UNIVERSITY OF ZIMBABWE

FACULTY OF SOCIAL STUDIES

DEPARTMENT OF ECONOMICS

The Extent and Determinants of Intra Industry Trade in the Food Industry:

The Case of Zimbabwe and its Five SADC Trading Partners (2000-2012)

BY

MATSURO LEON

A dissertation submitted in partial fulfilment of the requirements of the

Master of Science Degree in Economics (MSc Econ).

June 2014

i

DEDICATION

I dedicate this dissertation to my loving parents Jacob and Eunice, brothers, sisters, relatives and friends.

ii

ACKNOWLEDGEMENTS

Special mention goes to the University of Zimbabwe, the Economics Department in particular, for

affording me an opportunity to pursue my masters’ studies. I wish to mention the invaluable and priceless

support I received from my supervisor Dr. A. Makochekanwa, my International Trade lecturer at the

Joint Facility for Electives (JFE), Professor S. Buiguit and all Economics Department lecturers and staff.

The journey could not have been a fruitful one without my fellow college mates; Earnest, Dennis,

Runesu, Happiness, Godfrey, Cherish and Elson. To all of you I say thank you. To my friend Hillary

Makaya, thank you for being a true friend.

To my family, no words can describe all what you have been to me. I owe my success to you. You have

stood by me through all the turbulent times of my life, offering the much needed financial and emotional

support. You always make me believe in myself, indeed you motivate me to realize more than my average

potential.

My special gratitude also goes to the African Economic Research Consortium (AERC) for the financial

support and for affording me an opportunity to be part of the 2013 Joint Facility for Electives (JFE) in

Kenya (Nairobi).

Above all I praise my Lord for all that I am.

iii

ABSTRACT

Theoretical models of intra industry trade (IIT) have explained it using features of developed countries,

and to this end many studies have mainly focused on industrialised nations. The turn of the millennium

witnessed Zimbabwe reorienting its trade away from traditional partners, particularly the European

Union, towards the SADC region. Zimbabwe’s bilateral trade data with its SADC trade partners from

UN Comtrade shows evidence of two way exchange of goods within the same product category. This

study endeavors to ascertain the extent and determinants of IIT between Zimbabwe and its five SADC

trade partners (Botswana, Malawi, Mozambique, South Africa and Zambia) in the food manufacturing

industry.

The study calculated Grubel- Lloyd Indices for Zimbabwe’s bilateral trade with five of its trade partners

and found out that intra industry trade exists between Zimbabwe and its trade partners. However, IIT is

still low. Furthermore, the study employed the gravity model to find the significant country specific

determinants of IIT. Using panel data for five of Zimbabwe’ s trade partners over the period 2000 to

2012, the study estimated a pooled Ordinary Least Squares model in Stata. From the estimated results,

the study found that the product of partners’ GDP, the differences in partners’ GDP, weighted distance,

dummy variables for common boarder and common language were significant factors in explaining IIT

between Zimbabwe and its SADC partners in manufactured food products.

iv

LIST OF ACRONYMS

ASEAN Association of South-East Asian Nations

CZI Confederations of Zimbabwe Industries

COMTRADE Common Format for Transient Data Exchange

FAO Food and Agriculture Organization

FTA Free Trade Area

GDP Gross Domestic Product

HS Harmonized Commodity Description and Coding System

IDP Industrial Development Policy

IIT Intra Industry Trade

INT Inter Industry Trade

Mercosur MERcado COmún del SUR

MPS Monetary Policy Statement

PCI Per Capita Income

RISDP Regional Indicative Strategic Development Plan

RTA Regional Trade Agreement

SAARC South Asian Association for Regional Cooperation

SACU Southern African Customs Union

SADC Southern African Development Community

SAP Structural Adjustment Programme

UN United Nations

UNCTAD United Nations Conference on Trade and Development

WTO World Trade Organization

Zimstat Zimbabwe National Statistical Agency

v

TABLE OF CONTENT

DEDICATION............................................................................................................................................................... i

ACKNOWLEDGEMENTS ............................................................................................................................................ ii

ABSTRACT ................................................................................................................................................................ iii

LIST OF ACRONYMS ................................................................................................................................................. iv

TABLE OF CONTENT ........................................................................................................................................... v

LIST OF TABLES ...................................................................................................................................................... viii

LIST OF FIGURES ...................................................................................................................................................... ix

CHAPTER ONE ........................................................................................................................................................... 1

INTRODUCTION AND BACKGROUND ........................................................................................................................ 1

1.0 Introduction .............................................................................................................................................. 1

1.0.1. Importance of Intra Industry Trade................................................................................................... 2

1.1 Background .................................................................................................................................................... 3

1.1.1 Southern African Development Community (SADC) ............................................................................. 4

1.1.2. Economies of SADC Region .................................................................................................................. 5

1.1.3. Zimbabwe’s Manufacturing Sector ........................................................................................................ 5

1.1.4. Importance of the Manufacturing Sector ................................................................................................ 6

1.1.5. Zimbabwe’s Trade over the past two decades ........................................................................................ 7

1.1.6. Intra-SADC Trade Performance ........................................................................................................... 10

1.2. Statement of the problem ........................................................................................................................ 11

1.3. Study Objectives ..................................................................................................................................... 12

1.4. Research Hypothesis .............................................................................................................................. 13

1.5. Research questions ................................................................................................................................. 13

1.6. Significance of the study ........................................................................................................................ 13

1.7. Scope of the study .................................................................................................................................. 14

1.8. Organisation of the study ........................................................................................................................ 14

CHAPTER TWO ........................................................................................................................................................ 15

LITERATURE REVIEW............................................................................................................................................... 15

2.0. Introduction ................................................................................................................................................. 15

2.1. Theoretical review ....................................................................................................................................... 15

2.1.1. A brief review of traditional theories of trade ...................................................................................... 15

2.1.2. New Trade Theories ............................................................................................................................. 18

vi

2.2. Empirical Review ........................................................................................................................................ 22

2.3 Conclusion .................................................................................................................................................... 28

CHAPTER THREE ...................................................................................................................................................... 29

RESEARCH METHODOLOGY .................................................................................................................................... 29

3.0. Introduction ................................................................................................................................................. 29

3.0.1. Measuring IIT in the Food Manufacturing Industry ............................................................................. 29

3.0.2. The Gravity Model ............................................................................................................................... 31

3.1. The Empirical Model ................................................................................................................................... 32

3.2. Definition and Measurement of Variables ................................................................................................... 33

3.2.1. Intra-industry Trade Index ( ijkIITFM ) ............................................................................................... 33

3.2.2. Product of Gross Domestic Product between Zimbabwe and Partner k ( jkRGDP ) ........................... 34

3.2.3. Differences in Gross Domestic Product ( jkDGDP ) ............................................................................ 34

3.2.3. Dissimilarity in Per Capita Income ( DPCIjk ) ................................................................................... 34

3.2.4. Per Capita Income ( kPCI ) ................................................................................................................... 35

3.2.5. Weighted Distance ( jkWDIS ) ............................................................................................................ 35

3.2.6. Trade Intensity ( jkTI ) .......................................................................................................................... 35

3.2.7. Real Exchange Rate ( jkRER ) ............................................................................................................. 36

3.2.8. Common Border (D1) ........................................................................................................................... 37

3.2.9. Common Language (D2) ....................................................................................................................... 37

3.2.9. Free trade Area Dummy 3D ............................................................................................................. 37

3.3 Data Sources and Problems .......................................................................................................................... 38

3.4 Diagnostic Tests ........................................................................................................................................... 38

3.5 Conclusion .................................................................................................................................................... 39

CHAPTER FOUR ....................................................................................................................................................... 41

ESTIMATION AND RESULTS .................................................................................................................................... 41

4.0. Introduction ................................................................................................................................................. 41

4.1. Results of Intra Industry Trade Shares ........................................................................................................ 41

4.2. Descriptive Statistics ................................................................................................................................... 43

vii

4.3. Econometric Tests and Estimation of Results ............................................................................................. 44

4.4. Estimation of the Model .............................................................................................................................. 45

4.5. Conclusion ................................................................................................................................................... 49

CHAPTER FIVE ......................................................................................................................................................... 50

CONCLUSIONS AND POLICY RECOMMENDATIONS ................................................................................................ 50

5.0. Introduction ................................................................................................................................................. 50

5.1. Conclusions of the Study ............................................................................................................................. 50

5.2. Policy Implications and Recommendations ................................................................................................ 51

5.3. Study Limitations and Areas for Further Research ..................................................................................... 53

REFERENCES ........................................................................................................................................................... 54

APPENDICES ............................................................................................................................................................ 58

Appendix 1: Commodity List and Description ................................................................................................... 58

Appendix 2. Correlation Coefficients (Stata printout) ....................................................................................... 59

Appendix 3. F-test (Poolability test) ................................................................................................................... 59

Appendix 4. Breusch and Pagan Lagrange Multiplier Test for Random Effects (Stata printout) ...................... 59

Appendix 5. Hausman Test (Stata printout) ....................................................................................................... 60

Appendix 6. Heteroskedasticity Test ................................................................................................................... 60

Appendix 7. Unrestricted Pooled OLS Regression Results (Stata printout)....................................................... 61

Appendix 8: Restricted Pooled Ordinary Least Squares regression results ((Stata printout) ........................... 62

Appendix 9: Random Effects Model Estimation Results (Stata printout) ........................................................... 63

Appendix 10: Fixed Effects Model (within) Regression Results (Stata printout) .............................................. 64

viii

LIST OF TABLES

Table 1: Manufacturing Sector Statistics.................................................................................................................. 6

Table 2: Intra SADC trade shares (%) .................................................................................................................... 11

Table 3: G-L Indices for Zimbabwe’s Intra Industry Trade ................................................................................... 41

Table 4: Summary of Descriptive Statistics ........................................................................................................... 43

Table 5: The Restricted Pooled OLS Model .......................................................................................................... 46

ix

LIST OF FIGURES

Figure 1: Export Market Concentration 1991 ............................................................................................ 8

Figure 2: Export Market concentration 2011 ............................................................................................. 8

Figure 3: Percentage changes in exports and imports (2009-2013) ........................................................... 9

Figure 4: Zimbabwe’s trade balance (2009-2013) ................................................................................... 10

1

CHAPTER ONE

INTRODUCTION AND BACKGROUND

1.0 Introduction

Liberalisation, be it in trade or finance, has been the facet of globalization. For many developing

countries sustained growth hinges on trade and capital inflows (Santos-Paulino, 2012). While trade

has been viewed as ‘economically benign’ due to its effect of increasing market size, critics have

argued that it is ‘socially malign’ on a number of fronts especially on its implication to poverty

(Bhagwati and Srinivasan, 2002)

International trade involves the exchange of various commodities between countries and the

exchange can be broadly classified under inter industry trade (INT) and intra-industry trade (IIT)

(Marrewijk, 2008). Trade between countries has historically been characterized by INT, which

involves the exchange of products originating from different industries. Traditional models of trade

(Ricardo and Heckscher- Ohlin models) have explained this type of trade in terms of differing

relative technologies (comparative advantage) and factor endowments (Ates and Turkcan, 2010).

Over the past four decades it has been observed that a significant portion of world trade is

increasingly being conducted between similarly endowed nations and it takes the form of IIT

(Kocyigit and Sen, 2007). While the traditional theories are capable of explaining INT, some

scholars1 argue they fall short in explaining trade between similarly endowed countries. The late

1970’s witnessed the emergence of ‘new trade theories’ which aimed at explaining the phenomena

of IIT, taking into account imperfect competition, economies of scales, and product differentiation

(Leitão and Faustino, 2009). IIT arises if a country simultaneously imports and exports similar

type of goods and services. Similarity is identified by the goods or services being classified in the

same sector (Marrewijk, 2008).

1 Krugman (1979), Lancaster (1980), Helpman & Krugman (1985), Balasssa and Bauwens (1988)

2

IIT is measured as an index and studies have applied the Balassa index as well as the Grubel-

Lloyd (GL) index. This study employs the GL index whose values range between 0 and 100, with

a value of zero indicating total inter-industry trade while that of 100 representing total intra-

industry trade.

Trade data from UN Comtrade shows that Zimbabwe exports and imports commodities within the

same statistical category to and from its partners in SADC, leading us to suspect of the existence

of IIT. This study endeavors to separately examine the degree to which this two-way exchange of

goods from the same industrial classification takes place. Furthermore, it examines the country

specific determinants of IIT between Zimbabwe and its SADC trade partners in the food

manufacturing industry. Our focus will be limited to Zimbabwe and its five trade partners in SADC

that is, Botswana, Malawi, Mozambique, South Africa and Zambia. The choice was motivated by

the fact that these partners trade much with Zimbabwe than the rest of the SADC region.

1.0.1. Importance of Intra Industry Trade

IIT does not have to be based on comparative advantage (Ruffin, 1998), thus it can be argued that

regardless of the endowment characteristics, countries can gainfully trade with each other.

Greenaway and Milner (1983) argue that accurate measurement of IIT is important for two main

reasons. First it ‘can give some indication of the importance of determinants of international trade

other than relative factor proportions’ for instance the role of diversity of preference and

increasing returns to scale (p, 300). Secondly, more trade expansion is likely to have less

adjustment costs if it is IIT as opposed to INT. The second explanation confirms Krugman’s (1981)

observation that much trade has been conducted with minimal reallocation of resources and

distribution effects on income.

Cattaneo and Fryer (2003) argued that if trade liberalisation leads to IIT, then it will pose less

challenges in terms of job losses. The prospect of reemployment are higher compared to the case

of INT, thus IIT reduces the possibility of poor households slipping into deeper poverty upon

losing a wage-earning job. Havrylyshyn and Kunzel (1997) note that the degree of IIT gives an

3

impression of the industrial sector’s diversity and degree of specialization, hence the ability of a

country to be a competitive participant in the ever changing trade environment.

Theoretical models of IIT associate IIT with economies of scale in production, product

differentiation and imperfect competition (Krugman, 1979). IIT permits countries to exploit

economies of scale and thus enhances gains from trade as countries specialise on a limited variety

of products within an industry, effectively lowering per unit costs. Furthermore, specialisation in

a specific industrial category stimulates innovation, which increases efficiency and has a positive

contributory effect on long run economic growth (Ruffin, 1998).

IIT reduces the need for industrial players to call for protection as there will be both exporters and

importers within an industry thus unanimity is difficult to achieve in lobbying for protection

(Marvel and Ray, 1987). Mulenga (2012) argues that it is in this vain that many developing

countries are realizing the potential benefits of expanding trade amongst themselves in the form of

IIT. This resonates well with the envisaged SADC customs and monetary union, as well as the

proposed tripartite FTA (COMESA2- EAC3- SADC4), which all represent higher levels of

integration amongst relatively similarly endowed countries.

1.1 Background

The world has increasingly become a global village, and with economic interdependence now a

common feature, there has been a proliferation of Regional Trade Agreements (RTA) (Kalaba and

Tsedu, 2008). For developing countries, the challenge has been on how to participate more

effectively in the world economy especially given their relatively small economies (Aybodji,

2008). To this end, nations in the Southern African region have sought to integrate into the world

economy through the establishment of the Southern African Development Community (SADC).

2 Common Market for Eastern And Southern Africa 3 East African Community 4 Southern African Development Community

4

1.1.1 Southern African Development Community (SADC)

SADC is a fifteen member5 regional economic grouping formerly known as Southern African

Development Coordination Conference (SADCC), which was transformed into a formal treaty

based organisation in August 1992 (SADC Secretariat). The Windhoek Extra Ordinary Summit of

March 2001 gave impetus for the formulation of the Regional Indicative Strategic Development

Plan (RISDP).

In 2003 member states adopted the RISDP, a policy document which outlines the bloc’s regional

economic agenda. The RISDP defines four6 clusters within which policies and strategies are to be

evaluated with the intention of deepening regional integration and cooperation. Intra-regional trade

in the block is influenced by the SADC Protocol on Trade whose objectives as stated in Article 2

are:

1. ‘To further liberalise intra-regional trade in goods and services on the basis of fair, mutually

equitable and beneficial trade arrangements, complemented by Protocols in other areas’.

2. ‘To ensure efficient production within SADC reflecting the current and dynamic comparative

advantages of its members’.

3. ‘To contribute towards the improvement of the climate for domestic, cross-border and foreign

investment’.

4. ‘To enhance the economic development, diversification and industrialisation of the Region’.

5. ‘To establish a Free Trade Area in the SADC Region’.

To achieve the stated objectives as is enunciated in the RISDP and outlined in the amended Trade

Protocol (2005), the region sought to establish a free trade area (FTA) by 2008, a customs union

(CU) by 2010, a monetary union (MU) by 2016, with the ultimate goal of achieving a single

currency by 20187.

The FTA was attained in August 2008 after the minimum conditions of the FTA attainment were

met (85% of intra-regional trade had attained free duty). However, the tariff phase down process

5 SADC member states are Angola, Botswana, Democratic Republic of Congo, Madagascar, Lesotho, Malawi, Mauritius, Mozambique, Namibia, South Africa, Swaziland, Seychelles, Zambia and Zimbabwe 6 Trade, Industry, Finance and Investment (TIFI); Infrastructure and Services (IS); Food, Agriculture and Natural Resources (FANR); and the Social and Human Development and Special Programmes (SHDSP) cluster. 7 http://www.sadc.int/about-sadc/integration-milestones/free-trade-area/

5

was completed in January 2012, when complete tariff liberalization was achieved. Despite having

attained FTA status, the region has already missed the customs union date and it remains to be

seen if the monetary union and the single currency union will be achieved during the intended time

frames.

1.1.2. Economies of SADC Region

The characteristics of SADC member states mirror those of developing countries, all of them

except for South Africa and Mauritius (25% of GDP from manufacturing) have a higher

proportion of GDP originating from agriculture and mining (about 50%). Overall, SADC states

have relatively small, less diversified manufacturing sector which draw most of its raw material

from agriculture (SADC, RISDP, 2003).

1.1.3. Zimbabwe’s Manufacturing Sector

Zimbabwe’s manufacturing sector is relatively diversified, with the capacity to produce a variety

of commodities. The sector was nurtured through deliberate policies aimed at import substitution

as the then Rhodesian government sought to be self-sufficient in view of the sanctions imposed

during the period 1965 to 1980 (Zimtrade8). At independence in 1980, Zimbabwe inherited a dual

economy which by Sub Saharan Africa (SSA) standards was relatively a developed one.

Zimbabwe had an average of 12.2 % share of GDP derived from the agricultural sector, compared

to SSA average of 31.6% and manufacturing sector share of GDP averaged 23.3% for Zimbabwe

against the SSA average of 10.4% over the period 1980 to 1989 (Kanyenze, 2006). Noteworthy is

the fact that after independence Zimbabwe pursued the import substitution strategy that worked

well during the sanction period until the early 1990’s which ushered in Structural Adjustment

Programmes (SAPs). SAPs came with a cocktail of liberalisation measures which saw the current

8 http://www.zimtrade.co.zw/site/site.asp?section=2&page=2

6

account being opened up and a gradual movement towards market determined exchange rates

amongst other trade related reforms.

1.1.4. Importance of the Manufacturing Sector

The manufacturing sector has strong forward and backwards linkages with all sectors of the

economy, and due to the higher degree to which it is integrated with these sectors, its performance

has spiral effects on the macro economy (IDP, 2012). During the first decade after independence,

the sector absorbed 76% of domestic inputs with the rest coming from imports (Mudzonga, 2009).

The sector was the biggest contributor to GDP during the first decade after independence.

However, the economic challenges that faced Zimbabwe post 2000, witnessed a decline in

manufacturing sector contribution to GDP as low as 16.4% in 2007. At the same time Zimbabwe

witnessed massive deindustrialisation, with successive negative growth rates in manufacturing

which reached an all-time high of 17.1% in 2008, before rebounding in 2009 as the economy

adopted a multi-currency regime (see table 1.1). The adoption of multi-currency stopped the

economic haemorrhage, which had bled the economy as it shed a cumulative 40% of GDP over

the ten year period; a time frame which represents Zimbabwe’s own version of the lost decade

(IDP, 2012).

Table 1: Manufacturing Sector Statistics

Measure 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

% of GDP 14.6 13.3 13.6 15.1 16.4 16.9 16.4 16.7 17.8 17.9 18 16.8

% of exports 37 40 39 42 37 35 31 27 27 27 27

% growth rate -11 -5 -13 -10 4 -3 -5 -17.1 7.54 19.96 15 2.33

capacity % 56 58 49 56 35 32 17 8 31 42 57 44.9

Sources: World Bank, World Development Indicators, CZI annual surveys

Apart from being a significant contributor to GDP, the manufacturing sector during its peak period

provided employment and income to a significant proportion of the population. It also contributes

significantly to the overall domestic supply of goods as well as exports, supplying markets within

7

the region and beyond (IDP, 2012). Over the twelve year period, the manufacturing sector at its

peak contributed 42% to export proceeds in 2004, however, its contribution has remained

depressed over the years as manufacturing growth rates dropped to -17.1% in 2008. Despite the

improvements in capacity utilisation after the economic crisis, the manufacturing sector’s

contribution to export receipts has largely remained depressed at 27% over the years, signalling

the inability of local industry to effectively compete with its regional peers, particularly South

Africa which has a relatively more diverse and thriving manufacturing sector.

Zimbabwe’s policy documents, the Industrial Development Policy (IDP) and National Trade

Policy (NTP), launched in March 2012 recognized that industry should play a crucial role in the

country’s development, particularly in the production of value added products for both the

domestic and export markets. The IDP sets targets to raise manufacturing sector’s contribution to

exports from 26% in 2012 to 50% by 2015, and to increase its contribution to GDP from 15% in

2012 to 30% by 2015. However, with the challenges being continuously faced by the

manufacturing sector, the targets outlined above may not be achievable

The sector continues to be constrained by a myriad of challenges, including, scarcities of working

capital, obsolete equipment, unreliable power supply, unjustifiable wage demands, stiff

competition from imported products and depressed domestic demand (MPS9, 2011)

1.1.5. Zimbabwe’s Trade over the past two decades

Zimbabwe’s trade has undergone significant transformation over the past two decades.

Saungweme (2013) noted that the two time frames (1990-1999 and 2000-2009) exude sharp

differences in trade patterns particularly the composition of trade, its value and the associated

policies. Zimbabwe’s trade was more skewed towards the developed world in the early 1990’s.

Statistics from Zimstat indicate that about 60% of Zimbabwe’s trade was conducted with

Germany, the European Union, United States of America, United Kingdom and Japan.

Furthermore, no single trade partner accounted for more than 15% of Zimbabwe’s total trade, thus

there was spread of trade risk.

9 Monetary Policy Statement

8

Figure 1: Export Market Concentration 1991

Source: Zimstat (2013)

In sharp contrast, Zimbabwe’s trade has reoriented from its traditional partners towards its

neighbouring countries. South Africa is now the dominant partner in Zimbabwe’s trade accounting

for more than 60% of total trade over the past years. Figure 1 above and Figure 2 below give a

snapshot of the export market concentration for the two periods.

Figure 2: Export Market concentration 2011

Source: Zimstat (2013)

14% 11% 7% 6% 6% 5% 4% 4% 4% 4%

34%

% s

har

e

country

Export market concentration 1991

68%

6% 5% 4% 2% 2% 2% 2% 1% 1% 8%

% s

har

e

country

Export market concentration 2011

9

However, despite the reorientation of trade towards the SADC region, Zimbabwe has continuously

witnessed trade deficits. Merchandise exports increased by 44.6% from US$1,796 million in 2009

to US$3,243.2 million in 2010, before increasing further by 26.6% in 2011 as the economy

witnessed its first successive growth rates in a decade. On the other hand, imports rose by 29.1%

in 2010 from US$3,662.00 million in 2009 to US$5,161.8 million in 2010, before jumping to

US$7,562.00 million in 2011 (an equivalent 31.7% increase).

Both exports and imports declined in 2012, shedding approximately 16% and 12.7% respectively.

However, imports rose by 3.5% while exports fell by an additional 6.6% in 2013. Figure 3 and 4

below give a diagrammatic representation of these developments.

Figure 3: Percentage changes in exports and imports (2009-2013)

Source: Zimstat (2013)

-20

-10

0

10

20

30

40

50

2010 2011 2012 2013

PER

CEN

TAG

E

YEAR

percentage changes in exports and imports

% Exports % Imports

10

Figure 4: Zimbabwe’s trade balance (2009-2013)

Source: Zimstat (2013)

Overall, the economy witnessed persistent increases in trade deficits over the period, which

marginally fell in 2012 before taking a rebound in 2013. This comes on the backdrop of falling

industrial capacity utilisation and relatively weak GDP growth. Zimtrade in their manufacturing

sector capacity survey for 2013 reported that only 40% of the companies that used to export are

still exporting.

1.1.6. Intra-SADC Trade Performance

At country level, most SADC countries conduct their trade within the SADC region. However, for

the relatively more developed South Africa, its intra SADC trade share is relatively low, with

levels below 10% over the period 2000 to 2011. Zimbabwe’s trade on the other hand is tilted more

towards its SADC partners and it has the highest intra-SADC shares compared to the five countries

under consideration. At the aggregate level, intra SADC trade is relatively low and is dominated

with values below 20%. This however could be explained by South Africa’s relatively large extra

SADC trade shares compared to its partners in the SADC region. Despite the attainment of FTA

status in 2008, intra-regional trade shares are still lower compared to the 2002 level of 22%.

-4000

-2000

0

2000

4000

6000

8000

10000

2009 2010 2011 2012 2013

US$

MIL

LIO

N

YEAR

Zimbabwe's trade balance 2009 to 2013

Trade Balance Exports Imports

11

Table 2: Intra SADC trade shares (%)

Country Year

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Botswana 0.41 0.43 0.54 0.47 0.46 0.43 0.44 0.47 0.53 0.54 0.50 0.46

Malawi 0.39 0.41 0.45 0.44 0.47 0.51 0.51 0.47 0.44 0.44 0.35 0.34

Mozambique 0.33 0.33 0.38 0.32 0.30 0.35 0.29 0.31 0.24 0.35 0.46 0.32

South Africa 0.07 0.06 0.07 0.06 0.05 0.06 0.06 0.07 0.08 0.08 0.08 0.08

Zambia 0.51 0.50 0.57 0.58 0.53 0.41 0.37 0.38 0.39 0.39 0.37 0.39

Zimbabwe 0.72 0.82 0.66 0.56 0.62 0.58 0.68 0.68 0.73 0.75 0.61 0.67

SADC 0.20 0.19 0.22 0.20 0.19 0.16 0.16 0.16 0.16 0.18 0.18 0.18

Source: Own calculations10 from SADC Secretariat Statistics Unit, Trade Database (2013)

Statistics from the World Bank indicate that South Africa is the dominant import source for the

SADC countries. In 2012, South Africa accounted for 62.8%, 56.2%, 35.7%, 31.4% and 25% of

Botswana, Zimbabwe, Zambia, Mozambique, and Malawi’s imports respectively. This might be a

possible explanation for the relatively high intra SADC trade shares of the considered SADC

countries. These statistics confirm Cattaneo and Fryer (2003) observation that South Africa is the

dominant source of imports for a number of SADC countries.

1.2. Statement of the problem

Most empirical studies of IIT have mainly focused on developed countries because theories of IIT

are based on features of industrialized nations (Al-Mawali, 2005). Due to that fact the much of

SSA’s exports are driven by primary products (minerals, oil and agricultural produce), the general

view has been that developing countries do not engage in IIT amongst themselves. Zimbabwe’s

IIT is severely limited in literature, however, trade statistics from UN Comtrade show that

Zimbabwe is both an exporter and importer of manufactured food products belonging to the same

statistical category, and this is especially so for its trade with the SADC region.

10 Intra SADC trade share is calculated as a proportion of a country’s total trade with the SADC block as a fraction of that country’s total trade i.e. total SADC trade and trade with the rest of the world.

12

The volume of intra SADC trade has tripled over the past decade; however it has been noted that

traded commodities lacked diversification with four sectors11 accounting for about 98% of intra

SADC trade (SADC, 2012). Despite increases in intra-regional trade volumes, studies of IIT have

found that IIT in SSA is generally low compared to other developing regions of the world

(ASEAN, Mercosur and SAARC). The South- South trade monitor reports the GL intra-industry

trade index in the SADC region at around 0.05 (UNCTAD, 2013). Thus trade in general is

dominated by INT.

Proponents of IIT argue that it is beneficial as it allows exploitation of economies of scale,

stimulates innovation and has relatively minimal reallocation effects on factors of production and

hence their returns. However, with the low IIT, SSA and Zimbabwe in particular will potentially

lose out on benefits associated with IIT. Moves towards opening up for trade may face stiff

resistance from the import competing sectors, as these sectors may be hurt. This is particularly the

case with manufacturing companies in Zimbabwe, most of which are calling for higher import

duties. The question then is, what are the determinants of IIT between Zimbabwe and its five

SADC trade partners in the food manufacturing industry?

1.3. Study Objectives

The study examines the current trade patterns of Zimbabwe’s food manufacturing industry brought

by the several developments that reshaped the industry over the past decade. Furthermore, it

identifies country specific factors that determine the degree of IIT between Zimbabwe and its

SADC trading partners over the period 2000 to 2012. The specific objectives are:

To ascertain the pattern and magnitude of IIT between Zimbabwe and its SADC trading

partners in the food manufacturing industry.

To identify the determinants of IIT between Zimbabwe and its trading partners in SADC.

11 unprocessed agricultural products, food manufacture, textiles and clothing

13

1.4.Research Hypothesis

The study seeks to test the following hypothesis

a) IIT does not necessarily take place between countries of small economic size and at the

same level of development.

b) Similarity in per capita income is not the main determinant of IIT

1.5.Research questions

a) Is there IIT between Zimbabwe and its SADC trading partners in the food manufacturing

sector?

b) What is the extent of IIT between Zimbabwe and SADC?

c) What are the significant determinants of IIT between Zimbabwe and SADC?

1.6.Significance of the study

Empirical studies on IIT are many in the literature; however, ‘little research has been done on

developing countries’, Al-Mawali (2005, p. 407). To the best of our knowledge, with the exception

of Sunde et al (2009), Zimbabwe – SADC trade has been neglected in empirical studies of IIT.

Comparing Sunde et al (2009) with this study is impossible for two main reasons; firstly their

study covers part of the period when the country was sliding into an economic crisis (1990-2006)

while ours covers the period when the country was sliding into the economic crisis, the crisis period

and the post crisis period (2000-2012). Hence relying on their results for policy may be misleading.

Secondly our study specifies the industry concerned (food manufacturing industry) while theirs

had a macroeconomic bias.

Furthermore, no specific study to date has been published on our research area. However,

Zimbabwe - SADC trade in processed food products has become much more important than before

in recent years due to the surge in imported food products. The study, therefore, attempts to fill

this gap by examining the recent changes in the trade patterns of the food manufacturing industry

in Zimbabwe.

14

By evaluating the existence of IIT in food products, the study determines whether IIT takes place

among countries with similar economic structures. Furthermore, it sheds light on the likely effects

of further opening up of trade on Zimbabwe’s food manufacturing sector especially considering

the envisaged SADC customs and monetary union. This study will thus contribute to the empirical

literature on Zimbabwe’s IIT especially in the food manufacturing sector. Bringing to the fore,

evidence to ascertain the nature of trade in the food manufacturing industry and thus give policy

recommendations on whether it is justified for industry to call for protection.

1.7. Scope of the study

The study utilizes panel data for the period 2000 to 2012, for Zimbabwe and five of its major trade

partners in the SADC region. The countries included are Botswana, Malawi, Mozambique, South

Africa and Zambia. The choice of these countries is motivated chiefly by the availability of data

and also the fact that they are engaged in significant trade with Zimbabwe.

1.8. Organisation of the study

This chapter gave the background and overview of the study. The remaining part of the study is

organised as follows; Chapter 2 covers the literature review, Chapter 3 outlines the methodology,

with Chapter 4 presenting the estimation and results, while Chapter 5 presents the findings and

policy recommendations.

15

CHAPTER TWO

LITERATURE REVIEW

2.0. Introduction

This chapter gives an outline of the literature review, bringing to the fore prominent aspects in

trade theory as well as empirical findings related to the field of IIT. The theoretical review focuses

on the evolution of trade theory, explaining the developments from traditional trade theory to new

trade theories (explaining IIT). The section of empirical review looks at studies that have been

done on IIT, their findings and contribution to IIT literature.

2.1. Theoretical review

International trade can be broadly classified into inter-industry trade (INT) and intra-industry trade

(IIT). The two types of trade are largely distinguished through their sources and therefore the

theories that explain them. Ates and Turkcan (2010) define IIT as, ‘the simultaneous export and

import of products which belong to the same statistical category’ (p, 16), a definition which we

will use for the purpose of this study.

2.1.1. A brief review of traditional theories of trade

This section explains the traditional theories of trade, all of which explain INT which essentially

results from supply side factors related to differences in endowments. The theories predict that

similarly endowed nations have no reason to trade particularly if the trade involves an identical

commodity.

i. Absolute advantage (Adam Smith)

The theory of absolute advantage in trade can be traced to the work of Adam Smith (1776),

(Makochekanwa, 2007). The theory postulates that two countries can benefit from trade if they

specialize in the production of products in which they are more efficient and exchanging part of

16

the output for the product in which they are less efficient. Thus, a country would specialize in

production of a good in which it has absolute advantage and export the excess to finance purchases

of the good in which it has absolute disadvantage.

ii. Comparative advantage (David Ricardo)

According to Davis (1998), the Ricardian theory is premised on the fact that, ‘technical differences

matter for trade patterns when expansion of an individual sector does not drive up marginal

opportunity costs’ (p, 203). The basic insight of the theory which departs from the absolute

advantage theory is that trade is dependent on comparative not absolute advantage. Sen (2010)

states that the Ricardian theory postulates that comparative advantage is a necessary and sufficient

condition for mutually beneficial trade as it warrants complete specialisation in the commodity

with which a country has a comparative advantage in terms of labour hours devoted per unit output.

iii. Heckscher-Ohlin (HO) Factor Proportions Theory

The HO factor proportions theory of comparative advantage postulates that international trade

offsets the uneven geographical distribution of productive resources. It is premised on the

assumption that nations have different relative factor endowments and unlike Smith and Ricardo’s

theories, it considers capital as an additional factor of production. The theory predicts that a

country would specialize and export a good which uses intensively a factor in which it is relatively

abundantly endowed, and in turn import a good which uses intensively the factor in which it is

scarcely endowed. The basic insight of the model is that traded goods constitute bundles of factors

(labour and capital), thus “international exchange of commodities is therefore indirect factor

arbitrage, transferring the services of otherwise immobile factors of production from locations

where these factors are abundant to locations where they are scarce” (Leamer, 1995; p. 1).

The differences in endowments reflect differences in factor prices and hence the ultimate product

prices, thus in autarky differently endowed countries will have different terms of trade which forms

the basis for trade. Opening up for trade will result in relative commodity price equalization as

countries eliminate the excess supply and demand in their countries. Assuming there are no barriers

to trade, consumers will purchase goods from a cheaper source as long as there are price

17

differentials, until relative prices equalize in the two countries. At such a point there will not be

any excess demand or supply as export supply even out import demand (Markusen et al, 1995).

iv. The Factor Price Equalization Theorem (FPET)

The relative abundance of productive factors within a country determines those factors' relative

costs (Hanink, 1988). As countries reorganize production upon opening up for trade, there will be

more demand for the abundant factor and less demand for the scarce factor. This comes about as

countries specialize in the production of the product using the abundant factor intensively. The

prices of the abundant factor in both countries will increase while those of the scarce factor falls

until the relative factor prices in both nations are equalized as countries simultaneously realize

relative commodity price equalization.

v. Stolper - Samuelson Theorem (SST)

The theorem explains the distributive effects of trade on returns to factors of production. It predicts

that opening up for trade would reward the abundant factor at the expense of the scarce factor. A

capital abundant country would thus demand more capital to increase its output of a capital

intensive good to meet increased demand from the international market. It will draw some of this

additional capital from the labour intensive industry and in so doing increase the marginal

productivity of capital in both sectors (the labour and capital intensive industries), simultaneously

reducing that of labour. Since factors are paid their marginal productivities, capital in this case is

rewarded, and labour is at the losing end.

vi. The specific factors model (SFM)

According to Markusen et al (1995) work on the SFM, can be traced to Jones (1971) and

Samuelson (1971). The assumptions building this model are the same as those of the HO theorem,

with the only difference being the existence of capital specific to sectors. ‘With sector-specific

capital and mobile labour, the model shows that not all units of a factor have the same interest in

the opening or restriction of international trade’ Cattaneo and Fryer (2003, p. 8). Upon opening

18

up for trade, the real income of capital used in the production of the export good increases, while

the real income for the specific capital associated with the production of the import competing

good declines. The nominal wage for the mobile factor (labour) increases, however, the real wage

falls in terms of the export good and increases in terms of the import good. The theory implies that

with specific factors, there is an ambiguous gain of the specific factor used in the expanding sector

and a fall in the specific sector used in the contracting sector.

Summary

All the above theories are supply oriented and predict that trade is only feasible between countries

with different endowments. Thus they are plausible in explaining trade between industries, what

is often termed ‘inter-industry’, however they fail in explaining trade as well as its related effects

between countries with the same endowment characteristics; ‘intra-industry trade’.

2.1.2. New Trade Theories

According to Al-Mawali (2005), recent trade theories depart from traditional trade theories by

relaxing the assumptions of the HO theorem. The new theories take into account imperfect

competition, product differentiation and economies of scale in world trade. These theories explain

IIT.

i. Linder Theory

The theory was proposed by a Swedish economist Staffan Burenstan Linder in 1961. He took a

different perspective in explaining trade and argues that trade should not be addressed from the

supply side as in comparative advantage models, but should be viewed as an interrelationship

between similar markets (Hanink, 1988). Linder argues that trade amongst countries with similar

endowments results from overlapping demands. The theory postulates that consumer tastes and

preferences are influenced by their level of income, thus the per capita income level of a country

will yield a particular pattern of tastes. Producers differentiate their products to meet the demands

of domestic consumers, thus in essence income levels determine a pattern of tastes which in turn

trigger a production response (Appleyard, 2009).

19

In explaining the Linder theory, Appleyard (2009), hypothesised a three country scenario; country

I with a relatively low per capita income compared to II, and country III having the highest income

of the three. Due to lower per capita income in country I, consumers will demand products A, B,

C, D and E arranged in ascending order in terms of quality. A higher per capita income in country

II will yield demand of say products C, D, E, F and G, while an even higher per capita income in

country III will yield demand of E, F, G, H and I. Given the pattern of production in the three

countries, trade will only be observed for goods that have ‘overlapping demand’. Country I and II

will trade in goods C, D and E; country II and III will trade in goods E, F and G; while country I

and III will trade in product E. An important implication of this theory is that international trade

will be more intense between countries with similar per capita incomes.

ii. The Krugman Model (1979)

The model was put forward by Paul Krugman to explain three seemingly paradoxes or rather

stylised facts about modern day trade. He relaxed traditional theories assumptions of perfect

competition and constant returns to scale, opting for economies of scale and monopolistic

completion. He further assumed two factors of production (Labour 1 and Labour 2) which are

specific to industry 1 and 2, and whose wage rates are given by W1 and W2. Each industry

consisting of a number of firms specialising in differentiated products, operating on the portion

where average costs are falling. Due to fixed costs in production, a firm wishing to exploit

economies of scale will specialise in a certain line of products within an industry effectively

reducing it’s per unit cost. Thus economies of scale necessitate differentiation; which amongst

similarly endowed nations results in simultaneous exports and imports within an industry.

Opening up for trade will result in factor price equalisation; however, the pattern of production is

left unchanged. Two effects on welfare arise. First, real wage remain unchanged in terms of the

products of its industry, but may fall or rise in terms of the other depending on whether it’s a scarce

or abundant factor. The second is associated with an expanded market and hence increased variety

and it works to everyone’s benefit. However, overall IIT benefits all factors as the fall in wage of

the scarce factor is offset by the gains derived from a larger market. According to Krugman (1979)

20

‘…if economies of scale are sufficiently important both factors gain from trade’ (p. 14). It can

thus be argued that IIT is associated with fewer adjustment problems as compared to HO trade.

iii. The Falvey Model (1981)

Rodney Falvey (1981) developed a model of IIT, embodying elements of the Heckscher-Ohlin

model with the concept of product differentiation. The model assumes two factors of production

(capital and labour) and two countries (A and B) with different factor endowments. The model

further assumes that any given variety of a product X can be produced in the two countries,

however, the products differ in terms of quality. The quality differences arise from the differing

capital to labour (K/L) ratios used in the production processes. A higher K/L ratio indicates a

higher quality variety of good X and a lower K/L ratio is associated with a lower quality variety

of good X.

The differences in factor endowments implies that there will be different K/L ratios between

countries. If for simplicity we assume that country A is capital abundant and B is labour abundant;

given the endowment characteristics, A would have a comparative advantage in producing the

higher quality variety with B having a comparative advantage in the lower quality variety. In

autarky, the higher quality variety will be cheaper in country A, but expensive in B. Conversely

the lower quality variety will be cheaper in B but expensive in A. This forms the basis for trade.

Country A exports the higher quality variety to B in exchange for the lower quality version of

product X from B. The end result is a pattern of IIT based on factor endowments. Contrary to the

Krugman model, IIT is not explained by economies of scale and imperfect competition, but is

rather ‘…a result that reflects a linking of factor endowments and intensities to the phenomena of

product differentiation’ Appleyard et al (2009, p. 184)

iv. Explanations for IIT

There is general consensus among trade economists that comparative advantage theories of factor

endowments are of little help in explaining trade between countries with relatively the same

endowments (capital and labour). Theoretical work on IIT can be traced to the work of Grubel and

21

Lloyd (Al-Mawali 2005). In an extract from Grubel and Lloyd seminal discussion, Appleyard et

al (2009) identifies the following as some of the plausible explanations for IIT;

a) Product differentiation

According to Appleyard et al (2009), ‘product differentiation refers to products that are

seemingly the same but which are perceived by the consumer to have real or imagined

differences’ (p. 180). Consumers in both countries will thus purchase imports within the same

product category for the ‘perceived differences’ even though the products may be the same,

for instance different brands of rice or soft drinks.

b) Transport costs

When transport cost are significant it may necessitate consumers to import rather than buy

from within their boundaries and this is specially so for low value bulky products. For instance

a miller in Victoria Falls may find it cheaper to buy grain from Livingstone (Zambia, a distance

of less than 30km) than to drive a distance of 900km to Harare. Thus despite the fact that a

country produces enough for domestic consumption, consumers may find it desirable to import

the same product after factoring in transport costs.

c) Dynamic economies of scale

He termed this, “learning by doing’ which results in per unit cost reduction due to experience

in the production of a particular good. This explanation is related to the product differentiation

argument. Each producer produces for both domestic and export market, thus the cost reduction

necessitates an increase in sales of each of the versions of the product over time, which

enhances intra industry trade. Krugman (1981) argues that economies of scale in production

necessitates countries to concentrate on a ‘subset of products’ within a particular industry and

this gives rise to intra industry specialisation and trade.

d) Degree of product aggregation

Some economists argue that IIT is a ‘statistical artefact’ arising from the degree of aggregation

used (the way trade data is recorded and analysed). If a product category is broad for instance

beverages and tobacco we may have a higher degree of IIT compared to when its narrowed

22

down to a specific product like a certain brand of wine. Davis (1995) cites Finger (1975) and

Chipman (1985) as having argued that, ‘…existing classifications place goods of

heterogeneous factor proportions in a single industry, and so intra-industry trade is

unremarkable’ (p. 205).

e) Differing income distributions in countries

The explanation is akin to the Linder hypothesis and it was propounded by Herbert Grubel

(1970). His argument was that even though countries may have similar per capita incomes, the

distribution may be different. Hypothetically, consider two countries I and II, country I has a

larger proportion of low income consumers and II has a relatively even distribution of income.

Country I will be pre occupied with the need to produce a version of a product which suits the

desires of the majority (low income households), and country II will produce that which suits

a majority of its household’s (middle income). However there are some consumers in country

I who are richer and can afford the version of the country II product, at the same time there are

some within country II whose income is lower and hence are consumers of the version

produced in country I. This brings about IIT as both countries trade within the same industry.

2.2. Empirical Review

Studies of IIT can be broadly classified into two groups. According to Sharma (1999), one group

focused on developing theoretical explanations of IIT in the presence of ‘product differentiation

and increasing returns to scale’, while the other was concerned with the determinants of IIT within

an econometric setup (p. 1). There has been a general consensus amongst most international trade

theorists that conventional trade theory cannot explain trade in goods of similar factor content i.e.

intra-industry trade (Davis, 2005). Krugman (1979) and Lancaster (1980) are often acknowledged

for pioneering a theoretical framework explaining IIT in terms of economies of scale in production

and product differentiation (Neum, 2012).

Helpman (1986), contended that despite the success of theoretical models of IIT, focusing on

‘monopolistic competition and differentiated products’, and their seemingly plausible explanation

of larger trade volumes amongst similarly endowed nations, it was necessary to examine their

23

consistency with data (p. 63). He tested three hypothesis which were drawn from Helpman and

Krugman (1985). Two of these concern the behaviour of IIT and the other, trade volume. Using

annual data for fourteen industrial nations12, over the period 1956 to 1981, the study found that

with greater similarity in factor endowments, there will be a corresponding larger share of IIT. The

study also found out that as countries became similar over time their IIT shares increased.

Furthermore he also found that changes in country size over time can plausibly explain the

escalating trade- income ratios. These findings are consistent with the new trade theories, further

confirming their usefulness in explaining the IIT phenomenon.

In a significant departure from the work of other international economists who argued for

economies of scale and imperfect competition as the plausible explanations of IIT, Davis (1995)

refuted the idea and argued that, ‘…to the contrary, intra-industry trade arises quite naturally in

a constant returns setting’ (p. 223). In his paper, ‘Intra-industry trade: A Heckscher-Ohlin-

Ricardo approach’, Davis (1995) argues that empirical studies have produced mixed results on the

role of scale economies in IIT. While acknowledging that scale economies would result in intra-

industry specialisation (a key ingredient of IIT), he argues that they are not the only reason for

such specialisation. Technical differences can as well result in specialisation especially when it is

feasible to expand one sector without driving up ‘marginal opportunity cost’. According to him,

the basic characteristics of IIT, that is, ‘trade in goods of similar factor intensities’ and ‘large

number of goods produced and traded’ are synonymous with the Ricardian model (p. 222)

Greenway, Hine and Milner (1995); Al-Mawali (2005) and Abdd-el Rahman (1991) argue that it

is pertinent to distinguish between horizontal intra industry trade (HIIT) and vertical intra industry

trade (VIIT), arguing that different forces drive these two. For instance, VIIT is driven by

differences in factor endowments whereas similarities in endowments explain HIIT (Al-Mawali,

2005). It has been argued that trade liberalisation bring about adjustment costs, however the

distribution varies depending on the type of IIT, with costs being considerably lower for HIIT

compared to VIIT (Kandogan, 2003a)

12 Belgium, Canada, Denmark, France, Germany, Japan, Ireland, Italy, Luxembourg, Netherlands, Sweden, Switzerland, UK and USA

24

Ates and Turkcan (2010) examined the extent and composition of IIT in the automobile sector and

sought to determine the country specific factors influencing IIT between US and its 37 trading

partners. They decomposed trade into INT and IIT with its components; horizontal intra industry

trade (HIIT) and vertical intra-industry trade (VIIT). The study employed the adjusted Grubel-

Lloyd index for computing the overall IIT, and Greenway et al (1995) method for HIIT and VIIT.

An analysis of the indices showed that automobile trade is dominated by INT; however, the trend

exhibited an increase in share of IIT from 15% to 20% for the period 1989 to 2006 and the increase

in IIT was driven by substantial increases in VIIT. The study employed the logit transformation of

the gravity model for determining the determinants of IIT as well as its components VIIT and

HIIT. In addition to the traditional variables13, the study included foreign direct investment (FDI),

and used the Random Effects Model for estimation. Regression results for IIT and VIIT were

almost the same, however they were significantly different with those for HIIT. Factors aligned to

differences in endowment explained VIIT while those related to product differentiation and

similarity in per capita incomes explained HIIT.

Li et al (2003) examined the extent of IIT in insurance services between the United States and its

trade partners for the period 1995 and 1996. The study employed two-stage least squares (2SLS)

and two-stage nonlinear logit (2SNL) models to determine the significant determinants of IIT in

the insurance sector. The study found that both FDI and trade intensity have a positive relationship

with IIT. Differences in per capita income, differences in financial market size, differences in

trade openness and trade imbalance in goods and services were all found to be negatively related

with IIT. These empirical findings confirm the new theoretical trade models that take trade and

foreign direct investment as compliments rather than substitutes. Furthermore, the results confirm

predictions by theoretical models of IIT for instance Krugman (1979), in that similarly endowed

nations are likely to engage in more IIT, as all proxies for dissimilarity were found to be negatively

related to IIT.

The study by Li et al (2003) lends credence for extending the analysis of IIT beyond the usual

country specific determinant of trade to include the differences in trade openness and differences

13 GDP, differences in market size, geographical distance, exchange rate

25

in trade imbalances as explanatory variables. Furthermore, the study included industry specific

variables to capture differences in financial market size and insurance market. However, despite

this being the case our study will focus only on nation specific determining factors of IIT. This is

necessitated by the unavailability of data to capture food manufacturing industry specific variables.

Kocyigit and Sen (2007) analysed the extent and patterns of IIT for Turkey’s trade with the

European Union and the rest of the world for the period 1997 to 2005. They used 3 digit level data

of the Harmonised System (HS3) for Turkey’s leading export and import commodities to calculate

GL indices of IIT. The study found that different industries have different levels of IIT. IIT was

high for sophisticated manufactured products where economies of scale play an important role.

However, it was considerably lower for labour intensive industries where Turkey has a

comparative advantage relative to its trade partners. Furthermore, the study found out that Turkey’s

IIT has been on the increase over the years with both the EU and the rest of the world. This came

at a time when Turkey’s industry evolved to be more aligned to the industrial structure of the

developed countries. It was also observed that IIT with the EU rose significantly after the signing

of a custom union between Turkey and the EU.

Among the studies which empirically analyzed intra-trade in the SADC region is one by Sunde et

al (2009) who investigated the determinants of intra- industry trade (IIT) between Zimbabwe and

its seven trade partners in SADC. The partners included in the study were Botswana, DRC,

Malawi, Mauritius, Namibia, South Africa and Zambia. The study utilized panel data for bilateral

trade shares between Zimbabwe and its trade partners for the period 1990 to 2006. The authors

calculated the unadjusted G-L index of IIT, which was used as the dependent variable. Furthermore

they employed a modified gravity model equation to model the determinants of IIT between

Zimbabwe and its trade partners using pooled ordinary least squares. The empirical results

confirmed that IIT is explained by per capita income, trade intensity, distance, exchange rate and

gross domestic product.

In Zambia, a related study by Mulenga (2012) was done on the determinants of IIT between

Zambia and the SADC region for the period 1998 to 2006. The study employed the adjusted GL

index for determining the extent of IIT, and the modified gravity model to evaluate the

26

determinants of IIT. The empirical results confirmed the existence of IIT. Diagnostic tests noted

the presence of heteroskedasticity, however this was corrected by estimating a generalized least

square regression (GLS) of the random effects model (REM). The REM results showed that IIT

is explained by traditional gravity model variables; GDP, per capita incomes and distance.

However, the results further revealed that IIT is also explained by dissimilarity in per capita income

(DCPI), common border and common language. Despite having the expected signs, exchange rates

and trade intensity were found to be insignificant in explaining IIT between Zambia and its SADC

trade partners. GDP, common language and common border were found to have positive signs

while DCPI had a negative sign as is postulated by the Linder hypothesis.

Mulenga (2012) and Sunde et al (2009) are some of the notable researchers to empirically

investigate the country specific determinants of IIT for the Southern African region. Both studies

employed the modified gravity model to empirically model IIT, and they used the same

explanatory variables. However, Sunde et al (2009) used the unadjusted GL index, while Mulenga

(2012) used the adjusted GL index. The use of the unadjusted GL index is often argued to result

in estimates that are biased downwards, thus Sunde et al (2009) study suffers from this problem.

To avoid this potential bias in our study we are going to use the adjusted GL index for calculating

the IIT shares, this index was also used by Ates and Turkcan (2010). Despite both Sunde et al 2009

and Mulenga (2012) finding a positive GDP variable in their study, we find it awkward to explain

Zimbabwe’s IIT in terms of its partners’ GDP only. This may represent a possible spurious

regression problem, where an increase in the GDP of South Africa for instance results in an

increase in Zimbabwe’s IIT. To this end we borrow the GDP variable from Al-Mawali (2005) who

defined it as a product of the reporting country’s GDP and that of each of its trade partners.

Cattaneo and Fryer (2003) sought to establish the effects of opening up of trade on poverty, through

its impact on income distribution. To achieve this the authors investigated the effects of trade

liberalisation on IIT and INT, with special focus on the developing SADC region. Using 4 digit

SITC14 level trade data for SACU15, Mauritius, Malawi, Mozambique, Tanzania, Zambia and

Zimbabwe, the authors constructed Brullharts (1994) marginal intra-industry trade (MIIT) indices

14 Standard International Trade Classification 15 Southern African Customs Union

27

for the manufacturing sector of SACU’s trade with the mentioned trade partners for the period

1994 to 2004. The empirical results confirmed that there has been considerable trade liberalisation

as reflected by increases in intra-SADC trade. However, despite the increases in intra SADC trade

during this period, much of the trade has been inter industry in nature, implying that trade

liberalisation was associated with higher adjustment costs (potential unemployment and political

resistance). It was suggested that deliberate policy stance to stimulate IIT is required, with an

emphasis also aimed at not only reducing tariff related barriers but also addressing non-tariff

barriers (infrastructural inadequacies, border delays and administrative constraints as well as

supply constraints).

Al-Mawali (2005) investigated the country specific determinants of IIT for the South African

economy using the gravity model. He used Kandogan (2003a and 2003b) methodology for

computing HIIT and VIIT, and employed the gravity model using panel data for 50 trade partners

over the 1994 to 2000 period. Diagnostic test results confirmed the fixed effects model (FEM) as

the most appropriate in modeling IIT between South Africa and its trade partners. The study found

out that South Africa conducts much of its IIT with larger economies, furthermore geographical

distance was found to have a significant negative effect on IIT. Empirical findings of the

augmented gravity model showed that market size and standard of living variables had a significant

effect on IIT, VIIT and HIIT, while geography was found to repel IIT in all its forms. Overall,

political risk, technology gap and integration variables were found to be insignificant in explaining

both IIT, VIIT and HIIT.

The study by Al-Mawali (2005) as well as that by Ates and Turckan (2009) went a step further to

investigate the determinants of VIIT and HIIT. They however differed in their methodological

approach in estimating VIIT and HIIT, Al-Mawali (2005) employed the Kondogan (2003a)

methodology while Turckan (2009) used Greenway et al (1995) method. However, despite these

differences in the measures of VIIT and HIIT, both studies employed the modified gravity model

to investigate the determinants of IIT. The gravity model was also employed by Sunde et al (2009)

and Mulenga (2012). In our study we will employ the gravity model to investigate the determinants

of IIT between Zimbabwe and its five trade partners.

28

Dhakal, Pradhan and Upadhyaya (2009), tested the empirical validity of the Linder Hypothesis for

five Asian trade partners (Indonesia, Malaysia, Philippines, South Korea and Thailand) using a

modified gravity model. The study employed panel data pooled for the years 1997, 1999, 2001,

2003 and 2005; and estimated three models in fixed effects. The study found statistically

significant per capita income, ASEAN and distance variables, with the expected positive priori

signs for the first variable and a negative sign for the distance variable. However, the Linder

variable which was measured by differences in per capita income was found to have a significant

positive coefficient for one model and a negative (expected) coefficient but statistically

insignificant for the other. The study thus found no statistical evidence to support the Linder

hypothesis in explaining trade between the five countries.

2.3 Conclusion

From the empirical literature, there is consensus that an increasing proportion of world trade takes

the form of IIT. Furthermore, traditional theories of trade though plausible in explaining INT, fall

short in explaining IIT. While there is agreement that opening up for trade will result in factor

price equalization, effects on returns to factors of production vary remarkably depending on the

nature of trade, with costs being considerably higher for INT compared to IIT.

Most of the studies have sought to determine the country specific determinants of IIT, regressing

variables such as GDP, DCPI, distance, trade intensity, common language and boarder amongst

other variables on IIT using the gravity model. Findings from most studies confirm a positive

relationship between GPD, TI, common language and common boarders with IIT, while DCPI and

distance have been found to be negatively related to IIT.

Despite IIT being a widely researched topic, evidence suggests that not much has been done for

the African region and Zimbabwe in particular. Therefore, guided by empirical literature this study

seeks to establish the country specific determinants of IIT in food products between Zimbabwe

and some of its trade partners in the SADC region using the gravity framework.

29

CHAPTER THREE

RESEARCH METHODOLOGY

3.0. Introduction

This chapter presents the specification of the model as well as the econometric framework within

which the determinants of IIT between Zimbabwe and its five trade partners in the SADC region

are estimated. Firstly Grubel-Lloyd indices of IIT will be calculated to establish the nature16 of

trade in the food manufacturing industry. Furthermore, the chapter describes and justifies the

variables used in the model, linking some of the ideas raised in the previous chapter to the empirical

model.

3.0.1. Measuring IIT in the Food Manufacturing Industry

Trade flows can be identified as either IIT or INT. This section of the study presents the empirical

methodology on the measurement of intra industry trade. A number of measures of IIT have been

proposed in the empirical literature, including the Balassa index, the Aquinto index and the Grubel-

Lloyd index (Ates and Turkson, 2010).

a. The Balassa Index

Balassa (1966), is often credited for pioneering the work on a numerical measurement of IIT,

through his formulation of what is often termed the Balassa index. The algebraic formulation of

this index which expresses trade balance as a proportion of total trade is represented as follows;

jj

jj

jmx

mxINT

(1.0)

Where, jINT is inter industry trade index, while jx and jm are the values of exports and imports

of commodity j respectively.

16 Is it dominated in inter industry trade or intra-industry trade?

30

The index takes value of zero to indicate complete IIT and one to indicate complete INT. The zero

value for IIT is argued to be the major source of the index’s weakness, especially when it comes

to empirical analysis. This led to the development of yet another index; the Grubel-Lloyd index.

b. The Grubel Lloyd Index of IIT

The index was proposed by Grubel and Lloyd (1975) and is now commonly used for determining

the extent of IIT. The algebraic formulation of the index is given by;

ijkijk

ijkjki

ijkMX

MXIIT 1 (1.2)

where;

ijkIIT - is the intra-industry trade index in industry i between country j and partner k

Xijk - are country j ’s exports of industry i to country k

Mijk - are country j ’s imports of industry i from country k

However, Ates and Turkson (2010) argue that, over and above the aggregation bias, the unadjusted

G-L index is negatively related with an overall trade imbalance; a problem often cited in empirical

literature. Kocyigit and Sen (2007) argue that large trade imbalances result in GL indexes which

are biased downwards, hence the indexes are most likely to be underestimated. To correct for the

deficiencies, Grubel and Lloyd proposed the adjusted GL index which incorporates trade

imbalances (Kocyigit and Sen, 2007). This study follows Ates and Turkson (2010) formulation

of the adjusted G-L index modified by multiplying by 100 and computed as follows;

100*

1

11

n

i

ijkijk

n

i

ijkijk

n

i

ijkijk

ijk

MX

MXMX

IITFM (1.3)

31

Where, ijkIIIFM is the intra industry trade between Zimbabwe and trade partner k in the food

industry while ijkX and ijkM are Zimbabwe’s exports and imports of product category i in the food

industry to partner k respectively.

According to Ates and Turkson (2010), ‘the index computes the export and import flows with

country k in industry j , adjusted or weighted according to the relative share of the trade flows in

the i products included in industry j ’ (page, 18). It assumes values between 0 and 100, with zero

indicating complete international specialization (INT) and 100 signifying purely IIT. A GL index

of more than 50% signifies that the sector is IIT driven and a value less than 50% implies that it is

INT driven.

The first step in computing the G-L index is to select manufactured food products from UN

Comtrade bilateral trade data of Zimbabwe’s five trade partners. The bilateral trade flows utilized

in this study are classified at the 2 digit level of the Harmonized System (HS2). The food

manufacturing industry is represented by 11 product categories and the detailed description is

given in appendix 1.

Estimating the Determinants of IIT

This section outlines the framework within which country specific determinants of IIT are to be

modeled using the gravity model.

3.0.2. The Gravity Model

The basic gravity model explains trade between countries as depending upon their GDP,

population size and geographical distance. According to Bergstrand (1985), the model essentially

predicts that trade flows between countries depend on each other’s trade potential and ‘economic

forces either aiding or resisting the flow's movement from origin to destination’ (p. 447).

According to Deardorff (1998), two authors; Tienbergern (1962) and Poyhonen (1963) are often

credited for pioneering the use of the Gravity Model to analyze economic flows. However, it is

often argued that they did not give the theoretical justification of the use of the gravity model

32

prompting certain scholars to challenge its usefulness as an empirical device capable of explaining

trade flows.

The typical specification of the basic gravity model takes the form of;

ij

ji

ijD

MMAF (1.4)

Where ijF represents the trade flow, A is a constant, M is the economic mass of country i and

partner j and ijD is the physical distance between the two countries.

Despite early criticism of Tienbergen’s (1962) application of the gravity model in terms of its lack

of ‘theoretical underpinnings’, recent developments17 in trade theory have strengthened the

theoretical basis for the gravity model confirming its usefulness in empirical testing of bilateral

trade flows (Baldwin & Taglioni, 2006, p. 1). A number of studies in the IIT literature have

recently applied this model with success (Al-Mawali (2005); Simwaka (2006); Sunde at el (2009);

Ates & Turkcan (2010) and Mulenga (2012)). This study applies the Gravity Model, to estimate

the determinants of IIT between Zimbabwe and its SADC trade partners.

3.1. The Empirical Model

The empirical model is borrowed from studies by Sunde et al (2009) and Mulenga (2012).

However, we add two more variables in our model, (differences in gross domestic product and free

trade area dummy).

The dependent variable is intra industry trade ( ijkIITFM ) and the explanatory variables are the

product of gross domestic product ( jkRGDP ), differences in gross domestic product ( jkDGDP

), dissimilarity in per capita income ( jkDPCI ), trade intensity ( jkTII ), distance ( jkDIS ), real

exchange rate ( jkRER ), dummy variable for common border 1D , dummy variable for common

17 A number of authors have theoretically justified the use of the gravity model in empirical analysis including Anderson and Van Wincoop (2003), Bergstrand (1985) and Deardoff (1995)

33

language (2D ) and dummy variable for free trade area (

3D ). The general model and the expected

signs of the explanatory variables is as defined below.

?

),,,,,,,,,( 321 DDDRERWDISTIIPCIDGDPDPCIRGDPfIITFM jkjkjkkjkjkjkijk 1.5

where;

i - is the industry concerned (food manufacturing industry)

j - is the reporting country i.e. Zimbabwe

k - is the SADC trade partner

We use a panel data set and we follow Mulenga (2012) gravity model specifications. For the

purpose of interpreting our coefficients as elasticities we employ a log-liner function. Mulenga

(2012) argues that a log-linear function makes the estimates less sensitive to extreme observations.

The model to be estimated is specified as follows;

jkjkjk

jkkjkjkkjk

EDDDWDISRER

TIIPCIDPCIDGDPRGDPIITFM

31221111098

754310

lnlnln

lnlnlnlnlnln

(1.6)

where i is the slope coefficient and,

jkE is the error term.

3.2. Definition and Measurement of Variables

3.2.1. Intra-industry Trade Index ( ijkIITFM )

This is measured by the G-L index, and it represents the intra industry trade share between

Zimbabwe and trade partner k in the food manufacturing industry. It is computed as in equation

1.3 and is the dependent variable for our model.

34

3.2.2. Product of Gross Domestic Product between Zimbabwe and Partner k ( jkRGDP )

GDP is the total production of goods and services within the borders of a country and is usually

measured at annual intervals. Fillippini (2003) states that just like any other economic variable,

trade will generally increase with the increase in the size of the economy. The larger the economy,

the greater the scope for exploitation of economies of scale as producers pursue product

differentiation. Higher GDP levels represent larger markets and it is often argued that the larger

the market the greater the demand for foreign differentiated products. This increases the potential

for IIT (Ates and Turkcan, 2010). In this study we follow Al-Mawali (2005) who defined the

variable as the product of the partners GDP for each calendar year, algebraically denoted as;

kjjk GDPGDPRGDP (1.7)

The greater the product of the partners’ GDP’s, the greater their joint market size. This gives

greater opportunity for IIT, hence, it is hypothesized that this variable is positively related to IIT.

Data for GDP at constant 2005 prices is obtained from World Bank, World Development

Indicators (2013).

3.2.3. Differences in Gross Domestic Product ( jkDGDP )

The variable is used as a proxy for differences in market size, it is defined by the absolute

differences in the gross domestic products of partners, defined as follows;

kj GDPGDPDGDPjk (1.8)

According to Al-Mawali (2005), it is hypothesized that the lesser the differences in GDP, the

greater the similarity of markets thus the higher the IIT. We thus expect a negative sign between

this variable and IIT.

3.2.3. Dissimilarity in Per Capita Income ( DPCIjk )

Also known as the Linder term, DPCIjk is the absolute difference between the per capita incomes

of the trading countries, defined as follows;

35

kj PCIPCIDPCIjk (1.9)

We use it as a proxy for consumer tastes and preferences and this is in conformity with the Linder

theory which predicts that countries with similar PCI have over lapping demand which increases

IIT. Hence the share of IIT increases with declines in DPCI

3.2.4. Per Capita Income (kPCI )

PCI measures the level of a country’s economic development and is used in comparing levels of

economic development between countries. It is believed that IIT with any given trading partner

tends to be higher as PCI of the partner country increases. In this study PCI is measured in constant

base year prices denominated in United States Dollars (US$) and is expected to be positively

related to IIT.

3.2.5. Weighted Distance ( jkWDIS )

Geographical distance is the physical distance between Harare and capital cities of each of its trade

partner. In the trade literature, for instance in Krugman (1980), Balassa (1986) and Bergstrand and

Egger (2006), it was found that there is a negative relationship between IIT and geographical

distance. Ates and Turckan (2010) argue that distance increase transaction costs, in terms of

transport and insurance. For the purpose of this study we use weighted distance from

http://www.cepii.fr/. The use of weighted is justified on the basis that it takes into consideration

the areas in which economic activity is concentrated other than acting on the assumption that much

trade and production takes place in the main cities.

3.2.6. Trade Intensity ( jkTI )

TI measures the degree of trade between trade partners, it is hypothesized that the higher the TI

between trading countries the higher the IIT, we thus expect a positive sign between TI and IIT.

We follow Al-Mawali (2005) definition of trade intensity, which we redefine as the ratio of

36

Zimbabwe’s trade volume with a trade partner k to its total trade volume and it is computed as

follows;

jj

jkjk

jkMX

MXTI

(1.9)

where;

jkX - Zimbabwe’s exports to country k

jkM - Zimbabwe’s imports from country k

jX - Zimbabwe’s world exports

jM -Zimbabwe’s world imports

3.2.7. Real Exchange Rate ( jkRER )

The exchange rate is the price of a currency in terms of another currency. We calculate the real

exchange rate as the cross exchange rates between trading partners’ currencies adjusted for

inflation. However, since we are concentrating on two periods; the pre and post Zimbabwean dollar

($ZW), we use the United States Dollar as the local currency for the period under which the country

has been using multi-currency18. The use of the real exchange rate is justified on the basis that it

gives a measure of the economic competitiveness in terms of import and exports. However, there

is no consensus in the empirical literature as to the priori sign of exchange rate on IIT (Ates and

Turkson, (2010). The real exchange rate is calculated as;

j

kjkjk

p

pERER (2.0)

where;

jkRER = real exchange rate between Zimbabwe and trading partner k

jkE = nominal exchange rate between Zimbabwe and trading partner k ’s currency

18 The multi-currency system is the current monetary regime in Zimbabwe where a basket of currencies are used for everyday transactions, however, they are dominated by the United States dollar (US$).

37

jp = Zimbabwe’s GDP deflator

kp =Trading partner k’s GDP deflator

3.2.8. Common Border (D1)

The existence of a common border represents possibility of IIT due to locational advantages

(Balasa & Bauwens, 1987). Ceteris paribus IIT between countries sharing a common border is

likely to be higher compared to countries which do not share a common border.

otherwise

bordercommonasharecountriesifD

0

11

3.2.9. Common Language (D2)

The inclusion of the variable is justified on the basis that it aids information flow and lowers

transaction costs, thus it increases IIT between countries. It is measured as a dummy variable

specified as below

otherwise

languagecommonasharecountriesifD

0

12

We propose a positive relationship between common language and IIT

3.2.9. Free trade Area Dummy 3D

It is often hypothesized that trade increase with the openness of a country, a free trade area is thus

expected to be positively related to IIT. Kocyigit and Sen (2007) found a significant increase in

IIT between Turkey and the EU after the signing of a custom union agreement between Turkey

and the EU. The FTA dummy variable is defined as follows:

otherwise

statusFTAattainedhadSADCD

0

13

38

We include the variable to examine if the attainment of the FTA in SADC had any effect on intra

industry trade.

3.3 Data Sources and Problems

In constructing our empirical model, we employ panel data from a sample of six countries for a

thirteen year period spanning from 2000 to 2012. We justify the inclusion of these countries on

the basis that they conduct considerable trade with Zimbabwe. Most of the omitted SADC

countries conduct insignificant trade with Zimbabwe in food products. Apart from this, for some

countries, there is no reported bilateral trade flows between the omitted countries and Zimbabwe.

The use of panel data is justified on the superiority of panel data over both time series and cross

sectional data, as it enables us to identify and estimate effects that would otherwise not be

detectable in pure cross section or time series data (Koutsoyannis, 1977). Al-Mawali (2005),

argues that panel data has the advantage of controlling for individual heterogeneity, as ignoring

these unobserved individual specific effects lead to bias in the estimates.

We use two digit Harmonized System (HS) data from UN Comtrade for obtaining the bilateral

trade shares of manufactured food products; World Bank, World Development Indicators data for

GDP, per capita incomes, exchange rates and GDP deflators. For the bilateral geographical

distance we used http://www.cepii.fr/ website.

In the IIT literature HS2 level data is relatively aggregated, and this may present challenges of

aggregation bias (overstatement of IIT extent). However, there was insufficient data at higher

levels of the HS (HS4), and for this reason we had to rely on HS2 data for calculating IIT.

3.4 Diagnostic Tests

For the purpose of assessing the adequacy and relevance of the model, the study will carry out the

following diagnostic tests in Stata; multicollinearity, heteroskedasticity, Breusch and Pagan

Lagrange multiplier test for random effects, Poolability test (F-test) and the Hausman test for

Random effects.

39

Multicollinearity

Gujarrati (2003) defines multicollinearity as, ‘the existence of a perfect or exact linear relationship

among some or all explanatory variables of a regression model’ (p. 342). The presence of

multicollinearity presents challenges to the researcher as it results in estimates that are subject to

deficiencies in accuracy. The study will employ the correlation matrix to test for Multicollinearity.

A value of 0.8 and above between any two explanatory variables is evidence of multicollinearity.

We correct for this problem by dropping one of the variables that are correlated.

Heteroskedasticity

The study will test for heteroskedasticity using the Breusch-Pagan / Cook-Weisberg test to verify

the nature of the variances of the error terms, that is, are they homoscedastic or not?

F-test (Poolability test)

This test is conducted to determine the most appropriate model between the fixed effects model

(FEM) and the pooled OLS model. It will be carried under the null hypothesis that there are no

country specific heterogeneity against the alternative that the fixed effects approach is the best.

Breusch and Pagan Lagrange multiplier test for random effects

This test will be conducted to determine the best model between the random effects model (REM)

and the pooled OLS model. The test is run under the hypothesis that variances across entities is

zero, that is, there is no statistical evidence of panel effects.

Hausman Test for Random effects

The test will be conducted to determine the best model between the REM and FEM. The test is

run under the null hypothesis that there is no correlation between country specific heterogeneity

and the explanatory variables.

3.5 Conclusion

The study will employ the gravity model to investigate the determinants of IIT, between

Zimbabwe and its trade partners. Despite the earlier criticism of the methodological approach in

terms of its lack of theoretical underpinnings the gravity model has been confirmed one of the

40

most consistently successful empirical tool in modeling trade flows. The GL index will be used to

evaluate the existence (as well as the extent) of IIT. A number of variables have been considered

derived from theory and from other empirical studies.

41

CHAPTER FOUR

ESTIMATION AND RESULTS

4.0. Introduction

The purpose of this study is to determine the extent of IIT between Zimbabwe and its trade partners

as well as to determine the significant determinants of this type of trade. This chapter presents the

estimation, presentation and interpretation of empirical findings. The chapter begins with a

presentation and analysis of the G-L indices for Zimbabwe’s IIT with each of its SADC trade

partner over the period under review (2000-2012), followed by the econometric results.

4.1. Results of Intra Industry Trade Shares

IIT is measured by the GL indices, which occupies values between 0 and 100, with zero indicating

purely INT and 100 purely IIT. The larger the values the greater the IIT, thus it can be conjectured

that values greater than 50 indicate that the sector is IIT driven, while those below 50 indicate that

the sector is INT driven.

Table 3: G-L Indices for Zimbabwe’s Intra Industry Trade

G L indexes

Year Botswana Malawi Mozambique South Africa Zambia

2000 0.0 0.0 0.0 0.0 0.0

2001 11.1 11.0 5.6 15.5 6.3

2002 11.2 0.0 0.7 13.8 12.8

2003 6.0 9.4 3.3 13.3 36.7

2004 8.3 38.6 43.3 42.3 85.1

2005 0.0 28.3 7.6 66.9 49.3

2006 3.8 32.4 2.8 34.4 46.3

2007 4.4 40.0 3.6 50.9 80.3

2008 4.4 60.6 2.2 9.8 43.4

2009 11.2 62.6 0.1 8.8 36.4

2010 6.0 9.4 3.3 13.3 36.7

2011 8.5 4.9 2.8 8.7 4.2

2012 13.7 1.4 2.4 3.5 3.4

Source: own calculation using HS2 bilateral trade data from UN Comtrade (2013).

42

Table 3 above, is a summary of recorded IIT levels for Zimbabwe’s trade with its five SADC

partners in the food manufacturing sector. The recorded IIT levels were computed from bilateral

trade flows data as recorded at the 2 digit level of the harmonized system (HS2). The year 2000 is

dominated by purely INT, implying that for the food products that were traded between Zimbabwe

and each of its partners, there was no two way exchange. Zimbabwe’s trade with Botswana over

the 12 year period was INT driven, with two years (2000 and 2005) recording purely INT. For

Zimbabwe and Botswana trade, the highest GL index reported over the years was 13.7%.

Bilateral trade flows between Zimbabwe and Malawi exhibits some appreciable extent of IIT, with

the successive years of 2008 and 2009 recording GL indexes of 60.6% and 62.6% respectively.

From the year 2003 IIT shares increased from 9.4% to 62.6% in 2009 before declining successively

over the rest of the period. Surprisingly, the period which marked successive increases in IIT, the

manufacturing industry was witnessing modest declines in capacity utilization before reaching

record low levels in 2008. The period of economic decline witnessed Zimbabwe importing some

food items from traditionally export destinations for instance Malawi, Zambia and Mozambique.

Zimbabwe’s trade in processed food items with Mozambique is generally inter industry driven,

with the only highest GL index recorded being 43.3% in 2004, however for all other years the GL

Indexes are below 6%.

South Africa which is arguably Zimbabwe’s largest trade partner exhibits inter industry driven

trade, however, for 2005 and 2007, the GL indices for Zimbabwean trade with South Africa were

above 50%, indicating that for these particular years it was IIT driven.

Zimbabwe recorded relatively high GL indices with Zambia over the years, compared to the rest

of the nations considered in the study. Zimbabwe’s IIT with all its trade partners has no noticeable

trend. However, there has been a downward trend in IIT especially after the nation adopted the

multi-currencies.

Overall, there is evidence of the existence of IIT in Zimbabwe’s trade with each of the considered

trade partners in processed food products. However, for most periods the GL indices are below the

50% mark, signifying that IIT is not well pronounced. Trade in the sector is thus inter industry

43

driven and is associated with higher adjustment costs. Further opening up for trade would bring

about costly reallocation of resources between the import competing sector and the export sector,

potentially exposing workers in the contracting sector to structural unemployment especially in

the case where their skills are inflexible to the demands of the expanding sectors. Furthermore, the

specific capital used in the contracting sector will be made redundant. This is explained by the

specific factors model.

4.2. Descriptive Statistics

It is imperative that before one runs a regression model they should take a closer look at their data,

and this is done through an analysis of descriptive statistics. Table 4 below presents a summary of

the descriptive statistics of the variables used in the regression analysis.

Table 4: Summary of Descriptive Statistics

IITFM RGDP DGDP DPCI PCI WDIS RER

Mean 1.658972 36.52438 22.44764 6.521765 7.021646 3.819715 7.202278

Median 2.172456 45.20493 21.78701 6.061121 6.811665 3.926960 6.398373

Maximum 4.444353 49.27591 26.42416 8.863744 8.953452 4.529423 15.35060

Minimum -5.298317 -8.286166 19.40087 1.546312 5.379541 2.940388 0.472840

Std. Dev. 2.413371 19.36823 2.026083 1.711803 1.294644 0.424575 2.306266

Skewness -1.521169 -1.505076 1.052688 -0.275818 0.222946 -0.375164 1.645108

Kurtosis 4.646734 3.352830 2.877941 2.479702 1.370170 1.996200 7.717413

Jarque-Bera 32.41213 24.87742 12.04532 1.557325 7.732737 4.253725 89.59034

Probability 0.000000 0.000004 0.002423 0.459019 0.020934 0.119211 0.000000

Sum 107.8332 2374.085 1459.097 423.9147 456.4070 248.2815 468.1481

Sum Sq. Dev. 372.7591 24008.22 262.7207 187.5371 107.2706 11.53687 340.4072

Observations 65 65 65 65 65 65 65

Cross sections 5 5 5 5 5 5 5

44

Five of the variables; IITFM, RGDP, DGDP, PCI and RER are normally distributed as confirmed

by the JB probabilities that are all less than 0.05. However, two of the variable, that is, WDIS and

DPCI are not normally distributed. Despite the non fulfilment of the normality assumption we can

still progress with the regressions and get the intended results. According to Greene (2003), the

normality assumption, ‘…is often viewed as an unnecessary and possibly inappropriate addition

to the regression model’ (p. 17). Furthermore the central limit theory argues that if observations

are greater than 25, the variables tend to be normally distributed.

4.3. Econometric Tests and Estimation of Results

4.3.1. Econometric Tests

This section interprets the results of econometric tests conducted.

Multicollinearity Test

The econometric analysis of the study commences with testing for Multicollinearity. We employed

Pearson’s correlation coefficient approach to explore multicollinearity. From the correlation

matrix, the correlation coefficient between DPCI and PCI is highest at 0.8857 (see appendix 2 for

the correlation matrix). This implies a possible problem of multicollinearity between these

explanatory variables. The study will correct for the problem of multicollinearity by dropping the

PCI variable. PCI has not been consistently used for exploring IIT as compared to DPCI, and thus

dropping DPCI which is a variable under investigation in our study is not an option.

F-test (Poolability test)

In a bid to determine the best model between the fixed effects model (FEM) and the pooled OLS

model the study carried out poolability tests. The hypothesis to be tested is that there is no country

specific heterogeneity, that is, the pooled OLS model is the best against the alternative that the

fixed effects is the appropriate model. The F-test results supports the null hypothesis, the F(4,∂1)

=1.39 and the p-value =0.2502, implying that there is no country specific heterogeneity, thus the

pooled OLS model is the appropriate of the two models (See appendix 3 for the F-test results).

45

Breusch and Pagan Lagrange Multiplier Test for Random Effects (LM)

We carried out the LM test to determine the appropriate model between the random effects model

(REM) and the pooled OLS model. Results report a Chibar2 (01) =0 and a p-value =1, we therefore

accept the null hypothesis and conclude that there are no panel effects (see appendix 4) Thus the

pooled OLS model is considered preferable over the random effects model.

Hausman Test

The Hausman test was conducted to determine the best model between the FEM and the REM.

The hypothesis to be tested is that there is no correlation between country specific heterogeneity

and the explanatory variables i.e. the random effects model is the appropriate model against the

alternative that the fixed effects is the appropriate model. The results report a chi-square χ2 (7)

=4.46, with a p-value of 0.7259, thus we fail to reject the null hypothesis and we conclude that the

random effects is the best model (see appendix 5).

Heteroskedasticity Test

We conducted the Breusch-Pagan / Cook-Weisberg test for heteroskedasticity to verify the nature

of the variances of the error terms, that is, are they homoscedastic (constant) or not? The study

found a chi-square test statistic of 1.83, with a p-value of 0.1764 (see appendix 6). We accept the

null hypothesis and conclude that there is no statistical evidence of heteroskedasticity.

4.4. Estimation of the Model

4.4.1. The Unrestricted Pooled Ordinary Least Squares Model (POLSM)

Despite the Hausman test results confirming the REM as the appropriate model, the F-test as well

as the Breusch and Pagan Lagrange Multiplier test results supersede the Hausman test, and for this

reason the study used the pooled OLS model for estimating the determinants of IIT. The study ran

an unrestricted pooled OLS model (see appendix 7) The results found all other variables significant

at the conventional levels of significance (1%, 5% and 10%) except for dissimilarity in per capita

income ( DPCI ), real exchange rate ( RER); dummy variable for common boarder 1D , dummy

46

variable for SADC FTA 3D , and trade intensity TII . We therefore drop these variables one

by one, starting with the variable which is highly insignificant and we estimate another regression

which gives the restricted model.

4.4.2. Presentation of the Restricted Pooled OLS Model Results.

The table below is a summary of the pooled OLS model results after elimination of insignificant

variables of the unrestricted model. The explanatory variable of the restricted model are all

significant at either the 1%, 5% or 10% level of significance.

Table 5: The Restricted Pooled OLS Model

Source SS df MS Number of observations = 64

Model 1 91.47 5 18.29 F(5,58) = 4.21

Residual 1 252.24 58 4.35 Prob ˃ F = 0.0025

Total 343.71 63 5.46 R-squared

Adj R-squared

= 0.2661

= 0.2029

iitfm Coefficient Std. Err t p-value 95 % confidence interval

rgdp 0.091127 0.02342 3.89 0.00*** 0.4424858 0.138007

dgdp -0.79568 0.24613 -3.23 0.00*** -1.288359 -0.302998

wdis 3.846698 1.24118 3.10 0.00*** 1.362212 6.331184

d1 1.378269 0.74991 1.84 0.07* -1.122836 2.879373

d2 5.276342 1.19770 4.41 0.00*** 2.877889 7.673793

constant -3.71515 3.39173 -1.10 0.27 -10.50443 3.074131

***Significant at 1% level of significance. **Significant at 5% level of significance. * Significant at 10%

level of significance

The study used a balanced panel data set consisting of 384 observations obtained from pooling

time series data from five countries each contributing 64 observations as reported in the table

above. The regression results report an F (5, 58) statistic of 4.21, with a p-value of 0.0025. This

implies that there is statistical evidence to show that the model fits the data well at 1% level of

significance.

47

4.4.3. Discussion of Pooled OLS Model Results

Product of Gross Domestic Product between Zimbabwe and Partner k RGDP

The variable RGDP is found to be highly significant at 1% level of significant with the expected

positive sign. Our results confirm the findings of many other studies done on the country specific

determinants of IIT. For instance Mulenga (2012), Sunde at el (2010) and Al-Mawali (2005) all

found a positive value for the GDP variable with respect to IIT. A positive coefficient of 0.09

implies that a 1% increase in the product of GDP of two trade partners will result in an increase in

IIT share by approximately 0.09%, holding everything else constant. The results confirm the

importance of economic size in explaining IIT. Countries need to put in place policies that favor

economic growth if they are to benefit from IIT. The larger the countries the bigger the markets,

this fosters innovativeness amongst firms as they try to differentiate their products from those of

competitors. According to Krugman (1979), through product differentiation firms can concentrate

on a limited set of products, this results in lower per unit cost as firms exploit economies of scale.

Differences in Gross Domestic Products ( DGDP )

The variable was highly significant at 1% level of significance and it has the expected negative

sign. The negative coefficient of 0.80 implies that an increase by 1% of DGDP will result in a

decline in IIT of 0.79%, holding everything else constant. The results show the importance of

promoting economic growth in Zimbabwe, because for any period when SADC partners’

economies are growing whilst the Zimbabwean economy is stagnant or shrinking, IIT in the food

manufacturing industry will fall significantly, bringing about costly trade outcomes associated

with inter industry trade. The variable measures the similarity of markets, the greater the

dissimilarity, the lower the IIT. The findings confirm Al-Mawali (2005) findings for the same

variable.

48

Dissimilarity in Per Capita Income ( DPCI )

The variable was found to have the expected negative sign, however, it is statistically insignificant

at all conventional levels of significance. The results imply that the Linder hypothesis does not

explain intra industry trade between Zimbabwe and its SADC trade partners in the food industry.

Thus tastes and preferences do not influence intra industry trade. Our findings contradict those by

Sunde at el (2009) who found a statistically significant DPCI with the expected negative sign.

The findings however, confirm those by Dhakal, Pradhan and Upadhyaya (2009), who found a

negative (expected) coefficient but statistically insignificant Linder variable.

Weighted Distance (WDIS )

The variable is highly significant at 1 % level of significance, however, it has an unexpected

positive sign. The coefficient of 3.85 implies that for a 1% increase in distance, IIT more than

triples. The results defy economic theory, as it is expected that the further away the countries are

from each other, the higher the trade costs (transport and insurance), which repel trade and reduce

IIT. It contradicts findings by Balassa (1986), Krugman (1980), Begstrand and Eagger (2002).

However, these findings are the same as Mulenga (2012) findings. The results, though not

conforming to economic theory, could be justified on the grounds that South Africa (Zimbabwe’s

largest trade partner in the SADC region), is the furthest of all the considered SADC capitals from

Harare, with a weighted distance of 1930 km against all other partners’ weighted distances all

below 1000km19.

Common Boarder Dummy (1D )

The variable became significant after dropping other highly insignificant variable, and this is

confirmed by the fact that it is weakly significant (10% level of significance). The variable has the

expected positive sign with a coefficient of 1.38, implying that holding everything constant,

countries which share borders are likely to conduct more IIT compared to those that do not. This

19 936 for Botswana, 919 for Mozambique, 522 for Malawi and 397 for Zambia

49

confirms Balassa and Bauwens (1987) as well as Mulenga (2012) findings that IIT is likely to

increase due to locational advantages.

Dummy Variable for Common Language (2D )

Dummy variable for common language is highly significant at 1% level of significance, with a

high coefficient of 5.28. The variable has the expected positive sign and the coefficient of 5.28

implies that Zimbabwe’s trade in manufactured food products with a partner which shares the same

language is more likely to be IIT.

4.5. Conclusion

The study found that intra industry trade does exist between Zimbabwe and its trade partners in

the SADC block, thus despite the widely held belief that developing countries do not engage in

IIT data indicates otherwise. However, IIT was found to be low. Apart from this, the study found

that the product of countries’ GDP, differences in GDP, weighted distance and dummy variables

for common boarder and language are significant variables in explaining intra industry trade in the

food manufacturing sector. Furthermore the study found that real exchange rate, free trade area

dummy, dissimilarity in per capita income and trade intensity are insignificant in explaining intra

industry trade.

50

CHAPTER FIVE

CONCLUSIONS AND POLICY RECOMMENDATIONS

5.0. Introduction

This chapter summarizes and concludes the study, providing policy implications as well as

recommendations derived from the empirical findings. Limitations of the study, together with

opportunities for further research will be presented as well.

5.1. Conclusions of the Study

The study analyzed the extent and determinants of IIT between Zimbabwe and its five SADC trade

partners in the food manufacturing industry from 2000 to 2012. The study considered country

specific variables which can explain IIT, including the product of Zimbabwe and partner’s GDP,

differences in GDP, dissimilarities in per capita income, trade intensity, exchange rate, weighted

distance and dummy variables for common boarder, common language and free trade area. The

study carried out diagnostic tests to establish the best estimation method between the pooled OLS,

fixed effects and the random effects model. The F-test and the Breusch and Pagan Lagrange

Multiplier test confirmed the pooled OLS model as the best, as such it was the one used for

empirical analysis.

The study found out that contrary to widely held beliefs that developing countries do not engage

in intra industry trade, these countries do in actual fact conduct simultaneous exporting and

importing of goods within the same industrial classification. IIT does exist between Zimbabwe

and its trade partners in SADC in the food manufacturing industry. However, despite its existence,

it was found that it is relatively low, thus trade in manufactured food products is dominated in inter

industry trade. Botswana and Mozambique have the least shares of IIT with Zimbabwe, and over

the sample period Zimbabwe conducted most IIT with Zambia which recorded a GL index of

85.14% in 2004.

51

Apart from this, the study found no statistical evidence to support that dissimilarities in per capita

income (DPCI) is the major determinant of IIT between Zimbabwe and its trade partners. The

variable though having the expected negative sign is insignificant at all conventional level of

significance leading us to conclude that DPCI is not a determinant of IIT in the food manufacturing

industry. While the Linder hypothesis predicts that IIT is driven by overlapping demand, statistical

evidence from the study indicates otherwise.

The regression results reported that IIT between Zimbabwe and its SADC partners is explained by

the product of partners’ GDP’s, the differences in their GDP, weighted distance, common boarder

dummy, and common language dummy. All these variables except for weighted distance have the

expected signs.

5.2. Policy Implications and Recommendations

The study found evidence of the existence of IIT between Zimbabwe and its trade partners in the

SADC region, however, IIT is still low. Most of the reported GL indices are below 50%, indicating

that international exchange in the food manufacturing industry is dominated by inter industry trade.

This implies that opening up for trade will bring about costly reallocation of resources between

industrial sectors as resources shift between the import competing sectors and the export sector.

Furthermore, it is likely to result in redistribution of returns to factors of production and may render

those employed in the food industry jobless and unable to find other jobs from other industrial

sectors with their existing skills sets. The findings backs calls by industrial players of the need to

restrict import flows, if domestic players are to be protected from foreign competition.

It is thus recommended that government should put in place supportive policies that encourage

investments and recapitalization of the food manufacturing industry so that local products can

effectively compete both on the domestic and international markets. This could involve the revival

of special economic zones which are open to foreign direct investments. Furthermore, government

should put in place an enabling environment with enough incentives to spur innovativeness

amongst local food manufacturing firms, so that different varieties of products appealing to

different sections of consumers both domestically and internationally are readily available.

52

As is noted in the Confederations of Zimbabwe Industry (CZI) 2013 annual survey, most

companies are operating below full capacity owing to challenges related to working capital,

inadequate provision of supportive infrastructure (energy, water), obsolete equipment and low

consumer demand. The Zimtrade export capacity manufacturing survey of 2013 noted a decline in

companies exporting, thus there is need for government to pursue a vigorous export led

industrialization strategy following in the footsteps of the Asian tigers. However, it is imperative

that an enabling policy environment which recognizes the need to tap in foreign direct investments,

protect property rights and uproot corruption is put in place.

From the gravity model results, product of partners’ GDP as well as differences in GDP were

found to be significant explanatory variables of IIT. It is thus recommended that government

should put in place policies which nurture and support growth of all key sectors of the economy.

There is need to revive the agricultural, mining, manufacturing and service sectors so that the

strong backward and forward linkages that exist between them can spur sustained economic

growth.

The distance variable was found to be highly significant alas with an unexpected positive sign, this

reinforces the observation that South Africa is the dominant import source for the SADC region.

After independence Zimbabwe had the most advanced and diversified manufacturing sector in the

SADC region second only to South Africa, there is thus greater need for revival of the

manufacturing sector as there are potential markets for Zimbabwean products in the neighboring

countries (Zambia, Mozambique, Malawi, Botswana) some of which import their goods from

South Africa through Zimbabwe.

Furthermore there is need to look at other non-tariff barriers (NTB’s) to trade, which impede trade

flows and potentially reduce IIT. The FTA variable was found to be insignificant despite the

attainment of an FTA status by SADC in 2008, it is imperative that the block resolves the issues

surrounding postponements of custom union and in the meantime, improve the transport

infrastructure between them and set up one stop boarders. This will help smoothen out trade flows

and foster economic integration as well as growth in the region.

53

5.3. Study Limitations and Areas for Further Research

Due to data and time limitations, the study concentrated on five of Zimbabwe’s 14 trade partners

in the SADC region to draw conclusions on the country specific determinants of IIT between

Zimbabwe and the SADC regional block. Furthermore, the study had to rely on HS2 data which

may overstate the extent of IIT. Given sufficient data at higher levels of the HS (HS4 or HS6) the

study can be broadened to include other nations in and outside the SADC block especially

important trade blocks such as the European Union, and other emerging countries that have

become important trade partners for Zimbabwe of late including China, India and Turkey.

Furthermore the scope of the study was limited to investigating the country specific determinants

of IIT, however given data this could be extended to incorporate industry specific determinants of

IIT for instance foreign direct investment, the capital to labor ratios and proxies for productivity.

54

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58

APPENDICES

Appendix 1: Commodity List and Description

HS 96

Code

Commodity Description

02 Name: Meat and edible meat offal

Description: Meat and edible meat offal

04 Name: Dairy products, eggs, honey, edible animal product

Description: Dairy prod; birds' eggs; natural honey; edible products

09 Name: Coffee, tea, mate and spices

Description: Coffee, tea, matï and spices.

11 Name: Milling products, malt, starches, inulin, wheat gluten

Description: Products of milling industry; malt; starches; insulin; wheat gluten

15 Name: Animal, vegetable fats and oils, cleavage products, etc.

Description: Animal/veg fats & oils & their cleavage products; etc.

16 Name: Meat, fish and seafood food preparations

Description: Prep of meat, fish or crustaceans, mollusks etc.

17 Name: Sugars and sugar confectionery

Description: Sugars and sugar confectionery.

19 Name: Cereal, flour, starch, milk preparations and products

Description: Preparation of cereal, flour, starch/milk; pastry cooks' prod

20 Name: Vegetable, fruit, nut, etc. food preparations

Description: Prep of vegetable, fruit, nuts or other parts of plants

22 Name: Beverages, spirits and vinegar

Description: Beverages, spirits and vinegar.

24 Name: Tobacco and manufactured tobacco substitutes

Description: Tobacco and manufactured tobacco substitutes

Source: UN Comtrade

59

Appendix 2. Correlation Coefficients (Stata printout)

Appendix 3. F-test (Poolability test)

Appendix 4. Breusch and Pagan Lagrange Multiplier Test for Random Effects (Stata printout)

tii 0.0885 0.1567 0.7299 0.5227 0.1820 0.4201 0.5635 -0.0197 -0.1325 0.5725 1.0000

pci 0.0683 -0.5158 0.6166 0.8857 0.1416 0.5660 0.5060 0.4146 0.2032 1.0000

d3 0.0701 0.0098 0.1186 0.0188 0.1925 0.3714 0.0062 0.0062 1.0000

d2 0.2568 -0.2406 0.2338 0.4215 -0.0256 -0.3593 -0.2549 1.0000

d1 0.0070 -0.2154 0.1555 0.3837 0.0386 0.3883 1.0000

wdis -0.0610 -0.2880 0.4447 0.4451 0.2309 1.0000

rer 0.0100 -0.0341 0.1463 -0.0126 1.0000

dpci -0.0259 -0.4914 0.6146 1.0000

dgdp 0.0389 0.2236 1.0000

rgdp 0.1304 1.0000

iitfm 1.0000

iitfm rgdp dgdp dpci rer wdis d1 d2 d3 pci tii

(obs=64)

. correlate iitfm rgdp dgdp dpci rer wdis d1 d2 d3 pci tii

F test that all u_i=0: F(4, 51) = 1.39 Prob > F = 0.2493

Prob > chibar2 = 1.0000

chibar2(01) = 0.00

Test: Var(u) = 0

u 0 0

e 4.340742 2.083445

iitfm 5.455758 2.335756

Var sd = sqrt(Var)

Estimated results:

iitfm[country,t] = Xb + u[country] + e[country,t]

Breusch and Pagan Lagrangian multiplier test for random effects

60

Appendix 5. Hausman Test (Stata printout)

Appendix 6. Heteroskedasticity Test

. hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity

Ho: Constant variance

Variables: fitted values of iitfm

chi2 (1) = 1.83

Prob > chi2 = 0.1764

(V_b-V_B is not positive definite)

Prob>chi2 = 0.8039

= 4.56

chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Test: Ho: difference in coefficients not systematic

B = inconsistent under Ha, efficient under Ho; obtained from xtreg

b = consistent under Ho and Ha; obtained from xtreg

tii .3052268 .282917 .0223098 .1010393

d3 -.487413 -.7063769 .2189638 .5905121

d2 7.327805 6.17572 1.152084 1.689248

wdis 4.462381 4.888438 -.4260575 1.876398

rer -.0493314 -.0365245 -.0128068 .0157982

dpci -.108927 -.2235341 .114607 .

dgdp -1.034575 -.9257479 -.108827 .3929773

rgdp -.2229075 .0911747 -.3140821 .1503667

fe_1 re_1 Difference S.E.

(b) (B) (b-B) sqrt(diag(V_b-V_B))

Coefficients

. hausman fe_1 re_1

61

Appendix 7. Unrestricted Pooled OLS Regression Results (Stata printout)

_cons -4.107073 6.836669 -0.60 0.551 -17.81969 9.605548

tii .3041099 .4677896 0.65 0.518 -.6341572 1.242377

pci -.2185862 1.123418 -0.19 0.846 -2.471878 2.034706

d3 -.6575841 .8317349 -0.79 0.433 -2.325833 1.010665

d2 6.454396 2.028618 3.18 0.002 2.385504 10.52329

d1 1.316019 1.542035 0.85 0.397 -1.776911 4.408949

wdis 5.061265 1.850227 2.74 0.008 1.35018 8.77235

rer -.0369937 .1307283 -0.28 0.778 -.2992014 .225214

dpci -.2044933 .4219889 -0.48 0.630 -1.050896 .6419092

dgdp -.8884764 .434315 -2.05 0.046 -1.759602 -.0173509

rgdp .0868209 .041847 2.07 0.043 .0028863 .1707554

iitfm Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 343.71277 63 5.45575825 Root MSE = 2.1323

Adj R-squared = 0.1666

Residual 240.972784 53 4.54665631 R-squared = 0.2989

Model 102.739986 10 10.2739986 Prob > F = 0.0276

F( 10, 53) = 2.26

Source SS df MS Number of obs = 64

. regress iitfm rgdp dgdp dpci rer wdis d1 d2 d3 pci tii

62

Appendix 8: Restricted Pooled Ordinary Least Squares regression results ((Stata printout)

_cons -3.715149 3.391727 -1.10 0.278 -10.50443 3.074131

d2 5.276342 1.197697 4.41 0.000 2.87889 7.673793

d1 1.378269 .7499082 1.84 0.071 -.1228356 2.879373

wdis 3.846698 1.241177 3.10 0.003 1.362212 6.331184

dgdp -.7956786 .2461287 -3.23 0.002 -1.288359 -.3029984

rgdp .0911265 .0234202 3.89 0.000 .0442458 .1380071

iitfm Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 343.71277 63 5.45575825 Root MSE = 2.0854

Adj R-squared = 0.2029

Residual 252.242588 58 4.34901014 R-squared = 0.2661

Model 91.4701821 5 18.2940364 Prob > F = 0.0025

F( 5, 58) = 4.21

Source SS df MS Number of obs = 64

. regress iitfm rgdp dgdp wdis d1 d2

63

Appendix 9: Random Effects Model Estimation Results (Stata printout)

rho 0 (fraction of variance due to u_i)

sigma_e 2.0834446

sigma_u 0

_cons -3.747512 6.523278 -0.57 0.566 -16.5329 9.037879

d3 -.7063769 .7859341 -0.90 0.369 -2.246779 .8340256

d2 6.17572 1.423791 4.34 0.000 3.385141 8.966299

d1 1.11071 1.114424 1.00 0.319 -1.07352 3.294941

tii .282917 .4508614 0.63 0.530 -.600755 1.166589

rer -.0365245 .1295364 -0.28 0.778 -.2904111 .217362

wdis 4.888438 1.60855 3.04 0.002 1.735739 8.041138

dpci -.2235341 .4068119 -0.55 0.583 -1.020871 .5738026

dgdp -.9257479 .3863013 -2.40 0.017 -1.682885 -.1686112

rgdp .0911747 .0350456 2.60 0.009 .0224867 .1598627

iitfm Coef. Std. Err. z P>|z| [95% Conf. Interval]

corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0063

Wald chi2(9) = 22.97

overall = 0.2984 max = 13

between = 0.9913 avg = 12.8

R-sq: within = 0.2466 Obs per group: min = 12

Group variable: country Number of groups = 5

Random-effects GLS regression Number of obs = 64

. xi:xtreg iitfm rgdp dgdp dpci wdis rer tii d1 d2 d3, re

64

Appendix 10: Fixed Effects Model (within) Regression Results (Stata printout)

F test that all u_i=0: F(4, 51) = 1.39 Prob > F = 0.2493

rho .9178298 (fraction of variance due to u_i)

sigma_e 2.0834446

sigma_u 6.963152

_cons 10.94396 15.08588 0.73 0.471 -19.34222 41.23014

tii .3052268 .4620443 0.66 0.512 -.6223658 1.232819

d3 -.487413 .9830549 -0.50 0.622 -2.460978 1.486152

d2 7.327805 2.209239 3.32 0.002 2.892571 11.76304

d1 0 (omitted)

wdis 4.462381 2.471498 1.81 0.077 -.4993577 9.424119

rer -.0493314 .1304962 -0.38 0.707 -.3113134 .2126506

dpci -.108927 .4062478 -0.27 0.790 -.9245035 .7066494

dgdp -1.034575 .5510534 -1.88 0.066 -2.140861 .071711

rgdp -.2229075 .1543966 -1.44 0.155 -.5328716 .0870567

iitfm Coef. Std. Err. t P>|t| [95% Conf. Interval]

corr(u_i, Xb) = -0.9753 Prob > F = 0.0111

F(8,51) = 2.83

overall = 0.0000 max = 13

between = 0.1485 avg = 12.8

R-sq: within = 0.3078 Obs per group: min = 12

Group variable: country Number of groups = 5

Fixed-effects (within) regression Number of obs = 64

note: d1 omitted because of collinearity

. xi:xtreg iitfm rgdp dgdp dpci rer wdis d1 d2 d3 tii, fe