of both the
short and the long run dynamics in the growth model.
The following steps are involved when implementing the Johansen technique:
Step 1: Testing the order of integration
The first step in the Johansen approach is to test for the order of integration of the
variables under examination. All variables are preset to assess their order of
integration. When all the variables are integrated of the same order we can then
proceed with the co-integration test. The data must be plotted to see if a linear
time trend is present.
Step 2: Setting the appropriate lag length of the model
58
Estimate the model and determine the rank of П.
Step 3: Choosing the appropriate model regarding the deterministic components in the
multivariate system
Analyse the normalised cointegrating vector(s) and speed of adjustment coefficients.
Step 4: Determine the number of co-integrating vectors
Apply causality tests on the error correction model to identify a structural model and
determine whether the estimated model is reasonable.
Assume a vector: Xt = [LTII, LFDI, RGDP, DUMMY] and assume the vector is in VAR
representation of the form:
Xt = z + ∑ Xt-1 + µt....................................................................................................... 4.5
where z is a (n x 1) vector of deterministic variables, ε is a (n x 1) vector of white noise error
terms and ∏ is a (n x n) matrix of coefficients. In order to use the Johansen test, the VAR
(4.5), above, needs to be turned into a VECM; specification (Brooks, 2002: 403), which may
be specified as:
∆Xt= z + ∑ Xt-1 +µt....................................................................................................... 4.6
Where Xt is a vector of I (1) variables defined above, Δ Xt are all I (0) variables, Δ indicates
the first difference operator, B is a (n x n) coefficient matrix and ∏is a (n x n) matrix whose
rank determines the number of cointegrating relationships. The Johansen’s cointegration test
is to estimate the rank of the∏ matrix (r) from an unrestricted VAR and to test whether to
reject the restrictions implied by the reduced rank of ∏. If ∏is of full rank (r = n), it suggests
that variables are level stationary and if it is of zero rank (r = 0), no cointegration exists
among the variables. Alternatively, if ∏is of reduced rank (r<n), then there exists (n x r)
matrices α and β such that:
αβ`................................................................................................................................... 4.7
where α represents the speed of adjustment matrix, indicating the speed with which the
system responds to last period’s deviations from the equilibrium relationship and β is a
matrix of long run coefficients (Brooks, 2002: 404).
Before an attempt to rank the cointegrating relationship is made there are two steps that need
to be followed. The Johansen test requires an optimal lag length (k) to be selected and the
choice of the deterministic assumption. The rationale behind the selection of the lag length is
59
because the Johansen test can be affected by the lag length of the Vector Error Correction
Model (VECM). A set of information criteria is used in the selection of the optimal lag
length, this includes: the sequential modified likelihood ratio (LR), Akaike information
criterion (AIC), Final prediction error (FPE) Schwarz, information criterion (SC) and the
Hannan-Quinn information criterion (HQ). This information criteria usually centers around
one lag length but if its components are inconsistent then the AIC and SC are considered to
be the best predictors because of the prediction power.
The second step is the choice of the deterministic assumption that the Johansen test requires
in testing for cointegration. Various types of VAR can be estimated based on five
deterministic trend assumptions, for example, with or without a constant and trend in
cointegrating term and with or without a constant in the VAR equations. E-views 7
specifically provides the following deterministic trend assumptions that there is no
deterministic trend in the data and no intercept or trend in the VAR and in the cointegrating
equation (CE); there is no deterministic trend in the data, but an intercept in the CE and no
intercept in VAR; there is a linear deterministic trend in the data and an intercept in CE and
test VAR; there is a linear deterministic trend in data, intercept and trend in CE and no trend
in VAR; for a quadratic deterministic trend in data, intercept and trend in CE and linear trend
in VAR.
After the correct VAR order (k) and the deterministic trend assumption has been selected, the
rank of the ∏matrix can then be tested. The Johansen and Juselius (1990) has two variants of
the reduced rank test for determining the cointegration space, namely, the maximum
eigenvalue (λ-max) and the trace statistics (λ-trace). In interpreting the results of the null
hypothesis of no cointegrating vector can be rejected, it indicates that there is a long run
relationship among the variables in the model.
The Johansen and Juselius tests are represented by the following equations:
λ-max (r, r + 1) = -T ∑ (1- λi)……………………………………………..…..…......4.8
λ-trace (r) = -T∑ (1- λi) ................................................................................................4.9
Where: r is the number of cointegrating vectors, λi is the estimated values of the
characteristics root (also called eigenvalues) and T is the number of usable observations. The
larger λi, is, the more large and negative will be the test statistic. Therefore, if the eigenvalue
60
is non-zero, then ln (1λi) <0∀i>1., The largest for eigenvalue to have a rank of 1, it should be
significantly non-zero, while other eigenvalues will not be significantly different from zero.
The trace statistic sequentially tests the null hypothesis that the number of cointegrating
relations is r against the alternative of k cointegrating relations, where k is the number of
endogenous variables. The maximum eigenvalue conducts separate tests on each eigenvalue
and has as its null hypothesis that there are r cointegrating vectors against an alternative of
r+1 (Brooks, 2003: 405). Both these tests compare the eigenvalue and trace statistic values to
critical values. For both tests, if the test statistic is greater than the critical values, the null
hypothesis that there are r cointegrating vectors is rejected in favour of the corresponding
alternative hypothesis.
However, the trace and maximum eigenvalue statistics may yield contradictory results. To
deal with this problem, Johansen and Juselius (1990) recommend the examination of the
estimated cointegrating vector and basing one’s choice on the interpretability of the
cointegrating relations. Alternatively, Luintel and Khan (1999: 392) show that the trace test is
more robust than the maximum eigenvalue statistic in testing for cointegration. The two
approaches will be considered in this study when faced with such a problem.
After all the cointegrating vectors have been identified a Vector Error Correction Model may
be estimated. A VECM is merely a restricted VAR designed for use with non stationary
series that have been found to be cointegrated. The specified cointegrating relation in the
VECM restricts the long run behavior of the endogenous variables to converge on their
cointegrating relationships, while allowing for short run adjustment dynamics. Once
estimation is complete, the residuals from the VECM must be checked for diagnostic tests
such as normality, heteroskedasticity and autocorrelation, which are discussed in the next
sub-section
4.5.4 Diagnostic Tests
This is an important stage in the impact of a budget deficit on transport infrastructure
investment because it validates the parameter estimation outcomes achieved by the estimated
model. Diagnostic checks test the stochastic properties of the model such as residual
autocorrelation, heteroskedasticity and normality, among others. The multivariate extensions
of these residuals tests will be applied in this study; hence, they are briefly discussed herein.
61
4.5.4.1 Autocorrelation LM Test
The Lagrange Multiplier (LM) test used in this study is a multivariate test statistic for
residual serial correlation up to the specified lag order. Harris (1995: 82) argues that the lag
order for this test should be the same as that of the corresponding VAR. The test statistic for
the chosen lag order (m) is computed by running an auxiliary regression of the residuals (tμ)
on the original right-hand explanatory variables and the lagged residuals (m t− μ). Johansen
(1995) presents the formula of the LM statistic and provides details on this test. The LM
statistic tests the null hypothesis of no serial correlation against an alternative of auto
correlated residuals.
4.5.4.2 Heteroscedasticity test
According to Brooks (2003:148), there are a number of formal statistical tests for
heteroscedasticity. One such popular test is White’s (1980) general test for heteroscedasticity.
The test is useful because it has a number of assumptions; for example, it assumes that the
regression model estimated is of the standard linear. After running the regression residuals
are obtained and then test regression is run by regressing each product of the residuals on the
cross products of the regressors and testing the joint significance of the regression. 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.
4.5.4.3 Residual normality test
The residual normality test that will be used in this study is the multivariate extension of the
Jarque-Bera test which compares the third and fourth moments of the residuals to those from
the normal distribution. The joint test is based on the null hypothesis that residuals are
normally distributed. A significant Jarque-Bera statistic, therefore, points to non-normality in
the residuals. However, the absence of normality in the residuals may not render
cointegration tests invalid (Gujarati, 2003).
4.5.5 Impulse response and variance decomposition
The reaction of the dependant variable (TII) to shocks to each of the other variables is of
great importance in VAR estimation. This is because it shows how these transmitted shocks
affect transport infrastructure investment and how long it takes transport infrastructure
investment to recover from such shocks to the system. The usual block F-tests and an
62
examination of causality in a VAR show which of the variables in the model have statistically
significant influences on the future values of each of the variables in the system. However,
these tests will not reveal whether changes in the value of a given variable have a negative or
positive influence on the other variables in the system, or how long it would take for the
effect to work through the system (Brooks, 2003: 341). To provide such information,
Lütkepohl and Reimers (1992) and Mellander et al. (1992) developed impulse response and
forecast error variance decomposition analyses for a VAR process with cointegrated
variables, as discussed below:
4.5.5.1 Impulse response
Impulse response analysis traces the responsiveness of the dependent variable in the VAR to
shocks to each of the other variables. It shows the sign, magnitude and persistence of shocks
to transport infrastructure investment. A shock to a variable in a VAR not only directly
affects that variable, but is also transmitted to all other endogenous variables in the system
through the dynamic structure of the VAR. For each variable used in the equations separately,
a unit or one-time shock is applied to the forecast error and the effects upon the VAR system
over time are observed. The impulse response analysis is applied on the VECM and, provided
that the system is stable, the shock should gradually die away (Brooks, 2003: 341). There are
several ways of performing an impulse response analysis, but the Cholesky orthogonalisation
approach to impulse response analysis, which is a multivariate model extension of the
Cholesky factorisation technique, is preferred in this study. This approach is preferred
because, unlike other approaches, it incorporates a small sample of degrees of freedom
adjustment when estimating the residual covariance matrix used to derive the Cholesky factor
(Lütkepohl, 1991: 155-158).
4.5.5.2 Variance Decomposition
Variance decomposition measures the proportion of forecast error variance in a variable that
is explained by innovations (impulses) in itself and the other variables. Variance
decompositions performed on the VECM may provide some information on the relative
importance of shocks to the growth model. Variance decompositions give the proportion of
the movements in the dependent variables that are due to their ‘own’ shocks (innovations),
versus shocks to the other variables (Brooks, 2003: 342). Brooks also observed that own
series shocks explain most of the forecast error variance of the series in a VAR. The same
63
method and information that is used in estimating impulse responses is applied in variance
decompositions.
4.6 Conclusion
This chapter presented the methodology used in building an empirical model. Having
identified empirical studies by Hadiwibowo (2010) which looked at the relationship between
the fiscal policy, investments and economic growth, the model included the dependant
variable Transport Infrastructure Investment and a number of modified independent variables
including Foreign Direct Investment, Budget Deficit and Gross Domestic Product.
The Augmented Dickey-Fuller and Phillips Perron were chosen to test for stationarity whilst
the Johansen technique was chosen to test for cointegration and error correction. Diagnostics
residual checks are done to test for the validity and robustness of the model; to determine the
impact, magnitude and proportion of shocks to the growth model impulse response and
variance decomposition checks are done. Having outlined the research methodology in this
chapter, the next chapter presents the empirical findings of the study.
64
CHAPTER FIVE
EMPIRICAL RESULTS
5.1 Introduction
This chapter presents the empirical results of the econometric analysis used in the study. This
chapter is divided into four sections, namely; stationarity and unit root tests, the long run
relationship and short run parameters, diagnostics checks and impulse response as well as
variance decomposition.
5.2 Stationarity/unit root test
The first step in the procedure is to test whether the time series are stationary. 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.
Therefore, an informal graphical analysis is conducted before the Phillips Perron and the
Augmented Dickey-Fuller tests, which are the 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 one an idea of the trends and stationarity of the data set. Figures 5.1 and 5.2
show plots of all variables used in the model in their logarithm and first differences form
against time.
Figure 5.1 shows that all variables in their levels have a time variant mean and variance
suggesting that they are not stationary. The first two variables, LRGDP and LTII, have a trendy
behavior. They seem to have a growth trend even though there are fluctuations except for
LFDI which shows a downward trend until 1996 and begins to grow in the years after.
65
Figure5.1: Plots of all variables in logarithm form 1990q1-2009q4
Source: Own Computation using E-views 7
Figure 5.2 shows that some variables follow the stationarity after first differencing. The
variables DLRGDP and DLTII are stationary as they are hovering around their means.
DLFDI also shows the stationarity process as it seems to be hovering around the mean but the
7.6
8.0
8.4
8.8
9.2
9.6
90 92 94 96 98 00 02 04 06 08
LOG_TII
11.8
12.0
12.2
12.4
12.6
12.8
13.0
90 92 94 96 98 00 02 04 06 08
LOG_RGDP
14.5
15.0
15.5
16.0
16.5
17.0
90 92 94 96 98 00 02 04 06 08
LOG_FDI
66
variance is clearly not constant over time. To identify if time series data is stationary, one
checks if the plots on a graph are fluctuating around the zero mean. Data that fluctuates
around the zero mean indicates stationarity. However, one cannot precisely base conclusions
on the graphical analysis because it is an informal test for stationarity. Therefore, other
formal tests are conducted to support the graphical findings. In this regard, the Augmented
Dickey-Fuller and Phillips Peron are implemented and the results are presented in Tables 5.1
and 5.2.
Figure 5.2: Plots of all variables after first differencing 1990q1-2009q4
Source: Own Computation using E-views 7
-.4
-.2
.0
.2
.4
90 92 94 96 98 00 02 04 06 08
DLTII
-.04
-.02
.00
.02
.04
90 92 94 96 98 00 02 04 06 08
DLRGDP
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
90 92 94 96 98 00 02 04 06 08
DLFDI
67
Table 5.1 Unit Root/Stationarity Tests
Augmented Dickey-Fuller
Variable
With Constant
and no trend
With Constant
and Trend
No Constant
and Trend
Integration of
order
LTII
DLTII
1.139367
-9201294*
-1.002780
-9.702793*
2.092828
-8.774964*
I (1)
LRGDP
DLRGDP
1.331261
-6.102203*
-2.400727
-6.426005*
4.552974
-1.871206***
I (1)
LFDI
DLFDI
-1.770487
-8.720410*
-1.675260
-8.709704*
0.087719
-8.774964*
I (1)
Critical
Values
1%
-3.515536
-4.078420
-2.594946
5%
-2.898623
-3.467703
-1.944969
10%
-2.586605
-3.160627
-1.614082
Source:Own computation using E-views 7
* represents a stationary variable at 1% level of significance
** represents a stationary variable at 5% level of significance
*** represents a stationary variable at 10% level of significance
L represents Logarithms of variables
D represents that the variable has been differenced
Table 5.1 lists the Augmented Dickey-Fuller results. The test has a null hypothesis of unit
root. The null hypothesis of a unit root is rejected in favour of the stationary alternative in
68
each case if the test statistic is more negative than the critical value. Therefore, a rejection of
the null hypothesis means that the series does not have a unit root. It should also be noted that
the tests were carried out with no constant and trend, with constant but no trend, with both
trend and constant. The unit root using constant and trend suggests that all series become
stationary after first differencing.
The results show that LTII is not stationary in levels in the ADF but does however become
stationary after it has been differenced. The t-statistic -9.702793 becomes bigger than the one
percent critical value -4.078420. When the test is applied to first differences of the series, all
variables become stationary, which suggests that they are all I(1). The null hypothesis of unit
root is therefore rejected and the alternative of no unit root in the series is accepted. The unit
root test using constant and trend assumption shows the most robust results.
The results show that LRGDP is not stationary in all levels but becomes stationary after it has
been differenced. The t-statistic -6.426005 is greater than the Mackinnon value which is -
4.078420 significant at one percent. The null hypothesis of unit root is therefore rejected and
the alternative of no unit root in the series is accepted. The unit root test using constant and
trend assumption shows the most robust results. LFDI was found not stationary in all levels
but becomes stationary when it is first differenced. This is because the ADF t-statistic -
8.774964 is bigger than the one percent Mackinnon value -2.594946. The null hypothesis of
unit is rejected in favour of the alternative hypothesis and the variables are all integrated of
the order I(1).
69
Table 5.2 Phillips Perron
Phillips Perron
Variable
With Constant
& No Trend
With Constant
& Trend
No Constant &
Trend
Integration of
order
LTII
DLTII
1.850656
-9.229373*
-0.714486
-10.39757*
2.183700
-8.774964*
I (1)
LFDI
DLFDI
-1.844693
--8.720410*
-1.747122
-8.709704*
0.087719
-8.774964*
I (1)
LRGDP
DLRGDP
1.865186
-6.118283*
-2.809586
-6.420543*
6.553112
-3.444388*
I (1)
Critical
Values
1%
-3.515536
-4.078420
-2.594563
5%
-2.898623
-3.467703
-1.944969
10%
-2.586605
-3.160627
-1.614082
Values marked with * represent a stationary variable at 1% significance level and **
represent a stationary variable at 5% significance level and *** represent a stationary
variable at 10%.
Source:Own Computation used E-views 7
Table 5.2 lists the Phillips Perron results. The test has a null hypothesis of unit root. The null
hypothesis of a unit root is rejected in favour of the stationary alternative in each case if the
test statistic is more negative than the critical value. Therefore, a rejection of the null
hypothesis means that the series does not have a unit root. It should also be noted that the
70
tests were carried out with no constant and trend, with constant but no trend, with both trend
and constant. The unit root using constant and trend suggests that all series become stationary
after first differencing.
The results show that LTII is not stationary in levels but does however becomes stationary
after it has been differenced. The t-statistic 10.39757 becomes bigger than the one percent
critical value -4.078420. When the test is applied to first differences of the series, all
variables become stationary, which suggests that they are all I(1). The null hypothesis of unit
root is therefore rejected and the alternative of no unit root in the series is accepted. The unit
root test using constant and trend assumption shows the most robust results.
The results show that LRGDP is not stationary in all levels but becomes stationary after it has
been differenced. The t-statistic -6.420543 is greater than the Mackinnon value which is -
4.078420 significant at one percent. The null hypothesis of unit root is therefore rejected and
the alternative of no unit root in the series is accepted. The unit root test using constant and
trend assumption shows the most robust results. LFDI was found not stationary in all levels
but becomes stationary when it is first differenced. This is because the t-statistic -8.774964 is
bigger than the one percent Mackinnon value -2.594563. The null hypothesis of unit root is
rejected in favour of the alternative hypothesis and the variables are all integrated of the order
I(1).
5.3 Cointergration
In order to test for Cointergration it is important to test for lag length. The procedure involved
specifying the optimal leg length and choosing the deterministic assumption that the Johansen
test requires. Table 5.3, shows the lag length criteria obtained from the unrestricted VAR.
71
Table 5.3 Lag length Criteria
Lag LogL LR FPE AIC SC HQ
0
265.8943 NA 8.14e-09 -7.274842 -7.148361 -7.224490
1
320.1821 101.0355* 2.81e-09* -8.338390* -7.705983* -8.086627*
2
324.4051 7.390252 3.92e-09 -8.011251 -6.872918 -7.558078
3
327.4371 4.969269 5.68e-09 -7.651032 -6.006773 -6.996448
4
339.0442 17.73300 6.55e-09 -7.529006 -5.378821 -6.673011
5
342.6248 5.072460 9.56e-09 -7.184021 -4.527911 -6.126616
6
349.1891 8.570154 1.31e-08 -6.921920 -3.759884 -5.663104
7
358.5070 11.12966 1.69e-08 -6.736305 -3.068343 -5.276079
Source: Own Computation using E-views 7
* 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
However, as evidence in Table 5.3, the information criterion approach does not produce any
conflicting results because all the information criteria select lag length at 1. Therefore, the
information criteria AIC and HQ led to the conclusion to adopt 1 lag. Subsequently, the
cointegration test is conducted using 1 lag for the VAR. Table 5.4, shows the results obtained
from the Johansen Cointegration technique.
72
Table 5.4 Johansen cointegration rank test results
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.418422 105.0210 47.85613 0.0000
At most 1 * 0.360292 63.28620 29.79707 0.0000
At most 2 * 0.263376 28.88694 15.49471 0.0003
At most 3 * 0.067119 5.349785 3.841466 0.0207
Trace test
indicates 4
cointegrating
eqn(s) at the
0.05 level
Trace test indicates 4 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.418422 41.73476 27.58434 0.0004
At most 1 * 0.360292 34.39926 21.13162 0.0004
At most 2 * 0.263376 23.53716 14.26460 0.0013
At most 3 * 0.067119 5.349785 3.841466 0.0207
Max-eigenvalue test indicates 4 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Source: Own Computation using E-views 7
73
In Table 5.4, the first part represents the Trace Test whilst the second part represents the
Maximum Eigenvalue Test. The Trace test shows that the null hypothesis of no cointegrating
vector is rejected since the test statistic of 105.0210 is greater than the 5 percent critical value
of approximately 47.85613. The null hypothesis, that there is at most 1 cointegrating vector,
is rejected since its test of 63.28620 statistics is larger than the critical 5 percent value of
approximately 29.79707. However, the null hypothesis that there are at most 2 cointegrating
vectors is rejected as the test statistics of 28.88694 is larger than the 5 percent critical value of
approximately 15.49471. At the null hypothesis that there are at most 3 cointegrating vectors
is rejected since the test statistic of 5.349785 is larger than the 5 percent critical value of
approximately 3.841466. The Maximum Eigenvalue test results are similar to that of the
Trace tests as it rejects the null hypothesis of no cointegration at most 1 up to the null
hypothesis that there are at most 3 cointegrating vectors. The Trace and Maximum
Eigenvalue tests both suggest that there are 4 cointegrating relationships within the empirical
model. The cointegrating relationship within the model is graphically shown below. The
graph in Figure 5.3 below shows the cointegrated residual exhibit stationarity over the long
run. What remains, therefore, is the need to identify which vectors constitute the true or most
significant cointegrating relationship.
Figure 5.3: Johansen cointegrating Vector
Source: Own Computation using E-views7
-.5
-.4
-.3
-.2
-.1
.0
.1
.2
.3
.4
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Cointegrating relation 1
74
The discovery of a cointegration equation in the previous section implies that an error
correction model can be used. This allows us to distinguish between the long and short run
impacts of variables so as to establish the extent of influence that budget deficit has on
transport infrastructure investment. Using the outcomes from the cointegration test, the
VECM shall be specified in the next section.
5.4 Vector Error Correction Model and the long run relationship
The error correction model estimates the speed at which a dependent variable Y returns to
equilibrium after a change in independent variable X. If variables have a long run relationship
(cointegrated) there may still be a short-run deviation in their behaviour, thus there is
disequilibrium in the system. The Vector Error Correction Model (VECM) is therefore used
to correct the disequilibrium or tie down the short run relationship to its long run behaviour.
If the gap between the long run and short run rates is large, relative to the long run
relationship, the error correction model must be applied.
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 allows for the differentiation between long and short run parameters for
the empirical model. However, before the interpretation of the results from the VECM, the
four cointegrating relationships that have been suggested in the last section have to be
identified. This section therefore looks at the variables constituting the cointegrating
equations. Table 5.5 below shows the estimates of the VECM in both the long and short run.
Table 5.5 Results of both the Long Run and Short Run Relationship
Variable Coefficient Standard Error t-statistic
Constant 0.047578
D (RGDP (-1)) -3.995993 1.05821 -3.704180
D (FDI (1)) -0.127286 0.04701 -2.70755
D (DUMMY (1)) -0.026581 0.02163 -1.22865
Error Correction DLRGDP DLFDI DUMMY
Cont Eq1 0.058124 0.551528 0.290087
0.02104 0.662254 0.46117
2.76246 0.88593 0.62902
Source: Own Computation using E-views 7
75
The results in Table 5.5 suggest that there is a positive and statistically insignificant
relationship between a budget deficit and transport infrastructure investment in South Africa
during the period reviewed, with a t-statistic of 0.62902. However, in a long run the results
suggest at negative and statistically insignificant relationship between a budget deficit and
infrastructure investment in South Africa during the same period under review. The t-statistic
for this coefficient is -1.22865 at 1 percent significance interval pertaining that a percentage
increase in budget deficit lagged once leads to a decrease in the transport infrastructure
investment. This means that a persistent deficit causes a downward pressure on transport
infrastructure expenditure. According to Moudud (1998), increases in government deficit
tend to lead to a decline in investments which results in crowding out effects. Therefore, an
expansionary government deficit lowers the bond prices, raises the interest rate of bonds,
increases the demand for consumption and, lastly, raises the demand for money. A rise in a
budget deficit leads to a crowd-out effect because it increases the interest rate which later
negatively affects the investment and economic growth.
With respect to FDI, the results suggest a positive and statistically insignificant relationship
between FDIs and transport infrastructure investment in South Africa during the period under
review, with a t-statistic of 0.88593. However, in the long run there is a negative and
statistically significant relationship between FDIs and transport infrastructure investment in
South Africa with a t-statistic of -2.70755. Investment in transport infrastructure including
roads, rail to transport goods is a catalyst in attracting foreign direct investment (SAIIA,
2012). Increased FDI flows into South Africa are the outcome of more public sector
investment in economic infrastructure so as to facilitate more investment by local private
firms. Such increased investment increases confidence in the South African economy and
thereby attracts more foreign direct investment. However, in a long run, the results suggest a
decline in public investment in transport infrastructure. This could imply that, in a long run,
transport infrastructural facilities are sufficiently available meaning that the government’s
investment in transport infrastructure starts to diminish.
With regard to RGDP, the results show that there is a statistically significant but positive
relationship between RGDP and transport infrastructure in South Africa, with a t-statistic of
2.76246 under the period reviewed. However, in the long run there is a statistically
significant but negative relationship between RGDP and transport infrastructure investment
in South Africa, with a t-statistic of -3.74180. This can be explained by the fact that, in a long
76
run, the rate of investments in new transport infrastructure slows down and public
expenditure is merely targeted at maintaining the existing infrastructure.
5.4 Diagnostic Tests
Diagnostic checks are important in this analysis because, if there is a problem in the residuals
from the estimation of the model, it will show that the model is not efficient, so that
parameter estimates from such a model may be biased. The robustness of the model was
tested in three main ways: firstly, serial correlation was tested using the Lagrange multiplier
(LM) test, followed by the White test for heteroskedesticity and finally the Jarque-Bera test
for normality. The results of the diagnostics tests are presented in Table 5.6, below.
Table 5.6 Results of the Diagnostic Tests
Test Null Hypothesis t-statistic Probability
Langrage Multiplier No serial correlation 13.14312 (0.6623)
Heteroscedasticity There is no
heteroscedasticity
192.2666 (0.2523)
Normality Residuals are not
normally distributed
136.1803 (0.0000)
Source: Own Computation using E-views 7
The test for heteroskedesticity using White test with cross terms produced a CH sq. of
192.2666 at a probability of 0.2523. The null hypothesis of no heteroskedesticity or no
misspecification will thus be not rejected. Therefore, the model does not suffer from any
misspecification; hence, it can be relied on. The normality test showed a static of 136.1803
and a probability of (0.0000). This means that the null hypothesis is going to be rejected since
the probability is less than 5%. In this instance, the probability is smaller and we therefore
reject the null hypothesis of a normal distribution. The LM test showed a static of 13.14312
and a p-value of 0.6623. This indicates that the null hypothesis of no serial correlation is
accepted as the t-statistic is not significant.
77
5.5 Impulse response and Variance decomposition
Impulse response and variance decomposition tests show the wealth of information of the
dynamic effects on the short run parameter estimates. Impulse response analysis traces out
the responsiveness of the dependent variable in the VAR to shocks to each of the other
variables in the system. Variance decomposition analysis provides a means of determining
the relative importance of shocks in explaining variations in the variable of interest. Figure
5.4, below, presents the impulse response test results.
Figure 5.4 Impulse Response
Source: Own Computation using E-views 7
-.005
.000
.005
.010
.015
.020
.025
1 2 3 4 5 6 7 8 9 10
Response of DLTII to DLRGDP
-.005
.000
.005
.010
.015
.020
.025
1 2 3 4 5 6 7 8 9 10
Response of DLTII to DLFDI
-.005
.000
.005
.010
.015
.020
.025
1 2 3 4 5 6 7 8 9 10
Response of DLTII to DUMMY
Response to Cholesky One S.D. Innovations
78
These impulse response functions show the dynamic response of the transport infrastructure
investment to a one-period standard deviation shock to the innovations of the system and
indicate the directions and persistence of the response to each of the shocks over a 10 quarter
(2.5years) period. For the most part, the impulse response functions have the expected pattern
and confirm the results from the short run relationship. The first graph shows the response of
the independent variable to deviations by itself; this simply means that the effect or
responsiveness of transport infrastructure investment to changes in transport infrastructure
investment. The transport infrastructure investment had decreased in the second quarter for a
short run period but remained stable throughout the 10 quarter period. A one time period
standard deviation shock to RGDP the effect is stable but starts to appreciate in the second
quarter and starts to decrease until it reaches a stable state in the fourth quarter till the 10th
quarter. The effect of RGDP to TII explains the high coefficient and affects it in the long run.
A onetime standard deviation shock to FDI marginally decreases the TII in the fifth quarter
through-out the last quarter. Among all the analysed variables only RGDP is shown to have a
persistent and significant impact on transport infrastructure investment and conforms to the
results obtained, the rest seems to show a negative impact on transport infrastructure
investment.
5.6 Variance Decomposition
Variance decomposition analysis provides for a means of determining the relative importance
of shocks in explaining variations in the variable of interest. In the context of this study, it
therefore provides a way of determining the relative importance of shocks to each of the
budget deficits in explaining variations in transport infrastructure investment. The results of
the variance decomposition analysis are presented in Table 5.7 and these show the proportion
of the forecast error variance in transport infrastructure investment explained by its own
innovations and innovations in the budget deficit.
79
Table 5.7 Variance Decomposition
Period S.E TII RGDP FDI DUMMY
1 0.080988 100.0000 0.000000 0.000000 0.000000
2 0.085511 90.95220 5.580742 3.465938 0.001117
3 0.093800 79.38535 11.53988 9.067688 0.007082
4 0.099201 74.96531 14.40843 10.60768 0.018578
5 0.105101 71.05588 16.45970 12.46625 0.018161
6 0.110359 67.72587 18.45906 13.79698 0.018096
7 0.115526 64.93023 20.02024 15.03140 0.018136
8 0.120400 62.64957 21.34952 15.98260 0.018304
9 0.125116 60.68491 22.46696 16.82976 0.018376
10 0.129647 58.99261 23.44250 17.54646 0.018438
Source: Own Computation using E-views 7
In the first year, all of the variance in transport infrastructure investment is explained by its
own innovations (shocks), as suggested by Brooks (2002: 342). For the 5th year ahead,
forecast error variance, reported in column 2 of Table 5.6 under S.E., transport infrastructure
investment explains itself about 71 per cent of its variation, while other variables explain the
remaining 29 per cent. Of this 29 per cent, RGDP explains about 16.4 per cent, FDI about
12.46 per cent and Dummy about 0.01 per cent.
However, after a period of 10 years, transport infrastructure investment explains about 58 per
cent of its own variation, while other variables explain the remaining 42 per cent. The
influence of FDI increases substantially to about 1.54 per cent while the dummy remains the
same at 0.01 per cent and RGDP increases to about 23.44 per cent, explaining the largest
component of the 42 per cent variation in transport infrastructure investment. This result is
compatible with economic theory. Therefore, these results are similar to those from the
impulse response analysis in that all the variables have an impact on transport infrastructure
investment in the short run.
80
5.7 Conclusion
The first section presented the unit root test where the Augmented Dickey-Fuller and Phillips
Perron tests were used to test for stationarity. Both methods revealed that the data series are
non-stationary in levels and stationary when first differenced. Therefore, the series are
integrated of the same order I (1).
Cointegration tests were presented in the second section where the lag order information
criteria approach was applied as a direction in choosing the lag order. 1 lag was used in order
to permit adjustments in the model and accomplish well behaved residuals. All the
information criteria approaches used selected 1 lag; therefore, a conclusion to adopt 1 lag for
VAR was made. The Johansen cointegration tests provided evidence that there is no
cointegration between transport infrastructure investment and the explanatory variables.
The short run dynamics are not consistent with literature showing the Foreign Direct
Investment, RGDP have a negative impact on TII while the budget deficit shows consistent
results of having a negative effect on TII. The impulse response showed that RGDP is the
only variable that has been persistent on TII whilst variance decomposition showed that all
the variables have an impact on transport infrastructure investment in the short run, but the
RGDP has the most significant effect followed by the FDI and the budget deficit.
The last section of this chapter presented the results of the diagnostic checks, impulsive
response and variance decomposition. Diagnostic checks revealed the suitability of the
model; there is serial correlation and no misspecification while the errors are normally
distributed. Both the impulse response and variance decomposition produced results that are
compatible with economic theory. Therefore, the results from this research can be relied
upon. Compelling conclusions on the impact of budget deficit and transport infrastructure
investment can be deduced and applicable policies can be safely formulated. The summary of
the main findings, conclusions and recommendations are presented in the ensuing chapter.
81
CHAPTER SIX
SUMMARY OF THE MAIN FINDINGS, CONCLUSIONS, IMPLICATIONS AND
RECOMMENDATIONS
6.1 Summary of the study and conclusions
The main objective of this research was to assess the impact of a budget deficit on transport
infrastructure investment in South Africa. The objective of this study was addressed by
looking at all the aspects contributing towards budget deficits. These aspects include the
financial crisis, debts and increasing social welfare expenditure. This chapter begins by
presenting some highlights of each chapter, which is followed by conclusions. The last part of
the chapter offers a set of policy recommendations based on the findings of this study.
The first chapter outlined that the aim of the study, its objectives, problem statement and the
significance of the study. Chapter Two presented an analysis of the theoretical and empirical
literature pertinent to the study. In the main, this chapter presented a literature review on
growth theories; namely, the Harrod-Domar, neo-classical and endogenous growth models.
Based on the discussion of growth theories, this study adopted endogenous growth models as
a theoretical framework for this study.
Chapter Three presented an overview of the variables included in the econometric model
used to analyse the data in this study. In this regard, trends on transport infrastructure
investment, gross domestic product, foreign direct investment as well as a budget balance
during the period under review was presented.
Chapter Four presents the research methodology employed in this study. The study is based
on quantitative research techniques using econometric analysis. Specifically, the time series
data covering the period 1990 to 2009 (quarterly data) was analysed using a vector error
correction model. All the methodological steps used in analysing the data were outlined in
Chapter Four; this includes stationarity tests, tests for cointegration and diagnostic tests, all of
which were explained in this chapter.
Chapter Five presents the analysis of data and the results of the analysis. The main findings
of this analysis show that a budget deficit negatively impacts investments in transport
82
infrastructure in South Africa, during the period under review. Based on these findings, the
results suggest that a null hypothesis presented in chapter one of this study cannot be rejected.
6.2 Conclusions
The results from the analysis of the variables included in the econometric estimation model
show that the variations in transport infrastructure investment are mainly explained by
economic growth followed by foreign direct investment and, lastly, by a budget deficit. The
main insight that can be drawn from this analysis is that a budget deficit has a negative
impact on transport infrastructure investment in the long run. The findings show that the
budget deficit is consistent in both the short and the long run negatively affecting transport
infrastructure investments. With the budget deficit increasing it does not only affect the
transport infrastructure investments but it also lowers the bond prices, raises the interest rates
and has a crowding-out effect.
6.3 Recommendations
Based on the results of this study, the following recommendations are provided:
In order to ensure that transport infrastructure investment is maintained on a positive
level, foreign direct investments should be attracted. In fact, a good transport
infrastructure on its own attracts foreign direct investments.
Policy makers should ensure that pro-growth policies are devised and implemented in
South Africa. This will help maintain sustainable economic growth in South Africa.
Whilst expansionary fiscal policy is desirable for its economic benefits, the Treasury
should ensure that there is fiscal discipline in the budget. This will help curtail a
budget deficit which will in turn help boost infrastructural investments in South
Africa.
6.4 Delimitations and recommendations for future research
This study focused primarily on data from 1990 to 2009, due to the unavailability of data in
some of the variables. The main thrust of this study is on the impact of a budget deficit and its
impact on transport infrastructure investment. Other factors that may influence transport
infrastructure investment were excluded. For future research the researchers could apply the
same econometric model but use the different period to check how the budget deficit affected
the transport infrastructure investment during the apartheid years.
83
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APPENDIX
Data used in the regression analysis
Years TII FDI RGDP DUMMY
1990Q1 3209.25 9615750 159971 1
1990Q2 3209.25 9615750 158383 1
1990Q3 3209.25 9615750 158468 1
1990Q4 3209.25 9615750 158752 1
1991Q1 3224.75 11042750 160118 1
1991Q2 3224.75 11042750 160810 1
1991Q3 3224.75 11042750 161706 1
1991Q4 3224.75 11042750 162770 1
1992Q1 2951.5 13582250 162150 1
1992Q2 2951.5 13582250 161803 1
1992Q3 2951.5 13582250 162029 1
1992Q4 2951.5 13582250 162176 1
1993Q1 2882.5 15255000 162441 1
1993Q2 2882.5 15255000 162841 1
1993Q3 2882.5 15255000 162960 1
1993Q4 2882.5 15255000 163156 1
1994Q1 2988.75 16924500 165208 1
1994Q2 2988.75 16924500 167932 1
1994Q3 2988.75 16924500 170084 1
1994Q4 2988.75 16924500 171975 1
1995Q1 3457 21247750 172966 1
1995Q2 3457 21247750 174029 1
93
1995Q3 3457 21247750 175458 1
1995Q4 3457 21247750 176428 1
1996Q1 3398 2850325 180089 1
1996Q2 3398 2850325 184353 1
1996Q3 3398 2850325 188753 1
1996Q4 3398 2850325 193106 1
1997Q1 3646.5 2829250 194069 1
1997Q2 3646.5 2829250 195118 1
1997Q3 3646.5 2829250 195722 1
1997Q4 3646.5 2829250 196515 1
1998Q1 4167.75 3934625 197019 1
1998Q2 4167.75 3934625 199795 1
1998Q3 4167.75 3934625 201260 1
1998Q4 4167.75 3934625 201339 1
1999Q1 3124.25 5075900 206855 1
1999Q2 3124.25 5075900 209139 1
1999Q3 3124.25 5075900 211457 1
1999Q4 3124.25 5075900 212816 1
2000Q1 3479 6116325 213916 1
2000Q2 3479 6116325 214718 1
2000Q3 3479 6116325 217315 1
2000Q4 3479 6116325 221040 1
2001Q1 3537.75 5329600 227158 0
2001Q2 3537.75 5329600 232269 0
2001Q3 3537.75 5329600 236842 0
2001Q4 3537.75 5329600 241531 0
94
2002Q1 3542.5 4747775 247019 0
2002Q2 3542.5 4747775 250251 0
2002Q3 3542.5 4747775 243344 0
2002Q4 3542.5 4747775 250046 0
2003Q1 3832.5 4512675 255989 1
2003Q2 3832.5 4512675 259461 1
2003Q3 3832.5 4512675 263376 1
2003Q4 3832.5 4512675 265666 1
2004Q1 4547 5500900 273173 1
2004Q2 4547 5500900 278446 1
2004Q3 4547 5500900 281244 1
2004Q4 4547 5500900 285313 1
2005Q1 5898 5962250 288187 0
2005Q2 5898 5962250 293692 0
2005Q3 5898 5962250 299077 0
2005Q4 5898 5962250 301060 0
2006Q1 6273 8856350 312688 0
2006Q2 6273 8856350 320797 0
2006Q3 6273 8856350 328856 0
2006Q4 6273 8856350 333667 0
2007Q1 7562 11215725 339870 0
2007Q2 7562 11215725 344026 0
2007Q3 7562 11215725 353202 0
2007Q4 7562 11215725 360906 0
2008Q1 11098.75 11621050 368838 1
2008Q2 11098.75 11621050 370801 1