The relationship between foreign direct investment (FDI ...

The relationship between foreign direct investment (FDI)

and manufacturing exports and imports in South Africa

PIETER OPPERMAN

Research report presented in partial fulfilment

of the requirements for the degree of

Masters in Development Finance

at the University of Stellenbosch

Supervisor: Prof Charles Adjasi

Degree of confidentiality: A December 2012

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Declaration

By submitting this research report electronically, I, Pieter Opperman, declare that the entirety of the

work contained therein is my own, original work, that I am the owner of the copyright thereof

(unless to the extent explicitly otherwise stated) and that I have not previously in its entirety or in

part submitted it for obtaining any qualification.

16155904

P. Opperman

October 2012

Copyright © 2012 Stellenbosch University All rights reserved

Stellenbosch University http://scholar.sun.ac.za

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Acknowledgements

I would like to acknowledge and express my gratitude to my supervisor Professor Charles Adjasi

for his interest shown in the research report and for his ever timely feedback and guidance

throughout the year. I dedicate this thesis to my parents on whose support and encouragement I

could always count throughout my studies.


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Abstract

In recent years South Africa has started to embark on policies to increase FDI and boost the

country’s manufacturing sector. FDI inflows are important for their perceived role of bridging the

savings-investment gap, while increasing the country’s manufacturing capacity will help diversify

the economy and could contribute towards job creation. The literature has revealed that the debate

on causality between FDI and trade has not yet been resolved. Furthermore, the FDI/trade

relationship has not been adequately addressed in African literature.

The research study has investigated the causal link between FDI and manufacturing exports and

FDI and manufacturing imports in South Africa for the period 1994 – 2011. Unit root tests of

stationarity were performed on the respective time series and it was found that the included

variables are non-stationary at their levels, but stationary at first differences. Tests of cointegration

revealed that FDI and manufacturing exports as well as FDI and manufacturing imports and vice

versa were cointegrated, implying a long-run relationship between the two sets of variables. The

study then utilised causality tests based on the significance of the ECM coefficient as well simple

Granger causality tests in a bivariate setting.

The results indicate one-way causality from manufacturing exports to FDI and from manufacturing

imports to FDI. These results suggest that exports and imports of the manufacturing sector matter

in the locational inflows of FDI in South Africa. It is recommended that the South African

government should encourage FDI policies that have an export component or export strategy. This

could attract more FDI inflows that would close the investment gap in the manufacturing sector.


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Table of contents

Declaration ii

Acknowledgements iii

Abstract iv

List of tables vii

List of figures viii

List of acronyms ix

CHAPTER 1 ORIENTATION 1

1.1 INTRODUCTION 1

1.2 PROBLEM STATEMENT AND RESEARCH OBJECTIVES 4

1.3 OBJECTIVE OF THE STUDY 5

1.4 BROAD RESEARCH QUESTION 5

1.4.1 Sub Research Questions 5

1.5 IMPORTANCE OF THE STUDY 5

1.6 RESEARCH DESIGN AND METHODOLOGY 5

1.7 CHAPTER OUTLINE 7

1.8 CHAPTER SUMMARY 7

CHAPTER 2 LITERATURE REVIEW 8

2.1 THEORETICAL FRAMEWORK 8

2.2 EMPIRICAL LITERATURE 11

2.2.1 Determinants of FDI 11

2.2.2 Indirect (spillover) studies of FDI and exports 12

2.2.3 Direct studies of FDI and exports 14

2.2.4 FDI and trade 16


CHAPTER 3 FDI AND MANUFACTURING IN SOUTH AFRICA 19

3.1 FDI IN SOUTH AFRICA 19

3.2 MANUFACTURING IN SOUTH AFRICA 23

3.3 EXPORT PROMOTION 27


CHAPTER 4 RESEARCH METHODOLOGY 31

4.1 DATA COLLECTION 31

4.2 DATA ANALYSIS 31

4.2.1 Model 32


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4.2.2 Stationarity 32

4.2.3 Cointegration 34

4.2.4 Error Correction Model (ECM) 35


CHAPTER 5 RESULTS 36

5.1 STATIONARITY 36

5.2 COINTEGRATION 38

5.3 ECM 39

5.3.1 Granger causality 40

5.4 NON-ECONOMETRIC ANALYSIS OF MANUFACTURING LEVEL DATA 41

5.5 DISCUSSION 43


CHAPTER 6 CONCLUSION 45

6.1 SUMMARY 45

6.2 DISCUSSION 46

6.3 RECOMMENDATIONS 47

6.4 LIMITATIONS OF THE STUDY AND POSSIBLE FUTURE RESEARCH 48

REFERENCES 49

APPENDIX A1 ADF TESTS 55

APPENDIX A2 ADF TESTS ON FIRST DIFFERENCES 58

APPENDIX A3 COINTEGRATION TESTS 61

APPENDIX A4 ECM 62


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List of tables

Table 3.1: FDI inflows and outflows (US$bn) 19

Table 3.2: Regional sources of South African FDI, 1994–2005 20

Table 3.3: Individual country sources of FDI, 1994–2005 21

Table 3.4: Total direct investment in manufacturing as at 31 December (Rm) 21

Table 3.5: FDI related policies per institution 22

Table 3.6: Manufacturing sub-sectors’ share of total manufacturing by real value added (%) 25

Table 3.7: Total South African manufacturing trade (Rbn) 26

Table 5.1: ADF test results 37

Table 5.2: ADF test results for first differences 37

Table 5.3: Causality test results based on the significance of ECM coefficient 40

Table 5.4: Descriptive statistics 42

Table 5.5: Correlation coefficient 43


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List of figures

Figure 3.1: South African manufacturing trade balance 26

Figure 3.2: South African exports to Brazil, China and India 29

Figure 3.3: South Africa’s exports to Brazil, China and India as a percentage of total exports 30

Figure 5.1: Quarterly logarithms of FDI, EXP and IMP, South Africa, 1994–2011 36

Figure 5.2: Regression of LFDI and LEXP 38

Figure 5.3: Regression of LFDI and LIMP 38

Figure 5.4: PP unit root test on S (residuals of regression of LFDI and LEXP) 39

Figure 5.5: PP unit root test on S1 (residuals of regression of LFDI and LIMP) 39

Figure 5.6: Granger causality test of LFDI and LEXP 41

Figure 5.7: Granger causality test of LFDI and LIMP 41

Figure 5.8: Annual manufacturing FDI stock data, 1997–2010 (Rmillions) 42

Figure 5.9: Annual manufacturing stock data, manufacturing exports and manufacturing imports,

1997–2010 (Rmillions) 43


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List of acronyms

ADF augmented Dickey-Fuller

AEG augmented Engle-Granger

AIEC Automotive Industry Export Council

ANC African National Congress

ARDL autoregressive distributed lag

BRICS Brazil, Russia, China, India and South Africa

COSATU Congress of South African Trade Unions

CSA customs secured area

DSM decision support model

DTI Department of Trade and Industry

ECM error correction mechanism

ECT error correction term

EG Engle-Granger

EMIA Export Marketing & Investment Assistance Scheme

EPZ export processing zone

EXP manufacturing exports

FDI foreign direct investment

GDP gross domestic product

GEAR Growth, employment and redistribution

IDZ Industrial Development Zone Programme

IMP manufacturing imports

IPAP Industrial Policy Action Plan

ISC industries and services corridor

LEXP natural logarithm of manufacturing exports

LFDI natural logarithm of foreign direct investment

LIMP natural logarithm of manufacturing imports

MENA Middle Eastern and North African

MIDP Motor Industry Development Programme

MNE multinational enterprise

MNEs multinational enterprises

NAACAM National Association of Automotive Component and Allied Manufacturers

NGP New Growth Path

NPC National Planning Commission

OLI ownership, location and internalisation

OLS ordinary least squares

PP Phillips-Perron


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R&D research and development

ROI return on investment

SADC Southern African Development Community

SARB South African Reserve Bank

SATIEC SA Textile Industry Export Council

TIPS Trade & Industrial Policy Strategies

TISA Trade and Investment South Africa

UNCTAD United Nations Conference on Trade and Development


CHAPTER 1

ORIENTATION

1.1 INTRODUCTION

The integration of foreign direct investment (FDI) into trade theory has been the focus of recent

trade related theoretical studies (Bezuidenhout & Naude, 2008:1). Trade theories are concerned

with why countries trade with each other while FDI theories attempt to explain the pattern of FDI

between countries, i.e. why a firm would invest in production facilities in a foreign country (Hill,

2007:238). FDI is important for recipient countries as it closes the investment gap as well as

facilitates the transfer of new technologies and best practices (Yao, 2006:340). In attracting export-

oriented FDI, export promotion policies should not be neglected (UNCTAD, 2005:57). The

Southeast Asian experience has demonstrated the success of having an export orientation as part

of industrial policy (AbuAl-Foul & Soliman, 2008:5). The rationale for export-oriented industries is

further enhanced by the pace of urbanisation (UNCTAD, 2005:55).

South Africa appears to have embarked on policies to increase FDI and export flows. The recently

launched South African New Growth Path (NGP) consists of a package of macroeconomic and

microeconomic policy interventions. Development trade policies are part of the microeconomic

package and seek to promote exports (Republic of South Africa, 2010b:24). An industrial policy

that focuses on exports, as opposed to import replacement, has the potential of leveraging global

demand and by implication needs to be competitive (Laubscher, 2010:3). South Africa’s

manufacturing exports increased by nearly 12 percent in 2010 to R321bn after a decrease of

almost 29 percent in 2009. Regionally the European Union was the largest recipient of South

African manufacturing exports with the United States of America (USA) being the largest single

recipient country (Republic of South Africa, 2011).

In Africa, FDI as capital source has grown in importance for its perceived role of bridging the

savings-investment gap and the ability of assisting the attainment of Millennium Development

Goals (Ayayi, 2006:12). Hence, a major part of developing strategies for developing countries has

been to promote and attract FDI.

FDI in being a source of long-term capital for investment in infrastructure and other development

initiatives also provides the following ripple effects (Ernst & Young, 2011:8):

Job creation. The literature stated that FDI has helped create 1.6m new jobs in Africa the

previous eight years.

Developing local suppliers. Through local procurement policies, the supply chains of local

providers can be extended.


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Facilitating skill, technology and knowledge transfer.

Being a catalyst for economic diversification – something which would benefit most African

countries whose economies are mostly reliant on natural resources.

The literature on FDI identified four main motives for investing abroad, namely resource seeking,

market seeking, efficiency seeking and strategic asset seeking (Ajayi, 2006:15). Resource seeking

FDI pertains to investors locating abroad to secure inputs of raw materials while the goal of market

seeking FDIs is to open new markets in FDI recipient countries. Efficiency seeking FDI has the

goal of producing in as few countries as possible with each country having its own advantages of

location, endowment and government incentives. Finally, strategic asset seeking FDI chooses

locations where advantage can be taken of research and development and other benefits.

Ruane (2008:66) stated that globalisation has limited host countries’ policy options regarding

attracting FDI and has resulted in the liberalisation of product- and capital markets. Trade and

exchange rate policies may henceforth be of limited use. Challenges for smaller host economies

are increasing competition from other host economies seeing that smaller economies are

constrained by fiscal and political considerations. Furthermore, calculating the benefits and costs

associated with FDI incentives poses a further challenge as well as the ability of host economies to

clearly show how FDI will contribute to the economic development of the host country (Ruane,

2008:71).

Opportunities for smaller host economies to attract FDI require that, as globalisation and the

liberalisation of product- and capital markets continue, the size of the domestic market becomes

less of a factor considered by multinational enterprises (MNEs) (Ruane, 2008:72). Ruane noted an

apparent shift from market seeking- to efficiency seeking FDI.

However, relatively little is known empirically about FDI’s effect on the export behaviour of firms in

African countries (Abor, Adjasi & Hayford, 2008:446). Prior African studies revealed that FDI

positively affected either manufacturing export performance or export growth (Abor et al., 2008;

AbuAl-Foul & Soliman, 2008; Ancharaz, 2003). A South African study that investigated the

relationship between agricultural FDI and agricultural exports revealed a bi-directional causality

(Dlamini & Fraser, 2010). The essence of this research report was to examine the link between FDI

and manufacturing exports in South Africa. It has expanded on the work by Dlamini and Fraser by

concentrating on manufacturing exports and has also incorporated imports as has been suggested

in the theoretical literature.

After the 2009 general elections in South Africa a new power block within the ruling African

National Congress (ANC) emerged and has led to certain economic policy interventions by the

South African government. The NGP and the Industrial Policy Action Plan (IPAP2) being the


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aforementioned. The incumbent President Zuma, owing much of his support to alliance partner, the

Congress of South African Trade Unions (COSATU), duly created a Department of Economic

Development with a former union leader as Minister (Hirsch, 2011:56). The NGP, under the ambit

of the Department of Economic Development, has job creation as main concern with the goal of

creating five million jobs within the next decade (Republic of South Africa, 2010b:8). With an official

unemployment rate back up to 25 percent in the first quarter of 2011, it is not surprising that the

President indicated that 2011 would be the year of job creation as unemployment is politically

untenable. The NGP, by incorporating IPAP2, targets labour-absorbing activities across various

economic sectors, of which manufacturing is one. Increasing manufacturing capacity will diversify

the economy from a dependence on commodities and could provide opportunities for faster

productivity growth (UNCTAD, 2005:55).

As the South African government aims to increase its fixed investment from 20 to 25 percent of

gross domestic product (GDP), of which FDI comprises three to five percent, targeted initiatives

with other member countries of BRICS are being sought (Lund, 2011a:20). More specifically, the

state has targeted R115 billion in foreign investment within three years, this figure being part of the

Trade and Industry Minister’s performance contract signed with the President (Ensor, 2011).

According to the Minister, this target will be met by his department. Total FDI inflows in South

Africa have recently been declining. After increasing from $5.7bn in 2007 to $9bn in 2008, FDI

declined to $5.7bn and $1.3bn in 2009 and 2010 respectively (African Economic Outlook, 2011;

Hazelhurst, 2011:13). The decrease in FDI inflows in South Africa was in contrast to developing

countries overall where FDI in total increased by ten percent during 2010 (Hazelhurst, 2011:13).

In 2008, manufacturing accounted for the largest share of the production base in the South African

economy with those sectors that are capital- and energy intensive, performing relatively well

(Republic of South Africa, 2010a:11).

During the economic boom years of the previous decade, job creation mainly occurred in the

wholesale, retail and business service sectors with the former two service sectors largely reliant on

private credit extension (Republic of South Africa, 2010a:12).

Hence, many jobs were lost when the extension of credit contracted and this is a further indication

that consumption driven growth needs to be replaced by a stronger production base (Republic of

South Africa, 2010a:12). Economic growth further declined because of a decrease in trade and

thus South Africa experienced both a developed country credit contraction as well as a developing

country trade and investment decline (Hirsch, 2011:56).

A further consequence of the global economic crisis was that patterns of trade and investment

have changed as can be seen in the economic recovery of certain emerging market economies

(Republic of South Africa, 2010b:4). With South Africa having recently joined the group of BRICS


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countries – Brazil, Russia, China, India and South Africa – opportunities and challenges abound

which South Africa has to leverage to enhance trade and investment. Opportunities include

exposure to more export markets, while the current trade relationship with China could be seen as

a challenge.

Encouragingly, South Africa and China signed a Comprehensive Strategic Partnership in 2010

which is aimed at improving the current structure of trade between the two countries (Republic of

South Africa, 2010b:4). At a recent news conference the Trade and Industry Minister stated that

the South African government has indicated to China that it must increase its imports of

manufactured value-added products (Roos, 2011). BRICS membership should not only be about

political clout, but also translate into tangible economic benefits for South Africa. As wages

continue to rise in China and other parts of Asia, could Africa attract FDI in manufacturing? (Lund,

2011b:20). Some commentators have argued that China is already shedding manufacturing jobs,

pointing out Bangladesh’s garment exports are up to $12 billion from $5 billion in 2002 (Norbrook,

2010:67). Other reasons put forward why China will shed manufacturing jobs in the near future

include demographics, value-chain ambitions and currency pressure (Norbrook, 2010:70).

Therefore, could manufacturing exports also be a pre-requisite to attract FDI?

1.2 PROBLEM STATEMENT AND RESEARCH OBJECTIVES

The empirical work of Ahmed, Cheng and Messinis (2010) and Anwar and Nguyen (2011b) have

highlighted the need to take into account imports as well as exports when investigating the

relationship between FDI and manufacturing exports. As the previously mentioned studies have

shown, the interrelatedness of FDI, exports and imports necessitated the inclusion of imports.

Pacheco–Lopez (2005:1171) noted that possible bi-directional causality might not only exist

between exports and FDI, but also between imports and FDI. The latter can be explained as

follows - if imports signify evidence of an existing market, FDI might be attracted to the country in

order to produce the product locally. In turn, FDI could also stimulate imports as MNEs after having

been established in a country start importing certain supplies in order to satisfy internally required

standards.

The objective of the research was to investigate the relationship between FDI and manufacturing

trade in South Africa; specifically the causal link between manufacturing FDI, manufacturing

exports and manufacturing imports in order to recommend commensurate policies.

The debate on causality between FDI and trade has not yet been resolved (Bezuidenhout &

Naude, 2008:16). The research report aims to contribute regarding the FDI/trade causality debate

seeing that a lack of empirical studies, especially in South Africa, exists. The main question raised

is whether FDI contributes to promote manufacturing exports in South Africa.


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1.3 OBJECTIVE OF THE STUDY


trade in South Africa; specifically the causal link between manufacturing FDI, manufacturing

exports and manufacturing imports in order to recommend commensurate policies.

1.4 BROAD RESEARCH QUESTION

Does FDI promote manufacturing trade in South Africa? If so what kind of international trade does

FDI promote; exports or imports or both?

1.4.1 Sub Research Questions

Are there causal links between FDI and manufacturing exports in South Africa?

Are there causal links between FDI manufacturing imports in South Africa?

What is the nature of the link between FDI and manufacturing exports or imports in South

Africa?

1.5 IMPORTANCE OF THE STUDY

The current research project is relevant given the importance of the manufacturing sector for South

Africa in relation to the NGP and IPAP2 as the country seeks to broaden its production base and

lessen its dependence on commodities. Government’s proposed new legal framework for FDI is

further impetus for the current field of research. The National Planning Commission’s (NPC’s)

National Development Plan Vision for 2030 also identified labour intensive industries,

manufacturing and exports as those areas that have the greatest potential for growth and job

creation (GIBS, 2011:49)

Wider manufacturing capacity offers prospects of integration across different economic sectors,

between the urban and rural economies and consumers, intermediate and capital goods industries

and could create a demand structure connecting domestic wages and consumption with domestic

production (UNCTAD, 2005:55). The report noted that late-industrialising economies, by adopting

such a pattern of internal integration, attracted manufacturing FDI.

Of importance is that if a causal direction is known between FDI and manufacturing trade, an

investment-attraction programme specific to the manufacturing sector can be mapped that could

translate to increased FDI inflows, bolstering the government’s aim of increasing fixed investment.

1.6 RESEARCH DESIGN AND METHODOLOGY

The related literature review has revealed that FDI, manufacturing exports and manufacturing

imports are related. In addition, a complementary relationship between FDI and exports is


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predicted by an export platform model (Abor et al., 2008:452). The following causality relationships

are to be tested in a bivariate setting:

FDI ↔ EXP

FDI ↔ IMP

Where,

FDI is the aggregated FDI flow data;

EXP is manufacturing exports;

IMP is manufacturing imports.

Time series data of FDI in the manufacturing sector, manufacturing exports and manufacturing

imports are relevant for the current study and thus had to be considered when choosing the

appropriate method. Based on the theoretical and empirical literature review, it is expected that FDI

and trade hold some long-run relationship. Therefore, possible co-integrating relationships may

exist.

A natural starting point for time series data analysis was to test for stationarity of the particular time

series involved (Aziakpono, 2008: 198). According to Brooks (2008: 326), most economic time

series contain a single unit root and are thus non-stationary. Popular unit root tests are the Dickey-

Fuller (DF) test and the augmented Dickey-Fuller (ADF) test. In the ADF test, the lagged difference

terms of the regress are added to take into account possible serial correlation of the error terms

(Gujarati & Porter, 2009:758).

Co-integration between two time series occurs when their linear combination cancels out the

stochastic trends in the series (Gujarati & Porter, 2009:762). A linear combination of variables

containing a unit root will be stationary if the variables are cointegrated (Brooks, 2008: 336). As a

result the regression of one variable on another would be meaningful and not spurious.

The direction of causation between FDI and manufacturing trade is of interest. The causality test

method to use depends on the stationarity or not of the time series, and if non-stationary, whether

the time series are cointegrated or not (Aziakpono, 2008:200). According to Bashier and Bataineh

(2007:12), if the test results determine that the variables are integrated of order one and not

cointegrated, a Granger causality test should be implemented using the first differences of the

variables. They further posit that should the variables be stationary and cointegrated, an error

correction model should be used. The error correction mechanism (ECM) reconciles the short-run

behaviour of an economic variable with its long-run behaviour (Gujarati & Porter, 2009:769).

To summarise, economic time series are involved that, according to Brooks (2008:326), would

suggest non-stationarity. A unit root test was conducted to confirm the suspected non-stationarity


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or not. Following the unit root test, the time series were tested for cointegration after which the

appropriate causality test method was employed.

Software requirements for the research project were essentially EViews 7 and spread sheets. As

only secondary data was used and as all the data is available in the public domain, issues of

ethical considerations are not applicable.

1.7 CHAPTER OUTLINE

This research report is divided into six chapters. Chapter 1 has provided the introduction and

overview. Included were the context, problem statement and research design of the research

report. In Chapter 2 the related theoretical and empirical literature will be reviewed. Chapter 3 will

discuss FDI, the manufacturing sector and export promotion in South Africa post-apartheid; while

Chapter 4 will cover issues pertaining to the research methodology employed. Chapter 5 will

present and discuss the empirical results with Chapter 6 to conclude. The conclusion will seek to

address the limitations of the research project, possible policy implications arising and highlight

future research avenues.

1.8 CHAPTER SUMMARY

Chapter 1 has provided an introduction to the research report. The problem statement and

research objectives were stated next. The importance of the research report and the research

design were discussed and the chapter concluded with the chapter outline.


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CHAPTER 2

LITERATURE REVIEW

2.1 THEORETICAL FRAMEWORK

Initial research regarding the relationship between FDI and trade has focussed on whether the

relationship is complementary or whether FDI and trade act as substitutes (Xuan & Xing,

2008:190).

The seminal work of Mundell (1957) demonstrated capital mobility as a substitute for trade.

Accordingly, price differentials in goods and factor markets would be eliminated by factor mobility

as a result of differences in factor prices between countries. Thereafter, trade impediments

enhance factor movements and vice versa, resulting in FDI and exports being alternative means of

involvement in foreign markets (Bajo-Rubio & Montero-Munoz, 2001:312).

Markusen (1983) presented several models where factor mobility lead to an increase in world

trade. He stated that the models shared a common characteristic in that the basis for trade was not

brought about by differences in relative factor endowment. Other bases for trade put forward

include imperfect competition, production and factor taxes, returns to scale, different production

technology and factor market distortions. It is argued that in all these cases factor mobility leads to

differences in factor proportions which in turn indicate an additional motive for trade in goods (Bajo-

Rubio & Montero-Munoz, 2001:312).

Consequently, Markusen (1983) concluded that the idea of trade in goods and factors being

substitutes is a result and general characteristic of factor proportion models. In addition, Goldberg

and Klein (1999:2) indicated that subsequent theoretical work where models diverge from the

standard Heckscher-Ohlin-Samuelson model assumptions (which emphasises differences in factor

endowments), as used by Mundell (1957), can lead to findings of a complementary relationship.

The product-life cycle theory suggests that FDI and trade are substitutes (Vernon, 1966).

According to the theory, firms will undertake FDI at different stages in the product’s life cycle. In

Vernon’s time most new products were developed and introduced in the USA. His argument was

that as demand in the USA matures, cost considerations become the main concern and thereby

switch the locus of production to other developed and developing countries (Hill, 2007:182). The

USA then ceases being an exporter of the product. Another earlier theory used to explain FDI is

from Knickerbocker (1973) that suggests FDI can be explained from the perspective that firms

operating in oligopolistic industries imitate each other (Hill, 2007:249). The link between FDI and

trade is, however, not addressed.


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According to Bajo-Rubio and Montero-Munoz (2001:312), theories of the multinational enterprise

(MNE) first propagated by Hymer (1976) stated that MNEs must have some specific advantage

over local firms in the host country. They further posited that because of such an ownership

advantage it would be beneficial for the MNE to internalise it within the firm by way of FDI, on

condition that the foreign country has a location advantage over the local country, making FDI

preferred over exporting. The essence of Dunning’s ownership, location and internalisation (OLI)

paradigm is apparent.

Dunning’s OLI paradigm was the dominant analytical framework for accommodating economic

theories of the determinants of FDI and trade in the last part of the 20th century (Dunning,

2000:163). According to the OLI, or eclectic paradigm, foreign production of MNEs is determined

by the interaction of the interdependent three OLI variables, which comprise three sub-paradigms

as well.

Ownership (O), the first sub-paradigm, avers that MNEs with greater competitive advantage,

relative to other MNEs and domestic firms in the foreign country seeking to invest, are more likely

to engage or increase foreign production. The location (L) sub-paradigm avers that the more MNEs

resource endowments (natural resources, labour, etc.), which need to be used in conjunction with

their competitive advantage, favour a foreign location, MNEs will engage in FDI to exploit that

competitive advantage. The internalisation (I) sub-paradigm concerns ways in which MNEs

organise and exploit their competitive advantage, given the location advantages of the foreign

location. MNEs will engage in FDI rather than licensing, the greater the benefits of internalising

foreign intermediate product markets (Dunning, 2000:164). Internalisation is a way to protect

MNEs’ competitive advantage (Anwar & Nguyen, 2011a:179).

Di Mauro (2000:3) stated that the main problem concerning the OLI framework is that although

explaining the existence of MNEs, explaining more recent trends in FDI has become problematic.

He further elucidated that there has been a surge in FDI among similar countries, implying

horizontal FDI, and furthermore stated that there is an absence of soundly generated empirical

models to compare real data with theory.

Helpman (1984) incorporated OLI considerations in developing a simple general equilibrium model

of international trade. Ownership and location advantages are combined in a monopolistic

competition model that includes horizontally differentiated goods and where MNEs develop specific

and specialised inputs, i.e. management, marketing and product specific R&D, that cannot be

traded (Bajo-Rubio & Montero-Munoz, 2001:312). Existing differences in factor endowments will

enable firms from the country that is relatively abundant in headquarter services to become MNEs

resulting in both intra-industry trade in differentiated products to appear as well as intra-firm trade

in specialised inputs to appear (Bajo-Rubio & Montero-Munoz, 2001:312).


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Ethier (1986) incorporated direct investment in a simple general equilibrium model of international

trade with the analysis attempting to endogenise the internalisation decision. In contrast to

Helpman (1984), Ethier (1986) found that both greater uncertainties faced by the firm as well as a

greater similarity in factor endowments between countries will more likely translate in FDI that

leads to two-way FDI and relatively higher intra-industry and intra-firm trade (Bajo-Rubio &

Montero-Munoz, 2001:313).

New trade theory that emerged in the 1970s differs from the Heckscher-Ohlin theory which

highlights the importance of endowment of production factors for trade (Hill, 2007:186). The

importance of economies of scale and first mover advantages are emphasised by new trade theory

(Hill, 2007:184). Later new trade theory models incorporated FDI and thus trade and FDI were

classified as horizontal or vertical (Abor et al., 2008:451; Xuan & Xing, 2008:191).

According to Di Mauro (2000:3), new trade theory refers mainly to ownership and location

advantage that arises endogenously and where MNEs are introduced in general equilibrium

models. He posited that exponents of the theory’s early literature derived the activities of MNEs

when they tried to explain intra-firm trade being an additional component of international trade. The

theory assumed transport costs to be zero and MNEs to split their production process between a

headquarter activity and the foreign plant production. The rationale for multinational activities to

arise comes about from the factor proportions in the two MNE activities that differ and are

recognisable as vertical FDI where firms will take advantage of factor price differentials across

countries and separate the production process (Di Mauro, 2000:3).

Horizontal FDI is investment in the same industry at a foreign location, thus a duplication of the

production process while with vertical FDI the production process is decomposed into stages. To

attain economies of scale, production is located at different locations to exploit differences in factor

cost. Of importance concerning the relationship between FDI and trade is that horizontal FDI

reduces or substitutes trade while vertical FDI stimulates or complements trade (Aizenman & Noy,

2006:318). The same authors posited that according to economic reasoning, horizontal FDI would

be more common among industrialised countries, while vertical FDI would be found more often

between industrialised and developing countries.

The link between trade and FDI has received further attention following a recent contribution by

Helpman, Melitz and Yeaple (2003). They developed a model of international trade through which

firms can serve the local market, export, or engage in FDI with the aim to serve foreign markets.

The Helpman et al. (2003) model stressed the impact of heterogeneity on internationalisation

decisions and to test the implications of the model would require firm- or bank-level data (Buch &

Lipponer, 2007:807).


11

According to Helpman et al. (2003:28), because of heterogeneous firms, different productivity

levels exist that produce different organisational forms. The least productive firms would cease to

exist; low productivity firms would serve the local market with the remaining firms serving the local

and foreign markets. However, the mode of operation in foreign markets would differ with the most

productive firms in the group choosing the FDI route with less productive firms exporting.

Furthermore, FDI is assumed to be horizontal with the model containing a proximity-concentration

trade-off that predicts that with higher economies of scale or lower trade frictions, foreign markets

would be served more by exports relative to FDI.

The integration strategies of MNEs have become more complex in recent years, therefore the

traditional classification of FDI as horizontal or vertical has become less meaningful in practice

(Helpman, 2006:590). In particular, MNEs are investing in countries with a lower cost base to

create export platforms to serve other countries around the globe, resulting in large flows of FDI

across industrial nations that cannot be suitable classified as horizontal FDI (Helpman, 2006:591).

According to Abor et al. (2008:452), a complementary relationship between FDI and domestic

country exports is predicted by an export platform model.

2.2 EMPIRICAL LITERATURE

2.2.1 Determinants of FDI

The following section will review some of the determinants of FDI as found in the empirical

literature.

A South African study by Rusike (2007:73) revealed that long run determinants of FDI were

openness, the exchange rate and financial development. Increased openness and financial

development would attract FDI while a depreciating exchange rate would deter FDI to South Africa.

Policy recommendations were a relaxation of exchange controls and easing regulatory burdens for

foreign investors. The appeal of three cabinet ministers to the Competition Tribunal’s approval of

the R16,5bn Wal-Mart/Massmart merger in 2011 is therefore regrettable from an FDI viewpoint.

Fedderke and Romm (2006:738) investigated the growth impact and determinants of FDI in South

Africa from 1956–2003. Positive technological spillovers from foreign to domestic capital were

found with FDI in South Africa tending to be capital intensive, suggesting horizontal FDI.

Determinants of FDI found from their empirical findings allowed for the following policy

prescriptions: reducing political risk, lowering corporate tax rates, wage moderation and

strengthening growth in market sizes. The openness of the economy also impacted strongly on FDI

with increasing imports lowering FDI and increasing exports raising FDI.


12

In Mauritius, trade openness, wages and the quality of the country’s labour were found to be

instrumental factors (Seetanah & Rojid, 2011:24). A lesson for the South African context is that the

state should ensure that labour costs remain competitive and should not appreciate faster relative

to other FDI recipient countries. Singhania and Gupta (2011:79) found adjusted GDP, the inflation

rate and research and development to have significant impact on FDI inflows in India.

The determinants of FDI were investigated in another emerging economy, Brazil. De Angelo, Eunni

and Fouto (2010:203) found that aggregate consumer sales as proxy for internal market growth,

were a significant determinant of FDI inflows. The practical implication of the study was that in

larger emerging market economies, policy makers should avert using fiscal and monetary policies

to attract FDI but rather stimulate growth of their internal markets.

Morris and Aziz (2011:408) echoed the sentiments of De Angelo et al. (2010), namely that it seems

as if large multinationals are not always persuaded by ease of doing business factors when making

investment decisions regarding African and Asian countries. Asia, especially China and India, have

millions of middle class consumers that would provide a market for the goods and services of

multinationals. Currently Africa lacks the domestic markets compared to other BRIC economies.

Foreign investment decisions are complex and producing a list of determining factors cannot be

exhaustive. Ajayi (2006:16) stated that there is no unanimously accepted single factor that

determines the flow of investment. He summarised that FDI is influenced by factors that range from

the size of the market, quality of labour, the availability of resources and infrastructure and

institutions.

2.2.2 Indirect (spillover) studies of FDI and exports

It is important to make a distinction between the direct- and indirect (spillover) effect of FDI on

exports. The effect of FDI on the manufacturing exports of South Africa would be an example of

the direct effect. Most spillover studies focus on either technology spillovers or export spillovers

from FDI. Domestic firms becoming more export-oriented as a result of FDI would be an example

of positive export spillover effect (Anwar & Nguyen, 2011a: 177).

Most export spillover studies revealed positive spillovers from FDI (Sun, 2009:1203). More than a

decade ago Head and Ries (1999:2) already noted the aforementioned. They stated that most

theories of FDI postulated that firms would choose the mode of entry into foreign markets that

would yield the highest profit, depending on factors such as transportation costs, trade barriers and

fixed costs. Intuitively, one would thus expect FDI to displace exports. However, most empirical

work up to that point in time indicated a complementary relationship where countries, industries

and firms that invest heavily in foreign countries were also the source of large volumes of exports

to those FDI recipient countries (Head & Ries, 1999:2).


13

In their empirical work, Head and Ries (1999:19) stated that addressing the issue of a spurious

statistical relationship was an important aspect when investigating the effects of FDI on exports.

They argued that it would be incorrect to interpret a simultaneous rise in FDI and exports as proof

of a complementary relationship. In addition, firms that have superior products could follow both

modes of entry and as such it is not indicative that FDI generates more exports. Head and Ries

used a panel dataset of 25 years to address the previous issues and employed fixed firm effects

that would enable them to identify the influence of FDI on exports based on within-firm variation. In

addition, only lagged effects of investment on current exports were considered and

macroeconomic shocks affecting all firms were controlled for.

As most other studies up to that point in time, Head and Ries (1999:19) found FDI and exports to

have a complementary relationship. The panel dataset contained data on 932 Japanese firms and

Head and Ries demonstrated important differences across firms. Firms least likely to supply

foreign production facilities with intermediate inputs were less likely to have FDI stimulate exports.

Abor et al. (2008:446) used a probit model to indicate FDI’s positive influence on the export

decisions of Ghanaian firms. Using panel data regression, their results overall demonstrated that

FDI had a positive effect on firms’ decisions to export and on firms’ export performance. Their

results confirmed the findings of other empirical studies regarding the export decision and were

explained through the fact that as a result of FDI, improved technologies and management skills

were brought on board that would entail productivity enhancement and hence the decision to

export. Put forward as another explanation concerning the decision to export, where firms receiving

a foreign capital injection were more likely be in a better position to finance the sunk costs

associated with entering the export market and foreign-owned firms having links with foreign

markets and would as such be more motivated to export.

By estimating a Heckman sample selection model, Sun (2009:1221) demonstrated export

spillovers from FDI in China’s cultural, educational and sporting product manufacturing industry. He

found the export impact from FDI on firms to be varied in that the scale depended positively on firm

geographical location in Central China while in Western China a negative dependency was found

with regard to sales cost to sales revenue ration, ownership structure and geographical location.

Anwar and Nguyen (2011a:177), by also using a Heckman type selection model, found that the

presence of foreign firms in Vietnam, through horizontal and vertical linkages, had a significant

positive effect on the Vietnamese firms’ decision to export and their export share. Their results

continued to hold after taking into account factors such as domestic firms’ level of technology,

domestic firms’ ownership structure, the orientation of foreign firms and the geographical proximity

to foreign firms.


14

Wang and Yu (2007:404) provided clear evidence of a curvilinear relationship between foreign

presence and spillover benefits to firms in the Chinese manufacturing industry. This finding is in

contrast to a linear relationship found in previous studies with the curvilinear relationship being

particularly strong in labour-intensive industries.

An interesting study by Wei and Liu (2006:554), based on a panel of more than 10 000 Chinese

and foreign-invested firms, found that government incentives may hasten competition for exports

between domestic and foreign-invested Chinese firms in the same industry. Hence, they presented

evidence of negative intra-industry spillovers as opposed to most other export spillover studies

revealing positive export spillovers from FDI.

Results from Thangavelu and Owyong (2003:623) indicated that FDI intensive industries were the

key contributors to productivity growth in manufacturing industries with regard to export

performance and economies of scale when compared to non-FDI intensive industries. Data were

used from ten major industries in Singapore’s manufacturing sector with the ten industries divided

into FDI intensive and non-FDI intensive, based on the share of foreign equity.

Assessing the impact of inward FDI on the performance of Chinese manufacturing firms, findings

from Buckley, Clegg and Wang (2002:637) revealed that non-Chinese MNEs generate

technological and international market access spillover benefits for local firms with overseas

Chinese investors conferring only market access benefits.

2.2.3 Direct studies of FDI and exports

Regarding the direct effect of FDI on exports, an earlier study by Zhang and Song (2000:395)

using a simple regression model to calculate a correlation coefficient, found a strong correlation

between exports and FDI in China. Their findings supported the view that increased FDI positively

affects manufacturing exports.

Kim and Kang (1996:39) examined the relationship between outward FDI and exports in South

Korea and Japan by using cross-sectional data. The purpose of the study was to investigate

whether or not FDI substitutes or decreases exports from the domestic country that will help in

predicting the effect on domestic industries. Results indicated that FDI does not substitute exports

from the domestic country. Retaining foreign export markets as a determinant of FDI was found

more important in Japan than in South Korea which might imply that outward FDI in Japan is more

of a market-oriented type while in South Korea more of a cost-oriented type.

Ancharaz (2003) found that FDI contributed to export growth in Mauritius. The impact on export

competitiveness has however been negligible. The study examined FDI flows to the Mauritian

export processing zone (EPZ) with the economic model specifying FDI as a function of daily

average wages in the EPZ, a foreign interest rate variable, real exchange rate variable, the


15

domestic capital stock in the EPZ and a dummy variable to capture the period of trade liberalisation

in Mauritius.

AbuAl-Foul and Soliman (2008:4) investigated FDI’s effect on manufacturing export in four Middle

Eastern and North African (MENA) countries, namely Egypt, Morocco, Tunisia and Turkey. By

using panel data from 1975 – 2003 and making use of a gravity model, their results indicated that

FDI positively affects the domestic country’s manufacturing exports. The findings revealed that for

the respective host countries, a ten percent increase in FDI inflows would lead to a 1.2 percent

increase in manufacturing exports while a similar ten percent increase in FDI stock would lead to a

3.4 percent increase in manufacturing exports. What is of note to mention is that the authors

comment that country-level data might hide sector- or firm-specific aspects of FDI activities as well

as the nature of their interaction with local firms. Disaggregated data could thus also be considered

if available.

By employing a gravity model and equations, the findings of Xuan and Xing (2008:183)

demonstrated that FDI has been a major contributor in driving Vietnam’s export growth. FDI has

significantly aided Vietnam’s exports to the 23 FDI source countries on which the analysis was

based. Specifically, a one percent increase in FDI inflows would lead to a 0.13 percent increase in

Vietnam’s exports to those source countries.

Ahmed et al. (2010:23), using the autoregressive distributed lag (ARDL) approach in the

examination of a Granger test of causality, found evidence of a bi-directional causality between FDI

and exports in Ghana, Kenya and Nigeria. In South Africa the Granger causality was from FDI to

exports, while in Zambia the causality was from exports to FDI. When the findings were further

examined using Pedroni’s estimation approach that allows for heterogeneity across individual

countries, the evidence revealed that FDI had a significant positive impact on exports.

Bajo-Rubio and Montero-Munoz (2001) analysed the relationship between FDI and exports in

Spain by using quarterly data for the period 1977–1998. By means of Granger causality tests in a

cointegration framework, their results indicated a complementary relationship between the

variables with the Granger causality running in from FDI to exports in the short run, and a bilateral

causality in the long run.

The findings of Dlamini and Fraser (2010:57) indicated a bi-directional causality between FDI and

exports in the South African agricultural industry. The Dlamini and Fraser (2010) study used

Granger causality tests and an error correction model in a bivariate setting. The study’s economic

model was based on the assumption that FDI is a function of agricultural exports and GDP in the

agricultural sector. The authors recommended that since FDI had a complementary relationship

with agricultural exports, priority should be given to providing an enabling environment for


16

agricultural productivity rather than on attracting FDI because an increase in the rate of agricultural

productivity would translate into more FDI.

An indirect way of empirically testing FDI’s impact on employment is to gauge the relationship

between exports and FDI (Di Mauro, 2000:11). The objective would be to inspect whether FDI

substitutes for exports and as such indirectly harms local employment in the exporting sector. Di

Mauro (2000:11) adopted a methodology in which gravity equations for exports and FDI are

estimated with the residuals of the estimations and are then regressed against each other. The

method presumes to remove the influence of common factors on FDI and on exports by using the

gravity equations. Complementarity would be found if there is a positive correlation between the

two residuals.

Di Mauro’s (2000) working paper assessed the impact of economic integration on FDI and exports

by using a gravity approach and the paper also dealt with the complementarity versus

substitutability debate concerning the FDI and exports relationship. Economic integration between

European Union countries was the primary focus. In the working paper, the residuals from the FDI

gravity equation were the dependent variable and regressed against the residuals from the exports

gravity equation. For the three years considered, it was found that the coefficients were positive

and highly significant, implying a complementary relationship between FDI and exports.

Di Mauro (2000:21) highlighted that the previously mentioned method could only account for a

complementary or substitutability relationship between FDI and exports at an aggregate level and

that studies investigating different sectors of the economy might reveal different results.

As opposed to most of the empirical literature that focuses on non-financial MNEs, Buch and

Lipponer (2007:806) used bank-level data to investigate the FDI versus export decision for German

Banks. Their findings revealed that FDI and cross-border services were complements. They also

tested Helpman et al. (2003) theories of MNEs, which highlighted the importance of firm

heterogeneity, and found that the heterogeneity of banks had a significant impact on the

internationalisation decision.

2.2.4 FDI and trade

Aizenman and Noy (2006:333) stated that the feedback effect between FDI and trade was stronger

in developing countries than in industrialised countries. The aforementioned is in line with the

inference that the bulk of FDI heading to developing countries has been vertical. Furthermore,

Aizenman and Noy posited that this notion does offer a partial motivation for trade and financial

liberalisation policies undertaken by developing countries in recent times.

Anwar and Nguyen (2011b:39) used a gravity model to investigate FDI’s impact on exports,

imports and net exports in Vietnam. In addition, the study also considered FDI’s impact on the


17

previously mentioned trade during three sub periods concerning the Asian financial crisis; pre-

Asian crisis, post-Asian crisis and during the Asian financial crisis. The analysis was based on a

panel dataset covering 19 of Vietnam’s trading partners for the years 1990–2007. Their results

showed a complementary relationship between FDI and exports as well as between FDI and

imports. FDI’s impact on net exports was found to be significantly positive in the post-Asian

financial crisis period.

Ahmed et al. (2010:23), by using Granger-type causality tests to demonstrate the interrelatedness

of exports, FDI and import variables, confirmed the concern of Anwar and Nguyen (2011b:39)

regarding the effect of FDI on imports. Specifically, whether manufacturing industries attracting FDI

relies on significant inputs of imported goods? Hence, Anwar and Nguyen (2011b:40) hypothesized

that the direct effect of FDI on net-exports may not be significant or even negative.

According to Bezuidenhout and Naude (2008:16), inconclusive evidence still exists as to whether

FDI causes trade or whether trade causes FDI. In their working paper, they investigated the

relationship between trade and FDI in the Southern African Development Community (SADC).

Before using a gravity model, Bezuidenhout and Naude (2008:16) examined the causality between

trade and FDI by using a Granger causality test. The results indicated that in the case of the 20

countries involved in the study, trade caused FDI. Hence, in their model estimation, the FDI proxy

was treated as the dependent variable. Further findings revealed that distance and political

instability were negative determinants of FDI to SADC countries; while regarding trade patterns, a

complementary relationship between FDI and trade to SADC in the case of continental Europe was

observed.

Pacheco-Lopez (2005:1157) analysed the liberation of FDI in Mexico and its relationship with

exports and imports. By using Granger causality methodology and an ARDL model to estimate an

error correction model, it was found that although MNEs promoted exports in Mexico, the

displacement of local firms through higher import content has limited the country’s economic

development. Hence, bi-directional Granger causality between exports, imports and FDI were

found.

2.3 CHAPTER SUMMARY

The chapter provided the theoretical framework upon which the research report has been based.

The related empirical literature review distinguished between direct- and indirect studies of FDI and

manufacturing exports and FDI and trade. Based on the related empirical literature review, the key

message pertaining to the current research report suggested that FDI and manufacturing exports

are related. Few studies incorporated imports when investigating the aforementioned relationship.


18

If FDI has resulted in a significant increase in imports, the empirical literature has revealed that it

would make sense to also consider the relationship between FDI and imports.


19

CHAPTER 3

FDI AND MANUFACTURING IN SOUTH AFRICA

3.1 FDI IN SOUTH AFRICA

While accounting for only a small part of global FDI flow, South Africa dominates FDI in the

Southern Africa Development Community (Akinboade, Siebrits & Roussot, 2006:178). The authors

noted that FDI has been consistently positive although disappointing in terms of quantity after

1994. It is further mentioned that evidence of the relatively low levels of FDI inflows could be seen

from the fact that foreign portfolio capital flows have generally exceeded FDI inflows by a

considerable margin.

Rusike (2007:40) echoed the sentiment that FDI has appeared to increase over time since 1994,

but relative to the size of the South African economy and other similar developing markets, FDI is

below levels expected. Mergers and acquisitions as opposed to Greenfield investments was found

to account for most FDI activity. Greenfield projects would enhance more job creation opportunities

as large acquisitions only provide a once-off capital inflow and a subsequent drain of dividends.

Rusike (2007:40) also compared inward FDI as a share of GDP with other developing countries

and found net inflows in South Africa to be very low at generally less than one percent of GDP,

apart from a couple of exceptional years. Compared to other developing countries which average

three – five percent, this is not good. Table 3.1 provides FDI inflows for South Africa, Africa and

developing markets.

Table 3.1: FDI inflows and outflows (US$bn)

Year South Africa Africa Developing markets

2005 6,647 38,16 332,343

2006 -0,527 46,259 429,459

2007 5,695 63,132 573,072

2008 9,006 73,413 658,002

2009 5,696 60,167 510,578

2010 1,553 55,05 573,561

Source: African Economic Outlook, 2011; Thomas, 2011:45.

Disconcerting from Table 3.1 is that while developing markets attracted greater FDI inflows

following the financial crisis, South Africa’s inflows of FDI slumped to US$ 1,55bn in 2010. South

Africa’s attraction of less than three percent of the continents FDI inflow is also uninspiring.


20

Thomas (2011:45) reported that among 54 African countries, South Africa ranked 9th in terms of

FDI inflows, behind countries such as Angola, Egypt and Nigeria.

Thomas (2011:45) further reported that economists reckoned that the previously alluded to

portfolio inflows have replaced FDI because of the country’s strict labour policies and other

government interventions with owning shares on the JSE offering more liquidity. Other reasons put

forward for low FDI inflows were the size of the consumer market in South Africa being small and

constrained by excessive debt as well as the country’s export base being handicapped by transport

costs. As example Thomas cited research that indicated that the cost of loading or offloading a

container ship at the Durban port was between 80–170 percent greater than at European ports.

South Africa’s natural resource wealth will keep attracting investors and, compared to the rest of

Africa, the relatively well educated labour force helps to draw funds into non-resource sectors of

the economy as well (Ernst & Young, 2011:40). This is evident by inspecting the top five sectors in

number of FDI projects from 2003–2010, namely software and IT services, business services,

financial services, metals and the automotive sector. The Ernst & Young report listed the top five

investor countries in number of FDI projects from 2003–2010 as the US, UK, Germany, India and

Australia.

Rusike (2007:44) analysed FDI inflows to South Africa from 1994 – 2005 and found that Europe

was by far the largest regional source of FDI. Table 3.2 presents his findings.

Table 3.2: Regional sources of South African FDI, 1994–2005

Region Amount(Rm) Percentage

Europe 2 315 091 86

Americas 234 510 9

Africa 30 109 1

Australia 5 415 0.002

Asia 111 727 4

Source: Rusike, 2007:44.

Rusike (2007:45) further analysed FDI inflows from individual countries. Table 3.3 presents his

findings.


21

Table 3.3: Individual country sources of FDI, 1994–2005

Country Amount(Rm) Percentage

UK 1 803 309 70

USA 212 711 8

Germany 197 442 8

Netherlands 104 757 4

Switzerland 73 821 3

Malaysia 52 940 2

France 41 746 2

Other Europe 37 953 1

Japan 35 305 1

Luxumborg 16 106 1

Source: Rusike, 2007:45.

From Table 3.2 and Table 3.3 it is evident that European countries have been the most significant

contributors regarding FDI inflows to South Africa since the end of apartheid in 1994. It would

appear that the trend still persists. SARB Quarterly Bulletin International economic relations for

December 2010 revealed that for that year alone, Europe accounted for 84 percent of total FDI

with the UK again by far the greatest individual source country.

FDI annual manufacturing stock figures as collected from the South African Reserve Bank (SARB)

Quarterly Bulletin are presented in Table 3.4.

Table 3.4: Total direct investment in manufacturing as at 31 December (Rm)

Year FDI stock in the manufacturing sector

2003 75 427

2004 111 354

2005 136 028

2006 165 432

2007 197 099

2008 204 754

2009 242 217

2010 262 920

Source: SARB Quarterly Bulletins.

In 2010, FDI stock in the manufacturing sector was 25.89 percent of total FDI stock with mining

and quarrying at 38.28 percent and financial intermediation, insurance, real estate and business


22

services at 23.81 percent of total FDI stock. As Table 3.4 indicates, there has been an upward

trend in FDI manufacturing stock in recent years.

Given South Africa’s history of discriminatory laws, FDI was not actively pursued prior to 1994.

According to Rusike (2007:31), although the country acknowledged the importance of FDI within

the broader macroeconomic spectrum, no specific and coherent FDI policy that acted as guidance

to attract FDI existed. Hence, the macroeconomic strategy of GEAR (Growth, employment and

redistribution) of 1996 aimed to provide a favourable environment as point of origin in attracting

FDI. Specific sectors of the economy have pursued FDI related policies (Rusike, 2007:31). Rusike

noted that a number of departments and units including the DTI, the Department of Minerals and

Energy and the SARB, have all had the responsibility of formulating and implementing different FDI

related policies. Table 3.5 provides a summary of FDI related policies per institution.

Table 3.5: FDI related policies per institution

Department of Trade and Industry

Competition Commission

South African Reserve Bank

Department of Minerals and

Energy

Department of Environmental

Affairs and Tourism

Spatial Development Initiatives(SDIs)

Competition Policy Exchange control (being phased out)

Tax legislation for the mining industry

Investment incentives

Expropriation powers regarding land and can impose remedial costs on mining investors

Expropriation powers regarding land and can impose remedial costs on mining investors

Bilateral Investment and Protection Agreements

Companies Registration

Technology Transfer

Source: Moeti, 2005:162.

Moeti (2005:166) stressed that issues regarding administrative oversight between government

departments needed to be addressed. He noted that although the current South African Cabinet

committee system could in a way fulfil a coordinating function, it should be remembered that

coordination should not just happen at an Executive level. Moeti (2005:167) concluded that a

coordinating body or institution that would be concerned with all legislation and policies pertaining

to FDI might be needed.


23

Sello (2007:34) stated that most of South Africa’s incentive schemes in attracting FDI are

administrated by the DTI. According to him, examples of these schemes include the Industrial

Development Zone Programme (IDZ), Skills Support Programme, Critical Infrastructure Fund and

the Strategic Industrial Projects Incentive. Regarding IDZs, Sello (2007:35) noted that unlike Export

Processing Zones in other countries, these industrial zones are relatively new with the first one

designated in 2001. These zones have been developed in Port Elizabeth and East London,

Richards Bay and at the OR Tambo International Airport in Johannesburg.

Two operational zones characterises an industrial zone, a customs secured area (CSA) and an

industries and services corridor (ISC). Businesses operating within the CSA have duty-free imports

of production inputs, supplies procured from South Africa have zero VAT rate and these

enterprises can sell finished products in South Africa after paying normal input duties without the

imposition of quotas. In the ISCs service providers to CSA businesses find an industrial and office

park environment (Sello, 2007:35).

Disconcertingly, Sello (2007:39) found that FDI to South Africa was accompanied by small linkages

to the local economy. He concluded that although incentive packages may influence FDI inflow, the

main determinants of a host country’s FDI attractiveness may not be specific incentives. He also

provided an example of access to foreign markets, as in the case of Lesotho’s access to the US

market and in South Africa’s case the generally stable political and economic climate as possibly

more important determinants of FDI attractiveness.

3.2 MANUFACTURING IN SOUTH AFRICA

Mbeki (2011:61) contended that while the virtues of globalisation are quickly extolled, casualties

such as South Africa’s manufacturing sector also exist. He stated that in 1980, 30 percent of the

country’s output came from manufacturing a situation which does not compare favourably to 15

percent contribution of manufacturing output currently. This he said has contributed to the

unacceptable levels of unemployment. Unlike China and India, South Africa already had a

successful manufacturing sector and while developed countries rushed to supply the newly

industrialised Asian nations with capital goods to fuel their growth, the South African manufacturing

sector was left behind.

At sectorial level, the slowing down in South African growth could be attributed chiefly to the poor

performance of the secondary sector, particularly manufacturing (TIPS, 2009:1). The TIPS study

echoed Mbeki’s (2011) sentiment that this is important given that manufacturing has for many

decades been the main driver and measure of economic growth locally and internationally. The

report further noted that prior to the crisis, utilisation of manufacturing production capacity

increased faster than investments in manufacturing which is an important point, considering

investment is required to foster additional job creation.


24

Whereas a decade ago the manufacturing sector was the largest employer in the country, the

sector is currently well behind government services. Together with a parallel drop in the mining and

agricultural sectors, the South African economy has been totally reconfigured into a raw mineral

exporter and consumer economy (Mbeki, 2011:61). Mbeki alarmingly concluded that South Africa

is becoming a colony in economic relations with China and India and that the country is “eating

ourselves through our natural resource wealth”.

The South African Government is aware of the situation and through the IPAP2 attempts to

promote long-term industrialisation and industrial diversification that will hopefully bring about less

reliance on commodities. The lofty purpose of the IPAP2 is to expand production in value-added

sectors of the economy with high employment and growth multipliers that can be competitive in

export markets and compete locally against imports (Republic of South Africa, 2010a:1).

The IPAP2 document further accentuated the urgency of the situation in that it stated that South

Africa has no alternative action than the proposals in the policy action plan. Manufacturing is seen

as an engine of long-term sustainable growth and job creation for developing countries. The policy

document portrayed South African growth in the past as being reliant on growth in consumption,

fuelled by credit extension. The document stated that between 1994 and 2008, 7.7 percent annual

growth was recorded in consumption driven sectors compared to the 2.9 percent annual growth in

the productive sectors of the economy. This has meant that even at the height of the South Africa’s

average annual growth of 5.1 percent between 2005 and 2007, unemployment still hovered near

the 23 percent level.

The relative profitability of manufacturing, as part of value added production, has been low as a

result of a number of factors that include (Republic of South Africa, 2010a:5):

A volatile and lack of a sufficient competitive Rand;

The cost of capital relative to South Africa’s trading partners and particularly the capital

channelled towards value added sectors such as manufacturing that results in the limited

allocation of said capital;

Monopolistic provision and pricing of key inputs;

Aged, unreliable and expensive infrastructure system;

Weak skills base and system;

Inability to adequately leverage public capital and other areas of public expenditure.


25

Further worsening for the sector has been the recent electricity price hikes that will adversely affect

the economy’s production side. Recently, Stewart Jennings, the Chairperson of South Africa’s

Manufacturing Circle, argued that having already adopted compound increases of 140 percent

over the past four years, a further envisioned two increases at the 25 percent level would have

serious consequences for a sector that has shed 300 000 jobs since 2008 (Lamprecht, 2011).

Roger Pitot, Executive Director of the National Association of Automotive Component and Allied

Manufacturers (NAACAM), echoed the previous sentiment and reported that for some of their

affiliated member companies, electricity currently comprises 15 percent of their total cost

(Lamprecht, 2011). According to Pitot, in the short term relief is needed for manufacturers that are

heavy electricity users. In the medium term, government should upgrade infrastructure, especially

in ports and rail, as the amount of products transported per road is too high. Government should

also focus to ensure labour stability as strikes greatly dampen productivity.

The manufacturing sector revealed heterogeneous performances at sub-sector level prior to the

global financial crisis. Market changes were observed since the 2000s with some industries

shrinking while other sub-sectors were sustaining and even growing (TIPS, 2009:1). The report

further noted that prior to the global financial crisis, utilisation of manufacturing production capacity

increased faster than investment in manufacturing which is important considering investment is

required to create additional jobs. Table 3.6 indicates real value addition of certain manufacturing

sub-sectors.

Table 3.6: Manufacturing sub-sectors’ share of total manufacturing by real value added (%)

Sub-sector 1994 2000 2008

Food 6.7 4.4 5.3

Textiles 6.1 4.1 2.6

Paper and paper products 8.5 6.8 7.3

Coke and refined petroleum products

1.3 2.5 4.1

Basic chemicals 8.0 11.4 13.1

Plastic products 6.0 5.3 5.2

Basic iron and steel 7.2 8.7 8.9

Machinery and equipment 8.6 5.4 4.2

Motor vehicles, parts and accessories

8.6 15.5 19.4

Other manufacturing 1.2 0.9 0.8

Source: TIPS, 2009:1.

Analysing the above table, the TIPS report highlighted the performance of the motor vehicles, parts

and accessories sub-sector that has more than doubled its operations since 1994 and the report


26

attributed it to the Motor Industry Development Programme (MIDP). Textiles have performed poorly

and are significantly down from 1994 levels in terms of real value added.

Table 3.7 presents South African total manufacturing trade from 2005–2011 with Figure 3.1

displaying the country’s manufacturing trade balance.

Table 3.7: Total South African manufacturing trade (Rbn)

Year Manufacturing exports Manufacturing imports

2005 203,779 295,409

2006 242,392 381,821

2007 304,857 462,399

2008 404,211 563,779

2009 287,372 428,418

2010 320,952 477,195

2011 360,938 590,344

Source: Republic of South Africa, 2011.

Figure 3.1: South African manufacturing trade balance

Source: Republic of South Africa, 2011.

As seen from Table 3.7 and Figure 3.1, manufacturing imports have been consistently greater than

manufacturing exports. Pacheco-Lopez (2005:1168) stated that imports could be seen as evidence

that a market exists and as such FDI might be attracted to the local economy. Put differently,

evidence of a rise in imports in a local country could justify the investment and production of MNEs.

The importance of also taking manufacturing imports into consideration when investigating the

relationship between FDI and manufacturing exports as per Ahmed et al. (2010), Anwar and

Nguyen (2011b) and Pacheco-Lopez (2005) is therefore appropriate for South Africa.


27

3.3 EXPORT PROMOTION

In South Africa, the DTI’s trade and investment support body, Trade and Investment South Africa

(TISA), aims to increase export capacity and support direct investment flows through strategies

directed at targeted markets by managing a network of foreign trade offices (Republic of South

Africa, 2012). The four business units that comprise TISA are:

Investment Promotion and Facilitation

Export Promotion

Export Development

Foreign Service Management.

The export promotion directorate’s promotion offerings include market intelligence and advice,

trade-lead facilitation and in-market support, trying to facilitate exports by matching potential

exporters and buyers and administrating an incentive scheme, Export Marketing & Investment

Assistance Scheme (EMIA), that will partially compensate exporters for certain costs incurred

when products and services are marketed abroad. The following assistance is provided under the

EMIA scheme (Van Aarde, 2007:53):

National Pavilions

Individual Exhibitions

Primary Export Marketing Research Scheme

Foreign Direct Investment Research Scheme

Sector Specific Assistance

Outward Selling Trade Missions

Outward Investment Recruitment Missions

Inward Buying Trade Missions

Inward investment Missions.

According to the DTI, eligible enterprises are South African manufacturers and exporters, export

trading houses and commission agents from South Africa that represent at least three SMMEs or

businesses owned by previous disadvantaged individuals as well as South African export councils

and industry associations that represent a minimum of five local entities. In the manufacturing

sector, examples of industry-based export councils are the Automotive Industry Export Council

(AIEC) and the SA Textile Industry Export Council (SATIEC).

Van Aarde (2007) aimed to determine the return on investment (ROI) for national pavilions and

trade missions which were the largest programmes under the EMIA scheme. It was found that ROI

per sector for these export promotion programmes produced positive results. However, in order to


28

achieve sustainable export growth, the DTI would have to develop new evaluation criteria as well

as focus more on the manufacturing and services industries.

The Van Aarde (2007:92) study recommended that the DTI should establish a permanent

evaluation unit that would incorporate international best practices regarding export promotion

evaluations. The evaluation unit should be aligned with the department’s strategic objectives for if

programmes are to be managed effectively, they would have to be monitored appropriately.

Optimal resource allocation and policies targeted at higher value added programmes would follow.

Related to appropriate evaluation, Van Eldik and Viviers (2005:1) commented that many South

African companies experience difficulty in entering export markets and achieving sustainable

export growth. They stated that a possible reason is that companies are not export ready. It was

further noted that export promotion programmes in South Africa were based on results obtained

from export readiness questionnaires that were not scientifically designed and as a result should

not be used to gauge the export readiness of the evaluated companies.

For African countries new opportunities abound as the promise of international entrepreneurship is

enhanced due to the economic successes of Brazil, China and India. These countries have also

been described as the ‘southern engines of growth’ in the world economy (Pearson, Viviers,

Cuyvers & Naude, 2010:355).

By employing a decision support model (DSM) and adapting the context to South Africa, Pearson

et al. (2010:355) found that the results from the DSM suggested significant potential for further

exports to China and India, and to a lesser extent, Brazil. Export promotion activities in these

emerging markets should not be neglected. In Figure 3.2 the overall growth in exports to Brazil,

China and India is indicated.


29

Figure 3.2: South African exports to Brazil, China and India

Source: Pearson et al., 2010:348.

Pearson et al. (2010:347) noted that export growth to these three countries was relatively similar

up to 1999, from when export growth to China and India accelerated. Up to 2002, the higher export

growth to China and India was almost similar after which export growth to China increased faster.

The faster export growth to China does remind one of the South African Trade and Industry

Minister’s signing of the Comprehensive Strategic Partnership with China in 2010 and the South

African Government’s appeal to China to increase its imports of value-added products in order to

improve the current structure of trade between the two countries.


30

Figure 3.3: South Africa’s exports to Brazil, China and India as a percentage of total exports

Source: Pearson et al., 2010:347.

From Figure 3.3, it can be seen that South Africa’s relative presence is still small in the said

countries. Pearson et al. (2010:356) reported that because of a larger private sector involvement

through sectorial associations and export councils, a different blend of export instruments would be

needed in order to implement an offensive market expansion strategy. These instruments could for

example be incentives for participating in sectorial trade fairs and targeting press campaigns in the

local press.

3.4 CHAPTER SUMMARY

The chapter provided an overview of FDI, manufacturing sector and export promotion in South

Africa. It has been noted that the manufacturing sector is key for government in terms of job

creation potential. The sector’s current relatively low profitability is worrying and the chapter

highlighted the different performances at sub-sector level. Recent trends regarding South African

FDI were discussed. FDI inflows in South Africa have recently been disappointing and more

greenfield investments rather than mergers and acquisitions are required to facilitate job creation.

FDI manufacturing stock levels in recent years have trended upwards. Also noted was that no

specific and coherent FDI policy exists. The proposed new legal framework for FDI in South Africa

is welcomed.


31

CHAPTER 4

RESEARCH METHODOLOGY

4.1 DATA COLLECTION

The empirical literature review has revealed that FDI flow data and FDI stock data have been

mostly used as variables for FDI. FDI flow data is available in quarterly figures, but only in

aggregated form, i.e. there is no quarterly data available according to economic activity, for

instance manufacturing (Swart, 2012).

Further complicating the usability of FDI data was that according to Naidoo (2012), the South

African Reserve Bank (SARB) only started measuring FDI manufacturing stock data from 1997.

Therefore, with annual FDI manufacturing stock data having been obtained from 1997–2010, the

ensuing time series only had 14 data points that could hamper meaningful analysis. Dlamini and

Fraser (2010:60) sought to overcome the limitation of only having annual FDI stock data available

in the agricultural sector by deriving quarterly figures by using mainly the GDP of the agricultural

sector and a model suggested by Nijman and Palm (1986).

Employing the aforementioned method with regards to the current research project was

considered. However, it was decided to use aggregated quarterly FDI flow data for econometric

analysis and then use disaggregated FDI stock data for additional non-econometric analysis on the

variations rather than using interpolation or model-based approaches to account for the missing

quarterly observations. Aggregated quarterly FDI flow data was therefore also obtained from the

SARB and quarterly data for manufacturing exports and – imports were obtained from the DTI.

Data for the previous mentioned three variables were obtained from 1994–2011.

For the econometric analysis the data was transformed to natural logarithms to ease interpretation.

By using natural logarithms the relative change instead of the level change could be observed.

Another advantage of using natural logarithms is that data could possibly be made normal and the

variances decreased.

4.2 DATA ANALYSIS

The related literature review has revealed that FDI, manufacturing exports and manufacturing

imports are related. In addition, a complementary relationship between FDI and exports is

predicted by an export platform model (Abor et al., 2008:452). The following causality relationships

are to be tested in a bivariate setting:

FDI ↔ EXP

FDI ↔ IMP


32

Where,

FDI is the aggregated FDI flow data;

EXP is manufacturing exports;

IMP is manufacturing imports;

Time series data of FDI in the manufacturing sector, manufacturing exports and manufacturing

imports is relevant for the current study and thus had to be considered when choosing the

appropriate method. Based on the theoretical and empirical literature review, it is expected that FDI

and trade hold some long-run relationship. Therefore, possible cointegrating relationships may

exist.

As the direction of causality between FDI and manufacturing trade is of interest for the current

research project, Granger causality tests had to be employed. The causality test method to use

depends on the stationarity or not of the time series, and if non-stationary whether the time series

are cointegrated or not (Aziakpono, 2008:200). According to Bashier and Bataineh (2007:12), if the

test results determine that the variables are integrated of order one and not cointegrated, a

Granger causality test should be implemented using the first differences of the variables. They

further posited that should the variables be stationary and cointegrated, an error correction model

should be used.

4.2.1 Model

The letter L denotes the natural logarithm.

LFDI = a + a LEXP +

LFDI = a + a LIMP +

Where,

LFDI is the aggregated FDI flow data;

LEXP is manufacturing exports;

LIMP is manufacturing imports.

The a priori assumptions are that manufacturing exports have an associated effect on FDI and

manufacturing imports also have an associated effect on FDI.

4.2.2 Stationarity

Apart from whether a model should be estimated using a single equation approach or a systems

estimator, it is also important to consider the underlying properties of the processes involved when

generating the time series variables (Harris, 1995:14). The problem of spurious regression can

result from models containing non-stationary variables that would suggest a statistical significant


33

relationship in the regression model when all that was actually obtained was proof of

contemporaneous correlations rather than any causal relationship (Harris, 1995:14).

Furthermore, if a time series is non-stationary, one can only study its behaviour for a particular

period and as a consequence generalisation to other time periods and forecasting may be of little

practical value (Gujarati & Porter, 2009:741). Brooks (2008:320) stated that when non-stationary

variables are used in a regression model, it can be proven that standard assumptions concerning

asymptotic analysis will not be valid. Put differently, the t-statistics will not conform to a t-

distribution and likewise the F-statistic will not follow an F-distribution.

Practically, if we want to understand the relationship between variables using regression analysis

for time series data, some sort of stability over time is required (Wooldridge, 2009:379). Hence, a

natural starting point for time series data analysis would be to test for stationarity of the particular

time series involved (Aziakpono, 2008: 198). Wooldridge (2009:378) contended that the notion of

stationarity has played an important part when analysing time series data and defined a stationary

time series process as one where the probability distributions are stable over time. Gujarati and

Porter (2009:741) stated that if a time series is stationary, the mean, variance and auto covariance

(at different lags) will remain the same irrespective at what point they are measured. Thus, time

invariant.

If a non-stationary time series has to be differenced d times in order to induce stationarity, the time

series is said to be integrated of order d, I(d) (Brooks, 2008:326; Gujarati & Porter, 2009:747;

Harris, 1995:22). A stationary time series is said to be integrated of order zero, I(0), and according

to the authors, the majority of economic and financial time series are integrated of order one, I(1),

meaning that these time series normally become stationary only after taking their first differences.

By differencing I(1) variables before using them in linear regression models is a safe course to

follow, but by always differencing I(1) variables, the scope of the questions that can be answered is

being limited (Wooldridge, 2009:637). Gujarati and Porter (2009:760) also cautioned that when

transforming a non-stationary time series to make it stationary, the transformation method used will

depend on whether the time series is difference stationary or trend stationary. The authors pointed

out that if a difference stationary time series is treated as trend stationary then under-differencing

will occur and if a trend stationary series is treated as difference stationary then over-differencing

will result.

The unit root test is a test of stationarity (or non-stationarity) that has become popular in recent

years (Gujarati & Porter, 2009:754). Early pioneering work for unit root testing in time series data

was done by Dickey and Fuller and the Dickey-Fuller(DF) tests are also known as τ (tau) tests with

these authors having computed the critical values of the τ-statistic through Monte Carlo simulations


34

(Gujarati & Porter, 2009:755). These tests can be conducted to allow for an intercept, or intercept

and deterministic trend, or neither when applying the test regression (Brooks, 2008:327).

The DF test assumed that the error term was uncorrelated, but if that is not the case Dickey and

Fuller have developed another test known as the augmented Dickey-Fuller(ADF) test where the

lagged values of the dependent variable are added (Gujarati & Porter, 2009:757). A problem that

now arises is determining the optimal number of lags to be included (Brooks, 2008:329). If too few

lags are included not all of the autocorrelation will be removed and with too many lags the

coefficient of the standard errors will be increased.

Brooks (2008:329) suggested using two simple rules of thumb to determine the optimal lag length.

Firstly, the frequency of the data can be used so that if the data were quarterly, four lags would be

used and so forth. Secondly, information criterion such as Akaide, Schwarz and other could be

used. Gujarati and Porter (2009:757), in demonstrating an ADF-regression, used quarterly data

and then decided to use four lags.

4.2.3 Cointegration

If a linear combination of I(1) variables become I(0), the variables are said to be cointegrated

(Brooks, 2008:336). The linear combination therefore becomes stationary. Put differently, the linear

combination has cancelled out the stochastic trends in the respective time series (Gujarati &

Porter, 2009:762). Cointegration therefore makes regressions with I(1) variables potentially useful

(Wooldridge, 2009:637).

Many non-stationary time series display a characteristic of “moving together” over time which

would imply that the two series are bound in the long run by some relationship. The cointegrating

relationship is also said to be an equilibrium phenomenon for it is possible for the cointegrating

variables to deviate in the short run, but their long run relationship or association would return

(Brooks, 2008:336).

For Harris (1995:25), when non-stationary time series is involved, the cointegration concept is

synonymous with equilibrium. Further noted was that a failure to establish cointegrating

relationships would often lead to spurious regressions that would not be indicative of a long-run

economic relationship, but rather be a reflection of trends contained in the non-stationary time

series. Authors often cite Granger (1986) who aptly described a test for cointegration as a pre-test

to avoid situations where spurious regressions may result (Dlamini & Fraser, 2010:62; Gujarati &

Porter, 2009:762).

A comparatively simple method to test for cointegration is to estimate a regression, obtain the

residuals and use DF or ADF tests (Gujarati & Porter, 2009:763). However, caution should be

exercised since a test employed on the residuals of a model would change the critical values


35

compared to a DF or ADF test. These new critical values have been calculated by Engle and

Granger and the DF and ADF tests are now known as the Engle-Granger(EG) and augmented

Engle-Granger(AEG) tests (Gujarati & Porter, 2009:763). According to Brooks (2008:340), the

Phillips-Perron (PP) unit root test for non-stationarity of the residuals can also be used.

4.2.4 Error Correction Model (ECM)

As has been stated, if time series were cointegrated, there would be a long-term or equilibrium

relationship. In the short run there may be disequilibrium and the ECM that was popularised by

Engle and Granger would correct for this disequilibrium (Gujarati & Porter, 2009:764). The authors

noted that an important theorem, the Granger representation theorem, stated that if variables Y

and X were cointegrated, an ECM could be used to express the relationship between the variables.

An ECM would thus enable one to investigate the short-run dynamics in the relationship between Y

and X (Wooldridge, 2009:643). Harris (1995:25) concluded that cointegration and short-run ECM

provided a useful link in short- and long-run approaches to econometric modelling.

An ECM overcomes the problem of only using first differences (e.g. limited scope and equilibrium

relationship not investigated) by using a combination of first differences and lagged levels of

cointegrated variables (Brooks, 2008:338). The following equation is an example of an ECM:

( )

If and are cointegrated with cointegrating coefficient γ then( ), the error

correction term will be I(0) even though the other parts are I(1) (Brooks, 2008:338). OLS and other

statistical inference procedures are valid to use.

4.3 CHAPTER SUMMARY

To summarise, economic time series are involved that according to Brooks (2008:326) would

suggest non-stationarity. A unit root test would be conducted to confirm the suspected non-

stationarity or not. Following the unit root test the time series was tested for cointegration after

which the appropriate causality test method was employed. If cointegration was found, an ECM

was used.


36

CHAPTER 5

RESULTS

5.1 STATIONARITY

In Figure 5.1 the time series are plotted in order to obtain a glimpse of the growth rate of the

respective series. It appears that the LEXP and LIMP are trending upwards which could suggest

that the mean of LEXP and LIMP have been changing with the series perhaps being non-

stationary. The trend in LFDI is less apparently upwards than the other two series but could have a

stochastic trend and thus also possibly be non-stationary containing unit root processes.

Figure 5.1: Quarterly logarithms of FDI, EXP and IMP, South Africa, 1994–2011

The ADF test is used to test for stationarity. For this research report the test was used to test for

the existence of unit root processes in two cases: with an intercept only and with an intercept and a

trend. The null hypothesis stated that the respective time series has a unit root. Hence, if the ADF

test statistic is less (in absolute value) than the critical value, the null hypothesis of a unit root

cannot be rejected and it can be concluded that the time series is non-stationary at levels. Table

5.1 presents the results of the unit root tests for LFDI, LEXP and LIMP for their levels.

2

4

6

8

10

12

14

1994 1996 1998 2000 2002 2004 2006 2008 2010

LEXPORTS LFDI LIMPORTS


37

Table 5.1: ADF test results

Variable Intercept Trend and intercept

LFDI -1.547 (-2.981) -2.475 (-3.595)

LEXP -1.512 (-2.909) -2.243 (-3.478)

LIMP -0.890 (-2.911) -3.014 (-3.478)

Parenthesis () denote test critical values at 5% level of significance.

The results reported for LEXP (both intercept only and intercept and trend) and LIMP (intercept

only) used Akaike’s information criterion to automatically select the appropriate lag length in

EViews. If Schwarz’s information criterion had been used, the conclusions from those results would

have been the same. For LIMP (intercept and trend), the Akaike – and Schwarz’s information

criterion respectively used generated results from which different conclusions would be made. It

was therefore decided to run the ADF test again and since quarterly data was used to employ four

lags and that result is reported in Table 5.1.

Concerning LFDI (both intercept only and intercept and trend), when the natural logarithm of the

variable was taken, missing observations for some periods were generated as a logarithm for a

negative value cannot be generated. That has meant that the automatic selection option using the

chosen information criterion could not be used and thus it was also decided to run the ADF test

using four lags and that result is reported in Table 5.1.

The results presented in Table 5.1 indicate that at a five percent level of significance, the null

hypothesis of a unit root cannot be rejected for all three variables (both intercept only and intercept

and trend) and it can be concluded that the three time series are non-stationary at their levels.

In order to determine the stationary property of the three time series, the ADF test was also applied

to the first differences as presented in Table 5.2.

Table 5.2: ADF test results for first differences

Variable Intercept Trend and intercept

LFDI -2.812 (-3.012) -2.506 (-3.645)

LEXP -3.981 (-2.906) -4.356 (-3.479)

LIMP -3.849 (-2.911) -3.878 (-3.487)

Parenthesis () denotes test critical values at 5% level of significance.

The results reported for LIMP (both intercept only and intercept and trend) and LEXP (intercept

only) used Akaike’s information criterion to automatically select the appropriate lag length in


38

EViews. If Schwarz’s information criterion had been used, the conclusions from those results would

have been the same. For LEXP (intercept and trend), the Akaike – and Schwarz’s information

criterion respectively used generated results from which different conclusions would be made. It

was therefore decided to run the ADF test again and quarterly data was used to employ four lags

and that result is reported in Table 5.2. LFDI was reported using four lags.

The results presented in Table 5.2 indicate that at a five percent level of significance, the null

hypothesis of a unit root can be rejected for LEXP and LIMP (both intercept only and intercept and

trend) and it can be concluded that the two time series are stationary at first differences. The null

hypothesis of a unit root cannot be rejected for LFDI and it can be concluded that the LFDI time

series is still a non-stationary series, even at a first difference level. This implies it may be an I(2)

series, however, the problems with missing data generation could account for this.

5.2 COINTEGRATION

In Figure 5.2 and Figure 5.3 the long run equations are estimated. In Figure 5.2 LEXP is

statistically significant at a one percent level of significance. A one percent change in LEXP has a

2.8 percent associated increase, on average, on LFDI, ceteris paribus. In Figure 5.3 LIMP is

statistically significant at a one percent level of significance. A one percent change in LIMP has a

3.09 percent associated increase, on average, on LFDI, ceteris paribus.

Dependent Variable: LFDI

Method: Least Squares

Date: 09/07/12 Time: 09:48

Sample: 1994Q1 2011Q4

Included observations: 63 Variable Coefficient Std. Error t-Statistic Prob. C -19.47850 7.733037 -2.518868 0.0145

LEXPORTS 2.806376 0.833647 3.366383 0.0013

TIME -0.055464 0.028555 -1.942373 0.0568

Figure 5.2: Regression of LFDI and LEXP

Dependent Variable: LFDI


Date: 09/07/12 Time: 09:49

Sample: 1994Q1 2011Q4

Included observations: 63 Variable Coefficient Std. Error t-Statistic Prob. C -23.58602 11.10894 -2.123156 0.0379

LIMPORTS 3.092858 1.140214 2.712523 0.0087

TIME -0.059641 0.036671 -1.626407 0.1091

Figure 5.3: Regression of LFDI and LIMP


39

To test for cointegration, a regression between LFDI and LEXP and between LFDI and LIMP both

with a time trend would be run respectively and then a unit root test would be performed on the

residuals. Under the null hypothesis a unit root exists in the potentially cointegrating regression

residuals while the alternative hypothesis would state that the residuals are stationary. If the null

hypothesis of a unit root can be rejected, there would be evidence of a cointegrating relationship.

Null Hypothesis: S has a unit root

Exogenous: None

Bandwidth: 4 (Newey-West automatic) using Bartlett kernel Adj. t-Stat Prob.* Phillips-Perron test statistic -7.692528 0.0000

Test critical values: 1% level -2.608490

5% level -1.946996

10% level -1.612934 *MacKinnon (1996) one-sided p-values.

Figure 5.4: PP unit root test on S (residuals of regression of LFDI and LEXP)

Null Hypothesis: S1 has a unit root

Exogenous: None



5% level -1.946996


Figure 5.5: PP unit root test on S1 (residuals of regression of LFDI and LIMP)

Evident from Figure 5.4 and Figure 5.5 is that the absolute value of the test statistic is greater than

any of the critical values and as such the null hypothesis in both cases can be rejected. It is

concluded that the estimated residuals in both cases are stationary. Therefore, LFDI and LEXP,

and LFDI and LIMP and vice versa are cointegrated, implying a long-run relationship exists

between the two sets of variables.

5.3 ECM

As the previous test results have indicated cointegration between the two sets of variables, an

ECM can be used in order to ascertain the direction of Granger causality.


40

Table 5.3: Causality test results based on the significance of ECM coefficient

Dependent variable ΔLFDI

Variable Coefficient t-Stat R² DW

ΔLEXP 1.443 1.070 0.52 2.08

ECT -0.875(0.000)* 7.429

Dependent variable ΔLFDI

ΔLIMP 1.091 0.680 0.51 2.12

ECT -0.835(0.000)* 7.229

Dependent variable ΔLEXP

ΔLFDI 0.015 1.070 0.03 1.87

ECT 0.019(0.255) 1.152

Dependent variable ΔLIMP

ΔLFDI 0.008 0.680 0.02 1.62

ECT 0.015(0.302) 1.044

Parenthesis () denotes p-values. DW=Durbin-Watson stat. * denotes significance at 1% level. Δ denotes first

differenced data.

The focus in the ECM is the sign and statistical significance of the error correction coefficient. The

error correction coefficient is expected to be negative. The results in Table 5.3 indicate that the

effect of LEXP on LFDI and LIMP on LFDI is statistically significant and the error correction

coefficient is negative. These two empirical regressions’ ECMs are as follows:

ΔLFDI = 0.130017 + 1.443020ΔLEXP – 0.874523( )

ΔLFDI = 0.133836 + 1.090648ΔLIMP – 0.83484( )

The effect of LEXP on LFDI would suggest that LFDI adjusts to LEXP with a lag. The speed of

adjustment from short-run discrepancy to correction to long-run equilibrium is very fast with

87.5percent of the discrepancy corrected within a quarter. Regarding the effect of LIMP on LFDI,

the results would suggest that LFDI about 83 percent of the discrepancy from short-term to long-

term equilibrium is corrected within a quarter. The estimation results of the ECM therefore indicate

that a causality relationship runs from LEXP to LFDI and from LIMP to LFDI.

5.3.1 Granger causality

In Figure 5.6 and Figure 5.7 simple Granger causality tests are employed to investigate the

possibility of bi-directional causality between LFDI and LEXP and between LFDI and LIMP. In


41

Figure 5.6 the hypothesis that LEXP does not (Granger) cause LFDI is rejected at a five percent

level of significance. Hence, LEXP does (Granger) cause LFDI and it is therefore concluded that

the direction of causality runs from LEXP to LFDI. In Figure 5.7 the hypothesis that LIMP does not

(Granger) cause LFDI is rejected at a one percent level of significance. Hence, LIMP does

(Granger) cause LFDI and it is therefore concluded that the direction of causality runs from LIMP to

LFDI. No evidence of bi-directional causality is found and the simple Granger causality test results

confirm the causality test results based on the significance of the ECM coefficient.

Pairwise Granger Causality Tests

Date: 09/07/12 Time: 10:35

Sample: 1994Q1 2011Q4

Lags: 2 Null Hypothesis: Obs F-Statistic Prob. LEXPORTS does not Granger Cause LFDI 46 5.11764 0.0104

LFDI does not Granger Cause LEXPORTS 0.18010 0.8358

Figure 5.6: Granger causality test of LFDI and LEXP

Pairwise Granger Causality Tests

Date: 09/07/12 Time: 10:35

Sample: 1994Q1 2011Q4

Lags: 2 Null Hypothesis: Obs F-Statistic Prob. LIMPORTS does not Granger Cause LFDI 46 5.69908 0.0065

LFDI does not Granger Cause LIMPORTS 0.96657 0.3889

Figure 5.7: Granger causality test of LFDI and LIMP

5.4 NON-ECONOMETRIC ANALYSIS OF MANUFACTURING LEVEL DATA

The econometric analysis performed in the previous sections used aggregated quarterly FDI flow

data in order to obtain a suitable sample size. With no disaggregated FDI flow data available in

quarterly form, disaggregated annual FDI stock data for the manufacturing sector in total could be

obtained from 1997–2010. Table 5.4 provides the descriptive statistics. Figure 5.4 provides the

trend of the disaggregated FDI manufacturing stock data for the period while Figure 5.5 compares

the aforementioned trend with the annual manufacturing exports and – imports’ trend.


42

Table 5.4: Descriptive statistics

FDI Exports Imports

Mean 128048.4 198972.2 286594.7

Median 100398.5 181686.3 243547.9

Maximum 262290.0 404211.3 563779.8

Minimum 34688.0 69740.39 107462.6

Std.Dev 74347.14 101423.9 150202.1

Skewness 0.512173 0.479219 0.461223

Kurtosis 1.954767 2.270406 1.860840

From Figure 5.8 and Figure 5.9 it can be seen that manufacturing FDI stock has trended steadily

upwards, apart from 2002. The effect of the global financial crisis in 2009 on manufacturing exports

and – imports is further apparent. The standard deviation of the FDI manufacturing stock data is

smaller than the standard deviations of the other two variables implying that the variance of the FDI

stock data have been smaller than the variances of manufacturing exports and – imports. The FDI

stock data appear to move along with the other variables over time and the correlation coefficient

would be of interest. Whilst FDI may be lagging the other variables, it has a more steady

movement upwards over the period.

Figure 5.8: Annual manufacturing FDI stock data, 1997–2010 (Rmillions)

0

40,000

80,000

120,000

160,000

200,000

240,000

280,000

97 98 99 00 01 02 03 04 05 06 07 08 09 10

FDI


43

Figure 5.9: Annual manufacturing stock data, manufacturing exports and manufacturing

imports, 1997–2010 (Rmillions)

Table 5.5 presents the correlation coefficients.

Table 5.5: Correlation coefficient

FDI and EXP 0.899462

FDI and IMP 0.923832

In both instances evidence is found of a strong positive relationship between the respective

variables. Care should be taken not make any causal inferences since these are only degrees of

association effects.

5.5 DISCUSSION

The findings of the research report differ from the findings of Ahmed et al. (2010) which found the

Granger causality running from FDI to total exports in South Africa. In Mexico, Pacheco-Lopez

(2005) reported bi-directional causality between FDI and exports and between FDI and imports

respectively. Dlamini and Fraser (2010) reported bi-directional causality between FDI and exports

in the South African agricultural sector. The current findings are supported by the Bezuidenhout

and Naude (2008) study that investigated 20 SADC countries and found that the Granger causality

test results indicated in all of the 20 countries that trade caused FDI.

0

100,000

200,000

300,000

400,000

500,000

600,000

97 98 99 00 01 02 03 04 05 06 07 08 09 10

EXPORTS FDI IMPORTS


44

Pacheco-Lopez (2005:1170) explained the causality running from exports to FDI that as MNEs

start trading in foreign markets and after learning more about local conditions, i.e. economic,

social, political and the ruling conditions of their trading partners, these MNEs may open

subsidiaries in the local country or opt for joint ventures with local firms that would result in FDI

inflows. Regarding the causality running from imports to FDI, Pacheco-Lopez (2005:1171) stated

that imports could be seen as evidence of the existence of a market in a host country and as such

FDI might be attracted to that country in order to produce that product domestically.

With the findings revealing that the causal relationship running from LEXP to LFDI, the DTI’s trade

and investment support body, TISA’s aim of increasing export capacity through business units

focussing on export promotion and export development is vindicated. The development trade

policies of the NGP that seek to promote exports are encouraging.

Regarding South Africa’s current export promotion policies, the research report findings highlight

the need for having industry-based export councils within the manufacturing sector. With the

manufacturing sector currently under pressure, the DTI should investigate how the EMIA scheme

can be made more accessible for these councils. Van Aarde’s (2007:92) recommendation of

having a permanent evaluation unit within the DTI that would incorporate export promotion best

practices should be implemented.

5.6 CHAPTER SUMMARY

The chapter presented the results of the research report. ADF tests were performed in order to

ascertain the stationary properties of the time series involved and after finding that the respective

series contained unit root processes, tests for cointegration were conducted. It was found that LFDI

and LEXP as well as LFDI and LIMP and both vice versa were cointegrated, implying a long-run

relationship between the two sets of variables. An ECM was employed to ascertain Granger

causality and it was found that a causal relationship runs from LEXP to LFDI and from LIMP to

LFDI. Simple Granger causality test results confirmed the causality test results based on the

significance of the ECM coefficient. Additional non-econometric analysis of the disaggregated data

was also performed revealing a strong positive correlation between FDI and EXP and FDI and

IMP.


45

CHAPTER 6

CONCLUSION

6.1 SUMMARY

The importance of international capital flows has increased as a result of the globalisation and

regionalisation of economies (Bezuidenhout & Naude, 2008:1). In recent years South Africa has

started to embark on policies to increase FDI and export flows. The importance of the

manufacturing sector for job creation has also been highlighted in policy documents such as the

NGP and IPAP2. Examining the link between FDI and manufacturing exports was the essence of

the research report. However, the empirical literature has highlighted the need to take into account

imports as well when investigating the said relationship.

FDI inflows to South Africa have been disappointing in recent years compared to FDI inflows to

other developing countries. The relatively low profitability of the country’s manufacturing sector is

an area of concern and the sector displayed heterogeneous performances at sub-sector level. As

yet, no specific and coherent FDI policy in South Africa exists. Export promotion in South Africa is

mainly facilitated through the DTI’s trade and investment support body, TISA.

According to Bezuidenhout and Naude (2008:3) the relationship between FDI and trade has not

been adequately addressed in African literature. A complementary relationship between FDI and

host country exports is predicted by an export platform model (Abor et al., 2008:452). The key

message obtained from the related empirical literature review suggested that FDI and

manufacturing exports are related.


trade in South Africa. The research report therefore contributed towards understanding the

aforementioned relationship in South Africa seeing that there is a lack of empirical studies in that

regard. The author of the research report is not aware of another study that focuses on the

FDI/trade relationship specifically in the manufacturing sector of South Africa.

Aggregated quarterly FDI flow data as well as quarterly manufacturing exports and – imports from

1994–2011 were the three time series of interest. Causality relationships between FDI and

manufacturing exports as well as between FDI and manufacturing imports were tested in a

bivariate setting.

Unit root tests of stationarity were performed on the respective time series and it was found that

the included variables were non-stationary at their levels, but stationary at first differences. Tests of

cointegration revealed that FDI and manufacturing exports as well as FDI and manufacturing


46

imports and vice versa were cointegrated, implying a long-run relationship between the two sets of

variables.

Causality tests based on the significance of the ECM coefficient revealed a causal relationship

running from manufacturing exports to FDI and from manufacturing imports to FDI. A simple

Granger causality test between the two sets of variables confirmed the aforementioned finding.

Non-econometric analysis of disaggregated data using total FDI manufacturing stock as variable

revealed a strong positive correlation between FDI and manufacturing exports as well as between

FDI and manufacturing imports.

Bezuidenhout and Naude (2008) stated that for developed countries, most of the evidence

suggested that FDI causes trade and that little FDI is being caused by trade. The results of the

research project therefore suggest that in the case of South Africa FDI can be caused by trade. Put

differently, exports and imports of the manufacturing sector matter in the locational inflows of FDI in

South Africa.

6.2 DISCUSSION

The South African Government’s proposed new legal framework for FDI is welcomed seeing that

no specific and coherent policy on FDI exists. A long-term and consistent policy environment is

necessary in order to promote South Africa as a foreign investment destination. A single legal

framework governing all forms of investment is desirable as a repeat of the Walmart-Massmart

merger where Ministers from three different departments filed papers with the Competition Appeal

Court should be avoided. Incentives regarding FDI must be rules based and not on discretion

(Ruane, 2008:73). The new legal framework on FDI should include local procurement policies in

order for the supply chains of local providers to be extended. This could enhance the country’s

ability to compete against imports.

Closely related to the aforementioned point is Moeti’s (2005:167) recommendation of having a

coordinating body or institution that would be tasked with overseeing all legislation and policies

relating to FDI. This coordinating body would be able to ease administrative oversight between

government departments and it is recommended that this coordinating body be housed within the

DTI as the DTI already has a trade and investment support body, TISA. Seeing that challenges

identified for smaller economies were calculating benefits and costs associated with FDI as well as

the ability to demonstrate how FDI would aid in economic development, this coordinating

institution’s role could be enhanced to help overcome these challenges.

With the European Union, as South Africa’s largest regional source of FDI, embroiled in economic

turbulence, more investment from BRIC countries should be sought. In addition, South Africa’s

exports to the BRIC countries are still small in relation to total exports. Hence, considerable scope


47

for trade and investment opportunities exists. The importance of BRIC countries increasing their

imports of value-added products is essential in any trade and investment negotiations.

The need to invest more resources in order to upgrade the country’s human capital has never been

more urgent. As skilled workers are hard to come by, labour force training and re-training should

remain a high priority on the economic development policy agenda. The enhancement of the host

country’s labour force competence must be complemented by any FDI incentive package (Ruane,

2008:73).

The controversial issue of labour markets being inflexible in South Africa has a bearing on the

country’s manufacturing sector. For instance, in the struggling clothing and textile industry under

pressure from workers, the South African Clothing and Textile Workers Union agreed with industry

to offer 30 percent lower entry-level wages (Lund, 2011c:28). This agreement was rightly hailed in

the press as this particular industry faces a host of challenges including competition from legal and

illegal imports. This type of collaboration between unions and industry should be sought in other

struggling industries within the manufacturing sector.

Infrastructure upgrading is paramount for South Africa to attract substantial FDI inflows.

Government’s recently established Presidential Infrastructure Coordinating Commission being

chaired by President Zuma is welcomed.

According to Sello (2007:46), the notion that FDI can facilitate technology and knowledge spillover

in host countries is widely accepted although the benefit seen in Africa has been limited. Increased

competition for FDI and African countries’ limited bargaining power with large MNEs has made it

difficult for local governments to demand some form of performance requirement from MNEs that

would ensure that the local economy receives the benefits from FDI. Sello (2007:47)

recommended that African countries should pool their resources and align investment policies and

regulatory environments. Regional trade blocks should take the lead and address these issues.

Although greater regional collaboration would benefit South Africa as well, South Africa should

already leverage its position as dominant economy in Africa and its natural resources to demand

greater performance requirements from MNEs, such as requiring specific investment in certain

industries within the manufacturing sector.

6.3 RECOMMENDATIONS

Since the performance of manufacturing exports stimulates more FDI inflows to the country, the

South African government should encourage FDI policies that have an export component or export

strategy. This could attract more FDI inflows that would close the investment gap in the

manufacturing sector.


48

6.4 LIMITATIONS OF THE STUDY AND POSSIBLE FUTURE RESEARCH

As has been pointed out in the research methodology chapter, total aggregated FDI flow data was

used in the econometric analysis as opposed to using the more appropriate FDI flow data within

the manufacturing sector, if this data had been available. Another limitation was the sample size of

the disaggregated data, i.e. annual FDI stock in the manufacturing sector, used in the non-

econometric analysis.

In addition, considering the scope of the research report, the study was conducted in a bivariate

setting. Ahmed et al. (2010:17) stated that a multivariate causality setting could be more reliable

seeing that in a bivariate causality framework omitted variable bias may exist. Ahmed further stated

that relationships may be more complex than only a two-way causation. In a multivariate

framework, the possibility of multiple cointegrating relationships may also be uncovered through

the use of a systems approach to cointegration.

Possible future empirical research could investigate reasons for the heterogeneous performance at

sub-sector level within the manufacturing sector and the corresponding link between investment

and trade.


49

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APPENDIX A1

ADF TESTS

Null Hypothesis: LEXPORTS has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=11) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -1.795647 0.3798


5% level -2.902953



Exogenous: Constant

Lag Length: 9 (Automatic - based on AIC, maxlag=11) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -1.512278 0.5209


5% level -2.909206



Exogenous: Constant, Linear Trend



5% level -3.474363






5% level -3.478305

10% level -3.166788


56

*MacKinnon (1996) one-sided p-values.

Null Hypothesis: LIMPORTS has a unit root

Exogenous: Constant



5% level -2.902953



Exogenous: Constant



5% level -2.910860






5% level -3.474363






5% level -3.483970



57



Lag Length: 4 (Fixed) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -3.013957 0.1362


5% level -3.478305


Null Hypothesis: LFDI has a unit root

Exogenous: Constant



5% level -2.981038

10% level -2.629906


Null Hypothesis: LFDI has a unit root




5% level -3.595026



58

APPENDIX A2

ADF TESTS ON FIRST DIFFERENCES

Null Hypothesis: D(LEXPORTS) has a unit root

Exogenous: Constant



5% level -2.905519



Exogenous: Constant



5% level -2.903566






5% level -3.483970






5% level -3.475305



59





5% level -3.479367


Null Hypothesis: D(LIMPORTS) has a unit root

Exogenous: Constant



5% level -2.910860

10% level -2.593090



Exogenous: Constant



5% level -2.903566






5% level -3.486509



60





5% level -3.475305


Null Hypothesis: D(LFDI) has a unit root

Exogenous: Constant



5% level -3.012363


Null Hypothesis: D(LFDI) has a unit root




5% level -3.644963



61

APPENDIX A3

COINTEGRATION TESTS

Null Hypothesis: S has a unit root

Exogenous: None



5% level -1.946996


Null Hypothesis: S1 has a unit root

Exogenous: None



5% level -1.946996



62

APPENDIX A4

ECM

Dependent Variable: D(LFDI_DET)


Date: 08/14/12 Time: 12:34

Sample (adjusted): 1994Q2 2011Q4

Included observations: 54 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C 0.130017 0.132809 0.978978 0.3322

D(LEXPORTS_DET) 1.443020 1.348759 1.069887 0.2897

S(-1) -0.874523 0.117723 -7.428642 0.0000 R-squared 0.520847 Mean dependent var 0.146064

Adjusted R-squared 0.502057 S.D. dependent var 1.382742

S.E. of regression 0.975733 Akaike info criterion 2.842698

Sum squared resid 48.55483 Schwarz criterion 2.953197

Log likelihood -73.75284 Hannan-Quinn criter. 2.885313

F-statistic 27.71892 Durbin-Watson stat 2.079474

Prob(F-statistic) 0.000000

Dependent Variable: D(LFDI_DET)


Date: 08/14/12 Time: 12:40



D(LIMPORTS_DET) 1.090648 1.604773 0.679628 0.4998

S1(-1) -0.834841 0.115490 -7.228681 0.0000 R-squared 0.506139 Mean dependent var 0.146064

Adjusted R-squared 0.486772 S.D. dependent var 1.382742

S.E. of regression 0.990595 Akaike info criterion 2.872931

Sum squared resid 50.04523 Schwarz criterion 2.983430

Log likelihood -74.56915 Hannan-Quinn criter. 2.915547




63

Dependent Variable: D(LEXPORTS_DET)


Date: 08/14/12 Time: 12:43


Included observations: 54 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C -0.002972 0.013757 -0.216006 0.8298

D(LFDI_DET) 0.015212 0.014219 1.069887 0.2897

S(-1) 0.019834 0.017218 1.151882 0.2547 R-squared 0.027674 Mean dependent var -0.001151

Adjusted R-squared -0.010456 S.D. dependent var 0.099663

S.E. of regression 0.100182 Akaike info criterion -1.709694

Sum squared resid 0.511863 Schwarz criterion -1.599195

Log likelihood 49.16174 Hannan-Quinn criter. -1.667079



Dependent Variable: D(LIMPORTS_DET)


Date: 08/14/12 Time: 12:47



D(LFDI_DET) 0.008229 0.012109 0.679628 0.4998

S1(-1) 0.014744 0.014124 1.043847 0.3015 R-squared 0.021053 Mean dependent var 0.006526

Adjusted R-squared -0.017338 S.D. dependent var 0.085311

S.E. of regression 0.086048 Akaike info criterion -2.013874

Sum squared resid 0.377616 Schwarz criterion -1.903375

Log likelihood 57.37461 Hannan-Quinn criter. -1.971259




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