proposal

mmemo

FulltextThesis.pdf

Home >Business & Finance homework help >Economics homework help >proposal

NON-OIL EXPORTS, FINANCE AND ECONOMIC

DEVELOPMENT IN SAUDI ARABIA

A thesis submitted for the degree of Doctor of Philosophy

By Abdullah Alsakran

School of Social Sciences Brunel University

MARCH 2014

Abstract Oil is an important part of the Saudi economy. With the volatility of oil prices and

the pressing needs of economic growth and development, the Saudi Arabian

government has planned to diversify its sources of income. To this end, the majority

of effort has focused on developing the non-oil export sectors, particularly in

manufacturing. Despite government efforts to enhance the ratio of non-oil export

to total exports, it remains weak, amounting to 15 per cent of total exports in 2010

(which compares unfavourably with the average for other Middle East and North

Africa countries (MENA) which stood between 30-46 per cent in 2010). This

research aims to provide a comprehensive assessment of non-oil exports and their

financing in Saudi Arabia. This study uses unique, primary data, collected through a

custom designed questionnaire and a unique sample of Saudi exports. There is

currently no comparable database for Saudi firms, or for other MENA countries

trying to engage in export diversification strategies.

This dissertation comprises three main empirical parts which are; ownership

structure and operations, finance, and business climate, respectively (chapters 3, 4

and 5). In the first, the econometric analysis shows multiple factors have a

significant positive impact on export intensity, including: whether the firm is

shareholding, the age of the firm, internationally and locally recognised quality

certificate, length of export experience, supplies of domestic origin, independent

retail stores, TV or radio advertising, a foreign language website and finally an

export marketing plan. Regarding the impact of financial factors on exports at firm

level, the econometric analysis showed that younger firms are more likely to be

credit-constrained than older firms. Finally, this dissertation provides evidence of

the relationship between the business environment, competition and firm’s

exports. The main findings show that firm performance, measured as intensity of

exports, is boosted by an increase in experience of export and hindered by a high

level of labour, competition, custom and trade regulation, and the informal sector.

Table of contents ABSTRACT ......................................................................................................................................... I

TABLE OF CONTENTS ......................................................................................................................... I

ACKNOWLEDGMENTS .....................................................................................................................III

CHAPTER 1: INTRODUCTION ............................................................................................................ 1

1.1 MOTIVATIONS .................................................................................................................................. 1

1.2 AIMS AND OBJECTIVES ........................................................................................................................ 2

1.3 CONTRIBUTIONS OF THE RESEARCH ....................................................................................................... 3

1.4 THESIS STRUCTURE ............................................................................................................................ 5

CHAPTER 2: DATA DESCRIPTION ...................................................................................................... 6

2.1 SAUDI ECONOMY OVERVIEW ..................................................................................................... 6

2.1.1 INTRODUCTION .............................................................................................................................. 6

2.1.2 DEVELOPMENT OF INCOME SOURCES IN TERMS OF THE SAUDI ECONOMY .................................................. 6

2.1.3 DIVERSIFICATION OF INCOME SOURCES AND THE STRATEGIC PLANS OF THE INDUSTRIAL SECTOR ................... 10

2.1.4 DIRECT ROLE OF THE STATE IN THE DEVELOPMENT OF THE INDUSTRIAL SECTOR ......................................... 14

2.1.5 DEVELOPMENT AND GROWTH OF THE INDUSTRIAL SECTOR.................................................................... 21

2.1.6 DEVELOPMENT OF NON-OIL EXPORTS ............................................................................................... 26

2.1.7 CONCLUSION ............................................................................................................................... 39

2.2 THE STRUCTURAL CHARACTERISTICS OF SAMPLE FIRMS .......................................................... 43

2.2.1 INTRODUCTION ............................................................................................................................ 43

2.2.2 EMPIRICAL STUDIES OF OBSTACLES TO SAUDI ARABIAN EXPORTERS ........................................................ 48

2.2.3 DATA METHODOLOGY .................................................................................................................. 50

2.2.4 DESCRIPTIVE SAMPLE INFORMATION ................................................................................................ 53

2.2.4.1 Export intensity: Rate of firms’ exports ........................................................................... 53

2.2.4.2 Export experience analysis............................................................................................... 54

2.2.4.3 Export orientation of firms .............................................................................................. 54

2.2.4.4 Analysis of trade operations’ characteristics ................................................................... 54

2.2.4.5 Firms’ export marketing .................................................................................................. 55

2.2.4.6 Infrastructure ................................................................................................................... 56

2.2.4.7 Competition ..................................................................................................................... 56

2.2.4.8 Labour situation ............................................................................................................... 57

2.2.4.9 Production capacity ......................................................................................................... 57

2.2.4.10 Total annual costs .......................................................................................................... 58

2.2.4.11 Legal and security status ............................................................................................... 59

2.2.4.12 Firms’ credit position ..................................................................................................... 59

2.2.4.13 Access to finance ........................................................................................................... 61

2.2.4.14 Supporting capabilities that encourage exports ............................................................ 62

2.2.4.15 The impact of the main variables on the expansion of national sales ........................... 62

2.2.4.16 The impact of the main variables on expanding exports ............................................... 62

2.2.4.17 Analysis of the trade barriers reducing export level ...................................................... 63

2.2.4.18 The most and least important challenges reducing export level ................................... 63

2.2.5 DISCUSSION AND CONCLUSION ....................................................................................................... 63

CHAPTER 3: MAIN DETERMINANTS OF EXPORT INTENSITY: ........................................................... 90

3.1 INTRODUCTION ............................................................................................................................... 90

3.2 LITERATURE REVIEW ........................................................................................................................ 93

3.3 EMPIRICAL MODELS ANALYSIS ......................................................................................................... 108

3.3.1 Model (A): EMPIRICAL REPLICATION FRAMEWORK ......................................................... 108

3.3.2 Estimates of the Model (A) ............................................................................................... 110

3.3.3 Model (B): EMPIRICAL ENHANCED FRAMEWORK ............................................................. 112

3.3.4 Estimates of Model (B) ..................................................................................................... 114

3.3.5 Model (C): EMPIRICAL EXPORT INTENSITY FRAMEWORK ................................................. 116

3.3.6 Estimates of Model (C)...................................................................................................... 117

3.4 DISCUSSION AND CONCLUSION......................................................................................................... 122

CHAPTER 4: FINANCIAL CONSTRAINTS TO FIRMS’ EXPORTS ......................................................... 136

4.1 INTRODUCTION ............................................................................................................................. 136

4.2 LITERATURE ON THE FINANCIAL CONSTRAINTS AND FIRMS EXPORT BEHAVIOUR ......................................... 139

4.2.1 The effect of financial factors on export behaviour .......................................................... 140

4.2.2 A glance at measuring credit constraints ......................................................................... 142

4.3 THE ECONOMETRIC APPROACH......................................................................................................... 144

4.4 DESCRIPTIVE STATISTICS OF VARIABLES .............................................................................................. 148

4.5 RESULTS ...................................................................................................................................... 149

4.5.3.1 Testing over identifying restrictions .............................................................................. 150

4.5.3.2 Collinearity ..................................................................................................................... 150

4.6 ROBUSTNESS CHECKS ..................................................................................................................... 150

4.7 CONCLUSIONS .............................................................................................................................. 151

CHAPTER 5: BUSINESS ENVIRONMENT, COMPETITION, AND FIRM PERFORMANCE IMPACT ON

EXPORT BEHAVIOUR .................................................................................................................... 159

5.1 INTRODUCTION ............................................................................................................................. 159

5.2 LITERATURE REVIEW ...................................................................................................................... 161

5.3 SETUP OF THE MODEL .................................................................................................................... 166

5.4 DESCRIPTIVE STATISTICS ................................................................................................................. 168

5.5 MAIN FINDINGS ............................................................................................................................ 169

5.6 CONCLUSION ................................................................................................................................ 171

CHAPTER 6: CONCLUSIONS, LIMITATIONS AND FUTURE RESEARCH ............................................. 177

6.1 CONCLUSION ................................................................................................................................ 177

6.2 POLICY RECOMMENDATIONS ........................................................................................................... 180

6.3 LIMITATIONS AND FUTURE RESEARCH ............................................................................................... 183

REFERENCES ................................................................................................................................. 184

APPENDIX 1: ANALYSIS THE IMPACT OF FIRM LEVEL ON A FIRM’S CREDIT POSITION AND ACCESS

TO FINANCE ON EXPORT INTENSITY ............................................................................................. 199

A1.1 The impact of firm level on a firm’s credit position ........................................................... 199

A.1.2 Estimates of the impact of firm level on a firm’s credit position ...................................... 200

A.2.1 Access to finance and export intensity ............................................................................. 200

A.2.2 Estimates of access to finance and export intensity ......................................................... 202

APPENDIX 2: SAMPLE AND QUESTIONNAIRE ................................................................................ 205

1. INTRODUCTION ............................................................................................................................... 205

2. METHODS OF DATA COLLECTION ....................................................................................................... 206

3. CONTENT OF THE QUESTIONNAIRES .................................................................................................... 207

4. PILOT TEST .................................................................................................................................... 210

5. DATA PREPARATION ........................................................................................................................ 211

6. SAMPLE SIZE .................................................................................................................................. 211

7. SUMMARY ..................................................................................................................................... 213

APPENDIX 3: QUESTIONNAIRE. .................................................................................................... 214

III

Acknowledgments

All the praise goes to Allah for his generous blessings, without which I would not

have completed this work.

My deepest and most sincere gratitude goes to my supervisor, Professor Nauro F.

Campos, for his inspiration, enthusiasm, knowledge, encouragement, support,

criticism, professional guidance and constructive comments throughout this

project. Thank you very much, Professor Nauro, for your one-of-a-kind supervision.

I would also like to express my gratitude to my second supervisor, Professor

Menelaos Karanasos, for his help and support.

I am particularly grateful to H.E. Eng. Yousef Albassam, Vice Chairman and

Managing Director of the Saudi Fund for Development, for granting me leave to

come to Britain and carry out this research, and for his continuous efforts to

develop the Non-Oil Export supporting base. I should also like to thank all the

individuals who helped during my fieldwork in SFD, particularly Ahmed Al-Yhia D.G.

of the Research and Economic Studies Department, Ahmed Al-Ghanam D.G. of the

Saudi Export programme and other colleagues in the aforementioned departments.

I should also like to thank my friend Mr.Haithm Al-Abdulatif who helped me with

the questionnaire.

My special gratitude is due to my mother, my wife, my sons and daughters, and my

brothers and sisters, for their continuous encouragement and support.

Finally, I present this work to a person who has a significant impact on my life: my

father -God's mercy-.

Chapter 1: Introduction

1.1 Motivations

The Saudi economy is predominantly oil-based and as such faced with the

continuous volatility of oil prices, along with a pressing need for economic growth

and development. The risk of dependence on this one source is acute when income

is dependent on natural resources. The risk is particularly high when natural

resources are depleting and, simultaneously, prices in the world market are based

on political and economic variables beyond the control of the producing countries.

This is why the Saudi government intends to diversify its sources of income. To that

end, extensive effort has focused on the development of the non-oil export sector,

especially those supporting the industrial sector.

A vast literature in recent decades illustrates the role of export expansion on

the economy. Studies such as those by Cavusgil and Nevin (1981), Todaro (1986),

Barker and Kaynak (1992), Gumede (2000), Aynul and Hirohito (2004) and Fatih

(2009), among many others, find a positive causal link between the expansion of

exports and economic growth. This positive link has encouraged many countries

start and export performance programmes. There has been limited discussion in

relation to Saudi exports, by Al-Aali (1997) and Al-Qahtany (2001). They have

however used limited questions that do not cover many of the key affect exporting

behaviour. Other studies such as Al-Twuijri (2001) and Al-Jarrah (2008) use macro

data that support the positive link between exports diversification and economic

growth.

Saudi Arabia has taken steps designed to support and encourage industry. The

state’s role can be divided into three areas. The first is to provide support. For

example, the Ministry of Commerce and Industry allocated industrial cities to the

various regions of the country. To upgrade the quality of services provided by the

industrial cities, the state founded the Saudi Industrial Property Authority (Modon)

in 2001. The government gives soft, medium and long-term loans to industrial

establishments through the Saudi Industrial Development Fund (SIDF). The second

area is to provide support during the manufacturing process. This support comes

through the provision of raw materials; in particular, this is done through the Saudi

Basic Industries Corporation (SABIC). Also in this phase, through SIDF, the

government provides direct loans to manufacturers wishing to expand. The third

area involves supporting and facilitating the final product with regard to exporting.

This role is played chiefly by the Saudi Export Programme (SEP), which works under

the umbrella of the Saudi Fund for Development (SFD). SEP was established in 1999

in order to develop private sector exports, by firstly providing financing incentives

and credit to exporters, while also providing competitive credit terms for buyers

abroad or funding institutions working in this area. Non-oil exports, however, still

remain a small portion of total exports. Official organisation statistics show that the

contribution of non-oil exports to total exports remains weak, on average covering

15 per cent of the country’s total exports over the past 30 years, which compares

unfavourably with the average for other countries in the Middle East and North

Africa, which were in the range of 30-46 per cent in 2010.

1.2 Aims and objectives

The aim of this present thesis is to address a fundamental question: What are

the main issues that face non-oil exporters in Saudi Arabia to increase their level of

exports? To answer these questions, we carried out a unique survey of Saudi firms.

It covers all sector and regions of Saudi Arabia. It is based upon a detailed

questionnaire (appendix 3) that was applied in face-to-face interviews during 2011

to 175 Saudi firms. The data examine and test the behaviour of firms and their

performance towards exporting. This is the first study of this kind for Saudi Arabia.

The literature shows that factors such as ownership structure, the impact of

innovation, size of labour, age of the firm and the sector are influential

characteristics. Similarly, the characterisation of trade operations such as the origin

of supplies, ways of importing the firm’s raw materials, export experience and total

sales, sales channel distributions, importance of marketing activities, and finally

export support capabilities should also be considered in analysis.

The survey also covers the role of finance in export diversification. In terms of

financial analysis, the target of this thesis is to answer the following questions: 1)

Does finance have a significant effect on exporter behaviour in Saudi Arabia? 2) Are

there problems regarding access to finance? This work attempts, by relying on

recent methodology, to identify credit constraints. In addition, the analysis

considers the degree of access to finance, the most important factors affecting it,

and how financial factors impact the level of exports in the private sector in Saudi

Arabia. The last important goal of this dissertation is to examine the relationship

between the competition and business environment, and export performance in

Saudi Arabia. It will shed some light on the investment climate and government

actions directed to alleviating restrictions on business.

1.3 Contributions of the research

Given the lack of comprehensive studies covering manufacturing behaviour, in

both Saudi Arabia and other Gulf Cooperation Council (GCC) countries, this research

strives to examine the reasons behind the low non-oil exports contribution to total

exports. In this research, a survey was conducted using a specific questionnaire to

assist the special government organisation that supports the private sector (the

Saudi Fund for Development). Hence, this work relies on new survey data and a

representative sample of the Saudi Arabian manufacturing sector conducted at the

end of 2011. The survey includes details of specific export obstacles that firms face

when selling their products abroad. Furthermore, the cross-sectional structure of

the data allows detailed identification of export influential variables.

The current work analyses the environment of non-oil export operations. The

work also attempts to provide a complete view of the obstacles and barriers faced

by non-oil exporters. Finally, it presents business environment indicators for

investors in the industrial sector and micro data concerning Saudi manufacturers’

behaviour.

The objective of this study is, for the first time in Saudi Arabia, to test the

influence of different determinants on firms’ exporting behaviour. The findings are

expected to assist the government organisation which aims to support private

sector firms. In addition, we believe these findings will help policy makers to select

their tools to increase the level of non-oil exports.

The study develops, analyses and testes a framework of exporting behaviour.

The main procedure is to present a systematic assessment of this framework as an

empirical model of exporting behaviour. Our study is based on a questionnaire that

allows us to expand the model in the commonly literature used. We derived and

generated information relying on the foundations of the literature. This derived

model gave this study a wider scope and the capability to explain many factors

considered to have important effects on export intensity, such as ownership, firm

size, innovation, trade operations, sales distribution channels, marketing activities

and export capabilities.

The study also discusses the financial constraints of exporter firms. Models

were obtained from the literature on the credit effect on export behaviour. The

main measurement used in the current study classifies sample firms into categories

based on their level of credit constraint. The analysis employed a Two Stage Least

Squares (2SLS) to present evidence regarding the impact of credit on Saudi export

firms.

Although Saudi Arabia is a country rich in oil, few studies have discussed the

competition and business environment. This thesis contributes to the existing

literature by highlighting the importance of features in the business environment;

reviewing export determinants such as competition and business constraints, in

order to establish the framework used in this study. Our survey addresses issues

relating to firms exports and their business environment, such as access to finance,

access to infrastructure, competition and labour. Other contributions in this regard

are to the existing body of literature on the impact of regulations and the business

environment on export or trade in general. For instance, by distinguishing between

export and specific regulatory measures, the analysis provides estimates of how

important business regulations, typically outside the scope of trade and customs

authorities, affect exports.

1.4 Thesis structure

This thesis has seven chapters. Chapter One introduces the thesis topic and the

rationale behind the research. Chapter Two has two main sections: first, it reviews

the development of the Saudi Arabian economy, and an exploration of Saudi

economic policy assistance in terms of export diversification. The second section

begins with a discussion of the characteristics of firms in terms of their business

environment and trade operations. The analysis then moves on to define the main

factors encouraging or hindering firms when it comes to increasing their level of

export.

Chapter Three analyses the main determinants of export intensity by focusing on

the influence of ownership, innovation, trade operations, distribution channels,

marketing and export capabilities. Chapter Four investigates the determinants of

financial constraints and credit rationing, which have an effect on exporting firms. It

examines new methods to measure credit constraints. The current work applied

instrumental variable (IV) regressions.

Chapter Five explores which business factors may impact firm performance. The

main focus is on business constraints and competition. Firm performance in this

chapter is also measured by export intensity. Chapter Six presents the main findings

using the ordinary least squares (OLS) method.

Finally, Chapter Six summarises the major conclusions from this research. It also

offers some recommendations to policy makers and areas for further study.

The thesis contains three appendices. Appendix One provides more information

that support the analysis of Chapter Four, which is the analysis of the impact of firm

level on a firm’s credit position and access to finance on export intensity. The

second explains the sample and the questionnaire. The appendix also presents the

sample’s basic characteristics. The third appendix contains the questionnaire,

designed as part of this study and used to collect the data.

Chapter 2: Data Description

2.1 Saudi Economy Overview

2.1.1 Introduction

The dependence on oil as a single source of income has led to negative

consequences which may be devastating for the national economy. It can be seen

that in many countries of the world there is a strong positive relationship between

growth rates and stability, as well as on the diversity of the basis of the national

economy and multiple sectors of production. The risk is particularly acute when

national income is mainly dependent on natural resources. The risk is high when

the source is depleting and, at the same time, prices in world markets are based on

political and economic variables beyond the control of the producing country. This

chapter offers a brief overview of the development of the Saudi Arabian economy,

in an effort to understand ways in which the Saudi economy may face a sustainable

development risk. Moreover, an exploration of the Saudi economic policy would be

of assistance in terms of diversification and a consideration of the strategic plans

aimed at meeting the economic needs of the Saudi nation.

2.1.2 Development of income sources in terms of the Saudi economy

The character of the Saudi economy in the past three decades has led to the

acquisition of many varied features. After the oil boom at the beginning of the

Seventies, the Saudi economy began to modernise. Prior to that, it was

economically simple, consisting of agriculture, grazing, and some primitive

industries. In addition, the fees received from trade and services dependent on

pilgrims was the most important source of government revenue1.

In the mid-Seventies, the rise in oil prices led to the acquisition of enormous

financial resources, and this became the most important source of income. With

these financial resources, the government has implemented a number of

infrastructure projects. The economic and development policies of the state aimed

to encourage and support the private sector by providing loans and services, and by

1 Ministry of Planning, 2011. Achievements of the Development Plans;(28)Issue, 1390-1432h (1970-2011) Facts & Figures.

exempting them from taxes and customs duties. This resulted in generally increased

economic growth and, in particular, growth of the industrial sector. The lower oil

prices in the mid-Eighties decreased oil revenue. The lower revenues had a negative

effect on the government budget and on infrastructure project financing1.

The major problem that faces many developing countries is that they are

reliant on primary exports, where primary goods represent the main source of

income and foreign exchange revenue. There is a high degree of dependency on a

single commodity. This is supposed to be particularly risky in the case of oil exports

compared with any other primary commodities, due to the fact that the oil market

has seen a high degree of instability in the past forty decades, as can be seen in

Table 2A.1. As illustrated, the government oil revenues of about SR 7.0 billion in

1970, rose to SR 319 billion in 1980. Due to the decline in oil prices in 1986, there

was a decline in production as well as lower oil exports. As a result, government

revenues fell by 72% to around SR 88 billion in 1986. This result forced the state to

think seriously about the situation and try to diversify the economic base.

Table 2A.1: Saudi Arabian Oil Revenues, Price, Production and Exports (*)

Nominal Oil price

(in U.S$ per Barrel)

Annual Government Oil Revenues (Million

SAR)

Crude Oil Production (daily

average-Million Barrels)

Crude oil exports (Million Barrels)

1970 1.3 7122 3.8 1174.17

1975 10.72 93481 7.08 2409.39

1980 28.67 319305 9.9 3375.69

1986 13.73(**) 88425 3.17 780.72

1990 20.82 123148 6.41 1642.42

1995 16.73 105728 8.02 2296.13

2000 26.81 214424 8.09 2282.38

2005 50.15 504540 9.35 2631.24

2010 77.75 670265 8.17 2425.09 (*) Source: SAMA (2011)

The risks resulting from the dependency on oil exports as a major income

source can be summarized in terms of economic and financial risks such as

fluctuating oil prices leading to the instability of total income and reduced revenue

due to a lack of liquidity that has a direct effect on government spending. The

1 Ministry of Planning, 2011. Achievements of the Development Plans;(28)Issue, 1390-1432h (1970-2011) Facts & Figures.

government must pay for their commitments and it may require covering their

needs by loans plus interest, which will lead to an increase in government debt. The

other risk affects sustainable development. Strategic plans will be difficult to apply,

causing the government to put a hold on some infrastructure projects. The other

important risk associated with being dependent on oil exports as a major income

source is the political risk. When oil prices increase consumers decrease their

consumption of oil. In addition, most buyer countries set a high rate of tax on oil

sales, so government revenue are directly affected by the consuming countries.

To avoid these risks, the Saudi government has tried to reduce the country’s

dependence on oil. The process of development planning started in 1969 for the

development and mobilization of manpower and material resources, with the aim

of investing to achieve many of the government’s economic and social objectives. It

aimed to improve the standard of living of citizens, complete the infrastructure,

diversify the economic base and sources of national income, develop and improve

human resources, and encourage the private sector to contribute to an active role

in development efforts. During the period of the Seventies to the Nineties, the state

focused on manufacturing by supporting and establishing industrial projects1.

Moreover, the government encouraged the development of replacement industries

to meet the needs of the local market. It was also during that period that the

government encouraged the establishment of industries with a comparative

advantage in production, in order to support their ability to export their products to

foreign markets. The government encouragement and support expanded to the

private sector in various other economic fields.

In general, the development plans have led to progress in bringing about

fundamental changes in the main structure of the national economy, and have led

to the diversification of the production base, whereby the non-oil sectors have

contributed to the Gross Domestic Product (GDP). In addition, the private sector

has achieved a key role in terms of production, investment, and employment, all

steps which have reduced the reliance on government spending2. The development

1 Ministry of Planning, 2011. Achievements of the Development Plans;(28)Issue, 1390-1432h (1970-2011) Facts & Figures. 2 Saudi Arabian Monetary Association (SAMA), 2011. Annual Report. No. 47. Research and Statistics Department, Riyadh.

of the Saudi economy in terms of the development plans can be divided into three

phases:

The first phase (1970-1985) which was characterized by an expansion in

spending on infrastructure, providing public services and facilities, and offering

funding support to the private sector through development funds in the form of

soft loans, and by providing direct and indirect subsidies for industrial and

agricultural projects. On the other hand, the financing of development depended

on the growing oil revenues at this stage that led to an increase in crude oil

production and export.1

The second phase (1985-2005) saw intensive efforts to rationalize public

expenditure. This was due to the decline in oil revenues resulting from the

instability of global conditions in the oil markets. This stage also saw intensive

efforts to improve the performance of public institutions, the diversification of the

economic base, and the enhancement of the developmental role of the private

sector2.

The Eighth Development Plan saw the beginning of the third phase3 (2005-

2009), which was characterized by the adoption of an expansionary fiscal policy

supported by a flow of oil public revenues caused by high world prices of oil. A

feature of this period was the dependence on four five-year of strategic long-range

plans extending for twenty years. The Eighth Development Plan was the first of the

long-range system of strategic planning which was characterized in this stage by a

combination of developmental change and continuity. The continued focus was on

accelerating growth and diversifying the economic base with the aim of achieving

balanced development between regions. In addition, this stage saw the creation of

a shift towards a knowledge-based economy in order to intensify efforts to enhance

Saudi Arabia’s competitiveness, and to deal flexibly and efficiently with local,

regional and international challenges.

1 Ministry of Planning; The First Development Plan 1970. The Second Development Plan 1974.The Third Development Plan 1981. Riyadh: Ministry of Planning Press. 2 Ministry of Planning; The Fourth Development Plan 1985. The Fifth Development Plan 1990.The Sixth Development Plan 1993. Riyadh: Ministry of Planning Press. 3 Ministry of Planning; The Seventh Development Plan 1990. The Eighth Development Plan 2004.The Ninth Development Plan 2009. Riyadh: Ministry of Planning Press.

2.1.3 Diversification of income sources and the strategic plans of the

industrial sector

The industrial sector captured the early attention of the government in terms

of its five-year development plans. Since the Seventies the government realised the

importance of diversifying sources of income, by the encouragement of non-oil

sectors. It set a strategic objective by working on five-year development plans1:

Saudi Arabia’s first two development plans2, covering the 1970s, emphasized

infrastructure. The results were impressive the total length of paved highways

tripled, power generation increased by a multiple of 28, and the capacity of the

seaports grew tenfold

For the third plan (1980-1985)3, the emphasis changed. Spending on

infrastructure dropped, but it rose markedly on education, health, and social

services. Diversifying and expanding productive sectors of the economy (primarily

industry) were also addressed. The two industrial cities of Jubail and Yanbu were

largely completed around the use of the country's oil and gas reserves to produce

steel, petrochemicals, fertilizers, and refined oil products.

In the fourth plan (1985-90)4, the country's basic infrastructure was viewed as

largely complete, but education and training remained areas of concern.

Private enterprise was encouraged, and foreign investment in the form of joint

ventures with Saudi public and private companies was welcomed. The private

sector became more important, rising to 70% of Non-Oil GDP by 1987. While still

concentrated in trade and commerce, private investment increased in industry,

agriculture, banking, and construction companies. These private investments

were supported by generous government financing and incentive programmes.

The objective was for the private sector to have 70% to 80% ownership in most

joint venture enterprises.

1 Ministry of Planning, 1970. The First Development Plan. Riyadh: Ministry of Planning Press. 2 Ministry of Planning, 1975. The Second Development Plan. Riyadh: Ministry of Planning Press. 3 Ministry of Planning, 1981. The Third Development Plan. Riyadh: Ministry of Planning Press. 4 Ministry of Planning, 1985. The Fourth Development Plan. Riyadh: Ministry of Planning Press.

The fifth plan (1990-95)1 emphasized consolidation of the country's

defenses; improved and more efficient government social services; regional

development; and most importantly, creating greater private-sector employment

opportunities for Saudis by reducing the number of foreign workers.

The sixth plan (1995-2000)2 focused on lowering the cost of government

services without cutting them and sought to expand educational training programs.

The plan called for reducing the Kingdom's dependence on the petroleum sector by

diversifying economic activity, particularly in the private sector, with special

emphasis on industry and agriculture. It also continued the effort to "Saudize" the

labour force.

The seventh plan (2000-2005)3 focuses more on economic diversification and

a greater role of the private sector in the Saudi economy. For the period 2000-05,

the Saudi Government has aimed at an average GDP growth rate of 3.16% each

year, with projected growths of 5.04% for the private sector and 4.01% for the Non-

Oil sector. The government also has set a target of creating 817,300 new jobs for

Saudi nationals.

The Eighth Development Plan (2005-2009)4 featured a new structure. It was

different from the previous plans, in a manner that reflects that the country has just

gained accession to the WTO. This plan requires the management of the economy,

and the development process is different from that of the past, so that the

economy is more open.

The objectives of the plan focused on improving the productivity of the

national economy and strengthening its competitiveness, paying particular

attention to promising activities such as the strategic and manufacturing industries,

and attention in particular to energy-intensive industries and their derivatives, the

natural gas industry, mining, tourism, and information technology.

1 Ministry of Planning, 1990. The Fifth Development Plan. Riyadh: Ministry of Planning Press. 2 Ministry of Planning, 1993. The Sixth Development Plan. Riyadh: Ministry of Planning Press. 3 Ministry of Planning, 1999. The Seventh Development Plan. Riyadh: Ministry of Planning Press. 4 Ministry of Planning, 2004. The Eighth Development Plan. Riyadh: Ministry of Planning Press.

The Plan also paid great attention to the contribution of the private sector to

economic and social development. The plan provides the assistance needed to

support the competitiveness of national products, to support and encourage the

ways and methods of scientific research, and to develop trends towards a

knowledge-based economy. This is one of the pillars and the main support of the

Plan and emphasises the need to increase production and productivity, expanding

the horizons of investment. Finally, the Eighth Plan did not neglect the continued

expansion of basic equipment and maintenance, to meet the needs of the growth

of demand, and to facilitate the growth of all production and service sectors, due to

improvements in terms of efficiency and productivity.

As a result, the contribution of the non-oil sectors has grown in value at an

average annual rate of 5.5 per cent, with its share in GDP growing from 51 per cent

to 73.5 per cent during the same period.

Table 2A.2: Contribution Development of Production Sectors to GDP during the Economic Development Plans (actual figures)

Plan stage Industrial sector

Agriculture sector

Mining sector

Services sector

First plan 1970 8.3% 4.2% 35.9% 36.2%

1974 4.9% 0.8% 73.9% 15.8%

Second plan 1975 5.0% 0.9% 59.3% 26.9%

1979 5.1% 1.2% 49.9% 33.3%

Third plan 1980 4.1% 1.0% 59.0% 27.1%

1984 7.8% 2.8% 28.5% 50.2%

Fourth plan 1985 8.0% 3.7% 23.5% 53.9%

1989 8.8% 6.3% 23.5% 49.4%

Fifth Plan 1990 8.6% 5.9% 31.7% 43.9%

1994 9.4% 6.2% 29.2% 44.7%

Sixth plan 1995 9.6% 5.9% 30.7% 43.8%

1999 10.4% 5.7% 28.7% 45.3%

Seventh plan 2000 9.7% 5.9% 36.8% 39.8%

2004 10.2% 4.0% 40.6% 37.4%

Eighth plan 2005 9.4% 3.2% 46.0% 32.7%

2009 10.4% 2.9% 42.6% 37.2% Source: Central Department of Statistics & Information, Ministry of Economy and Planning (2011). (Different chosen years at current prices, Million Rls)

3.1 The National Strategy of Industry (NSI) 1

During the Eighth Plan (2005-2009), the Saudi government established

national strategic plans for the industrial sector. It presents a vision for growth,

development, and wealth-creation in the Kingdom, and offers a roadmap for

maximising the proceeds of its resources, both natural and human. It includes

mechanisms for effective management, updated laws and funding. The Strategy

offers a detailed analysis of the current industrial situation in the Kingdom and

examines trends in economics and technology from all over the world to help in

forming the industrial strategy of the Kingdom. Both public and private-sector

industries have participated in extensive discussions on the characteristics of the

economy, Saudi society, and experiments conducted worldwide in the field of

industrial development (NSI, 2009).

The strategy utilises the achievements of industry as the Kingdom trends

towards a knowledge-based economy, including knowledge gained in the fields of

energy and petrochemicals, the strengthening of innovative and competitive

capacities, and industrial diversification, all leading to a balanced development of

the Kingdom at regional level.

The country designed the manufacturing sector development strategic plan

based on the following eight main objectives2:

1- Increase the economy’s capacity to produce a range of commodities at costs

that will enable it to compete effectively in domestic and foreign markets.

2- Exploit the advantages of low-priced energy inputs, the abundance of

derivatives extracted from petroleum and the agricultural, mineral, and

fishery resources that are available, to diversify the industrial base.

3- Encourage the full utilisation of the capacities of the manufacturing

industries in the private sector.

4- Expand and deepen links with international technology utilising companies.

5- Promote balanced regional industrial development.

1 NIS (2009), Ministry of Commerce and Industry 2 NSI (2009) , Ministry of Commerce and Industry

6- Raise industrial productivity by encouraging high-capacity utilisation.

7- Lessen the dependence of industry on non-Saudi labour by intensifying the

education of Saudi citizens and promoting the on-the-job training of Saudis.

8- Intensify cooperation and economic integration within existing industries.

On the other hand, the objectives of the Ninth Development Plan (2009-2014)

include improving the standard of living, developing human resources, increasing

the recruitment of Saudi nationals, diversification of the economy ensuring

balanced regional development and, enhancing the competitiveness of the

economy and of Saudi products.

The Government's National Strategy for Industry aims to greatly develop and

diversify the economy by 2020. Its objectives are shown in Table 2A.31:

Table 2A.3: National Industrial Strategy goals for 2020

Objective Current

indicators Future

indicators

Expand manufacturing per cent of GDP 11% 20%

Double Saudi industrial employment 15% 30%

Increase industrial exports 18% 35%

Increase the proportion of technology-based manufactured products

30% 60%

Increase national employment 15% 30%

Increase economic 'value added' 8% a year Source NSI (2009), Ministry of commercial and industry.

2.1.4 Direct role of the state in the development of the industrial

sector

The country has taken steps to support and encourage industry. The state’s

role can be divided into three phases. The first is to provide support before

industrialisation. For example, the Ministry of Commerce and Industry allocated

industrial cities to the various regions of the country. It has constructed and

developed these cities throughout the country and has provided them with all

required services and utilities. Beginning in 1970, the country has implemented a

plan (under the First five-year Development Plan) to develop industrial cities.

1 NSI (2009) , Ministry of Commerce and Industry.

Industry is seen as and fundamental source of national income, and the basic plan is

to resettle or establish factories in these cities, where all the elements such as basic

services and equipment are provided, and where the environmental conditions,

safety requirements, employment opportunities, and distribution of resources are

considered in a way that ensures each of the country’s regions has a carefully-

considered balance of each sector. The first three cities were established in Riyadh,

Jeddah, and Dammam in 1974 on a total area not exceeding 1.4 million square

meters. The success of these projects led to the expansion of the programme in the

Second five-year Plan in 1975-1980 and in coming development plans.

To upgrade the quality of services provided by the industrial cities, the state

founded the Saudi Industrial Property Authority (Modon) in 2001, as an

independent public agency to oversee the establishment and management of

industrial cities and technology zones, in addition to the operation, maintenance

and development of these cities in collaboration with the private sector. By the end

of the Seventh Plan (2000-2004) there were 14 industrial cities in regions such as

Riyadh 1st. and 2nd., Jeddah 1st. and Dammam 1st. and 2nd., Makkah, Qassim, Al

Ahsa, Madinah, Assir, Al-Jouf, Tabuk, Hail, and Najran.

Also in this stage, the government founded the Saudi Industrial Development

Fund (SIDF), which aims to gives soft, medium, and long-term loans to industrial

establishments for up to 50 percent of the total cost of a project. The payback

period is up to fifteen years. There is also a two-year grace period from the start of

production. SIDF by providing funding also reviews and analyses the feasibility

study submitted due to its requirements request by factories. This procedure by the

SIDF plays an important role in determining the needs of either the domestic

market or the international market. On the other hand, the country, through other

funding organizations such as the Saudi Credit and Savings Bank, plays a supporting

role in providing funding for the financing of small and medium-sized enterprises,

aimed at promoting development and creating employment opportunities for Saudi

citizens in less-developed cities. There is also the Public Investment Fund (PIF),

which focuses on very large firms. This is because the private sector sometimes

cannot implement development alone because they may have insufficient

experience, inadequate capital resources, or both.

The second phase was designed to provide support during the manufacturing

process. This support comes through the provision of raw materials, whether

through customs exemptions, or the creation of a private entity for the provision of

raw materials. In particular, this is done through SABIC, which has specialized and

obtained a concession from the state to provide raw materials to companies in the

petrochemical, chemicals, and plastics industries. Also in this phase, through SIDF,

the government provides direct loans to manufacturers which want to expand.

The third phase involves supporting and facilitating the final product with

regard to exporting. This role is played by the Saudi Export Program, which works

under the umbrella of the Saudi Fund for Development. This programme provides

companies with finance for the purchase of Saudi exports, as well as providing

insurance risk services for exports with regard to non-payment by buyers.

Additionally, the government also established the General Investment Authority

(SAGIA) in 2000, which seeks to attract foreign direct investment in Saudi business

in order to take advantage of the technical development of products and to

improve production, thereby enhancing the competitiveness of local products in

international markets. The government believes that investment is closely related

to economic performance, and plays an important role in the form of an impact

multiplier on all sectors of the economy. Investment promotes economic growth,

the diversification of income sources, provides new employment opportunities,

encourages technology transfer and indigenisation, aids export development,

strengthens commercial relations, and represents an essential component in

achieving the objectives of overall economic development.

4.1 The Saudi Industrial Development Fund (SIDF)

The Saudi Industrial Development Fund plays a pivotal role in the fulfilment of

the objectives and policies of programmes devised for the industrialisation of Saudi

Arabia. Since its inception, SIDF has assumed a leading role in the achievement of

goals, as well as the formulation of policies and programmes geared towards

assisting the private sector in the process of industrial conversion. Financial support

in the form of soft loans provided by the SIDF represents one of its major

supportive functions in encouraging industrial development within the Kingdom.

The favourable response of the private sector has had a significant influence on the

establishment and expansion of the industrial base. Besides the provision of loans,

the SIDF provides borrowers with a variety of technical, administrative, financial,

and marketing consultation services, which in turn, help to raise the level of their

performance and overcome obstacles. The role of the SIDF in industrial

development requires the verification and confirmation of the feasibility of the

macro and micro economic implications of borrowers' projects. It also calls for the

increase of projects’ potential for success through the optimum allocation of

invested capital. Among the SIDF's prime objectives in the context of industrial

development in the Kingdom are:

 Achievement of a good return on investment.

 A suitable added value.

 Replacement of imports by local products.

 Promotion of non-oil industry related exports.

 Realisation of industrial integration.

 Creation of employment opportunities for Saudi nationals.

 Exploitation of the Kingdom's natural resources and raw materials.

 Attraction of foreign capital as well as the transfer of technology.

 Protection of the natural environment.

From its foundation up to the end of 2010, there were 3,226 industrial loans

given by the SIDF with a total value in SR 87,391 million (around £14,565 million)1.

These were approved for the support of 2,284 new industrial projects Kingdom-

wide. The chemical industry still leads all other sectors in terms of the total amount

and number of loan commitments since SIDF’s inception up to the end of the fiscal

year 2010. The cumulative commitments extended to the sector totalled SR 35,147

1 Calculated rely on the exchange rate of one GBP equal six riyals.

million, representing 40 per cent of the total value of loans approved by the fund

during that period (Table 2A.4).

The engineering industries sector came second in terms of the value and

number of approved loans since the inception of the fund up to the end of the fiscal

year 2010. Cumulative commitments extended to this sector totalled SR 17,802

million, representing 20 per cent of the total loans approved by the SIDF. Third

place in terms of the cumulative value of approved loans is held by the consumer

industries. By the end of 2010, cumulative commitments extended to this sector

totalled SR 14,551 million, representing 17 per cent of the total loans approved by

SIDF since its inception up to the end of the period under discussion. Another

important sector is the cement industry, the amount of loans committed to this

sector since the inception of the fund up to the end of the fiscal year 2010 totalled

SR 9,695 million or 11 per cent of total loans approved, thereby ranking the sector

fourth in terms of the amount of loan monies committed. Finally, by the end of

2010, the loans SIDF committed to the “Other Building Materials” sector totalled SR

9,319 million, or 10 per cent of the cumulative loans approved to industrial projects

since the inception of the fund. Thus, the sector was ranked fifth in terms of the

size of the loans approved.

The SFDI also established a programme known as Kafalah (Guarantee), in 2006.

It aims to overcome the obstacles of financing that face small and medium sized

enterprises (SMEs). Because there are firms which do not have the ability to provide

the guarantees required by financing organisations, it has been established to cover

a percentage of the risk associated with financing an organisation, in the event that

the organisation fails to repay its funding or part thereof, it encourages banks to

finance SMEs, but could not provide the guarantees or accounts receivable which

prove their eligibility for funding. This programme has been established between

the Ministry of Finance represented by the Saudi Industrial Development Fund, and

ten local Saudi banks.

Since its initiation at the beginning of 2006 up to the end of 2011 the Small

and Medium Enterprises Loan Guarantee Program has issued a total of 2,109

guarantees amounting to SR 804.4 million against a total of commercial-bank

financing to the tune of SR 2,016 million extended to 1,390 SMEs.

Table 2A.4: Number and value of approved SIDF industrial projects and loans by minor sector

(SR millions)

Sector 1970 2010 Cumulative Total

Number Value Number % Value %

Consumer Products 22 830 624 27.3% 14551 16.7% Food 10 555 290 12.7% 7356 8.4% Beverages 6 163 55 2.4% 1581 1.8% Textiles 1 1 64 2.8% 2037 2.3% Leather & substitutes 0 - 24 1.1% 133 0.2% Carpentry products 0 - 14 0.6% 205 0.2% Wooden furniture 2 12 53 2.3% 368 0.4% Paper products 3 99 88 3.9% 2620 3.0% Printing 0 - 36 1.6% 215 0.2% Chemical Products 15 3726 562 24.6% 35147 40.2% Chemicals 11 1833 267 11.7% 27566 31.5% Oil & gas products 3 1800 32 1.4% 3114 3.6% Rubber Products 0 - 17 0.7% 477 0.5% Plastic Products 1 93 246 10.8% 3990 4.6% Building Material 14 674 366 16.0% 9319 10.7% Ceramic Products 0 189 13 0.6% 1332 1.5% Glass Products 2 98 59 2.6% 2563 2.9% Other Building Material 12 387 294 12.9% 5424 6.2% Cement 0 0 30 1.3% 9695 11.1% Engineered Products 17 1358 659 28.9% 17802 20.4% Metal Products 13 1225 391 17.1% 12988 14.9% Machinery 1 11 88 3.9% 872 1.0% Electrical Equipment 3 122 126 5.5% 3010 3.4% Transport Equipment 0 - 54 2.4% 932 1.1% Other Manufacturing 0 0 43 1.9% 877 1.0%

Total 68 6588 2284 (a) 100% 87391(b) 100% (a) Of which 454 loans were terminated. (b) Of which SR 12.197 million were terminated or reduced

* source : SIDF Annual report 2010

4.2 Saudi Basic Industries Corporation (SABIC)

SABIC’s creation by royal decree in September 1976 was a bold step for a

developing country. The headquarters are in Riyadh, the capital of Saudi Arabia. It

marked a new move into the use of the by-products of oil extraction to produce

value-added commodities such as chemicals, polymers, and fertilizers, for export.

These commodities were also intended to create new industries, helping Saudi

Arabia to diversify and to develop.

SABIC products and services are extensive. They are organized into four

categories; Chemicals (Chemicals and Performance Chemicals), Plastics (Polymers

and Innovative Plastics), Fertilizers, and Metals

SABIC began production in 1981. The total production in 1985 was 6.3 million

metric tons (MMT), but by the end of 2010 it had reached 66 MMT (Table 2A.5). It

is a market leader in key products such as ethylene, ethylene glycol, methanol,

MTBE, and polyethylene. Chemicals, SABIC’s largest strategic business unit, account

for around 60 per cent of the firm’s total production. It is also the world’s fourth-

largest producer of polyolefins. In addition to this it is the world’s third-largest

producer of polyethylene and the fourth-largest producer of polypropylene. It also

achieved 11th position among the top 500 companies in the world in 2005 as

ranked by the Financial Times.

It is also the world’s largest producer of mono-ethylene glycol, MTBE, granular

urea, polyphenylene, and polyetherimide. The Saudi Iron and Steel Company

(HADEED), owned by SABIC, is one of the world’s largest fully-integrated steel

producers. SABIC’s European subsidiary produces over 2 MMT of polymers and over

5 MMT of basic chemicals. The forecast shows that the annual production capacity

of SABIC will reach over 130 MMT by 2020. At the end of 2010, SABIC operated in

more than 40 countries across the world and has 60 world-class manufacturing and

compounding plants in locations across the Middle East, Asia, Europe, and the

Americas.

Table 2A.5: Total production by business unit

2009 2010 Growth per cent

Chemicals 37,479 42268 13%

Performance Chemicals ** - 458 -

Innovative Plastics 1,033 1231 19%

Polymers 8,666 10667 23%

Fertilizers 6,542 7043 8%

Metals 4,776 5191 9%

Total 58496 66858 14%

** Performance Chemicals to start production in 2010 ‘000 metric tons

*Source : SABIC annual reports 2009 and 2010

SABIC’s overall total assets stood at SR 317 billion at the end of 2010,

compared with SR 297 billion in 2009. The value of its sales revenue was about SR

103.1 billion at the end of 2009 and rose to about SR 151.9 billion in 2010. Net

profits in 2009 touched SR 9 billion, rising to SR 21.5 billion in 2010.

Figure 2.1: Main Organisations supporting the Industrial Sector in Saudi Arabia

MODON 2001

SABIC 1976

Managing industrial cities Providing raw materials

SIDF 1974

SEP 1999

Industrial financing Exports credit & Financing

2.1.5 Development and growth of the industrial sector

Although industry in Saudi Arabia is considered as beginning in the Seventies,

it has witnessed a steady development and has achieved a number of impressive

accomplishments. Due to the importance and support that has been provided by

the state, it has played a solid role in achieving the strategic objectives and

economic goals of the country.

The state's efforts have included the support of industrial development in

several basic fields, including the implementation of the necessary infrastructure.

During the late 1970s and the early 1980s, the fishing villages of Al-Jubail and Yanbu

were transformed into modern industrial cities, with the Royal Commission for

Jubail and Yanbu overseeing the infrastructure development. It also constructed

industrial cities in various regions of the country, as well as establishing the Saudi

Industrial Development Fund (SIDF) in addition to providing a number of other

industrial supports and incentives. The response and co-operation of the private

sector with the government's plans and efforts have had an effective impact on the

actualisation of industrial development's objectives.

INDUSTRIAL SECTOR

As can be seen from Table 2A.6, the industrial base in the Kingdom has

expanded considerably over the last four decades. The total number of operating

industrial units has jumped from 198 in 1974 to 4,744 in 2010. In parallel, invested

capital has increased by SR 12 billion in 1974 to SR 439.7 billion in 2010. The

employment figures have also seen a huge growth in numbers from 34,000 workers

in 1974 to 577,499 workers in 2010.

Table 2A.6: Growth of Operating Industrial Units, Finance of Operating Industrial and Workers during 1974-2010

Industrial activities

Operating Industrial Units

Finance of Operating Workers

1974* 2010** 1974* 2010** 1974* 2010**

Products of Animals, Food & beverages: 39 754 2028 40948 7199 112187

Products of Wood, Paper, Leather and Textiles: 52 882 1116 28578 5930 101177 Textiles products 1 87 20 4987 60 15137 Cloth products 1 82 38 976 249 9505 Leather products 2 46 7 636 50 3967 Wood industry and products 4 61 65 2884 839 7164 Paper industry and its products 9 157 177 7865 843 22290 Printing press and copying of recorded multi-media 18 119 809 3913 2594 10741 Furniture and products unclassified elsewhere 17 330 0 7317 1295 32373

Products of Chemical, petrochemical, plastic, Rubber and Medical care :

24 1119 3840 237976 7811 119629

Refined petroleum and nuclear fuel products 4 90 364 164513 3487 25970 Chemical materials and products 9 506 2954 60835 2429 48335 Rubber and plastic products 11 507 522 12076 1895 44314 Recycling 0 16 0 552 0 1010

Products of Building Material and Glassware: 58 1437 4165 109281 7512 172111 Other non-metal products 25 771 3771 56567 3780 88481 Basic metal products 24 311 234 42398 2801 46627 Construction metal products 9 355 160 10316 931 37003

Products of Electrical, Machinery, Transport and Medical equipment:

24 552 1183 22929 5476 72395

Machines and Equipment industry 12 223 808 6037 4357 27285 Office and accounting terminals as well as computers 0 5 0 660 0 2704 Electric machines and terminals (unclassified elsewhere) 2 132 127 10887 464 23779 Radio, TV and telecommunication equipment and terminals

0 19 0 1041 0 2931

Medical terminals, optic tools and all types of watches 2 14 78 212 33 859 Engine and trailer motors 8 138 0 3216 622 12230 Other transportation equipment 0 21 170 876 0 2607

Total 197 4744 12332 439712 33928 577499

* source: (SDIF,2010) ** source: (SAMA,2011)

By looking into the composition of the industry sector in Saudi Arabia, we can

see that the 'other non-metallic minerals' sector heads all other sectors in terms of

operating industrial units (771), representing 16.3 per cent of the total number of

factories operating at the end of 2010. The other four sectors in the top five in

terms of operating industrial units are the food and beverages products sector (15.9

per cent), rubber and plastic products (10.6 per cent), chemical materials and

products (10.6 per cent), and the construction of metal products (7.4 per cent).

The refined petroleum products sector is in the top five in terms of the volume

of investment (SR 164 billion), representing 37.4 per cent of the total investment in

operating factories, followed by the chemical materials and products with SR 60

billion, representing 13.8 per cent of total investment in operating factories. Then

comes with other non-metal products (12.8 per cent), basic metal products, and the

food and beverages products sectors with 9.6 per cent and 9.3 per cent

respectively.

The top sectors in terms of number of employees is the food and beverages

products sector (112,187 workers) representing 19.4 per cent of the total

employment in operating factories. The second in terms of numbers of employees

is the other non-metallic minerals sector (15.3 per cent), then the chemical

materials and products, and basic metal products with 8.3 per cent and 8.0 per cent

respectively, and lastly rubber and plastic products (7.6 per cent).

The added value of the manufacturing industrial sector in Saudi Arabia was

estimated at SR 109.7 billion in 2010 as shown in Table 2A.7, which is about 12.6

per cent of the GDP (at constant 1999 prices). We can understand the significance

of this development when compared with the added value of the sector, which

amounted to SR 10.3 billion in 1970 and made up only 5.9 per cent of the GDP.

The added value of the manufacturing sector has risen from SR 10.3 billion in

1970, to SR 15.2 billion at the end of the First Development Plan, and then to SR

20.8 billion at the end of the Second Development Plan.

The industrial sector has continued to perform outstandingly in successive

development plans. The added value contribution has risen since the end of the

Third Development Plan up until the current Ninth Development Plan. In general,

the annual real growth in terms of added value amounted to 6.5 per cent during

the period 1970-2010, a rate of growth which is more than the rate of real average

growth for the national economy as a whole.

The oil refining industry plays an important role in the manufacturing industry,

and offers an added value of about SR 21.9 billion in 2010, up from SR 6.98 billion in

1970 (Table 2A.8). In addition, the added value of the oil refining industry rose from

about SR 6.98 billion in the first year of the First Development Plan to SR 7.4 billion

in the last year of the plan. After that the contribution of the added value of oil

refining continued to increase with regard to real GDP during the subsequent

development plans, in that the added value rose to SR 9.44 and SR 10.83 billion

respectively in the last years of the Second and Third Development Plans. The

added value continued to increase through the Fourth to Seventh Development

Plans (except for the occasional bad year) in that it rose from SR 15.1 billion in 1989

to SR 17.1 billion in 1994, and to SR 18.02 billion at the end of the Sixth

Development Plan. In the Seventh Development Plan, the added value of the oil

refining industry rose by 3.58 billion in 2000 to SR 21.6 billion in 2004 and to about

SR 21.9 billion in 2010.

Table 2A.7: Added value of the total manufacturing industry sector, 1970-2010 By SAR billion

years Total manufacturing output

Value (a) Annual Growth Added value (b) Annual Growth

First plan 1970 1.99 28.3% 10.32 24.2

1974 7.86 170.3% 15.21

Second plan 1975 8.30 5.5% 15.18 -0.2

1979 19.06 53.3% 20.77

Third plan 1980 22.41 17.5% 22.08 6.3

1984 32.67 16.1% 32.35

Fourth plan 1985 30.02 -8.1% 35.56 9.9

1989 31.34 3.9% 38.65

Fifth Plan 1990 37.63 20.1% 40.56 4.9

1994 48.34 7.3% 46.11

Sixth plan 1995 51.35 6.2% 49.31 6.9

1999 62.80 8.1% 62.80

Seventh plan 2000 57.96 8.7% 65.79 4.8

2004 95.82 11.1% 81.31

Eighth plan 2005 110.70 15.5% 86.94 6.9

2009 146.67 -0.8% 105.10

Ninth plan 2010 167.83 14.4% 109,75 4.4

(a) At producers’ values at current prices. (b) At producers’ values at constant price for 1999 *** Source: Ministry of Economy and Planning. Achievements of the Plan (2011).

For the petrochemical industry, the added value achieved rose from SR 0.71

billion in 1984 to SR 4.95 billion in 1989, the last year of the Fourth Development

Plan. At the end of the Fifth Development Plan, the added value amounted to SR

3.45 billion. Contributions from the industry to GDP rose during the Sixth

Development Plan to SR 6.0 billion in 1999. In the Seventh Development Plan, the

added value in 2000 amounted to SR 6.0 billion, rose to SR 8.95 billion in 2004, and

amounted SR 14.6 billion in 2010.

Table 2A.8: Added value of industries by sector during 1970-2010 (By SAR billion)

Plans Years

Other Manufacturing Petrochemical Oil refining

Value* Annual Growth

% of GDP

Added value

Value* Annual Growth

% of GDP

Added value

Value* Annual Growth

% of GDP

Added value

1th plan 1970 0.64 12.7% 2.60 3.34 - - - - 1.35 37.40% 5.60 6.98

1974 1.6 68.4% 1.00 7.86 - - - - 6.26 219.4% 3.90 7.35

2th plan 1975 2.7 68.6% 1.60 8.65 - - - - 5.6 -10.6% 3.40 6.53

1979 8.49 26.5% 2.30 11.34 - - -

10.56 84.3% 2.80 9.44

3th plan 1980 10.38 22.2% 1.90 12.7 - - - - 12.02 13.80% 2.20 9.37

1984 18.23 13.1% 4.30 20.8 0.62 185.7% 0.10 0.71 13.82 17.0% 3.30 10.82

4th plan 1985 18.82 3.2% 5.00 21.56 1.00 61.60% 0.30 1.13 10.2 -26.2% 2.70 12.86

1989 17.54 15.4% 4.90 18.59 4.39 -17.0% 1.20 4.95 9.4 -2.8% 2.60 15.11

5th plan 1990 19.4 10.6% 4.40 19.13 3.75 -14.6% 0.90 4.03 14.47 53.80% 3.30 17.39

1994 27.86 8.2% 5.50 25.57 3.78 6.8% 0.80 3.45 15.68 -0.5% 3.10 17.09

6th plan 1995 30.27 8.7% 5.70 29.03 4.06 7.40% 0.80 3.87 17.01 1.90% 3.20 16.4

1999 38.77 4.1% 6.40 38.77 6.00 13.6% 1.00 6.00 18.02 15.9% 3.00 18.02

7th plan 2000 40.18 3.6% 5.70 41.03 7.02 17.00% 1.00 6.1 21.08 17.00% 3.00 18.66

2004 52.61 9.2% 5.60 50.72 10.77 28.8% 1.10 8.95 32.43 9.1% 3.40 21.63

8th plan 2005 58.15 10.5% 4.90 52.89 13.1 21.60% 1.10 11.71 39.45 21.60% 3.30 22.33

2009 83.64 -1.1% 5.90 69.48 16.15 -6.8% 1.10 14 46.87 2.0% 3.30 21.61

9th plan 2010 92.04 10.0% 5.50 73.26 17.97 11.30% 1.10 14.6 57.82 23.40% 3.40 21.88

(a) At producers’ values at current prices. (b) At producers’ values at constant price for 1999 *** Source: Ministry of Economy and Planning. Achievements of the Plan (2011).

For other manufacturing industries, which include various metal industries,

food, construction, clothing production, and others, the added value has increased

in constant 1999 prices from SR 3.3 billion in the first year of the First Development

Plan to SR 7.9 billion in the last year of the plan. In the Second Development Plan,

this added value increased from SR 8.6 billion in 1975 to SR 11.3 billion in 1979. In

the Third Development Plan, it rose to SR 20.8 billion in 1984. But it dropped in the

Fourth Development Plan to about SR 18.6 billion in 1989, then it reversed and

continued to increase in these industries through successive development plans,

reaching about SR 25.6 billion in 1994, and SR 38.8 billion at the end of the Sixth

Development Plan. It has made a contribution of SR 50.7 billion to GDP in 2004

during the Seventh Development Plan, compared with SR 41.03 billion in 2000. The

results for 2010 confirmed the continuation of the rapid growth of these industries,

in that the added value rose to SR 73.3 billion.

In general, the total real average annual growth rate in terms of the added

value of the other manufacturing sector was 8.1 per cent during the period 1970-

2010. Along the same lines, petroleum refining contributed a real average annual

growth rate of 3.5 per cent during that period, while the petrochemical industry

contributed an average annual growth rate of 16.2 per cent during the period 1983-

2010. It is also the case that many of the national industries have shown significant

growth in the past few decades. For example, cement production increased from

667 thousand tons in 1970 to 48 million tons in 2010, which indicates an average

annual growth rate of 11.3 per cent. The achievements of the industrial sector are

reflected in an increase in the volume of exports for products and manufactured

goods in terms of different categories and value, which confirms the increase in

Saudi good's competitiveness in domestic and foreign markets.

2.1.6 Development of non-oil exports

Saudi Arabia has devoted a great deal of resources and effort to the

development of non-oil exports. In parallel with overall economic development

strategies, the government aimed to expand the production base and diversify its

income sources. Despite the relative recent emergence of industry in Saudi Arabia,

particularly the experience of the private sector in terms of exports, Saudi non-oil

exports have made great strides in this area. Petrochemical exports have had a

head start in terms of penetration of global markets, and this has also contributed

to a positive image of Saudi products in terms of quality and price.

Total Saudi exports recorded an average growth rate of about 84.4 per cent

during the period 1970-1974 (Table 2A.9). The highest growth rate was in 1974

when it reached 279.4 per cent, while in 1975 it recorded a lower growth rate of -

17.6 per cent. During the Eighties, total exports recorded their highest growth rate,

with an increase of 70 per cent in 1980. While there were negative growth rates

during the period 1982-1986, Saudi Arabia recorded consistently positive growth

rates after that period.

The highest growth rate in the Nineties was in 1990, when it reached 56.5 per

cent as a result of higher oil prices and volume production during the Gulf War

crisis. 1998 recorded the lowest average, reaching a growth rate of 36.1 per cent

due to lower oil prices in world markets. Hand in hand with the improvement in oil

prices in 2000, the growth rate of exports recorded an increased rate of 52.9 per

cent. Total exports have continued to rise since 2000, when they amounted of SR

290.5 billion. They reached 1.1754 trillion in 2008, but in 2009 dropped sharply by -

38.7 per cent before increasing by 30.6 per cent in 2010. The following table shows

the growth of the values and contributions of oil and non-oil exports during the

five-year Development Plans (1970 to 2010).

Table 2A.9: The development of Total Saudi exports(a) 1985 – 2010

Plan Years Oil Exports Non-Oil exports(*) Total Exports

Value per cent

of exports Annual Growth Value

per cent of exports

Annual Growth Value

Growth of plan

1th plan 1970 10,879 99.74% - 28 0.26% - 10,907 84.4% 126,223 1974 125,939 99.78% 279.4% 284 0.22% 153.6%

2th plan 1975 103,674 99.29% -17.7% 738 0.71% 159.9% 104,412 11.1% 213,183 1979 211,244 99.09% 54.1% 1,939 0.91% 71.6%

3th plan 1980 359,865 99.17% 70.4% 3,021 0.83% 55.8% 362,886 -9.1% 132,299 1984 127,860 96.64% -17.4% 4,439 3.36% 24.5%

4th plan 1985 93,953 94.40% -26.5% 5,583 5.60% 26.0% 99,536 -4.3% 105,678 1989 90,224 85.38% 19.5% 15,454 14.62% 2.8%

5th plan 1990 150,868 90.70% 67.2% 15,471 9.30% 0.1% 166,339 8.5% 159,590 1994 142,829 89.50% -1.2% 16,761 10.50% 18.6%

6th plan 1995 163,083 87.00% 14.2% 24,320 13.00% 45.1% 187,403 3.6% 190,084 1999 168,045 88.41% 37.8% 22,039 11.59% -5.9%

7th plan 2000 265,747 91.50% 58.1% 24,806 8.50% 12.6% 290,553 20.0% 472,491 2004 414,254 87.67% 34.5% 58,237 12.33% 38.3%

8th plan 2005 606,371 89.50% 46.4% 72,482 10.50% 24.5% 677,144 8.8% 721,109 2009 611,490 84.80% -42.1% 109,619 15.20% -9.9%

9th plan 2010 808,220 85.80% 32.2% 133,565 14.20% 21.8% 941,785 30.6% (*)including Re-exports (a) At producers’ values at current prices, SAR Million actual figures.

** Source: Ministry of Economy and Planning, Achievements of the plan (2011).

The composition of exports indicates the dominance of oil exports (mainly

unrefined oil), with an average (2005-09) of 88 per cent of total exports. Oil exports

posted a rising trend, with an average growth of 20.8 per cent (2005-08), until it

dropped sharply in 2009. Other exports such as chemical products, plastic products,

and re-exports only constituted an average of 15.2 per cent and 14.3 per cent of the

total exports in 2009 and 2010 respectively.

In general, Table 2A.9 shows that total exports have achieved a rapid growth

over the development plan period. The value of these exports showed an annual

growth rate of 11.8 per cent during the period 1970-2010, increasing from about SR

10,907 million in 1970 to about SR 941,785 million in 2010. The significant increase

in the total value of exports could result from increasing oil prices and oil

production. It shows an annual growth rate with regard to oil exports of 20.3 per

cent during 1970-2010. The table also illustrates the annual growth rate of non-oil

exports at 23.6 per cent during 1970-2010, as a result it is a sector which has

clearly achieved rapid growth over the past few years.

It can be seen from the data in Table 2A.9 that the non-oil exports of Saudi

Arabia have been growing rapidly and significantly over the period under

discussion. The Saudi industrial average growth was 19 per cent annually during the

period 1985-2010, and a rise in value from SR 5.58 Billion in 1985 to SR 133.56

Billion in 2010. It is interesting to consider the significant rise in the value of

industrial exports in 2003 with the implementation of the Gulf Cooperation Council

(GCC) customs union, as well as Saudi Arabia's accession to the WTO in 2005. In

terms of the industrial export ratio to Saudi Arabia's non-oil GDP, it grew from 1.5

per cent in 1985 to 14 per cent in 2010, indicating the importance of exports as a

factor in terms of industrial development. Table 2A.10 clarifies the value of the

most important exported goods.

Table 2A.10 illustrates some of the main characteristics of the Saudi non-oil

exports composition at the end of 2010. Chemical and plastic products had reached

the highest non-oil exports value during 2010 at SR 82.33 Billion which made up 61

per cent of total non-oil exports with an increase of SR 29.1 billion, 54.8 per cent

higher than in 2009. Some of this was plastic products with a total value of SR 42.1

billion. This shows an increase of SR 18.6 billion, making it 79 per cent higher than

in the previous year. Although re-export products had a value of SR 19.6 billion

making up 14.7 per cent of the total non-oil export value, it decreased by SR 4.1

billion, making it 17.4 per cent lower than in the previous year. On the other hand,

food substances had achieved a value of SR 11.07 billion in 2010 making up 8.3 per

cent of the total non-oil export value with an increase of SR 915 million which was 9

per cent higher than in the previous year. after that, textiles, clothes, carpets, paper

and its products, with a value of SR 9.5 billion made up 7.2 per cent of the total

non-oil export value with a decrease of SR 277 million, 2.8 per cent lower than in

the previous year.

Table 2A.10: The development of Saudi industrial exports by major sectors: 1985 – 2010

Year Foodstuffs

Wood, Paper, Leather and Textiles &

Other

Chemical and Plastic

Products

Base Metals and Articles of Base Metals

Electrical Machines, &

Tools Re-exports Total

1985 257 119 2,737 392 6 2,072 5,583 (4.6%) (2.1%) (49.0%) (7.0%) (0.11%) (37.1%) (100%)

1990 1,182 924 9,419 1,231 301 2,414 15,471

(7.6%) (6.0%) (60.9%) (8.0%) (1.9%) (15.6%) (100%)

1995 1,589 1,866 15,621 2,631 851 1,762 24,320

(6.5%) (7.7%) (64.2%) (10.8%) (3.5%) (7.2%) (100%)

2000 1,700 2,357 15,930 1,982 951 1,886 24,806

(6.9%) (9.5%) (64.2%) (8.0%) (3.8%) (7.6%) (100%)

2005 4,361 5,809 42,055 4,991 2,784 10,773 70,773

(6.2%) (8.2%) (59.4%) (7.1%) (3.9%) (15.2%) (100%)

2009 10,159 9,840 53,182 6,998 4,818 23,768 108,765

(9.3%) (9.0%) (48.9%) (6.4%) (4.4%) (21.9%) (100%)

2010 11,074 9,563 82,338 7,205 3,744 19,641 133,565

(8.3%) (7.2%) (61.6%) (5.4%) (2.8%) (14.7%) (100%)

average annually Growth

20.3% 26.4% 19.4% 20.9% 37.6% 11.5% 15.4%

** Source: SAMA (2011).

However, the value of the base metals and articles, and its related products

during this period had a total value of SR 7.2 billion. This was 5.4 per cent of the

total non-oil export value, with an increase of SR 207 million, which was 3 per cent

higher than in the previous year. Finally, the export of machinery, equipment, and

electrical appliances during this period totalled SR 3.7 billion which made up 2.8 per

cent of the total non-oil export value, with a decrease of SR 1.0 billion which was 22

per cent lower than in the previous year. Table 2A.11 shows the details of the

export trend during the period 2008 - 2010:

Table 2A.11: Exports by Country Grouping For the Years of 2008 - 2010

Region 2008 2009 2010

Value % of Total Value %of Total Value %of Total

Gulf Cooperation Council 82,744 7.0% 71,543 9.9% 76,953 8.2%

Other Arab League Countries 63,880 5.4% 41,590 5.8% 49,753 5.3%

Asian not Arabic 647,259 55.1% 419,716 58.2% 554,981 58.9%

African not Arabic 25,104 2.1% 14,615 2.0% 16,521 1.8%

Australia and Oceania 3,235 0.3% 1,989 0.3% 1,890 0.2%

North America 203,207 17.3% 91,014 12.6% 131,997 14.0%

South America 12,973 1.1% 7,476 1.0% 10,221 1.1%

European Union 123,840 10.5% 66,421 9.2% 89,473 9.5%

Europe not European Union 13,167 1.1% 6,735 0.9% 9,992 1.1%

Total 1,175,409 100% 721,099 100% 941,781 100%

* Source: Export Statistics (2010) (SAR Millions)

Table 2A.11 and figure 2.3 show the most important groups of countries which

were exported to. Asian countries (non-Arabic) took the first position in terms of

the total export value during this period by SR 518.5 billion. This made up 58.9 per

cent of total exports with an increase of SR 135.2 billion which was 32 per cent

higher than in the previous year. Official statistics show that the most important

country of this group was Japan with SR 135.6 billion, which accounted for 26 per

cent of the total of this group, followed by the Chinese Mainland with SR 112.2

billion, making up 22 per cent of the total of this group.

In second position was North American countries with a total value of SR 131.9

billion making up 14 per cent of total exports, with an increase of SR 40.9 billion

which was 45 per cent higher than in the previous year. The most important

country of this group was the U.S.A with SR 124.6 billion, 95 per cent of the total of

this group, followed by Canada with SR 7.3 billion, 6 per cent of the total of this

group.

However, the European Union Countries took third position with a total value

of SR 89.4 billion making up 10 per cent of total exports with an increase of SR 23.0

billion which was 35 per cent higher than in the previous year. The most exported

to country within this group was Spain with SR 17.7 billion (20 per cent), followed

by France with SR 15.7 billion making up 18 per cent of the total of this group.

In fourth position was the Gulf Cooperation Council countries with a total of SR

76.9 billion making up 8.2 per cent of total exports with an increase of SR 5.410

billion which was 8 per cent higher than in the previous year. The most important

country of this group was the United Arab Emirates with SR 32.923 billion making

up 43 per cent of the total of this group, followed by Bahrain with SR 29.8 billion, 39

per cent of the total of this group.

The total export value to Other Arab Countries was SR 49.7 Billion making up 5

per cent of total exports with an increase of 8.1 billion, 20 per cent higher than in

the previous year. The most exported to country within this group was Jordan with

a value of SR 12.8 billion making up 26 per cent, followed by Egypt with a value of

SR 9.7 billion, 20 per cent of the total of this group.

However, the export value to the African Countries was totalled at SR 16.5

billion accounting for 1.8 per cent of exports with an increase of SR 1.9 billion which

was 13 per cent higher than the previous year. The most exported to country was

South Africa with a value of SR 11.2 billion (68 per cent) followed by Kenya with SR

1.9 billion which accounted for 12 per cent of the total of this group. The export

value of the countries of the Rest of the World was SR 13.0 billion making up 1 per

cent of total exports.

2.1.6.1 The Saudi Export Program (SEP)

The governments of developed countries have, for several decades, attempted

to establish national agencies for the provision of export credit, to enhance the

competitiveness of their own exports, and to enable exporters to access global

markets. Intense competition between these countries has recently led

governments to seek to adopt systems and regulation controls working through

these agencies. The arrangement referred to as the "Consensus Agreement" was

agreed under the auspices of the Organisation for Economic Co-operation and

Development (OECD)1.

1OECD website,(http://www.oecd.org/about/0,3347,en_2649_34171_1_1_1_1_1,00.html)

The globalisation of trade has imposed the need for developing countries to

support exporters in their attempts to enter new international markets. On this

basis, governments have provided insurance and guarantees directly to exporters

or importers, and it is often supported by reassurance arrangements and risk

distribution with regional or international agencies. It is also noted that the national

agencies dealing with export credits sought to create relationships with major

international insurers and reinsurer companies under the strategic alliance

agreements, in order to take advantage of their expertise in terms of risk

assessment and the development of an information base.

The government of Saudi Arabia established the Saudi Export Program (SEP) in

1999 within the Saudi Fund for Development, to promote the export sector in Saudi

Arabia, and assist in diversifying the national economic base, leading to a greater

contribution to the GDP and minimising the dependence of the economy on a single

commodity in the form of "crude oil". The main objectives of SEP are the

development and diversification of Saudi non-oil exports, to maximize the

competitiveness of Saudi exports by providing credit to foreign buyers and/or

institutions, to motivate Saudi exporters to discover and enter new markets by

mitigating the risks associated with non-payment, and to enhance the facilities

offered by the programme and mitigate the associated risks through technical

cooperation, joint financing, and reinsurance arrangements with the international

and regional institutions involved in this area.

The SEP, as well as other government initiatives, will encourage the Saudi

business community to develop the export sector which will result in greater

production of better quality products, thus enabling Saudi Arabian exporters to

increase the volume of Saudi exports and obtain many benefits, notably: an

increase in sales volume and revenue, improved inventory management, improved

capital turnover, a more competitive industry locally and abroad, to utilise the full

capacity of their factories, increase their market share in various geographical

locations, and to develop national products and industries.

SEP aims to assist national industry and Saudi exporters to achieve their goals

of export development, and an increase in export volume, by providing them with

funding and the guarantee/insurance facilities needed to increase competitiveness

and mitigate the risks associated with international trade transactions which

exporters may face, in particular, when entering new markets. The SEP offers such

financial facilities based on pre-determined eligibility criteria and rules. In general,

based on the risks involved and the specific nature of each export transaction, the

SEP could support up to 100 per cent of the value of an eligible export transaction.

In addition, the Saudi domestic value added for any product to be eligible for SEP

support should be 25 per cent or more. The minimum value for any transaction

should not be less than SR 100,000. The Program offers funding and guarantees

facilities in Saudi Riyals or U.S. dollars. Besides that, the SEP contributes greatly to

promotional activities by organising or participating in seminars, exhibitions, and by

publishing brochures and newsletters introducing most of the national exports.

Table 2A.12 clarifies the total numbers of exporters who have participated in the

SEP financing programs until the end of 2011:

Table 2A.12: The SEP participating firms, ratio to total operating industrial units at end 2011.

Sector

Total firms participating of SEP(*) Ratio of SEP participating firms to Total operating

industrial units No. per cent

Share Products of Animals, Food & Beverages

59 11.8% 8%

Products of Wood, Paper, Leather and Textiles

30 6.0% 3%

Products of Chemical, Petrochemical, Plastic, Rubber and Medical care

102 20.4% 9%

Products of Building Material and Glassware

103 20.6% 7%

Products of Electrical, Machinery, Transport and Medical equipment

96 19.2% 17%

Services firms 110 22.0%

Total 500 100% 11% (**)Source: Saudi Export Program (SEP)

From the time the SEP was established until 2011, the total number of

exporters registered on the SEP database amounted to 500 firms. Table 2A.12

shows that service firms represent 22 per cent of the total of firms registered with

the SEP. These firms work as brokers in the local market by buying national

products to sell in the foreign market. Firms that produce building material,

glassware, and chemical, petrochemical, plastic, rubber, and medical care products

represent 20.6 per cent and 20.4 per cent respectively of the total number of firms

registered with the SEP. In addition, they respectively make up 9 per cent and 7 per

cent of total operating industrial units in Saudi Arabia. On the other hand, firms

that produce electrical, machinery, transport, and medical equipment make up only

17 per cent of the total number of operating industrial units, and 19.2 per cent of

the total number of firms registered with the SEP. In total, such firms registered

with the SEP make up 11 per cent of the total number of operating industrial units

in Saudi Arabia.

Table 2A.13 indicates that the SEP has attempted to spread internally through

the different areas of the country, but that there is a concentration (55.4 per cent)

in Riyadh province, where the capital is. This concentration may be attributed to

the presence of two industrial cities in Riyadh. In second place the western region

has the most registration and benefits of services provided by the SEP. 27.4 per

cent of the total SEP registered firms hail from this region. After that the eastern

region has only 13.6 per cent registered.

Although the cities of Jubail and Yanbu have been customised as industrial

cities, it is noticeable that the firms located there have a low participation rate in

the SEP. The participation rates of Jubail and Yanbu are 2.4 per cent and 0.4 per

cent respectively. Table 2A.13 also shows that Riyadh and the western region have

the same ratio in terms of the number of factories operating in the wood, paper,

leather, and textile products sector, which are registered with SEP. It is half that in

the Eastern Region. There are 25 food industry factories which are registered with

the SEP, while in the central region there are only 20 factories in Riyadh. 22

factories are registered in the western region. These two regions account for 79 per

cent of the total number of firms registered with the SEP with regard to Food &

Beverages.

The number of factories in the building material and glassware sector which

are registered with the SEP in Riyadh represents 52 per cent, whilst factories in the

Western and Eastern regions are both around 43 per cent. In the chemical,

petrochemical, plastic, rubber, and medical care industries, the number of factories

in Riyadh (54) accounted for 52 per cent, while the proportion in Jeddah city is

about 27 per cent, and in the eastern region there are 17 per cent registered with

18 factories, including 7 factories in Jubail.

Table 2A.13: the total number of SEP registrations by regions and sectors until the end of 2011.

Products of Food and Beverages

Wood, Paper, Leather

and Textiles

Building Material

and Glassware

Chemical, Petrochemical, Plastic, Rubber

and Medical care

Electrical, Machinery, Transport, Tools and Medical

equipment

Services Total per cent

Central Area 25 12 56 55 62 77 287 57.4%

Riyadh Capital 20 12 54 54 62 75 277 55.4%

others area 2 - 1 1 - 4 0.8%

Qassim 3 - 1 - - 2 6 1.2%

EASTERN 9 6 22 18 8 5 68 13.6%

Dammam + others 9 6 18 11 8 4 56 11.2%

Jubayl 0 - 4 7 - 1 12 2.4%

WESTERN 22 12 23 28 26 26 137 27.4%

JEDDADH 16 11 22 27 26 25 127 25.4%

Maddinah 5 1 - 1 - 1 8 1.6%

Yanbu 1 - 1 - - - 2 0.4%

NORTHERN 2 - - - 2 4 0.8%

SOUTHERN 1 - 2 1 - 4 0.8%

Total 59 30 103 102 96 110 500 100.0%

Similarly, the factories operating in the electronics and equipment sector,

totalled 96 firms which have registered with the SEP, of which 62 factories are in

Riyadh representing 64 per cent of the total, followed by 26 factories in Jeddah,

then Dammam with 8 factories. The service companies, both those working in the

field of buying local products and selling them to overseas buyers, or companies

working in contracting, or operating in consultancy and engineering services, have a

very high proportion of firms registered with the SEP. Around 68 per cent of them

registered are from Riyadh and 26 per cent from Jeddah. In terms of the factories in

the Northern and Southern regions, the proportion of firms registered with the SEP

is very small - four firms from each region at the end of 2011. Three of these

factories are in the food and beverages industry, two are in the building material

and glassware sector, and one firm is in the plastic sector.

Although the SEP was established in 1999, it only actually started its operation

of providing finance, in 2001. Moreover, the credit guarantee service was launched

as one of the services provided by the SEP at the end of 2003. Since the SEP’s

inception, and until 2011, it has achieved a great deal. The total financing and

guarantee facilities support SR 22.7 billion worth of exports (shown in Table 2A.14),

reaching to about 48 countries1.

Table 2A.14: Finance and Guarantees of Saudi Exports (*)

Operations

Manufactured metal,

Machines and equipment

Chemical and Plastic Products

Capital projects

Other** Sub-Total Credit

line total

2001 Finance 69 37 - - 106 235 341

Guarantee - - - - - - -

2002 Finance 9.67 72.75 - - 82.42 18.19 100.61

Guarantee - - - - - - -

2003 Finance 12.46 117 217.94 - 347.4 19.88 367.28

Guarantee - 0.71 - 1.27 1.98 - 1.98

2004 Finance 110 199.5 - - 309.5 40.5 350

Guarantee 1.46 2.5 - 38.37 42.33 - 42.33

2005 Finance - 604.66 169.52 135.3 909.48 173.96 1083.44

Guarantee 24.01 67.43 80.88 22.49 194.81 - 194.81

2006 Finance - 140.35 167 202.5 509.85 1215.25 1725.1

Guarantee 5.08 602.606 6.13 101.44 715.256 - 715.256

2007 Finance 3.15 110.47 40.88 - 154.5 277.5 432

Guarantee 8.76 1450.99 24.73 275.85 1760.33 - 1760.33

2008 Finance 231.25 483.75 - - 715 123.75 838.75

Guarantee 1.9 3222.12 23.19 278.24 3525.45 - 3525.45

2009 Finance 311.38 451 37.5 20 819.88 145 964.88

Guarantee 22.8 2102.4 - 136.78 2261.98 - 2261.98

2010 Finance 313.25 506.25 - - 819.5 396.25 1215.75

Guarantee 7.66 2285.32 - 205.4 2498.38 - 2498.38

2011 Finance - 1266 1005 188 2459 240 2699

Guarantee 14 2857 - 30 2901 - 2901

Total Finance 1060.16 3988.73 1637.84 545.8 7232.53 2774.28 10006.81

Total Guarantee 85.67 12591.076 134.93 1089.84 13901.516 - 13901.516

(*) source: Saudi Fund for Development, SEP ( Million Riyals)

**Others: animal products, foodstuffs and beverages, wood, paper, leather and textile products.

Table 2A.14 illustrates that the volume of financing and credit insurance

provided by the SEP to cover non-oil exports has risen noticeably. In 2003 there was

1SFD (2010), Annual Report.

SR 367.28 and SR 1.98 million available for finance and insurance respectively which

rose to SR 2,699 million and SR 2,901 respectively in 2011. The products which were

funded by the SEP with regard to manufactured metal, machines, and equipment

amounted to a financing volume of SR 1,145.83 million, including SR 1060.16 million

for financing operations and SR 85.67 million for credit insurance operations.

Chemical and plastic products were funded to the tune of SR 16,582.8 million

including SR 3,988 million for financing operations and SR 12,591 million for credit

insurance operations. The funding of capital projects amounted to SR 1,772 million,

including SR 1,637 million for financing operations and SR 134.9 million for credit

insurance operations. Other products consisting of animal products, food-stuffs and

beverages, wood, paper, leather, and textile products received funding of SR

1635.64, including SR 545 million for financing operations and SR 1,089 million for

credit insurance operations. There are different mechanisms of direct funding

which work in three ways:

(1) Supplier credit assists Saudi exporters to provide the required credit to

foreign importers.

(2) Local Buyer Credit that is offered by the SEP. Such credit facilities are

available to Saudi businessmen (local buyers) and to investors who execute projects

outside the KSA and need financing from the SEP to help export Saudi goods and

services which they then use for project implementation.

(3) Foreign Buyer (Importer) Credits. These credit facilities assist importers

from outside the KSA to obtain the required financing directly from the SEP.

However, indirect funding uses lines of credit from banks, financial institutions,

and large firms. Usually it is provided to commercial banks and financial institutions

which will be acting as agents for the SEP in the importer’s country. It focuses on

SME’s as the main beneficiaries. The repayment period depends on the type of

exports; short term is up to 2 years for consumable goods and raw materials.

Medium term is up to 7 years for consumable durable goods and semi-capital

goods. Long term is up to 15 years for capital and durable goods, turnkey contracts,

and projects.

The total financing applications approved by the end of 2011 reached a value

of SR 10 billion compared to SR 7.3 billion in 2010. The SEP provides facilities

including direct funding operations amounting to SR 7,232.53 million. In addition to

this, the program has opened lines of credit with several foreign banks amounting

to SR 2,774.28 million. These lines of credit are mainly used by small to medium

sized enterprises. The SEP operations have tended to focus on Asia and Africa with

55.1 per cent and 43.8 per cent respectively, and there were three direct export

finances available to firms dealing with Europe, and North and South America.

Table 2A.15 shows the geographical distribution of financing activities by the SEP

until the end of 2011:

Table 2A.15: Geographical Distribution of Financing Activities by SEP until the end of 2011 (Million Riyals)

Region Number Amount** per cent

Africa 76 4382.98 43.8%

Asia 48 5513.73 55.1%

Others (Europe,America North and South)

3 110.1 1.1%

Total 127 10006.81 100.0%

(*) source: Saudi Fund for Development, SEP

On the other hand, The Guarantee Service's gross coverage under the program

amounts to SR 13.9 Billion compared to more than SR 11.0 billion in 2011.

Export credit insurance and guarantee facilities aim to offer guarantees for

exporters against non-payment risk. It also offer guarantees for commercial banks

which are prepared to finance local exporters. In addition the SEP covers non-

payment risks such as commercial risks, it covers up to 90 per cent for commercial

activity and covers political risks up to 90 per cent.

There are several types of export credit insurance policies:

(1) Whole turn-over policy; this policy covers all of an exporters’ risks involved

in dealing with registered importers in different countries. The SEP studies and

evaluates each importer and assigns an adequate credit limit for that importer.

Subject to the approval of the SEP, Saudi Exporters (Policy Holders) can add other

importers to the policy during the term of its validity. Whole turn-over policy is

usually short term (about one year).

(2) Specific transaction policy; this policy covers all export risks involved in a

single transaction. Under this policy, the SEP could cover an open account of a

customer, or confirm documentary credits.

(3) Fields of co-operation with local banks; bank’s acceptance to finance SEP

insurance policyholders (post shipment),and guarantees to financing working

capital (Pre-Shipment) for exporters (Policy Holders). It also covers documentary

credit insurance policy (DCIP) as well as confirming incoming L/C’s (Specific

Operations). Finally, it offers an exchange of credit information and reports.

2.1.7 Conclusion

The government of Saudi Arabia has paid special attention to the subject of

the diversification of the economic base. This was due to its dependence on oil as a

main source of income through exporting it as a raw material. This interest

manifested itself when the state followed an economic strategy which sought to

exploit the income generated by oil exports as a means of diversifying the structure

and the number of commodity exports, thus contributing to a reduction in the

political and economic risks associated with a heavy reliance on oil. Like other

countries in the world, the government has pursued an industrial development

strategy by working on import substitution and export development. It has found

the facilities to ensure the development and promotion of the role of the private

sector, primarily focusing on industrial exports, trying to reduce the impact of the

risks which businesses face, and encouraging the creation and activation of

appropriate institutional frameworks to support these exports. In this regard, the

government has established the Saudi Industrial Development Fund which plays an

important role in lending to enterprises and new small sized companies, in order for

them to expand in terms of size and quality, and therefore enabling them to target

both close foreign markets and those further away. The government also

established the General Investment Authority (SAGIA) which seeks to attract

foreign direct investment in order to take advantage of the technical development

of products and improve production, thereby enhancing the competitiveness of

local products in international markets.

Another important government institutional framework which was established

was the "Saudi Export Program" under the umbrella of the Saudi Fund for

Development, in order to develop national non-oil exports and encourage

diversification, this program provides financial incentives and credit to exporters on

the one hand, and on the other provides competitive credit terms for buyers

abroad or for funding institutions working in this area.

This program indirectly assisted the improvement of Saudi products and an

increase in their quality. It also overcame the financial obstacles that prevented

exports gaining access to foreign markets. On the other hand, the SEP identified

these products in other importing countries, thus creating a demand from

consumers and manufacturers in those countries for these products, and the

continuation of this demand and growth over time.

It is clear from the above that the institutional frameworks have contributed

effectively to supporting export industries, which has led to an increase in the

percentage of their contribution to GDP, and has improved the national balance of

payments, supported the economy, diversified its resources, as well as created

more job opportunities for national workers. However, it remains the case that

firms which aim to export should seek to improve their export ability and

competitiveness. They also need access to specialised information in terms of the

global markets and with regard to benchmarking competitiveness, creating

effective systems for export, exploiting opportunities associated with e-commerce,

implementing international quality, and applying standards of environmental

conservation.

Figure 2.2 : Map outlining the regions of Saudi Arabia

Figure 2.3 : Map of % of Saudi exports by country grouping at end 2010

2.2 The structural characteristics of sample firms

2.2.1 Introduction

Exports are an important component of the Saudi national income and they play a

key role in the economy. The main Saudi export is oil, which has been led by the Saudi

Arabian Oil Company (ARAMCO). ARAMCO produces, manufactures, markets and ships

crude oil, natural gas and petroleum products. The petroleum sector accounts for roughly

75% of budget revenues, 45% of GDP, and 90% of export earnings. About 40% of GDP

comes from the private sector (SAMA, 2011). With the continuous volatility of oil prices

along with the pressing need for economic growth and development, Saudi Arabia had

planned to diversify its sources of income by making changes in the infrastructure of the

national economy to expand the country’s productivity base. To that end, most efforts

are currently focused on the development of the non-oil export sector.

In an attempt to encourage the private sector to play its intended role in the

economy, Saudi Arabia has created different organisations and financial institutions to

assist this sector. The private sector’s contribution to the export sector, however,

remains weak, amounting to 15 per cent of the country’s total exports.

The concept of expanding current exports in developing countries has received

considerable attention in recent decades in development literature. Studies such as those

by Cavusgil and Nevin (1981), Todaro (1986), Barker and Kaynak (1992), Gumede (2000),

Aynul and (2004) and Fatih (2009), among many others, find a positive causal link

between the expansion of exports and economic growth. This positive link has resulted in

many countries expanding their export performance programs.

Some firms choosing not to export, it have been analysed reasons (i.e. Bilkey and

Tesar, 1977; Bilkey, 1978; Bijmolt and Zwart, 1994; Sharkey et al., 1989; Westhead et al.,

1995) and how to convert these non-exporters to exporters. The aforementioned

literature discussed reasons for firm that not have been encouraged to export in

developed countries, on the other hand, there is literature that also studies the issues

firms face when exporting in developing countries.

However, Al-Twuijri (2001), Al-Jarrah (2008) Shirazi and Abdulmanap (2005), and

Dastjerdi, et al. (2012) Alimi and Muse (2012) amongst few studies discuss the

importance of exporting for income diversification, especially for countries such as Saudi

Arabia, Nigeria and Iran, all of which depend on exporting single goods, such as oil. A

number of studies such as Al-Aali (1995), Crick et al. (1998), Al-Qahtany (2001) have

identified some major factors that contribute towards export barriers. Macro variables

include those related to government policy which supports manufacturing and exports.

External variables include the sector characteristics and business environment of the

firm. Micro level factors or internal variables include the size of the firm.

The impact of exports on economic development has been published on a

considerable amount of literature, along with the challenges and obstacles of exporting

i.e Balassa, B. (1978), Feder, G. (1983), Al-Yousif,(1997), Al-Yousif,(1997), Al-Yousif,(1997),

Al-Yousif,(1997), Alimi and Muse(2012), Mehraraet al. (2012), Ben Jebli and Ben Youssef

(2013), Elbeydi et al. (2010 and Lee and Huang (2002). These studies cover three distinct

areas: The effects of export expansion on economic growth, firms’ export behaviour and

why some firms export more than others (export barriers), and empirical studies of

obstacles faced by Saudi exporters’. The effects of export expansion on economic

development in developing countries have received considerable attention in

development literature during recent decades. Exports play an important role in

accelerating economic growth and they increase the use of human resources and capital

(Todaro, 1977). The expansion of exports has positive effects on both the growth of the

economy and individual firms (Cavusgil and Nevin, 1981).

Positive outcomes of exporting include: increased profitability, improved capacity

utilization, avoidance of risky reliance on one market, increases in employment, improved

trade balance, and improved quality of life (Barker and Kaynak, 1992). In addition, studies

such as those by Gumede (2000), Aynul and Hirohito (2004) and Fatih (2009) find a

positive causal link between the expansion of exports and economic growth. This positive

link has resulted in many countries developing export performance programs. Bilkey’s

(1978) research analyses motivation for exporting. Some firms are pushed into exporting

by external variables (e.g., a foreign customer or agent); some with no evident objectives

take advantage of export opportunities if they come their way, whilst others are

motivated to begin exporting deliberately. Although marginal exporters involve low level

exporting, they have learnt the export basics which could also lead to them perceiving

more barriers in exporting than actually exist. Sharkey et al. (1989) explain that marginal

exporters are those exploring exporting opportunities and may have filled some

unsolicited orders. Active exporters have mastered the technicalities of exporting, have

learnt that exporting is an important means for achieving organisational goals and have

learnt to cope with various export barriers (Sharkey et al., 1989).

Dejo-Oricain and Ramírez (2009) divide the determinants of firms’ export behaviour

into three groups: firm sector to which the exporting firm belongs, firm-specific

characteristics (export level, firm size, organisational experience, product diversification,

international experience, export regularity, geographical diversification, and level of ICT

implementation) and the export destination market. After a cluster analysis, Dejo-Oricain

and Ramirez (2009) identified five exporting firm profiles that show different grades of

commitment in the international expansion of the firms. These range from those with the

least commitment (firms in group one) to the greatest international commitment (group

five). These five different export profiles reflect varying degrees of commitment and

different strategies in their international expansion. The main results, however, show

that Spanish SME’s show large differences among them. Group five on average is present

in 32 countries from almost all international regions, whilst the number of markets in

group one is the lowest and in widely dispersed geographical areas. In terms of sector

effect, the results reported that firms’ export behaviour was influenced by their sector, as

there are specific characteristics for each sector that affect export opportunities.

Although exporters perceive more barriers to exporting than non-exporters (Bilkey

and Tesar, 1970), several researchers claim that non-exporters do perceive considerable

barriers to exporting (Ahmed et al., 2004; Bilkey and Tesar, 1977; Kedia and Chhokar,

1986). Bilkey (1978) outlines the most common obstacles to exporting: foreign formal

constraints; lack of finances; inadequate connections to the foreign market; lack of

knowledge about potential export markets; and deficiency of sufficient channels of

distribution abroad. Cavusgil and Nevin (1981) list two factors that explain why firms in

general are reluctant to export. The first is the lack of macro-level incentives and a

stimulating national export policy. The second factor contends that the real problems are

internal to the firm. The real barriers to a firm’s involvement in export marketing are

internal rather than external.

In a comprehensive study of the barriers to exporting, Bauerschmidt et al. (1985)

analyse the U.S. paper industry. The study principally covers experienced exporters who

were asked to rank the importance of seventeen potential export barriers. The findings

suggest that the high value of the U.S. dollar relative to foreign currencies was perceived

to be an extremely important barrier, whilst high transportation costs were also

considered to be extremely important. Medium importance was attached to the risks

involved in selling abroad, high foreign tariffs on imported products and management

emphasis on developing domestic markets.

Barker and Kaynak (1992) showed that the most important obstacles encountered

by exporters were too much red tape, trade barriers, transportation difficulties, lack of

export incentives and lack of trained personnel for export operations and co-ordinated

export assistance. On the other hand, the most important obstacles that confronted non-

exporters were a lack of foreign market contacts, high initial investment, trade barriers, a

lack of information about exporting and a lack of personnel. Alexandrides (1971)

published one of the first papers to investigate the barriers to exporting. Alexandrides’

research proposed that the major problems preventing firms from initiating exporting

operations were the existence of intense competition in foreign markets, a lack of

knowledge of exporting, inadequate understanding of export payment procedures and

difficulties in locating foreign markets.

Dejo-Oricain and Ramírez (2009) discussed the impact of firm size on exporting, they

found four reasons why large firms enjoy advantages related to their size that make them

more active in terms of exporting. Firstly, large firms have more financial, material and

human resources available, which are pivotal for developing and maintaining an export

programme. Secondly, managers in large firms have higher levels of expertise and are

more dynamic. They are capable of appreciating the advantages of exporting and, as a

result, they develop strategies to export effectively. Size not only supports entry into

foreign markets it also provides a greater ability to respond effectively to the demands of

customers abroad. Thirdly, larger firms are more competitive as they are able to produce

more economies of scale and have greater potential in the market. Fourthly, they are

potentially better able to bear export risks because they have easy access to information

sources and they have the ability to withstand the impact of international errors. The size

of large firms is also associated with lower average or marginal costs that positively

reflect as advantages in terms of exports. However, Dejo-Oricain and Ramírez (2009)

point out that a firm’s smaller size is not a hindrance for exporting, as exporting is the

form of internationalisation that requires fewer resources compared with other forms of

entry into foreign markets.

The aim of this chapter is to identify for further analysis the following topics of

increased the level of exports. To answer this question a number of sub-questions also

need to be addressed: What are the characteristics of trade operations? Where are the

sales moving to? Are there problems in obtaining raw materials? Are there problems in

product marketing? Does theft or damage occur during the exporting process? However,

a major problem that this application is faced with is lack of data to answer the research

questions.

Moreover, there is lack of comprehensive research, failing to cover manufacturing

behaviour in Saudi Arabia and other Gulf countries. In particular, no studies have

examined the potential reasons for the low contribution from non-oil exports to the total

exports in the previous decades. This chapter will analyse the environment of non-oil

export operations. The work also attempts to provide a complete view of the obstacles

and barriers faced by non-oil exporters when selling their products abroad by using new

survey data

This section has been divided into five parts. The following part briefly reviews the

literature that focuses empirical studies of obstacles to Saudi Arabian exporters. Part

three presents the methodology involved in the different stages of the analysis, whilst

part four describes and showcases the data. The final part of this chapter summarises the

main results and the conclusion.

2.2.2 Empirical studies of obstacles to Saudi Arabian exporters

There is a lack of empirical studies that examine Saudi Arabian export barriers. Al-Aali

(1995) illustrates some obstacles facing Saudi exporters. This study focuses on two types

of industry: chemicals and food. It examined responses from 58 food and chemical

exporters in Saudi Arabia. Out of 447 exporters, a random sample of 148 firms was

selected to participate in the study. One hundred-forty usable responses were obtained,

which included responses from 58 firms pertaining to this study: 30 in the food and

beverage industry and 28 in the chemical and petrochemical industry. Managerial

perceptions on 24 export obstacles that were derived from the literature were analysed

and reported. The single most important obstacle perceived by the Al-Aali sample is

severe competition in foreign markets. Competition is followed by the high cost of

imported raw materials, absence of information about foreign markets, wide fluctuations

in the foreign exchange rate, and high overseas transportation costs. The eight categories

of the obstacles are: market information, competition, shipping, government policy,

foreign market risks, export procedures, production/marketing cost, and

internal/technical problems. The Al-Aali study relies on MANOVA analysis, which showed

that chemical and food exporters are statistically different in their mean response to

these obstacles. ANOVA determined the variables that are different at the .05 level. They

are: risks involved in selling abroad; language and cultural differences; complex export

procedures; lack of an adequate export revenue insurance programme; and absence of

an export management and consulting company. Managerial and policy implications are

discussed. Furthermore, recommendations for tackling the top export obstacles are

presented.

Crick et al. (1998) is considered to be important to differentiate between firms in

relation to their export involvement (non-exporters are not considered in the Crick et al.

study) to establish whether differences exist between low and high involvement

exporting firms concerning perceptions towards the variation in importance of obstacles

to exporting.

The firms in the Crick et al. study did not have an export ratio at approximately the

50 per cent level; Crick et al. believed this allowed firms to be clearly defined as having

either a low or high export involvement. In practice therefore, low involvement exporters

had an export ratio of below 35 per cent, whereas those with a high export involvement

had an export ratio of above 60 per cent. Furthermore, Crick et al. argued that since there

is no single agreed method by which to categorise particular sizes of firms, different

statistical results are likely to result from particular subjective classifications. The effect of

firm size was considered to be a co-variate in the study investigation. As Crick et al.

illustrated this leads to the following hypothesis (placed in the conventional null

hypothesis format): there are no significant differences between the perceived obstacles

to the exporting of particularly sized low and high involvement Saudi Arabian exporters of

non-oil products. In total, the questionnaire was mailed to 411 firms, which was all the

Saudi Arabian exporters of non-oil products with at least two years of export experience,

as identified by the Saudi Export Development Centre from the Saudi Export Directory. In

calculating the overall response rate, 108 questionnaires were returned although nine

were considered to be unusable. In total, 99 responses were obtained, representing an

overall response rate of 24 per cent.

- Analysing the results relied on the factor analysis: the existence of an underlying

structure in the data was first explored and that was subsequently followed by analysis

using MANCOVA to establish whether statistical differences existed between low and

high involvement Saudi Arabian exporters of non-oil products in relation to the derived

factor scores; firm size was used as a co-variate. The findings list the most important

common obstacles encountered by firms: competition in export markets; lack of market

information; fear of imposed dumping policies; increasing tariffs; a lack of clarity

concerning trade agreements; import restrictions; the cost of importing raw material; and

a lack of suitable personnel. Al-Qahtany (2001) explores the obstacles facing Saudi

exporters of non-oil products. The sampling frame was comprised of 411 firms that have

been involved in exporting for at least two years as identified by the Saudi Export

Development Center (under the umbrella of the Council for Saudi Chambers of

Commerce and Industry). Al-Qahtany reported the difficulties faced with the responses.

Owing to the poor quality of the postal service, some firms did not receive the

questionnaire, and in some cases the researcher had to deliver it by hand. Some

respondents thought the questionnaire was a waste of time because they did not

recognise the importance of such research to their export development. By the middle of

November 1998, which was the cut-off date, 108 questionnaires had been returned, of

which nine were unusable, leaving 99 usable responses. The response rate was,

therefore, 24 per cent, which was considered to be an acceptable response rate. The

study investigated twenty five obstacles that have some relation to non-oil export

products. Competition with foreign firms was found to be the most substantial obstacle,

followed by a lack of information about potential export markets. These are categorised

as external barriers, and are, to some extent, uncontrollable. Additionally, some firms

complained about the high electricity connection fees and therefore the investigation

suggests that special electricity connection fees for export firms could be adopted.

On the other hand, relying on time series analysis for period from 1969 to 1996, Al-

Twuijri (2001) investigates the causal relationship between economic growth and exports

in Saudi Arabia. The results show a strong bi-directional causal pathway. Also, the study

by Al-Jarrah (2008) examines the relationship between economic development and the

performance of non-oil exports in Saudi Arabia, the study period of 1970-2003. The

results support previous evidence of the positive effect of non-oil exports on the

economic development of Saudi Arabia. The study also finds that the growth of non-oil

exports has a positive impact on investment and production in the country.

2.2.3 Data Methodology

This study is based on an empirical investigation of the barriers Saudi Arabian firms

face when engaging in exportation. The study depends upon primary data obtained by a

specific questionnaire designed to generate data from Saudi exporters. The data was

collected between September and December 2011. This chapter focuses on the different

parts of the questionnaire such as the general information given about firms, their

infrastructure, labour, production capacity and trade analysis. The analysis presented in

this chapter relies upon statistical analysis such as mean, standard error, variance, F-test,

factors impact ranking and a one-way ANOVA test.

The sample consisted of different sized firms from a variety of industries with

different levels of operation and export experience and other measurable characteristics.

These firms were trying either to expand the level of exports or the level of sales in the

domestic market. Table B2.1 shows the sector distribution of the sample by main region.

It consists only of firms that export manufacturing products in Saudi Arabia within the

following sectors: food and beverages, wood, paper, leather and textiles, chemical,

petrochemical, plastic, rubber and medical care, building materials and glassware, and

electronics, machinery, transport, tools and medical equipment. Table B2.2 also includes

information about time periods in relation to formal registration, type of current legal

status, females amongst the owners of the firm, and the possession of a locally or

internationally recognised quality certification.

The largest group of firms was based in Riyadh or the Central Region (97 firms). 47.4

per cent of the total sample in the Central Region was working in chemical,

petrochemical, plastic, rubber and medical care products. Firms reporting operations in

both the East and West consisted of 77 respondents, but only one firm was based

exclusively in the Northern region. The chemical, petrochemical, plastic, rubber and

medical care products represented 49.3 per cent of the total sample in both Eastern and

Western regions.

A major factor that describes the data sample is experience; 77.14 per cent of the

total sample was established more than sixteen years prior to participating in this study.

17.71 per cent of the sample companies had been running between 6 – 15 years - this

period is from the WTO being established in 1995 and Saudi Arabia's accession to the

WTO in 2005-. Firm had been in manufacturing for less than five years around 5 per cent,

reflecting the period after Saudi Arabia joined the WTO in 2005.

The classified of size of firm is relying on total sales and employee volume

categories. Firms were grouped into the following five size categories dependent on total

sales figures: (1) micro firms with annual sales of up to 10 million SAR, (2) small-sized

firms with sales between 11 million and 25 million SAR, (3) medium-sized firms with sales

between 26 million and 50 million SAR, (4) more-than-medium, less-than-large firms with

sales between 51 million and 100 million SAR, and finally (5) large firms with sales in

excess of 100 million SAR. Using the number of employees, firms were grouped into four

size categories: (1) micro firms with less than five employees; (2) small-sized firms with

six to 20 employees; (3) medium-sized firms with 21 to 99 employees, and (4) large firms

with more than 100 employees.

The majority of firms had total sales in excess of 100 million SAR as describe a large

firm represent 39 per cent; sales of 10 million SAR and less had 11 per cent. The

remaining firms’ sales ranged from 11 million to 25 million SAR, between 26 million and

50 million SAR and between 51 million and 100 million SAR representatives 50 per cent.

The sample showcases that the largest proportion of firms, around 75 per cent of

the total sample, had more than 100 employees. Around 48 per cent of companies

considered in the large group in terms of employees were in the chemical, petrochemical,

plastic, rubber and medical care raw materials sector, which represent 37 per cent of

total employees.

The majority of respondents' firms, 48 per cent of the total sample, were working in

chemical, petrochemical, plastic, rubber and medical care production. As can be seen in

Table B2.5, two of the groups were representative of 16 per cent of the sample, namely

the firms categorised as electrical, machinery, transport and medical equipment and

firms producing wood, paper, leather and textiles. The firms involving electronics,

machinery, transport and medical equipment represent 93 per cent in the same group of

the SEP participants, compared with the firms producing wood, paper, leather and textile

products with a percentage 29 per cent in the same sector in the SEP.

Table B2.5 shows figures concerning ownership from the study sample.

Approximately one-third of the sample consisted of limited partnerships, 26 per cent

were partnerships and 21 per cent were sole proprietorships22. The rest (less than 20 per

cent) of the firms had a shareholding firm and three per cent of them just had trade

shares in the stock market.

22This indicates that one person owns and manages the business and is personally responsible for its debts.

2.2.4 Descriptive Sample Information

2.2.4.1 Export intensity: Rate of firms’ exports

Local sales in the domestic or national market and exports to foreign markets are

the two principal networks through which a firm’s total sales are received. In selling

products to consumers, certain organisations use direct marketing to move their goods

(Table 2B.6b). A proportion of sales are typically sold through indirect marketing which

uses third parties, such as agents or distributors, to sell products in order to export them

to overseas markets. Table 2B.6a presents the average percentage of export intensity

categorised by type of ownership, labour and sales while Table 2B.6b presents the

average percentage of export intensity and national sales by sector. In the present study,

the questionnaire responses given by 107 of the 175 respondent firms reveal that their

proportion of exports of total sales is at 23 per cent. The other sixty-eight firms

interviewed reported export value percentages of either above 23 per cent (23-80 per

cent) or below 20 per cent.

The ratio export intensity for all manufacturing firms (Table 2.6a) was 20% for small

firms (5-19 employees), 24.55% for medium-sized firms (20-99 employees) and 22.73%

for firms with more than 100 employees in 2011 according to our survey data. This ratio

showed a greater increase for medium-sized firms than for large firms. On the other hand

the proportion of export intensity for all exporting firms was 13.93% for small firms (sales

of 10 million SAR and less), 26.73% for firms with total sales of 11-25 million, 13.38% for

firms with total sales of 26-51 million, 26.76% for firms with total sales of 51-100 million,

and 22.8% for firms with sales of more than 100 million. On the total sales measurement,

this ratio showed fluctuated, export intensity rise from firms their sales (10 million SAR

and less) to firms had total sales (11-25 million) then fall for firms had total sales (26-51

million) then grow for firms had total sales (51-100 million) and decrease for firms with

sales is more than 100 million. Table 2.6b reveal that the average value of all of the firms’

exports for the food and beverage industry this percentage drops to only 16 per cent. For

the electrical, transport, tools, medical equipment and machinery sector the average

value of exports rises to 30 per cent.

2.2.4.2 Export experience analysis

It can be seen from the data in Table 2B.7 panel (a) that around 5 per cent of

exporters have been exporting for less than two years, 30 per cent between three to

twelve years, 50 per cent between thirteen to twenty-two years and 23 per cent for more

than twenty-three years. Additionally, the statistics show that 32 per cent of exporters

sold over 20 per cent of their sales through exports and, for 67 per cent of the firms,

exports represented less than 20 per cent of their annual gross sales. This can lead to the

conclusion that many firms are attempting to export but are not capable of doing so

successfully.

Approximately 14 per cent of exporting firms started exporting after the government

established the Saudi Industrial Development Fund (SIDF) in 1974 and created the first

industrial cities in the country. Around 50 per cent of firms began exporting after the

government pushed into the local market large raw material provider Saudi Basic

Industries Corporation (SABIC) which began production in 1981. In 2000, the Saudi Export

Program (SEP) established services to provide exporters with funding and to guarantee

the percentage of export sales for firms that began to export was around 30 per cent.

2.2.4.3 Export orientation of firms

It is expected that the majority of exports go to nearby markets. Table 2B.7 panel (b)

shows that 86 per cent of the sample firms stated that their principal export destination

is the Gulf Cooperation Council (GCC) markets, 83 per cent the Arabian region (not

including GCC countries), 29 per cent African countries (not including Arabian countries),

22 per cent Asian countries (not including GCC and Arabian countries), 13 per cent

Europe and only 5 per cent of the companies listed the USA as their principal export

destination. The fact that the GCC and the Arabian region are major export destinations

and geographically proximate makes them attractive markets. Many of these nearby

economies are structurally similar to the Saudi economy (Al-Aali, 1995).

2.2.4.4 Analysis of trade operations’ characteristics

Table 2B.8 shows the mean values of the trade operations’ characteristics indicators

of the representative sample of Saudi Arabian exporters. It is important to note that the

sources of supplies for manufacturers are 65 per cent domestic in origin and 35 per cent

foreign in origin. It also shows that, on average, the firms imported 90 per cent of their

foreign-sourced raw materials directly from foreign nations.

Table 2B.8 also shows that the mean amount of days it takes to clear imported raw

material through Saudi customs is around eight days. Chemical, petrochemical, plastic,

rubber and medical care raw material takes around eleven days to clear and it takes five

days on average for food and beverage raw material to clear Saudi customs. Exported

goods take five days on average to clear customs. This decreases to two days for food and

beverage products and increases to eight days for building material and glassware

products and electrical, machinery, transport, tools and medical equipment products. The

proportion of exporters who have been affected by loss of exports through breakage or

spoilage during the export process is 10 per cent of the total sample; a loss of around 1.9

per cent of total sales. There was no loss of exports due to theft.

2.2.4.5 Firms’ export marketing

Export firms can be involved in different distribution channels, especially when

marketing abroad. The most important current distribution channels are the firm’s sales

force, independent agents, distributors or wholesalers and firm-owned retail stores and

independent retail stores. As can be seen from Table 2B.7 panel (a), 86 per cent of firms

depend upon their sales force whilst the use of other channels remains low.

Export marketing is also engaged in providing an offer that attracts buyers. The offer

is communicated to the buyer using sales promotion activities. The promotional activities

listed in the questionnaire include: trade association participation; trade fair exhibitions;

print advertising; TV and radio advertising; family and personal links; direct mail

advertising; firm and product brochures; and the internet. Table 3.4b shows the mean

value of each method, with 86 per cent of firms using trade fair exhibitions and firm and

product brochures. Only 30 per cent of firms participated in trade associations, who

provide easy access to the market with member benefits such as the Saudi Exports

Centre. However, as Table 2B.9 panel (c) shows, export marketing efforts through various

activities also focuses on nearby markets. This has resulted in the risk element in export

marketing being very low for Saudi exporters.

2.2.4.6 Infrastructure

The data in Table 2B.10 shows the status of electric services by type of ownership,

size and sector. Approximately 46 per cent of exporters have been experiencing electric

failures over the past year, five times the average number of power outages that typically

occur during a year, and with an average power outage duration of 1.17 hours. The

resulting losses due to power outages come to around 0.74 per cent of total annual sales.

The food and beverage sector recorded the highest loss amongst all sectors with a loss of

2.14 per cent of total annual sales. The Middle East and North Africa (MENA) countries

average value of sales losses due to power outages is 5.59 per cent and for all other

countries it is 4.90 per cent. It is clear that Saudi Arabian infrastructure is better

according to both regional and world averages.

Table 2B.11 presents the status of water services categorised by type of ownership,

size and sector. Approximately 17 per cent of firms that faced water insufficiencies and

the average number of incidents of water insufficiency per month was around 1.4.

However, the average duration of insufficient water supply was 7.63 hours. This period

increased to 10.3 hours in the building material and glassware sector. The percentage of

the water supply used in the production process from public sources was around 7.9 per

cent for firms in this sector but it rose to 27 per cent in the food and beverage sector.

Table 2B.12 illustrates the status of communication services according to type of

ownership, size and sector. It can be seen from the data in Table 2B.12 that most firms

have engaged very well with communication services, for example, firms use e-mail to

communicate with clients or suppliers, firms use their own websites, firms have a high-

speed internet connection on their premises and firms use the internet to make

purchases for the firm, to deliver services to clients or to undertake research and develop

for new products and services.

2.2.4.7 Competition

It can be seen in Table 2B.13 that approximately a fifth of firms use the international

market to sell their primary product, just over 27 per cent sell through their local market

and over half deal with the national market. More than 50 per cent of the national

market share and over a quarter of the international market share is held by the

chemical, petrochemical, plastic, rubber and medical care sector. The market leader of

the local market is the wood, paper, leather and textiles industry which holds over 44 per

cent of the local market share. It can be concluded that Saudi exporters endeavour to sell

their new goods in the international market as Table 2B.13 also illustrates that 92 per

cent of firms have registered patents overseas.

2.2.4.8 Labour situation

The average proportion of a firm`s production workers in terms of total employees

for the total sample is around 76 per cent as is presented in Table 2B.14, while non-

production workers (e.g. managers, administration and sales) account for approximately

24 per cent of the workforce. The data show that 50 per cent of the production workers

are skilled workers. In terms of training, approximately 68 per cent of firms that offer

formal training programmes for their permanent or full-time employees and the

percentage of production employees that received training is approximately 79 per cent.

The chemical, petrochemical, plastic, rubber and medical care sector is highly dependent

on skilled workers in comparison with other sectors, while the food and beverages sector

is highly reliant on unskilled workers. Hence, the percentage of production employees

that received training in the food and beverages sector is the highest.

In regards to full-time temporary employees that a firm employs throughout the

fiscal year, Table 2B.15 presents full-time temporary employees by type of ownership,

size and sector. 26 per cent of firms employed temporary employees which accounted for

between 1 per cent and 10 per cent of their permanent full-time employees, while 39 per

cent of firms did not engage temporary employees. Table 2B.16 shows the duration of

employment of temporary employees by type of ownership, size and sector. It can be

seen in the table that 30 per cent of firms engage temporary employees for a period of

one month to three months, and approximately 14 per cent of the sample retained

temporary employees for a period of three to six months.

2.2.4.9 Production capacity

Firms may not use their total production capacity for a variety of reasons. Table

2B.17 displays unused production capacity by type of ownership, size and sector. Around

40 per cent of the total sample leaves less than 25 per cent of their production capacity

unused, 35 per cent of firms leave 25 per cent to 50 per cent of their production capacity

unused, while around 14 per cent have no unused production capacity. The food and

beverages sector, the chemical, petrochemical, plastic, rubber and medical care sector

and the electrical, machinery, transport, tools and medical equipment sector recorded a

high average of unused production capacity of less than 25 per cent, while the wood,

paper, leather and textiles sector and the building material and glassware sector

recorded a high average of between 25-50 per cent of unused production capacity.

As for why firms did not run the total production capacity available, Table 2B.18 lists

seven reasons that answer this question, which are: limited local market, lack of funding

to increase production, difficulty in expanding exports, cost of production inputs,

difficulty of marketing the product, difficulty in obtaining skilled workers and other

reasons not listed in the questionnaire. The figure in Table 2B.18 indicates that 28 per

cent left production capacity unused because of difficulty in obtaining skilled workers, 22

per cent due to the limited local market and 22 per cent for other reasons not listed in

the study. By looking within each sector, figures illustrate that 35 per cent of food and

beverage firms attributed their production capacity use to other reasons not listed, while

46 per cent of wood, paper, leather and textiles firms and 66 per cent of building material

and glassware firms blamed difficulties in expanding exports. Of the firms that work in

chemical, petrochemical, plastic, rubber and medical care, 46 per cent refer to difficulties

in obtaining skilled workers. Finally, of the electrical, machinery, transport, tools and

medical equipment firms, 57 per cent responded that the reason why they do not run the

total production capacity available was due to a limited local market.

2.2.4.10 Total annual costs

Table 2B.19 compares the total annual sales among the sectors of the sample firms.

It is apparent from this table that around 20 per cent of total costs went on paying the

costs of labour, including wages, salaries, bonuses and social security payments, while 63

per cent went on covering the cost of raw materials and intermediate goods used in

production. The cost of fuel and electricity is very low compared with other costs at

approximately 3.7 per cent. The building material and glassware sector pays low costs for

labour compared with other sectors by around 17 per cent, while the food and beverages

sector pays low costs to cover raw materials and other types of costs not listed on the

questionnaire.

2.2.4.11 Legal and security status

As can be seen from the data in Table 2B.20, 12 per cent of firms have legal cases

against their business currently pending. The electrical, machinery, transport, tools and

medical equipment sector recorded the highest number of all sectors, with 50 per cent of

firms being engaged in legal cases. Also, the table illustrates that 48 per cent of the

sample have submitted an application to obtain an import licence, in particular the

chemical, petrochemical, plastic, rubber and medical care sector which account for

around half of the firms that submitted an application. According to the security status

data, approximately 40 per cent of firms pay for security, for example, equipment,

personnel or professional security services, and the percentage paid for security out of a

firm's total annual sales or as a percentage of the firm`s total costs is around 5.71 per

cent.

2.2.4.12 Firms’ credit position

Table 2B.21 shows the credit position of firms by sector and main region of the

sample. It can be seen from Table 2B.21 that about 44 per cent of the total sample used a

line of credit or loan during the observed year. In addition, approximately 46 per cent of

the sample purchased fixed assets, such as machinery, vehicles, equipment, land or

buildings. The figures in Table 2B.22 show that the level of operating a checking or

savings account in firms is low; furthermore, they take advantage of the overdraft facility.

Firms that applied for any loans or lines of credit consisted of about half of the sample,

and 46 per cent of firms that had a line of credit or a loan from a financial institution and

a very large proportion of firms had their financial statements checked and certified by an

external auditor. The average percentages that used borrowed funds from private and

state-owned banks and non-bank financial institutions to fund working capital are shown

in Table 2B.23 at around 28.4 per cent and 17.5 per cent respectively, while 62 per cent

and 13 per cent on average funded the purchase of fixed assets, such as machinery,

vehicles, equipment, land or buildings by using funds borrowed from banks (whether

private or state-owned) and non-bank financial institutions respectively.

Table 2B.24 presents the distributions of the Guarantor Financial Institutions by

ownership and type of sector. This table shows that commercial banks represent the

largest proportion among the Guarantor Financial Institutions that provide loans or lines

of credit to firms. Also, the figures demonstrate that the role of government financial

institutions marginally did not exceed 10 per cent and this percentage increased to 20 per

cent when providing finance sharing with commercial banks. The value of loans or lines of

credit provided for exporting firms was distributed around the mean values of the loans.

Table 2B.25 shows that loans ranging between one to 10 million riyals accounted for 48

per cent of the total loans provided for exporting firms. In addition, 28 per cent of

exporting firms obtained loans of over 10 million and less than 100 million riyals. The

guarantees required to secure these loans and lines of credit from financial organisations

to exporters are somewhat acceptable. Table 2B.26 shows that more than half of the

firms claimed to have provided a rate of 100 per cent equivalent value of the loan or line

of credit. Meanwhile, figures show that a quarter of firms have been asked to provide

collateral exceeding the value of funding, including from 150-200 per cent.

Most required guarantees from exporting firms to obtain a loan or line of credit of a

type that are not classified in the study questionnaire, by around 43 per cent. The

guarantees which were listed in the questionnaire responses are presented in Table

2B.27 (i.e. land, buildings under ownership of the firm, machinery and equipment

including movables, accounts receivable and inventories, personal assets of owner and

other forms of collateral). Land and buildings under the ownership of the firm were the

most common guarantees submitted to a financial institution for a loan or line of credit at 23

per cent. Table 2B.28 illustrates the firms that had no loans or line of credit in our sample.

The figure shows that 54 per cent of firms did not need a loan because the firm had sufficient

capital. Some 21 per cent of firms attributed the lack of funding to the difficulty of providing

guarantees.

Table 2B.29 shows that payment in advance was the most widely used form of

payment received by exporting firms when exporting their goods. Payment in advance is

used in limited partnerships and sole proprietor firms. The wood and leather sector and

the building materials and contracting sector were the sectors in which this manner of

payment was used the most often. The table also illustrates that the selling via credit or

selling debt was the second most widely used method employed by exporters. This could

be seen as proof that Saudi exporters rely on traditional methods to sell their products

abroad, and after a period of time and an increase in the amount of trust with business

associates, are will to sell them goods on credit.

In addition, Table 2B.30 highlights the currency that firms used in their export

operations. The table reveals that the U.S. dollar was the most important currency and

was used by 90 per cent of firms, while the use of the euro averaged 26 per cent. These

figures present evidence that Saudi exporting firms receive a low level of different foreign

currency. Moreover, the figures in Table 2B.30 show that the food and beverage products

sector is more reliant on the euro than other sectors.

Table 2B.31 provides more of an explanation about the payment methods used for

purchases and sales according to type of ownership. Approximately 54 per cent of payment

for purchases of material inputs or services is paid after delivery, approximately 74 per cent

of shareholding firms with shares trading on the stock market have paid for purchases of

material inputs or services after delivery, while around 55 per cent of sole proprietorship and

partnerships have paid before delivery. Regarding the payment method for a firm's total

annual sales of its goods or services, payment after delivery of the firms’ output was

approximately 74 per cent and, at around 83 per cent, shareholding firms recorded the

highest incidence of receiving their dues after the delivery the output to buyers.

2.2.4.13 Access to finance

Access to finance is described in Table 2B.32 which shows the distribution of firms

by legal status, sector and firm size according to annual sales with respect to finance

availability, cost of finance, interest rates, fees and collateral requirements. The statistics

are based on the extent of the obstacle in gaining access to finance that is present in

regards to the current operations of a firm using a five-point scale: no obstacle, minor

obstacle, moderate obstacle, severe obstacle and very severe obstacle. This type of

variable has often been used in some of the literature as a proxy for being credit

constrained (Kuntchev et al, 2012). Furthermore, Table 2B.32 displays the frequencies

and means of the extent of an obstacle to finance that are faced by an exporter. It is

apparent that the availability of finance is not an obstacle compared with collateral

requirements. Taking into consideration the costs, interest rates and fees represent a

minor obstacle.

2.2.4.14 Supporting capabilities that encourage exports

The most important supporting capabilities that have an impact on the decision to

export are presented in Table 2B.33. A one-way ANOVA test was conducted. The test

concluded that those capabilities that significantly supported an increase of exports

were: multi-lingual sales staff, a fax machine, a foreign-language website and product

information on the website. Table 2B.33 shows the capabilities that had little impact in

terms of supporting an increase in exports included: foreign language ability, email, an

export marketing plan and export document preparation.

2.2.4.15 The impact of the main variables on the expansion of national sales

To test decision-makers’ positions in regards to the different barriers to increasing

national sales, a one-way ANOVA test was conducted, as presented in Table 2B.34. The

responses were given according to four categories: (1) not at all important; (2) somewhat

important; (3) important; and (4) very important. It was concluded that the behaviour of

firms’ decision-makers had little impact on the following barriers to increasing national

sales: low demand; taxes on labour; supply of skilled labour; taxes on capital; access to

credit; distribution problems; competitiveness; limited export diversification; and

informal restrictions. However, from Table 2B.34 it was concluded that the behaviour of

firms’ decision-makers had a significant impact in terms of the following barriers to

increasing national sales: inadequate transport links; standards compliance; and customs

and border procedures for raw materials.

2.2.4.16 The impact of the main variables on expanding exports

To examine the position of decision-makers in relation to the different barriers to

exporting, a further one-way ANOVA test was conducted, as presented in Table 2B.35. It

concluded that the behaviour of firms’ decision-makers had little impact on the following

barriers to exporting: access to credit; taxes on capital; the cost of exporting; inadequate

transport links; foreign marketing costs; and competitiveness. As Table 2B.35 shows, it

was also concluded that the behaviour of firms’ decision-makers did have a significant

impact on the following barriers to exporting: low regional demand; import tariffs and

charges; port charges or delays; tariffs or quotas in export markets; freight charges;

standards compliance; customs and border procedures; informal restrictions; taxes on

labour; supply of skilled labour; product quality; and limited export.

2.2.4.17 Analysis of the trade barriers reducing export level

What are the most common issues that confront Saudi exporters? To examine this

issue, one-way ANOVA tests were conducted to analyse the effect of the ratio of exports

of total sales on the seventeen obstacles to exporting. The purpose of this test was to see

whether the positions of firms towards these seventeen variables differed according to

the level of export intensity. The results are presented in Table 2B.36. The p-values

shown in Table 2B.36 are greater than .05 in six of the seventeen obstacles to exporting.

The price-competitiveness of a firm’s products, demands offshore, hidden costs, export

market risk or taking on more export market risk, non-tariff barriers and a lack of

knowledge about potential export markets do not represent barriers to exporting.

Therefore, the obstacles confronting exporters are: freight costs; the cost of raw materials or

components; the cost of finance; a lack of skilled staff; exchange rate volatility; economic

conditions overseas; tariff barriers overseas; a lack of export skills or knowledge; a lack of

skills in logistics and knowledge of trade regulations; and language or cultural barriers.

2.2.4.18 The most and least important challenges reducing export level

These challenges were measured by policy-makers in Saudi firms. The list ranges from

(1) for the most important challenges to (12) for the least important challenges. Table 2B.37

shows that increasing the current level of sales in domestic markets is the most important

challenge to policy-makers in Saudi firms, followed by increasing the current level of exports

and maintaining the current level of sales in domestic markets. On the other hand, training

workers in the skills required, developing a business plan and identifying and engaging

trained workers are recorded as being the least important challenges.

2.2.5 Discussion and Conclusion

This study has highlighted the role of the Saudi Arabian government in motivating

manufacturers to export. It has illustrated the facilities needed to ensure the

development and promotion of the role of the private sector, primarily focusing on

industrial exports, as well highlighting how to reduce the impact of the risks that

businesses face and how to encourage the creation and activation of appropriate

institutional frameworks to support exports. It is clear from the data presented that the

institutional frameworks have contributed effectively to supporting export industries,

which has led to an increase in the percentage of their contribution to GDP and has

improved the national balance of payments, supported the economy and diversified its

resources, as well as created more job opportunities for national workers. However, it

remains the case that firms that aim to export should seek to improve their export ability

and competitiveness. They also require access to specialised information in terms of

global markets and with regard to benchmarking competitiveness and creating effective

systems for export, exploiting opportunities associated with e-commerce and

implementing international quality and environmental conservation standards.

It can be concluded from the firms surveyed that the policy- or decision-makers in

exporting firms face ten key problems. The most common barriers and obstacles faced by

decision-makers were: freight costs; the cost of raw materials or components; the cost of

finance; a lack of skilled staff; exchange rate volatility; economic conditions overseas;

tariff barriers overseas; a lack of export skills or knowledge; a lack of skills in logistics and

knowledge of trade regulations; and language or cultural barriers.

Another conclusion that can be drawn in this chapter is that policy-makers’ positions

towards expanding national sales or expanding exporting are affected by the same

variables. Level of export or national sales had insignificant with taxes on capital, access

to credit and competitiveness and significant with standards compliance and customs and

border procedures. However, taxes on labour, supply of skilled labour, limited export

diversification and informal restrictions are significant variables in terms of decision-makers’

positions towards expanding exporting whilst inadequate transport links is a significant

variable in terms of decision-makers’ positions towards expanding national sales.

Table 2B.1: Profile of the sample, Sector distributions by region

Sector Main Region Central Western Eastern Northern Total

Food and Beverages 9 3 1 1 14 64.29 21.43 7.14 7.14 100 9.18 7.5 2.78 100 8

Wood, Paper, Leather and Textiles 17 7 4 - 28 60.71 25 14.29 - 100 17.35 17.5 11.11 - 16

Chemical, Petrochemical, Plastic, Rubber and Medical care

46 18 20 - 84 54.76 21.43 23.81 - 100 46.94 45 55.56 - 48

Building Materials and Glassware 11 4 6 - 21 52.38 19.05 28.57 - 100 11.22 10 16.67 - 12

Electronics, Machinery, Transport, Tools and Medical equipment

15 8 5 - 28 53.57 28.57 17.86 - 100 15.31 20 13.89 - 16

Total 98 40 36 1 175 56 22.86 20.57 0.57 100 100 100 100 100 100

Table 2B.2: Characteristics of the sample

Years indicating formal registration: No. % Total Obs.

1 Less than 5 years 9 5.14 175

2 Six to 15 years 31 17.71

3 More than sixteen years 135 77.14

Females amongst the owners of the firm: 166

1 Yes 45 27.10

2 No 121 72.90

Having a locally recognised quality certification: 170

1 Yes 127 74.71

2 No 43 25.29

Having an internationally recognised quality certification: 171

1 Yes 127 74.27

2 No 44 25.73

Table 2B.3: Firm size determined by sales figures

sector 10 million and less

11-25 million

26-51 million

51-100 million

More than 100 million

Total

Food and Beverages

2 4 3 - 5 14

14.29 28.57 21.43 - 35.71 100

10 10.53 23.08 - 7.35 8 Wood, Paper, Leather and Textiles 9 6 4 4 5 28

32.14 21.43 14.29 14.29 17.86 100

45 15.79 30.77 11.11 7.35 16 Chemical, Petrochemical, Plastic, Rubber and Medical care

1 27 3 26 27 84

1.19 32.14 3.57 30.95 32.14 100

5 71.05 23.08 72.22 39.71 48 Building Materials and Glassware 5 1 1 1 13 21

23.81 4.76 4.76 4.76 61.9 100

25 2.63 7.69 2.78 19.12 12 Electronics, Machinery, Transport, Tools and Medical equipment

3 - 2 5 18 28

10.71 - 7.14 17.86 64.29 100

15 - 15.38 13.89 26.47 16

Total 20 38 13 36 68 175

11.43 21.71 7.43 20.57 38.86 100

Table 2B.4: Firm size deduced by labour volume

Sector Less than 99 More than 100 Total Food and Beverages 3 11 14

21.43 78.57 100

6.98 8.33 8

Wood, Paper, Leather and Textiles 7 21 28

33.33 75.00 108

16.28 15.91 16

Chemical, Petrochemical, Plastic, Rubber and Medical care 21 63 84

25.00 75.00 100

48.84 47.73 48

Building Materials and Glassware 6 15 21

28.57 71.43 100

13.95 11.36 12

Electrical, Machinery, Transport, Tools and Medical equipment 6 22 28

21.43 78.57 100

13.95 16.67 16

Total

43 132 175

24.57 75.43 100

100 100 100

Table 2B.5: Sample size by type of ownership and sector

Type of current legal of firm Food

produce

Wood, Paper, Leather,

Textiles and Other

Chemical and Plastic Products

Base Metals and Articles

of Base Metals

Electrical Machines and Tools

Total

Shareholding firm with shares trade in the stock market

3 - 1 - 1 5 60 - 20 - 20 100 21.43 - 1.19 - 3.57 2.86

Shareholding firm with non- traded shares or shares traded privately

- 6 15 2 6 29 - 20.69 51.72 6.9 20.69 100 - 21.43 17.86 9.52 21.43 16.57

-Sole proprietorship 2 9 22 4 - 37 5.41 24.32 59.46 10.81 - 100 14.29 32.14 26.19 19.05 - 21.14

-Partnership 4 6 26 4 5 45 8.89 13.33 57.78 8.89 11.11 100 28.57 21.43 30.95 19.05 17.86 25.71

Limited partnership 5 7 20 10 16 58 8.62 12.07 34.48 17.24 27.59 100 35.71 25 23.81 47.62 57.14 33.14

Total 14 28 84 21 28 175

Table 2B.6a Exports intensity by type of ownership, size and sector.

Status Freq. Mean

Exports intensity by type of ownership Shareholding firm with shares trade in the stock market. 5 38.00

Shareholding firm with non-traded shares or shares traded privately. 29 26.57

Sole proprietorship 37 20.00

Partnership 45 28.95

Limited partnership 58 16.86

Exports intensity by type of labour: Micro (< 5 employees) 1 10.00

Small (>5 employees <20) 2 20.00

Medium (20-99 employees) 40 24.55

Large (100+ employees) 132 22.73

Exports intensity by type of Total Sales: 10 million and less 19 13.93

11-25 million 38 26.73

26-51 million 13 13.38

51-100 million 36 26.76

More than 100 million 68 22.80 Total 23.01

Table 2B.6b The Proportion of Exports intensity and national sales by sectors.

Sector National sales Direct exports

Food and Beverages. 83.9 16.1 2.97 2.97

Wood, Paper, Leather and Textiles. 82.59 14.44 3.61 1.74

Chemical, Petrochemical, Plastic, Rubber and Medical care. 73.95 25.1 2.51 2.52

Building Material and Glassware. 78.06 20.83 1.94 1.82

Electrical, Machinery, Transport, Tools and Medical equipment. 70 30 2.38 2.38

Total sample 75.89 23.01 1.50 1.42

Note: Top number is the mean; the lower is the standard error.

Table 2B.7 The sample’s export experience

a) Category of first Export Between

1980-1989 Between

1990-1999 Between

2000-2009 After 2010 Total

Food and Beverages. 2 7 1 0 10 20 70 10 - 100 8.7 8.64 2.04 - 6.21 Wood, Paper, Leather and Textiles. 3 15 8 1 27

11.11 55.56 29.63 3.7 100 13.04 18.52 16.33 12.5 16.77

Chemical, Petrochemical, Plastic, Rubber and Medical care.

10 42 23 6 81 12.35 51.85 28.4 7.41 100 43.48 51.85 46.94 75 50.31

Building Material and Glassware. 2 6 9 1 18 11.11 33.33 50 5.56 100 8.7 7.41 18.37 12.5 11.18

Electrical, Machinery, Transport, Tools and Medical equipment.

6 11 8 0 25 24 44 32 - 100 26.09 13.58 16.33 - 15.53

Total 23 81 49 8 161 14.29 50.31 30.43 4.97 100 100 100 100 100 100

b)Exports’ Destination GCC Arabian Asian African European American Australian

Food and Beverages. 0.7143 0.2857 . . . . . 10 4 . . . . .

Wood, Paper, Leather and Textiles.

0.8929 0.9643 0.3929 0.3095 0.1667 0.0595 0.0119 75 81 33 26 14 5 1

Chemical, Petrochemical Plastic, Rubber and Medical care.

0.9643 0.8214 0.0357 0.1786 0.1429 . . 27 23 1 5 4 . .

Building Material and Glassware. 0.8571 0.8571 0.1429 0.3810 . . . 18 18 3 8 . . .

Electrical, Machinery, Transport, Tools and Medical equipment.

0.7857 0.7143 0.1071 0.4286 0.2143 0.1786 . 22 20 3 12 6 5 .

Total 0.8686 0.8343 0.2286 0.2914 0.1371 0.0571 0.0057 152 146 40 51 24 10 1

Table 2B.8 Trade operations’ characteristics indicators

Variable Mean SE(mean) SD Variance N

Supplies of domestic origin 65.83 1.89 25.01 625.52 175

Supplies of foreign origin 34.17 1.89 25.01 625.52 175

Raw materials imported directly 90.32 1.51 19.74 389.80 171

Raw materials imported indirectly 9.72 1.46 17.30 299.26 141

Days to clear Imports customs 8.29 0.81 4.78 22.86 35

Days to clear Exports customs 5.44 0.32 4.04 16.30 158

Exported loss by breakage or spoilage 1.96 0.42 1.78 3.16 18

Domestic products loss by breakage or spoilage 2.30 0.39 1.73 2.98 20

Domestic products lost by theft 0.50 0.17 0.30 0.09 3

Exported lost by theft (no observation) Ø Ø Ø Ø Ø

Table 2B.9 Export marketing characteristics

a) Sales distribution channel B) Sales Promotion Activities Attitudes towards promotional activities’

List Mean List Mean List Mean

Firm Sales Force 0.863 Trade Association participation 0.309 National 0.897 Independent Agents 0.217 Trade Fair Exhibition 0.863 GCC 0.749 Distributors/Wholesalers 0.377 Print Advertising 0.554 Arab countries 0.469 Firm -Owned Retail Stores 0.154 TV/Radio Advertising 0.131 Asia countries 0.143 Independent Retail Stores 0.086 Family/Personal Links 0.183 African countries 0.120

Direct Mail Advertising 0.183 European countries 0.126

Firm & Product Brochures 0.863 American countries 0.046

Internet 0.749

Figure 2.4

Figure 2.5

Micro (< 5 employees)

Small (>5 employees <20)

Medium (20-99 employees)

Large (100+ employees)

Export intensity by labour

10 million and less

11-25 million 26-51 million 51-100 million More than 100 million

Exports intensity by type of Total Sales:

Table 2B.10 Electric services status by type of ownership, size and sector.

Status Experience

electric failures

Average number of power

outages during a year

Average duration of

power outages

Loss as per cent of total annual

sales due to power outages

Electric services status by type of ownership:

Shareholding firm with shares trade in the stock market.

3 3.67 1.00 7.50 60 0.67 0.00 2.50

3.7 Shareholding firm with non-traded shares or shares traded privately.

8 4.57 1.00 - 27.59 0.57 0.00 -

9.88 Sole proprietorship 19 8.11 1.00 0.06

51.35 2.04 0.00 0.06 23.46

Partnership 14 5.90 1.53 0.64 31.11 1.45 0.24 0.31 17.28

Limited partnership 36 4.77 1.17 1.17 62.07 0.61 0.08 0.85 44.44

Electric services status by type of labour:

Micro (< 5 employees) 1 . . -

100 . . .

1.23

Small (>5 employees <20) 2 10.00 1.00 -

100 . 0.00 -

2.47

Medium (20-99 employees) 22 5.71 1.63 1.35

55 1.12 0.19 0.55

27.16

Large (100+ employees) 56 5.27 1.03 0.53

42.42 0.62 0.03 0.42

69.14

Electric services status by type of sector:

Food and Beverages 10 3.63 1.00 2.14

71.43 0.46 0.00 1.49

12.35

Wood, Paper, Leather and Textiles 12 6.00 1.00 -

42.86 1.07 0.00 -

14.81

Chemical, Petrochemical, Plastic, Rubber and Medical care

39 6.92 1.21 0.94 46.43 0.99 0.10 0.63

48.15

Building Material and Glassware 11 4.25 1.18 -

52.38 0.75 0.18 -

13.58

Electrical, Machinery, Transport, Tools and Medical equipment

9 3.13 1.31 0.80 32.14 0.48 0.13 0.51

11.11

Total 81 5.48 1.17 0.74

46.29 0.54 0.06 0.33

The second row of figures represents the totals for each category; the third row represents per cent of total.

Table 2B.11 Water services status by type of ownership, size and sector.

Status Experience

water insufficiency

Average number of incidents of water insufficiency per

month

Insufficient water supply per hour

per cent of water supply, used from

public source

Electric services status by type of ownership:

Shareholding firm with shares trade in the stock market.

. . . 20.00

. . . 20.00 . . . Shareholding firm with non-traded shares or shares traded privately.

2 0.00 12.00 2.95 6.9 0.00 12.00 2.05

6.67 Sole proprietorship 12 3.75 12.00 16.88

32.43 0.70 3.62 6.48

Partnership 3 2.80 14.40 1.08

6.67 1.16 5.88 0.65

Limited partnership 13 0.12 4.39 7.35

22.41 0.12 2.77 4.29

43.33

Electric services status by type of labour:

Small (>5 employees <20) 1 . 1.00 .

50 . . .

3.33

Medium (20-99 employee) 4 . 0.33 13.29

10 . 0.33 5.55

13.33

Large (100+ employees) 25 1.59 8.31 5.82

18.94 0.38 2.26 2.05

83.33

Electric services status by type of sector:

Food and Beverages 2 . . 27.27

14.29 . . 14.08 6.67

Wood, Paper, Leather and Textiles 3 . . . 10.71 . . .

10 Chemical, Petrochemical, Plastic, Rubber and Medical care

21 2.81 9.86 8.41 25 0.56 2.54 3.28 70

Building Material and Glassware 1 0.43 10.29 6.33 4.76 0.43 10.29 3.33 3.33

Electrical, Machinery, Transport, Tools and Medical equipment.

3 . 7.20 3.54 10.71 . 3.67 2.49

Total 30 1.41 7.63 7.90

17.14 0.34 2.08 2.16

The second row of figures represents the total for each category; the third row represents per cent of total.

Table 2B.12 Communication services status by type of ownership, size and sector.

Status

Uses e-mail Own website

High-speed Internet

Make purchases

Deliver services

Research and developme nt

Communication services status by type of ownership

Shareholding firm with shares trade in the stock market.

5 5 5 3 2 3 100 100 100 60 40 60 2.87 2.98 3.09 1.91 1.37 1.92

Shareholding firm with non-traded shares or shares traded privately.

29 29 26 25 26 28 100 100 89.66 86.21 89.66 96.55 16.67 17.26 16.05 15.92 17.81 17.95

Sole proprietorship 37 34 33 37 32 32

100 91.89 89.19 100 86.49 86.49

21.26 20.24 20.37 23.57 21.92 20.51

Partnership 45 45 42 35 42 45

100 100 93.33 77.78 93.33 100

25.86 26.79 25.93 22.29 28.77 28.85

Limited partnership 57 54 55 56 44 47

98.28 93.1 94.83 96.55 75.86 81.03

32.76 32.14 33.95 35.67 30.14 30.13 Communication services status by type of labour

Micro (< 5 employees) 1 1 1 1 1 1

100 100 100 100 100 100

0.57 0.6 0.62 0.64 0.68 0.64

Small (>5 employees <20) 1 1 1 2 1 1

50 50 50 100 50 50

0.57 0.6 0.62 1.27 0.68 0.64

Medium (20-99 employees) 40 37 34 38 27 30

100 92.5 85 95 67.5 75

22.99 22.02 20.99 24.2 18.49 19.23

Large (100+ employees) 132 129 126 116 117 124

100 97.73 95.45 87.88 88.64 93.94

75.86 76.79 77.78 73.89 80.14 79.49 Communication services status by type of sector

Food and Beverages 14 11 14 12 9 9

100 78.57 100 85.71 64.29 64.29

8.05 6.55 8.64 7.64 6.16 5.77

Wood, Paper, Leather and Textiles 28 28 23 25 26 25

100 100 82.14 89.29 92.86 89.29

16.09 16.67 14.2 15.92 17.81 16.03 Chemical, Petrochemical, Plastic, Rubber and Medical care

83 80 80 71 73 77 98.81 95.24 95.24 84.52 86.9 91.67 47.7 47.62 49.38 45.22 50 49.36

Building Material and Glassware 21 21 17 21 10 17

100 100 80.95 100 47.62 80.95

12.07 12.5 10.49 13.38 6.85 10.9 Electrical, Machinery, Transport, Tools and Medical equipment

28 28 28 28 28 28 100 100 100 100 100 100 16.09 16.67 17.28 17.83 19.18 17.95

Total 174 168 162 157 146 156

99.43 96 92.57 89.71 83.43 89.14 The second row of figures represents the total for each category; the third row represents per cent of total.

Table 2B.13 Competition status by type of ownership, size and sector.

Main market

Number of competitors

Patents registered abroad Local National International One 2-5 > 5

Status by type of ownership

Shareholding firm with shares trade in the stock market.

20.63 41.23 38.13 - 1 4 5

20.63 9.44 18.96 2.00 3.45 3.14

Shareholding firm with non-traded shares or shares traded privately.

20.56 53.33 25.56 1 11 17 25

5.61 5.47 5.01 11.11 22.00 14.66 15.72

Sole proprietors 34.92 47.79 17.29 1 12 24 36

5.38 5.32 3.05 11.11 24.00 20.69 22.64

Partnership 28.78 43.11 28.11 4 7 34 42

4.64 4.84 3.92 44.44 14.00 29.31 26.42

Limited partnership 24.53 61.3 13.98 3 19 36 50

2.85 3.05 1.33 33.33 38.00 31.03 31.45

Status by type of labour

Micro (< 5 employees) 40 50 10 - - 1 1

. . . 0.86 0.63

Small (>5 &<20 ) . 90 20 - 1 1 2

. 10 10 2.00 0.86 1.26

Medium (20-99) 29.72 47.31 22.97 8 9 23 36

4.2 5.1 5.07 88.89 18.00 19.83 22.64

Large (100+ ) 26.06 53.89 19.86 1 40 91 120

2.47 2.46 1.4 11.11 80.00 78.45 75.47

Status by type of sector

Food and Beverages 43.91 42.64 13.45 - 5 9 14

5.05 4.9 3.04 10.00 7.76 8.81

Wood, Paper, Leather and Textiles

44.23 42.88 12.12 1 7 20 28

3.43 3.6 1.12 11.11 14.00 17.24 17.61

Chemical, Petrochemical, Plastic, Rubber and Medical care

19.62 55.12 25.27 8 24 52 71

3.59 4.61 3.45 88.89 48.00 44.83 44.65

Building Material and Glassware 35.17 50.11 14.72 - 4 17 21

5.15 5.95 1.92 8.00 14.66 13.21

Electrical, Machinery, Transport, Tools and Medical equipment

35.17 50.11 14.72 - 10 18 25

5.15 5.95 1.92 20.00 15.52 15.72

Total 27.02 52.31 20.52 9 50 116 159

2.12 2.22 1.59 5% 28% 67% 90%

The second row of figures represents the total for each category; the third row represents % of total.

Table 2B.14 Labour status by type of ownership, size and sector.

Total workers Production Workers Received training

% Production % Non %Skilled % Unskilled Formal training

% Production

% Non- Production

Labour status by ownership

Shareholding firm with shares trade in the stock market.

81.66 18.33 51.00 49.00 3 76.67 23.33

Shareholding firm with non-traded shares or shares traded privately.

78.48 27.41 54.55 45.45 20 78.33 21.67

Sole proprietors 77.30 22.70 45.27 54.73 22 50.00 50.00

Partnership 76.84 23.16 54.00 46.00 32 83.33 16.67

Limited partners 73.90 26.10 45.60 54.40 41 78.67 21.33

Labour status by size

Micro (< 5) 60.00 40.00 80.00 20.00 - 70.00 30.00

Small (>5 &<20) 80.00 10.00 50.00 50.00 - 80.85 19.15

Medium (20-99) 74.62 30.51 41.00 59.00 24 76.67 23.33

Large (100+) 77.02 22.98 51.80 48.20 95 78.33 21.67

Labour status by sector

Food and Beverages

73.58 26.42 24.29 75.71 5 90.00 10.00

Wood, Paper, Leather and Textiles

78.82 21.18 41.14 58.86 8 40.00 60.00

Chemical, Petrochemical, Plastic, Rubber and Medical care

74.85 25.15 53.45 46.55 70 76.25 23.75

Building Material and Glassware

77.05 32.48 48.81 51.19 11 87.22 12.78

Electrical, Machinery, Transport, Tools and Medical equipment

79.46 20.54 58.93 41.07 25 80.24 19.76

Sample 76.42 24.74 49.47 50.53 119 79.27 20.73 S.E 0.70 1.33 1.96 1.96 1.64 1.64

Table 2B.15 Full-time temporary employees by type of ownership, size and sector.

Status Temporary employees as per cent of full-time employees

1 per cent- 10 per cent

11 per cent- 25 per cent

26 per cent-50 per cent

More than 50 per cent N/A Total

By ownership Shareholding firm with shares

trade in the stock market. - 2 1 - 2 5 - 40 20 - 40 100 - 4.88 9.09 - 2.9 2.86

Shareholding firm with non- traded shares or shares traded privately.

10 8 - 2 9 29 34.48 27.59 - 6.9 31.03 100 21.28 19.51 - 28.57 13.04 16.57

Sole proprietorship 9 11 5 - 12 37

24.32 29.73 13.51 - 32.43 100

19.15 26.83 45.45 - 17.39 21.14

Partnership 10 4 3 2 26 45

22.22 8.89 6.67 4.44 57.78 100

21.28 9.76 27.27 28.57 37.68 25.71

Limited partnership 18 16 2 2 20 58

31.03 27.59 3.45 3.45 34.48 100

38.3 39.02 18.18 28.57 28.99 33.14 By size

Micro (< 5 employees) - 1 - - - 1

- 100 - - - 100

- 2.44 - - - 0.57

Small (>5 employees <20) 1 1 - - - 2

50 50 - - - 100

2.13 2.44 - - - 1.14

Medium (20-99) 9 10 - 2 19 40

22.5 25 - 5 47.5 100

19.15 24.39 - 28.57 27.54 22.86

Large (100+) 37 29 11 5 50 132

28.03 21.97 8.33 3.79 37.88 100

78.72 70.73 100 71.43 72.46 75.43 By sector

Food and Beverages - 6 3 - 5 14

- 42.86 21.43 - 35.71 100

- 14.63 27.27 - 7.25 8 Wood, Paper, Leather and Textiles

11 5 - 1 11 28 39.29 17.86 - 3.57 39.29 100

23.4 12.2 - 14.29 15.94 16 Chemical, Petrochemical, Plastic, Rubber and Medical care

26 25 8 - 25 84 30.95 29.76 9.52 - 29.76 100 55.32 60.98 72.73 - 36.23 48

Building Material and Glassware 1 - - 4 16 21

4.76 - - 19.05 76.19 100

2.13 - - 57.14 23.19 12

Electrical, Machinery, Transport, Tools and Medical equipment

9 5 - 2 12 28 32.14 17.86 - 7.14 42.86 100 19.15 12.2 - 28.57 17.39 16

Total 47 41 11 7 69 175

26.86 23.43 6.29 4 39.43 100

100 100 100 100 100 100 The second row of figures represents the total for each category; the third row represents per cent of total.

Table 2B.16 Length of employment of temporary employees by type of ownership, size

and sector.

Status 1month 1 to 3 months 3 to 6 More than 6 N/A Total

By ownership

Shareholding firm with shares trade in the stock market.

2 - 1 - 2 5 40 - 20 - 40 100 15.38 - 4 - 2.9 2.86

Shareholding firm with non-traded shares or shares traded privately.

3 8 7 2 9 29 10.34 27.59 24.14 6.9 31.03 100 23.08 15.09 28 13.33 13.04 16.57

Sole proprietorship 1 6 12 6 12 37

2.7 16.22 32.43 16.22 32.43 100

7.69 11.32 48 40 17.39 21.14

Partnership - 14 3 2 26 45

- 31.11 6.67 4.44 57.78 100

- 26.42 12 13.33 37.68 25.71

Limited partnership 7 25 2 4 20 58

12.07 43.1 3.45 6.9 34.48 100

53.85 47.17 8 26.67 28.99 33.14

By size

Micro (< 5 employees) 1 - - - - 1 100 - - - - 100 7.69 - - - - 0.57 Small (>5 employees <20) 1 1 - - - 2 50 50 - - - 100 7.69 1.89 - - - 1.14 Medium (20-99) 2 13 - 6 19 40 5 32.5 - 15 47.5 100 15.38 24.53 - 40 27.54 22.86 Large (100+) 9 39 25 9 50 132 6.82 29.55 18.94 6.82 37.88 100 69.23 73.58 100 60 72.46 75.43 By sector

Food and Beverages 2 4 3 - 5 14 14.29 28.57 21.43 - 35.71 100 15.38 7.55 12 - 7.25 8 Wood, Paper, Leather and Textiles 2 13 - 2 11 28 7.14 46.43 - 7.14 39.29 100 15.38 24.53 - 13.33 15.94 16 Chemical, Petrochemical, Plastic, Rubber and Medical care.

6 25 22 6 25 84 7.14 29.76 26.19 7.14 29.76 100 46.15 47.17 88 40 36.23 48

Building Material and Glassware - - - 5 16 21 - - - 23.81 76.19 100 - - - 33.33 23.19 12 Electrical, Machinery, Transport, Tools and Medical equipment.

3 11 - 2 12 28 10.71 39.29 - 7.14 42.86 100 23.08 20.75 - 13.33 17.39 16

Total 13 53 25 15 69 175

7.43 30.29 14.29 8.57 39.43 100

The second row of figures represents the total for each category; the third row represents per cent of total.

Table 2B.17 Unused production capacity by type of ownership, size and sector.

Status Less than 25% 25-50 % 51-75% More than 76 % No unused Total

By ownership Shareholding firm with shares trade in the stock market.

1 3 - - 1 5 20 60 - - 20 100

1.45 4.84 - - 4.17 2.86 Shareholding firm with non-traded shares or shares traded privately.

16 4 1 - 8 29 55.17 13.79 3.45 - 27.59 100 23.19 6.45 9.09 - 33.33 16.57

Sole proprietorship 14 17 1 3 2 37 37.84 45.95 2.7 8.11 5.41 100 20.29 27.42 9.09 33.33 8.33 21.14 Partnership 9 21 2 4 9 45 20 46.67 4.44 8.89 20 100 13.04 33.87 18.18 44.44 37.5 25.71 Limited partnership 29 17 6 2 4 58 50 29.31 10.34 3.45 6.9 100 42.03 27.42 54.55 22.22 16.67 33.14 By size Small (>5 employees <20) 1 1 - - - 2 50 50 - - - 100 1.45 1.61 - - - 1.14 Medium (20-99) 5 21 4 - 7 40 12.5 52.5 10 7.5 17.5 100 7.25 33.87 36.36 33.33 29.17 22.86 Large (100+) 63 39 7 6 17 132 47.73 29.55 5.3 4.55 12.88 100 91.3 62.9 63.64 66.67 70.83 75.43

By sector Food and Beverages 6 4 - - 4 14 42.86 28.57 - - 28.57 100 8.7 6.45 - - 16.67 8 Wood, Paper, Leather and Textiles 5 16 - - 7 28 17.86 57.14 - - 25 100 7.25 25.81 - - 29.17 16 Chemical, Petrochemical, Plastic, Rubber and Medical care.

37 27 4 6 10 84 44.05 32.14 4.76 7.14 11.9 100 53.62 43.55 36.36 66.67 41.67 48

Building Material and Glassware 4 7 4 3 3 21 19.05 33.33 19.05 14.29 14.29 100 5.8 11.29 36.36 33.33 12.5 12 Electrical, Machinery, Transport, Tools and Medical equipment.

17 8 3 - - 28 60.71 28.57 10.71 - - 100 24.64 12.9 27.27 - - 16

Total 69 62 11 9 24 175

39.43 35.43 6.29 5.14 13.71 100

The second row of figures represents the total for each category; the third row represents per cent of total.

Table 2B.18 The main reasons why firms did not run the total production capacity

available.

Limited local market

Lack of funding to increase production

Difficulty in expandin g exports

Cost of product ion inputs

Difficulty of marketing the product

Difficulty in obtaining skilled workers

Other No unused

Food and Beverages 3 - 2 - 3 - 5 4

21.43 - 14.29 - 21.43 - 35.71 28.57

7.69 - 4.08 - 15.79 - 12.82 28.57

Wood, Paper, Leather and Textiles

8 6 13 2 3 1 9 7

28.57 21.43 46.43 7.14 10.71 3.57 32.14 25

20.51 19.35 26.53 15.38 15.79 2 23.08 50

Chemical, Petrochemical, Plastic, Rubber and Medical care.

4 20 10 4 9 39 13 -

4.76 23.81 11.9 4.76 10.71 46.43 15.48 -

10.26 64.52 20.41 30.77 47.37 78 33.33 -

Building Material and Glassware

8 5 14 7 4 5 - 3

38.1 23.81 66.67 33.33 19.05 23.81 - 14.29

20.51 16.13 28.57 53.85 21.05 10 - 21.43

Electrical, Machinery, Transport, Tools and Medical equipment.

16 - 10 - - 5 12 -

57.14 - 35.71 - - 17.86 42.86 -

41.03 - 20.41 - - 10 30.77 -

Total 39 31 49 13 19 50 39 14

22.29 17.71 28 7.43 10.86 28.57 22.29 8

100 100 100 100 100 100 100 100

The second row of figures represents the total for each category; the third row represents per cent of total.

Table 2B.19 Total annual costs.

Industry ( per cent) Labour1

Raw materials2 Fuel Electricity Other

Food and Beverages 20.07 58.57 4.79 4.86 11.71

Wood, Paper, Leather and Textiles 19.91 64.44 4.01 3.91 10.3

Chemical, Petrochemical, Plastic, Rubber and Medical care.

20.31 63.85 3.35 3.56 9.49

Building Material and Glassware 17.14 65.48 4.57 4.19 8.62

Electrical, Machinery, Transport, Tools and Medical equipment.

20.32 63.39 3.64 3.29 10.08

Total 19.85 63.64 3.77 3.75 9.77

1 Including wages, salaries, bonuses, social security payments. 2 Including intermediate goods used in production.

Table 2B.20 Indictors of legal import licence and security status of firms.

Status Have legal cases pending

Submit an application to obtain an import license

Pay for security

Suffer losses as a result of theft, robbery, vandalism or arson

By ownership Shareholding firm with shares

trade in the stock market. - 2 5 2 - 40 100 40 - 2.38 7.04 20

Shareholding firm with non- traded shares or shares traded privately.

3 18 15 - 10.34 62.07 51.72 - 13.64 21.43 21.13 -

Sole proprietorship 4 15 5 - 10.81 40.54 13.51 - 18.18 17.86 7.04 - Partnership - 22 14 - - 48.89 31.11 - - 26.19 19.72 - Limited partnership 15 27 31 8 25.86 46.55 53.45 13.79 68.18 32.14 43.66 80 By size Small ( <20) 1 1 1 1 50 50 50 50 4.55 1.19 1.41 10 Medium (20-99) 5 16 12 2 12.5 40 30 5 22.73 19.05 16.9 20 Large (100+) 16 66 58 7 12.12 50 43.94 5.3 72.73 78.57 81.69 70 By sector Food and Beverages - 6 8 4 - 42.86 57.14 28.57 - 7.14 11.27 40 Wood, Paper, Leather and Textiles

5 14 8 - 17.86 50 28.57 - 22.73 16.67 11.27 -

Chemical, Petrochemical, Plastic, Rubber and Medical care.

5 40 34 6 5.95 47.62 40.48 7.14

22.73 47.62 47.89 60 Building Material and Glassware

1 3 5 - 4.76 14.29 23.81 - 4.55 3.57 7.04 -

Electrical, Machinery, Transport, Tools and Medical equipment.

11 21 16 - 39.29 75 57.14 -

50 25 22.54 -

Total 22 84 71 10

12.57 48 40.57 5.71

100 100 100 100

The second row of figures represents the total for each category; the third row represents per cent of total.

Table 2B.21 Credit position of firms by type of sector and region.

Sector Central Western Eastern Northern Total

(A) (B) (A) (B) (A) (B) (A) (B) (A) (B)

Food and Beverages 4 2 1 2 1 - - 1 6 5

9.09 3.92 5 13.50 7.69 - - 100 7.79 6.17

Wood, Paper, Leather and Textiles

4 8 - 1 1 2 - - 5 11

9.09 15.69 - 6.67 7.69 15.3 - - 6.49 13.58

Chemical, Petrochemical, Plastic, Rubber and Medical care.

19 26 16 9 6 7 - - 41 42

43.18 50.98 80 56.25 46.15 53.8 - - 53.25 51.85

Building Material and Glassware 5 3 - - 3 1 - - 8 4

11.36 5.88 - - 23.08 7.69 - - 10.39 4.94

Electrical, Machinery, Transport, Tools and Medical equipment.

12 12 3 4 2 3 - - 17 19

27.27 23.53 15 25 15.38 18.1 - - 22.08 23.46

Total 44 51 20 16 13 13 - 1 77 81

100 100 100 100 100 100 - 100 100 100

% of sample by region 44.90 54.04 50.00 40.00 36.11 36.1 - 100. 44.00 46.28

% of total sample 25.14 29.14 11.43 9.14 7.43 7.43 - 0.57

(A)Firms purchase of fixed assets, such as machinery, vehicles, equipment, land or buildings. (B) Firms have a line of credit or a loan from a financial institution.

Table 2B.22 Firms’ financial characterisation by type of ownership.

Ownership legal status Purchase fixed assets

A checking or savings account

An overdraft facility

Apply for any loans or lines of credit

Have a line of credit or a loan from a financial institution

Financial statements checked by an external auditor

Shareholding firm with shares trade in the stock market.

3 2 1 2 2 5

3.9 4.88 1.69 2.35 2.47 3.03 Shareholding firm with non-traded shares or shares traded privately.

13 5 8 13 15 29

16.88 12.2 13.56 15.29 18.52 17.58

Sole proprietorship 21 5 15 11 16 34

27.27 12.2 25.42 12.94 19.75 20.61

Partnership 15 12 18 23 27 41

19.48 29.27 30.51 27.06 33.33 24.85

Limited partnership 24 17 17 35 21 55

31.17 41.46 28.81 41.18 25.93 33.33

Other 1 - - 1 - 1

1.3 - - 1.18 - 0.61

Total 77 41 59 85 81 165

per cent of total sample 44 23 34 49 46 94

Table 2B.23 Financial sources of firms by type of ownership.

Source of finance Freq. Internal funds

Borrowed from banks (private)

Borrowed from non- bank financial

Purchases on credit and advances

Other

A) The proportion of finance of firm's working capital Shareholding firm with shares trade 5 100 - - - - Shareholding firm with non-traded 29 87.00 24.00 30.00 46.25 20.00 Sole proprietorship 37 79.19 28.33 30.00 28.00 20.00 Partnership 45 88.93 35.45 - 15.00 - Limited partnership 58 88.52 25.63 8.57 18.75 0.00 Other 1 100 - - - -

Mean 86.71 28.42 17.50 28.27 10.00 se(mean) 1.68 2.08 3.92 5.68 3.78 variance 467.39 225.58 184.09 837.88 114.29

B) The proportion of finance of firm's total purchase of fixed assets Shareholding firm with shares trade 5 73.33 80.00 - - - Shareholding firm with non-traded 29 49.38 52.00 9.00 57.43 32.50 Sole proprietorship 37 96.67 68.33 - 50.00 - Partnership 45 75.00 85.71 - - 100 Limited partnership 58 53.91 57.20 15.00 23.33 - Other 1 100 - - - -

Mean 70.94 62.04 13.80 38.94 46.50 se(mean) 4.38 4.87 4.63 9.14 15.13 variance 1323.73 1160.33 214.40 1420.31 2289.17

Table 2B.24 Distribution of Guarantor Financial Institution by type of sector and ownership.

Sector

(1) Commercial banks

(2) Governmen t agency

(3) (1 + 2)

(1) + Non-bank financial institutions

(2) + Non- bank financial institutions

Other Total

A) Distribution of Guarantor Financial Institution by type of ownership: Shares on stock market

1 - 1 - - - 2 50 - 50 - - - 100 1.89 - 10 - - - 2.38

Non-traded shares 4 6 4 - 1 - 15 26.67 40 26.67 - 6.67 - 100 7.55 60 40 - 100 - 17.86

Sole proprietorship 10 1 3 1 - 1 16 62.5 6.25 18.75 6.25 - 6.25 100 18.87 10 30 16.67 - 25 19.05

Partnership 26 - - - - 3 29 89.66 - - - - 10.34 100 49.06 - - - - 75 34.52

Limited partnership 12 3 2 5 - - 22 54.55 13.64 9.09 22.73 - - 100 22.64 30 20 83.33 - - 26.19

B) Distribution of Guarantor Financial Institution by type of sector: Food and Beverages 2 - 2 1 - - 5

40 - 40 20 - - 100 3.77 - 20 16.67 - - 5.95

Wood, Paper, Leather and Textiles

6 1 2 - 1 1 11 54.55 9.09 18.18 - 9.09 9.09 100 11.32 10 20 - 100 25 13.1

Chemical, Petrochemical, Plastic, Rubber and Medical care.

28 6 6 1 - 3 44 63.64 13.64 13.64 2.27 - 6.82 100 52.83 60 60 16.67 - 75 52.38

Building Material and Glassware

1 3 - - - - 4 25 75 - - - - 100 1.89 30 - - - - 4.76

Electrical, Machinery, Transport, Tools and Medical equipment.

16 - - 4 - - 20 80 - - 20 - - 100 30.19 - - 66.67 - - 23.81

Total 53 10 10 6 1 4 84

63.1 11.9 11.9 7.14 1.19 4.76 100

Table 2B.25 Value of loan or line of credit by type of sector, annual sales and ownership.

Type Less than 1 million

SAR

Between 1-5

million SAR

Between 6-10

million SAR

Between 10-50 million

SAR

Between 50-100 million

SAR

Total

A) bValue of loan or credit by type of ownership:

Shareholding firm with shares trade in the stock market.

- - 1 1 - 2 - - 50 50 - 100 - - 5 5.26 - 2.47

Shareholding firm with non- traded shares or shares traded privately.

- 3 5 4 3 15 - 20 33.33 26.67 20 100 - 10.71 25 21.05 33.33 18.52

Sole proprietorship 2 10 - - 4 16 12.5 62.5 - - 25 100 40 35.71 - - 44.44 19.75

Partnership - 12 10 4 - 26 - 46.15 38.46 15.38 - 100 - 42.86 50 21.05 - 32.1

Limited partnership 3 3 4 10 2 22 13.64 13.64 18.18 45.45 9.09 100 60 10.71 20 52.63 22.22 27.16

B) Value of loan or credit dependant on annual sales:

1-10 million 4 2 - - - 6 66.67 33.33 - - - 100 80 7.14 - - - 7.41

11-25 million 1 11 - - - 12 8.33 91.67 - - - 100 20 39.29 - - - 14.81

26-51 million - 3 - - - 3 - 100 - - - 100 - 10.71 - - - 3.7

51-100 million - 9 7 1 4 21 - 42.86 33.33 4.76 19.05 100 - 32.14 35 5.26 44.44 25.93

More than 100 million - 3 13 18 5 39 - 7.69 33.33 46.15 12.82 100 - 10.71 65 94.74 55.56 48.15

C) Value of loan or credit by sector:

Food and Beverages - 2 - 3 - 5 - 40 - 60 - 100 - 7.14 - 15.79 - 6.17

Wood, Paper, Leather and Textiles

2 4 5 - - 11 18.18 36.36 45.45 - - 100 40 14.29 25 - - 13.58

Chemical, Petrochemical, Plastic, Rubber and Medical care.

- 20 7 7 7 41 - 48.78 17.07 17.07 17.07 100 - 71.43 35 36.84 77.78 50.62

Building Material and Glassware

- - - 4 - 4 - - - 100 - 100 - - - 21.05 - 4.94

Electrical, Machinery, Transport, Tools and Medical equipment.

3 2 8 5 2 20 15 10 40 25 10 100 60 7.14 40 26.32 22.22 24.69

Total 5 28 20 19 9 81 6.17 34.57 24.69 23.46 11.11 100 100 100 100 100 100 100

Table 2B.26 Value of collateral required for loan or line of credit by type of sector, annual

sales and ownership.

Type %100 of facility value

%101-%125 of facility value

%126-%150 of facility value

%156-%200of facility value

Total

Value of Collateral required of loan or credit by type of ownership:

Shareholding firm with shares trade in the stock market.

1 - - 1 2 50 - - 50 100 3.03 - - 6.25 3.17

Shareholding firm with non-traded shares or shares traded privately.

4 - - 6 10 40 - - 60 100 12.12 - - 37.5 15.87

Sole proprietorship 8 1 2 4 15 53.33 6.67 13.33 26.67 100 24.24 20 22.22 25 23.81

Partnership 9 3 6 - 18 50 16.67 33.33 - 100 27.27 60 66.67 - 28.57

Limited partnership 11 1 1 5 18 61.11 5.56 5.56 27.78 100 33.33 20 11.11 31.25 28.57

Value of collateral required for loan or credit depending on annual sales:

1-10 million 3 1 2 - 6 50 16.67 33.33 - 100 9.09 20 22.22 - 9.52 11-25 million 6 - - 2 8 75 - - 25 100 18.18 - - 12.5 12.7 26-51 million 2 - 1 0 3 66.67 - 33.33 0 100 6.06 - 11.11 0 4.76 51-100 million 2 4 6 4 16 12.5 25 37.5 25 100 6.06 80 66.67 25 25.4 More than 100 million 20 - - 10 30 66.67 - - 33.33 100 60.61 - - 62.5 47.62 Value of Collateral required of loan or credit by sector:

Food and Beverages 2 - - 3 5 40 - - 60 100 6.06 - - 18.75 7.94

Wood, Paper, Leather and Textiles

2 1 4 - 7 28.57 14.29 57.14 - 100 6.06 20 44.44 - 11.11

Chemical, Petrochemical, Plastic, Rubber and Medical care.

17 3 5 10 35 48.57 8.57 14.29 28.57 100 51.52 60 55.56 62.5 55.56

Building Material and Glassware

- 1 - 3 4 - 25 - 75 100 - 20 - 18.75 6.35

Electrical, Machinery, Transport, Tools and Medical equipment.

12 - - - 12 100 - - - 100 36.36 - - - 19.05

Total 33 5 9 16 63 52.38 7.94 14.29 25.4 100 100 100 100 100 100

Table 2B.27 Collateral required for loan or line of credit by type of sector, annual sales

and ownership.

Type

Land, buildings under ownership of the firm

Machinery and equipment including movables

Accounts receivable and inventories

Personal assets of owner

Other forms of collateral

Total

Collateral required for loan or credit by type of ownership:

Shareholding firm with shares trade in the stock market.

2 1 - - 1 4 50.00 25.00 - - 25.00 100 8 6.25 - - 2.22 3.81

Shareholding firm with non-traded shares or shares traded privately.

9 7 1 - 9 26 34.62 26.92 3.85 - 34.62 100 36 43.75 8.33 - 20 24.76

Sole proprietorship 6 - - 6 8 20 30.00 - - 30.00 40.00 100 24 - - 85.71 17.78 19.05

Partnership - 1 6 1 20 28 - 3.57 21.43 3.57 71.43 100 - 6.25 50 14.29 44.44 26.67

Limited partnership 8 7 5 - 7 27 29.63 25.93 18.52 - 25.93 100 32 43.75 41.67 - 15.56 25.71

Collateral required for loan or credit depending on annual sales:

1-10 million 5 - - 2 - 7 71.43 - - 28.57 - 100 20.00 - - 28.57 - 6.67

11-25 million 2 2 - - 11 15 13.33 13.33 - - 73.33 100 8.00 12.50 - - 24.44 14.29

26-51 million 2 1 1 - - 4 50.00 25.00 25.00 - - 100 8.00 6.25 8.33 - - 3.81

51-100 million 4 1 6 5 11 27 14.81 3.70 22.22 18.52 40.74 100 16.00 6.25 50.00 71.43 24.44 25.71

More than 100 million 12 12 5 - 23 52 23.08 23.08 9.62 - 44.23 100 48.00 75.00 41.67 - 51.11 49.52

Collateral required for loan or credit by sector:

Food and Beverages 3 3 - - 2 8 37.50 37.50 - - 25.00 100 12.00 18.75 - - 4.44 7.62

Wood, Paper, Leather and Textiles

4 4 4 3 1 16 25.00 25.00 25.00 18.75 6.25 100 16.00 25.00 33.33 42.86 2.22 15.24

Chemical, Petrochemical, Plastic, Rubber and Medical care.

13 6 - 4 34 57 22.81 10.53 - 7.02 59.65 100 52.00 37.50 - 57.14 75.56 54.29

Building Material and Glassware

- 3 - - 1 4 - 75.00 - - 7.14 100 - 18.75 - - 2.22 3.81

Electrical, Machinery, Transport, Tools and Medical equipment.

5 - 8 - 7 20 25.20 - 40.00 - 35.00 100 20.00 - 66.67 - 15.56 19.05

Total 25 16 12 7 45 105 23.81 15.24 11.43 6.67 42.86 100 100 100 100 100 100 100

Table 2B.28 Main reasons for not applying for loan or line of credit by type of ownership,

annual sales and sector.

Status No need for a loan

Complex procedures

Interest rates were not favourable

Highly collateral

Did not think it would be approved

Other total

Main reasons for not applying for a loan or credit by ownership:

Shareholding firm with shares trade in the stock market.

3 - - - - - 3 100 - - - - - 100 6.12 - - - - - 3.33

Shareholding firm with non-traded shares or shares traded privately.

2 - 1 5 - - 8 25.00 - 12.50 62.50 - - 100 4.08 - 9.09 26.32 - - 8.89

Sole proprietorship 17 1 2 4 1 1 26 65.38 3.85 7.69 15.38 3.85 3.85 100 34.69 25.00 18.18 21.05 100 16.67 28.89

Partnership 9 - 1 2 - 3 15 60.00 - 6.67 13.33 - 20.00 100 18.37 - 9.09 10.53 - 50.00 16.67

Limited partnership 18 3 7 8 - 2 38 47.37 7.89 18.42 21.05 - 5.26 100

36.73 75.00 63.64 42.11 - 33.33 42.22

Main reasons for not applying for a loan or credit depending on annual sales:

1-10 million

10 1 1 2 1 2 17 58.82 5.88 5.88 11.76 5.88 11.76 100 20.41 25.00 9.09 10.53 100 33.33 18.89

11-25 million 14 - 5 8 - - 27 51.85 - 18.52 29.63 - - 100 28.57 - 45.45 42.11 - - 30.00

26-51 million 7 - 4 2 - 1 14 50.00 - 28.57 14.29 - 7.14 100 14.29 - 36.36 10.53 - 16.67 15.56

51-100 million 14 - - - - 1 15 93.33 - - - - 6.67 100 28.57 - - - - 16.67 16.67

More than 100 million 4 3 1 7 - 2 17 23.53 17.65 5.88 41.18 - 11.76 100 8.16 75.00 9.09 36.84 - 33.33 18.89

Main reasons for not applying for a loan or credit by type of sector:

Food and Beverages 9 - 3 - - - 12 75.00 - 25.00 - - - 100 18.37 - 27.27 - - - 13.33

Wood, Paper, Leather and Textiles

9 1 1 8 1 3 23 39.13 4.35 4.35 34.78 4.35 13.04 100 18.37 25.00 9.09 42.11 100 50.00 25.56

Chemical, Petrochemical, Plastic, Rubber and Medical care.

24 - 4 8 - 3 39 61.54 - 10.26 20.51 - 7.69 100 48.98 - 36.36 42.11 - 50.00 43.33

Building Material and Glassware

4 3 3 3 - - 13 30.77 23.08 23.08 23.08 - - 100 8.16 75.00 27.27 15.79 - - 14.44

Electrical, Machinery, Transport, Tools and Medical equipment.

3 - - - - - 3 100 - - - - - 100 6.12 - - - - - 3.33

Total

49 4 11 19 1 6 90 54.44 4.44 12.22 21.11 1.11 6.67 100 100 100 100 100 100 100 100

Table 2B.29 Export payment terms by type of sector and ownership.

Type Payment in advance

Bank draft as sight

Bank draft as time

Letter of credit at sight

Letter of credit at time

Credit Open account

A)Export payment terms by type of ownership:

Shareholding firm with shares trade in the stock market.

60.00 - 20.00 40.00 40.00 20.00 - 54.77 - 44.72 54.77 54.77 44.72 - 30.00 - 20.00 30.00 30.00 20.00 - 3 - 1 2 2 1 -

Shareholding firm with non-traded shares or shares traded privately.

62.07 31.03 34.48 48.28 58.62 44.83 65.52 49.38 47.08 48.37 50.85 50.12 50.61 48.37 24.38 22.17 23.40 25.86 25.12 25.62 23.40 18 9 10 14 17 13 19

Sole proprietors 72.97 40.54 24.32 35.14 29.73 37.84 16.22 45.02 49.77 43.50 48.40 46.34 49.17 37.37 20.27 24.77 18.92 23.42 21.47 24.17 13.96 27 15 9 13 11 14 6

Partnership 51.11 33.33 33.33 28.89 53.33 73.33 22.22 50.55 47.67 47.67 45.84 50.45 44.72 42.04 25.56 22.73 22.73 21.01 25.45 20.00 17.68 23 15 15 13 24 33 10

Limited partnership 75.86 48.28 25.86 31.03 20.69 25.86 39.66 43.17 50.41 44.17 46.67 40.86 44.17 49.35 18.63 25.41 19.51 21.78 16.70 19.51 24.35 44 28 15 18 12 15 23

Other - - 100 100 100 - - - - - - - - - - - - - - - - - - 1 1 1 - -

B)Export payment terms by type of sector:

Food and Beverages 64.29 14.29 42.86 35.71 - - 35.71 49.72 36.31 51.36 49.72 - - 49.72 24.73 13.19 26.37 24.73 - - 24.73 9 2 6 5 - - 5

Wood, Paper, Leather and Textiles

96.43 32.14 35.71 32.14 42.86 78.57 46.43 18.90 47.56 48.80 47.56 50.40 41.79 50.79 3.57 22.62 23.81 22.62 25.40 17.46 25.79 27 9 10 9 12 22 13

Chemical, Petrochemical, Plastic, Rubber and Medical care.

58.33 42.86 15.48 36.90 28.57 45.24 21.43 49.60 49.78 36.38 48.54 45.45 50.07 41.28 24.60 24.78 13.24 23.57 20.65 25.07 17.04 49 36 13 31 24 38 18

Building Material and Glassware

80.95 47.62 19.05 38.10 71.43 14.29 47.62 40.24 51.18 40.24 49.76 46.29 35.86 51.18 16.19 26.19 16.19 24.76 21.43 12.86 26.19 17 10 4 8 15 3 10

Electrical, Machinery, Transport, Tools and Medical equipment.

46.43 35.71 64.29 28.57 57.14 46.43 42.86 50.79 48.80 48.80 46.00 50.40 50.79 50.40 25.79 23.81 23.81 21.16 25.40 25.79 25.40 13 10 18 8 16 13 12

Total 65.71 38.29 29.14 34.86 38.29 43.43 33.14

sd 47.60 48.75 45.57 47.79 48.75 49.71 47.21

variance 22.66 23.76 20.77 22.84 23.76 24.71 22.29

sum 115 67 51 61 67 76 58

Table 2B.30 Foreign currency used by firms during export by ownership, total

sales and sector.

status Freq. US dollars Euro Pound Others

Shares on stock market 5 83.33 21.00 3.00 68.33 Non-traded shares 29 90.00 26.50 10.00 10.00 Sole proprietorship 37 91.18 59.20 60.80 Partnership 45 94.27 15.00 68.33 Limited partnership 58 87.50 27.40 57.50 Other 1 95.00 5.00 10 million and less 2 93.44 26.25 100 11-25 million 38 87.03 37.73 100 26-51 million 13 99.17 10.00 51-100 million 36 90.28 19.71 15.00 More than 100 million 68 89.70 27.00 6.50 4.14 Food and Beverages 14 76.25 62.00 75.50 Wood, Paper, Leather and Textiles 28 90.00 20.42 43.33 Chemical, Plastic, Rubber, Medical 84 91.16 30.74 6.50 68.33 Building Material and Glassware 21 95.95 21.67 5.00 Electrical, Machinery, Tools 28 88.08 19.38 100

Average 90.00 26.00 6.5 55.7

Table 2B.31 Payment method for purchases and sales by type of ownership.

Payment method Freq. Paid for before delivery Paid on delivery Paid for after delivery

A) Purchases of material inputs or services

Shareholding firm with shares trade 5 41.00 0.00 73.75

Shareholding firm with non-traded shares 29 31.21 17.89 61.30

Sole proprietorship 37 55.00 14.41 46.09

Partnership 45 54.89 17.86 41.88

Limited partnership 58 35.11 22.41 62.33

Other 1 10 30 60

mean 44.41 18.33 54.17 se(mean) 2.42 1.71 2.32

sd 30.73 15.95 29.53 variance 944.18 254.46 871.82

B) Firms’ total annual sales of goods or services

Shareholding firm with shares trade 5 25.00 33.33 83.33

Shareholding firm with non-traded shares 29 17.59 15.28 74.83

Sole proprietorship 37 20.33 11.25 79.19

Partnership 45 18.91 21.85 76.33

Limited partnership 58 33.55 28.27 69.31

Other 1 5 - 95

mean 23.68 20.66 74.75 se(mean) 1.52 2.34 1.47

sd 17.68 22.34 18.95 variance 312.68 499.00 359.08

Table 2B.32 Degree of access to finance.

Type . Availability Cost Interest rate

Fees Collateral requirement

(1) No obstacle 41.38 12.75 3.35 14.09 3.35 (2) Minor obstacle 24.14 28.19 30.2 45.64 4.7 (3) Moderate obstacle Freq 14.48 22.15 26.85 27.52 32.89 (4) Major obstacle 12.41 22.82 16.11 0.67 20.81 (5) Very Severe Obstacle 7.59 14.09 23.49 12.08 38.25

Status Freq .

……..…………… ……………..Means………………………………………...

Shares on stock market 5 3.50 2.50 3.00 2.00 4.00 Non-traded shares 29 2.14 2.64 2.96 2.18 4.54 Sole proprietorship 37 2.88 3.17 3.45 2.97 4.48 Partnership 45 1.67 2.95 2.90 2.05 3.75 Limited partnership 58 2.28 3.08 3.62 2.82 3.76

10 million and less 20 2.31 2.76 3.24 2.88 4.00 11-25 million 38 2.68 3.29 3.59 2.97 4.15 26-50 million 13 2.15 3.92 4.31 3.00 3.46 51-100 million 36 2.04 3.46 3.08 2.19 4.19 More than 100 million 68 2.00 2.42 2.93 2.17 3.59

Food and Beverages 14 2.17 3.25 3.08 1.83 3.00 Wood, Paper, Leather, Textiles 28 1.57 2.21 2.79 1.68 3.89

Chemical, Plastic, Rubber, Medical 84 2.48 3.21 3.37 2.87 4.22 Building Material and Glassware 21 2.08 2.76 3.18 2.53 3.06 Electrical, Machinery, Tools 28 2.28 3.20 3.64 2.80 3.80

Mean Mean 2.2 2.97 3.26 2.51 S.E S.E 0.108 0.103 0.099 0.926

F_stat* F_stat* 1.28 5.91 4.68 7.08 Prob> F Prob> F 0.2807 0.0002 0.0014 0.0000

* Calculation of F_stat relies on ANOVA one-way analysis to test the impact of access to finance on export intensity.

Table 2B.33 The supporting capabilities that encourage exportation

Capabilities Mean SE (mean) Variance F-Value p-Value

Foreign Language Ability 3.70 0.04 0.30 1.36 0.26

Multi-Lingual Sales Staff 3.12 0.07 0.70 4.90 0.00

Fax Machine 3.29 0.06 0.65 4.01 0.01

Email 3.95 0.02 0.05 0.38 0.54

Foreign-Language Website 3.74 0.05 0.33 2.81 0.04

Product Information on Web 3.56 0.06 0.62 12.74 0.00

Export Marketing Plan 3.40 0.06 0.50 1.58 0.20

Export Document Preparation 3.19 0.06 0.68 2.29 0.08

Table 2B.34 The main trade barriers for decision-makers in expanding national sales

Variables Mean SE (mean) Variance F-Value p-Value

Low foreign demand 2.16 0.08 1.18 2.42 0.069

Taxes on labour 2.55 0.08 1.01 0.25 0.859

Supply of skilled labour 2.88 0.08 1.00 2.06 0.108

Taxes on capital 2.74 0.10 1.15 2.31 0.081

Access to credit 2.87 0.08 1.08 1.70 0.170

Distribution problems 2.68 0.08 0.99 1.92 0.129

Competitiveness 3.40 0.06 0.60 1.90 0.132

Limited export diversification 2.78 0.07 0.74 0.64 0.590

Inadequate transport links 2.77 0.08 1.02 3.85 0.011

Standards compliance 2.93 0.07 0.89 7.16 0.000

Customs and border procedures 3.02 0.07 0.82 3.86 0.011

Informal restrictions 2.61 0.06 0.68 2.02 0.113

Table 2B.35 The main barriers for decision-makers in expanding the level of exports

Variables Mean SE (mean) Variance F-Value p-Value

Low regional demand 2.61 0.09 1.41 7.62 0.0001

Import tariffs and charges 2.95 0.08 0.95 2.79 0.0424

Port charges/delays 2.86 0.08 0.90 2.92 0.0360

Tariffs or quotas in export markets 2.73 0.11 1.15 6.66 0.0004

Freight charges 2.84 0.08 1.08 8.94 0.0000

Standards compliance 2.88 0.06 0.65 4.48 0.0048

Customs and border procedures 2.99 0.07 0.87 4.77 0.0033

Informal restrictions 2.76 0.07 0.81 2.69 0.0483

Access to credit 3.37 0.06 0.66 0.90 0.4438

Taxes on labour 2.64 0.06 0.50 14.26 0.0000

Supply of skilled labour 2.85 0.06 0.66 19.84 0.0000

Taxes on capital 2.81 0.07 0.45 0.94 0.4245

Cost of exporting 2.91 0.05 0.48 0.33 0.8017

Inadequate transport links 3.01 0.07 0.71 0.78 0.5052

Product quality 3.42 0.06 0.51 5.74 0.0039

Foreign marketing costs 3.04 0.07 0.70 1.00 0.3935

Competitiveness 3.22 0.05 0.49 0.95 0.4170

Limited export diversification 3.02 0.06 0.54 4.61 0.0041

Table 2B.36 Effect of obstacles and barriers on direct exports N Variables Mean SE Variance F-Value p-Value

1 The price competitiveness of a firm’s products 3.34 0.07 0.73 0.83 0.478 2 Freight costs 3.08 0.07 0.79 2.75 0.045 3 Cost of raw materials/components 3.22 0.06 0.65 4.85 0.003 4 Cost of finance 3.26 0.06 0.67 8.23 0.000 5 Lack of skilled staff 2.91 0.07 0.75 6.28 0.001 6 Exchange rate volatility 2.53 0.08 1.04 9.64 0.000 7 Economic conditions overseas 3.37 0.06 0.53 3.5 0.017 8 Demand offshore 3.42 0.05 0.51 3.04 0.051 9 Hidden costs 2.65 0.07 0.78 2.29 0.081 10 Export market risk or taking on more export market risk 2.69 0.07 0.81 2.12 0.100 11 Tariff barriers overseas 2.57 0.07 0.88 3.04 0.031 12 Non-tariff barriers 2.30 0.08 0.93 2.43 0.068 13 Insufficient funds for developing further export markets 2.47 0.07 0.83 3.31 0.022 14 Lack of knowledge about potential export markets 2.62 0.08 1.02 1.7 0.170 15 Lack of export skills/knowledge 2.55 0.08 0.94 4.1 0.008 16 Lack of skills in logistics and knowledge of trade regulations 2.64 0.08 1.03 6.11 0.001 17 Language or cultural barriers 2.40 0.08 1.01 5.22 0.002

Table 2B.37 Ranking of strategic challenges confronting Saudi exporters

Variables Mean SE (mean) Variance Rank

Increasing the current level of exports 5.69 0.31 16.30 2 Maintaining the current level of exports 6.35 0.28 14.17 7 Generating new markets 6.22 0.28 14.12 5 Maintaining the current level of sales in domestic markets 5.81 0.24 10.25 3 Increasing the current level of sales in domestic markets 5.28 0.24 10.29 1 Ensuring adequate raw material supply 6.15 0.26 11.78 4 Obtaining new working capital 6.26 0.25 10.55 6 Providing funds for the current operations 6.38 0.21 7.46 8 Obtaining new capital for plants and equipment 7.03 0.20 7.32 9 Identifying and engaging trained workers 7.43 0.26 11.43 10 Training workers in the skills required 7.92 0.25 10.51 12 Developing a business plan 7.47 0.27 12.62 11

Chapter 3: Main Determinants of Export Intensity: (Influence of ownership, innovation, trade operations, distribution

channels, marketing and export capabilities.)

3.1 Introduction

Economic policies and plans in the Kingdom of Saudi Arabia are seeking to

diversify the country’s sources of income. The country has sought to find an

industrial base to allow it to benefit from the comparative advantages featured by

its economy. Hence, the country encourages current manufacturing firms to export

or find new industries that have the benefit of the availability of raw materials, that

are capital-intensive, enjoy low cost infrastructure (electricity, telecoms, water, and

transportation), have a developed industrial base, offer quality products and

internationally competitive prices. These elements assist the government in

pursuing a policy of export-oriented industrialisation. Therefore, national planning

pays great attention to exporting, which has become a central target. With this aim

in mind, the government has established several institutions and organisations in

order to encourage and assist firms to export. However, non-oil exports are still at a

low level as part of total exports, and this does not correspond to the minimum of

the incentives provided. Official statistics, as presented in chapter 2, report that the

contribution of non-oil exports in the export sector remains weak, as it amounts to

only 15 per cent of the country’s total exports.

There is a lack of literature covering manufacturing behaviour in Saudi Arabia;

in addition, there is a need to examine the reasons for this low non-oil contribution

to total exports in the –case of Saudi Arabia. However, a significant problem with

this kind of analysis is that there is little data available on Saudi Arabia. To this end,

this chapter will analyse some of the factors impacting on non-oil export intensity.

The study methodology framework adopted in this chapter was initially

developed by Fernandez and Nieto (2006)1. The main procedure is to present a

systematic assessment of the Fernandez and Nieto framework as an empirical

1 Fernandez and Nieto (2006), examined a sample of Spanish SMEs obtained from the Survey of Business Strategies (SBS). This is a firm-level panel of data compiled by the Spanish Ministry of Science and Technology from 1991 to 1999. The SBS covers a wide range of Spanish manufacturing firms operating in all industry sectors.

model of the behaviour underlying exports by firms. Our study is based on a specific

questionnaire designed to gather data from Saudi export manufacturing firms, with

the data set containing a number of variables allowing us to expand the Fernandez

and Nieto model. The model generated relies on the Empirical Primary framework

namely the Empirical Export intensity framework. This derived framework gave this

study a wide scope and the capability to explain many of the factors considered to

have important effects on exports intensity, such as ownership, firm size,

innovation, trade operations, sales distribution channels, marketing activities and

export capabilities.

Ownership structure can influence a firm’s export behaviour because it is

related to different grades of risk aversion. Studies such as Fernandez and Nieto,

(2006) and Filatotchev et al., (2008) have discussed and illustrated the main types

of ownership. These studies refer to the roots of ownership of two types: family

and partner (Fernandez and Nieto, 2006). In their analysis some studies expanded

upon this by adding the participation of foreign investment in the firm’s ownership

in order to investigate the influence of whether this participation represents an

active element in driving firms to export (Filatotchev et al., 2008)1.

Much of the literature has discussed the impact of innovation on export

behaviour. There are different ways to measure innovation (Beveren and

Vandenbussche, 2010)2 and some studies touch on the influence of innovation on

firms’ behaviour (Wagner, 2004)3. In our study, innovation is assessed using

variables to measure the effect of locally or internationally recognised quality

certificates and patents registered in Saudi Arabia or abroad on export intensity. At

1 Filatotchev et al. (2008) examined a hand-collected data set of 434 foreign-invested firms in Poland, Hungary, Slovenia, Slovakia and Estonia between 2002 and 2003.. 2 Beveren and Vandenbussche (2010) examined data from an innovation survey for Belgium, obtained from BELSPO (2006). The survey is conducted every four years; the data we used are for the years 2000 and 2004. The population for each survey is selected on the basis of the full population of Belgian firms registered at the National Office for Social Security at the end of the period considered (2000 and 2004). Of these, all firms with at least 10 employees are selected. The full sample of firms in 2000 amounts to 2100 firms, while for 2004 data is available for 3322 firms. 3 The data used by Wagner (2004) was collected in interviews conducted as part of a panel study. The population covered encompasses all manufacturing establishments with at least 5 employees that were active in 1994 in the state of Lower Saxony, one of the 'old' German federal states. The data was collected in personal interviews with the owner or top manager of the firm.

the same time, there are some conceptual grounds for a relationship between a

firm’s size and its export intensity. As the literature review explains, there are firm

characterisations such as region, labour volume and age of the firm to take into

account.

The sectors that are included in our study are the food and beverages sector,

the wood, paper, leather and textiles sector, the chemical, petrochemical, plastic,

rubber and medical care sector, the building material and glassware sector and the

electrical, machinery, transport, tools and medical equipment sector. The

characterisation of trade operations, such as origin of supplies, ways of importing

the firm’s raw materials, export experience and total sales, are considered to be

elements that identify the position of the firm, which gives the relationship

significant control (Enterprise Surveys, 2012).

Another important factor to be considered by a manufacturer who has decided

to enter their firm’s product on the international market is whether the product

should be distributed indirectly or directly. Most sales channel distributions depend

on a firm’s sales force, independent agents, distributors or wholesalers, firm-owned

retail stores and independent retail stores (Leonidou, 1995)1.

Some studies focus on the importance of marketing activities which encourage

firms to contact a foreign buyer or to seek new markets (Vinh and Julian, 2008)2.

There are different types of marketing methods that attract importers. This study

examines trade association participation, trade fair exhibitions, print advertising, TV

or radio advertising, family or personal links, direct mail advertising, firm and

product brochures and using the internet as the most common marketing activities.

Many studies that focus on a firm’s export intensity do not consider its export

support capabilities. Our empirical analysis builds on export capabilities. It can be

1 In the study by Leonidou (1995), primary research provided the main input of the study and consisted of 165 in-depth interviews with different components of the distribution chain, namely manufacturers (17 per cent), distributors/agents (19 per cent), wholesalers (11 per cent) and retailers (53 per cent). It included various types of outlets, such as department stores, supermarkets, boutiques and pharmacies. 2 In the study by Vinh and Julian (2008), data was gathered using a self-administered mail survey of 315 Australian firms involved in exporting. The sample included 315 firms who were a priori identified as being involved in direct exporting, a sample of 133 Australian export ventures.

seen from the literature review that the empirical models provide evidence of the

importance of capabilities such as foreign language capability, multilingual sales

staff, fax machines, e-mail, a foreign-language website, product information on the

web, export marketing plans and export document preparation (Ahmed and Rock,

2012)1. There are good reasons to examine export support capabilities to explain

how a firm's management behaves as regards exports.

To achieve the objective of this thesis, this study examines a cross-sectional

data representative sample of the Saudi Arabian manufacturing sector collected at

the end of 2011. This data provides a very comprehensive and detailed view of

export activity within the country. Moreover, the cross-sectional structure of the

data allows us to better isolate how the variables the study considers important

influenced exports. This work will make five contributions to the current literature.

Firstly, in general, to provide micro-data covering Saudi Arabian manufacturing

behaviour; this data examines the reasons for the low non-oil contribution to total

exports. Secondly, this study aims to test the influence of different determinants on

firms’ behaviour towards exporting. Thirdly, the findings are expected to generate

strong policy implementation. Finally, the study will showcase some indicators for

investors in the industrial sector in Saudi Arabia.

The remainder of the chapter is structured as follows. The next section reviews

existing literature in the area of characterising firms’ exporting behaviours. Section

3 outlines the specifications of the models employed in the analysis. Section 4 will

discuss the results.

3.2 Literature Review

Export behaviour is built principally on two variables: export intensity and

export propensity. Export propensity is measured by a dummy variable (0 or 1) so

that when the firm is an exporter (there are export sales) it takes a value of one and

it takes a value of zero if it does not export (export sales equal zero). The second

variable, export intensity, which the current study relies on as a main dependent

1 In the study by Ahmed and Rock (2012), data for this study was collected through an Internet survey of Chilean manufacturers that export. Of the 480 companies in the sample, 133 responded to the questionnaire.

variable for whole thesis, is a percentage and indicates the proportion of total sales

represented by exports.

However, the question of how firms decide whether or not to export is

discussed based on empirical research that used micro data to focus on the

manufacturing sectors of industrial and developing countries. Studies such as

Baldwin (1988), Bernard and Jensen (1999), Roberts and Tybout (1997) and DAS et

al. (2007) used data from several different countries. These studies investigate the

factors influencing the export decision of the firm. It has been reported that

exporting firms are more efficient than non-exporting firms.

Moreover, in this investigation there are studies that analysed the role of the

sunk costs on exporting. Baldwin (1988) argues that it is natural to consider the

costs associated with entering international markets and that they may have the

character of being sunk in nature. These might include the cost of information

about demand situations abroad or costs of founding a distribution system. It

shows that temporary exchange rate fluctuations can have constant (i.e. hysteresis)

effects on trade quantities and prices. Baldwin (1988) revealed that if market-entry

costs are sunk, sufficiently large real exchange rate shocks can change the domestic

market structure and thereby induce hysteresis.

Roberts and Tybout (1997) empirically addressed the question of entry and

exit costs in the decision to export by the profit maximising firm. It introduced the

idea that large exchange rate swings can cause slowdown effects when market

entry costs are sunk. The results also reveal that exporting experience depreciates

once firms cease servicing foreign markets. After a two-year absence the re-entry

costs are not significantly different from those faced by a new exporter. Roberts

and Tybout (1997) were consistent with the view that an important source of sunk

entry costs for Colombian exporters is the need to accumulate information about

the demand side, information that is likely to depreciate upon exit from the market.

The sunk costs are a significant source of export-market persistence, and both

observed and unobserved firm characteristics also contribute to an individual firm's

export behaviour. Firms that are owned by corporations, or are old or large are all

more likely to export. For firms with "average" observable characteristics and no

past exporting experience, variation in unobserved sources of variance in

profitability can lead to as much as a 36 percentage point difference in the

probability of exporting.

Bernard and Jensen (1999) have documented the superior performance

characteristics of exporting firms compared to non-exporters. It discussed whether

good firms become exporters or whether exporting improves firm performance.

They consider the sources of the substantial performance advantages in exporting

and non-exporting firms. The advantages that they found are substantial: at any

point in time exporters produce more than twice as much output and are 12%–19%

more productive. Their analysis shows that exporters pay higher wages to all types

of workers. The study looks at both the characteristics of firms before they export

and the performance of firms once they enter the international market. The main

finding result is that good firms become exporters. Several years before they

actually ship any goods abroad, future exporters have many of the same, desirable

performance characteristics. The analysis showed that in the years just prior to the

start of exporting, these firms are growing faster than their non-exporting

counterparts. The study presents evidence that exporters have significantly lower

failure rates than non-exporters with comparable characteristics. The results show

that among surviving firms, employment growth is higher in exporters across all

areas. Bernard and Jensen (1999) provide substantial evidence that the export

market is one of substantial dynamism: more than 10% of manufacturing firms

enter or exit every year. Entry and exit are associated with large changes for the

firm. Entry is a time of growth and improved performance, while the firm that stops

exporting is performing poorly. Knowing the export status of a firm today is not

sufficient to identify faster growth in the future. On the other hand, there is

substantial evidence provided by Bernard and Jensen (1999) that success and new

products lead to exporting, and that exporting is associated with growth in firm

size. However, the lack of productivity gains suggest that firms entering the export

market are unlikely to substantially raise their productivity, even if they export

continuously. According to Bernard and Jensen (1999), exporting shows little

evidence of boosting firm productivity. However, exporting does provide expanded

market opportunities for the most productive firms in a sector. As these firms

expand, the overall economy may grow as resources are reallocated from less

productive to more productive activities. Other major potential benefits may be

due to the number of jobs and, through higher firm survival rates, the stability of

those jobs.

DAS et al. (2007) offers an explanation for exchange rate, foreign demand, and

production costs evolve, domestic producers are continually faced with two choices

: whether to be an exporter, and if so, how much to export. It develops a dynamic

structural model of export supply that characterises these two decisions. The model

embodies firm-level heterogeneity in export profits, uncertainty about the

determinants of future profits, and market entry costs for new exporters. Using a

Bayesian Monte Carlo Markov chain estimator, it fits this model to firm-level panel

data on three Colombian manufacturing industries. They obtain profit function and

sunk entry cost coefficients and use them to simulate export responses to shifts in

the exchange-rate process and several types of export subsidies. In each case, the

aggregate export response depends on entry costs, expectations about the

exchange rate process, past exporting experience, and producer heterogeneity.

Export revenue subsidies are far more effective at stimulating exports than policies

that subsidise entry costs.

Özler et al. (2007) investigate the factors influencing the export decision of the

Turkish manufacturing firms over the 1990-2001 period. Their results support the

presence of high sunk costs of entry to export markets, as well as the hypothesis

that the full history of export participation matters for the current export decision.

Moreover, it shows that the effect of past export experience on the current export

decision rapidly depreciates over time: recent export market participation matters

more than participation further in the past. Another important finding shows that

while persistence in exporting helps lower the costs of re-entry today, there are

diminishing returns to export experience.

Van Beveren et al. (2010) found analysed the relationship between firm-level

innovation activities and firms’ propensity to start exporting for firms in a small

open economy. They measured innovation through innovative effort (R&D) as well

as innovative output (product and process innovation). The evidence points to firms

self-selecting into innovation in anticipation of their entry into export markets,

rather than product and process innovation triggering entry into the export market.

These results suggest that governments can foster firm-level innovation through

trade liberalisation.

Although there are many studies in the literature regarding the determinants

of export behaviours of firms, most of them are focused on export propensity while

other studies analyses of export intensity receives very little examination. The

Helpman et al. (2008) study was designed to determine the effect of trade frictions

on trade flows into the intensive and extensive margins. Helpman et al. (2008)

developed a simple model of international trade with heterogeneous firms that are

consistent with a number of formalised features of the data. The analysis model

predicts positive as well as zero trade flows across two combinations of countries,

allowing the number of exporting firms to vary across destination countries. The

results of this study show that the impact of trade frictions on trade flows can be

decomposed into the intensive and extensive margins, as Helpman et al. (2008)

argue, where the former refers to the trade volume per exporter and the latter

refers to the number of exporters. They mentioned that their model earnings a

generalised gravity equation that accounts for the self-selection of firms into export

markets and their impact on trade volumes. The main results show that traditional

estimates are biased and that most of the bias is due not to selection but rather

due to the omission of the extensive margin. Moreover, the effect of the number of

exporting firms varies across country pairs according to their characteristics. This

variation is large and particularly so for trade between developed and less

developed countries and between pairs of less developed countries.

Although economic literature covers a number of issues and relationships

between the determinants that may influence export behaviour, the relationship

between different ownership types and their strategic behaviour towards exports

has been paid scant attention so far. In addition, firm owners’ behaviour is an

important factor that must be taken into consideration when analysing export

intensity. Firms’ resources are one of the important central determinants that can

be influenced by the type of ownership. Their founders or other financial and

non-financial firms usually manage firms. Some literature has specifically covered

the impact of managers on export behaviour in most continental European

countries such as Germany, Spain and Italy, and the rest of the non-Anglo-Saxon

world (Fernandez and Nieto, 2006).

It is interesting to examine different ownership types more accurately. The

differences in firm ownership types have an effect on their strategic behaviour.

Fernandez and Nieto (2006) reported that managers’ levels of equity participation

are an important element in launching an acceptable incentive scheme. In addition,

a firm’s operations will also be affected as a consequence of concentrating

ownership, which relies on ownership behaviour towards availability of resources,

particularly if the firm is managed by non-managerial shareholders such as banks or

institutional shareholders with adequate motivation and information (Filatotchev et

al., 2008). Based on work by Fernandez and Nieto (2006), there are three types of

ownership which identify firms: firms owned by a family, a corporation and a family

with another corporation as a shareholder. One question to be discussed is whether

all types of owners behave in a similar way or whether there are variations that

result in different kinds of management.

Although family ownership has its advantages including long-term orientation,

flexibility, speedy decision-making and family culture and commitment (Poza,

2004), family firms also face disadvantages which are: limited access to the

resources and capabilities needed, especially for the international market and the

ability to sustain a competitive advantage (Kets de Vries, 1996). In addition, in

family firms the division between business and personal objectives often becomes

indistinct (Davis and Tagiuri, 1991). Moreover, family firms can be expected to be

risk-averse as regards the family’s investments because a high proportion of the

owners’ family wealth is invested in the business (Demsetz and Lehn, 1985; Fama

and Jensen, 1985; Donckels and Fröhlich, 1991).

Fernandez and Nieto (2006) explain the advantage of the second type of

ownership, which is the corporate blockholder (i.e. the owner of a large amount of

a company’s shares and/or bonds, or block. In terms of shares, these owners are

often able to influence the company due to the voting rights awarded with their

holding). The corporation can fund the firms or provide guarantees. Moreover,

corporation ownership can be described as a manner to alleviate the problems of

information asymmetry and opportunism in financial markets that makes it difficult

to obtain the finances required to grow (Allen and Phillips, 2000). In addition, the

investments of corporate shareholders are usually diversified making them more

risk-neutral (Nieto, 2001). Furthermore, important advantages can be provided by

corporation ownership including: technological, commercial and organisational

knowledge (Allen and Phillips, 2000). These facilities and funding are essential for

firms to bring their competitive advantage to international markets.

The third type of ownership is a family and corporation. In some family firms,

the family shares the firm’s capital with another company (new shareholder).

Fernandez and Nieto (2006) mentioned two consequences of this. Firstly, this type

of ownership can assist the firm to build up the strategic resources needed to

compete in international markets. These firms can acquire the resources they lack,

including technology resources, labour skills, customer networks etc., and thus

locate themselves in a better position to market. Secondly, this new type of

ownership (original ownership and new shareholder) of these firms will support the

introduction of mechanisms aimed at mitigating and resolving the conflicts of

interest traditionally present in family firms. In addition, this type of ownership

requires formal control schemes to separate family and business systems. Hence,

when a family firm has a corporate shareholder, this is likely to encourage

international expansion (Cooper et al., 1994).

Another aspect within ownership analysis is the phenomenon of female

owners. Robson et al. (2012) argues that female management commonly have

fewer opportunities to develop the relevant experience, they have fewer contacts

and they have greater difficulty in accumulating resources (Cooper et al., 1994).

Saffu and Manu (2004) assert that female-owned firms were more likely to

encounter financial constraints. Brush (1992) suggests that female owners are less

likely than male owners to pursue uniquely economic aims. Owing to the

disadvantages they face, as well as the industrial sectors they select, some female

entrepreneurs are unable to capitalise on identified foreign market opportunities

(Robson et al., 2012).

100

The foreign ownership of firms has been discussed as an important factor that

plays a role in the international market. Hiep and Nishijima (2009) report that

foreign ownership or firms that have direct imports of inputs are found to have

higher export intensity. Even though studies of the effects of ownership on access

to international market strategy are comparatively infrequent, some recent papers

(Rodriguez et al., 2005; Filatotchev et al., 2008) suggest that shareholders generally,

and foreign investors in particular, encourage international expansion of their firms’

portfolios. Moreover, foreign investors have a positive influence on managerial

risk-taking and the extent to which local firms join the international market.

Filatotchev et al. (2008) produced empirical evidence that a large amount of foreign

investment could possibly provide access to the resources needed for restructuring

and improving international activities.

The encouragement of turning family businesses in Saudi Arabia into shared

(public) companies is another step the government has taken in an attempt to build

a strong industry base which supports the contribution of non-oil products to total

exports. The expectation of the contribution of family businesses to the economy is

around 350 billion Saudi Riyals for 2011, which represents more than 25 per cent

total GDP and over 90 per cent of the total non-oil GDP (Sama, 2011). These firms

number more than five thousand and are from all regions and cities in the country.

There are only 156 family firms listed on the Saudi Stock Exchange (CMA, 2011).

The country believes there is an economic benefit to restructuring family business

sets (CMA, 2011). The observed results of the transformation of family businesses

into public stock companies reveal more commitment from family and

administration in order to increase returns to shareholders and continued growth in

terms of sales and profits. The transformation of family businesses into public

companies also leads to on-going work to develop and attract the best talent from

outside the family. Finally, this procedure also facilitates access to sources of

funding and strengthens the company's competitive position (CMA, 2011).

Various studies have evaluated the relationship between export intensity and

innovation. Innovation is an important factor researched by several empirical

studies in an attempt to explain export performance (see Ito and Pucik, 1993;

101

Molero, 1998; Wakelin, 1998; Basile, 2001; Wagner, 2004; Rodriguez et al., 2005;

Beveren and Vandenbussche, 2010). Some of the previous studies use R&D

expenditures to control the prospect action of innovation efforts on exporting

behaviour. Beveren and Vandenbussche (2010) points out two types of innovation

measures have been used in the literature: the first measure is the ratio of R&D

over sales, second; a dummy variable have been used indicating of innovation

output measures. Rodriguez et al. (2005) emphasises that the use of this type of

dummy variables is complemented by other variables measuring whether the firm

undertakes product innovation or not. There are different determinants suggested

to measure the innovation by dummy variable such as; whether firm rely on export

marketing research, if firm registered local patents or International patents.

Wagner (2004) uses dummy variables to measure research and development R&D

intensity, three dummies for a range of groups, and patents (whether or not a firm

registered as a minimum one patent). In addition, Wagner (2004) explains that

R&D, and patents in the export behaviour model, because firms from a highly

industrialized country should have a comparative advantage in new and advanced

goods produced by highly qualified labour. However, some literatures offer

evidence that R&D positively influences export intensity (Gruber et al., 1967;

Cavusgil, 1984; Benvignati, 1990; Braunerhjelm, 1996; Ito and Pucik, 1993; Salomon

and Shaver, 2005). Others find no significant relationship between R&D and export

intensity (Cooper and Kleinschmidt, 1985; Kravis and Lipsey, 1992; Ito and Pucik, 1993).

The influence of firm size on export behaviour has been widely discussed in

previous and contemporary literature. For example, Bilkey (1978), Verwaal and

Donkers (2002), Gourlay and Jonathan (2004), Kundu and Katz (2003), Majocchi et

al. (2005), Lages et al. (2008) and Beveren and Vandenbussche, (2010) report that

firm size is the most important determining impact on different levels of export

intensity. Some studies (e.g. Moini, 1995; Wagner, 1995; Verwaal and Donkers,

2002; Majocchi et al., 2005; Jauhari, 2007; Bezic et al., 2010) have found that the

relationship between firm size and export intensity is a positive, while other studies

state that firm size has little or no influence on export intensity (e.g. Wolf and Pett,

2000; Bonaccorsi, 1992). Conversely, some literature states that there is a negative

102

relationship between firm size and export intensity (e.g., Gripsaud, 1990;

Patibandla, 1995; Moen, 1999; Basile, 2001).

Bernard and Jensen (2004) found that large firms have advantages in terms of

exporting as long as their size is associated with lower average or marginal costs.

Dejo-Oricain and Ramírez-Alesón (2009) report that large firm have advantages

related to their size that makes them more effective in terms of export for four

reasons. Firstly, because they have more funds, labour and material resources

available which are essential for developing and maintaining export schemes

(Cavusgil and Naor, 1987). Secondly, size not only facilitates entry into an

international market but also provides a better ability to respond efficiently to the

demands of international customers (Katsikeas et al., 1995). Their leaders are more

competent and active, capable of appreciating the worth of exporting and of

developing a strategy to export effectively (Tookey, 1964).Thirdly, Samiee and

Walters (1990) state that large firms are more competitive as they are able to

create more economies of scale and hold greater power in the market. Fourthly,

they bear risk because they have easier access to information sources and they

have the ability to resist the impact of international risk (Bonaccorsi, 1992;

Balabanis and Katsikea, 2003). However, the small size of firms is not a barrier to

exporting (Sterlacchini, 2001). Gripsaud (1990) also found that small-sized firms had

a more positive attitude toward exporting than those that were larger in size.

Cooper and Kleinshmidt (1985) found that the export performance of a firm was

related to its size. Smaller firms performed better than larger firms.

The industry affiliation of the firm is another important factor in controlling for

industry effects. The type of sector is essential to an analysis of the export

environment, whereas reflect the other factors in the model on each sector. Also,

analysis determines the impact of reflect each sector on the model. The specific

characterisations for each sector will affect export opportunities (Dejo-Oricain and

Ramírez-Alesón, 2009). In addition, the sector of the firm provides information

about its features level. Nachum and Zaheer (2005) found that the presence of

strong competitiveness in the sector forces pressure on firms to search for new

markets for their output. Another important determinant is location. A firm’s

103

decision regarding location depends on the interaction between production costs

and ease of access to markets (Venables, 1996; Bezie et al., 2010). According to

Koeing (2009), export behaviour is likely affected by agglomeration both positively

and negatively. Location impact analysis indicates rises in congestion in export

infrastructure and greater competition related to exported goods (Bezie et al., 2010).

Research studies that discuss the relationship between firm age and export

behaviour show different empirical results. According to some studies (e.g.

Balabanisand Katsikea, 2003; Majocchi et al., 2005; Bezie et al., 2010), there is no

evidence that age influences export performance. Some empirical studies

(Leonidou, 2000; Welch and Wiedersheim-Paul, 1980) have observed that newly

established firms have more difficulty in overcoming export barriers due to a lack of

organisational resources, managerial experience and market and business

knowledge. Majocchi et al. (2005) and Fryges (2006) found that a firm’s age has

different effects on its export intensity. They found that it has positive effects in

Italy but the opposite was found when examining German and British technology-

oriented firms. Hiep and Nishijima (2009) reported that long-established firms may

have some experience or advantages in terms of export. On the other hand, Hiep

and Nishijima (2009) stated that newly born firms may have higher export intensity

than older ones due to their target of doing business abroad from birth. This

concept suggests that new small- or medium-sized firms would plan from inception

to export products or services as an integral part of their strategy (Kundu and Katz,

2003).

In addition to different types of firm ownership, innovation, size, age, sector

and location, another important factor is a firm’s exports experience. Many studies

(e.g. Bilkey, 1978; Davidson, 1980; Archarungroj and Hoshino, 1998; Erramilli, 1991;

Majocchi et al., 2005; Lages et al., 2008; Bezie et al., 2010) have mentioned the

importance of experience in a firm’s ability to export. Majocchi et al. (2005) stated

that the better the knowledge of international business opportunities, both locally

and internationally, generated by the accumulation of experience, the more the

international involvement of firms increases as time passes. Firms must learn how

to behave in a different market context and, therefore, international experience is

104

very important (Majocchi et al., 2005). In addition, Robson et al. (2012) found that

firms with longer business ownership experience are more likely to export and to

report higher export intensity. In contrast, Bertrand and Mol (2008) argued that

past studies suggest that firms with less export experience are likely to be more

eager to execute international internet marketing activities.

One of the most important issues confronting firms in relation to increasing

exports is suppliers (Enterprise Surveys, 2012). The International Finance

Corporation of the World Bank takes into account the role of raw material sources.

The Enterprise Survey questionnaire, in the manufacturing module of the

questionnaire, divides supply sources into two origins: domestic and foreign.

Domestic supplies are often purchased directly, for example, most firms, in our case

in Saudi Arabia; obtain their raw materials from SABIC. Foreign production imports

are either imported directly or through a local intermediary. The sample shows an

average ratio of 65.8 per cent for domestic supply and 34.2 per cent for foreign

supply. However, firms import 90 per cent of raw materials directly from foreign

supplies, and 10 per cent are purchased from local markets by intermediaries. In

general, the total number of imports to Saudi Arabia represents a high value versus

total Saudi exports. Official statistics indicate that imports represent 43 per cent of

the total volume of exports, noting that oil constituted 85 per cent of total exports

in 2010 (SAMA, 2011).

The distribution channel is another important factor that has an impact on

export behaviour. Distribution is perhaps the most critical way of gaining a

competitive edge in the Saudi market (Leonidou, 1995) because of the valuable

contacts, experiences, specialisations and services that channel intermediaries can

offer in making goods available to end-users (Kaynak, 1984; Leonidou, 1991).

Leonidou (1995) analysed the distribution system in Saudi Arabia, which consists of

local manufacturers, distributors or agents and wholesalers and retailers, each

playing a distinct role. Leonidou (1995) argued that in the absence of a strong

indigenous manufacturing base, distributors or agents play a crucial role.

105

Analysis and discussion of the impact of distribution channels indicates the

importance of taking into consideration the issue of marketing (e.g. Leonidou, 1995;

Johansson, 2000; Salomon and Shaver, 2005; Lages et al., 2008; Ural and Acaravci,

2006). Channels of distribution are marketing intermediaries through which the

product reaches the consumer. The most important tool in export marketing is

trade association participation. Chambers of commerce are particularly useful

associations. The scope of activities of chambers of commerce covers export

promotion by special participation managed by the chambers. The other aims of

export participation are to issue certificates of origin, provide information on

foreign buyers, analyse and supply information about markets abroad, obtain

advantages offered by the government, and organise meetings, seminars and

workshops related to export opportunities. A further aim is to send delegations of

members to potential export countries for survey purposes, to supply information

regarding their products and to discuss common problems in relation to the

accessibility of exports. Other tools used in export marketing target communication

with overseas importers and how to present the products to the final consumer.

Participation in foreign trade fairs and exhibitions is a method of reaching large

numbers of buyers directly, quickly and economically (Leonidou, 1995). The

internet can be used to provide information about the product via webpages, which

allows importers to get product information from the internet and to contact the

exporter (Vinh and Julian, 2008). Bertrand and Mol (2008) mentioned that firms

with little export experience gain more from the use of internet export channels

than do firms with high levels of export experience. Another tool is family and

personal links; this tool allows communication with one or more potential overseas

buyers (Johansson, 2000). A further tool is direct mailing; by this method the

exporter sends sales literature by mail to select or potential buyers. The main

exporters in Saudi Arabia are industrial firms; hence, advertising in newspapers, on

TV and on radio is not an efficient method to reach buyers. One of the most cost-

effective methods is providing foreign markets with company product brochures. In

contrast, print advertising is costly in markets abroad compared to domestic

markets (Leonidou, 1995); firms that use this method of print advertising focus on

individual consumers instead of wholesale buyers.

106

However, Di Maria et al. (2014) analyze whether the firm’s experience,

product and process innovation as well as a clear international marketing strategy

affect firms’ probabilities of entering export markets and their export intensities. In

the aforementioned paper empirically investigates how experience, innovation and

international marketing strategy influence export behavior at the firm level in order

to explore how these determinants act as export drivers for a firm and the

consequences measured in terms of export intensity. It carried out a quantitative

analysis based on a dataset on 582 Italian manufacturing firms observed over the

three-year periods 2001-2003 and 2004-2006. Their results show that the internal

capabilities of a firm to efficiently manage internal processes (productivity)

together with a proactive marketing strategy toward internationalization influence

the decision to enter new foreign markets and to effectively obtain positive

performances (export intensity). Moreover, Oyeniyi (2009) in his study aims at

explaining the effects of firms’ strategic factors on export performance of Nigerian

companies. It reported that the key strategic factors on export and its marketing

plan will cover all aspects of the product, promotion, pricing and distribution. The

most important result of the present study was that marketing strategies was

strongly related to export performance. As such, product adaptation, promotion

adaptation and firm marketing position affected the firm export performance.

Salomon and Shaver (2005) found that investment in advertising does not

significantly affect export behaviour, which is in line with the results of Cavusgil and

Naor (1987). Furthermore, Benvignati (1990) and Kravis and Lipsey (1992) reported

that export sales are negatively related to advertising. Another study by Cavusgil

and Zou (1994) found a negative and moderate relationship between promotion

and export marketing performance. Salomon and Shaver (2005) argued that in both

cases these findings are consistent with Cavusgil and Nevin (1981) view that

advertising does not carry well across domestic borders.

There are other factors that should be taken into consideration during analysis

of export behaviour. Export support capabilities involve a measure of the level of

willingness to export as well as the expenditure on export marketing tools in

relation to their effect on export intensity (Zou and Zao, 2003). From the

107

perspective of resource-based theory (Rodriguez et al., 2005) some studies have

found that generating and sustaining competitive advantages resides in the set of

strategic resources and capabilities available to the firm. Ahmed and Rock (2012)

mentioned that many recent studies have examined the contribution of capabilities

and resources to the achievement of competitive advantage in export markets. He

reported that competitive advantage rooted in export intensity is derived from a

firm’s ability to respond successfully to the external environment. For example,

multi-language skills can significantly improve export success (EC, 2005; Lawless and

Whelah, 2008). Small- and medium-sized enterprises (SMEs) that have a languages

strategy and invest in staff with language skills are shown to be able to achieve

more export sales than those that do not. The EC (2005) reports that the analysis of

survey responses identified some key elements of language management which

were associated with strong export performance, and there could be very

significant gains to the EU economy if all exporting SMEs employed these

techniques. It would, thus be beneficial to support businesses in becoming more

expert at managing language skills and in applying the four elements of language

management, which are: having a language strategy, appointing native speakers,

recruiting staff with language skills and using translators/interpreters. These

elements of language management were found to be associated with successful

export performance (EC, 2005).

Some literature, for example the study done by Ural and Acaravci (2006), uses

websites to represent the level of firms’ commitment to export activities. In our

study the analysis we have used the variable of email as a proxy of the website

variable. Ural and Acaravci (2006) believes that email use is important in regards to

the decision to export or not but not in regards to export intensity, while website

use may reflect the orientation of firms in involving more intensive export activities.

These capabilities provided to firms increase the level of organisation and thus

impact on their export behaviour. Alegre et al. (2012) examined the effect of

organisational learning capability on export intensity. He argued that the concept of

organisational learning capability could provide a useful insight in determining such

management initiatives.

108

3.3 Empirical Models Analysis

Our study concentrates on the relationship between the proportion of exports

in total sales and in firm characteristics. Hence, the dependent variable in the

empirical models of the study is the share of exports in total sales. This

measurement has been widely used in the literature, such as by Kundu and Katz

(2003), Wagner (2004), Majocchi et al. (2005), Fernandez and Nieto (2006), Lages et

al. (2008), Lockett et al. (2008) and others. Most of these studies list the dependent

variable as export intensity.

In our study, all firms have taken the decision to export, whether they are

already exporters or firms intending to export by registering with the Saudi export

programme. The study looks at export firms only and does not take into account

the decision to export or not. However, the average export intensity in the sample

is about 23 per cent. The export intensity shows that more than one fifth of the

output of those Saudi firms was sold in foreign markets (more statistical details on

table 2B.6 –chapter 2-).

In this study, the ordinary least square (OLS) estimation method is applied.

Wagner (2004) argued that if the estimation regress the export/sales ratio on an

independent variable using OLS, there is no room for firm heterogeneity of this

kind. In addition, OLS assumes that the conditional distribution of the export/sales

ratio, given the set of firm characteristics, is homogeneous. This implies that at no

matter what point the conditional distribution is analysed, the estimates of the

relationship between the export/sales ratio (the dependent variable) and the firm

characteristics (the independent variables) are the same.

3.3.1 Model (A): EMPIRICAL REPLICATION FRAMEWORK

The empirical models in the literature review consisted of different

independent variables in a theoretical framework. Fernandez and Nieto (2006)

explained the effects of the different types of ownership. The model distinguishes

between three basic categories: first, a family to which the firm belongs, with one

or more members in managerial positions; second, a corporation in which another

company is a shareholder; third, a family and corporation where the firm belongs to

109

the family, with one or more family members in managerial positions, and with a

corporate shareholder. Table 3.1 panel A presents the variables included in the

Fernandez and Nieto (2006) model, and counterpart variables of our first study

framework that is Model (A) are shown in Table 3.1 panel B.

By relying on the Fernandez and Nieto (2006) model variables, the framework

uses sole proprietorship as a proxy for the family firm in the Model (A). For the

same reason, a variable of SMEs with a corporate blockholder (with at least 5 per

cent of equity holdings) is replaced by a shareholding company, and a family firm

with a corporate blockholder is replaced by a limited partnership with trade shares

on the stock market. The value of these dummy variables is 0 if not chosen as the

type of ownership or 1 if the ownership of the firm is chosen.

The analyses also controlled the model for the following variables: firm age

was measured as the number of years in operation. Internationalisation studies by

Reuber and Fischer (1997), Preece et al. (1998) and Chen and Martin (2001) have

used this variable to control firms’ level of experience and accumulated resources

(Dierickx and Cool, 1989). The size of the firm is measured as the number of

employees in the models to control the possibility that size may influence the

resources available to support firm internationalisation.

Fernandez and Nieto (2006) included variables to measure the

internationalisation process by agreements, alliances and cooperatives (Welch,

1992; Keeble et al., 1998; Lu and Beamish, 2001). International performance can

improve firms by providing resources and mitigating uncertainty. This variable

describes whether the firm has agreements with retailers and wholesalers or not; 0,

1 dummy is used in the models. In the Model (A), a dummy variable is used if firms

use distributors / wholesalers to sell their products.

The sector variable is used to obtain sector characteristics. In the empirical

model this includes the mean export intensity by industry and year; in Model (A) it

is replaced by the mean of each sector. The Fernandez and Nieto (2006) model

takes into account the origin of the corporate shareholder investing in the firm, as

this will influence the behaviour of the firm and its knowledge of international

110

markets. For this reason, in our estimation, the model includes a variable reflect rile

of foreign ownership. Foreign investment in firms is represented by whether a firm

is managed or owned by private foreign individuals, companies / organisations or

not; 0, 1 dummy is used in replicated models. An important factor established by

empirical work is that innovation explains export performance. Model (A) suggests

that this can be represented by three variables measured in the current study. The

study relies on export market research and on registered local and international

patents as a proxy for innovation (Rodriguez et al., 2005).

3.3.2 Estimates of the Model (A)

Table 3.2 and Table 3.3 contain the descriptive statistics and correlations

between variables. The study relies on selected variables in the Table 3.1B Model

(A). The correlation matrix in Table 3.3 presents the independent variables related

to export intensity. The correlation between the independent variables and export

intensity is very low; the highest correlation is 0.27 between export intensity and

local and international patents registered. Also, the correlation matrix shows that

the level of correlation is low between the independent variables themselves.

However, a high correlation is given between the two innovation measures, local

and international patents registered, because they both act as significant drivers of

firms’ behaviour to intensify export. Beveren and Vandenbussche (2010) argued

that the insignificance of the process innovation variable does not reflect its true

impact. Moreover, while including the innovation measures one by one avoids the

multicollinearity issues discussed above, the analysis fails to take into account

potential complementarities between firms’ product and process innovation in

shaping their future export prospects.

The results display four specifications with different sets of independent

variables (i.e. ownership, firm size, innovation and sales distribution channel) for

export intensity to illustrate the influence of each characteristic. The results for the

determinants of export intensity are presented in Table 3.4, which addresses the results

of Model (A).

111

As can be seen from Table 3.4, the F statistics and Chi-square analysis reveals

that the null hypothesis that the regression coefficients are together equal to zero

can be rejected at the 1 per cent significance level for all models’ regressions. As in

Fernandez and Nieto (2006), sole proprietorship in models 1 and 2 are statistically

significant below the 1 per cent level. Moreover, in line with the Fernandez and

Nieto (2006) model, the study also found the coefficient of this variable was

negative and significant, showing that this type of ownership negatively affects

export intensity. The coefficient of the variable that identifies family and limited

ownership variable that proxy by corporate ownership is also negative and

significant in the model; the results show that if a firm is owned by limited

partnership, export intensity is on average between 9.93 and 10.05 per cent lower

across all estimations in Model (A), holding other independents constant. Although

shareholding firm status has a positive coefficient, it is insignificant in these models,

which do not reflect a definitive relationship between shareholding and export

intensity.

Among the independent variables, sector has a positive role in export intensity

in models 1, which is consistent with Westhead et al. (2001) who found differences

in internationalisation across industry sector types. The significant coefficient of

sectors in general leads us to divide the impact of each sector in model 2. Our

Model (A) results show that coefficients of two sectors are positive significant:

chemicals, petrochemicals, plastics, rubber and medical care; and electrical,

machinery, transport, tools and medical equipment.

The results of Table 3.4 reveal that foreign ownership and shareholding firms

are not related to export behaviour, so there is no influence on export intensity in

this model. Moreover, the results show that all of the other variables included in

the model are insignificant; none of the innovation measurements, such as export

marketing plan, patents registered locally or abroad, firm size by labour volume and

age of firm, are shown to be effective.

112

3.3.3 Model (B): EMPIRICAL ENHANCED FRAMEWORK

The Empirical Enhanced Model (Model (B)) is an expansion of the Empirical

Replication Model (Model (A)) and the model by Fernandez and Nieto (2006).

Model (B) relies on multiple regressions, which allow us to add more variables to

the model and estimate their influences on the dependent variable. To analyse the

effects of ownership structure and firm characteristics on export intensity, five

different indicators of firm characteristics (i.e. ownership, firm size, innovation,

trade operations and sales distribution channel) are used as independent variables.

Model (B) presumes that firms’ ownership structure and characteristics could affect

their export intensity, together with other firm-specific covariates. Table 3.5

presents the variables included in Model (B).

Ownership variables in Model (A) consist of three independent variables: sole

proprietorship, limited partnership and shareholding firm with trade shares on the

stock market. In our survey, the analysis benefited from adding more forms of firm

ownership which are partnership and ‘other’ (e.g. philanthropist organisation). Also

the analysis divided shareholding companies according to two variables: those with

trade shares on the stock market and a shareholding firm with non-traded shares or

shares traded privately. The 0, 1 dummy is used for these variables in the empirical

models. Moreover, in our survey, there are six further variables that identify firm

ownership structures and whether a firm is owned privately or by companies /

individuals or domestic of foreign organisations. These variables are used in Model

(A), which analyses the behaviour of the firm and its knowledge of international

markets. The investing in firms in our analysis is a foreign firm represented by

private foreign individuals or companies; 0, 1 dummy is used in the empirical

model. Also in our study, the analysis takes into consideration the impact of female

ownership on export behaviour.

Firm size was classified using labour volume, sector, firm age and main region.

Labour volume refers to the number of employees. Firms were grouped into four

size categories: (1) micro-firms with less than 5 employees; (2) small-sized firms

with 6 to 20 employees; (3) medium-sized firms with 21 to 99 employees; and (4)

113

large firms with more than 100 employees. Because there are no firms in the two

smaller categories, the analysis use dummy variable that size=1 if firm in the large

group otherwise 0. The study identified sector variables to understand the

influence of characteristics for each sector on export behaviour. In the empirical

models, the sector value variable used in the analysis shows the mean of export

intensity by each sector. The sectors contained in the model were: food and

beverages; wood, paper, leather and textiles; chemicals, petrochemicals, plastics,

rubber and medical care; building material and glassware; and electrical,

machinery, transport, tools and medical equipment. The firm age variable looked at

the issue of the impact of firm experience on export intensity and whether long

experience in manufacturing has more of an impact on the level of export than

those younger firms. Main region is included as a variable to show the impact of

services and facilities provided in each region and in particular, to examine the

indirect effect of infrastructure on manufacturing, such as transportation,

electricity, water, services and communications, as well as the flow of this effect on

export procedures. The region variable used in the estimation is the mean region,

which is the mean of export intensity by each region.

Innovation in the study was measured by alternative variables. The aim was to

identify the prospective action of innovation efforts on export behaviour. In our

case, the analysis added two more variables to the three variables used in Model

(A). Locally and internationally recognised certification variables were added to

export marketing plan, patents registered in Saudi Arabia and patents registered

abroad.

The export experience variable is an important factor used in this study to

examine the effects of international experience on export intensity. The length of

export experience was measured by the number of years since the firm started

exporting. Moreover, variables were obtained through surveys concerning what the

firm depends on to run its operation, such as raw materials, origin of supplies and

position of imports. The analysis used two variables: percentage of supplies of

domestic origin and percentage of direct imports. Another important variable that

affects export intensity is total sales of the firm. The discussion point shows

114

whether an increase or decrease of total sales influences export intensity. Total

sales figures are grouped by firm into five size categories: (1) micro-firms with

annual sales of 10 million SAR and less; (2) small-sized firms with sales between 11

million and 25 million SAR; (3) medium-sized firms with sales between 26 million

and 50 million SAR; (4) more-than-medium and less-than-large firms with sales

between 51 million and 100 million SAR; and (5) large firms with sales in excess of

100 million SAR.

The sales distribution channel in the Fernandez and Nieto (2006) model

included one variable to measure the internationalisation process by agreements,

alliances and cooperatives. Wholesalers and retailers can be used in Model (B) as

well as firms’ sales forces, independent agents, firm-owned retail stores and

independent retail stores. These sections of Model (B) allow us to analyse the effect of

the sales distribution channel on export behaviour in Saudi Arabia. Export firms can be

involved in different distribution channels, especially when marketing abroad.

3.3.4 Estimates of Model (B)

Table 3.2 shows the variable definition and descriptive statistics of enhanced

study variables. The correlation matrix in Table 3.3 presents insights into which of

the independent variables are related to export intensity. The highest correlation

with export intensity is for locally-recognised quality certification (0.267) and

internationally-recognised quality certification (0.254). Both correlations are low

and positive. On the other hand, limited partnership firms (0.230) and patents

registered abroad (-0.160) have the highest negative correlations. The correlation

matrix, as shown in Table 3.3, shows the correlation between the independent

variables as being either low or moderate, which suggests the absence of

multicollinearity between independent variables. Only one issue of low correlation

is noted between the age of firm and length of export experience. These correlated

variables play the same role in the regression, which means that the estimation will

use one of them in the model. However, although Model (A) reveals the high

correlation given between the two innovation measures between locally- and

internationally-recognised quality certification, as well as local and international

115

patents registered. Beveren and Vandenbussche (2010) claim the regression

includes all innovation measures because they drive firms into the export market.

Precisely, Beveren and Vandenbussche include two innovation measures in the

regression, which are product and process innovation, although given high

correlation between the two innovation variables, they may both act as significant

drivers of firms’ probability to enter the export market. Moreover, while including

the innovation measures one by one avoids the multicollinearity issues, it fails to

take into account potential complementarities between firms’ product and process

innovation in shaping their future export prospects.

The estimation employed OLS regression (Table 3.5). In this model, the

estimation isolated the effects of firm age and export experience; the aim of this

step is to measure the impact of each variable separately. The following two

models were generated: model 1 analyse the relationship between export intensity

and different firm characteristics depending on age of firm measured by number of

years; model 2 involves export experience instead of firm age, which is measured

by length of export experience. Table 3.5 shows the results of all types of Model (B).

Statistical results show that the F statistics and Chi-square analysis in all

models are significant. The coefficient estimates of firm ownership as shareholding,

internationally-recognised quality certification, age of firm, length of export

experience, independent agents and independent retail stores are all positive and

significant in all models. In contrast, the coefficient estimates of females amongst

owners of the firm, annual sales and supplies of firms dependant on domestic origin

are negative and significant in all models.

Thus, the estimation results show that the firm’s export intensity is, on

average, between 50.46 to 53.3 per cent higher for firms owned by shareholding

ownership (at the 5 per cent significance level). Also, having internationally-

recognised quality certification increases the export intensity by 15.19 to 16.76 per

cent, on average.

The Independent retail stores magnitude between 10.56 and 11.57 points

while independent agents around 5.07 points. The results of the impact of

116

experience show that for each extra year of exports experience, the exports

intensity increases by 0.431 per cent, while a one year change in age of firm results

in between 0.168 per cent increase in exports intensity, holding other independent

variables constant. For firms owned or managed by females, exports intensity is on

average between 8.73 and 9.14 per cent lower, and analysis of annual sales show

that one unit change in total sales results in between -2.22 and 2.70 per cent

decrease in the level of exports intensity. Finally, one per cent increase in supplies

of domestic origin results in between 0.09 and 0.10 per cent increase in exports

intensity. The study also found that the estimated coefficient of non-trade

shareholding firm, partnership amongst types of ownership and main region as well

as locally-recognised quality certification, patents registered abroad amongst

innovation variables, and distributors or wholesalers amongst sales distribution

channels are positive but not statistically significant for export intensity.

Additionally, the coefficient of sole proprietorship and limited partnership foreign

ownership as types of ownership, firm size (measured by labour volume and mean

of sector), market research and patents registered in Saudi Arabia as innovation

variables, imported directly as trade operation and firm sales force and firm-owned

retail stores as sales distribution channels are negative but insignificant for export

intensity.

3.3.5 Model (C): EMPIRICAL EXPORT INTENSITY FRAMEWORK

The Empirical Export Intensity Model (Model (C)) is an extension of Model (B).

In this model, the analysis adds two further groups of indicators, marketing

activities and export capabilities, to the five different indicators of firm

characteristics in Model (B) (i.e. ownership, firm size, innovation, trade operations

and sales distribution channel) which are used as independent variables.

The first group of indicators added to Model (C) is export marketing which

involves providing an offer that attracts buyers. The offer is communicated to the

buyer using sales promotion activities. The activities listed in the questionnaire

include: trade association participation; trade fair exhibitions; print advertising; TV

and radio advertising; family and personal links; direct mail advertising; firm and

117

product brochures; and the internet. These tools are assumed to assist exporters in

attracting customers to their products as well as acting as valuable marketing

communication tools for exporters from developing countries.

The Model (C) framework includes several variables to control the impact of a

firm’s work environment on export intensity. Hence, the export support capabilities

were measured using the responses of managers. The most important support

capabilities factors in regards to export found in the framework are: foreign language

ability; multi-lingual sales staff; fax machine; foreign language website; product

information on the web; export marketing plan; and export document preparation.

3.3.6 Estimates of Model (C)

Table 3.2 presents descriptive statistics for the variables employed in Model

(C): Empirical Export Intensity Framework. The correlation matrix in Table 3.3, in

addition to the correlation discussed in the aforementioned Model (B), did not

show high correlation between explanatory variables and export intensity.

Nevertheless, high correlations between firm age and export experience were

observed (the correlation between firm age and export experience variables was 77

per cent). In order to avoid multicollinearity the empirical analysis for influence of

export intensity (Table 3.6) was carried out using different models. In each case the

first two models combined firm age and sector in total form and each sector’s

separate variables, whereas the third and fourth models combined export

experience with sector and each sector’s separate variables; a similar approach to

dealing with the issue is used by Ma (2002), Qian (2010) and Ganotakis and Love

(2012). The R2 specifications range from 0.59 to 0.67 while adjusted R2 ranges from

0.42 to 0.54. The study obtained the following findings.

The regression coefficient for shareholding firms with trade shares on the

stock market has a positive impact on all Model (C) models among types of

ownership. In contrast, foreign and female ownership has no statistically significant

impact on the level of export intensity. There was support for these results, with

other types of ownership lacking effort and strategy to increase their level of

118

international marketing compared to that of a large firm managed by shareholding

owners.

As regards the role of innovation, the measure of locally-recognised quality

certification has a positive impact on export intensity in model 1 in Table 3.6. This

model relied on the firm age variable rather than on export experience and were

run with the mean of the sector as a whole. Another measure, internationally-

recognised quality certification, also had a positive impact on models 2 and 4 in

Table 3.6. The effect of this variable on export intensity in the aforementioned

models was relied on the firm age variable rather than export experience, as well as

being run with the sector level. This suggests that greater research effort is

potentially reflected in improved product quality, which is significant for both initial

entry and expansion into export markets. However, locally- and internationally-

recognised quality certifications are an input measure of research effort and,

therefore, may not be an accurate indication of innovative activity. Patents

registered abroad or locally amongst innovation measures are insignificant

variables. As shown by Roper and Love (2002), the innovation-export relationship is

sensitive to the measure of innovation (Gourlay and Seaton, 2004). Gourlay and

Seaton (2004) argued that it might be the case that an output measure of

innovative activity would have yielded a different result for the export probability

equation.

The size of the firm, measured by labour, is found in all models to have a

significant and negative impact on export intensity. As explained by Majocchi et al.

(2005), this does not mean that larger firms export less in an absolute sense; larger

export firms may have a large domestic market as well. Iyer (2010) provided

empirical evidence that larger exporting firms may have a large domestic market as

well, pulling down the export intensity. Iyer (2010) argued, in line with Majocchi et

al. (2005), that the empirical findings do not mean that larger firms export less in an

absolute sense. Across all Iyer (2010) models, the estimation appears that firm size

should not be a criterion for the intensity level, given that size is negatively

associated with export intensity. Iyer (2010) does not suggest discrimination against

large firms per se since they might be exporting more in an absolute sense. The

119

export intensity of a firm is positively influenced by the number of export markets

services and, possibly, by product diversification.

In regards to the negative sign of the estimated coefficient of firm size,

Majocchi et al. (2005) found it supported the argument that large firms that could

undertake advertisement expenditure would be able to derive relative advantages

specific to the protected domestic market. Patiblanda (1995) argues that large firms

may have an advantage over small firms in selling their products in the domestic

market. In addition, small firms might be in better situation to take advantage of

information externalities in exports that might take place through inter-firm

linkages. To that end, these firms should be given assistance to break into export

markets and to export at high intensity. The argument provided by Patiblanda,

(1995) is that in the presence of capital market imperfections and sub‐optimal

contractual arrangements, small firms face higher transaction or selling costs in the

domestic market. Consequently, small firms seeking to overcome the mobility

barriers imposed by high transaction costs in the domestic market follow one of the

strategic responses to break into the competitive world market. Small firms that

can recognise a critical level of production efficiency and possible information

externalities that arise through inter‐firm linkages might be the ones able to

succeed in exports.

In the same line of study, Mittelstaedt et al. (2003) found that firms must

achieve a minimum size in order to export successfully. However, Verwaal and

Donkers (2002) found that, in comparison to manufacturing firms with large export

relationships, small firms have even higher export intensities than large firms, as

increases in firm size result in shifts to curves with lower export intensities. With

sizeable export relationships, small firms seem to have a competitive advantage in

exports compared to large firms. Small firms with large export relationships seem to

benefit from their flexibility. For example, Mittelstaedt et al. (2003) states that the

firm size and export intensity relationship is positive if export relationship size is

smaller than approximately 10,000 euros and is approximately flat, and beyond

about 25,000 euros it even becomes negative.

120

All models show sector to be positive and significant in terms of export

intensity. Moreover, the models that run regression by relying on each sector

separately (Models 2 and 4 in table 3.6) show that firms working in the wood,

paper, textiles and leather sector have no impact on export intensity as shown in

the regression coefficients. However, firms working in the other sectors (i.e. the

chemicals, petrochemicals, plastics, rubber and medical care sector, the building

material and glassware sector and the electrical, machinery, transport, tools and

medical equipment sector) have a positive impact on export intensity. Across all

models, the results found that the age of a firm explains export intensity; the

impact of firm age is positive and significant in export intensity for all models.

Similarly, the impact of export experience on export intensity is significant for all

models. This result is supported by Kundu and Katz’s (2003) argument that firms

that have had international experience display stronger export performance.

The results also show the influence of other firm trade operation

characteristics on export intensity, such as annual sales and suppliers. Models 4

show negative signals for annual sales. This result illustrates that a low level of

annual sales by firms increases intensity of export. The influences of other firm

trade operation characteristics on export intensity, such as supplies of domestic

origin, have a significantly positive impact. In contrast, the direct importation of a

firm’s material supplies of foreign origin is statistically insignificant.

In terms of a firm’s distribution of its sales, although 86 per cent of the firms

distribute their products through the firms’ sales forces (Table 2B.9 in panel a), the

regression results in terms of the sector as a whole show a negative significant

relationship due to the fact that pursuing this means of distribution decreases the

level of exports. Moreover, there is a negative impact on the level of export that

results from firm-owned retail stores; firms using this method represent 15 per cent

of the total sample size (Table 2B.9 in panel a). In addition, there is no significant

effect when firms use either independent agents or distributors and wholesalers.

However, the results from firms relying on independent retailers found a positive

influence on export intensity, as shown by models 2 and 4 in table 3.6.

121

Among the marketing promotion activities used by the firms, the study found a

negative impact for trade fair exhibitions in all models. Moreover, the results for

firms that used brochures to promote the firm and its products, showed that, in the

case of models run by sector variable as a whole and in relation to firm age or

export experience in all cases of the regression method (models 1 and 3 in table

3.6), they have a negative impact on export intensity. In contrast, the positive effect

of an increased level of exports was observed when the firm relied on TV and radio

advertising and used the internet. Models that support firms dependant on TV and

radio advertising are significant in export intensity that relies on firm age, export

experience and sector level variables (models 1 and 3 in table 3.6).

The results also show that a foreign language website and export marketing

plan amongst export support capabilities have significant effects on increasing the

level of exports. Models 1 and 3 in Table 3.6, which relied on sector variables

whether in the case of firm age or export experience, show the positive effects of

foreign language websites. Further estimates show that firms with export

marketing plans had a positive impact on export intensity as shown by models 1

and 3 using sector variables in total form,.

In contrast, the results in all models show that firms that paid more attention

to involving staff who had foreign language ability had a negative impact on export.

Based on the interview stage of this study, the negative impact can be attributed to

the fact that the majority of the sample of firms is owned by sole proprietorship or

family or partners and in these firms the authority for all firm activities is typically

the owner. The owner does not empower staffs that have foreign language ability

to market and offer quotes on firm's products. When firms involve these staff it

adds costs to the firm’s budget and entails more bureaucracy which negatively

affects the firm’s behaviour in relation to export. Another reason may be that staffs

holding qualifications in foreign languages do not have marketing skills.

In addition, multi-lingual sales staff in models 1 and 3 in Table 3.6 also had a

negative influence on export intensity. Furthermore, model 2 reported that firms

using email as a support capability to export had a negative impact on export

122

intensity. An argument to explain this negative impact can be provided by relying

on observation during the collection of data. For instance, a firm may depend only

on email as the sole method of marketing without integration with other marketing

methods, or a firm may misuse this method and be unfamiliar with how to market

using it.

3.4 Discussion and conclusion

The objective of this work has been to analyse the effect of different types of

determinants and factors on export behaviour. To achieve this target the study has

adopted two methods and three frameworks to analyse the effects on export

behaviour, which allows us to determine if there is some sensitivity in the variables

measuring export intensity.

This project uses a new survey and unique data generated by a specific

questionnaire. The survey includes details of specific export obstacles that firms

face when selling their products abroad. In addition, the study contains data that

describes the situation and position of 175 firms as a representative sample of

export-manufacturing firms in Saudi Arabia.

Table 3.7 shows the steps taken to derive and build upon the Model (C)

Empirical Export Intensity framework. As a first step, the analysis looks at the

Fernandez and Nieto (2006) model, including the three dummies representing

whether the firm is owned by sole proprietorship, shareholding (i.e. is a

shareholding firm with shares in the stock market), or limited partnership. The

empirical framework covers the following factors: ownership, foreign investment in

firms, innovation represented by patents registered locally or abroad, alliance

measured by whether the firm had agreements with retailers and wholesalers or

not, and controlled the Model (A) by firm age, size measured by number of

employees and mean of sector. In the second step, the study embarked on Model

(B). Once the estimation added the additional variables into the Model (A) the

study expected the model to be more robust. In ownership, the estimation added

three more variables, which were Shareholding Firm with non-traded shares or

shares traded privately, partnership, and females amongst the owners of the firm.

123

The study also controlled the model by use of variables that reflects the impact of

region or location characterization. Regarding innovation, the study found that

locally and internationally recognised certification were important variables which

gave researchers insight allowing an explanation of the influence of innovation on

export intensity. Moreover, the model extended alliances by adding more variables

under the sales distribution channels chain, which were; firm sales force,

independent agents, firm-owned retail stores, and independent retail stores, in

addition to distributors or wholesalers. However, the Model (B) was supported by

four added variables to control the estimate; length of export experience, supplies

of domestic origin, direct imports, and annual sales. In step three, the derivative

model is a component of vital factors in addition to the Model (B). The marketing

activities and export support capabilities appeared as pivotal factors Model (C). The

activities most used to market exports Model (C) are trade association

participation, trade fair exhibitions, print advertising, TV or radio advertising, family

and personal links, direct mail advertising, firm and product brochures, and the

internet. However, the variables of foreign language ability, multi-lingual sales staff,

fax machine, email, foreign language website, product information on the web,

export marketing plan, and export document preparation represented the export

support capabilities.

In our empirical results, the estimation found sole proprietorship and limited

partnership amongst owners are statistically significant and have a negative effect

on export intensity only in the Model (A) estimation, while these kinds of ownership

are insignificant in all the other models, whether Model (B) or Model (C). The

analysis found that this result is reflected in export performance in family-owned

firms; in the literature context, family firms have limited access to the resources

and capabilities needed (e.g. Kets de Vries, 1996; Poza, 2004). One type of owner is

the shareholding firm with shares in the stock market; this type of owner had no

effect in Model (A), but had a positive impact on export intensity in Model (B) and

Model (C). The shareholding firm's results are a repercussion of a larger firm size

that has easier access to information sources, the ability to resist the impact of

international risk, more funds, labour and material resources available, which are

124

essential for developing and maintaining an export scheme. The results show that

females amongst owners of the firm have a negative impact on export intensity in

Model (B), whilst it is insignificant in all Model (C) model estimations. This result is

supported by a female owner as one part of a family firm’s ownership. Moreover,

the results reveal that foreign ownership is not related to export behaviour, which

implies that it does not influence export intensity in our study models.

Firm size, as measured by number of employees, type of sector, and location,

has various impacts on export intensity. Coefficient signal of type of sector has a

significant positive impact in Model (A) and Model (C), the magnitude found in the

Model (A) is less than in Model (C), it is 1.25 point, while in model (C) it is found

between 34.0 and 39.24 point. In contrast, Model (B) where coefficient of type of

sector is not significant. The analysis divided the impact of sector by each sector

separately. The estimation found that the chemicals, petrochemicals, plastics,

rubber, and medical care sector had a positive impact by a magnitude between

24.99 and 25.31 in model (C) depending on firm age and export experience

respectively, the electrical, machinery, transport, tools, and medical equipment

sector also had a positive impact on export intensity by a magnitude 18.92 point in

model (A), and between 33.16 and 37.84 in model (C) depending on firm age and

export experience respectively. Meanwhile the building material and glassware

sector has a positive impact on export intensity in Model (C) estimations, the

magnitude is between 20.85 and 25.56 relying on firm age and export experience

respectively. These results are a reflection of the Saudi economy’s dependence on

oil-related industries.

From our findings, the estimation observed that number of employees as a

measure of firm size has a negative influence on export intensity in Model (C)

estimations, and no significant impact in Model (A) or Model (B) estimations. In

fact, several studies have inferred from the results of their empirical analysis that

the relationship between the number of employees and export intensity is not

significant or are negatively significant. For example, Wolf and Pett (2000) found

that small firms are able to pursue an export strategy by employing a specific skill

base. Another important positive effect on export intensity is the role of innovation.

125

Our results found the positive impact that recognised quality certification has on

Model (B) and Model (C) estimations. Locally recognised quality certification had a

positive effect in model Model (C) relying on the firm age variable rather than

export experience, as well as being run with the mean of each sector in total form.

In addition, internationally recognised quality certification also had a positive effect

in model Model (C) that was run by using both methods relying on the firm age

variable and mean of each sector in total form, in addition to Model (B) estimations

which showed that there were positive effects in all models. However, innovation

had no significantly negative impacts on export intensity.

With regard to the age of firm, it had a positive and highly significant effect on

export intensity in all models except in the replicated model (A). In the same way,

but to a minimum degree, length of export experience used in Model (B) and Model

relying on mean of sector in total form in Model (C) estimations. This result implies

that firm age and export experience played similar roles in the estimations. In

contrast, the location of firms in all models used in our study had no effect on

export intensity. Another aspect of trade operation characteristics of firms is annual

sales; the effects were shown to be negative. All models in Model (B) estimations

supported the exclusion of one model relying on export experience. Similarly, the

models in Model (C) estimations showed annual sales had a negative impact, by

relying on firm age and export experience within both sectors in the level. One

reason for the negative impact of annual sales on export intensity is that firms with

large sales, especially in the local market; does not strive to export more. The

estimations show the influence other firm trade operation characteristics have on

export intensity; supplies of domestic origin are significant with a positive impact in

one model using both methods in Model (C); the mentioned model deals with firm

age and each sector separately. In Model (B) estimations the influence of domestic

origin suppliers is contradictory; the coefficient signal is negative, which shows that

a one unit change in supplies of domestic origin results between 0.09 and 0.10 per

cent in export intensity, but with more robust Model (C) the impact is in line with

economies depending on local raw materials based on petroleum derivatives, the

126

coefficient magnitude for domestic origin suppliers in the mentioned model is

positive and shows a one unit change in supplies of domestic origin results 0.19 per

cent in export intensity. While the direct import of a firms’ raw material supplies

being of foreign origin is statistically insignificant.

The regressions are also controlled by sales distribution channels to measure

the influence of firm alliance on export behaviour (e.g. Kaynak, 1984; Leonidou,

1991; Leonidou, 1995). The estimations show that distributors and wholesalers do

not affect export intensity in any model. Firms relying on independent retail store's

results found a positive influence on export intensity in all models of Model (B)

estimations and in all models in Model (C) depending on each sector separately.

Another positive impact amongst sales distribution channels was felt when firms

relied on independent agent; all models in Model (B) estimation are supported, but

this result is not reflected by Model (C) estimations. In contrast, the negative effect

of the sales distribution channel on the export intensity appear when the firms rely

on their firm sales force, in the Model (C) estimations especially when the model is

controlled by means of sector. Similarly, firms relying on owned retail stores have a

negative impact in Model (C) estimations, except one insignificant model which

relies on export experience and sector in total form.

Analysing the factors that have an impact on export intensity has taken into

account marketing promotion activities. In Model (C) estimations, firms that rely on

TV and radio advertising and use the internet increased their export intensity. In

contrast, the export intensity decreased in firms that depend on trade fair

exhibitions, brochures to promote the firm and its products, and print advertising.

There were negative effects because some firms do not rely on clear strategies for

marketing their products, whilst the study did not find any significance in trade

association participation, family or personal links, and direct mail advertising had an

effect on export intensity in all models in Model (C) estimations.

The additional factors that added to the derivation model for export intensity

is export support capabilities. The results show the significant effects of foreign

language websites, which had a positive impact on export intensity with a

127

coefficient magnitude between 8.13 and 8.65 across all estimations in model (C),

while export marketing plans had a positive impact on export intensity with a

coefficient magnitude between 6.26 and 7.78 point. In contrast, firms paying more

attention to involving staff with foreign language ability as support capabilities to

export had a negative impact on export intensity. The magnitude of coefficient for

involving staff with foreign language ability is between -11.50 points and -15.47

points, while the multi-lingual sales staff coefficient magnitude is -4.00 points. The

other variables that had no significance were fax machine, product information on

the web, and export document preparation. The impact of distribution channels,

marketing activities, and supportive capacity was negative, due to a lack of

marketing expertise or ability to penetrate distribution channels in foreign markets.

Another aspect was the novelty of some firms, or the absence of strong strategies

toward export marketing. The antecedent aspects should be developed by firms to

help promote and grow sales in international markets.

128

Table 3.1 Variables included in replicated study analysis

A) Fernandez and Nieto variable B) Model A variable Value

EXPINT Export sales/total sales The proportion of exports in total sales

per cent

Independent Variables

FAM A Family firm; (0, 1) dummy used in models. Sole proprietorship (0,1) dummy

COR A SME with a corporate blockholder (with at least 5 per cent of equity holdings); (0, 1) dummy used in models

Shareholding firm with trade shares on the stock market

(0,1) dummy

FAMCOR A family firm with a corporate blockholder (with at least 5 per cent of equity holdings); (0, 1) dummy used in models

Limited partnership (0,1) dummy

PID

Total R&D expenditure/total sales (lagged one period)

Export marketing research (0,1) dummy

Local patents registered (0,1) dummy

International patents registered (0,1) dummy

ALLIANC The firm has agreements with retailers and wholesalers; (0, 1) dummy used in models

Distribution channels; Distributors/Wholesalers

(0,1) dummy

AGE Number of years since the first year of firm’s operations until the year of observation

Number of years since the first year of firm’s operations until the year of observation

Number

FOREIGN The company investing in firms is a foreign firm; (0, 1) dummy used in models

Private foreign individuals, companies or organizations

(0,1) dummy

SIZE Number of employees Size=1 for firm in large group (0,1) dummy

SECTOR Mean by industry (sector) and year of EXPINT

Mean of industry (sector) of export intensity

per cent

129

Table 3.2: Variables definition and descriptive statistics

Variable Obs Mean Std. Dev. Min Max Definition

1 Export intensity 159 23.01 17.90 4 90 Export sales on total sales 2 Shareholding Firm (Shares trade) 175 0.03 0.17 0 1 Dummy variable (0,1) 3 Partnership Firm (Non-traded shares) 175 0.17 0.37 0 1 Dummy variable (0,1) 4 Family Firm (Sole proprietorship) 175 0.21 0.41 0 1 Dummy variable (0,1) 5 Partnership Firm 175 0.26 0.44 0 1 Dummy variable (0,1) 6 Limited partnership Firm 175 0.33 0.47 0 1 Dummy variable (0,1) 7 Foreign investment 175 0.10 0.30 0 1 Dummy variable (0,1) 8 Main Region 175 20.60 3.97 4 29.6 Means 9 Central 175 0.56 0.497 0 1 Dummy variable (0.1)

10 Eastern 175 0.20 0.405 0 1 Dummy variable (0.1 11 Size 175 0.75 0.43 0 1 Dummy variable (0,1) 12 Mean of sector 175 0.30 0.17 0.08 0.48 Means 13 Age of firm 175 21.81 10.37 2 62 Number 14 Export experience 161 13.56 6.85 1 31 Number 15 females amongst the owners of the firm 166 0.27 0.45 0 1 Dummy variable (0,1) 16 Locally-recognised quality certification 170 0.75 0.44 0 1 Dummy variable (0,1) 17 Internationally-recognised quality certif. 171 0.74 0.44 0 1 Dummy variable (0,1) 18 Patents registered abroad 166 0.05 0.21 0 1 Dummy variable (0,1) 19 Patents registered in Saudi Arabia 169 0.06 0.24 0 1 Dummy variable (0,1) 20 Supplies of domestic origin 175 65.83 25.01 4 100 Percentage 21 Imported directly 171 90.32 19.74 5 100 Percentage 22 Annual sales 175 4.53 1.49 1 6 Category variable (1-6) 23 Firm Sales Force 175 0.86 0.34 0 1 Dummy variable (0,1) 24 Independent Agents 175 0.22 0.41 0 1 Dummy variable (0,1) 25 Distributors/Wholesalers 175 0.38 0.49 0 1 Dummy variable (0,1) 26 Firm -Owned Retail Stores 175 0.15 0.36 0 1 Dummy variable (0,1) 27 Independent Retail Stores 175 0.09 0.28 0 1 Dummy variable (0,1) 28 Trade Association Participation 175 0.31 0.46 0 1 Dummy variable (0,1) 29 Trade Fair Exhibition 175 0.86 0.34 0 1 Dummy variable (0,1) 30 Print Advertising 175 0.55 0.50 0 1 Dummy variable (0,1) 31 TV/Radio Advertising 175 0.13 0.34 0 1 Dummy variable (0,1) 32 Family/Personal Links 175 0.18 0.39 0 1 Dummy variable (0,1) 33 Direct Mail Advertising 175 0.18 0.39 0 1 Dummy variable (0,1) 34 Firm & Product Brochures 175 0.86 0.34 0 1 Dummy variable (0,1) 35 Internet 175 0.75 0.44 0 1 Dummy variable (0,1) 36 Foreign Language Ability 164 3.70 0.54 2 4 Category variable (1-4) 37 Multi-Lingual Sales Staff 164 3.12 0.83 1 4 Category variable (1-4) 38 Fax Machine 164 3.29 0.81 1 4 Category variable (1-4) 39 Foreign Language Web Site 158 3.74 0.58 1 4 Category variable (1-4) 40 Product Information on Web 158 3.56 0.79 1 4 Category variable (1-4) 41 Export Marketing Plan 161 3.40 0.71 1 4 Category variable (1-4) 42 Export Document Preparation 161 3.19 0.82 1 4 Category variable (1-4)

130

Table 3.3: Correlation matrix of empirical models

1 2 3 4 5 6 7 8 9 10 11 12 13

1 Export intensity 1

2 Shareholding Firm 0.12 1

3 Non-traded Shares Firm 0.09 -0.08 1

4 Family Firm (Sole proprietorship) -0.09 -0.09 -0.23 1

5 Partnership Firm 0.20 -0.10 -0.26 -0.30 1

6 Limited partnership Firm -0.23 -0.12 -0.31 -0.36 -0.41 1

7 Foreign investment 0.07 -0.06 0.11 -0.03 -0.02 -0.03 1

8 Main Region 0.15 0.20 0.17 -0.28 -0.03 0.07 0.02 1

9 Labour Volume 0.00 0.02 0.21 -0.34 0.05 0.06 -0.02 -0.10 1

10 Mean of sector 0.12 -0.13 0.05 0.12 0.11 -0.19 -0.01 0.04 0.01 1

11 Age of firm 0.01 -0.01 0.09 0.02 -0.11 0.01 -0.06 0.02 0.31 -0.07 1

12 Export experience 0.18 0.14 0.18 -0.15 -0.04 -0.03 0.02 0.16 0.44 -0.11 0.77 1

13 Females amongst the owners of the firm 0.17 0.13 -0.03 -0.26 0.27 -0.01 -0.12 0.15 0.17 0.14 0.08 0.23 1

14 Locally-recognised quality certif. 0.27 -0.06 0.19 -0.38 0.20 0.02 0.05 -0.02 0.52 0.04 -0.03 0.15 0.17

15 Internationally-recognised certif. 0.25 -0.06 0.19 -0.37 0.21 0.00 0.06 -0.07 0.64 0.15 0.06 0.21 0.18

16 patents registered abroad -0.14 -0.04 -0.02 0.01 -0.13 0.14 -0.07 0.14 -0.15 0.04 -0.08 -0.17 0.06

17 patents registered in Saudi Arabia -0.16 -0.04 -0.04 -0.07 -0.14 0.24 -0.08 0.17 -0.06 0.13 0.06 -0.07 0.18

18 Supplies of domestic origin -0.03 0.11 -0.05 0.19 -0.10 -0.05 -0.04 0.16 -0.07 0.01 0.01 0.12 -0.02

19 Imported directly 0.20 0.08 0.10 -0.29 0.08 0.07 0.15 0.07 0.24 -0.10 0.16 0.26 0.00

20 Annual sales 0.05 -0.06 0.33 -0.33 0.01 0.03 0.00 0.00 0.61 0.06 0.24 0.35 0.26

21 Firm Sales Force -0.01 -0.03 0.04 0.08 0.04 -0.14 -0.09 0.00 0.18 0.24 0.17 0.16 0.23

22 Independent Agents -0.02 0.08 0.03 -0.07 -0.25 0.25 -0.13 0.00 0.09 -0.08 -0.10 -0.07 0.15

23 Distributors/Wholesalers 0.01 0.08 0.16 -0.29 -0.08 0.18 -0.02 -0.06 0.11 -0.14 -0.11 0.04 0.00

24 Firm -Owned Retail Stores -0.05 -0.07 0.19 0.09 -0.14 -0.07 0.07 0.02 0.07 0.16 0.23 0.22 0.04

25 Independent Retail Stores 0.11 -0.05 0.03 0.09 -0.04 -0.04 -0.10 -0.17 0.16 0.27 0.16 0.13 0.09

26 Trade Association Participation 0.18 0.11 0.07 -0.26 0.26 -0.10 -0.05 0.11 0.26 0.07 0.17 0.30 0.67

27 Trade Fair Exhibition 0.04 -0.13 0.00 -0.12 0.04 0.10 -0.04 0.16 0.08 0.14 0.25 0.19 -0.12

28 Print Advertising -0.14 0.15 0.09 -0.13 0.08 -0.08 0.06 0.06 0.02 -0.09 -0.02 -0.08 0.09

29 TV/Radio Advertising -0.01 0.24 0.24 -0.20 -0.15 0.05 0.10 0.20 0.21 -0.16 0.23 0.30 0.08

30 Family/Personal Links -0.12 0.10 -0.05 0.19 -0.01 -0.14 -0.01 0.02 -0.22 0.05 -0.09 -0.13 -0.11

31 Direct Mail Advertising -0.04 0.10 -0.21 -0.03 0.20 -0.02 0.14 -0.02 -0.16 -0.04 -0.32 -0.31 -0.11

32 Firm & Product Brochures 0.04 -0.03 0.04 -0.36 0.23 0.07 0.13 0.08 0.08 -0.06 -0.19 -0.06 -0.19

33 Internet 0.02 -0.06 -0.06 0.20 -0.05 -0.07 -0.03 -0.08 -0.05 -0.14 0.17 0.15 -0.27

34 Foreign Language Ability 0.13 0.08 0.10 -0.15 -0.01 0.04 -0.10 0.06 0.09 0.12 0.23 0.16 0.11

35 Multi-Lingual Sales Staff -0.22 0.03 0.03 0.14 -0.29 0.12 -0.02 -0.12 -0.05 0.15 0.08 0.04 -0.09

36 Fax Machine -0.11 0.01 0.08 0.20 -0.16 -0.10 -0.12 0.12 -0.20 -0.16 0.01 0.03 -0.12

37 Email 0.05 0.03 -0.10 -0.01 0.15 -0.07 -0.20 -0.04 -0.07 0.13 0.03 -0.02 0.14

38 Foreign Language Web Site 0.22 0.06 -0.14 -0.04 0.16 -0.04 -0.04 -0.03 0.07 -0.01 0.03 0.05 0.09

39 Product Information on Web -0.26 0.08 -0.03 0.08 -0.31 0.22 -0.01 -0.07 0.16 -0.11 0.12 0.05 -0.15

40 Export Marketing Plan -0.05 0.12 0.06 0.02 -0.22 0.09 -0.06 0.04 0.08 -0.30 0.17 0.07 -0.12

41 Export Document Preparation -0.09 0.14 0.06 0.08 -0.28 0.10 -0.10 0.03 0.13 -0.01 0.22 0.17 -0.01

131

Continued Table 3.3: Correlation matrix of empirical models

14 15 16 17 18 19 20 21 22 23 24 25 26

15 Internationally-recognised

certif.

0.86 1

16 Patents registered abroad 0.07 0.07 1

17 Patents registered in Saudi -0.02 -0.02 0.93 1

18 Supplies of domestic origin -0.17 -0.09 -0.06 0.08 1

19 Imported directly 0.24 0.41 0.12 0.13 -0.10 1

20 Annual sales 0.51 0.64 -0.07 -0.05 -0.23 0.27 1

21 Firm Sales Force -0.08 0.11 0.09 0.09 0.24 0.02 0.01 1

22 Independent Agents 0.12 0.12 0.08 0.05 0.05 0.07 0.23 0.01 1

23 Distributors/Wholesalers 0.24 0.22 0.05 -0.05 0.07 -0.05 0.23 -0.14 0.28 1

24 Firm-Owned Retail Stores 0.14 0.14 0.15 0.11 0.06 0.17 0.20 -0.01 0.08 -0.01 1

25 Independent Retail Stores 0.18 0.18 -0.07 -0.08 0.02 -0.01 0.14 0.12 0.14 -0.07 0.49 1

26 Trade Association Participation 0.19 0.20 -0.03 0.10 0.08 0.09 0.21 0.12 0.07 0.02 -0.11 -0.07 1

27 Trade Fair Exhibition 0.19 0.19 0.09 0.10 -0.03 0.27 0.20 -0.16 0.13 0.17 0.08 0.12 0.12

28 Print Advertising 0.20 0.08 0.15 0.08 0.07 -0.03 -0.05 -0.02 0.11 0.22 -0.16 -0.18 0.28

29 TV/Radio Advertising 0.15 0.15 0.18 0.14 0.10 0.19 0.25 -0.19 0.16 0.19 0.26 -0.12 0.44

30 Family/Personal Links -0.03 -0.20 -0.11 -0.12 0.12 -0.30 -0.27 -0.16 -0.25 0.15 0.21 0.12 -0.12

31 Direct Mail Advertising 0.07 -0.09 -0.10 -0.11 0.09 -0.28 -0.16 -0.07 -0.03 0.21 -0.16 -0.14 -0.09

32 Firm & Product Brochures 0.15 0.15 0.09 0.10 0.02 0.40 0.03 -0.16 -0.11 0.14 -0.29 -0.53 0.09

33 Internet -0.19 -0.19 -0.13 -0.09 0.46 -0.14 -0.23 0.19 -0.01 -0.01 -0.04 -0.10 -0.07

34 Foreign Language Ability 0.27 0.26 0.01 0.09 0.15 -0.12 0.14 0.06 -0.01 0.21 -0.05 0.17 -0.10

35 Multi-Lingual Sales Staff 0.15 0.08 0.17 0.15 0.04 -0.13 0.05 0.00 0.05 0.18 -0.02 -0.02 -0.05

36 Fax Machine -0.08 -0.14 0.05 -0.07 0.32 -0.17 -0.15 0.06 0.17 -0.10 0.14 0.23 -0.19

37 Email 0.00 -0.01 -0.09 0.06 0.10 0.02 -0.06 0.05 -0.07 -0.03 -0.20 0.08 0.05

38 Foreign Language Web Site 0.14 0.02 0.11 0.12 -0.19 -0.15 0.03 0.21 0.01 0.13 -0.35 0.03 0.04

39 Product Information on Web -0.10 -0.14 0.13 0.14 0.05 -0.21 0.03 0.23 0.19 0.17 -0.15 0.10 -0.04

40 Export Marketing Plan 0.13 0.11 0.19 0.00 -0.06 -0.04 0.20 -0.23 0.20 0.25 -0.03 -0.06 -0.04

41 Export Document Preparation 0.18 0.16 0.05 -0.03 0.03 -0.13 0.20 -0.10 0.04 0.06 0.01 0.03 0.07

Continued Table 3.3: Correlation matrix of empirical models

27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

28 Print Advertising 0.24 1

29 TV/Radio Advertising 0.16 0.35 1

30 Family/Personal Links 0.10 0.13 -0.14 1

31 Direct Mail Advertising 0.10 0.42 -0.10 0.39 1

32 Brochures 0.13 0.18 0.16 -0.24 0.15 1

33 Internet 0.11 0.04 -0.09 0.27 0.27 0.08 1

34 Foreign Language Ability -0.15 -0.16 -0.10 0.06 -0.15 -0.21 -0.05 1

35 Multi-Lingual Sales Staff 0.03 0.10 0.00 0.22 0.01 -0.10 0.17 0.28 1

36 Fax Machine -0.08 0.01 -0.04 0.18 -0.09 -0.31 0.30 0.12 0.33 1

37 Email -0.08 -0.10 -0.21 0.11 -0.09 -0.09 0.00 0.41 0.29 0.12 1

38 Foreign Language Web Site 0.09 -0.01 -0.13 -0.09 0.02 -0.06 -0.01 0.41 0.11 -0.06 0.41 1

39 Product Information on Web 0.07 0.09 0.03 0.09 0.07 -0.13 0.19 0.20 0.34 0.17 0.24 0.50 1

40 Export Marketing Plan 0.14 0.23 0.14 0.02 0.02 -0.08 -0.09 0.16 0.31 0.16 0.10 -0.05 0.25 1

41 Export Document Preparation 0.04 0.21 0.10 0.11 -0.07 -0.16 -0.04 0.21 0.55 0.34 0.19 -0.10 0.28 0.69 1

132

Table 3.4 Estimation of Model (A)

Dependent Variable: Model 1 Model 2 Export intensity Coef. t_stat Coef. t_stat

Ownerships: Family Firm -10.92 -2.87** -11.03 -2.82** Shareholding Firm 11.48 1.16 11.47 1.13 Limited partnership Firm -10.05 -3.12** -9.937 -2.98** Foreign investment 6.625 1.37 6.654 1.35 Innovation: Research and development 0.545 0.19 0.832 0.24 Patents registered abroad -13.65 -0.79 -13.39 -0.76 Patents registered in Saudi Arabia 28.07 1.52 28.01 1.49 Distribution Channel: Distributors/Wholesalers -5.421 -1.67 -5.375 -1.62 Firm size: Firm Age 0.087 0.59 0.0916 0.61 Size -3.525 -0.93 -3.605 -0.93 Mean of sector 1.25 4.54*** sector of (Food and beverages) 1.541 0.23 sector of (Chemical, Petrochemical, …) 7.337 1.33 sector of (Building Material, …) 13.36 3.42*** sector of (Electrical, Machinery, …) 18.92 3.42*** Constant -11.46 -1.11 6.373 0.75

Observation 152 152 R2 0.243 0.2427 Adj R2 0.183 0.165 F_stat 4.075 3.136 Prob> F 0.00 0.0003

*** Significant < 0.01 ** Significant < 0.05 * Significant < 0.10 “ Note that 152 observations were used in the analysis, rather than the full 175, because we restricted the sample and removing non-response categories such as ‘do not know,’ ‘no answer,’ ‘not applicable (more details on page 210 ).

133

Table 3.5: Estimation of Model B Empirical Enhanced Framework

Model specification Export intensity Model 1 Model 2 Coef. t_stat Coef. t_stat

Ownership Shareholding trade on the stock market firm 50.46 2.92*** 53.3 3.12*** Shareholding with non-traded shares firm 2.43 0.2 5.554 0.46 Sole proprietorship firm -5.308 -0.44 -2.759 -0.23 Partnership firm -1.668 -0.14 1.521 0.13 Limited partnership firm -12.36 -1.02 -8.83 -0.73 Foreign investment -3.198 -0.7 -4.874 -1.07 Females amongst the owners of the firm -8.736 -2.45** -9.149 -2.62**

Firm size , sector and location

Central -3.551 -1.26 -2.905 -1.06 Eastern -0.51 -0.13 -2.311 -0.57 size 0.937 0.21 -1.021 -0.22 sector of (Food and beverages) 7.895 1.31 7.34 1.24 sector of (Building Material, …) 5.612 1.1 8.001 1.58 sector of (Chemical, Petrochemical, …) 3.619 0.94 4.895 1.27 sector==5 (Electrical, Machinery,…) 16.06 3.02*** 17.1 3.47*** Age of firm 0.168 1.21

Innovation Locally-recognised quality certification -1.098 -0.19 -1.773 -0.32

Internationally-recognised quality certification 15.19 1.87* 16.76 2.08** Marketing Research -2.344 -0.79 -1.954 -0.67 Patents registered abroad -10.66 -0.76 -9.209 -0.67 Patents registered in Saudi Arabia 18.37 1.28 16.11 1.14

Trade operation

Supplies of domestic origin -0.0919 -1.39 -0.106 -1.62 Imported directly 0.011 0.15 0.00284 0.04 Annual Sales -2.221 -1.77* -2.708 -2.14** Length of export(export experience)

0.431 2.14**

Distribution channels

Firm Sales Force -0.825 -0.15 -0.885 -0.17 Independent Agents 5.073 1.59 5.072 1.64 Distributors/Wholesalers -0.822 -0.29 -1.634 -0.59 Firm -Owned Retail Stores -4.258 -1.05 -3.472 -0.89 Independent Retail Stores 11.57 2.70*** 10.56 2.47**

Constant 17.48 1.05 16.97 1.04

Observations 138

138

R-squared 0.5114

0.5247

Adjusted R-squared 0.386

0.403

F 4.074

4.298

Prop > F 0.00 0.00

Significant *** p<0.01, ** p<0.05, * p<0.1, “ Note that 138 observations were used in the analysis, rather than the full 175, because we restricted the sample and

removing non-response categories such as ‘do not know,’ ‘no answer,’ ‘not applicable. Moreover, with added more

independent variables lead to drop more observations more details on page 210 .

134

Table 3.6 Estimation of Model (C)

Model specification

Model 1 Model 2 Model 3 Model 4

Coef t_value Coef t_value Coef t_value Coef t_value

Ownerships Shareholding Firm 37.35 2.16** 30.92 1.86* 36.91 2.08** 33.29 2.01** Partnership Firm 1.45 0.12 -3.37 -0.28 -0.30 -0.02 -1.88 -0.15 Family Firm -12.79 -1.01 -14.11 -1.15 -11.19 -0.86 -11.55 -0.95 Partnership Firm -3.82 -0.32 -9.65 -0.81 -3.37 -0.27 -7.71 -0.65 Limited partnership Firm -13.91 -1.15 -16.51 -1.39 -13.48 -1.09 -13.70 -1.15 foreign investment -0.64 -0.13 0.02 0.00 -2.18 -0.41 -2.89 -0.58 females amongst the owners -7.30 -1.54 -5.72 -1.08 -7.94 -1.62 -6.21 -1.18

Firm size, location and sector

Central -4.07 -1.38 -0.63 -0.21 -3.90 -1.28 0.73 0.24 Eastern -3.63 -0.90 1.72 0.40 -4.22 -1.01 0.92 0.21 Labour Volume -11.73 -2.30** -10.98 -2.25** -11.70 -2.23** -11.76 -2.40** Mean of sector 39.24 2.84***

34.00 2.44**

sector==2(wood, Paper, Textiles,)

8.19 0.80

8.21 0.81

sector==3 (Chemical, Petrochemical.)

24.99 2.68***

25.31 2.74*** sector==4 (Building Material,…)

20.85 1.90*

25.56 2.28**

sector==5 (Electrical, Machinery,…)

33.16 3.19***

37.84 3.58*** Age of firm 0.45 2.34** 0.39 2.12**

Innovation Locally-recognized quality certify. 13.20 1.73* 5.74 0.74 9.93 1.28 0.99 0.13

Internationally-recognized qu. certif. 12.09 1.14 21.71 2.08** 11.69 1.07 25.59 2.41** patents registered abroad -5.53 -0.37 -20.62 -1.39 4.00 0.27 -18.07 -1.26 patents registered in Saudi Arabia 7.50 0.48 34.37 2.09** -0.52 -0.03 33.57 2.07**

Trade operations and Experience

length of export 0.34 1.34 0.59 2.44** Supplies of domestic origin 0.05 0.55 0.19 1.80* 0.00 0.00 0.16 1.63 Imported directly 0.03 0.28 0.01 0.07 0.03 0.27 0.02 0.19 annual sales -0.55 -0.41 -2.25 -1.67* -0.33 -0.25 -2.87 -2.06**

Sales Distribution channel

Firm Sales Force -18.38 -2.99*** -9.09 -1.07 -13.99 -2.41** -6.42 -0.80 Independent Agents 0.84 0.20 -0.28 -0.05 0.00 0.00 -0.65 -0.13 Distributors/Wholesalers 2.68 0.75 -1.81 -0.46 2.00 0.54 -4.01 -1.01 Firm -Owned Retail Stores -8.98 -1.37 -11.19 -1.74* -4.96 -0.77 -8.23 -1.32 Independent Retail Stores 8.12 1.21 16.74 2.31** 7.95 1.15 16.61 2.31**

Marketing activities

Trade Association Participation -4.22 -0.82 -2.03 -0.41 -3.04 -0.58 -1.17 -0.24 Trade Fair Exhibition -22.14 -4.05*** -27.52 -4.06*** -21.06 -3.79*** -27.60 -4.11*** Print Advertising -1.92 -0.58 -2.93 -0.89 -0.62 -0.18 -1.82 -0.56 TV/Radio Advertising 2.88 0.52 10.73 1.83* 2.65 0.46 11.04 1.90* Family/Personal Links -7.05 -1.29 -1.54 -0.24 -8.47 -1.51 -0.36 -0.06 Direct Mail Advertising 0.64 0.13 1.05 0.22 -0.73 -0.15 1.62 0.34 Firm & Product Brochures -15.79 -2.31** -4.17 -0.55 -17.01 -2.45** -3.20 -0.42 Internet 13.74 2.59** 4.14 0.70 16.25 3.07*** 2.59 0.43

Export capabilities

Foreign Language Ability -14.26 -3.49*** -15.47 -3.80*** -11.50 -2.93*** -14.26 -3.67*** Multi-Lingual Sales Staff -4.00 -1.77* -2.98 -1.20 -2.92 -1.31 -2.06 -0.87 Fax Machine -1.79 -0.69 -1.89 -0.74 -2.51 -0.96 -2.30 -0.93 Foreign Language Web Site 8.13 1.71* 5.46 1.20 8.65 1.79* 5.19 1.15 Product Information on Web 0.46 0.14 1.42 0.41 -0.26 -0.08 2.02 0.59 Export Marketing Plan 6.26 1.83* 2.75 0.81 7.78 2.29** 2.96 0.89 Export Document Preparation 0.60 0.20 0.71 0.22 -0.06 -0.02 0.12 0.04

Constant 53.22 2.06** 46.58 1.51 44.14 1.70* 36.96 1.20

Observations2 132 132 132 132

R2 0.60

0.66

0.59

0.67

Adjusted R2 0.44

0.50

0.41

0.51

F 3.61

4.12

3.38

4.21

Significant *** p<0.01, ** p<0.05, * p<0.1, Model (1) estimated by using Length of export and a mean of sector as whole, whiles

the Model (2) using Length of export and sector level. Model (3) estimated by using Age of firm and a sector as whole, whiles the Model (4) using Age of firm and sector level. “ Note that 132 observations were used in the analysis, rather than the full 175, because we restricted the sample and removing non-response categories such as ‘do not know,’ ‘no answer,’ ‘not applicable. Moreover, with added more independent variables lead to drop more observations (more details on page 210 ).

135

Table (3.7) Derivation of Empirical Exports Models

Model specification

Fernandez and Nieto Model

Model (A)

Model (B)

Model (C)

ownerships Family firm Sole proprietorship Sole proprietorship Sole proprietorship A SME with a corporate blockholder Shareholding firm with shares trade in the stock market Shareholding firm with shares trade in the stock market Shareholding firm with shares trade in the stock market A family firm with a corporate blockholder Limited partnership Limited partnership Firm Limited partnership Firm Partnership Firm Partnership Firm Shareholding firm with non-shares trade Shareholding firm with non-shares trade The firm investing in firms is a foreign firm Private foreign individuals, companies or organizations foreign investment foreign investment females amongst the owners females amongst the owners

Firm size Main Region Location (Main Region) Number of employees Labour Volume Labour Volume Labour Volume Mean by industry Mean of Sector Mean of sector Mean of sector age of firm since the starting until the observation age of firm age of firm age of firm

Innovation Total R&D expenditure/total sales Export Market research locally-recognized quality certify. locally-recognized quality certif. Internationally-recognized qu. certif. Internationally-recognized qu. certif. International patents registered patents registered abroad patents registered abroad Local patents registered patents registered in Saudi Arabia patents registered in Saudi Arabia

Trade operations

length of export International experience (length of export) Supplies of domestic origin Supplies of domestic origin Imported directly Imported directly annual sales annual sales

Sales Distribution channel

Firm Sales Force Firm Sales Force Independent Agents Independent Agents Retailers or Wholesalers agreement Distributors or Wholesalers agreement Distributors/Wholesalers Distributors/Wholesalers Firm -Owned Retail Stores Firm -Owned Retail Stores Independent Retail Stores Independent Retail Stores

Marketing activities

Trade Association Participation Trade Fair Exhibition Print Advertising TV/Radio Advertising Family/Personal Links Direct Mail Advertising Firm & Product Brochures Internet

Export capabilities

Foreign Language Ability Multi-Lingual Sales Staff Fax Machine Foreign Language Web Site Product Information on Web Export Marketing Plan Export Document Preparation

136

Chapter 4: Financial constraints to firms’ exports

4.1 Introduction

Financial aspects have been found to be highly important in impacting on

firms’ activities. Firms may require short, medium, and long-term finance. The

short-term finance is required to pay working capital needs such as purchases of

raw materials, payment of wages and salaries etc. On the other hand, medium-term

and long-term finance includes operations like loans to finance fixed assets and

long-term working capital needs. For this reason, financial constraints are often

cited as an important factor in firms’ exports. Moreover, a firm that is involved in

foreign markets can earn benefits from exporting that enhance its financial

position: human capital skills and production experience come from different

internationally recognized standardisation, as well as production capacity and offer

the opportunity to expand. As a result, the growth in the firm reflects on the

country’s economic development, which supports income diversification, creates

employment opportunities, provides a source of foreign exchange, and so on

(Cavusgil and Nevin, 1981; Pinho and Martins, 2010). Despite the importance of

studying the financial constraints and its impact on export activity at the

macroeconomic level, which was noted for example by Beck (2002), and Becker and

Greenberg (2007), these studies tried to address the link between financial

development and exports. These theoretical and empirical studies reveal a positive

impact of financial development on foreign exporting markets, and countries with

well-developed financial systems tend to export goods produced in industries that

use external finance effectively (Lancheros and Demirel, 2012). However these

literatures remain silent regarding such effects at the firm level (Kiendrebeogo and

Minea, 2012).

In contrast, some literature finds that financially constrained firms are less

likely to export (e.g. Greenaway et al., 2005; Bridges and Guariglia, 2008; Kuntchev

et al, 2012). It is important to understand the different factors that can help or

hinder firms’ creation and development. Recent research (e.g. Goldman and

Viswanath, 2009; Damijan et al, 2010; Bellone et al., 2010; Minetti and Zhu, 2011;

137

Manuel, 2011; Lancheros and Demirel, 2012) that discussed different cases around

the world, provides evidence that small and medium sized firms in particular face

greater financing obstacles than large firms. Moreover, the literature finds that

small firms use less external finance, especially bank finance. However, Goldman

and Viswanath (2009) and others report that export status might very well be

correlated negatively with financial leverage. A lot of evidence (e.g. Damijan et al,

2010; Bellone et al., 2010; Minetti and Zhu, 2011) has supported the view that

exporting firms are better and more efficient than other firms. These firms have a

good influence through intangible assets like human capital, and do not support

high debt. Hence, relying on this theory, exporting firms would have lower financial

leverage.

There are good grounds for supposing that financial constraints might limit

levels of exports. Jun-Du and Girma (2007) found that financial sector development

based on International trade theory is a source of comparative advantage and

consequently a determinant of international trade flows. Manuel (2011) found that

firms with a longer credit period (because of delays in payments to creditors) faced

more difficulties in entering export markets, and also found a negative and

statistically significant relationship between financial constraints and export

intensity. Damijan and Kostevc (2011) identified that financial constraints will

therefore provide an important barrier not only to entry into export markets, but

also to new exporters’ expansion dynamics in foreign markets. Moreover,

Greenaway et al. (2007) reported that financially constrained firms, for whom it is

difficult or too expensive to obtain external finance such as loans, will in fact only

invest if it has sufficient internal funds, and will invest more the higher its cash flow.

On the other hand, firms rely less on external loans to finance the fixed and variable

costs of exporting, examined by Lancheros and Demirel (2010). Bellone et al. (2010)

argued that leverage and liquidity are strongly negatively correlated; more liquid

firms are also less leveraged, meaning that these two measures of financial health

go hand in hand. Kiendrebeogo and Minea (2012) defined ‘financially constrained’

as a firm that does not have access to sufficient external liquidity and is not

productive enough to generate sufficient internal liquidity. Goldman and Viswanath

138

(2009) showed that the greater the ability of a firm to generate cashflow, the

greater its ability to support debt (a positive relationship between financial leverage

and measures of cashflow).

The impact of credit frictions on the intensive rather than on the extensive

margin of export was investigated by Minetti and Zhu (2011), by looking at the

effect of credit rationing on foreign sales. Bridges and Guariglia (2008) argued that

global engagement may shield firms from financial constraints, and consequently

improve their performance. Kuntchev et al. (2012) discussed the link between a

firm’s higher performance and credit constraints; firms with higher performance, as

measured by labour productivity, are less likely to be credit constrained, advice

which is taken as an indication of well-functioning financial markets. An antecedent

study which examined this result shows that this relationship is weaker for small

firms than for medium and large firms. Secchi et al. (2012) studied the effects of

financial constraints which are large, and in general larger than what is estimated

when corrections are not taken into account. Additionally, financing constraints

increase the probability to reduce products or destinations, and reduce the

probability to add new products or new destinations. Secchi et al. (2012) also

concluded that financing constraints tend to hinder an effective reallocation of

resources.

In Saudi Arabia, the country takes into consideration the importance of

financing for exporters. As preceding chapters reported the government

established an institutional framework, which is the "Saudi Export Program" (SEP) in

1999, in order to develop private sector exports, by providing financing incentives

and credit to exporters on the one hand, and on the other hand through the

provision of competitive credit terms for buyers abroad or funding institutions

working in this area. However, as shown in the statistics in chapter two section one

the private sector’s contribution in the export sector remains weak, as it amounts

to 15 per cent of the country’s total exports. For this reason, the current work aims

to address a fundamental question: What are the major obstacles and barriers that

confront Saudi exporting firms in terms of finance? To answer this question, a

number of sub-questions also need to be addressed: one major hypothesis is does

139

the financial factor have a significantly high effect on exporter behaviour? Are there

problems regarding access to finance? This study attempts to identify the main

credit constraints.

The present study aims to contribute to this developing field of research by

studying the role of finance in exporting for manufacturing firms, an issue that has

not been previously explored especially in Saudi Arabia. The study follows empirical

models that measure the credit effect by classifying sample firms into four

categories: not credit constrained, maybe credit constrained, partially credit

constrained, and fully credit constrained.

The rest of the chapter is organized as follows: the next section presents the

literature on financial constraints and firm export behaviour. Section 3 presents the

econometric approach employed to measure financial constraints and illustrates

the methodology that present. Section 4 contains the final data set and descriptive

statistics. This study tests the hypothesis that less constrained firms self-select into

exporting, and analyses the link between access to finance and credit constraints,

then looks at how selling abroad improves firms’ finances under credit constraints

in section 5: these results are discussed as well as the testing of the model’s health

and robustness check. Finally, Section 6 is the conclusion.

4.2 Literature on the Financial Constraints and Firms Export

Behaviour

The economic literature illustrates how data from firm level surveys collected

by studies under a standard methodology can be used to analyse the financial

issues confronting firms. Kuntchev et al (2012) addresses questions about the type

of credit firms use to finance their working capital and their investments, as well as

which firms are satisfied with the credit they have and which ones are credit-

constrained. Kuntchev et al. argues that firms are better financed themselves by

analysing the link between access to credit and firm performance, and the

association between access to credit at the firm level and equivalent macro

variables. In theory, access to finance is more likely to be reported as an increasing

obstacle as firms are credit constrained.

140

Two frameworks have been discussed in the literature to analyse the

relationship between the credit (financial) factors, and firms’ behaviour towards

exports. Firstly, some literature has studied the impact of different levels of firm

characteristics such as firm size (labour and sales), age and ownership as control

variables, and incentive to export on credit or financial constraints (e.g. Bridges and

Guariglia, 2008; Damijan et al., 2010; Kuntchev et al., 2012).Secondly, there is

literature comprising research on how export intensity is influenced by credit or

financial constraints, checking the analysis by taking into account variables as

labour, sales, age, and ownership(e.g. Goldman and Viswanath, 2009; Damijan et

al., 2010; Bellone et al., 2010; Minetti and Zhu, 2011; Manuel, 2011; Kiendrebeogo

and Minea, 2012; Lancheros and Demirel, 2012; Secchi, 2012). Those studies have

used export intensity to test the impact of financial constraints. In addition, there

are studies that rely on credit or financial constraints to analyse export propensity,

such as Greenaway et al. (2007).

4.2.1 The effect of financial factors on export behaviour

The definition of credit or financial constraints has often been the subject of

argument in the literature. Some studies that discussed the effects of financial

factors on export behaviour addressed liquidity and leverage ratio as a main factors

(e.g. Greenaway, 2005; Greenaway, 2007; Bridges and Guariglia, 2008; Bellone et

al., 2010; Minetti and Zhu, 2011; Manuel, 2011; Kiendrebeogo and Minea, 2012).

On the other hand, other studies use loan and debt measurement (e.g.Damijan et

al, 2010; Manuel, 2011; Lancheros and Demirel, 2012; Kiendrebeogo and Minea,

2012). Both of these literatures involve financial measures and firm characteristics

to provide perceptions of credit-constrained firms.

Despite the fact that much of the literature of financial constraint focuses on

liquidity and leverage variables to analyses the impact of financial constraint on

export behaviour, there are differences in definitions of liquidity and leverage. A

literature review found that Greenaway et al. (2005) used definitions through four

measures of leverage ratio. Firstly, the short-term debt to total assets ratio.

Secondly, the total debt to assets ratio, which are indicators of the general

141

indebtedness of the firm. Thirdly, the short-term debt to current assets ratio, which

shows whether short-term liabilities are backed with relatively liquid assets. Finally,

the short-term debt to current liabilities ratio, which can be seen as a measure of

bank dependence. Greenaway et al. (2007) defined leverage ratio as the firm’s ratio

of short-term debt to current assets. Furthermore, Bridges and Guariglia (2008)

used a similar definition that is calculated as the firm’s short-term debt to assets

ratio. In addition, Bridges and Guariglia mentioned that leverage and collateral are

financial variables proxying respectively for the degree of indebtedness of the firm

and its degree of collateralisation, similar to those used by Fotopoulos and Louri

(2000). While Manuel (2011) measured a firm’s leverage as the ratio of total debt to

total assets. Manuel was also concerned about the influence that could result from

the variations in long-term debt on short-term funds and inventories. On the other

hand, Minetti and Zhu (2011) defined leverage ratio as a firm's ratio of total

liabilities to equity. Meanwhile, liquidity ratio was defined as current assets over

current liabilities by Greenaway et al. (2005) and Bellone et al. (2010). However,

Greenaway et al. (2007) and Minetti and Zhu (2011) calculated the liquidity ratio as

a firm's current assets less current liabilities over total assets. There is another way

of identifying liquidity ratio suggested by Manuel (2011), where this variable is

computed as the ratio of cash flow (net income plus depreciation plus changes in

deferred taxes) over total assets. In addition, Kiendrebeogo and Minea (2012)

measured liquidity by a score Index in a range from 1 to 10, 10 being the situation

of the most liquid firms. However, leverage and liquidity are strongly negatively

correlated; as mentioned by Bellone et al. (2010), more liquid firms are also less

leveraged, meaning that these two measures of financial health go hand in hand.

The second method used to define credit constraints is by measuring loan and

debt. Damijan et al (2010) relied on ratio of total debt-to-assets, which represents

various measures of financing employed such as the debt-to-assets ratio, Earnings

before Interest, Taxes, Depreciation, and Amortization (EBITDA)-to-sales. The

empirical model uses the share of collateral and share of loans from associated

firms. Lancheros and Demirel (2010) examined two types of loans: long-term

borrowing (LTB) is calculated as the stock of long-term debt normalised by total

142

assets, and short-term borrowing (STB) is measured as the flow of short-term

borrowing normalized by total assets. The disequilibrium of a firm in the model is

used by Manuel (2011); this is a dummy that takes on the value 1 if the loan

demand is higher than loan supply. Kiendrebeogo and Minea (2012) uses the value

of the last loan obtained by the firm from a financial institution and the value of the

collateral required as a percentage of the loan value. Also a dummy variable is

equal to 1 if the firm currently has an overdraft facility or line of credit.

4.2.2 A glance at measuring credit constraints

The manner in which financial constraints are measured is a very sensitive

topic in the literature. There is limited guidance offered in this area of the literature

(Bellone et al., 2010). The current analysis provides a glance at measure credit

constraints for Saudi Arabia using the (Kuntchev et al., 2012) framework and applies

to finance dates that have been provided by the Saudi Fund for Development (SFD)

surveys. Our measures of credit rationing are based on firms' responses to the

questions in the survey. Firms that are credit-constrained can be divided into four

groups (figure 4.1). The first group, named Fully Credit Constrained (FCC), includes

firms that have no external loans because loan applications were rejected or the

firm did not even bother to apply, even though they needed additional capital. The

firms that meet all the following conditions jointly are fully credit constrained;

firstly, the firm did not use external sources of finance for both working capital and

investments during the previous fiscal year; it applied for a loan during the previous

fiscal year, and does not have a loan outstanding at the time of the survey which

was disbursed during the last fiscal year or later. These conditions are in the context

of the questionnaire, that these firms applied for a loan and were rejected and do

not have any type of external finance. Secondly, firms did not use external sources

of finance for either working capital and investments during the previous fiscal

year, did not apply for a loan during the previous fiscal year, do not have an

outstanding loan at the time of the survey, and the reason for not applying for a

loan was other than having enough capital for the firm’s needs. Some

characteristics of the potential loan’s terms and conditions deterred these firms

from applying. It is thus concluded that they were rationed out of the market.

143

The second group, named Partially Credit Constrained (PCC), includes firms

that manage to find some other forms of external finance. This group of firms

meets the following conditions: they used external sources of finance for working

capital and/or investments during the previous fiscal year and/or have a loan

outstanding at the time of the survey, and did not apply for a loan during the

previous fiscal year and the reason for not applying for a loan was other than

having enough capital for the firm’s needs. Some of these reasons may indicate that

firms may self-select out of the credit market owing to prevailing terms and

conditions; thus some degree of rationing is assumed, or they applied for a loan but

were rejected.

The third group, named Maybe Credit Constrained (MCC), includes firms that

have had access to external finance and there is evidence of them having bank

finance, they are classified under the possibility of maybe being credit constrained

as it is impossible to ascertain whether they were partially rationed on the terms

and conditions of their external finance. The questions in the survey that placed

firms in this group asked whether firms used external sources of finance for

working capital and/or investments during the previous fiscal year and/or have a

loan outstanding at the time of the survey and applied for a loan during the

previous fiscal year

Finally, the fourth group, named Non-Credit Constrained (NCC), includes firms

that fit the following description: firms that did not apply for a loan during the

previous fiscal year and the reason for not applying for a loan was having enough

capital for the firm’s needs. This fourth group can be further divided according to

their usage of external finance, since this group includes both firms that use

external finance and ones that do not. The important characteristic of this group is

that, independently of their current level of external finance, these firms are happy

with their current financing structure for both working capital and investments30.

30 Appendix one provides more information that support analysis of current chapter , which is analysis the impact of firm level on a firm’s credit position and access to finance on export intensity..

144

4.3 The econometric approach

Following Minetti and Zhu (2011), this research investigated the impact of

credit on intensity of exports looking at the effect of credit rationing on direct

exports. In practice, the analysis uses the specification below:

yi= α + β Ci+ γZi + νi …(4.1)

where yi = direct exports;

Ci = Credit measurement

Zi = The vector of controls for firm characteristics such as labour, age consortium, sector and corporation.

By estimating the intensive margin of firm exports as the following empirical

specification:

EXPORTSi= α1+ β1CCSi+ γ1CashFi + γ2ProdVi + γ3SIZEi+ γ4EDUi

+γ5FixEMPi+ γ6AGEi +γ7ISOi+γ8CONSi +γ9CORPi +γ10INDi + νi (4.2a)

EXPORTSi= α2+ β2LEVi + γ1CashFi + γ2ProdVi + γ3SIZEi+ γ4EDUi

+γ5FixEMPi+ γ6AGEi +γ7ISOi+γ8CONSi +γ9CORPi +γ10INDi + νi (4.2b)

EXPORTSi= α3 + β3LIQi + γ1CashFi + γ2ProdVi + γ3SIZEi+ γ4EDUi

+γ5FixEMPi+ γ6AGEi +γ7ISOi+γ8CONSi +γ9CORPi +γ10INDi + νi (4.2c)

The credit measurement identified in Eq.(4.2) as:

CCS =Credit rationing in Eq. (4.2a). A category variable that takes the value of 1 if

firm is Not Credit Constrained-NCC, 2 1 if firm is Maybe Credit Constrained-

MCC, 3 1 if firm is Partially Credit Constrained-PCC, and 4 1 if firm is Fully

Credit Constrained-FCC.

LIQ =Liquidity ratio in Eq. (4.2b).

LEV =Leverage ratio in Eq. (4.2c).

Where the rest of variables in Eq.(4.2) are:

EXPORTS =Dependent variable; export intensity.

CashF =Cash flow.

ProdV =Labour productivity.

SIZE =Dummy measure for firm size value 1 if firm in the large size.

145

EDU =Workforce composition by the shares of secondary school graduates and

college graduates.

FixEMP =Capital intensity by fixed assets per worker.

AGE =Firm age.

ISO =Dummy variable indicating whether the firm has an international

recognized quality certification.

CONS =Consortium: It belongs to a consortium or a business group.

CORP =Corporation: Dummy variables indicating whether a firm is a corporation.

IND =Sector.

The methodology that used to estimate Eq. (4.2) relied on that some literature

(e.g. Baum, P184 2006 Edition; Minetti and Zhu, 2011) has mentioned that there

are three common instances where the zero-conditional-mean assumption may be

violated in economic research: endogeneity (simultaneous determination of

response variable and regressors), omitted-variable bias, and errors in variables

(measurement error in the regressors). In each of these cases, OLS is not capable of

delivering consistent parameter estimates. Instrumental Variables (IV) estimation is

designed to deal with this problem. The general concept is that of the instrumental

variables estimator is known as two-stage least squares (2SLS). The IV approach

provides consistent estimators of the parameters when the OLS estimators are

inconsistent (in situations such as omitting a relevant variable, measurement errors,

and simultaneity). Econometrically, OLS estimators of the model parameters are

invalid (e.g. inconsistent) in the case of endogenous explanatory variables to obtain

consistent estimators of the model parameters in the presence of endogenous

explanatory variables using instrumental variables and applying the two-stage least

squares estimation (2SLS).

Based on the results obtained from estimating the Eq. (4.2) that will rely on

simple regression of foreign sales on credit rationing and control variables, some

literatures propose that the results in this case may still overstate or understate the

effect of rationing. The most important is the omitted variable bias. Whether a firm

is rationed or not is likely to be correlated with several firm characteristics. Even

146

though the study includes different characteristics as controls, rationing may

correlate with unobserved firm characteristics.

Following Minetti and Zhu (2011), the econometric technique to address these

endogeneity issues is to identify exogenous restrictions on the local supply of

banking services. The study expects these restrictions to directly influence the

firms' ability to obtain financing and, hence, the probability of rationing. On the

other hand, the study does not expect these restrictions to affect firms' export

directly. The explanation of instruments relies on the role of monetary policy in

Saudi Arabia. The government, as a controller and monitor of monetary policy, has

not allowed the formation of new banks in the country; there were 10 banks before

accession to the WTO (the negotiations to join the WTO took place between

1995-2005). The other aspect is that the government also prevents foreign banks

from entering the local economy. In 2011, after new regulations were implemented

because of the WTO, the total number of banks increased to 22, with 1,607

branches distributed around the country, while the total number in 2005 was

around 1,202 branches (SAMA, 2011). The preceding arguments imply that

locations that have seen the expansion of new branches, as determined by WTO

regulations, are unlikely to be correlated with structural characteristics of the

different areas of the country. For this reason, to capture the local structure of

regulation, the study included the provincial number in 2011.

Credit measurement may be endogenous in Eq. (4.1). For this reason, the

study estimated the effect of credit on foreign sales using an instrumental variable

using Eq. (4.2) with the regional measure of the number of banks and effect of

foreign ownership. The endogenous variable Ci in Eq. (4.2a) is category; the first

stage is to obtain fitted probabilities of credit rationing ĉi rely on definition listed on

section (4.2.2), and then use ĉi as the instrument for Ci in the two-stage least square

(2SLS) estimation of Eq. (4.2a). Minetti and Zhu (2011) affirm that this method is

robust to misspecification of the probit model of credit rationing. The estimates of

Eq. (4.2b) rely on Leverage ratio while the Eq. (4.2c) estimates rely on Liquidity ratio

both using the instrumental variable rely on the regional measure of the number of

banks and effect of foreign ownership.

147

However, a number of studies find supporting evidence using different

variables to identify constrained firms. Hence, variables such as size, labour, age,

labour productivity, concentration of ownership, whether managed by females, or

foreign participation are used to capture ways to overcome having imperfect

information (Bellone et al., 2010), which hinders access to capital markets and

credit constraints status. For example, the effect of firm size on credit status;

researchers such as Kuntchev et al. (2012) found that smaller firms rely more on

trade credit and informal sources of finance and less on equity and formal debt

than large firms.

In the export intensity model, the analysis follows Minetti and Zhu (2011), who

consider the measures of firms' financial conditions as controls; these are liquidity,

leverage ratio, and cash flow. Also the study adds controls for these factors. By

using the questionnaire variables, the analysis measures firm size by the number of

workers, computes labour productivity as value added per worker, workforce

composition by the proportion of college graduates, and capital intensity by fixed

assets per worker. Additionally, the estimation uses dummy variables to capture

whether a firm is a corporation or belongs to a consortium or a business group. The

role of using a consortium or a group to allow a firm to share a distribution network

with other firms reduces the cost of entering international markets (Minetti and

Zhu, 2011). In addition, a consortium or a group could provide a firm with financial

resources for sustaining export costs through, for example, internal capital markets.

Also, firms distributing their products through specialised intermediaries can

significantly save on the costs of setting up foreign distribution networks. The study

also includes a dummy variable indicating whether the firm has an internationally-

recognized quality certification, which is a system required by most importers to

certify the efficiency of production, and hence, the quality and productivity of a

firm. Finally, the analysis includes the number of current bank branches in each

province.

As regards financial constraints, the most common proxy used is the sensitivity

of investment to cash flow (Bellone et al., 2010). It defines firms as financially

constrained or unconstrained based on their dividend layout ratio, then shows that

148

likely constrained firms (low dividend layout) display higher investment–cash flow

sensitivity. Bellone et al. (2010) find that larger firms (less likely to be constrained)

exhibit a higher cash flow coefficient in the regression equation, even after

controlling for sector heterogeneity. However, Bellone et al. (2010) argue that the

usefulness of investment–cash flow sensitivity as a measure of financial constraint

has been definitely questioned, because there are arguments which have discussed

evidence of a negative relationship between investment–cash flow sensitivity and

financial constraints. The main finding provided by Kuntchev et al. is that firms with

higher performance, as measured by productivity reliance on labour, are less likely

to be financially constrained, which the literature takes as an indication of well-

functioning financial markets. This analysis shows that this relationship is weaker

for small firms than for medium and large firms. All these variables can reflect the

extent of a firm's credit risk and its financial health and, hence, help grasp the

probability of credit rationing (Greenaway et al., 2007).

4.4 Descriptive statistics of variables

The present chapter uses results from a section of the survey which looks at

the financial operations of the respondents and general information about the

companies. Table 4.1 weighs up the credit constraint level among the sample

companies. Roughly 12 per cent of the companies that were surveyed were fully

credit constrained. Only 7 per cent were partially credit constrained, while most of

them were maybe credit constrained. Also, 37 per cent of the companies showed as

not credit constrained. We can see from this table that most of the western firms

are not credit constrained - about 57 per cent - but the majority of central firms 51

per cent are not credit constrained (figure 4.2). However, 37 per cent of eastern

firms are maybe credit constrained, and 25 per cent are fully credit constrained.

The food and beverages sectors are less impacted by credit constraints, whilst the

wood, paper, leather, and textiles sectors have the most issues regarding credit

constraints (figure 4.3). Additionally, examining the characteristics of size of sales

and labour, the data showed that the larger the firm, the fewer credit constraints

they faced (figure 4.4).

149

4.5 Results

After controlling for a firms' financial conditions and other firm characteristics,

for some firms the estimations lack data on firm characteristics, especially liquidity

and leverage ratios, cash flows, and capital intensity. Moreover, the analysis

restricted the sample and removing non-response categories such as ‘do not know,’

‘no answer,’ ‘not applicable. Moreover, with added more independent variables

lead to drop more observations (more details on page 210). As a result, in the

analysis that follows the sample reduces from 175 to 139 and to 81 firms.

The estimation results of the models in Eq. (4.2i) are presented in Tables 4.4,

4.5 and 4.6. While Table 4.4 lists the ordinary least square (OLS) estimates in which

rationing is considered exogenous. The regression results show that Model 1

estimated by using the credit rationing, while Model 2 estimated by using the

leverage ratio and Model 3 estimated by using the liquidity ratio. There is no

evidence that credit rationing and leverage ratio have a statistically significant

effect on foreign sales, while the liquidity ratio is statistically significant at the 10%

level. On the other hand, more productive, larger, more capital intensive firms,

firms that have a lower cash flows, and better educated workers with recognized

quality certification have significantly higher foreign sales. These results are

consistent with results obtained by Minetti and Zhu (2011).

Table 4.5 column 2 shows 2SLS estimates that when the endogeneity of

rationing is accounted for, there is a significantly negative effect of rationing on

foreign sales. The magnitude of the effect found by Minetti and Zhu (2011) is large,

but in our study the point estimate is small: it is -0.50 with a 90 per cent confidence

interval between 0.09 and -1.09. While Table 4.6 column 2 shows 2SLS estimates

that when the endogeneity of leverage ratio is accounted for, there is a

insignificantly effect on foreign sales. Moreover, column 2 in Table 4.7 shows 2SLS

estimates that when the endogeneity of liquidity ratio is accounted for, also there is

insignificantly effect of liquidity on foreign sales.

In general, the 2SLS estimates of credit rationing with other variables are more

statistically significant than the OLS estimates. Under the 2SLS estimators more

150

productive and larger firms, and those that have a lower cash flow and better

educated workers with recognized quality certification have significantly higher

foreign sales. These results are consistent with results obtained by Minetti and Zhu

(2011).

4.5.3.1 Testing over identifying restrictions

The testing procedure was carried out using STATA v12. From a regression of

the IV or 2SLS estimation, the Sargan-Hansen test is a test of over identifying

restrictions (Baum, 2006). The joint null hypothesis is that the instruments are valid

instruments, i.e. uncorrelated with the error term, and that the excluded

instruments are correctly excluded from the estimated equation. Under the null,

which is: H0: over identifying restrictions are valid. The results obtained by Sargan

statistic are:

Credit rationing Leverage ratio Liquidity ratio

Sargan statistic chi2-sq(i) 0.221 0.002 0.017 Prob>chi2 0.638 0.966 0.896

The test statistic is distributed as chi-squared.

It quite clearly indicates that the analysis cannot reject the null, which is a

good indicator.

4.5.3.2 Collinearity

The analysis checked the lists of included instruments, excluded instruments

and endogenous regressors for collinearity. The estimation using a new version of

Stata program, the estimation dropped one variable endogenous that is one sector

amongst sectors is collinear with another, after that the model is far from having

any econometrics problems.

4.6 Robustness checks

The robustness check is using the different measures of the credit constraints,

it were taken in this study to ensure that the model in Eq. 4.2i was robust, these are

displayed in Table 4.5, Table 4.6, and Table 4.7. The first measure (Table 4.5) rely on

151

credit rationing only and did not use the liquidity ratio and the leverage ratio when

performing the regressions, and discovered that the same results were obtained.

The second measure, shown in Table 4.6 utilised the impact of leverage ratio

without using credit rationing and liquidity ratio. Lastly, the research only uses

liquidity ratio, and is displayed in Table 4.7.

4.7 Conclusions

This Chapter has given an overview of the latest empirical studies on exporters

by focusing on financial factors that have an effect upon firms regarding export

behaviour. The descriptive statistics highlight some financial indicators and

elements, which Saudi firms have shown during their existence in the export

markets. The study has analysed the firms’ export behaviour, the influence of credit

constraint on the firm’s attributes, the importance of access to finance for firms,

and the export intensity influenced by credit constraints.

The importance of access to credit for firms, in particular for exporting firms,

has been considered in the related literature. The study relied on the latest

measures of the credit constrained status based on micro data, and describing what

type of firms are more likely to be credit constrained and which ones are not. The

value of the measure of credit constraint comes from firms' responses to the

questionnaire instead of firms' financial statements. The analysis tested the

hypothesis that internationalisation leads to better access to financial markets and

found no support for that hypothesis. Another main finding is that more productive

firms are less likely to be credit constrained. In terms of the financial constraints,

the results show that there exists a negative relationship between credit constraint

and exports, which means that financial constraints constitute a common problem

for firms that are involved in foreign markets. However, our results found that

there exists a positive relationship between a firm's export behaviour, productivity

and capital intensity.

The present chapter has important implications. The financial factors are an

additional reason that Saudi Arabia should undertake an expansion of its exports.

Consequently, it is important to enhance the Saudi export programme (which

152

provides finance and guarantees to the Saudi exporter) to face the increasing needs

of finance by export firms.

Figure 4.1: Correspondence between the questions in SFD Surveys and the

credit-constrained firms (Kuntchev et al., 2012)

Figure 4.2: Credit constraint status by Region

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Central Western Eastern Northern

FCC

PCC

MCC

NCC

153

Figure 4.3: Credit constraint status by industry

Figure 4.4: Credit constraint status by firm size (Labour)

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Food & beverages Wood, Paper, Leather and

Textiles

Chemical ,Plastic, Rubber, Medical

Building Material and Metals

Electrical Machines, & Tools

FCC

PCC

MCC

NCC

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Small ( employees <20) Medium (20-99 employees) Large (100+ employees)

FCC

PCC

MCC

NCC

154

Table 4.1: % firms by level of credit constraint, by sector, total sales and labour.

Type Not Credit Constrained

Maybe Credit Constrained

Partially Credit Constrained

Fully Credit Constrained

NCC MCC PCC FCC

Central 30.65 51.61 8.06 9.68 Western 57.69 26.92 7.69 7.69 Eastern 33.33 37.5 4.17 25.00 Northern - 100.00 - -

Food & beverages 64.29 35.71 - - Wood, Paper, Leather and Textiles 38.89 16.67 5.56 38.89 Chemical ,Plastic, Rubber, Medical 38.00 42.00 8.00 12.00 Building Material and Metals 50.00 12.25 37.5 - Electrical Machines, & Tools 13.04 82.61 - 4.35 10 million and less 56.25 12.5 6.25 25.00 11-25 million 54.17 12.5 8.33 25.00 26-51 million 58.33 25.00 - 16.67 51-100 million 42.86 52.38 4.76 - More than 100 million 10.00 75.00 10.00 5.00 Small ( employees <20) 50.00 - - 50.00 Medium (20-99 employees) 41.38 34.48 3.45 20.69 Large (100+ employees) 35.37 47.56 8.54 8.54 All firms 37.17 43.36 7.08 12.39

155

Table 4.4: financial constraints and export intensity

OLS Regression Parameters

Export intensity Model 1 Model 2 Model 3 Coef. t_stat Coef. t_stat Coef. t_stat Credit rationing -0.045 -0.56 Leverage Ratio -0.013 -0.89 Liquidity Ratio -0.3 -1.84* Cash flow -0.54 -4.94*** -0.19 -2.12** -0.22 -2.47** Labour productivity 0.24 5.47*** 0.19 5.02*** 0.19 5.10*** Logarithm of labour 0.21 1.38 0.21 1.87* 0.21 1.90* Workforce by the shares of graduates -0.13 -0.26 0.11 0.25 0.13 0.29 Fixed assets per worker 0.02 2.00** 0.024 2.18** 0.033 2.71*** Firm age log 0.31 2.44** 0.17 1.81* 0.19 2.11** Internationally-recognized quality certification 0.57 2.42** 0.57 2.70*** 0.57 2.78*** Consortium -0.041 -0.27 0.022 0.19 -0.0064 -0.057 Corporation 0.015 0.14 -0.065 -0.83 -0.054 -0.7 sector of (Food and beverages) -0.69 -2.53** -0.36 -1.36 -0.34 -1.3 sector of (Building Material,…) 0.026 0.091 0.51 2.80*** 0.55 3.00*** sector of (Chemical, Petrochemical.) 0.14 0.77 0.094 0.61 0.15 0.96 sector of (Electrical, Machinery,…) 0.44 2.02** 0.58 2.90*** 0.64 3.20*** Bank 0.19 0.2 0.91 1.1 0.84 1.03 Foreign investment 0.35 1.65 0.15 0.84 0.14 0.81

Constant 0.9 0.83 0.11 0.15 0.16 0.21

Observations” 81 139 139

R 2 0.6273 0.4039 0.4163

Adjusted R2 0.53 0.33 0.34 F 6.73 5.17 5.44

t statistics in * p< 0.10, ** p< 0.05, *** p< 0.01

Model (1) estimated by using Credit rationing, while Model (2) estimated by using Leverage Ratio and Model (3)

estimated by using Liquidity Ratio

“ Note that 81 observations in model (1) and 139 in models (2)and (3) were used in the analysis, rather than the full 175, because we restricted the sample and removing non-response categories such as ‘do not know,’ ‘no answer,’ ‘not applicable. Moreover, with added more independent variables lead to drop more observations (more details on page 210). In addition, observations reducing in model (1) more than in both models (2) and (3) because Credit rationing generated rely on four categories Groups not directed of the sample.

156

Table 4.5: Credit rationing and export intensity

2SLS Regression Parameters

Export intensity (1) 1st stage (2) 2SLS

Coef. Std. Err. t_stat Coef. Std. Err. t_stat

Credit rationing

-0.50 0.30 -1.66*

Cash flow -0.08 0.17 -0.50 -0.57 0.13 -4.56***

Labour productivity 0.04 0.07 0.54 0.26 0.05 4.96***

Logarithm of labour 0.18 0.23 0.77 0.30 0.18 1.61

Workforce by the shares of graduates -1.09 0.74 -1.48 -0.49 0.50 -0.97

Fixed assets per worker 0.00 0.02 -0.29 0.02 0.01 1.63

Firm age log -0.64 0.18 -3.61*** 0.01 0.20 0.07 Internationally-recognized quality certification -0.18 0.36 -0.50 0.47 0.27 1.77*

Consortium 0.12 0.24 0.50 0.03 0.16 0.21

Corporation 0.02 0.17 0.11 0.02 0.12 0.14

Mean of Sector -0.05 0.02 -2.67*** 0.00 0.02 0.12

sector of (Building Material,…) -1.09 0.39 -2.81*** -1.22 0.47 -2.62***

sector of (Wood, paper, Textiles…) -0.68 0.41 -1.66* -0.48 0.36 -1.34

sector of (Chemical, Petrochemical.) -0.19 0.20 -0.98 -0.24 0.16 -1.55

Bank branches -1.37 1.44 -0.95 Foreign Ownership -0.65 0.32 -2.06*** _cons 5.18 1.56 3.31*** 2.67 1.48 1.81*

Number of obs 81

81 F 3.45

5.2

Prob > F 0.0003

0 Centered R2 0.4434

(overidentification test of all instruments): Sargan statistic

0.221

Chi-sq P-val

0.638 t statistics in * p< 0.10, ** p< 0.05, *** p< 0.01

157

Table 4.6: Leverage Ratio and export intensity

2SLS Regression Parameters

Export intensity (1) 1st stage (2) 2SLS

Coef. Std. Err. t_stat Coef. Std. Err. t_stat

Leverage Ratio

-0.23 0.24 -0.95

Cash flow 0.65 0.52 1.24 -0.05 0.18 -0.28

Labour productivity 0.06 0.22 0.28 0.20 0.06 3.36***

Logarithm of labour -0.08 0.68 -0.11 0.20 0.17 1.15

Workforce by the shares of graduates -3.31 2.60 -1.27 -0.58 0.72 -0.80

Fixed assets per worker 0.32 0.06 5.44*** 0.09 0.07 1.26

Firm age log -1.21 0.55 -2.22** -0.09 0.30 -0.29

Internationally-recognized quality certification 2.53 1.23 2.05** 1.10 0.63 1.74*

Consortium 1.09 0.68 1.60 0.25 0.31 0.81

Corporation -0.38 0.46 -0.82 -0.15 0.15 -0.97

Mean of Sector 0.03 0.08 0.35 0.04 0.02 2.06**

sector of (Building Material,…) -1.66 1.53 -1.08 -0.77 0.57 -1.35

sector of (Wood, paper, Textiles…) 0.05 0.95 0.06 0.29 0.24 1.19

sector of (Chemical, Petrochemical.) -0.21 0.69 -0.30 -0.34 0.18 -1.88*

Bank branches -4.44 4.94 -0.90 Foreign Ownership -0.67 1.07 -0.63 _cons 1.97 4.57 0.43 -0.01 1.04 -0.01

Number of obs 139

139 F 5.36

2.28 Prob > F 0

0.0084 Centered R2 0.3952

(overidentification test of all instruments): Sargan statistic

0.002 Chi-sq P-val

0.9661

t statistics in * p< 0.10, ** p< 0.05, *** p< 0.01

“ Note that 139 observations were used in the analysis, rather than the full 175, because we restricted the sample and removing non-response categories such as ‘do not know,’ ‘no answer,’ ‘not applicable. Moreover, with added more independent variables lead to drop more observations (more details on page 210).

158

Table 4.7: Liquidity Ratio and export intensity

2SLS Regression Parameters

Export intensity (1) 1st stage (2) 2SLS

Coef. Std. Err. t_stat Coef. Std. Err. t_stat

Liquidity Ratio

-2.43 2.46 -0.99

Cash flow -0.07 0.05 -1.43 -0.36 0.24 -1.55

Labour productivity 0.01 0.02 0.27 0.20 0.06 3.53***

Logarithm of labour 0.00 0.06 -0.03 0.21 0.16 1.28

Workforce by the shares of graduates -0.09 0.24 -0.39 -0.04 0.51 -0.08

Fixed assets per worker 0.04 0.01 8.07*** 0.13 0.10 1.21

Firm age log 0.02 0.05 0.37 0.23 0.15 1.58

Internationally-recognized quality certification 0.12 0.11 1.07 0.83 0.39 2.13**

Consortium -0.05 0.06 -0.73 -0.10 0.20 -0.52

Corporation 0.02 0.04 0.47 -0.01 0.12 -0.10

Mean of Sector 0.01 0.01 2.11** 0.07 0.04 1.73*

sector of (Building Material,…) -0.03 0.14 -0.23 -0.47 0.38 -1.24

sector of (Wood, paper, Textiles…) 0.03 0.09 0.30 0.34 0.24 1.40

sector of (Chemical, Petrochemical.) 0.03 0.06 0.42 -0.23 0.18 -1.28

Bank branches -0.43 0.46 -0.94 Foreign Ownership -0.05 0.10 -0.55 _cons 0.05 0.42 0.12 -0.34 1.05 -0.32

Number of obs 139

139 F 10.22

2.51

Prob > F 0

0.0035 Centered R2 139

139

(overidentification test of all instruments) Sargan statistic

0.017

Chi-sq P-val

0.8966 t statistics in * p< 0.10, ** p< 0.05, *** p< 0.01

159

Chapter 5: Business Environment, Competition, and Firm Performance impact on export behaviour

5.1 Introduction

The overall business environment plays an instrumental role and does have an

impact export performance. In reform economies accurately, what determines the

success can respond to the demands of the market environment? And where can

the most important gains in business environment be expected to come from? In

this kind of question, some of the literature reports results that show the relative

influence of competition and other features of firms’ external environment on their

restructuring actions and subsequent sales and productivity performance. This

literature shows realistically that competition matters, but it matters in an

intriguing and complex way.

The current work contributes to this literature and sheds light on the

importance of features of the business environment. This work begins with a review

of the export determinants, such as the competition and business constraints

literature, in order to establish the framework used for the study. Most

importantly, the work coincides with a recent methodology to test for the existence

of interactions among export and business regulations, while this work often uses

the same or similar dependent variables of the studies mentioned in the literature,

such as Commander and Svejnar (2011). All of the literature focuses on a particular

set of explanatory variables and usually does not take into account the explanatory

variables deemed important in other strands of research. This raises the issue of

whether existing studies generate biased estimates on account of omitted

variables. Basically, microeconomic data are better suited for such analyses of

productivity, are better able to capture possible obstacles to firm performance, and

are thus more likely to shed light on the key policy implications.

The investment environment plays an important role and increasingly in the

developed economies. Numerous studies have been allocated to present its

relationship with firm performance, especially in developing countries issue

countries (e. g. Dollaret et al.,2005; Asaftei et al., 2008; Goedhuyset al., 2010; Xu.

160

2010; Commander and Svejnar, 2011; Augier et al., 2012). The recent dependence

on micro level datasets, collected whether by World Bank surveys or other

institutions, supports raising research to link the business environment to firm

performance, which would lead to a better perception of economic development.

Hence, this work aims to examine the relationship between the competition and

business environment on one side and export performance on the other side in

Saudi Arabia. To explore this issue in our case more intensively, we examine in this

study examines how Saudi firms’ characteristics, perceived competition intensity,

and constraints in the domestic markets affect their efforts to increase the level of

export. Although Saudi Arabia is an interesting and rich oil country, there are no

studies discussing the competition and business environment, and no data have

been collected to provide a knowledge base for this matter. To do so, we employ a

cross-sectional micro-data set obtained by preparing a specific questionnaire; the

outcome is a unique dataset covering 175 firms spanning different attributes. The

objective of the survey is to obtain feedback from export enterprises in Saudi

Arabia on the operation of the state and of the private sector as well as to help in

building a panel of enterprise data that will make it possible to track the situation in

the business environment. This survey primarily addresses issues related to the

exports of firms and their business environment, i.e. access to finance, access to

infrastructure, competition, labour, etc. In the macroeconomic policy, Saudi Arabia

has striven to take great steps in improving the stability and predictability of laws,

regulations and procedures that firms must comply with in order to start and run

their business operations. We expect to have a better insight into the impacts of

internal and external factors on the export intensity of firms in Saudi Arabia, a

country so far pursuing export-led growth strategies, setting some policy and

intensive for implications in enhancing firm exports.

To that end, the purpose of this project is therefore to evaluate the potential

contribution of both competition and specific business constraint measures to trade

and export competitiveness, as well as the potential gains from adopting a more

integrated and coherent approach to trade and business (investment) facilitation.

The work makes numerous new contributions to the existing body of literature on

161

the impact of behind the border regulations and the business environment on

export or trade in general. For instance, by distinguishing between export and

general specific regulatory measures, the analysis provides estimates of how

important business regulations, typically outside the purview of trade and customs

authorities, affect exports.

This chapter is outlined as follows. The next section, Section 2, outlines the

literature review. Section 3 sets up the model and the empirical methodology.

Section 4 contains the descriptive statistics. Section 5 presents the empirical

findings using the ordinary least squares (OLS) method. Section 6 presents the

results of the robustness check. And Section 7 raises some concluding remarks.

5.2 Literature Review

Trade theories that depend on heterogeneous firms constitute a large body of

economic theoretical background on the export behaviour of firms. Firms in these

kinds of theory take into consideration different in terms of efficiency. Further, they

experience different variable and fixed costs when engaged in trade. The

heterogeneity in firm-specific efficiency and trade costs determines the difference

in export behaviour among firms (Hiep, 2009). To this end, whatever factors affect

the efficiency levels and trade costs of firms will be possible determinants of their

export behaviour. These findings coincided with Yan Aw et al. (2000) and Melitz

(2003) that the more efficient firms have higher levels of export intensity. This

prediction is then confirmed by others, such as Arnold and Hgussinger (2005), Cirno

et al. (2008), Lages et al. (2008), Beveren and Vandenbusshe (2010), Powell and

Wagner (2010) and others. Hiep (2009) discussed theoretical that argue that export

sales, and hence export intensity, are negatively linked to trade costs. In summary,

economic theories support the argument that a firm’s attributes and business

environment characterise its export intensity by affecting efficiency and firm-

specific trade costs. In the current work the analysis is principally based on this

argument. Factors such as competition and domestic business constraints may

constitute the exporting strategy of the firm, besides affecting the firm’s efficiency

and costs.

162

However, the impact of competition on productivity is not so simple to

evaluate. The degree of competition in a specific industry is difficult to measure and

is determined by many different elements. On the other hand, it is not easy to

determine the effect of productivity on competition, whether in a direct or any

measurable way. Carlin et al. (2001) argues that the measurement of competitive

pressure in the economy is very difficult. Additionally, in much of the literature,

only industry level proxies for competition in the form of indicators of market

structure are available. The issue is that the “industry” may be completely distant

from the concept of the “market” that is relevant to a firm’s products.

Nevertheless, the role of competition is not easy to Identify. Some literature relies

on the level of competition which is constructed from responses of firms to the

following inquiry asked in the questionnaire. In addition, the degree of competition

faced by a firm is not particularly easy to measure, especially as competition could

be affecting performance through a range of quite different means and changes in

performance would be expected in turn to affect market structure. Carlin et al.

(2001) reported that, even if the degree of competition it faces has no direct causal

influence on the behaviour of any individual firm, it may be that more competitive

market environments see a faster replacement of the relatively inefficient by

relatively efficient firms. As a result of this, a correlation appears over time

between a measure of competition at industry level and the average efficiency of

those firms.

Although some economic models show that the effect of competition on

export behaviour may be ambiguous, others argue that it is quite likely that

competition has a direct influence on behaviour. Willig’s (1987) and Carlin et

al.(2001) demonstrates two offsetting effects of raised competition on the

incentives for managers to exert effort. Whilst increased competition makes profits

more sensitive to managerial effort, it also depresses demand for the firm’s output,

which dampens profits and hence blunts the incentive. Although some economic

models shown that competition is indirect to measure, there are reasons for

thinking that the economic environment in different economies provides a more

productive setting in which to test hypotheses about the effects of competition by

163

taking into consideration the environments in the market. Bombardini (2011) found

that, under a competitive environment, the relatively higher productivity of goods

exporting firms translates into more competitive economic conditions for firms

exporting out of the same country and industry. This result reflects that higher

relative productivity in an industry leads to a relatively higher wage of the specific

factor associated with that industry. For this reason, this raises all the costs,

including the fixed costs of exporting, and lowers the probability of exporting and

the level of exports for a firm with a given productivity level. Industry-specific

inputs can be thought of as factors of production that cannot easily be moved from

industry to industry. Bombardini summarised these results that can be industry-

specific knowledge of workers or physical capital that reduces in capacity if moved

from one industry to another. Heckman and Pages (2000) look at labour market

regulations in Latin America. They find that labour market regulations in Chile and

Colombia make labour quite immobile due to extensive hiring and firing costs based

on different reasons. They find evidence of this channel in the data, as the industry

wage correlates negatively with firm performance after having been purged of

country- and industry-specific effects.

Mayer et al. (2011) show how firm-level measures of exported output per

worker as well as shrunk sales per worker for a given export destination increase

with tougher competition in that destination. This effect of competition on firm

productivity holds even when one fixes the set of products exported, thus removing

any potential effects from the extensive (product) margin of trade. Then, the firm-

level productivity increase is entirely driven by the response of the firm's product

mix: producing relatively more of the better-performing products raises measured

firm productivity. Mayer et al. described how tougher competition affects the

selection of both the firms in a market, and of the products they produce: high cost

firms exit, and firms drop their high cost products. These selection effects induce

productivity improvements at both the firm and the aggregate level. Hiep (2009)

analysed theoretically that competition is a determinant of export decision making,

which was discussed by Morgan (1999) who reported the intensity of competition

in a market was negatively associated with the market’s attractiveness. The

164

empirical findings of Hiep show no significant evidence of the relationship between

perceived competitive intensity in the domestic market and export strategy

development. The theoretical view that Hiep reported included a positive

relationship between intense domestic market competition and greater export

involvement. However, he also argued other theoretical points that reported non-

significant results.

The direct impact of competition on firms also listed a number of studies by

Carlin et al. (2001). They found a positive effect of larger market share on

performance that was applied in Bulgaria, by Jones et al. (1998). Using a measure of

competition at industry level, Konings (1998) also found in a study of Bulgaria and

Estonia that more competitive pressure in the industry enhanced firm performance

in Bulgaria but not in Estonia. For Russia, Earle and Estrin (1998) found that greater

competition in the market complemented the effect of privatisation in enhancing

performance. Brown and Earle (2000) reported strong positive effects of domestic

and import competition in the product market on total factor productivity. A study

of Georgian firms (Djankov and Kreacic, 1998) found that competition from foreign

producers tended to be associated with employment cuts and changes in suppliers

(but tended to reduce the likelihood of the disposal of assets, renovations and

computerisation). In contrast, firms with a larger market share were more likely to

engage in computerisation, renovations and the establishment of a new marketing

department and the disposal of assets. Djankov and Murrell (2000) pool 17 studies

and report a positive impact of competition on performance. Whereas for the non-

CIS, both domestic and foreign competition is effective, for the CIS countries, only

domestic competition is significant.

The World Bank (2005) has noted that the barriers to doing business vary

widely across regions and countries. Some literature (e.g. Colin Xu, 2010 and

Commander and Svejnar, 2011) supported the World Bank policy regarding must

take into consideration the investment environment as a strategy for economic

development. The business environment covers whatever external environment

has an impact on the returns and risks faced by exporters. To that end, the

measurement of the business environment has confronted major methodological

165

challenges that may have generated biased estimates on account of issues such as

errors in variables, omitted variables and the endogeneity of regressors.

Commander and Svejnar (2011) reported that, to the investigations of the effects of

business environment, researchers have been analysing the effects on firm

performance of three key structural features: the extent of the firm’s export

orientation, competition, and other firm attributes. They found a number of studies

and findings in the overall sense that the performance effects of exports are found

to be positive.

Augier (2012) discussed relevant recent studies in this field, such as Dollar et

al. (2005) who consider Bangladesh, China, India and Pakistan and point to the

negative role of power outages, customs delays and access to finance on firm-level

performance. An important result of their papers is that the empirical link between

the investment climate indicators and firm performance is robust to the inclusion of

country dummies, confirming that the business environment is not constant within

a country, and emphasising the need to use firm-level data. Similarly, Fernandes

(2008) focuses on Bangladesh and examines the relationship between TFP and

business environment indicators. By using protection payments as proxy for

criminal activity, Fernandes finds that firms with lower TFP are those making larger

protection payments. The main result of Fernandes study is to show the negative

effect of crime and corruption on firm performance TFP. Fernandes also discusses

the positive correlation of TFP with access to short‐term credit proxied by overdraft

facilities, but the negative correlation with longer term financing needs proxied by

loan facilities. However, it is important to point out these results are not statistically

significant. For China, Hallward-Driemer et al. (2006) show that ownership and

investment climate measures matter for the investment rate, TFP and sales growth.

In particular, light regulatory burdens, limited corruption, technological

infrastructure and labour market flexibility appear to have a positive impact on firm

performance, while gains from improved access to banking and physical

infrastructure are quite limited. The paper of Asaftei et al. (2008) underlines the

importance of market structure and soft budget constraints in ensuring that

privatisation improves firm productivity in Romania. Eifert et al. (2008), in analysing

166

17 poor African countries, show that productivity is inversely linked to the cost and

importance of indirect inputs, related to infrastructure and public services, in

production. Finally, Gatti and Love (2008) show that improved access to credit

impacts positively on the productivity of Bulgarian firms while, as does Goedhuys et

al. (2010), who focus on labour productivity for Tanzania. Moreno-Badia and

Slootmaekers (2009), admittedly with a different methodology, do not confirm this

relationship for Estonian firms.

5.3 Setup of the model

Our framework extends Commander and Svejnar’s (2011). The estimation

begin with a production function for firm i. The model relies on augmented Cobb-

Douglas function, which is:

yi= β0 + β1Ci+β3 xi + ρZi+ δIi+ θSi+ ςTi+ ξi ….(5.1)

Where:

yi = Exports intensity;

C’i =Competition variable;

x’s =Represents the capital and labour inputs;

Zi =A vector of the business environment;

I’i =Structural variables (export orientation of the firm and total sales);

S’s =Dummy variable for industries;

T’s =Dummy variable for regions;

ξ =An independently distributed error term.

Estimating Eq.(5.1) allows export efficiency to vary across institutional and

structural variables, industries, and regions. The equation represents our basic

specification. Our main explanatory variable is the level of competition, which is

constructed from the responses of firms to the question which was asked in the

survey to evaluate: Competitive Advantages of firms products in Domestic market

and in Foreign market is: Their responses were on a 1-5 scale defined as: no

advantage (1), tend to advantage (2), advantage (3), strongly advantage (4) and

very strongly advantage (5). The analysis defined the measure of competition as the

167

average score on each parts of question ([a] price, [b] quality, and [c] service), then

the average score of all parts. The level of competition cannot be used directly in

the estimation due to the possibility that they could be endogenous to industry

characteristics such as size, age, etc. In addition, there is an average level of

competition faced by firms within each region. Being a group average, it suffers less

from the measurement errors and endogeneity problems associated with industry

or sector responses, although these problems cannot be ruled out totally.

Commander and Svejnar (2011) reported that controlling adequately for

endogeneity is not an easy task in survey data that does not come from a natural

experiment. However, in our model the analysis included variables as proxy for the

capital and labour inputs in Eq.(5.1), which are wages and employment, in the

vector of the business environment our survey includes: access to finance, tax rates,

cost of financing, tax administration, customs and trade regulations, business

licensing and permits, labour regulations, political instability, courts, corruption,

crime, theft and disorder, practices of competitors in the informal sector, and

average level of infrastructure. The indicators that the study has used in the models

follow the World Bank indicators to measure a business, which is measured on a

scale of 1 to 5. In addition, the analysis include in Eq. (5.1) structural variables which

are export experience and total sales, and a dummy variable for industry and for

region to control for the heterogeneity between firms. In the event of direct

estimation of a firm's revenue garnered from exporting in a competitive

environment, many factors mean that it is unlikely to be ruled out completely,

when considering the facets of regulation, infrastructure, etc. that are, to some

extent, commonly shared by firms in a given region. For instance, more stringent

business regulations are known to reduce competition by their effect on new

products or new markets.

Moreover, the relationships between business environment constraints is

measured by Commander and Svejnar (2011). This method was used to examine

the relationships among the various constraints, the aim is to find out whether

these business environment constraints are highly correlated or not. This pair-wise

correlation is also detected in an ANOVA regression that was carried out to assess

168

the extent to which the variation in the value of any given constraint can be

explained by the other constraints. In what follows, the study enters only one of

each of these pair-wise correlated constraint variables, noting that it generally does

not matter which of the two is entered. Regression coefficients are from a

regression of the dependent variable in each column on the other constraints, and

the R2 values are from the reported regression as correlation coefficients among

these constraints.

5.4 Descriptive Statistics

A questionnaire was used in this study to look at the specifics of the business

environment, and the interaction between firm and state across a multitude of

variables and interactions. The present research uses the responses of the sample

to evaluate the impact of competition on the sampled firms. The question

evaluating this was as follows: "Competitive Advantages of firm's products in

Domestic market and in Foreign market is: (1) No advantage; (2) tend to advantage;

(3) advantage; (4) strongly advantage; (5) Very strongly advantage and (.) Don’t

Know for (a) price, (b) quality and (c) service". The question was analysed by

producing an average score for all parts of the question (price, quality, service).

Table 5.1 displays the results of this question and shows that none of the

respondents described their competitive environment as “no advantage”, 3 per

cent as “tend to advantage”, 22 per cent as “advantage”, 62 per cent as “strongly

advantage” and the remaining 13 per cent as “very strongly advantage”. Table 5.1

also displays the levels of competition by firm ownership structure, size of firm, and

the sector of the respondent. Table 5.1 tells us that the food and beverages sector

operates in a highly competitive environment in terms of both price and quality,

whether at home or abroad. However, the wood, paper, leather and textiles sector

find higher competition when looking at their level of service.

Table 5.2 displays the key areas to focus on by exploiting information derived

from the firms' own perceptions as to the most crucial obstacles they confronted.

The major obstacles recognised by the firms were related to the labour regulations

(average degree 8.22), inadequately educated workforce (average degree 7.67),

169

and practices of competitors in the informal sector (average degree 7.10). Other

key obstacles where a degree of between 10 and 12 is identified by firms were:

access to finance (average degree 6.97), transport (average degree 6.17), and

electricity (average degree 6.14), while the business environment that takes a low

degree is court (average degree 1.82), crime theft and disorder (average degree

1.92), and corruption (average degree 2.04). In addition, Table 5.3 provides a

summary of the descriptive statistics of the variables used on the models, the

comprehensive descriptive was discussed in chapter 2.

5.5 Main findings

The base performance equation having been estimated, the estimation then

proceed to consider the impact of business environment constraints on firm

performance. Throughout the analysis, the study used, for each constraint, the

average value of responses. As can be seen from Table 5.4, the partial correlation

coefficients among these eight constraints are relatively low, and the total R2 in the

reported regressions of each constraint on others is at or below 0.5 in all, while the

rest of the constraints (i.e. access to finance, tax administration, business licensing

and permits, and corruption) are below 0.65. Main constraints are not highly

correlated, and collinearity among the constraints is limited as a result.

The literature argues that one should enter each variable individually to check

its effect on export efficiency (e.g. Commander and Svejnar, 2011). Table 5.5

provides a first pass at including the constraints in the performance regression:

individually (Columns 1–13), and with all constraints entered together (Column 14).

Despite the obvious omitted variable problem, the estimation reports the

specifications with the constraints entered one at a time because this approach has

been used frequently in the literature, and much of the accepted wisdom on the

effects of institutions and regulation on performance derives from these types of

specifications (Commander and Svejnar, 2011). With the model in Table 5.5, the

competition coefficient is negative and significant in Column 14, and the R2 are

higher than other models. It can be seen that, when entered individually, nine

constraints enter negatively, as would be expected from the existing literature,

170

while four constraints are contrary to the existing literature and are entered

positively. Tax administration, customs and trade regulations, political instability,

corruption, and the practices of competitors in the informal sector amongst negative

business constraints are significant, while infrastructure and labour regulation amongst

positive business constraints is significant at 1 per cent test levels.

However, when all the constraints are entered together in Table 5.5, the

customs and trade regulations, tax administration and practices of competitors in

the informal sector constraints remain negative and significant, Access to finance

appears a negative and significant, but labour regulation loses significance or, in the

case of tax rate and infrastructure, becomes positive and significant. Hence, the

negative effect of most business environment constraints on performance

disappears. The analysis can impute the positive effect of tax rate to the fact that

Saudi Arabia has effectively applied a lower taxation rate, and has facilitated foreign

ownership of business and investment ventures within the Kingdom. Recently it has

become the largest recipient of FDI in the Arab world. As may be seen from Table

5.5, the corresponding ordinary least squares (OLS) estimates are very similar for

the individually entered constraints (Columns 1–13). Also the estimation is the

same when all the constraints are entered together (Column 14). As a final remark,

all the mentioned results are close to consistent with the literature concerning firm

performance and business environment.

However, Commander and Svejnar (2011) argued that the lack of exposure

effect of the reported severity of various constraints in the business environment

could reflect the fact that (a) firms can get around these constraints at a relatively

low cost and the effect is hence not detectable in the data, the example listed by

Commander and Svejnar being that the firms may pay a bribe to obtain a licence,

but the cost of the bribe is small; or (b) managers who face severe constraints

compensate for the presence of these constraints and report lower severity than is

actually the case, another example being that firms that need more external

financing may ‘‘pre-save’’ from retained earnings and consequently report a lower

severity of the financing constraint than is in fact the case. Regarding the

observation by Commander and Svejnar of the significant variation in reported

171

constraints across firms, the latter phenomenon of compensating for constraints may

reduce the observed effect of constraints, but it is unlikely to eliminate it altogether.

5.6 Conclusion

The purpose of this work has been to jointly consider two issues: first, how the

competitive advantage and experience of Saudi firms affect export behaviour; and

second, the joint linkages between competition and the business environment that

are faced by Saudi firms, and their performance measured by the intensity of

exports. These issues are generally considered in isolation, but this study argues

that they must be considered together if analysis is to develop a fuller picture of

their role in the intensity of exports. The principal contributions of the work regard

the consideration of the impact of the business environment while fully allowing for

the endogenous relationship between exporting and competition.

Furthermore, the current work provides a deeper analysis of the structural

characteristics of the infrastructure, labour situation, production capacity,

competition, and business environment. The high cost of infrastructure services and

low quality of infrastructure will affect the production costs of certain products

more than others. In the same vein, poor infrastructure conditions have an effect

on goods exports. The econometrics results also support the positive impact of

Infrastructure on export intensity.

In this chapter, the study has addressed the challenge by using unique firm

level data to analyse the performance effects of a firm’s competition and the

business (institutional) environment. The estimations found evidence that

competition does not have an impact on performance, but the effect appears

negative once the model takes into account business environment constraints. The

export experience of the firm is found to have a positive effect on export intensity

in same situation, while the number of employees has a negative effect. When the

study examined the impact of perceived business environment constraints, the

estimation found that few appear to have explanatory power, once they are entered

together rather than one at a time, whereas wage and export experience have a

positive effect.

172

Table 5.1: Degree of Competition by type of ownership, size and sector.

Advantage In Domestics market

In foreign market

Average of competition

Price Quality Service

freq. per cent

(1) No advantage 2.33 - - 4.49 - - - -

(2) Tend to advantage 5.23 2.91 4.65 3.85 - 1.94 5 3

(3) Advantage 29.65 2.91 12.21 27.56 5.13 7.1 38 22

(4) Strongly advantage 37.79 23.26 30.23 30.77 18.59 32.26 106 62

(5) Very strongly advantage 25 70.93 52.91 33.33 76.28 58.71 23 13

172 100%

In Domestics market

In Foreign market

Freq Price Quality Service Freq Price Quality Service

Status by type of ownership

Shares in stock in market 5 4.8 4.6 5 3 4.67 5 5

Non-traded share 29 3.86 4.83 4.55 28 3.82 4.79 4.64

Sole proprietors 37 3.92 4.68 4.43 35 4.43 4.89 4.79

Partnership 42 3.29 4.31 4 42 3.55 4.55 4.29

Limited partners 58 3.91 4.71 4.28 47 3.64 4.66 4.28

Status by type of labour

Micro < 5 employ) 1 3 3 4 1 3 3 .

Small (>5 &<20 ) 2 3.5 4.5 4 1 4 5 5

Medium (20-99) 40 4 4.58 4.13 31 4.32 4.61 4.58

Large (100+ ) 129 3.72 4.65 4.38 123 3.73 4.75 4.45

Status by type of sector

Food & beverages 14 4.21 4.86 4.07 10 4.3 5 4.1

Wood, Paper, Lea 27 3.75 4.68 4.82 27 4.15 4.74 4.73

Chemical ,Plastic 81 3.54 4.44 4.16 78 3.83 4.67 4.51

Building Material 21 4 4.86 4.48 18 3.39 4.67 4.61

Electrical Machine 28 4.11 4.79 4.25 23 3.7 4.74 4.13

Mean 172 3.78 4.62 4.31 156 3.85 4.71 4.48

s.e 98% 0.073 0.052 0.065 89% 0.086 0.043 0.057

173

Table 5.2: Degree of obstacle of some element of business environment *

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Element Access to finance

Access to land

Business licensing

Corruption Courts Crime, theft and disorder

Customs and trade regulations

Electricity Educated workforce

Labour regulations

Political instability

informal competitors

Tax admin.

Tax rates Transport

By ownership 6.00 4.40 4.60 1.00 1.00 1.80 3.40 6.40 5.60 5.00 1.60 3.60 1.00 3.00 5.60 shares in stock 6.31 4.21 6.28 1.62 1.38 1.03 6.14 4.52 8.28 8.62 2.41 3.90 1.52 2.14 5.55 Non-traded share 7.70 5.16 6.08 1.35 1.43 1.76 6.76 6.89 8.51 8.57 2.73 7.54 3.65 3.00 6.68 Sole proprietors 7.87 6.69 6.76 1.20 1.22 1.33 6.27 7.78 7.07 9.09 1.91 7.69 1.56 2.27 6.89 Partnership 6.28 5.93 5.45 3.41 2.76 2.93 5.03 5.12 7.36 7.31 2.09 8.24 4.33 4.12 5.55 Limited partners 4.00 10.00 8.00 3.00 5.00 2.00 6.00 9.00 14.00 13.00 1.00 8.00 1.00 1.00 11.00 Other By labour 10.00 9.00 7.00 1.00 1.00 1.00 3.00 5.00 5.00 6.00 1.00 10.00 1.00 6.00 5.00 Micro < 5 employ 7.50 6.50 10.00 2.00 7.00 8.50 12.00 9.50 9.50 11.00 2.00 12.50 7.00 6.00 8.50 Small (>5 &<20) 7.00 6.08 6.15 3.08 1.75 1.83 5.55 5.88 7.35 8.75 2.98 7.10 3.42 2.42 6.63 Medium (20-99) 6.93 5.49 5.95 1.73 1.77 1.86 5.88 6.17 7.76 8.03 1.99 6.99 2.68 3.14 6.00 Large (100+) By industry 5.29 5.07 4.07 1.00 1.00 1.71 3.86 6.93 5.93 6.21 1.00 4.79 1.00 2.07 5.57 Food & beverages 6.71 5.79 4.79 1.29 1.54 1.32 5.39 6.61 7.00 8.00 1.14 7.96 1.75 2.29 6.68 Wood, Paper 6.76 4.55 6.26 1.85 1.80 1.81 6.50 6.65 8.61 8.30 2.27 7.50 2.77 2.64 5.38 Chemical ,Plastic 7.29 6.00 5.67 2.05 2.95 3.19 4.33 4.14 7.76 9.38 3.71 7.43 3.10 3.52 6.62 Building Material 8.46 8.89 7.93 3.89 1.71 2.00 6.54 5.21 6.32 8.32 2.57 5.93 5.18 5.04 7.96 Electrical Machine

6.97 5.66 6.05 2.04 1.82 1.92 5.86 6.14 7.67 8.22 2.21 7.10 2.89 3.03 6.17

Average 12 7 9 3 1 2 8 10 14 15 4 13 5 6 11 Average Rank 6.00 4.40 4.60 1.00 1.00 1.80 3.40 6.40 5.60 5.00 1.60 3.60 1.00 3.00 5.60 Number of firms reporting a business environment element to be the top obstacle Obstacle degree (13-15) 24 10 13 7 1 0 9 14 28 22 3 16 0 0 1 (10-12) 32 29 27 1 3 3 20 14 37 56 7 45 14 6 29 (7-9) 28 42 36 3 3 1 31 43 40 42 4 35 14 27 52 (4-6) 42 19 31 7 11 18 57 45 37 36 11 38 17 21 53 (1-3) 49 75 68 157 157 153 58 59 33 19 150 41 130 121 40 Total 175 175 175 175 175 175 175 175 175 175 175 175 175 175 175

* (1-3) No Obstacle, (4-6) a Minor Obstacle, (7-9) a Moderate obstacle, (10-12) a Major Obstacle, (13-15) a Very Severe Obstacle

174

Table 5.3 Descriptive statistics summary

Variable Obs Mean Std. Dev. Min Max

Export intensity 159 23.01 17.90 4 90 Employment 175 5.32 1.06 1.39 6.21 Sales 175 3.54 1.47 1 5 Wages 175 19.85 8.87 4 55 Sector 175 0.30 0.17 0.08 0.48 Mean Region 175 0.20 0.03 0.04 0.29 Export experience 161 13.56 6.85 1 31 Access to finance 175 2.66 1.41 1 5 Tax rates 149 2.97 1.26 1 5 Cost of financing 175 1.53 0.88 1 4 Tax administration 175 1.50 0.95 1 4 Customs and trade regulations 175 2.23 1.18 1 5 Business licensing and permits 175 2.35 1.33 1 5 Labour regulations 175 3.15 1.20 1 5 Political instability 175 1.30 0.84 1 5 Courts 175 1.17 0.59 1 5 Corruption 175 1.25 0.86 1 5 Crime, theft and disorder 175 1.17 0.50 1 4 Practices of competitors in the informal sector 175 2.75 1.31 1 5 Infrastructure 175 2.35 0.89 1 4 competition 156 3.85 1.07 1 5

175

Table 5.4: Linear relations among constraints

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) VARIABLES

(1)Access to finance 0.407*** -0.0106 -0.132** -0.102 0.421*** 0.259*** 0.0447 0.0297 0.0966* 0.106*** 0.0493 0.281*** (0.107) (0.0614) (0.0508) (0.0883) (0.0751) (0.0968) (0.0639) (0.0369) (0.0540) (0.0337) (0.110) (0.0616) (2)Cost of financing 0.238*** 0.0455 -0.0298 0.0298 -0.0863 -0.280*** 0.0260 -0.0652** 0.0910** -0.00768 -0.159* 0.0196 (0.0623) (0.0468) (0.0397) (0.0678) (0.0633) (0.0721) (0.0489) (0.0278) (0.0410) (0.0267) (0.0829) (0.0506) (3)Tax rates -0.0206 0.152 0.473*** -0.0886 0.0135 -0.201 -0.0118 0.147*** -0.373*** -0.0474 -0.0292 0.253*** (0.120) (0.156) (0.0603) (0.124) (0.116) (0.138) (0.0893) (0.0501) (0.0692) (0.0486) (0.153) (0.0898) (4)Tax administration -0.357** -0.138 0.659*** 0.183 0.205 -0.248 0.250** -0.272*** 0.691*** 0.275*** -0.205 0.0546 (0.138) (0.184) (0.0839) (0.145) (0.136) (0.162) (0.103) (0.0563) (0.0677) (0.0525) (0.180) (0.109) (5)Customs and trade regulations

-0.0953 0.0477 -0.0424 0.0631 0.509*** -0.0674 0.0147 0.0592* -0.165*** -0.0892*** 0.0739 0.124* (0.0824) (0.108) (0.0592) (0.0500) (0.0676) (0.0958) (0.0618) (0.0354) (0.0508) (0.0329) (0.106) (0.0631)

(6)Business licensing and permits

0.446*** -0.156 0.00734 0.0802 0.578*** 0.146 -0.0459 -0.0771** 0.0851 0.0244 0.0198 -0.0231 (0.0796) (0.115) (0.0632) (0.0532) (0.0768) (0.101) (0.0658) (0.0375) (0.0557) (0.0359) (0.113) (0.0681)

(7)Labor regulations 0.193*** -0.357*** -0.0769 -0.0681 -0.0538 0.103 0.132** -0.0687** 0.0409 -0.0218 -0.0804 0.0956* (0.0721) (0.0918) (0.0526) (0.0446) (0.0765) (0.0714) (0.0541) (0.0314) (0.0470) (0.0301) (0.0946) (0.0565) (8)Political instability 0.0803 0.0800 -0.0109 0.165** 0.0282 -0.0779 0.318** 0.164*** -0.0943 0.0181 -0.0843 -0.231*** (0.115) (0.150) (0.0823) (0.0683) (0.119) (0.111) (0.130) (0.0476) (0.0727) (0.0468) (0.147) (0.0865) (9)Courts 0.159 -0.598** 0.405*** -0.539*** 0.340* -0.390** -0.495** 0.489*** 0.843*** 0.381*** -0.237 0.129 (0.198) (0.255) (0.138) (0.111) (0.203) (0.190) (0.226) (0.142) (0.104) (0.0740) (0.254) (0.153) (10)Corruption 0.238* 0.384** -0.472*** 0.628*** -0.436*** 0.198 0.135 -0.130 0.388*** -0.282*** 0.537*** -0.0708 (0.133) (0.173) (0.0876) (0.0615) (0.134) (0.130) (0.156) (0.0999) (0.0477) (0.0493) (0.166) (0.104) (11)Crime, theft and disorder 0.639*** -0.0792 -0.147 0.611*** -0.576*** 0.139 -0.176 0.0606 0.428*** -0.687*** 0.0686 -0.0356 (0.203) (0.275) (0.150) (0.117) (0.212) (0.204) (0.243) (0.157) (0.0831) (0.120) (0.270) (0.162) (12)Practices of competitors in the informal sector

0.0300 -0.166* -0.00912 -0.0460 0.0482 0.0114 -0.0657 -0.0285 -0.0269 0.132*** 0.00693 0.176*** (0.0668) (0.0863) (0.0479) (0.0404) (0.0691) (0.0650) (0.0773) (0.0498) (0.0288) (0.0410) (0.0272) (0.0494)

(13)Infrastructure 0.472*** 0.0561 0.218*** 0.0338 0.223* -0.0366 0.215* -0.215*** 0.0404 -0.0481 -0.00993 0.485*** (0.103) (0.145) (0.0773) (0.0674) (0.113) (0.108) (0.127) (0.0808) (0.0478) (0.0706) (0.0453) (0.136) Constant -1.372*** 3.689*** 0.603* -0.0170 1.293*** -0.214 3.570*** 0.483 0.451** 0.0971 0.690*** 1.868*** 0.339 (0.444) (0.511) (0.325) (0.279) (0.462) (0.446) (0.435) (0.340) (0.195) (0.292) (0.178) (0.567) (0.353) Observations 149 149 149 149 149 149 149 149 149 149 149 149 149 R-squared 0.613 0.228 0.480 0.649 0.439 0.604 0.341 0.223 0.486 0.660 0.412 0.284 0.485 F 17.93 3.349 10.47 20.97 8.879 17.29 5.856 3.246 10.70 22.03 7.925 4.505 10.68

Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

176

Table 5.5: Impact of individual business environment constraints and competition on intensive of export, OLS estimation VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)

Competition -1.289 -2.709** -1.244 -1.128 -1.596 -1.166 -1.044 -1.133 -1.129 -1.168 -1.325 -1.642 -2.050 -3.922*** (1.302) (1.352) (1.304) (1.294) (1.267) (1.299) (1.298) (1.294) (1.298) (1.272) (1.301) (1.274) (1.259) (1.091) Employment -3.302 -2.638 -3.067 -3.001 -2.250 -3.150 -2.892 -4.008 -2.929 -2.819 -3.311 -2.637 -3.430 -3.073 (2.974) (3.110) (2.967) (2.939) (2.893) (2.950) (2.942) (2.980) (2.949) (2.896) (2.967) (2.895) (2.833) (2.365) Sales 0.917 0.791 0.744 0.697 0.541 1.257 0.680 0.951 0.467 0.0647 1.011 0.504 1.172 0.491 (2.128) (2.167) (2.130) (2.108) (2.065) (2.135) (2.108) (2.108) (2.126) (2.094) (2.132) (2.075) (2.032) (1.719) Wages 0.874*** 0.846*** 0.885*** 0.880*** 0.831*** 0.902*** 0.853*** 0.895*** 0.838*** 0.836*** 0.854*** 0.828*** 0.892*** 0.980*** (0.180) (0.187) (0.181) (0.177) (0.174) (0.180) (0.177) (0.178) (0.179) (0.175) (0.179) (0.175) (0.171) (0.156) sector of (Wood...) 2.535 2.756 2.390 2.246 1.150 2.071 3.460 2.824 2.495 2.526 3.045 0.121 2.054 -4.454

(5.809) (6.046) (5.811) (5.756) (5.654) (5.791) (5.772) (5.757) (5.767) (5.666) (5.824) (5.726) (5.548) (4.525) sector of (Chemical...) 7.661 1.810 7.433 7.768 6.561 7.115 6.732 10.21* 9.903* 10.03** 7.645 7.614 8.672* 13.94***

(5.049) (5.496) (5.045) (5.000) (4.906) (5.030) (5.020) (5.237) (5.240) (5.005) (5.041) (4.916) (4.828) (4.988) sector of (Building…) 11.90*** 9.467** 11.79*** 12.57*** 12.40*** 12.09*** 11.84*** 13.33*** 12.79*** 13.93*** 11.83*** 11.86*** 13.28*** 17.50***

(3.753) (4.091) (3.752) (3.739) (3.645) (3.737) (3.717) (3.815) (3.774) (3.737) (3.749) (3.656) (3.603) (3.214) sector of (Electrical..) 19.24*** 16.58*** 17.78*** 21.35*** 20.19*** 19.13*** 19.16*** 21.07*** 20.09*** 21.01*** 18.93*** 15.48*** 16.60*** 12.44** (5.032) (5.272) (5.262) (5.145) (4.877) (4.985) (4.964) (5.116) (5.030) (4.945) (5.005) (5.035) (4.826) (5.217) Central 2.290 3.070 2.402 2.338 2.421 2.346 2.500 0.618 1.680 2.101 1.783 1.667 0.891 2.382 (3.058) (3.365) (3.066) (3.014) (2.950) (3.029) (3.019) (3.134) (3.030) (2.965) (3.060) (2.965) (2.920) (2.583) Eastern 6.288 9.025** 6.621 7.070* 6.558* 6.351 7.142* 5.642 5.956 6.314 6.433 2.227 7.112* 5.108 (4.082) (4.470) (4.057) (4.031) (3.937) (4.039) (4.034) (4.057) (4.048) (3.961) (4.056) (4.238) (3.878) (4.120) export experience 0.567** 0.470** 0.604*** 0.563** 0.425* 0.489** 0.553** 0.680*** 0.634*** 0.703*** 0.558** 0.476** 0.779*** 0.980*** (0.226) (0.235) (0.229) (0.224) (0.224) (0.233) (0.224) (0.232) (0.228) (0.225) (0.226) (0.223) (0.222) (0.220) Access to finance -0.554 -2.720** (0.949) (1.304) Cost of financing 0.190 -0.737 (1.201) (1.153) Tax rates 1.211 5.836** (1.649) (2.349) Tax administration -2.765* -6.210** (1.575) (2.678) Customs and trade regulations

-3.340*** -2.769** (1.094) (1.341)

Business licensing and permits

-1.498 -1.380 (1.101) (1.638)

Labor regulations 1.812* 0.831 (1.030) (1.023) Political instability -2.756* -4.389** (1.617) (1.812) Courts -4.430 2.868 (2.897) (3.689) Corruption -5.964*** -5.102* (2.170) (2.839) Crime, theft and disorder -3.037 3.782

(3.743) (3.758)

competitors in the informal sector

-3.167*** -4.209*** (1.119) (1.307)

Infrastructure 5.449*** 11.93*** (1.448) (1.887)

Constant 7.277 9.588 2.660 7.272 12.58 7.088 -1.726 10.35 9.098 10.01 9.875 17.71 -7.218 8.892 (13.50) (14.93) (13.75) (13.09) (12.99) (13.16) (13.70) (13.36) (13.29) (12.96) (14.20) (13.54) (13.04) (13.74)

Observations 156 138 156 156 156 156 156 156 156 156 156 156 156 138 R-squared 0.318 0.341 0.319 0.331 0.358 0.325 0.331 0.330 0.327 0.350 0.319 0.352 0.378 0.686 F 5.549 5.387 5.573 5.883 6.643 5.733 5.885 5.862 5.792 6.428 5.588 6.484 7.234 10.27

Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

177

Chapter 6: Conclusions, Limitations and Future Research

6.1 Conclusion

Saudi Arabia has recognised the need to diversify their economy away from oil

as the main source of income. Due to the fact that oil is an exhaustible resource and

the oil price fluctuates considerably. It is clear from this thesis that the country was

especially concerned with the subject of the diversification of the economic base.

The state has followed an economic strategy that encourages industrial

development. It has provided facilities to ensure the development and promotion

of the role of the private sector, primarily focusing on industrial exports, trying to

reduce the impact of the risks which businesses face, and encouraging the creation

and activation of appropriate institutional frameworks to support these exports. In

this regard, the government has established an critical institutional framework in

the shape of the "Saudi Export Programme" under the umbrella of the Saudi Fund

for Development, in order to develop national non-oil exports and encourage

diversification by providing financing incentives and credit to exporters on the one

hand, and on the other hand through the provision of competitive credit terms for

buyers abroad or funding institutions working in this area.

This work has attempted to investigate the obstacles and barriers faced by

Saudi exporters through a survey of 175 manufacturing firms, employing data which

was first collected in Saudi Arabia at the end of 2011. The questionnaire content

has been discussed in appendix two. The current study analysed the firm export

behaviour from the point of view of three methodologies: the firm’s trade

operations, access to finance and credit constraints and, finally, competition and

the business environment.

Chapter Two has highlighted the role of the Saudi government in motivating

manufacturers to export. In addition It, was a description of the data that was used

in the analysis. In this chapter, the study summarised statistically the structure of

infrastructure, labour situation, production capacity, and competition. Then, it

concluded with some problems that hindered the firms surveyed, such as freight

178

costs, the cost of raw materials or components, the cost of finance, a lack of skilled

staff, exchange rate volatility, economic conditions overseas, tariff barriers

overseas, a lack of export skills or knowledge, a lack of skills in logistics and

knowledge of trade regulations, and language or cultural barriers.

Moreover, this chapter presented the policy-makers’ firm position regarding

expanding national sales or expanding exports, the study benefited by comparing

the influence between the same independent variables. Standards compliance and

customs and border procedures are the most important factors affecting decision-

makers and whether they expand national sales or expand exports. Taxes on

labour, the supply of skilled labour, limited export diversification, and informal

restrictions are factors for the firms positions towards expanding exports, whilst

inadequate transport links are a factor in the decision-makers’ position towards

expanding national sales.

In Chapter Three’s results we found support for the idea that the country

encourages the turning of family businesses into companies. The main result is that

firms managed individually or by family members have a negative effect on export

behaviour, while companies that are run through shareholders have a positive

impact on export intensity. In this chapter the results also show that foreign

ownership does not have an influence on export behaviour. For this reason, these

results indicate that foreign investment in Saudi Arabia may be taking advantage of

domestic demand rather than the international market.

The age of firms and export experience in international markets both have a

positive impact on export intensity. The effect of age is an indication of the

importance of benefiting from the experience of those firms, and analysing their

methods of achieving success and overcoming obstacles for the purpose of

designing programmes that support the firms that are interested in exporting more.

Recognised quality certifications and patents registered are aspects that show that

firms are interested in developed countries as an indicator of the role of innovation

in manufacturing and export. Recognised quality certifications prove to be an

associated factor in increasing export intensity, whether locally or internationally.

179

The patent results did not show any role in export behaviour. Another aspect

observed by the present study was that the increasing volume of firm sales led to

less willingness for export expansion, thus these firms will be under conditions of

domestic demand in the future. It is important to encourage these firms to diversify

their markets.

Although for some firms in the current study, using TV, radio, and the internet

has had a positive impact on export behaviour, other aspects have emerged which

negatively affect export intensity, such as firms depending on the firm’s sales force

to distribute their products or firm-owned retail stores. In addition, there were

negative impacts with regard to export marketing firms participating in trade fair

exhibitions and relying on brochures to promote the firm and its products; this may

be due to the firm carrying costs additional to the cost of export. We also argue

that this negative impact may be plausible because of the importance of spending

on promotion in order to market the products. Support capabilities for export were

discussed by some studies; our results show the importance of the firm engaging

with capabilities for export expansion. The results show it is more important to

prepare a marketing plan for export and to use foreign languages to identify

products. From the results, export capabilities are also shown to have a negative

impact on export behaviour. The firms responsible believe that having multilingual

sales staff is very important for export expansion, in addition to the use of email.

This is their perception of an important factor in the expansion of exports that is

not supported by the study results.

Chapter Four focuses on how financial factors affect firms. In this chapter we

have analysed the firms’ export behaviour by relying on three methodologies: the

influence of credit constraint on the firm’s attributes, the importance of access to

finance for firms, and how export intensity is influenced by credit constraints. We

relied on the latest measures of credit constrained status based on micro data and

describing what type of firms are more likely to be credit-constrained and which

ones are not. The results of testing the hypothesis that internationalisation leads to

better access to financial markets, was to find no support for this hypothesis in our

analysis. In general, the main finding is consistent with literature, especially the

180

works which argue that younger firms are more likely to be credit-constrained than

large, older firms. Another interesting result is that younger firms are also more

likely to use trade credit and informal sources of finance as funds for investment

and working capital than large firms. Furthermore, involvement in foreign markets

leads to a negative relationship between credit constraint and exports which mean

the credit rationing reduces foreign sales by more than 8 per cent.

The purpose of Chapter Five has been to analyse the competition and business

environment, and firms’ performance measured by the intensity of exports. The

current study has taken into account the business environment constraints to

analyse the performance effects of a firm’s competition. The main finding is that

competition does not have an impact on performance isolated in business

constraints, but the effect appears negative once the model takes into account the

business environment constraints. Also in this chapter the export experience of the

firm is found to have a positive effect on intensity as well as wages, while the effect

of the number of labourers also has a negative effect.

6.2 Policy Recommendations

Saudi Arabia strives to expand its productive base and alleviate its dependence

on oil. For this purpose, the country fosters policies and plans to support and

encourage the private sector to play a role in the economy by exporting and

benefiting from the comparative advantages of the economy. The Saudi economy is

characterised by the comparative advantages that support industry. The study

results show that several industry sectors have a positive impact on export. The

country encourages the turning of family businesses into sharing (public)

companies. There are economically beneficial results of restructuring family

businesses, such as more commitment from family and administration in order to

increase the returns to shareholders, continuation of sales and profit growth,

ongoing work to develop and attract the best talent from outside the family, ease

of access to sources of funding, and a strengthening of the company’s competitive

position. One of the main results of the present study is that the management of

firms by individuals or family members has a negative effect on export behaviour. In

181

contrast, the running of companies through shareholders has a positive impact on

export intensity. The present study results also show that foreign ownership does

not have an impact on export behaviour. This implies that foreign investment in

Saudi Arabia is oriented toward taking advantage of domestic demand for firms’

output.

The age of the firm and export experience in international markets both have a

positive impact on export intensity. The effect of age is an indication of the

importance of benefiting from the experiences of those firms. An analysis of their

methods in success and overcoming barriers can help in the design of programmes

to support firms that are interested in exporting more. Recognised-quality

certifications and patents registered are aspects that show that firms are interested

in developed countries as an indicator of the role of innovation in manufacturing

and export. Recognised-quality certifications prove to be an associate factor in

increasing export intensity, whether locally or internationally. The patent results did

not show any role in export behaviour. Another aspect observed by the present

study where the result was marginal was that the increasing volume of firm sales

led to less willingness for export expansion, and thus these firms will be under

conditions of domestic demand in the future. It is important to encourage these

firms to diversify their markets.

Although for some firms in the current study the use of TV, radio and the

internet has had a positive impact on export behaviour, other aspects have

emerged that negatively affect export intensity, such as firms depending on the

firm’s sales force to distribute their products, or firm-owned retail stores. Also,

there were negative impacts with regard to export marketing firms participating in

trade fair exhibitions and relying on brochures to promote the firm and its

products. This may be due to the firm carrying costs additional to the cost of

export. The analysis also argues that this negative impact may be plausible because

of the importance of spending on promotion in order to market the products.

Support capabilities for export were discussed by some studies; our results

show the importance of the firm engaging with capabilities for export expansion.

182

The results show it is more important to prepare a marketing plan for export and

use foreign languages to identify products. Also from the results, export capabilities

are shown to have a negative impact on export behaviour. The firms responsible

believe that having multilingual sales staff is very important for export expansion.

Their perception of an important factor toward the expansion of export is not

supported by the study’s results.

Moreover, In terms of the financial constraints, the results show that there

exists a negative relationship between credit constraint and exports, which means

that financial constraints constitute a problem for firms that are involved in foreign

markets. In addition, our results found that there exists a positive relationship

between firms’ export behaviour and productivity, size and capital intensity.

Controlling for productivity and other firm characteristics, and accounting for the

endogeneity of credit, we find that credit rationing reduces foreign sales by more

than 8 per cent. The present work has the important implication that the need for

an expansion of exports is an additional reason why Saudi Arabia should enhance its

Saudi Export Programme (SEP), which provides finance and guarantees to Saudi

exporters. The government should provide more efforts to use the finance

incentives of the Saudi Export Programme to encourage the private sector to fund

their export operation. In this thesis, we have explored the linkages between the

competition and business environment faced by firms and their performance as

measured by employment, sales and wages. In the first place, we have considered

the role of the business environment individually based on the firm-level; second,

we have used the business environment entered together based on the firm-level.

The thesis highlights several results that are highly relevant from a policy point of

view: To attract investment, the country should give top priority to improving their

country’s business climates, such as customs and trade regulations, and practices of

competitors in the informal sector. In line with improving business climates the analysis

show that competition has a negative impact on export intensity in Saudi Arabia.

However, the findings cannot claim that a firm is guaranteed success if it only takes

good care of these success factors. However, it is likely that a firm that does not

183

deal adequately with these factors will decrease its intensity of export compared

with firms that pay adequate attention to these issues.

6.3 Limitations and Future Research

The study have learned from this study to continue building a base of

knowledge of the private sector by survey analysis, because it is the first time this

kind of analysis has been done in Saudi Arabia. As for future lines of research in this

area, there are many questions to consider. First, and with respect to the current

work, this analysis could be extended to take into account the influence of different

firm attributes on export intensity.

The research attempted to include, in our current questionnaire, variables

that we found in the literature. The researcher found evidence that there are some

important questions that must be included in the questionnaire that will develop

and enhance the database for exports in the future. The main question would be to

analyse how export behaviour is related to the number of products (export

diversification: the total number of products exported to each destination). This

would contribute to enriching our comprehension of the effects on firm export

behaviour by investigating the exporting behaviour of multi-product firms in Saudi

Arabia. There should also be more questions about productivity, such as measures

of productivity (sales per worker). In terms of finances, it could be worth asking

about cash flow in firms and the percentage of stock leftover at the end of the year.

Moreover, although we collected data from Saudi Arabian exporters, it is important

to expand our analysis by including firms that have not exported to form a control

group and assess whether there are firms that could be exporting but are not, and

why they are not.

Although the empirical results documented in this thesis are plausible, at this

stage we cannot be sure that they hold in general. Testing whether our results extend

beyond Saudi firms is therefore a promising area for future research. Finally, the

general results of this study could assist in highlighting the main determinants in term

of ownership structure, finance, competition and business environment that confront

the Saudi exporting firms

184

References

Alimi, S. R., & Muse, B. O. (2012)." Export-led growth or growth–driven exports? Evidence

from Nigeria". Available at: http://mpra.ub.uni-muenchen.de/53468/

A. Colin Cameron and Pravin K. Trivedi. 2005. “Tobit and Selection Model in

Microeconometrics: Methods and Applications“. United States of America: Cambridge

University Press. 1035.

Ahmed, Sadrudin A.; Rock, Juan. 2012. "Exploring the Relationship between Export

Intensity and Exporter Characteristics, Resources, and Capabilities: Evidence from Chile",

Latin American Business Review. Jan-Mar2012, Vol. 13 Issue 1, p29-57.

Ahmed, Z .U., Craig, J. C., Baalbaki, I. and Hadadian, T. V., 2004. “Export barriers and firm

internationalisation: a study of Lebanese entrepreneurs“. Journal of Management and

World Business Research, 1(1), pp.11-22.

Al-Aali, Abdulrahman,1995. “Obstacles facing Saudi-Arabian food and chemical

exporters.“International Journal of Commerce and Management, 5(3), pp.17-31.

Al-Aali, Abdulrahman,1997. “Saudi Non-oil Export: The Challenges and Prospect.“The

Annual Meeting of Middle East Studies Association of North America (MESA) 22-25

November 1997.

Al-Aali, Abdulrahman,1999. “Obstacles perceived by exporters in Saudi Arabia“.King Saud

University Admin Sci, 11(1), pp.19-38.

Alegre, J, Pla-Barber, J, Chiva, R, &Villar, C. 2012, “Organisational learning capability,

product innovation performance and export intensity”, Technology Analysis & Strategic

Management, 24, 5, pp. 511-526.

Alexandrides, C. G.1971. How the Major Obstacles to Exporting can be Overcome. Atlantic

Economic Review: 12–15.

Al-Jarrah, Mohammed , 2008. “Non-Oil Export Growth and Economic Development in Saudi

Arabia: A Simultaneous Equations Approach“. Journal of the Gulf and Arabian Peninsula

Studies, 34(129),

Allen, J. and Phillips, G.M. 2000. “Corporate equity ownership, strategic alliances and

product market relationships”, Journal of Finance 55(6): 2791–2815.

Al-Qahtany, Mohammed, 2001.“Obstacles Facing Saudi Exporters of Non-Oil Products“.The

Lahore Journal of Economics, 6(1), pp.129-142.

Al-twuijri, H, 2001. “The relationship between exports and economic growth: An Empirical

Study on the economy of Saudi Arabia“. King Saud University Journal, Administrative

Sciences 1(13), pp. 219-234

185

Al-Yousif, K., 1997." Exports and economic growth: Some empirical evidence from the Arab

Gulf Countries", Applied Economics, 29, 693-697.

Archarungroj P. and Hoshino, Y. 1998,"The Impact of Firm Size on Export Performance and

Attitudes: An Empirical Study on Thailand Exporters", Japanese Journal of Administrative

Science Volume 12, No. 2, 1998, 79 - 88.

Arnold, J.M. and Hussinger, K., 2005. “Export behavior and firm productivity in German

manufacturing: a firm-level analysis“. World Economics, Vol. 141, No. 2.

Asaftei, G., Kumbhakar, S. C. and Mantescu, D. 2008. “Ownership, business environment

and productivity change“, Journal of Comparative Economics, 36, pp. 498–509

Augier, P., Dovis, M. and Gasiorek, M. 2012.“The business environment and Moroccan firm

productivity“.Economics of Transition, 20: 369–399.

Aw, Bee Yan, Mark J. Roberts and Tor Winston, 2007, Export Market Participation,

Investments in R&D and Worker Training, and the Evolution of Firm Productivity ,The World

Economy (2007) doi: 10.1111/j.1467-9701.2007.00873.x

Aw, Bee Yan, Sukkyun Chung and Mark J. Roberts. 2000. “Productivity and the Turnover in

the Export Market: Micro-level Evidence from the Republic of Korea and Taiwan (China),“’

The World Bank Economic Review, 14 (1): 65-90.

Aynul, Hasan M. and Hirohito Toda, 2004. “Export Diversification and Economic Growth:

The Experience of Selected Least Developed Countries", Development Papers No. 24,

UNESCAP Reference No.: ST/ESCAP/2314.

Balabanis, G. I., &Katsikea, E. S. 2003. ”Being an entrepreneurial exporter: Does it

pay?”,International Business Review, 12, 233–252.

Baldwin, Robert. 1988. “Hysteresis in Import Prices: The Beachhead Effect.” American

Economic Review, 78(4) pp.773-785.

Balassa, B. (1978). "Exports and economic growth: further evidence". Journal of

development Economics, 5(2), 181-189.

Barker, A. T. and Kaynak, E., 1992.“An Empirical Investigation of the Differences Between

Initiating and Continuing Exporters“.European Journal of Marketing, 26(3), pp.27-36.

Basile, Roberto ,2001. ”Export behaviour of Italian manufacturing firms over the nineties:

the role of innovation”, Research Policy, Volume 30, Issue 8, October 2001, Pages 1185-

1201,

Bauerschmidt, A., Sullivan, D. and Gillespie, K. 1985. “Common Factors Underlying Barriers

to Export Studies in the U.S. Paper Industry.“Journal of International Business Studies,

16(3), pp.111-123.

Baum, C.F., 2006. “An Introduction to modern econometrics using stata“.Stata Press, 2006.

186

Beck, T., 2002. “Financial development and international trade. Is there a link?” Journal of

International Economics 57, 107–131.

Beck, T., and Demirguc-Kunt, A., 2006.“Small and medium-size enterprises: access to

finance as a growth constraint“. Journal of Banking and Finance, 30, 11, 2931-2943.

Bellone, F., Musso, P., Nesta, L. and Schiavo, S., 2010.“Financial constraints and firm export

behaviour“. The World Economy, 33, 3 , 347-373.

Ben Jebli, M., & Ben Youssef, S. (2013)." Combustible renewables and waste consumption,

exports and economic growth: Evidence from panel for selected MENA countries".

Available at http://mpra.ub.uni-muenchen.de/47767/

Benvignati A. 1990. ”Industry determinants and ‘differences’ in U.S. intrafirm and arms-

length exports”.Review of Economics and Statistics 72: 481–488.

Bernard, A. B.; Jensen, J. B., 2004. "Why some firms export". Review of Economics and

Statistics 86, 561-569.

Bertrand, O. and Mol, M. J. 2012.”The antecedents and innovation effects of domestic and

offshore R&D outsourcing”. Forthcoming in Strategic Management Journal

Bezic, H., Vojvodic, K. and Stojcic, N., 2010. “Export Competitiveness, Firm Behaviour and

Obstacles for doing Business.“Business Logistics in Modern Management, 10, pp.11-26.

Bijmolt, T.H.A., and Zwart, P.S. 1994, "The Impact of Internal Factors on the Export Success

of Dutch Small and Medium-Sized Firms", Journal of Small Business Management, Vol. 3 2

pp.69-83.

Bilkey, W. J. and Tesar, G. 1977.“The Export Behaviour of Smaller-Sized Wisconsin

Manufacturing Firms.“Journal of International Business Studies, 8, pp.93-98.

Bilkey, W. J., 1978. ”An Attempted Integration of the Literature on the Export Behaviour of

Firms”.Journal of International Business Studies, 9(1), pp.33-46.

Bombardini M. & Christopher J. Kurz & Peter M. Morrow, 2012."Ricardian trade and the

impact of domestic competition on export performance," Canadian Journal of Economics,

Canadian Economics Association, vol. 45(2), pages 585-612, May.

Bonaccorsi, Andrea ,1992."On the Relationship between Firm Size and Export Intensity",

Journal of International Business Studies, Vol. 23, No. 4 (4th Qtr., 1992), pp. 605-635

Published.

Braunerhjelm, P. (1996). ”The relation between firm-specific intangibles and export”.

Economic Letters, 53, 213–219.

Bridges, S. and Guariglia, A. 2008. “Financial constraints, global engagement, and firm

survival in the United Kingdom: evidence from micro data“. Scottish Journal of Political

Economy, 55, 4, 444-64.

187

Brown J.D. and J.S. Earle 2000. “Competition and firm performance: lessons from Russia.“

MIMEO, SITE, March, 2000.

Bruce E. Hansen, 2011. "ECONOMETRICS", University of Wisconsin,

(www.ssc.wisc.edu/~bhansen).

Brush, S. 1992. ”Farmers' rights and genetic conservation in traditional farming

systems”.World Development, 20, 1617-1630.

Byeong-Joon, M, & Jain, S 2007. “Determinants and Outcomes of Internet Marketing

Activities of Exporting Firms”, Journal Of Global Marketing, 20, 4, pp. 55-71

Cavusgil ST, Naor J. 1987. ”Firm and management characteristics as discriminators of

export marketing activity”.Journal of Business Research 15: 221–235.

Cavusgil ST. 1984. ”Organizational characteristics associated with export activity”. Journal

of Management Studies 21: 3–22.

Cavusgil, S. T. and Nevin, J. R., 1981. “Internal Determinants of Export Marketing Behaviour:

An Empirical Investigation.“ Journal of Marketing Research, 18, pp.114-119.

Cavusgil, S. T., &Zou, S. 1994. ”Marketing strategy performance relationship: An

investigation of the empirical link in export market ventures”.Journal of Marketing, 58(1),

1–21.

Central Department of Statistics and Information, Foreign Trade, Ministry of Economy and

Planning, 2010. Export Statistics, Riyadh.

Chen, R. and Martin, M.J. 2001. ”Foreign expansion of small firms: the impact of domestic

alternatives and prior foreign business involvement”, Journal of Business Venturing 16(6):

557–574.

Commander S. and Svejnar, J., 2011.“Business Environment, Exports, Ownership, and Firm

Performance.“The Review of Economics and Statistics, February 2011, Vol. 93, No. 1, Pages

309-337, Posted Online January 21, 2011.

Cooper ,Robert G. and Kleinschmidt, Elko J. 1985. "The Impact of Export Strategy on Export

Sales Performance", Journal of International Business Studies, Vol. 16, No. 1 (Spring, 1985),

pp. 37-55Published

Cooper, A. C., Gimeno-Gascon, F. J., & Woo, C. Y. 1994. ”Initial human and financial capital

as predictors of new venture performance”.Journal of Business Capital Market Authority,

2011. Annual Report.Venturing, 9(5): 371–395.

Crick, D., Al Obaidi, M. and Chaudhry, S., 1998.“Perceived obstacles of Saudi- Arabian

exporters of non-oil products“.Journal of Marketing Practice: Applied Marketing Science,

4(7), pp.187-199.

188

Crinò, R. and Epifani, P. 2008.,“Productivity and Export Intensity to High-Income and Low-

Income Countries“, The Institute for Economic Analysis (IAE), Working

paper,(http://www.iae.csic.es/investigatorsMaterial/a8159161138archivoPdf22944.pdf)

D. Fu a, Y. Wu a and Y. Tang b.2011. "The effects of ownership structure and industry

characteristics on export performance Evidence from Chinese Manufacturing Firms". 19th

International Congress on Modelling and Simulation, Perth, Australia, 12–16 December

2011

Damijan, J.P. and Kostevc, Č., 2011.“Firms' pattern of trade and access to finance“.LICOS

Discussion Paper No. 278. [Online] Available at: http://ssrn.com/abstract=1786343 or

http://dx.doi.org/10.2139/ssrn.1786343

Damijan, J.P., Kostevc, Č. and Polanec, S. 2010. “Firm size, financing constraints and

intensive export margin“. Working Paper, Forum for Research in Empirical International

Trade.

Das, S.,Roberts,M. And Tybout, J.,2007, “Market entry costs, producer heterogeneity and

export dynamics”, Econometrica, Vol. 75, No. 3 (May, 2007), 837–873

Dastjerdi, R , Mohseni, R and Jafari, S .2012. "Studying the Effects of non-oil Exports on

Targeted Economic Growth In Iranian 5th Development Plan: A Computable General

Equilibrium Approach", Available at: http://works.bepress.com/reza_moosavi_mohseni/10

Davidson, W. 1980.”The location of foreign direct investment activity: Country

characteristics and experience effects”.Journal of International Business Studies, 11(2), 9–

22.

Davis, J.A. and Tagiuri, R. 1991. ”Bivalent Attributes of the Family Firm”, in C.E. Aronoff and

J.L. Ward (eds.) Family Business Sourcebook, Omnigraphics: Michigan, 62–73.

Dejo-Oricain, N. and Ramírez-Alesón, M., 2009.”Export Behaviour: a Study of Spanish

SMEs”.GCG Georgetown University–Universaria, 3(2),

Demsetz, H. and Lehn, K. 1985. ”The structure of corporate ownership: causes and

consequences”, Journal of Political Economy 93(6): 1155–1171.

Dierickx, I. and Cool, K. 1989. ”Asset stock accumulation and sustainability of competitive

advantage”, Management Science 35(12): 1504–1511.

Djankov and P. Murrell 2000. “Enterprise restructuring in transition: a quantitative survey.“

Mimeo, World Bank, April, 2000.

Djankov and V.-G.Kreacic 1998. “Restructuring of manufacturing firms in Georgia: four case

studies and a survey.“ world bank Occasional paper. Washington. D.C.

Dollar, D. &Kraay, A. 2003, "Institutions, trade, and growth", Journal of Monetary

Economics, vol. 50, no. 1, pp. 133-162.

189

Dollar, D., Hallward-Driemeier, M. and Mengistae, T. A. 2005.“Investment climate and firm

performance in developing countries“, Economic Development and Cultural Change, 54(1),

pp. 1–31.

Dominic H. Chai, 2010. "Firm ownership and philanthropy", Working Paper No. 400, Centre

for Business Research, University of Cambridge,

Donckels, R. and Fro¨hlich, E. 1991. ”Are family businesses really different? European

experiences from STRATOS”, Family Business Review 4(2): 149–160.

Earle J.S. and S. Estrin 1998, “Privatization, competition and budget constraint: disciplining

enterprises in Russia.“, Social Science Research Network Working Paper Series.

Eaton, J., Kortum, S. and Kramarz, F., 2011. “An Anatomy of International Trade: Evidence

from French Firms.“ Econometrica, 79(5), pp.1453-1498.

Eifert, B., Gelb, A. and Ramachandran, V. 2008.“The cost of doing business in Africa:

Evidence from enterprise survey data“, World Development, 36(9), pp. 1531–1546.

Elbeydi, K. R. M., Hamuda, A.M., Gazda, V., 2010." The relationship between export and

economic growth in Libya Arab Jamahiriya". Theoretical and Applied Economics, 1, 69-76.

Enterprisesurveys, 2009.“Enterprise survey and indicator surveys sampling methodology.“

[online] Available at: <www.enterprisesurveys.org> [Accessed 29 August 2009].

International Finance Corporation, World Bank.

Enterprisesurveys, 2012. “Enterprise Surveys Indicator Descriptions”,[online] Available at:

<www.enterprisesurveys.org> [Accessed May, 2012]. International Finance Corporation,

World Bank.

Erramilli, M. K. 1991. ”The experience factor in foreign market entry behaviour of service

firms”.Journal of International Business, 21, 479–501.

European Commission, 2005. "Effects on the European Economy of Shortages of Foreign

Language Skills in Enterprise", the National Centre for Languages.

Fama, E.F. and Jensen, M.C. 1985. ”Organizational forms and investment decisions”,

Journal of Financial Economics 14(1): 101–119.

Fatih, Y., 2009. “Causal Relationships between Financial Development“,Trade Openness

and Economic Growth: The Case of Turkey. Journal of Social Sciences, 5(1), pp.33-42.

Feder, G. (1983). "On exports and economic growth". Journal of development economics,

12(1), 59-73.

Fernandes, A. M. 2008. “Firm productivity in Bangladesh manufacturing industry“, World

Development, 36(10), pp. 1725–1744.

Fernandez, Z. & Nieto, M. 2006.“Impact of ownership on the international involvement of

SMEs”.Journal of International Business Studies, 37 (3): 340-351.

190

Filatotchev, Igor, Stephan, Johannes Florian and Jindra, Bjoern,2008. ”Ownership structure,

strategic controls and export intensity of foreign-invested firms in transition economies”,

Journal of International Business Studies, 39, issue 7, p. 1133-1148

Fryges, H.,2006. “Hidden Champions - How Young and Small Technology-Oriented Firms

Can Attain High Export-Sales Ratios,” ZEW Discussion Papers 06-45.

Ganotakis, Panagiotis and Love,James H .2012. "Export propensity, export intensity and

firm performance: The role of the entrepreneurial founding team" ,Journal of International

Business Studies advance online publication 2 August 2012; doi: 10.1057/jibs.2012.16

Gatti, R. and Love, I. 2008. “Does access to credit improve productivity?“ Economics of

Transition, 16(3), pp. 445–465.

Goedhuys, M., Janz, N. and Mohnen, P. 2010. “What drives productivity in Tanzanian

manufacturing firms: Technology or business environment?” The European Journal of

Development Research, 20(2), pp. 199–218.

Goldman, E. and Viswanath, P.V., 2009. “Export intensity and financial policies of Indian

Firms.“IGIDR Proceedings and Project Reports Series [Online] Available at: PP-062-11.

<http://www.igidr.ac.in/pdf/publication/PP-062-11.pdf>.

Gourlay A. and J S Seaton, 2004.”UK Export Behaviour at the Firm Level”.Economic Issues,

Vol. 9, Part 2, 2004

Greenaway, D., Guariglia, A. and Kneller, R., 2005. “Do financial factors affect exporting

decisions?“. GEP Research Paper 05 / 28, University of Nottingham: Leverhulme Centre.

Greenaway, D., Guariglia, A. and Kneller, R., 2007.“Financial factors and exporting

decisions“, Journal of International Economics, Elsevier, Vol. 73, 2 , pp. 377-395, November.

Gripsrud, G. 1990. “The determinants of export decisions and attitudes to a distant market:

Norwegian fishery exports in Japan“. Journal of International Business Studies, 21(3) 469–

485.

Gruber W, Mehta S, Vernon R. 1967. ”The R&D factor in international trade and

international investment of United States industries”.Journal of Political Economy 75: 20–

37.

Gumede, V., 2000. “Growth and Exporting of Small and Medium Enterprises in South Africa:

Some Thoughts on Policy and Scope for Further Research.“ [online] Available at:

<http://www.tips.org.za/files/402.pdf>.

Hallward-Driemeier, M., Wallsten, S. and Xu, L. C. 2006.“Ownership, investment climate

and firm performance: Evidence from Chinese firms“, Economics of Transition, 14(4), pp.

629–647.

191

Hansen, Thorsten 2010.”Exports and Productivity: An Empirical Analysis of German and

Austrian Firm-Level Performance”.MünchenerWirtschaftswissenschaftlicheBeiträge (VWL)

2010-22

Heckman, J.J. and C. Pages 2000.`The Cost of Job Security Regulation: Evidence for Latin

American Labor Markets,' NBER Working Paper 7773.

Helpman, E., M. Melitz and Y. R ubinstein. 2008. “Estimating Trade Flows: Trading Partners

and Trading Volumes”, Quarterly Journal of Economics. 123: 441-487.

Hiep N. &Nishijima S. 2009. “Export intensity and impacts from firm characteristics,

domestic competition and domestic constraints in vietnam: a micro-data analysis“.

Research Institute for Economics and Business Administration, Kobe University

(http://ideas.repec.org/p/kob/dpaper/238.htm)

Ilke Van Beveren&HylkeVandenbussche, 2010. "Product and process innovation and firms'

decision to export," Journal of Policy Reform, Taylor and Francis Journals, vol. 13(1), pages

3-24.

International Finance Corporation, 2010. “Understanding the Questionnaire”. World Bank’s

Enterprise survey 2010, [online] Available at: <www.enterprisesurveys.org>

Ito, K. and Pucik, V. 1993. ”R&D spending, domestic competition, and export performance

of Japanese manufacturing firms”, Strategic Management Journal 14(1): 61–75.

Iyer, Kris .2010. “The Determinants of Firm-Level Export Intensity in New Zealand

Agriculture and Forestry” .Economic Analysis & Policy, Vol. 40 No. 1, march 2010

Jauhari, V. 2007. "Analysing Export Intensity of The Select Electronics Firms In India",

International Journal of Innovation Management, Vol. 11, No. 3 (Sept. 2007) pp. 379–396

Jens Matthias Arnold and KatrinHussinger, 2005. “Export Behavior and Firm Productivity in

German Manufacturing: A Firm-Level Analysis“, World Economics, Vol. 141 No.2

Johansson, J. 2000. ”Global Marketing: Foreign Entry, Local Marketing, and Global

Management”, 2nd edition, Boston: McGraw-Hill

Jones, D., Klinedinst M and C. Rock, 1998. “Productive efficiency during transition: evidence

from Bulgarian Panel Data.“, Journal of Comparative Economics, Vol. 26, No. 3, pp. 446 .

464.

Jun Du and Girma, S., 2007.“Finance and firm export in China.“KYKLOS, Vol. 60, No. 1, 37-

54.

Katsikeas, C. S., Piercy, N. F., and Ioannidis, C. 1995, ”Determinants of export performance

in a European context”, European Journal of Marketing, 30(6), pp. 6–34.

Kaynak, Erdener, 1984. "International Marketing Management", Greenwood Publishing

Group, Incorporated

192

Keeble, D., Lawson, C., Smith, H.L., Moore, B. and Wilkinson, F. 1998. ”International

processes, networking and local embeddedness in technology-intensive small firms”, Small

Business Economics 11(4): 327–342.

Kellman, M. and Shachmurove, Y., 2012. “The Ability to Adapt and Overcome Obstacles:

Machinery Exports of Poland“, PIER Working Paper 12-004. [online] Available at:

<http://ssrn.com/abstract=2002733>.

Kets de Vries, M.F.R. 1996 “Family Business: Human Dilemmas in the Family Firm”,

International Thomson Business Press: London.

Kiendrebeogo, Y. and Minea, A., 2012. “Financial factors and manufacturing exports:

theory and firm-level evidence from Egypt“. CERDI Working Papers 2012, 21.

Koenig, Pamina 2009."Agglomeration and the export decisions of French firms", Journal of

Urban Economics 66 2009.186–195.

Konings J. 1998. “Firm performance in Bulgaria and Estonia: the effects of competitive

pressure“, financial pressure and disorganization.,The Davidson Institute Working Paper

Series, No. 185.

Kravis I, Lipsey R. 1992.“Sources of competitiveness of the United States and of its

multinational firms“.Review of Economics and Statistics 74: 193–201.

Kundu, S. and Katz, J. 2003. “Born-international SMEs: BI-level impacts of resources and

intentions“. Small Business Economics, 20 (1): 25- 47.

Kuntchev, V., Ramalho, R., Rodríguez-Meza, J. and Yang, J.S., 2012. “What have we learned

from the Enterprise Surveys regarding access to finance by SMEs?“ Unit of the Finance and

Private Sector Development of the World Bank Group, February 14, 2012.

Lages, L.; Japs, S. & Griffith, D. 2008. “The role of past performance in export ventures: A

short-term reactive approach”. Journal of International Business Studies, 39 (2): 304-325.

Lancheros, S. and Demirel, P. 2012. “Does finance play a role in exporting for service firms?

Evidence from India.“ The World Economy, Vol. 35, Issue 1, pp. 44-60.

Lawless M. and K. Whelan,2008. “Where Do Firms Export, How Much, and Why?"Working

Papers 200821, School Of Economics, University College, Dublin.

Lee, C.H., Huang, B. N., 2002. The "Relationship between exports and economic growth in

East Asian countries: a multivariate threshold autoregressive approach". Journal of

Economic Development, 27, 45-68.

Leonidou L.C. 1991. "Marketing in the Gulf States", Management Decision, Vol. 39, No. 8,

pp. 20-35.

Leonidou, L. C. 2000. “Barriers to export management: An organizational and

internationalization analysis“. Journal of International Management, 6, 121–148

193

Leonidou, L. C., Katsikeas, C. S., & Piercy, N. F. 1998. “Identifying managerial influences on

exporting: past research and future directions“. Journal of International Marketing, 6(2),

74–102.

Leonidou, L., 1995. “Empirical Research on Export Barriers: Review, Assessment and

Synthesis“. Journal of International Marketing, 3(1), pp.29-43.

Leonidou, L., 1995. “The Saudi distribution system structure, operation and

behaviour“.Marketing Intelligence and Planning, Vol. 13 No. 11, 1995, pp. 27-35

Lixin Colin Xu. 2010. “The Effects of Business Environments on Development: Surveying

New Firm-Level Evidence.“ World Bank, Policy Research Working Paper 5402

Lockett, A.; Wright, M.; Burrows, A., Scholes, L. & Paton, D. 2008. “The export intensity of

venture capital backed companies“. Small Business Economics, 31 (1): 38-58.

Lu, J. W., & Beamish, P. W. 2001. “The internationalization and performance of

SMEs“.Strategic Management Journal, 22(6–7), 565–586.

Ma, Z. 2002. Social-capital mobilization and income returns to entrepreneurship: The case

of return migration in rural China. Environment and Planning A, 34(1): 1763–1784.

Majocchi, A.; Bacchiocchi, E. &Mayrhofer, U. 2005. "Firm size, business experience and

export intensity in SMEs: A longitudinal approach to complex relationships". International

Business Review, 14 (6): 719-738.

Manuel, J., 2011. “Firm exports, liquidity management, and financial constraints.“

University of Granada, presented at XXXVI Simposio de la Asociación Española de

Economía, Universidad de Málaga, 17 December 2011.

Mayer, T. and Ottaviano, G. I. P., 2008. “The Happy Few: The Internationalisation of

European Firms.“ Intereconomics: Review of European Economic Policy, 43(3), pp.135-148.

Mayer, T., Melitz, M., and Ottaviano, G.I.P. 2011. “Market Size, Competition, and the

Product Mix of Exporters,“ NBER Working Papers 16959.

Mehrara, M., Musai, M., & Nasibparast, S. (2012)."The Causality between Non-Oil Exports

and GDP in Petroleum Exporting Countries". International Journal of Advanced Research in

Management and Social Sciences, 1(5), 22-34.

Melitz, M.J., 2003. “The impact of trade on intra-industry reallocations and aggregate

industry productivity“.Econometrica 71, 1695–1725.

Minetti and Zhu, 2011R.Minetti, S.C. Zhu, 2011. “Credit constraints and firm export:

microeconomic evidence from Italy“. Journal of International Economics, 83 2011 , pp.

109–125

Ministry of Commerce and Industry, 2009. “The National Strategy for Industry, Until 2020

and the mechanics of implementation "to industry competition and knowledge-based

economy”,The National Strategy for Industry (version 4).

194

Ministry of Planning, 1970. The First Development Plan. Riyadh: Ministry of Planning Press.

Ministry of Planning, 1975. The Second Development Plan. Riyadh: Ministry of Planning

Press.

Ministry of Planning, 1981. The Third Development Plan. Riyadh: Ministry of Planning Press.

Ministry of Planning, 1985. The Fourth Development Plan. Riyadh: Ministry of Planning

Press.

Ministry of Planning, 1990. The Fifth Development Plan. Riyadh: Ministry of Planning Press.

Ministry of Planning, 1993. The Sixth Development Plan. Riyadh: Ministry of Planning Press.

Ministry of Planning, 1999. The Seventh Development Plan. Riyadh: Ministry of Planning

Press.

Ministry of Planning, 2004. The Eighth Development Plan. Riyadh: Ministry of Planning

Press.

Ministry of Planning, 2009.“The Ninth Development Plan.“ Riyadh: Ministry of Planning

Press.

Ministry of Planning, 2011. “Achievements of the Development Plans“;(28)Issue, 1390-

1432h (1970-2011) Facts & Figures. Sixteenth Issue. Riyadh: Ministry of Planning Press.

Mittelstaedt, John D., George N. Harben and William A. Ward .2003. “How Small Is Too

Small? Firm Size as a Barrier to Exporting from the United States.” Journal of Small Business

Management, Vol. 41(1) pp. 68-84.

Moen, Ø. 1999. “The relationship between firm, size, competitive advantages and export

performance revisited“. International Small Business Journal, 18, 53–72

Moini, A. H. 1995. “An inquiry into successful exporting: An empirical investigation using a

three-stage model“. Journal of Small Business Management, 33, 9–25.

Molero, J. 1998. “Patterns of internationalization of Spanish innovatory firms“, Research

Policy 27(5): 541–558.

Moreno-Badia, M. and Slootmaekers, V. 2009. “The missing link between financial

constraints and productivity“, IMF Working Paper No. 09/72, Washington, DC: International

Monetary Fund.

Nachum, L.; Zaheer, S. 2005.“The Persistence of Distance? The Impact of Technology on

MNE Motivations for Foreign Investment“, Strategic Management Journal, Vol. 26, pp. 747-

767.

Nguyen Hiep& Shoji Nishijima, 2009. "Export intensity and impacts from firm

characteristics, domestic competition and domestic constraints in Vietnam: a micro-data

195

analysis”. Research Institute for Economics & Business Administration, Kobe University

(http://ideas.repec.org/p/kob/dpaper/238.htm)

Nieto, M.J. 2001. “Tipos de Propiedad y ComportamientoEstrate´gico de la Empresa“,

Departamento de Economı´a de la Empresa, Universidad Carlos III de Madrid, Getafe,

Spain.

Okpara, J. and Koumbiadis, N., 2009. “Strategic export orientation and internationalization

barriers: evidence from SMEs in a developing economy“. Journal of International Business

and Cultural Studies,1.

Patiblanda,M. 1995. “Firm size and export behaviour: An Indian case study“. Journal of

Development Studies, 31, 868–882.

Pinho, C.J. and Martins, L., 2010. “Exporting barriers: Insight from Portuguese small- and

medium‑sized exporters and non-exporters“. Journal of International Entrepreneurship, 8

3, 254-272.

Pla-Barber, J. &Alegre, J. 2007. “Analysing the link between export intensity, innovation and

firm size in a science-based industry“. International Business Review, 16 (3): 275-293.

Powell D. and Wagner J.,2010. “The Exporter Productivity Premium along the Productivity

Distribution: First Evidence from a Quantile Regression Approach for Fixed Effects Panel

Data Models“, IZA Discussion Paper No. 5112 ,August 2010

Poza, E. 2004.” Family Business”, Thomson South-Western Publishing, Mason: Ohio.

Preece, S.B., Miles, G. and Baetz, M.C. 1998. “Explaining the international intensity and

global diversity of early-stage technology-based firms“, Journal of Business Venturing 14(3):

259–281.

Presutti, M.; Boari, C. &Fratocchi, L. 2007. “Knowledge acquisition and the foreign

development of high tech startups: A social capital approach“. International Business

Review, 16 (1): 23-46.

Qian, H. 2010. Talent, creativity and regional economic performance: The case of China.

Annals of Regional Science, 45(1): 133–156.

Reuber, A.R. and Fischer, E. 1997 “The influence of the management team’s international

experience on the internationalization behaviors of SMEs“, Journal of International

Business Studies 28(4): 807–825.

Robert E. Morgan, Constantine S. Katsikeas, 1997. “Obstacles to export initiation and

expansion“, Omega, Volume 25, Issue 6, December 1997, Pages 677-690

Robson, P. , Charles K. Akuetteh, Paul Westhead and Mike Wright. 2012. "Exporting

intensity, human capital and business ownership experience", International Small Business

Journal 2012 30: 367

196

Rodriguez, J.L. & Garcia Rodriguez, R.M. 2005. “Technology and export behavior: A

resource-based view approach“. International Business Review, 14 (5): 539-557.

Roper S and Love J H. 2002. “Innovation and Export Performance: Evidence from the UK

and German Manufacturing Plants“, Research Policy, 31, 1087-1102.

Saffu, K., & Manu, T. 2004. “Strategic capabilities of Ghanaian female business owners and

the performance of their ventures.” A paper presented at the National Women’s’ Council

and the International Council of Small Business conference in South Africa. Retrieved

March 24.2006, from

Salomon, R. & Shaver, J. M. 2005. “Export and domestic sales: Their interrelationship and

determinants“. Strategic Management Journal, 26 (9): 855-871.

Samiee, Saeed and Peter G.P. Walters 1990."Influence of Firm Size on Export Planning and

Performance," Journal of Business Research, 20 (3), 235-48.

Saudi Arabian Monetary Association (SAMA), 2011.Annual Report.No. 47.Research and

Statistics Department, Riyadh.

Saudi Basic Industries Corporation (SABIC), 2010. annual reports (34).

Saudi Basic Industries Corporation (SABIC),2009. annual reports (33).

Saudi Industrial Development Fund (SIDF), 2010.Annual report

Secchi, A., Tamagni, F. and Tomasi, C. 2012. “Exporting under financial constraints:

margins, switching dynamics and prices“. Development Working Papers 338, Centro Studi

Luca d'Agliano, University of Milano, revised 16 July 2012.

Sergaki P and Semos A.,2005. ”The Impact of Financial Parameters on Agricultural

Cooperative and Investor-Owned Firm Performance in Greece”, Cahiers Options

Méditerranéennes, (ISSN:10221379), vol.64 [2005] p.259-265

Sharkey, T. W., Lim, J. S. and Kim, K. I. 1989. “Export Development and Perceived Export

Barriers: An Empirical Analysis of Small Firms.“ Management International Review, 29(2),

pp.1-11.

SHIRAZI,NASIM and ABDULMANAP, TURKHAN. 2005."EXPORT-LED GROWTH HYPOTHESIS:

FURTHER ECONOMETRIC: EVIDENCE FROM SOUTH ASIA". The Developing Economies, XLIII-

4 (December 2005): 472–88

Sterlacchini, A. 2001. “The determinants of export performance: A firm-level study of

Italian manufacturing“. Review of World Economics, 137(3): 450–472.

Sule Özler, Erol Taymaz and Kamil Yılmaz, 2007,”History Matters for the Export Decision

Plant Level Evidence from Turkish Manufacturing Industry” ,ERC Working Papers in

Economics 07/04.

Todaro, M. P. 1977. Economic for a Developing World, Longman, London, UK.

197

Todaro, M. P.1986. Economic Development in the Third World. New York: Longman.

Tookey, D. A. 1964. “Factors associated with success in exporting“. Journal of Management

Studies, 1(1): 48–66.

Ural T., and Acaravcı S., 2006, “The Effects of Firm’s Strategic Factors on Export and Firm

Performance: A Comparison of Permanent and Sporadic Exporters“, Problems and

Perspectives in Management. Volume 4, Issue 4, 2006

Van Beveren, I and Vandenbussche, Hylke, 2010 “Product and process innovation and

firms' decision to export“, Journal of Economic Policy Reform, 13: 1, 3 — 24

Venables, A. J. 1996. "Equilibrium Locations of Vertically Linked Industries", International

Economic Review , Vol. 37, No. 2, May, 1996

Verwaal en Bas Donkers, E., 2001. “Firm size and export intensity: a transaction costs and

resource‑based perspective.“ Rotterdam: Erasmus Research Institute of Management,

ERIM, 2001.

Verwaal, E. &Donkers, B. 2002. “Firm size and export intensity: Solving an empirical

puzzle“. Journal of International Business Studies, 33 (3): 603-613.

VinhNhat, L, & Julian, C. 2008. “The Internet, Strategy and Performance: A Study of

Australian Export Market Venture”', Journal Of Global Marketing, 21, 3, pp. 231-240

Wagner, Joachim, 1995. “Exports, firm size, and firm dynamics“.Small Business Economics,

7(1): 29–39

Wagner, Joachim, 2004. “Export Intensity and Plant Characteristics: What Can We Learn

from Quantile Regression?“, Review of World Economics, April 2006, Volume 142, Issue 1,

pp 195-203

Wakelin, K. 1998. “Innovation and export behaviour at the firm level“, Research Policy

26(7/8): 829–841.

Welch, L.S. 1992 “The use of alliances by small firms in achieving internationalization“,

Scandinavian International Business 1(2): 21–37

Welch, L.S. and F. Wiedersheim-Paul, 1980. “Initial Exports – A Marketing Failure?“,

Journal of Management Studies, 17 (3), 333-344.

Wendy Carlin, Steven Fries, Mark Schaffer and Paul Seabright, 2001. “Competition and

Enterprise Performance in Transition Economies: Evidence from a Cross-country Survey“.

Working Paper No. 376.

Westhead, P.; Wright, M. &Ucbasaran, D. 2004. “Internationalization of private firms:

Environmental turbulence and organizational strategies and resources“. Entrepreneurship

& Regional Development, 16 (6) 501-522.

198

White, H. and Xun Lu, 2010. “Robustness checks and robustness tests in applied economics.

Department of Economics,“ University of California, San Diego, 18 June 2010. [Online]

Available at www.asian-studies.uci.edu/files/economics/docs/micro/s11/white.pdf.

Willig, R. 1987.“Corporate governance and market structure.“ in A. Razin and E. Sadka

(eds.), Economic Policy in Theory and Practice, Macmillan, London.

Wolff, J.A. and Pett, T. 2000. “Internationalization of small firms: an examination of export

competitive patterns, firm size, and export performance“, Journal of Small Business

Management 38(2): 34–48.

World Bank 2005.;World Economic Forum 2005..

Yavas, U., Cavusgil, S.T. and Tuncalp, C. 1987, "Assessment of Selected Foreign Suppliers by

Saudi Importers: Implications for Exporters", Journal of Business Research, Vol. 15, pp. 237-

346.

Yildirim, H.S., Akci, Y. and Eksi, I.H., 2013. “The effect of firm characteristics in accessing

credit for SMEs.“Journal of Financial Services Marketing, 2013 18, 40-52, received in

revised form: 6 December 2012.

Zahra, S.A. 2003. ”International expansion of US manufacturing family businesses: the

effect of ownership and involvement”, Journal of Business Venturing 18(4): 495–512.

Zou, S., Fang, E., &Zao, S. 2003. “The effect of export marketing capabilities on export

performance: An investigation of Chinese exporters“. Journal of International Marketing,

11(4), 32–55.

199

Appendix 1: Analysis The impact of firm level on a firm’s credit position and Access to finance on export intensity

In order to analyse the relationship between financial constraints and intensity

of exports across the firms sampled, the analysis utilized more two econometric

methodologies. Firstly, the analysis identified the credit position of the firm as

above. Secondly, the study analysed the relationship between access to finance and

export intensity under the effect of credit constraints.

A1.1 The impact of firm level on a firm’s credit position

To test the relationship between firm characteristics and credit constraint

status, the estimation relies on the ordinal regression model, which is commonly

presented as a latent variable model. Defining credit constraint status c∗ as a latent

variable ranging in our case from 1 to 4. The measurement model for outcomes

responses are linked to the latent variable by the measurement model explained in

Figure 4.1 and Table 4.1:

ci=

1 ⇒NCC Not Credit Constrained

2 ⇒MCC Maybe Credit Constrained 3 ⇒PCC Partially Credit Constrained 4 ⇒FCC Fully Credit Constrained

The structural model is:

y*i= α +xi β’ + εi …(a.1)

The credit constraint status takes the form below:

Credit constraint status*i = α + β1Exportsi + β2ProdVi

+ β3sizei + β4agei + β5femalei + β6foreigni + εi …(a.2)

Where i is the firm observation and ε is a random error, x in Eq. (a.2) is a list of

independent and control variables. Thus, to motivate our empirical analysis, the

analysis draws upon the model of Kuntchev et al. (2012). Higher values of the

dependent variable denote higher levels of credit constraint. The study chose

explanatory variables based on theory. The literature review shows that besides the

level of export (exports), the study add variables as shown in Eq.(a.3) i.e. labour

200

productivity (Prodv), size of firm, firm age, females amongst owners, and

participation of foreign experience.

A.1.2 Estimates of the impact of firm level on a firm’s credit position

According to the descriptive statistical results in Table 4.1, the estimations of

Eq. (a.2) are confirmed through an ordered logit model where the dependent

variable is the credit constrained status and the independent variables of firm size

and age, female and foreign ownership dummies were used as controls. Table a.2

presents the result of the regression. There is a significant negative relationship

between firm age and credit constraint, i.e. the younger the firm, the higher the

probability of being credit constrained. Labour productivity is significant and

negatively correlated with credit constraint, i.e. more productive firms are less

likely to be credit constrained. Kuntchev el al. (2012) say that this result is explained

because the cross-sectional nature of the data does not permit establishing

whether this is the result of proper client selection by financial markets, or greater

financial access causing greater productivity; the positive correlation is indicative of

well-functioning financial markets

A.2.1 Access to finance and export intensity

The perception of access to credit as an obstacle is based on a direct question.

The degree of obstacle access to finance represents to the current operations of the

firm is a five-point scale: no obstacle, minor obstacle, moderate obstacle, severe

obstacle, and very severe obstacle. This type of variable has often been used in the

literature as a proxy for being credit constrained (Kuntchev et al., 2012). The

hypotheses is the perception of the obstacle is positively correlated to objective

measure of credit constraint. The hypothesis also shows a negative correlation with

size and with age: smaller firms and younger firms tend to find access to credit to

be more of a constraint to their operations than larger and older firms.

According to the preceding discussion on access to finance, identifying the

effect of firm characteristics on access to finance, given the ordered nature of the

dependent variable, an ordered Logit approach is followed which is specified as Eq.

201

(a.1), where y*i in the current empirical model is an unobservable latent variable, xi

is a set of control variables and εi represents the error term. Then the following:

yi = 0 if y*i ≤ λ0 yi = 1 if λ0 ˂ y*i ≤ λ1 yi = 2 if λ1 ˂ y*i ≤ λ2 yi = 3 if λ2 ˂ y*i ≤ λ3 yi = 4 if y*i > λ3

with ‘No Obstacle’, ‘Minor Obstacle’, ‘Moderate Obstacle’, ‘Major Obstacle’,

and ‘Very Severe Obstacle’ coded as 0, 1, 2, 3 and 4, respectively and the λi's as

unknown parameters that will be estimated together with β.

In the access to finance model, xi that is mentioned in Eq. (a.2) contains a set

of firm-level characteristics, in similar variables in Eq.(a.2) as well as adding credit

constraint position and excluding the effect of labour productivity, because the

estimation uses firm size in category form. The equation considered to estimate our

access to finance model is given as:

Access to finance*i =α + β1 Credit constrainti+β2Exportsi + β3sizei + β4agei +

β5femalei + β6foreigni + εi …(a.3)

On the basis of the previous discussion, access to funds may be more difficult

for firms that, by this time, have a loan or line of credit from a financial

organization. The firm size variable is included to reflect the role of the size of the

firm by employee numbers in determining the ease of accessing funding. The

hypotheses and some empirical views argue that older firms depend more heavily

on internal funds as opposed to younger firms, and refrain from using other sources

such as lenders, friends, or other sources to finance their needs. In addition, our

sample data analysis as shown in Table 2B.32 (Chapter 2) explains that on average

smaller firms (measured by sales) report significantly higher obstacles to finance

than larger firms. Moreover, foreign ownership of firms may take advantage of

internal and external funds even though external sources may be more expensive,

limited, or difficult to access owing to cost and credit rationing. However, some

empirical studies demonstrate that foreign-owned firms actually enjoy easier access

to finance. Dummy variables are included in Eq. (a.3) for foreign as well as female

ownership.

202

A.2.2 Estimates of access to finance and export intensity

Table a.3 shows the estimation of Eq. (a.3) from an ordered logit regression of

the perception of access to credit as a barrier. The perceived obstacle is positively

and significantly correlated to our objective measure of credit constraint. The

perception also shows a negative significant correlation with foreign ownership,

female ownership, and with age: younger firms, firms owned by females, and

foreigners ownership tend to find access to finance to be more of a constraint to

their operations than older firms.

203

Table b.1: Dependent variable: Credit constraints status, Ordered Logit.

Credit constraints status Model (1) Model (2) Model (3) Model (4)

Coef. S. E. Z_test Coef. S. E. Z_test Coef. S. E. Z_test Coef. S. E. Z_test

Export intensity -0.007 0.012 -0.57 0.008 0.012 0.65 -0.01 0.01 -0.43 0.01 0.01 0.83

Labour productivity -0.247 0.122 -2.03 -0.265 0.123 -2.15 -0.31** 0.14 -2.21 -0.33** 0.14 -2.41

Firm size -0.393 1.611 -0.24 -0.953 1.547 -0.62 -1.00 1.93 -0.52 -1.53 1.67 -0.91

Firm Age -2.25*** 0.462 -4.87 -2.44*** 0.50 -4.90 Exports experience -1.57*** 0.346 -4.56 -1.73*** 0.38 -4.51

Female -0.757 0.519 -1.46 -0.710 0.505 -1.41 -1.05* 0.54 -1.94 -0.97** 0.53 -1.84

Foreign ownership -3.42*** 0.928 -3.70 -2.46*** 0.778 -3.17 -3.52*** 0.98 -3.58 -2.51*** 0.82 -3.05

Group 1.31*** 0.36 3.65 1.21*** 0.35 3.47

/cut1 -9.653 2.668 -7.183 2.446 -9.310 3.078 -6.712 2.644

/cut2 -6.574 2.564 -4.223 2.381 -5.855 2.984 -3.443 2.591

/cut3 -6.001 2.547 -3.651 2.371 -5.215 2.979 -2.817 2.588

Observations” 90 90 90 90

LR chi2 46.77 41.50 61.79 54.87

Prob> chi2 0.00 0.00 0.00 0.00

Pseudo R2 0.231

0.205 6

0.31 0.27

Log likelihood -77.53 -80.16 -70.01 -73.47

Note: Model (1) estimated by using age of firm, while Model (2) used export experience instead of age of firm, Model (3) estimated by using age of firm and has an added (Group) variable, which is a dummy variable equal to 1 if the firm is part of a group, and 0 otherwise. Model (4) estimated by using export experience instead of age of firm and a (Group) variable. “ Note that 90 observations were used in the analysis, rather than the full 175, because we restricted the sample and removing non-response categories such as ‘do not know,’ ‘no answer,’ ‘not applicable. Moreover, with added more independent variables lead to drop more observations (more details on page 210 ).

204

Table b.2: Dependent variable: Access to finance, as a Major constraint

Access to finance Model (1) Model (2) Model (3) Model (4)

Coef. Std. Err. z Coef. Std. Err. z Coef. Std. Err. z Coef. Std. Err. z

Credit constraints status 3.255* 1.745 1.87 2.98** 1.33 2.24 3.46* 1.81 1.91 2.62* 1.35 1.94

Export intensity -0.013 0.011 -1.21 0.00 0.01 -0.47 -0.01 0.01 -0.93 0.00 0.01 -0.14

Firm size -2.001 1.254 -1.59 -1.91 1.14 -1.68 -2.42* 1.28 -1.89 -2.42** 1.18 -2.05

Firm Age -1.607** 0.552 -2.91 -1.65** 0.57 -2.89

Experts experience

-1.53*** 0.37 -4.16 -1.37*** 0.37 -3.69

Female -1.81*** 0.496 -3.66 -1.61*** 0.48 -3.35 -1.89*** 0.50 -3.77 -1.64*** 0.48 -3.39

Foreign ownership -2.991** 1.07 -2.80 -2.57*** 0.84 -3.05 -3.04** 1.11 -2.73 -2.36** 0.86 -2.73

Group 1.06*** 0.28 3.73 0.88*** 0.29 3.07

/cut1

/cut2

/cut3

/cut4

Observations 132 132 132 132

LR chi2 43.89 54.00 58.46 63.85

Prob> chi2 0.000 0.000 0.000 0.000

Pseudo R2 0.1336 0.1644 0.1779 0.1943

Log likelihood -124.34 -137.28 -135.05 -132.36

Note: The dependent variable is the response to the following question: ‘Is access to financing, which includes availability and cost No Obstacle (0), a Minor Obstacle (1), a Moderate Obstacle (2), a Major Obstacle (3), or a Very Severe Obstacle (4) to the current operations of this establishment?’. Methodologically, an ordered logit approach is taken. Model (1) estimated by using age of firm, while Model (2) using export experience instead of age of firm, Model (3) estimated by using age of firm and has an added (Group) variable, which is a dummy variable equal to 1 if the firm is part of a group , and 0 otherwise. Model (4) estimated by using export experience instead of age of firm and (Group) variable. “ Note that 132 observations were used in the analysis, rather than the full 175, because we restricted the sample and removing non-response categories such as ‘do not know,’ ‘no answer,’ ‘not applicable. Moreover, with added more independent variables lead to drop more observations (more details on page 210).

205

Appendix 2: Sample and Questionnaire

1. Introduction

Saudi Arabia is an oil rich country where the private sector represents a major

factor in the economy. The government encourages and establishes different

programmes to attract foreign investment. Despite these two factors, there is no

database to provide micro data, whether periodically or for one instance, on the

industrial sector. Similarly, there is no micro data collected by World Bank’s

enterprise surveys. There is no data presenting information that would enable one

to analyse the business environment. The current work has explored a number of

organisations that are relevant to the private sector. It aimed to obtain empirical

studies, provide data of Saudi exporting and firm performance. The focus was on

studies prepared by the micro data. The most important organisations that were

contacted were as follows: Local Banks in Saudi Arabia (Riyadh Bank, The National

Commercial Bank, Al-Rajhi Capital, SABB Bank and SAMA Bank); Regional

Organisations (Federation of Gulf cooperation Council-GCC Chambers, The Islamic

Development Bank, The Islamic Corporation for the Insurance of Investment and

Export Credit, The Council of Saudi Chambers of Commerce and Industry, Saudi

Export Development Centre (SEDC) and the Riyadh Chamber of Commerce and

Industry); Government Organisations (Ministry of Economy and Planning and the

Saudi Arabian General Investment Authority); International Organisations

(Association for Financial Professionals AFP, International Chamber of Commerce

ICC, International Monetary Fund and Bankers’ Association for Finance and Trade

and The Exporta Group) and Universities (King Fahd University of Petroleum and

Minerals-King Saud University)

Due to the lack of data, there are no recent studies discussing the needs of the

private sector in terms of financing their trade. In general no studies have been

conducted by survey or prepared by using secondary data in Saudi Arabia or the

GCC. In short, there are no studies discussing the firm export behaviour and

relationship with finance factors. For this reason, the current study intends to

answer the research aims by obtaining the perspectives of those responsible for the

management of industrial exporting firms. This questionnaire was conducted to

206

survey the most important processes and elements around the non-oil export

sector. The analysis relied on the quantitative and qualitative data collected. A

further rationale for conducting this survey was to provide a knowledge base

regarding this matter to policy makers and investors. These results that there is no

database to provide micro data of Saudi exporters lead this research and

encourages designing a specific questionnaire to collect data from original sources.

The questionnaire was funded by the Saudi Fund for Development (SFD) and is

entitled “Trade and finance questionnaire in 2011”. The micro data were collected

in Saudi Arabia for the first time. They were obtained by this specific questionnaire

designed to generate information from Saudi exporters between September and

December 2011.

2. Methods of Data Collection

The current work was dependent upon quantitative data. The questionnaire

was distributed between September and December in 2011. For some this entailed

meeting the responsible managers of industrial exporting firms face to face in

central, western and eastern provinces. Alternatively, some questionnaires were

sent by mail and email to firms based in other provinces. Only industrial firms were

included in the sample which was also registered with the Saudi Fund for

Development (SFD). There were 500 participants recorded at the end of 2011.

There were some considerations in preparing the questionnaire. It is important

to consider the order in which questions are presented. Sensitive questions, such as

questions about real income, actual sales, or total number of nationalities of

workers, should be presented as category levels. This encourages respondents to

answer questions. The questionnaire was designed to avoid using emotionally

loaded or biased words and phrases. The questionnaire used: Open format

questions that give dates and percentages, ‘yes’ or ‘no’ (dichotomous) questions,

which are simple questions that ask respondents to choose either one answer or

‘don’t know’, and closed format questions that include multiple-choice answers.

The topics included in this category were questions about sector, labour volume,

total sales size, sales orientation and types of loans and credit lines.

207

The next set of questions was importance questions or rating scale questions

where the respondents were asked to rate the importance of a particular issue. The

range consisted of: not applicable (0), not at all important (1), somewhat important

(2), important (3), and very important (4). Bipolar questions were used where there

were two extreme answers. The respondent was asked to mark responses between

the two opposite ends of the scale from (1) no obstacle to (5) very severe obstacle

and (1) no advantage to (5) very strong advantage.

The manufacturing firms classified in the questionnaire whereas a follows:

food and beverages, textile products, cloth products, leather products, wood

industry and products, paper industry and its products, printing press and copying

of recorded multi-media, refined petroleum and nuclear fuel products, chemical

materials and products, rubber and plastic products, other non-metal products,

basic metal products, construction metal products, machines and equipment

industry, office and accounting terminals as well as computers, electric machines

and terminals (unclassified elsewhere), radio, TV and telecommunications

equipment and terminals, medical terminals, optic tools and all types of watches,

engine and trailer motors, other transportation equipment, furniture and products

unclassified elsewhere, and recycling.

3. Content of the Questionnaires

Based on the background above, this research attempts to provide a complete

overview of the non-oil export environment, including the financing and business

environment. The main procedure is based on analysing the relationship between

private sector exports and different aspects such as trade operation structure,

financing, infrastructure, and competition. In addition, the micro data will assist by

illuminating the investment climate and government actions directed to alleviate

the restrictions on doing business. For questionnaire see Appendix 3. The

questionnaire was divided into 8 sections, consisting of 95 questions. Eight

questions contained 68 sub-questions, meaning that the full number of questions

totalled 146.

208

The cover letter of the questionnaire invites the management or particular

responsibility for exporting firms. This cover letter provided an endorsement from

the director general of the Saudi Export Programme (sponsor of the questionnaire)

to assist in encouraging a reasonable response rate. Furthermore, this letter

showcases that the results will contribute to and develops Saudi export programme

services. Even so, it was recognised that the use of a key respondent may have

biased the results (Crick, 1998), particularly if this respondent thought that Saudi-

Arabian policy makers could identify them, in spite of promises of anonymity.

PART A: General Information

This section aimed to obtain the general profile of the firm and its

manufacturing sector. It included gathering information about firm size, legal

situation, ownership, years of formal registration, in what year the firm began

operations, and discovering some innovation measurements such as local and

internationally recognised quality certifications.

PART B: Infrastructure and services

The aim of this section was to explore whether the firm is faced issue of the

Infrastructure services. The data from this section was also used to evaluate the

quality of the industrial environment. This section asked questions regarding

electricity and water services, such as if this firm experiences electricity failures or

insufficient water, and the average number of power outages or incidents of

insufficient water during a year, as well as an estimate of the losses that resulted

from power outages or incidents of insufficient water, either as a percentage of

total annual sales or as a total of annual losses. Finally, this section also explored

the communication base in the firms to measure the benefit of these technologies.

PART C: Trade Analysis

This section reviewed the trade analysis, for example asking the respondent to

report the time waiting for imports and exports to clear customs, and also to gage

the benefits of international trade for firms in terms of less expensive inputs and

new markets for exporting their products. Other questions were analysed sales

209

environment and operation. The respondent was asked to characterise the type of

activity that they engage in when exporting. Most firms would not be exporting to a

single country, but rather to different countries, and they may face difficulties and barriers.

PART D: Financial analysis

This section of the questionnaire focused on access to finance. It searched the

credit resources and loan requirements that work around Saudi industry. This

section also attempted to evaluate the financial statement of firms, especially of

the depth affecting firms’ operations and expansion. Most exporters face the

problem of obtaining export financing. In general, this section in the questionnaire

points to a firm‘s ability to provide trade credit arrangements with suppliers and

customers.

PART E: Degree of competition

The study in this section believes that there is a competition facing Saudi firms

whether in the local, national or international market. The questionnaire collected

data that reflect the impact of price, quality and service of the exporting firms’

products against the competition in different markets. The questionnaire defines

the measurement of competition as an average score on a 1-5 scale defined as: no

advantage (1) tends to be an advantage (2), advantage (3), strongly advantageous

(4) and very strongly advantageous (5). This section also aims to define the number

of competitors and consider the firms’ main markets.

PART F: Labour

This section measures the employment position in the industrial sector. It

discusses permanent full-time employees, and temporary or seasonal employees.

This section also determines production workers and non-production workers, as

well as skilled and unskilled production workers. Finally, it identifies the average

length of employment of temporary or seasonal employees.

PART G: Production capacity

This section shows the proportion of unused production capacity in the

industrial sector, as well as listing some reasons which prevent obtaining maximum

210

production capacity. On the other hand it provides the percentage of total annual

labour costs, including wages, salaries, bonuses, social security payments, raw materials

and intermediate goods used in production, costs of fuel and electricity and other costs

of production not included above.

PART H: Business environment

This final section of the questionnaire focuses on the customs and trade

regulations which represent obstacles for the current operation of this firm, as well

as if the firm has any legal cases against their business currently pending in judicial

authorities. On the other hand, it looks at whether the firm submitted an

application to obtain an import licence and approximately how many days it took.

This section gathers data about the firm in terms of what they pay for security, for

example equipment, personnel, or professional security services, and if this firm

suffered losses as a result of theft, robbery, vandalism or arson. In this section of

the questionnaire, the survey provides the main elements of the business

environment, if any, currently representing the biggest obstacle faced by the firm.

4. Pilot Test

The questionnaire was developed based on the findings derived from previous

literature on the Saudi economy and the notion of expanding current exports. An

element of practical experience was also involved in the questionnaire

development, and a pilot study was conducted to determine any potential

problems that the questionnaire presented. In this particular study, the pilot test

was presented to six firms. The problems considered were the wording of questions

to be certain that their meaning was clear, response criteria were clear and that

data entry following the responses posed no difficulties. A further advantage to

conducting a pilot study is that the questionnaires validity can be determined;

meaning the questionnaire assesses what it set out to asses. Overall the pilot test is

an important component prior to distributing the questionnaire for the study as it

allows for the correction of any presenting issues and therefore the presentation of

a reliable measure in the data collection stages.

211

5. Data Preparation

This work takes into consideration that an important part of any survey is data

preparation. The research has dealt with data by editing and entry. The respondent

questionnaire was revised and edited before data entry to ensure the quality of the

collected data. On the other hand, the questionnaires which were not completed

(around twenty questionnaires) were edited for completeness by the respondent

using email or phone to correct their answers. Then the data was coded and

cleaned by removing non-response categories such as ‘do not know,’ ‘no answer,’

‘not applicable,’ ‘not sure’ and ‘refused.’ These were removed as their presence

could distort the mean or regression results as outliers. Therefore this avoids

misinterpretation. This step of the surveying process was accomplished using Ms

Excel computer programs, then transferring the data to a STATA file.

6. Sample Size

Studies have reported various population sizes in relation to export research.

For example, Crick et al. (1998) used all the Saudi exporters of non-oil products that

were identified by the Saudi Export Development Centre from the Saudi Export

Directory. The questionnaire was mailed to a total of 411 firms. The response was

108 questionnaires, although nine were deemed to be unusable. Overall, 99

responses were obtained, representing a response rate of 24 per cent. In his study

concerning obstacles perceived by exporters in Saudi Arabia, Al-Aali (1999) used

manufacturing exporters who obtained certificates of origin from the Ministry of

Commerce in a two-year period preceding the study (n=447). A total of 148

exporters participated in Al-Aali’s (1999) study. Finally, Al-Qahtany (2001)

determined the target population of his study as all non-oil producing exporting

firms in Saudi Arabia. The source of his information is the Saudi Export Directory

(1995) which is published by the Saudi Export Development Centre.

The survey population is the 500 firms who registered with the Saudi Export

Programme (SEP). Although the number of Operating Industrial Unites

(Manufacturing firms) is 4744 units at end of 2011 (more details is provided in

chapter 2 section 1), there were no officially statistic shows number of exporters

212

whether each year or for one year in Saud Arabia. The study assumes these 500

firms are representative of exporters in the Saudi export sector. The study concerns

that every member of the SEP has an equal chance of being selected. A final

sample of 175 firms that represent 35 per cent of total member of the SEP was

collected to participate in the study (Figure B.1 shows map of the study sample size

to the manufacturing sector at end 2010). The study considers that the 175 firms in

the sample are themselves representative of the 500 SEP firms. On the other hand,

firms register with the SEP have been benefited of its services and facilities, chapter

2 in section (2.1.6.1 The Saudi Export Program) that SEP have provided insurance

and guarantees directly to exporters, this program provides financial incentives and

credit to exporters on the one hand, and on the other provides competitive credit

terms for buyers abroad or for funding institutions working in this area.

The survey faced difficulties during period of data collection. First, the period is

limited to three months because the administration producers in the UK

immigration are restricted, as well the fact that the Saudi Arabia sponsorship

system limits the period to three months. Second, the questionnaires were sent by

mail and e-mail to the respondents and asked them to choose the date for the face-

to-face interview. Due to the poor quality of the postal service in the industrial

areas, some firms did not receive the questionnaire, and in some cases the

researcher had to deliver it by hand. Some respondents thought the questionnaire

was a waste of time because they did not recognise the importance of such

research to their export development.

However, according to the world bank sample selection standards via

enterprise surveys (2009), it reported the minimum sample sizes of a population for

500 firms is 176 firms at 5 per cent and 97 firms at 7.5 per cent precision. It was

consequently decided to send 500 questionnaires to the whole population -which

are all firms who registered with SEP at end of 2011-. The study concerns that every

member of the SEP has an equal chance of being selected. A final sample of 175

firms that represent 35 per cent of total member of the SEP was collected to

participate in the study.

213

7. Summary

The research aimed to obtain sufficient data and to meet the research

questions outlined in the introduction. The main difficulty faced by the researcher

was in regards to the length of the questionnaire. It is difficult to create a

comprehensive questionnaire using a small number of questions. Therefore the

questionnaire was 18 pages long. The length of the questionnaire caused a number

of managers to withdraw, under the pretext of lack of time, or that the

questionnaire required participation from more than one department within the

firm to answer it.

Figure B.1 : Map of the study sample size to the manufacturing sector at end 2010

Operating industrial unites (Manufacturing firms)

4744 units at end of 2010

Exporting firms (=No official statistics)

Not exporting firms

Two organisation directly work with exporting firms at end of 2011

Saudi Export Programme (SEP) under umbrella of the Saudi Fund

for Development (SFD)

=500 firms at end of 2011

Saudi Export Development Centre (SEDC) under umbrella of the Council for Saudi Chambers of

Commerce and Industry

= 90 firms but SEDC directory present around 500 firms

The current study sample =175 firms

214

Appendix 3: Questionnaire.

Trade and Finance Questionnaire 2011

Dear General Manager First of all, I would like to express my sincere appreciation to you for your consideration and interaction in participating in the questionnaire directed to the business environment for products of the private sector in Saudi Arabia. This study aims to examine the development of non-oil exports in the Kingdom and assess both their contribution in the Saudi Economy and discover obstacles hindering them. Therefore, it can be noted that the questionnaire has been prepared carefully in a manner that caters to the fact that the original source of information is the private sector, most of which can be used to test the efficiency and adequacy and unbiasedness of the export sector, in addition to the efficiency in playing an active role in the development of an additional source of National Income though the export of surplus production, or otherwise benefit from the comparative advantages that are features of the Saudi Economy. Furthermore, to asses the situation of the export sector, it is necessary for the completion of this study in a satisfactory manner to support it with other enhancing factors, to assist in promoting the export sector and for carrying-out its functions, such as infrastructure, financial facilities, labor, the degree of competition and business environment in general. Undoubtedly, this study seeks to lead to results making an applied scientific, realistic addition to the planners and government programs related to the development of exports, taking into account that this study and the data conducted in it was generated from its original source. Hence, in this study, the role of the private sector is prominent in pushing the government in this area, and on the other hand, the study sets-out to assess and explore the past and the future of the export environment in the Kingdom. Important note: All information collected by this questionnaire are only for scientific purposes, the firm shall not bear any responsibility based on it. For more information, please use the contact information provided below. Best wishes, D.G of Saudi Export Program Mr. Ahmed M. Al Ghanam Sent to: The Saudi fund for development P.O.Box (50483) RIYADH 11523

- By Fax : (014647450)

Download electronic copy: www.sep.gov.saSent to: alsakran@sfd.gov.sa For more information contact :Mr. Abdullah Alsakran, mobile 0555201903

http://www.sep.gov.sa/

mailto:alsakran@sfd.gov.sa

215

SFD Questionnaire: Trade and Finance Questionnaire 2011

PART (A) -General Information:

(A1)- Region (1)Central (2)Western(3)Eastern(4)North(5)South

Name of firm (preferred)

(A2)- City /town

Subject (√)

(A3)-Labor Volume 1- Micro < 5 employees

2- Small (>5 employees <20)

3- Medium (20-99 employees)

4- Large (100+ employees)

(A4)-Sector

1-Food & beverages

2-Products of wood, Paper, Leather and Textiles

3-Products of Chemical, Petrochemical, Plastic, Rubber and Medical care

4-Products of Building Material and Glassware

5-Products of Electrical, Machinery, Transport, Tools and Medical equipment

(A5)-Describe your manufacturing area; tick (√) more than one if needed:

Sector of Manufacturing (√)

1- Food & beverages

2- Textiles products

3- Cloth products

4- Leather products

5- Wood industry and products

6- Paper industry and its products

7- Printing press and copying of recorded multi-media

8- Refined petroleum and nuclear fuel products

9- Chemical materials and products

10- Rubber and plastic products

11- Other nonmetal products

12- Basic metal products

13- Construction metal products

14- Machines and Equipment industry

15- Office and accounting terminals as well as computers

16- Electric machines and terminals (unclassified elsewhere)

17- Radio, TV and telecommunication equipment and terminals

18- Medical terminals, optic tools and all types of watches

19- Engine and trailer motors

20- Other transportation equipment

21- Furniture and products unclassified elsewhere

22- Recycling

(A6)-Years indicating formal registration.

formally registered (√)

1- <5 (after 2005)

2- 6-15 (between 1996-2005)

3- 16+ (before 1995)

216

(A7)- Current legal status

Type of current legal status (√)

1- Shareholding firm with shares trade in the stock market

2- Shareholding firm with non-traded shares or shares traded privately

3- Sole proprietorship

4- Partnership

5- Limited partnership

6- Other

(A8)-Ownership

Type of Ownership (√) (%)

1- Private domestic individuals, companies or organizations a8a

2- Private foreign individuals, companies or organizations a8b

3- Government or State a8c

4- Other a8d

(A9)- Is this firm part of another firm, whereas

Type of Ownership (√)

1- This firm is only one entity

2- This firm is the headquarters and has another branch

3- This firm is a branch / is part of another firm

4- Don’t know

(A10)-Are there any females amongst the owners of the firm,

(√)

1- Yes=1

2- No=0

3- Don’t know =.

(A11)-In what year did this firm begin operations?

(A12)- Does this firm have a locally-recognized quality certification?

(√)

1- Yes=1

2- No=0

3- Don’t know =.

(A13)- Does this firm have an internationally-recognized quality certification?

(√)

1- Yes=1

2- No=0

3- Don’t know =.

217

PART(B)- Infrastructure and services

Electric services

B1-Did this firm experience electric failures? list (√)

1- Yes =1

2- No(go to Q.5) =0

3- Don’t know =.

B2-How many out gages (on average) did this firm experience during this year? (√)

Average number of power outages during a year ………..

Don’t know =.

B3-How long (periods) did these power outages last on average?

list hour

Average duration of power outages

Less than one hour =1

Don’t know =.

B4-Please, estimate the losses that resulted from power outages either as a percentage of total annual sales or as a total of annual loses.

Loss as percentage of total annual sales due to power outages

(OR)Loss as percentage of total annual losses due to power outages

None =0

Don’t know =.

Water services

B5-Did this firm experience insufficient water supply for production?

list (√)

1- Yes

2- No ( go to Q.8)

3- Don’t know

B6-How many incidents of insufficient water supply did this firm experience?

list No.

Average number of incidents of water insufficiency per month(no insufficient water supply experience =.)

Don’t know =0

B7-How long did these incidents of insufficient water supply last on average.

list hour hour

insufficient water supply

Less than one hour =1

Don’t know =.

B8-What percentage of this firm`s water supply, used in the production process, was from public sources?

Water from public sources

None =0

Don’t know =.

218

Communication

B9-Does this firm use e-mail to communicate with clients or suppliers?

list (√)

1. Yes =1

2. No =0

3. Don’t know =.

B10- Does this firm use its own website? list (√)

1. Yes =1

2. No =0

3. Don’t know =.

B11-Does this firm have a high-speed Internet connection on its premises?

List (√)

1. Yes =1

2. No =0

3. Don’t know =.

B12- Is this firm`s Internet connection used to:(√)

list Yes No Don’t know

(B12A)Make purchases for this firm

(B12B)Deliver services to this firm`s clients

(B12C)Do research and develop ideas on new products and services

219

PART(C)-Trade Analysis

C1- what percentage of this firm`s material inputs or supplies were:

list (%)

(C1A)Supplies of domestic origin

(C1B)Supplies of foreign origin

C2-Were any of the material inputs or supplies purchased, imported directly or indirectly:

list (%)

(C2A)Imported directly

(C2B)Imported indirectly

C3-At the time inputs of production (raw material, supplies etc..) were imported, please define the number of days i,e period on average from the time of arrival (point of entry- airport, port) to customs claim?

Average number of days to clear customs ….

Less than one day =1

Don’t know =0

C4-How many years ago did you begin exporting? Year

(C4A)Began exporting directly

(C4B)Began exporting indirectly Didn’t export

C5- The total of this firm's annual sales amount to the tune of: (√)

1. Less than 1 million Saudi Riyal

2. 1-10 million

3. 11-25 million

4. 26-51 million

5. 51-100 million

6. More than 100 million

C6-Sales of this firm were oriented towards: (%)

(C6A)National sales (c6y category variable;1 "1-20", 2 "21-40", 3 "41-60", 4 "61-80", 5 "81-100")

(C6B)Indirect exports (sold domestically to third party that exports products)

(C6C)Direct exports(c6x category variable;1 "1-20", 2 "21-40", 3 "41-60", 4 "61-80", 5 "81-100")

C7-Current Distribution Channels (√)

(C7A)Firm Sales Force

(C7B)Independent Agents

(C7C)Distributors/Wholesalers

(C7D)Firm -Owned Retail Stores

(C7E)Independent Retail Stores

C8-At the time the firm exported its products directly, please define the number of days i.e. period on average from the time of arrival (point of exit,- airport, port) to customs claim.

Days

Don’t know=.

220

C9-Export Destinations

Countries & Regions (√)

(C9A) GCC

(C9B) Arabian countries

(C9C) Asian countries (Excluded Arab States)

(C9D) African countries (Excluded Arab States)

(C9E) European countries

(C9F) American countries

(C9G) Australian

(C9H) Didn’t export

C10- The ratio of exports by region

Countries & Regions (%)

(C10A) GCC

(C10B) Arabian countries

(C10C) Asian countries (Excluded Arab States)

(C10D) African countries (Excluded Arab States)

(C10E) European countries

(C10F) American countries

(C10G) Australian

(C10H) Didn’t export

C11-Export Delivery Terms :the percentage of this firm for exports delivery mechanism

(%)

(C11A) EXW (ex-works) (C11B) CIF (cost, insurance, and freight) (C11C) FOB (free on board) (C11D) FAS (free alongside ship)

(C11E) Don’t know =0

C12- The percentage of the value of the products exported directly was lost while in transit because of breakage or spoilage?

(%)

Percentage of breakage or spoilage (No losses= 0%)

Don’t know =.

C13-What percentage of the consignment value of products this firm shipped to supply domestic markets was lost while in transit because of breakage or spoilage?

(%)

Percentage of breakage or spoilage (No losses =0%)

Don’t know=.

C14-What percentage of the value of the products exported directly was lost while in transit because of theft?

(%)

Percentage of theft (No losses= 0%)

Don’t know=.

C15-What percentage of the value of the products this firm shipped to supply domestic markets was lost while in transit because of theft?

(%)

Percentage of theft (No losses= 0%)

Don’t know=.

221

16-Please, provide details of costs incurred for each unit after factory and up to Free on Board (FOB) in the country of export. Such costs may include (%):

Type of costs )%(

Export packing

Storage

Inland freight from factory to port

Insurance

Handling

Export taxes

Export inspection fees

Customs brokers’ fees

Commissions

Other taxes

C17-Current Sales Promotion Activities Chosen =1 Not chosen =0

Activities (√)

(C17A) Trade Association Participation (C17B) Trade Fair Exhibition (C17C) Print Advertising

(C17D) TV/Radio Advertising (C17E) Family/Personal Links (C17F) Direct Mail Advertising (C17G) Firm & Product Brochures (C17H) Internet

(C17I) No Activities

C18-Did this firm participate in promoting activities for its products nationally or internationally Chosen =1 Not chosen =0

Fair (√) (C18A) National

(C18B) GCC

(C18C) Arab countries

(C18D) Asia countries

(C18E) African countries

(C18F) European countries

(C18G) American countries

(C18H) Australian

(C18I) No participation

(C18J) Don’t know

C19- How would you categorize the supporting capabilities to exports……:

Capabilities (1)Not at all

important

(2)Somewhat

important (3)Important

(4)Very

important

(.)Not

applicable

(C19A)Foreign Language Ability

(C19B)Multi-Lingual Sales Staff

(C19C)Fax Machine

(C19D)EMail

(C19E)Foreign Language Web Site

(C19F)Product Information on Web

(C19G)Export Marketing Plan

(C19H)Export Document Preparation

222

C20- How important is on your firm’s ability to expand domestic sales? Would you say….?

List (1)Not at all important

(2)Somewhat important

(3)Important (4)Very

important (.)Not

applicable

(C20A) low demand

(C20B) Taxes on labor

(C20C) Supply of skilled labor

(C20D) Taxes on capital

(C20E) Access to credit

(C20F) Distribution problem

(C20G) Competitiveness

(C20H)Limited export diversification

(C20I) Inadequate transport link

(C20J) Standards compliance

(C20K) Customs and border procedures

(C20L) Informal restrictions

C21- How important is on your firm’s ability to expand exports? Would you say….?

List (1)Not at all important

(2)Somewhat important

(3)Important (4)Very

important (.)Not

applicable

(C21A) Low regional demand (C21B) Import tariffs and charges

(C21C) Port charges /delays (C21D) Tariffs or quotas in export markets

(C21E) Freight charges (C21F) Standards compliance (C21G) Customs and border procedures (C21H) Informal restrictions (C21I) Access to credit (C21J) Taxes on labor (C21K) Supply of skilled labor (C21L) Taxes on capital (C21M) Cost of export (C21N) Inadequate transport link (C21O) Product quality (C21P) Foreign marketing costs (C21Q) Competitiveness (C21R) Limited export diversification

223

C22-The barriers preventing your exporting or exporting more were, in order of importance

Barriers (1)Not at all

important

(2)Somewhat

important (3)Important

(4)Very

important

(.)Not

applicable

(C22A)The price competitiveness of our products

(C22B)Freight costs

(C22C)Cost of raw materials /components

(C22D)Cost of finance

(C22E)Lack of skilled staff

(C22F)Exchange rate volatility

(C22G)Economic conditions overseas

(C22H)Demand offshore

(C22I)Hidden costs ( government approvals) unpredictable regulations

(C22J)Export market risk or taking on more export market risk

(C22K)Tariff barriers overseas

(C22L)Non-tariff barriers, eg, sanitary restrictions overseas

(C22M)Insufficient funds for developing further export markets

(C22N)Lack of knowledge about potential export markets

(C22O)Lack of export skills/knowledge

(C22P)Lack of skills/knowledge of international logistics and trade regulations

(C22Q)Language or cultural barriers

C23- Which of these challenges do you think has most/least importance. Place them in order from the list bellow for the highest to (12) for the lowest challenge think has most/least importance. Place them in order from the list bellow (1) for the highest to (12) for the lowest challenge

Strategic Challenges Order (C23A)Increasing the current level of exports (C23B)Maintaining the current level of exports (C23C)Generating new markets

(C23D)Maintaining the current level of sales on domestic markets (C23E)Increasing the current level of sales on domestic markets

(C23F)Ensuring adequate raw material supply (C23G)Obtaining new working capital (C23H)Providing funds for the current operations (C23I)Obtaining new capital for plants and equipment

(C23J)Identifying and engaging trained workers (C23K)Training workers for the skills required

(C23L)Developing a Business Plan

224

PART(D)-Financial analysis

D1-What percentage, as a proportion of the value of total annual purchases of material inputs or services, were:

list (%)

D1A-Paid for before the delivery?

D1B-Paid for on delivery?

D1C-Paid for after delivery?

D2-what percentage of this firm's total annual sales of its goods or services were:

list (%)

D2A-Paid for before the delivery?

D2B-Paid for on delivery?

D2C-Paid for after delivery?

D3-Export Payment Terms Chosen =1 Not chosen =0

Terms (√)

D3A-Payment in Advance

D3B-Bank Draft at Sight

D3C-Bank Draft at Time

D3D-Letter of Credit at Sight

D3E-Letter of Credit at Time

D3F-Barter

D3G-Credit

D3H-Open Account

D3I-Don’t know =.

D4-Did this firm purchase any fixed assets, such as machinery, vehicles, equipment, land or buildings, what percentage of this purchase of total assets?

list D4A D4B

1. Yes=1 (_ _ _%)

2. No =0

3. Don’t know=.

D5-Please estimate the proportion of this firm's working capital that was financed from each of the following sources?

(%)

D5A-Internal funds or retained earnings D5B-Borrowed from banks (private and state-owned)

D5C-Borrowed from non-bank financial institutions D5D-Purchases on credit from suppliers and advances from customers D5E-Other (moneylenders, friends, relatives, etc.)

D6-Please estimate the proportion of this firm's total purchase of fixed assets that was financed from each of the following sources:

(%)

D6A-Internal funds or retained earnings D6B-Borrowed from banks (private and state-owned)

D6C-Borrowed from non-bank financial institutions D6D-Purchases on credit from suppliers and advances from customers D6E-Other (moneylenders, friends, relatives, etc.)

D7- Does this firm have a checking or savings account?

List (√)

1. Yes=1

2. No=0

3. Don’t know=.

225

D8-Does this firm have an overdraft facility?

List (√)

1. Yes=1

2. No=0

3. Don’t know=.

D9-Did this firm in the fiscal year apply for any loans or lines of credit?

List (√)

1. Yes=1

2. No=0

3. Don’t know=.

D10- Does this firm have a line of credit or a loan from a financial institution?

List (√)

1. Yes=1

2. No=0

3. Don’t know=.

D11- Referring to the most recent line of credit or loan, what type of financial institution granted this loan:

(%)

(1)Private commercial banks, (2) State-owned banks or government agency, (3) Non-bank financial institutions, (4) 1+2, (5) 1+3, (6) 2+3, (7) 1+8,(8)Other,(9)Don’t know

D11A-Private commercial banks D11B-State-owned banks or government agency

D11C-Non-bank financial institutions (microfinance institutions, credit cooperatives, credit unions, or finance companies) D11D-Other (9)-Don’t know

D12- Referring only to this most recent line of credit or loan, in what year was the most recent line of credit or loan approved?

Year

Year most recent loan/line of credit approved

Don’t know =.

D13- Referring only to this most recent loan or line of credit, what was its value at the time of approval? Chosen =1 Not chosen =0

list value

1. Less than 1 million SAR

2. Between 1-5 million SAR

3. Between 6-10 million SAR

4. Between 10-50 million SAR

5. Between 50-100 million SAR

6. More than 100 million SAR

7. Don’t know=.

D14- Referring only to this most recent loan or line of credit, did the financing require collateral?

1. Yes=1

2. No=0

3. Don’t know=.

D15- Referring only to this most recent loan or line of credit, what type of collateral was required? Chosen =1 Not chosen =0

List of collateral (chose one or more) (√)

D15A-Land, buildings under ownership of the firm

D15B-Machinery and equipment including movables

D15C-Accounts receivable and inventories

D15D-Personal assets of owner (house, etc.)

D15E-Other forms of collateral not included in the categories above

226

D16- Referring only to this most recent line of credit or loan, what was the approximate value of the collateral required?

Value of collateral (√)

1. %100 of facility value

2. %101-%125 of facility value

3. %126-%150 of facility value

4. %156-%200 of facility value

5. more than %200 of value

6. Don’t know=.

D17- What was the main reason why this firm did not apply for any line of credit or loan? Chosen =1 Not chosen =0

List (chose one or more) (√)

D17A-No need for a loan - firm had sufficient capital

D17B-Application procedures were complex

D17C-Interest rates were not favorable

D17D-Collateral requirements were too high

D17E-Size of loan and maturity were insufficient

D17F-Did not think it would be approved

D17G-Other

D17H-Don’t know =.

D18-Please place your perception as to regarding the access to finance, in which availability, cost, interest rates fees, and collateral requirements, as obstacles to meet current obligations:

Access to finance (0)No

obstacle (1)Minor obstacle

(2)Moderate obstacle

(3)Major obstacle

(4)Very Severe

Obstacle

(.)Don’t Know

(.)Does Not Apply

D18A-Availability D18B-Cost D18C-Interest rate D18D-Fees D18E-collateral requirements

D19- Has the recent Global Economic Crisis have an impact on this firm's operations.

Impact (√)

1. Direct impact

2. Indirect impact

3. No

4. Don’t know =.

D20- Please estimate the proportion of the majority of foreign currencies of which your exports are priced

list (%)

D20A-US dollars

D20B- Euro.

D20C-Pound

D20D-Others

D21- Did this firm has its annual financial statements checked and certified by an external auditor?

(√)

1. Yes =1

2. No =0

3. Don’t know=.

227

PART(E):Degree of Competition

E1-Which of the following was the main market in which this firm sold its main product?

list (%)

E1A-Local – main product sold mostly in same area where firm is located

E1B-National – main product sold mostly across the KSA

E1C-International

E2- For the main market in which this firm sold its main product, how many competitors did this firm`s main product face?

list (√)

1-One

2-2-5

3-More than 5

4-Don’t know=.

E3- Does this firm have any patents registered abroad?

list (√)

1. Yes

2. No

3. Don’t know =.

E4- Does this firm have any patents registered in Saudi Arabia?

list (√)

1. Yes

2. No

3. Don’t know =.

E5-Competitive Advantages of firms products in Domestic market (√)

Advantages (0)No advantage

(1)tend to advantage

(2)advantage (3)strongly advantage

(4)Very strongly

advantage (.)Don’t Know

E5A-Price

E5B-Quality

E5C-Service

E6-Competitive Advantages of firms products in Foreign market(√)

Advantages (0)No advantage

(1)tend to advantage

(2)advantage (3)strongly advantage

(4)Very strongly

advantage (.)Don’t Know

E6A--Price

E6B-Quality

E6C-Service

PART(F)- LABOR

F1-Please estimate the proportion of this firm`s Production workers of total employees

list %

F1A-Production workers

F1B-Non-production workers [e.g., managers, administration, sales]

100%

228

F2-Please estimate the proportion of this firm`s Production workers were:

list %

F2A-Skilled production workers

F2B-Unskilled production workers

100%

F3 How many full-time temporary employees did this firm employ throughout fiscal year

full-time temporary employees (√)

1- 1%-10% of full time employees 2- 11%-25% of full time employees 3- 26%-50% of full time employees 4- More than 50% of full time employees 5- Does Not Apply =.

6- Don’t know =.

F4- What was the average length of employment of all full-time temporary employees in fiscal year

list (√)

1- One month or Less 2- More than month and less than 3 months. 3- More than 3 month and less than 6 months. 4- More than 6 months

5- Does Not Apply =. 6- Don’t know =.

F5-Did this firm have formal training programs for its permanent, full-time employees?

list %

1- Yes =1

2- No =0

3- Don’t know =.

F6- What percentage of employees of the following categories received training?

list %

F6A-Production employees trained F6B-Non-production employees trained

F7-What percentage of the nationalities of employment in this firm?

list %

F7A-Arabs (including the Saudis) F7B-Asian Non Arab country F7C-African Non Arab country F7D-European F7E-Australian F7F-America

PART(G)- Production capacity

G1- The proportion of unused production capacity of the total capacity available Unused production capacity: (√)

1- Less than 25%

2- 26-50%

3- 51-75%

4- More than 76%

5- NO unused production =0

6- Don’t know =.

229

G2-What was the main reasons why this firm did not run the total production capacity available Reasons (Chosen=1, Not chosen =0) (√)

G2A-Limited local market

G2B-Lack of funding to increase production

G2C-Difficulty in expanding export

G2D-Cost of production inputs (raw materials)

G2E-Difficulty of marketing the product in the local market

G2F-Difficulty in obtaining skilled workers

G2G-Others

G2H-NO unused production

G2I-Don’t know =.

G3-please provide the percentage of following information about this firm

Total annual costs %

G3A-Total annual cost of labor including wages, salaries, bonuses, social security payments

G3B-Total annual cost of raw materials and intermediate goods used in production

G3C-Total annual costs of fuel

G3D-Total annual costs of electricity

G3E-Other cost of production not included above

100%

PART(H)- Business Environment

H1-To what extent do custom and trade regulations represent obstacles to the current operations of this firm?

Rank 1-5

(1) No Obstacle

(2) Minor (3)

Moderate (4) Major

(5)Very Severe

H1A-Transport

H1B-Customs and trade regulations

H2-Does the firm have any legal cases against their business currently pending in judicial authorities.

list (√)

1- Yes =1

2- No =0

3- Don’t know =.

H3-Over the last two years, did this firm submit an application to obtain an import license?

list (√)

1- Yes =1

2- No =0

3- Don’t know =.

H4-Approximately how many days did it take to obtain this import license from the day of the application to the day it was granted?

list H4a

Wait for import license =2

One day or less =1

Don’t know =.

H5-What percentage of ownership does the firm have in its lands

list %

H5A-Owned by this firm

H5B-Rented by this firm

H5C-Other

230

H6-Did this firm pay for security, for example equipment, personnel, or professional security services?

list (√)

1- Yes =1

2- No =0

3- Don’t know =.

H7- What percentage of this firm's total annual sales was paid for security, or what percentage of this firm`s total costs?

list %

Percentage of total annual sales for security

(OR) Percentage of total annual cost?

Don’t know =.

H8-Did this firm experience losses as a result of theft, robbery, vandalism or arson?

list (√)

1- Yes =1

2- No =0

3- Don’t know =.

H9-As a percentage of total annual sales or as total annual losses, define the estimated losses as a result of theft, robbery, vandalism or arson that occurred on this firm's premises.

list %

Losses as percentage of total annual sales

(OR) Losses as percentage of total annual losses

Don’t know =.

H10-which of the elements of the business environment included in the list, if any, currently represents the biggest obstacle faced by this firm (1-3) No Obstacle, (4-6) a Minor Obstacle, (7-9) a Moderate obstacle, (10-12) a Major Obstacle, (13-15) a Very Severe Obstacle

Rank from 1-15 Rank

H10A-Access to finance

H10B-Access to land

H10C-Business licensing and permits

H10D-Corruption

H10E-Courts

H10F-Crime, theft and disorder

H10G-Customs and trade regulations

H10H-Electricity

H10I-Inadequately educated workforce

H10J-Labor regulations

H10K-Political instability

H10L-Practices of competitors in the informal sector

H10M-Tax administration

H10N-Tax rates

H10O-Transport

Please type any information you want to add: