LONG-RUN RELATIONSHIP BETWEEN GOVERNMENT

EXPENDITURE AND ECONOMIC GROWTH: EVIDENCE

FROM SADC COUNTRIES

By

KABEYA CLEMENT MULAMBA

DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of

MASTER OF COMMERCE

In

ECONOMICS

In the

FACULTY OF ECONOMIC AND FINANCIAL SCIENCES

At the

UNIVERSITY OF JOHANNESBURG

SUPERVISOR: Dr. A. N. KABUNDI

January 2009

2

ABSTRACT

This study attempts to investigate the validity of Wagner’s law and the Keynesian

perspective of a long-run relationship and causality between government expenditure and

economic growth in SADC countries from 1988 to 2004. In order to determine the

existence of the long-run relationship and causality, a univariate analysis is carried out to

assess whether panel series are integrated at the same order. Subsequently, this study

finds that all panel series under investigation are indeed integrated of the same order.

Therefore, the second stage consists of assessing whether there is cointegration between

government expenditure and economic growth. This study applies two procedures of

panel cointegration, namely, the Pedroni panel cointegration test and the Kao panel

cointegration test. Both procedures find that certainly a long-run relationship exists

between government expenditure and economic growth in the SADC. Moreover, since

two equations are estimated in this study, there is unidirectional causality. In both

equation 1 and 2, the study finds that economic growth Granger causes government

expenditure in both the long and the short-run which is consistent with the Wagner’s law

than the Keynesian stance.

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Acknowledgements

Thanks are given to the almighty God and Lord Jesus-Christ for the grace upon my life

which has allowed me to complete this degree. Besides, it is a moral obligation for me to

recognise the mentorship merit of my supervisor, Dr Kabundi from whom I have learnt

perseverance despite difficulties.

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Dedication

I dedicate this work to my lovely wife, Celine, and my beloved sons, Gershom, Mark and

Benjamin for their support during my studies. This work is also dedicated to Tshibola and

Katolo, respectively my late younger sister and younger brother who passed away when I

was still far away from home and unable to attend their funeral.

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Contents

1 Introduction………………………………………………………….8

2 Literature review …………………………………………………..11

2.1 Introduction……………………………………………………………………..11

2.2 Theoretical explanation of Wagner’s law………………………………………14

2.3 Keynesian approach of government expenditure and economic growth

relationship…………………………………………………………………………..15

2.4 Government expenditure and economic growth: some empirical evidence……..16

2.5 summary…………………………………………………………………………20

3 Methodology………………………………………………………..22

3.1 Introduction……………………………………………………………………..22

3.2 Model specification……………………………………………………………..23

3.3 Panel unit root tests and cointegration analysis………………………………...24

3.3.1 Panel unit root tests………………………………………………………….24

3.3.1.1 The Levin, Lin and Chu (LLC) test……………………………………….24

3.3.1.2 The Im, Pesaran and Shin (IPS) test………………………………………25

3.3.2 Panel cointegration analysis…………………………………………………26

3.3.2.1 The concept of cointegration……………………………………………...26

3.3.2.2 Pedroni panel cointegration test………………………………………….27

3.3.2.3 Kao panel cointegration………………………………………………......28

3.4 Estimation methods…………………………………………………………….29

3.4.1 Fixed effects method…………………………………………………………..29

3.4.2 Pooled least squares method………………………………………………… 30

3.4.3 Random effects methods…………………………………………………….. 32

3.4.3.1 The concept of random effects method…………………………………32

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3.4.3.2 The Hausman test………………………………………………………33

3.5 Granger causality……………………………………………………………….34

3.6 Data description and overview of government expenditure in SADC ………...35

3.6.1 Data description……………………………………………………………….35

3.6.2 Overview of government expenditure in SADC………………………………36

4 Empirical results………………………………………………….39

4.1 Introduction…………………………………………………………………….39

4.2 Panel unit root results and order of integration………………………………...39

4.3 Panel cointegration tests………………………………………………………..41

4.3.1 Pedroni panel cointegration test………………………………………………41

4.3.2 Kao panel cointegration test………………………………………………….43

4.4 Estimation results…………………………………………………………….....44

4.4.1 Pooled least squares and fixed effects estimates……………………………...44

4.4.2 Random effects estimates…………………………………………………….46

4.5 Reports on Granger causality test……………………………………………...48

5 Conclusion…………………………………………………………52

6 References………………………………………………………….53

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List of Tables

1 Annual average of general government expenditure as % of GDP …………………37

2 Correlation statistics between government expenditure and economic growth for

individual SADC countries from 1988 to 2004………………………………………….38

3 Panel series unit root test for LE and LGDP………………………………………….40

4 Panel series unit root tests for LE/P and LGDP/P……………………………………41

5 Pedroni panel cointegration test between LE and LGDP…………………………….42

6 Pedroni panel cointegration test between LE/P and LGDP/P………………………...42

7 Reports on Kao panel cointegration test……………………………………………...43

8 Pooled least squares estimates………………………………………………………..45

9 Fixed effects estimates………………………………………………………………..46

10 Random effects estimates…………………………………………………………….47

11 Hausman test……………………………………………………………………….....47

12a Vector error correction estimates equation 1……………………………………...49

12b VECM Granger causality test equation 1………………………………………….49

13a Vector error correction estimates equation 2………………………………………50

13b VECM Granger causality test equation 2………………………………………….51

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CHAPTER 1

Introduction

The relationship between government expenditure and economic growth has, since many

decades, received much attention by economists. Economists have two opposing views

about the nature of this relationship between government expenditure and economic

growth. Firstly, the hypothesis of Wagner (1883) states that the growth of national

income causes the growth of government expenditures and a long-run equilibrium

relationship between them exists. Secondly, in the Keynesian stance, government

expenditure is treated as an exogenous policy variable that causes the growth of national

income.

As a consequence of this debate, there exists an extensive empirical literature with the

objective of testing the validity of each hypothesis mentioned above. For instance, Biswal

et al. (1999) test the Wagner’s hypothesis using disaggregated public expenditure data for

Canada. Al-Faris (2002) examines the long-run relationship between public expenditure

and economic growth in the Gulf Cooperation Council Countries. Whereas Ansari et al.

(1997) test Keynes versus Wagner’s hypotheses on how public expenditure commoves

with national income in three African countries.

Most of these studies use econometric techniques of cointegration and Granger causality

in order to empirically establish the possibility of a long-run relationship and the

direction of causality between public expenditure and national income. It appears that

there is a lack of consensus again across empirical studies. Some empirical studies

support the Wagner’s law rather than the Keynesian stance while other studies endorse

the Keynesian hypothesis.

This lack of a consensus both in theories and empirical studies on the relationship

between government expenditure and economic growth is the motivation of this study.

Firstly, economic growth, as an indicator of economic performance within a country, is

9

considered as an objective that most of the countries would anticipate because of its

impact in raising the standards of living, the state benefits, and the employment levels.

Understanding the determinant factors capable of causing economic growth is crucial. In

this regard, government expenditure needs to be analysed in order to determine if it is

indeed a determinant factor to cause economic growth.

Secondly, government being an economic agent has an important role to play in the

economy. It has to provide public goods and services. A country that experiences a

positive growth would take that advantage to provide more public goods and services in

order to ensure an egalitarian society in which members have more or less equal

opportunities. In this regard, economic growth becomes an objective that government

pursues in order to accumulate necessary resources for the provision of public goods and

services. On the other hand, government involvement in the economy through

government expenditures can have negative effects as well on the economy, such as the

debt burden of the national budget, inflation and taxes that might raise non conducive

environment signals amongst investors. Getting an insight on the possibility of growth to

explain government expenditures appears to be the most important for aforementioned

the reasons.

The lack of a consensus on the relationship between the size of government and the

growth of national income is enough for this study to ask whether the government

expenditure causes economic growth and vice versa in SADC countries and whether a

long-run equilibrium relationship exists between them. The purpose of this study is to

empirically investigate the causal relationship between the government expenditure and

economic growth in a longitudinal analysis for SADC countries.

Most studies assess the validity of Wagner’s law for developed countries and emerging

economies. In some instances, studies are done on individual African states. But as far as

this study is concerned, this is the first time that the Wagner’s law is studied in a group of

African countries.

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The study is structured as follows. Chapter 2 focuses on the literature review where

previous studies that investigate the relationship between the government expenditure and

economic growth are discussed. In chapter 3, the study presents the methodology that is

undertaken. It consists of the presentation and explanation of econometric models and

variables as well as the description of the data used. Chapter 4 presents the empirical

results. It consists of economic and statistical interpretation of the regression outputs.

Chapter 5 draws conclusions from the findings of the investigation.

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Chapter 2

Literature review

2.1 Introduction

This chapter provides on one hand a discussion on literature that seeks to understand the

sources of long-term relationship between government expenditure and economic growth

as stated by the Wagner’s law. On the other hand, a concern is placed in studies that test

the Keynesian stance of government expenditure which is considered as an exogenous

variable and generally determined by politicians and which influences the level of output

in an economy. To test either the Wagner’s law or the Keynesian hypothesis, most

empirical studies use econometric techniques of cointegration and Granger causality tests

that are applied in this study.

Moreover, the rationale in this study is explained trough the following raisons. Firstly,

this chapter anticipates in determining whether any similar study on the long-run

equilibrium between government expenditure and economic growth has been undertaken

in the context of panel data for SADC countries for the same period. Secondly, this study

aims at reviewing existing empirical studies on the comovement of the aforementioned

variables in order to disentangle and get deep insight of different approaches used up to

now in testing either the Wagner’s law or the Keynesian stance. Lastly, after mastering

different techniques used in the literature, the objective of the discussion is to ensure that

this study applies these techniques adequately in order to investigate the long term

relationship as well as the direction of causality between government expenditure and

economic growth in a of longitudinal analysis framework of SADC countries.

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2.2 Theoretical explanation of the Wagner’s law

The hypothesis that there is a relationship between economic growth and government

expenditure is supported in the demand-side view. In essence, the demand-side economic

theory advocates active intervention of government in the economy through government

expenditure, money supply in order to stimulate the demand for goods and services and

ensure economic growth and stability. However, this view is in contradiction with the

supply-side approach. In the supply-side approach of public finance, government

expenditure involves bureaucratic waste and considered as a distortion to economic

growth through inflation that it causes if the not directed to infrastructure creation and

investment (Buchman and Tullock, 1962).

Another demand-side approach, which is considered in this study, is that of Wagner’s

law. According to Levitt and Joyce (1987), “Wagner’s law predicts and advocates the

growth of government expenditure (as a share of national income) on social services and

transfers, on infrastructure, and on a range of economic services”. This hypothesis

stipulates that there is a tendency by fiscal authorities to increase the level of public

spending as the level of output is expanding. The increment of government expenditure is

justified by the role that government ought to play in the society. According to Abizadeh

and Yousefi (1988), the size of government grows as an effect of industrialisation, in

other words, the richer a society becomes, the more the government spends in order to

alleviate social and industrial stress. Peacock and Scott (2000) state that the interpretation

of the Wagner’s law should be comprehensive in the sense that government expenditure,

which must include public enterprises, is considered as a key element to stimulate a

measure of government control on the economy which is at a stage of infancy.

Likewise, Stiglitz (1988) argues that the government needs finances because of its role in

the society. The government performs different kinds of activities in the society. Firstly,

the government provides legal and institutional frameworks in which corporate and

private individuals can engage in economic activity. This is a primary role of a

government. It consists of providing a conducive environment in which property rights,

13

antitrust laws and incentives for competition are guaranteed. In brief, the provision of a

legal framework implies that government will constantly need resources to maintain law

and order. Secondly, government has the responsibility to finance social activities such as

housing, sport and recreation, education, primary health care. To ensure that it maintains

this role, government produces goods and services as any other private corporate.

Thirdly, government purchases goods and services in order to provide for the functioning

of its different organs such as national defense, education, police, fire protection,

environment management. Lastly, government has the responsibility to intervene in the

economy in order to correct the inequalities caused by the market system and alleviate the

phenomenon of poverty. For this purpose, government can redistribute income, wealth

through the expenditure side of its budget.

By stating that the expansion of the economy stimulates states to increase the volume of

government expenditure, there is an assumption that the Wagner’s approach treats

government expenditure like any consumption behavior is treated in the economy with

regard to its relation with national income. Private absorption is caused or explained by

the level of income. In the same way, economic growth causes government expenditure

through an increase in demand for public goods and services, and demand for

redistribution. Furthermore, the increase of government expenditures is mostly explained

by the fact that government wants to maximise its utility function consisting of public

service delivery. In addition, the Wagner’s law implicitly suggests that, beside a

unidirectional causality, there exists equilibrium between government expenditures and

economic growth.

2.3 Keynesian approach of government expenditure and economic

growth relationship

The main concern of most economists regarding government expenditure is to understand

how government expenditure affects the economy. Many argue that government

expenditure can help to improve the level of output in the economy. This point of view is

based on the hypothesis of the Keynesianism. In the Keynesian point of view,

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government expenditure is considered as a tool that fiscal authorities can use in order to

influence economic activity. For instance, to correct short-term cyclical fluctuations in

aggregate expenditure, government can use government expenditure (Singh and Sahni,

1984). According to Ram (1986) government expenditure can help improve the level of

productive investment, hence economic growth and development can be secured. Thus,

government expenditure has a positive impact on economic growth.

However, there exists an opposing view that postulates a negative impact that

government expenditure might have on economic growth. According to Barro (1990) and

Barro (1991), government expenditure is generally associated with higher taxation. If

there is an excessive intervention of government in economic activity through

government expenditure and higher taxation, this can result in distortion of economic

incentives, such as incentives to save and invest, incentives for innovation and

enterprises, and hence retard the process of economic growth and development.

2.4 Government Expenditure and Economic Growth: Some empirical

evidence

Some studies use aggregate data of government expenditures to test either the Wagner’s

law or the Keynesian stance while others use disaggregated data in order to get insight of

the long-run relationship and the direction of causality between individual component of

government expenditure and economic growth. In addition, different studies use different

indicators to estimate the economic growth. For instance, there are some studies that use

per capita gross domestic product while others use gross national income in nominal,

real, logarithm and percentage terms.

Secondly, studies that empirically test the long-run relationship and the direction of

causality between government expenditure and economic growth employ different

methodological approaches and techniques. For instance, most of the studies apply the

cointegration and Granger causality techniques on time series data and find contradicting

15

results. Whereas, only a few studies approach the question using cross-section and panel

data regressions and still, the results are contradictory.

For example, Landau (1983), Mo (2007) and Schaltegger and Torgler (2006) use cross-

country approach. Firstly, Landau (1983) in a study of 104 developed and developing

countries finds that government expenditure retards economic growth. Indeed, the study

by Landau (1986) confirms the statement of negative impact of government expenditure

on economic growth. Secondly, contrary to findings by Landau (1983), Mo (2007)

empirically investigates the effect of government expenditure on economic growth using

the data collection of Barro and Lee (1994) comprising 138 countries. Following Mo

(2007), government expenditure affects economic growth via three channels: the total

factor productivity, the investment and the aggregate demand. According to Mo (2007),

the Keynesian hypothesis is satisfied across all countries and, moreover, government

spending on investment has a positive marginal effect on productivity and GDP growth.

Thus, governments considered in the study should reallocate an important share of public

spending towards government investment in order to enhance their national productivity

and economic growth. Thirdly, Schaltegger and Torgler (2006) argue that the empirical

research on the relationship between government expenditure and economic growth in

cross-country regressions approach is still inconclusive. Therefore, their study

empirically investigates this relationship within a rich country using a sample of state and

local governments from Switzerland over the period 1981-2001. The authors find that

current government spending significantly affects economic growth. Whereas, there is no

confirmation of a significant impact of capital public spending on economic growth for

state and local governments in Switzerland.

Contrary to the approaches in Landau (1983) and Mo (2007), Biswal et al. (1991) use

aggregated and disaggregated data on government expenditure to test both the Wagner’s

law and the Keynesian hypothesis in Canada. Using aggregate data, the Engle-Granger

cointegration test supports both Wagner’s law and the Keynesian hypothesis. In addition,

the growth of total current expenditure and current expenditure on goods and services

exhibits a long-run equilibrium relationship with the growth of GDP. The Wagner’s law

16

as well as the Keynesian hypothesis concerning the direction of causality are satisfied for

both the total current expenditure and the current expenditure on goods and services

towards the gross domestic product and vice versa. In short-run, the causality related to

Wagner is confirmed from gross domestic product to government transfers to person and

transfers to business, government investment and expenditure on wages, salaries and

supplementary labour income of civilian and military personnel. In the Keynesian sense,

the short-run causality is confirmed from government investment, public debt,

expenditure on wages, salaries and supplementary labour income of civilian and military

personnel and expenditure on other goods and services to GDP. These empirical results

lead Biswal et al. (1991) to conclude that government transfers both to persons and

business could not be considered as instruments of stabilisation policy in Canada.

However, public debt and other expenditure on goods and services can be utilised as

stabilisation policy tools in Canada. While the total current expenditure, the current

expenditure on goods and services, the expenditure on wages and salaries and

government investment do not offer any clear cut way out whether to be regarded as

stabilisation policy tools.

Furthermore, testing the long-run relationship and causality between government

expenditure and economic growth at both aggregated and disaggregated level in a panel

of 30 developing countries, Bose et al. (2007) find that the share of government capital

expenditure in GDP is positively and significantly correlated with economic growth, but

current expenditure is statistically insignificant. Disaggregated data on government

expenditure, particularly government investment in education and total expenditures in

education are the only components that are significantly associated with economic

growth once the budget constraint and omitted variables are included in the model.

Similarly, after observing the success that the Gulf Cooperation Council Countries are

experiencing, the concern of Al-Faris (2002) is to determine the nature of the relationship

between government expenditure and economic growth. This is more or less motivated

by the domination of the public sector in the economy through redistribution of oil wealth

strategies such as high public wages, extensive public employment and subsidies. This

17

has resulted in an increase of public spending in the Gulf Countries. For this purpose, Al-

Faris (2002) uses a multivariate cointegration test of Johansen and the Granger causality

tests on both public expenditure aggregated and disaggregated data. The Al-Faris findings

reveal that there exists a long-run relationship between national income and total

spending, capital spending and current spending. In addition, for the majority of the

countries in the Gulf, the Wagner’s hypothesis of government expenditure expansion is

satisfied; which means that in these countries economic growth is a determinant factor

that explains the escalating role of government through the increase of public spending.

Conversely to most countries in the region, there is one case in which a bi-directional

relationship between government expenditure and national income, the case of Bahrain.

Surprisingly, the author refutes the hypothesis of the Keynesian paradigm. Al-Faris

argues that despite its important role played in the earlier stages of development in the

Gulf, public spending does not cause economic growth and could not be considered as a

stabilisation policy tool for the period under consideration. According to Al-Faris (2002),

the fact that government spending does not Granger cause economic growth is perhaps

explained by a large share of public spending devoted to non productive sectors such as

spending on defense, subsidies, and socially and politically motivated recruitment in the

public sector and the long time lag between social spending and development. In

addition, according to Kolluri et al. (2000) the long-run relationship between total

government spending and economic growth in the sense of Wagner is satisfied in the G7

countries for the sample period 1960-1993. Moreover, the authors estimated the

relationship between key components of government expenditure individually with

economic growth in order to get insight on the future distribution of government

resources among various categories of public spending. Kolluri’s result reveals that, in

disaggregated data, government expenditure is caused by economic growth on the long

run and the significance of the short-run, measured by the error correction mechanism,

strengthens this long term relationship.

In particular, Abisadeh and Yousefi (1998) test the idea shared by most economists and

political scientists that there exists a one-way relationship from economic growth towards

the size of government. For instance, Abisadeh and Yousefi (1998) argue a priori that

18

government expenditure is an important macroeconomic variable in developing countries

and absorbs a lager proportion of national economic resources and, hence affects

economic performance. Their objective is to empirically investigate the appropriateness

of government expenditure as a policy tool capable of helping improve the economic

performance of South Korea according to the Keynesian paradigm or the phenomena of

industrialisation and economic growth causing a higher demand of public goods and

services according to the Wagner’s hypothesis of expansion of government size. The

econometric test of Granger causality is used in order to determine if indeed government

expenditure can cause economic growth in South Korea and vice versa. Empirical results

in Abisadeh and Yousefi (1998) reveal firstly that, indeed economic growth Granger

causes government expenditure in South Korea and the results are statistically significant.

Hence the hypothesis of Wagner is satisfied. Secondly, the Keynesian stance is not

validated in the South Korean context. For instance, government expenditure is

negatively and significantly related to economic growth, meaning that government

expenditure does not Granger cause economic growth in South Korea. These findings in

Abisadeh and Yousefi (1998) are in conformity with the conclusions of most pro-free

market studies and snub the Keynesian paradigm of fiscal stabilisation in which

government expenditure can be used as a tool.

Similar to the idea of Abisadeh and Yousefi (1998), the main concern of Tulsidharan

(2006) is to empirically investigate the nature of the relationship that exists between

government expenditure and economic growth in India. Tulsidharan (2006) contributes to

the ongoing debate amongst economists in favour of market driven economy rather than

government managing and controlling the economic activity. To advocate for

privatisation or not, the author analyses the causal relationship between government final

consumption expenditure and gross national product at market price both in nominal and

real terms in India. For this purpose, the test of integration, cointegration and Error

correction mechanism are used. The result of Tulsidharan (2006) reveals that the uni-

directional causality from gross national product to government final consumption

expenditure is confirmed when the data is used in nominal terms and, hence satisfying the

hypothesis of Wagner. On the level of real terms, the causality is not confirmed. The

19

study of Tulsidharan (2006) suggests that, in the context of an early phase of a growing

economy as India, government can not only expand its activities, but initiate new public

expenditure.

In contradiction to the methodological approach applied in most of the studies on the

long-run relationship and causality between government expenditure and economic

growth, Chang (2002) argues on one hand that most empirical studies do not apply the

Augmented Dickey-Fuller and KPSS tests of integration. On the other hand, the author

notices that most studies apply the two step cointegration procedure proposed by Engle

and Granger. Therefore, the author’s objective is to apply the ADF and KPSS for testing

the order of integration of the series as well as the Johansen cointegration in the context

of a bivariate analysis. Six countries, among which three are from the emerging markets

(South Korea, Taiwan and Thailand) and the rest from the industrialised world (USA,

Japan and the UK) are chosen for testing the Wagner’s law. Indeed, Chang (2002)

concludes that the Wagner’s law is satisfied in all countries except for Thailand. The

contribution of Chang to scientific knowledge applies to the methodology and techniques

to use in order to confirm the validity of either the Wagner’s law or the Keynesian

hypothesis.

In addition, from this methodological point of view, Loizides and Vamvoukas (2005)

estimate the long-run relationship between government expenditure and economic growth

using both a bivariate and trivariate analysis. In the bivariate analysis, simple regressions

are estimated to establish the relation from government expenditure towards economic

growth and vice versa. Whereas, in the trivariate analysis either the unemployment rate

or inflation rate is added separately as explanatory variable in order to affirm the validity

of either the Keynesian hypothesis or Wagner’s law in Greece, UK and Ireland.

Loizides and Vamvoukas (2005) conclude that in the short run government size Granger

causes economic growth in all countries. While, in the long-run, economic growth

Granger causes the size of government in Greece, and when inflation is added in the UK.

This implies that government expenditure indeed constitutes a stabilisation policy tool to

affect economic growth in the short term for all the three countries under investigation.

20

Furthermore, Wahab (2004) and Ansari et al. (1997) apply respectively the Granger and

Holmes-Hutton tests as well a new specification in order to investigate Wagner’s law and

Keynesian stance. Following Wahab (2004) a new specification is presented in order to

disentangle the effect of positive economic growth and negative economic growth on

growth of government expenditure in the OECD countries for the period between 1950

and 2000. The general finding of Wahab (2004) reveals that in times of positive

economic growth, government expenditure tends to grow less than proportional to the

increase in growth. Whereas, in periods of recession, public spending decreases more

than proportional to the decrease in economic growth. This implies that the Wagner’s law

is valid on limited basis only.

Asanri et al. (1997) apply the Granger and Holmes-Hutton statistical techniques in the

context of three African countries (Ghana, Kenya and South Africa). The Keynesian

paradigm is not satisfied for these three countries under investigation. In addition, the

authors conclude that the possibility of long term association between government

expenditure and economic growth does not hold. Asanri et al. (1997) note that

government expenditure persistently deviate from economic growth for the period under

study. However, the Wagner’s law has been satisfied in the case of short-run relation for

Ghana only. Moreover, the authors insist on the fact that this finding on the economic

growth causing government expenditure in the short-run is based on the decay in the

economy and government expenditure, but they remain reserved in the case of economic

performance.

2.5 Summary

This chapter presents an overview of previous empirical studies that test either the

Wagner’s law of government size or the Keynesian paradigm in which government

expenditure constitutes a stabilisation policy tool. Emphasis was put on empirical studies

that use the cointegration and Granger causality econometrical procedures to test the

aforementioned hypotheses. The literature reveals that findings from empirical inquiries

21

on the issue of long term relationship and causality between government expenditure and

economic growth differ. For instance, this study finds that methodological approach, the

issue of business cycle affecting the sample period or category chosen, the issue of data

on government expenditure and economic growth could explain the disparity in the

conclusions. The identification of this gap constitutes the major motivation in the present

study. The objective of this study is to gain insights on the impact of government

expenditure on economic growth and vice versa in the SADC countries.

22

Chapter 3

Methodology

3.1 Introduction

To assess the long-run or equilibrium relationship between government expenditure and

economic growth across SADC countries and the direction of causality between these

two macroeconomic variables, this study adopts the procedure developed by Narayan at

al. (2008). Their approach consists in testing at the same time the Wagner’s law and the

Keynesian hypothesis in the framework of the panel cointegration and the estimation of a

dynamic error correction model. The panel cointegration technique is used in order to

determine the long-run elasticity of government expenditure with respect to economic

growth on one hand. On the other hand, within the framework of panel data analysis, the

study attempts to assess whether the elasticity of government expenditure to economic

growth varies across countries in SADC community or can be considered as having a

common characteristic for all countries together.

Moreover, a priori, there is a possibility that the regression estimates of such kind can

lead to invalid conclusion as simultaneity or feedback could arise due to the fact that the

Keynesian paradigm treats government expenditure as an exogenous policy determined

variable and economic growth as endogenous and explained by the government

expenditure whereas, the Wagnerian view emphasises on the association between

government expenditure and economic growth and as the economy grows, government

tends to increase its influence through government expenditure. This has lead most of the

researchers to interpret the Wagner’s law as follows. Economic growth precedes the

expansion of government expenditure. For instance, based on that Narayan et al. (2008),

in the context of government expenditure and economic growth, use the Granger

causality test in a framework of panel data, in which panel cointegration between series

is statistically proven to exist, as an appropriate tool to assess whether economic growth

23

causes government expenditure and vice versa. The Granger causality procedure helps

mop out simultaneity as variables will be interchanged to assess causality in both

directions.

This chapter presents in section 3.2, the specification of the model. In section 3.3, panel

unit root tests and cointegration analysis are presented. Section 3.4 presents the

estimation methods. Granger causality test is described in section 3.5. Lastly, data

description is presented in section 3.6.

3.2 Model specification

This study is build upon existing literature in the framework of the Wagner’s law. Hence,

the specification does not differ from this literature. Three major versions of models have

been developed in order to formulate the Wagner’s law, such as Peacock and Wiseman

(1961), Musgrave (1969), Gupta (1967), Goffman and Mahar (1971) and others. In the

context of this study, the procedure adopted is that of Narayan et al. (2008) in which

general government final consumption and per capita general government final

consumption are used as proxies of government expenditure and per capita gross

domestic product and gross domestic product as proxies of economic growth so that two

different equations are estimated in order to assess the sensibility of government

expenditure with respect to economic growth within the region under investigation. The

following models are specified:

ttt LGDPLE (1)

ttt PLGDPPLE // (2)

where LE denotes logarithm of government expenditure, LE/P indexes the logarithm of

per capita government expenditure, LGDP denotes logarithm of gross national product

and LGDP/P indexes the logarithm of per capita gross national product. Equation (1) is

first used by Peadock and Wiseman (1961), then by Goffman and Mahar (1971).

24

Equation (2) is used by Gupta (1967) and Michas (1975 in order to inquire whether the

elasticity of per capita government expenditure with respect to per capita GDP is greater

than the unity.

3.3 Panel unit root tests and cointegration analysis

3.3.1 Panel unit root tests

The validity of a cointegration property in the relationship between government

expenditure and economic growth entails that the data be integrated of same level. A

stationary process or variable is a stochastic process whose parameters such as the mean

and variance do not change over time or position. A variable whose observation changes

over time or position is described as a nonstationary or having a unit root. A number of

statistical tests have been developed in the context of univariate time series analysis in

order to test whether a variable is indeed stationary or nonstationary, for instance the

Dickey-Fuller test (DF), the augmented Dickey-Fuller test (ADF), the Phillips-Perron test

(PP). Furthermore, based on most of these time series tests, new statistical tests of

stationarity have been developed in the framework of panel data like the Levin, Lin and

Chu test (2002), Im, Pesaran and Shin test (2003) and the Maddala and Wu test (1999). A

brief overview of assumptions of each of the most popularly used panel unit root tests is

presented in this section, namely the LLC and IPS tests.

3.3.1.1 The Levin, Lin and Chu (LLC) test

Let consider the following AR (1) process of panel data:

itititiit xyy 1 (3)

where i = 1, 2…..N cross- sections and t = 1, 2…T periods, x indexes the explanatory

25

variable, i represents the individual autoregressive coefficients and it represent the

panel error terms that are assumed to be mutually independents. If │θi│ < 1, ity as then a

stationary. However, if it happens that │θi│= 1, then ity is a nonstationary process.

The Levin at al. (2002) procedure as well the Breitung (2000) assume a homogenous

behaviour of unit root in across all cross-sections in such a way that i is identical in all

units. The procedure consists in testing the null hypothesis of a common unit root against

the alternative of no common unit root. Mathematically, the null and alternative

hypothesises are represented respectively as follows: 0:0 iH ; 1:1 iH . The LLC

test is suitable in the context of a pooled regression where cross-sections are considered

to have a common behaviour.

Contrary to what this test offers as a tool to assess stationarity in a panel series, the LLC

test has some limitations. Firstly, because the LLC test assumes independence across

units, therefore it is not applicable once this assumption is violated, in other word; the

LLC test becomes irrelevant when there is correlation between cross-sections. Secondly,

under the assumption that the autoregressive parameters are indentical across all units,

the alternative hypothesis stands strong in any empirical case while the null applies only

in some situations as note Maddala and Wu (1999).

3.3.1.2 The Im, Pesaran and Shin (IPS) test

Conversely to what the LLC panel unit root assumes, Im at al. (2003) offer a test under

the assumption that some individual, not necessarily all, series have a unit root. The test

is based on assessing the null hypothesis of individual unit roots in the panel against a

heterogeneous stationarity. In the context of this study, the IPS will be an appropriate

procedure to weigh up the order of integration of both government expenditure as well as

economic growth taking into consideration the issue of country-specific effects.

Moreover, the ADF Fisher panel unit test proposed by Maddala and Wu (1999) is built

26

under the same assumption as the IPS. But in addition to that, the ADF Fisher panel unit

root test combines the probability values of the test statistic of unit root in each cross-

section (Arpaia and Turrini, 2008).

From this discussion, it is obvious to notice that the objective of IPS test is to correct the

drawback of the LLC test. This explains why the IPS is used is used as a complementary

tool order assess the stationarity nature of the panel series under investigation.

3.3.2 Panel cointegration analysis

3.3.2.1 The concept of cointegration

Consider a case of a bivariate regression with the variable Y as dependent and X the

explanatory variable. If Y and X are both integrated of order one, in other words Y and

X contain each a unit root in level and only after differencing they each become

stationary. This is mathematically noted as )1(~ IY ; )1(~ IX . Y and X are described

as cointegrated, if there is a parameter τ of the linear combination of the form:

XY (4)

where is a stationary process.

Subsequently, the statistical notion of cointegration has been, since its introduction in the

literature by Granger (1981), used in economics in order to determine long-run

relationships between economic variables. The application of the statistical approach in

determining long-run or equilibrium relations in economics implies that these variables

are commoving over time so that in case of any deviation, economic forces coming into

play will bring the equilibrium relationship back to normal.

Furthermore, the examination of cointegration in the relationship between variables

entails that the order of integration of variables be determined. It is at this stage that the

27

level of integration is determined for each variable under investigation. The following

step will be to assess whether variables that are integrated of the same order are

cointegrated indeed. For comparison purposes, two most used approaches of panel

cointegration tests are used in this study, the Pedroni panel cointegration test and the Kao

panel cointegration test. Both approaches are residual based and mostly appropriate in

bivariate regression.

3.3.2.2 Pedroni panel cointegration tests

Pedroni (1999) proposes a set of seven tests which consist of four panel statistics and

three group statistics. Each of these panel and group tests is distributed asymptotically as

a normal distribution under appropriate standardisation and can be expressed as follows:

)1,0(/)( NvNNT (5)

where NT indicates the corresponding type of cointegration test, and v index

respectively the mean and the variance simulated and provided by Pedroni (1999, 2001).

The numerical values of and v depend on the inclusion of a constant, a time trend and

the number of independent variables in the cointegration regression. The critical value at

5% is −1.65 for all tests apart from the panel-υ statistic for which is at 1.65. As per

Pedroni, there is an assumption of a single cointegrating vector and the procedure resides

in testing the null hypothesis of no cointegration against the alternative of cointegration

based on the residuals. To reject the null hypothesis of no cointegration, the computed

test statistic must exceed in absolute value the critical value (1.65 or −1.65 according to

the case). Subsequently, if the null hypothesis of no cointegration is rejected, it implies

that there is a long-run or equilibrium relationship between economic variables under

investigation and the following step is to estimate with a dynamic error correction

representation that helps capture, in addition to short-run dynamics, the speed of

adjustment to equilibrium after external shocks to the system as well as the direction of

causality as argued by Engel and Granger (1987).

28

In addition, Rao (2007) argues that the Pedroni tests have an increased power of

inference due firstly to the fact that information from both dimensions, cross-sections as

well as time series are taken into consideration. Actually, the panel statistics are based on

pooling information along the within dimension, while the group statistics are pooling

information between dimensions. Secondly, the assumption of a homogeneous behaviour

is considered when computing the panel tests, while the group tests assume a

heterogeneous behaviour. Thirdly, the tests allow a greater flexibility in the presence of

heterogeneity of cointegrating vectors. Furthermore, Rao (2007) contends that panel ADF

statistic followed by ADF group statistic perform better in terms of power than other tests

following Monte Carlo simulation on small sample properties of the tests undertaken by

Pedroni (1997). Therefore, this study is restricted to the application of these two tests, the

panel and group ADF rather than the all seven.

Similarly, Arpaia and Turrini (2008) argue that the null hypothesis of no cointegration for

both the panel and the group statistics is a residual-based so that the statistics hypothesize

that residuals possess a unit root. In addition, the group statistics assume stationarity in

the residuals with cross-section specific autocorrelation coefficients of these residuals.

While, the alternative hypothesis for the panel statistics stipulates that residuals are

stationary with exactly the same autocorrelation coefficient of residuals across cross-

sections.

3.3.2.3 Kao panel cointegration test

Kao (1999) proposes a residual based test of cointegration in the context of panel data

using the augmented Dickey-Fuller type tests. Consider the following equation

ititiit XY (6)

According to Kao (1999), the slope coefficient is assumed common for all members in

the panel, implying that there is a common cointegrating relationship. The Kao test

consists of applying the Dickey-Fuller and the ADF like tests on the residuals of the OLS

29

panel estimating in (6) in this form:

ititit vee 1ˆˆ (7)

ititit vee 1ˆˆ itpjitj ve (8)

where ite denotes the residuals from equation (6). Equation (7) and (8) represent the

Dickey-Fuller and the ADF like tests respectively and the null hypothesis of no

cointegration (implying unit root in the residuals), 1:0 H is tested against the

alternative of stationarity in the residuals, 1:1 H

3.4 Estimation methods

Unlike in most of the previous empirical studies on panel data framework this study uses

the above mentioned equations in a framework of panel data analysis for a group of

African states. Therefore, three estimation methods are proposed in which the behaviour

of the long-run relationship and causality between government expenditure and economic

growth is evaluated. Firstly, it is assumed that there is unobserved heterogeneity that

impact additively on the long-run relationship between government expenditure and

economic growth in SADC countries. Secondly, the long-run relationship is assumed

common for all members of the panel implying the absence of country-specific effects as

additional explanatory variable (pooled regression). Secondly, the unobserved

heterogeneity across countries is considered but it is assumed that it is uncorrelated with

the explanatory variable so that the random effects method is used. The following

paragraphs explain in details the settings of each approach used in this study.

3.4.1 Fixed effects model

The objective of the fixed effects model is to control, in addition to the observed effects,

the unobserved heterogeneity within the panel. This unobserved heterogeneity may be

considered constant over time but changing across cross-sections or constant across

30

cross-sections and changing over time. The distinctiveness of the fixed effects method

resides in the fact that it allows arbitrary correlation between the unobserved

heterogeneity and the explanatory variables. For the purpose of this study the unobserved

heterogeneity is assumed time-invariant but changing across cross sections. The

following equations are estimated:

LEit = αi + βLGDPit + εit (9)

LE/Pit = αi + βLGDP/Pit + εit (10)

for i = 1, 2,…..N countries over t = 1, 2,…T time periods; i indexes specific cross

section effect, symbolises common slope indicating the elasticity of either LGDP or

LGDPP with respect to change in LE or LE/P; it indexes the error term which is

assumed to be distributed normally with means zero and constant variance.

There are different ways of estimating equations (9) and (10) using OLS in order to have

consistent and efficient estimates. But, in this study only one variant of these techniques

is explained. The Least Squares Dummy Variable (LSDV) estimator. The LSDV

estimator assumes that any differences across countries could be captured by shifts in the

intercept term of a standard OLS regression. This implies a definition of a set of dummy

variables, for instance Ai, where Ai is equal to 1 in the case of an observation relating to

country i and 0 otherwise. Following this, equations (9) and (10) can be rewritten as:

ititiiit LGDPALE

ititiiit PLGDPAPLE //

31

3.4.2 Pooled model

The purpose of the pooled method is to restrict all two specification equations so that any

cross-country heterogeneity is not considered. This results in estimating the following

models:

ititit LGDPLE (11)

ititit PLGDPPLE // (12)

Here represents the common intercept coefficient, denotes firstly common slope

coefficient, in other words, it indicates that the elasticity of government expenditure with

respect to economic growth is similar in all SADC countries. Secondly, must be

positive and greater than one in order to satisfy the Wagner’s law of expansion of

government expenditure; indexes the disturbance error term with the assumption that

it is homoscedastic and normally distributed, ),0(~ 2 N . In addition, the standard

assumptions of ordinary least squares must be fulfilled in order to estimate (11) and (12)

respectively.

Besides, the choice between the restricted regression (in this case the pooled regression)

and the unrestricted (the fixed effects) is technically based on the evaluation of the F

ratio which follows the Fisher distribution statistic. According to Gujarati (2003), the

rationale of the F ratio resides in the fact that, if indeed there is no heterogeneity in the

panel, then the pooled regression and the fixed effects regression are the same so that the

pRSS (residuals sum of squares of the pooled) and the FRSS (residuals sum of squares of

the fixed effects) are not statistically different under the null hypothesis. The F ratio is

computed as follows:

dfRSSmRSSRSSF

F

FP

//)(

32

where m is the number of cross-sections, df is the number of degree of freedom

remaining after estimating the fixed effects regression. Therefore, if the computed F

value is greater than the critical F value at a given level of significance, the null

hypothesis of pooled regression and fixed regression being not statistically different is

rejected. This implies that there is heterogeneity in the panel, hence the fixed effect

regression is suitable. Conversely, if the computed F value does not exceed the critical

F value at a given significance level, the null hypothesis is not rejected, meaning that

there is no heterogeneity in the panel and the pooled regression is suitable.

Therefore, the study presents both the results of the pooled regression and the fixed

effects regression in parallel. In this way, the F ratio is used for both versions of

equations in order to assess the presence of heterogeneity in the relationship between

government expenditure and economic growth in SADC countries for the period under

investigation.

3.4.3 Random effects model

3.4.4.1 The concept of random effects model

The method of random effects acknowledges the unobserved heterogeneity, but unlike in

the fixed effects, the unobserved heterogeneity is treated as any random error rather than

a parameter to be estimated. Therefore, the ideal of the random effects method is to shift

the unobserved heterogeneity in the error term in order to estimate the equation. Given:

ititit LGDPLE (9)

ititit PLGDPPLE // (10)

Shifting the unobserved heterogeneity in the error term will result in,

ititit uLGDPLE (13)

33

ititit uPLGDPPLE // (14)

Here, denotes a common intercept; itiitu t described as the compounded error

term. The compounded error term is autocorrelated due to fact that time varies at each

stage while cross-sections change after T periods. Therefore, estimating (13), and (14),

with OLS will yield non consistent estimators. The appropriate estimator, in this case, is

the generalized least squares in order to correct the issue of autocorrelation.

3.4.4.2 The Hausman test

Consequent to the assessment of the presence of heterogeneity in the panel using the F

ratio, if the null hypothesis is rejected, implying that, indeed there is heterogeneity in the

panel; the fixed or random effects estimator can be used. However, technically the

decision to use fixed effects rather than random effects or vice versa is firstly based on

the size of the sample, that means whether the sample is drawn form the population or it

is the all population that is considered. Secondly, the Hausman test is used in order to

assess whether indeed there is no correlation between the error term and the unobservable

effects. Based on these preconditions, the sample of countries under investigation is

drawn from the population of SADC community and the study applies in addition the

Hausman test which is explained in the following paragraph.

Knowing that the objective of either the fixed effects or random effects being the

estimation of a particular coefficient in a linear panel data framework in which there

is an assumption of heterogeneity in the intercepts i , hence, the rationale of the

application of the statistic of Hausman (1978) in the context of fixed effects and random

effects estimators would be to determine that indeed the coefficient is consistent and

not correlated with the unobservable effects. The Hausman test assumes that the error

term is uncorrelated with the unobservable effects under the null hypothesis. The

rejection of the null hypothesis implies that the fixed effects model is appropriate for the

given data, otherwise the random effects would be suitable. In the case the random

34

effects model is suitable, this means that the estimator would yield consistent and

efficient .

3.5 Granger causality

Most empirical studies use econometric techniques in order to investigate the validity of

either the Wagnerian approach or the Keynesian stance or both concerning the direction

of causality. Therefore, this study applies the Granger causality test in a context of panel

data in order to determine the direction of causality between government expenditure and

economic growth in a panel of SADC countries.

According to Engel and Granger (1987), the traditional vector autoregression (VAR) is

not appropriate in order to assess the direction of causality where variables are

cointegrated. Instead, the vector autoregression needs to be augmented of one period

lagged of the residuals from the cointegrating equation in a dynamic error correction

representation. This is done in order to capture short-run direction of causality, the speed

of return to equilibrium as well as the long-run direction of causality. Granger causality

model will have the following form:

Equation 1

itittpitpit

itpitpititit

ECTLGDPLGDPLGDPLELELELE

11)1(2222

121)1(121211110

1......

(15)

itittpitpit

itpitpititit

ECTLGDPLGDPLGDPLELELELGDP

121)1(5252

151)1(424214130

2......

(16)

Equation 2

itittpitpit

itpitpititit

ECTLGDPPLGDPPLGDPPLEPLEPLEPLEP

11)1(2222

121)1(121211110

1......

(17)

35

(18)

Where indicates the first difference of the concerned variable, ECT1 and ECT2 are

respectively the residuals from the cointegrating equation itititit LGDPLE ,

itititit PLGDPPLE // and ititititit PLEPLGDP //

ititititit LELGDP ; i indicates the intercept coefficients in equations (15) to

(18); p indicates the maximum lag order in the VAR, which translates into a lag of p-1

in the VECM; denotes the speed of adjustment or time period that takes for the

deviating variable to return to equilibrium. Equations (15) to (18) are described as the

vector error correction mechanisms or error correction models (VECM).

Following Engle and Granger (1987), economic growth (LGDP or LGDPP) Granger

causes government expenditure (LE or LE/P); in short-run if all φ2 are statistically

significant and in the long-run if t1 is statistically significant. Similarly, government

expenditure is said to Granger causes economic growth, in the short-run if all φ5 are

statistically significant and in the long-run if t2 is statistically significant.

3.6 Data description and Overview of government expenditure in

SADC

3.6.1 Data description This study uses annual observations from 1988 to 2004 for the 13 countries of SADC1

community. Data are from African development indicators of the World Bank. General

government final consumption and per capita general government final consumption are 1 The SADC countries considered in this study are Angola, Botswana, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, South Africa, Swaziland, Tanzania, Zambia and Zimbabwe.

itittpitpit

itpitpititit

ECTLEPLEPLEPLGDPPLGDPPLGDPPLGDPP

121)1(5252

151)1(424214130

2......

36

used as proxies of government expenditure in US Dollar at constant prices (base=2000).

Concerning the variable economic growth, this study uses per capita gross domestic

product and gross domestic product in US Dollar constant term with the year 2000 as

base. All the variables enter the equations in logarithmic terms in order to facilitate the

interpretation in terms of elasticity of government expenditure with respect to a marginal

change in economic growth.

3.6.2 Overview of government expenditure in SADC

One way of interpreting the Wagner’s law is that this law assumes an increase of

government influence in the economy through government expenditure associated with

the expansion of the economy. Although the ideal would be to consider all aspects of

government expenditure including public finances and public enterprises, this study uses

annual average general government expenditure as percentage of GDP, as comprehensive

data on government expenditure for the group of countries under investigation is difficult

to obtain. The purpose of this exercise is help get an insight of the proportion of the

aforementioned influence of government in the economy in each individual member state

of SADC.

Table 1 presents averages of government expenditure as a share of GDP for 13 SADC

countries. On average, government expenditure as a share of GDP in Angola is the

highest in the region. Likewise, Botswana devotes 24% of its GDP to government

expenditure for the period 1988-2004. The third country in Southern Africa with a high

share of GDP accounted for government expenditure is Swaziland, with an annual

average of 21%. The data reveal that the proportion of GDP accounted for by government

expenditure in Madagascar is less than 10% and is the minimum in Southern Africa for

the period under investigation.

37

Table 1 Annual average general government expenditure as % of GDP from 1988 to

2004

COUNTRY ANNUAL AVERAGE GOV. EXP. AS % SHARE OF GDP

SOUTH AFRICA 19.12888

ZIMBABWE 18.093

ZAMBIA 15.73143

BOTSWANA 24.8943

SWAZILAND 21.26654

LESOTHO 16.626554

NAMIBIA 29.58342

MOZAMBIQUE 10.89028

MADAGASCAR 8.117444

MAURITIUS 13.706881

MALAWI 16.27484

TANZANIA 13.05131

ANGOLA 39.83309

Source: Own calculus based on data from African development indicators database.

Furthermore, table 2 presents statistics of correlation between government expenditure

and economic growth where data enter in logarithm terms and two versions of the

relationship are analysed country by country. LE represents log of government

expenditure, LEP indexes log of per capita government expenditure, LGDP and LGDPP

represent respectively log of GDP and per capita GDP.

Firstly, the correlation statistics reveal that there is a strong and positive relationship

between government expenditure and economic growth for the majority of SADC

countries with the exception of Zambia, Zimbabwe and Malawi where the relation is

negative and weak for the two first countries.

38

Lastly, like in the previous case, there is a negative but strong relationship between per

capita government expenditure and per capita growth in Malawi. However, the

correlation statistics reveal a positive relationship for the rest of the SADC countries and

show that this relationship between per capita government expenditure and per capita

growth is strong in Mozambique, Namibia, Lesotho, Swaziland, Botswana, Tanzania and

Mauritius. But, in South Africa, Zimbabwe, Zambia, Madagascar and Angola this

relationship is weak individually.

Table 2 Correlation statistics between government expenditure and economic growth for

individual SADC countries from 1988 to 2004

COUNTRY

LE.LGDP

CORRELATION

LEP. LGDPP

CORRELATION

SOUTH AFRICA 0.85224 0.372879

ZIMBABWE -0.15462 0.365378

ZAMBIA -0.45517 0.574404

BOTSWANA 0.994525 0.988175

SWAZILAND 0.799275 0.343488

LESOTHO 0.86492 0.758895

NAMIBIA 0.977318 0.493617

MOZAMBIQUE 0.853501 0.697153

MADAGASCAR 0.659605 0.8823

MAURITIUS 0.998417 0.997348

MALAWI -0.52556 -0.83092

TANZANIA 0.884285 0.919315

ANGOLA 0.808844 0.406

39

Chapter 4

Empirical results

4.1 Introduction

This chapter discusses empirical findings on the relationship and causality between

government expenditure and economic growth for SADC countries from 1988 to 2004.

As mentioned in the previous chapter, the necessary condition for the long-run

relationship between the aforementioned variables to hold is that a thorough investigation

of stationarity for each panel series must be carried out on one hand. On the other hand,

panel series must be integrated of same order. Following this, the chapter presents

empirical results on panel unit roots in section 4.2. Section 4.3 exposes the results on

panel cointegration tests. Section 4.4 presents the reports on estimates according to the

pooled least squares and the random effects estimator. The reports on causality between

government expenditure and economic growth in the SADC region according to the

approach of Granger causality test are presented in section 4.5.

4.2 Panel unit roots results and order of integration

In table 3, panel unit root statistics of government expenditure and economic growth are

reported according to the LLC and IPS approaches. Following the LLC and IPS tests,

panel series of government expenditure and economic growth are both integrated of order

1 at 5 per cent, )1(~ ILEit ; )1(~ ILGDPit

40

Table 3 Panel series unit root tests for LE and LGDP

Order

Panel

series

Statistic LLC p-Value LLC Statistic IPS

p-Value

IPS

Level LEit - 1.45033 0.073483 0.31694 0.6244

First

difference LEit

-9.34573

0.000

-7.96817

0.000

Level LGDPit 1.715614 0.956884 0.000

0.958842

First

Difference LGDPit -7.33554 0.000 -6.60425 0.000

Likewise, statistical tests on panel series of per capita government expenditure (LE/P)

and per capita economic growth are reported in table 4. The statistical tests reveal that

LE/P and LGDP/P are both integrated of order 1 according to the IPS procedure at 5 per

cent. However, the LLC approach suggests that LE/P is stationary in level at 5 per cent.

Since the aim of the study is to assess the long-run relationship between LE/P and

LGDP/P in considering country-specific effects, the approach of IPS seems appropriate

as the null hypothesis suggests an individual unit root process within the panel.

Therefore, the study considers that LE/Pit and LGDP/Pit are both integrated of order 1,

)1(~/ IPLE it ; )1(~/ IPLGDP it

41

Table 4 Panel series unit root tests for LE/P and LGDP/P

Order Panel series

Statistic

LLC

p-Value

LLC

Statistic

IPS p-Value IPS

Level LE/Pit -2.8619 0.002106 0.371814 0.644984

First

DIFFERENCE LE/Pit - - -7.61712 0.000

Level LGDP/Pit 0.129911 0.551682 0.129911 0.551682

First

DIFFERENCE LGDP/Pit -6.86943 0.000 -5.83514 0.000

Following the reports on table 3 and 4, the necessary condition for assessing the

possibility of long-run comovement between LEit and LGDPit as well as LE/Pit and

LGDP/Pit is fulfilled as all panel series under investigation are integrated of the same

order. The next step consists of testing the cointegration using the Pedroni approach as

well as the Kao panel cointegration test.

4.3 Panel cointegration results

This study uses the residual based panel cointegration tests according to the Pedroni

approach and the Kao cointegration test.

4.3.1 Pedroni panel cointegration results

The table 5 presents the Pedroni cointegration tests results between government

expenditure and economic growth. According to the Panel ADF- stat, there is

cointegration between government expenditure and economic growth at 5 per cent. The

computed statistic is greater than - 1.65 and the probability value is less than 0.05, hence

the null hypothesis of no cointegration is rejected. Conversely, the Group ADF-stat result

suggests that there is no cointegration between government expenditure and economic

42

growth at 5 per cent level. Therefore, the conclusion is reached when these results are

compared with those from Kao’s procedure in 4.4.2.

Table 5 Pedroni panel cointegration tests between LE and LGDP

Test Statistic Probability Weighted Stat. weighted Prob.

Panel ADF-

Statistic -3.90362 0.000196 -1.70822 0.09274

Group ADF-

Statistic -1.15375 0.20505 - -

Lastly, Pedroni panel cointegration tests between logarithm of per capita government

expenditure (LE/P) and per capita economic growth (LGDP/P) are presented in table 6.

Following the Pedroni approach of Panel ADF and Group ADF statistics, there is indeed

cointegration between LE/Pit and LGDP/Pit in 13 SADC countries from 1988 to 2004.

This is justified as in both Panel and Group ADF statistics the probability value is smaller

than 0.05 and the computed statistics are both greater than the critical statistic (-1.65),

hence the null hypothesis of no cointegration is rejected at 5 per cent.

Table 6 Pedroni panel cointegration tests between LE/P and LGDP/P

Test Statistic Probability Weighted Stat. Weighted Prob.

Panel ADF-

Statistic -3.69481 0.000433 -2.22536 0.033539

Group ADF-

Statistic -2.88309 0.006251 - -

In general, the Pedroni panel cointegration procedure reveals that indeed, the long-run

relationship between government expenditure and economic growth in a panel of 13

countries when the within dimension is considered (Panel ADF test) of SADC holds

according to the Wagner’s law of government size for all three versions specified in this

43

study. But, taking into consideration the between dimension (Group ADF test),

cointegration is proved to exist only between LE/P and LGDP/P and not between LE and

LGDP. The next section presents results on Kao cointegration test in order to reach a

conclusion on whether indeed there is cointegration for both pairs of panel series

abovementioned

4.3.2 Kao panel cointegration test

Reports on the Kao panel cointegration test (table 7) reveal that indeed there is

cointegration at 5% level of significance between government expenditure and economic

growth in the first instance, as the probability value is less than 0.05, as a result, that the

null hypothesis of no cointegration is rejected. Secondly, there is cointegration between

per capita government expenditure and per capita economic growth because the null of

no cointegration is rejected at 0.05 level of significance.

Table 7 Reports on Kao panel cointegration tests

LEit and LGDPit LE/Pit and LGDP/Pit

ADF t-statistic -1.74483

-2.04607

ADF p-value 0.0405

0.0204

Following the results from both the Pedroni and Kao panel cointegration tests, this study

finds that government expenditure and economic growth are related and move together in

the long-run for the sample of SADC countries. This implies, the regression based on two

nonstationary panel series is not spurious and hence the validity of Wagner’s law could

be assessed. Essentially, the direction of causality will be assessed as well.

44

4.4 Estimation results

The elasticity of government expenditure with respect to economic growth is estimated

according to three approaches in this study. Firstly, the study presents the pooled least

square estimate for both equations in parallel with fixed effects estimates. Secondly the

random effects estimates are presented followed by an evaluation of the consistency and

efficiency of the slope coefficient using the Hausman statistic.

4.4.1 Pooled least squares and fixed effects estimates

The tables 8 and 9 present respectively the reports on the pooled least squares estimates

and the fixed effects estimates for both equations. Additionally, the t-statistic and

probability values are corrected for autocorrelation and heteroscedasticity according to

Newey-West test, as the residuals exhibit serial correlation and their variance is not

constant.

The pooled least squares estimates reveal that the sign of the slope coefficients in both

models is positive and in conformity with the theory. This implies that a percentage

increase in economic growth results in the increase of 0.98 percent in government

expenditure for equation 1 and more than 1 percent for equation 2. Moreover, at 5 percent

level, the slope coefficients are significant. However, to satisfy the Wagner’s hypothesis

as stated by Goffman (1968), only equation 2 can be considered as relevant. In equation

2, the elasticity of government expenditure with respect to economic growth is

significantly greater than a 1 percent. However, the sufficient condition is to prove if

government expenditure is caused by economic growth.

Moreover, table 9 does not include the figures on specific effects for space reason. The

objective of table 9 is to allow a discussion on the eventuality of heterogeneity in SADC

as long as the relationship between government expenditure and economic growth is

concerned. Therefore, given the reports on the residuals sum of squares for both pooled

estimates and fixed effect, the F ratio is calculated as follows:

45

Equation 1

F(13, 206) = [(10.052 – 1.802)/13] / [(1.082)/206] = 72.52

where 13 is the number of cross-sections; 206 is the degree of freedom after estimation in

fixed effects. The F(13, 206) critical value in the tables is given by 1.76. Following this, the

null hypothesis of no statistical significance between the pooled and the fixed effects

estimators is rejected at 5 percent level as F computed is greater than F critical. This

implies that there is heterogeneity in the panel with regard to Equation 1.

Equation 2

F(13, 206) = [(7.534 – 1.716)/13] / [(1.716)/206] = 53.7254

The F(13, 206) critical value in the tables is given by 1.76. Similarly to Equation 1, equation

2 rejects the null hypothesis of no heterogeneity in the panel as the computed F statistic

is greater than the critical F value at 5 percent level of significance.

In consequence of the reported F-statistics for both equations, this study finds that the

thirteen countries of SADC under examination are not similar as far as government

expenditure and economic growth relationship is concerned. This implies that, the

estimation procedure should take into account the issue of country specific unobserved

patterns as additional variables in explaining the considered dependent variable.

Table 8 Pooled least squares estimates

Constant Slope R2 AJ. R2 RSS

Equation 1 -0.62943

(-1.85)

[ 0.0649 ]

0.983324*

(27.58)

[0.000]

0.853 0.852 10.052

Equation 2 -1.447*

(-8.203)

[0.00]

1.2306*

(20.47)

[0.00]

0.9054

0.9050 7.534

* indicates significance at 5% level The reported t-statistics are presented in between parenthesis, whereas the probability values are in between square brackets.

46

Table 9 Fixed effects estimates

Constant Slope R2 AJ. R2 RSS

Equation1 -0.717

(-0.970)

[ 0.3328]

0.992470*

(12.9)

[0.000]

0.973 0.972 1.802

Equation 2 -1.076602

(-3.22)

[0.00]

1.099*

(9.304)

[0.00]

0.978

0.977 1.716

* indicates significance at 5% level. The reported t-statistics are presented in between parenthesis, whereas the probability values are in between square brackets.

4.4.2 Random effects estimates

The table 10 presents the reports on the long-run relationship between government

expenditure and economic growth according to the random effects approach. The t-

statistic, given in between brackets, and the probability values in between square

brackets, are corrected using the cross-section SUR (seemingly unrelated regression) for

standard errors and covariance. The reason for using SUR is to take into account

heteroscedasticity and contemporaneous correlation in the error across cross-sections.

Following the random effects estimates, the sign of the slope coefficient in both equations

is positive and complies with the theory. This means that government expenditure and

economic growth are positively related in the SADC countries under the period of

investigation. Moreover, the size of the cointegrating parameter is significantly greater

than the one in equation 2 at 5 percent level and in equation 1, it is less than one. This

implies that a positive change of 1 percent in government expenditure, ceteris paribus,

will result in more that one percent increase of government expenditure for SADC

countries for equation 2.

47

Table 10 Random effects estimates Constant Slope R2 AJ.R2 Durbin-Watson

Equation 1 -0.688

(-1.455)

[0.147]

0.985*

(20.23)

[0.00]

0.527 0.525 0.55

Equation 2 -1.285*

(-5.108)

[0.00]

1.173*

(15.143)

[0.00]

0.502 0.50 0.589

* indicates significance at 5 percent.

The table 10 presents the Hausman test reports on comparison between fixed effects

estimator and the random effects. According to the random effect estimator, the null

hypothesis of consistency and efficiency of in equations 1 and 2 is not rejected at 5

percent level. This implies that, for equation 1 and equation 2, the random effects

estimator is appropriate. This is justified for the following reason. Firstly, the thirteen

countries under investigation constitute the sample data that is drawn from SADC

countries. Secondly, the error term is not correlated with the unobservable effects, which

render the coefficient slope consistent and efficient.

Table 11 Hausman test

Dependant

variable

Explanatory

variable

Fixed

effects

Random

effects

Variance P-value

LEit LGDPit 0.992 0.989 0.001 0.9465

LE/Pit LGDP/Pit 1.0993 1.1730 0.0069 0.3755

48

4.5 Reports on Granger causality test

Equation 1

The tables 12a and 12b present respectively the estimates of VECM for equation 1 and

the reports on Granger causality test. The procedure consists in the selection of the

maximum lag length in VAR. Given that the time frame of the data is relatively short (17

years) and the frequency is annual, the reasonable maximum lag length is assumed at one

period.

Following the reports in both tables 12a and 12b and the fact that causation takes place

from economic growth to government expenditure, the negative slope coefficient of

ECMit-1 indicates that panel series adjust themselves to the long-run equilibrium after a

deviation at relatively a high speed. Additionally, the fact that the slope coefficient of

ECMit-1 is significant at 10 percent level, this indicates that government expenditure is

Granger caused by economic growth in the long-run. The reported p-values for Chi-

square for only all lagged first difference of economic growth and for all lagged first

difference and ECMit-1 in the VECM are less at 5 per cent level, hence the rejection of the

null hypothesis of no Granger causality from economic growth to government

expenditure. This implies that economic growth Granger causes government expenditure

both in short and long-run in SADC.

Secondly, when causation takes place from government expenditure to economic growth,

the positive slope coefficient of ECMit-1 in table 12b indicates that once equilibrium

relationship is broken, panel series do not adjust thereafter to that equilibrium and since

this slope is insignificant, it translates that in the long-run government expenditure does

Granger cause economic growth. Besides, in the short-run government expenditure does

not Granger cause economic growth since the p-values for both reported Chi-square for

49

only all lagged first difference of government expenditure and for all lagged first

difference of government expenditure and ECTit-1 are greater at 5 per cent.

Therefore, Wagner’s law is validated in equation 1 as the statistics reveal that both in the

long and short-run, economic growth Granger cause government expenditure. The

Keynesian stance is however not confirmed in this instance.

Table 12a Vector error correction estimates equation 1

Source of

causation→

Constant ΔLEit-1 ΔLGDPit-1

ECMit-1

ΔLGDPit

0.0072 -0.0404

[ -0.561]

0.539*

[ 2.2756]

-0.031

[-1.613]

ΔLEit

0.0112 0.1108

[0.5117]

0.1898*

[ 2.6618]

0.0082

[1.3892]

Figures in brackets indicate the t-stat.

Table 12b VECM Granger causality test equation 1

Dependent variable Chi-sq for lagged coeff.of exp. Var.

Chi-sq for exp.var and ECTit-1

D(LE) 5.178391 [ 0.02295 ] 5.178391 [ 0.0229 ] D(LGDP) 0.261891 [ 0.6088] 0.261891 [ 0.6088]

Figures in [ ] indicate p-value

Equation 2

The tables 13a and 13b present respectively reports on VECM estimates and Granger

causality for model 2. The maximum lag length is determined at one period for the same

reason as in equation 1. Firstly, let us consider that causality runs from per capita

50

economic growth to per capita government expenditure. The statistics reveal that in long-

run, there is evidence of causality from per capita economic growth to per capita

government expenditure, since the slope coefficient of ECTit-1 is negative, which

indicates a return to equilibrium after shock and its correspondent t-statistic significant at

5 per cent level. Additionally, in the short-run, causality is again confirmed given that

the reported p-values of Chi-square are less at 5 per cent level. This implies a rejection of

the null hypothesis of no Granger causality from per capita economic growth to per capita

government expenditure in SADC countries under examination.

Nevertheless, when causation runs from per capita government expenditure to per capita

economic growth, there is no confirmation of causality in either the long or the short-run.

Firstly, the slope coefficient of ECTit-1 is positive and insignificant. Secondly, in the short

term, the reported p-values of Chi-square are greater at 5 per cent level, which implies

that the null of no Granger causality is not rejected at 5 percent level. In conclusion, the Wagner’s assertion of the relationship and causality from per capita

economic growth towards per capita government expenditure in SADC countries is

validated in both the long and short-run. Whereas the Keynesian point of view is not

confirmed in any case.

Table 13a Vector error correction estimates equation 2

Source of

causation→

Constant ΔLEPit-1 ΔLGDPPit-1

ECMit-1

ΔLGDPPit

0.0034 -0.0348

[ -0.483]

0.507*

[ 2.188]

-0.057

[-2.138]

ΔLEPit

0.0037 0.123

[0.556]

0.2259*

[ 3.181]

0.008

[0.9745]

Figures in brackets indicate the t-stat.

51

Table 13b VECM Granger causality test equation 2 Dependent variable

Chi-sq for lagged coeff.of exp. Var.

Chi-sq for exp.var and ECTit-1

D(LEP) 4.790677 [ 0.0286] 4.790677 [ 0.0286 ] D(LGDPP) 0.310113 [ 0.5776 ] 0.5776 [ 0..286 ]

Figures in brackets indicate p-value

52

Chapter 5

Conclusion

This study focused on testing the validity of the Wagner’s law of long-run relationship

between government expenditure and economic growth using econometrical techniques

of cointegration and Granger causality in the context of panel data for thirteen SADC

countries from 1988 to 2004. Subsidiary to this objective, this study tested, for

comparison purpose, the Keynesian stance of government expenditure as a policy tool in

hands of political authority to impact economic growth. Furthermore, this study presented in chapter 2 studies that, using econometric techniques,

empirically investigated the validity of Wagner’s law as well as the Keynesian stance.

Therefore, the literature reveals that the investigation of the long-run relationship

between government expenditure and economic growth is mostly carried out in the

context of time series rather than panel data. Following this, the approach of panel data

was used in this research in order to examine the cointegration and causality between

government expenditure and economic growth in SADC countries.

Prior to determining whether government expenditure and economic growth in SADC

countries are cointegrated, a univariate analysis was carried out in order to assess the

level of integration of each panel series. The rationale for this procedure is in the

compliance with cointegration theory which states that series must be integrated of same

level. Consequently, the study found that the panel series under investigation are all

integrated of the same order; hence the next step consisted of testing whether

cointegration exists by applying the Pedroni as well as the Kao panel cointegration tests

and the Granger causality test in order to determine causal and effect variable.

53

Besides, the findings of this study, after applying the described methodology, are

consistent with the Wagner’s law applicable to countries at their earlier stages of

development than the Keynesian stance for the following reasons. Firstly, this study finds

that indeed government expenditure and economic growth have a long-run relationship in

SADC countries. Secondly, there is a unidirectional causality from economic growth to

government expenditure in both the long and the short-run.

54

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