AN ANALYSIS OF POLICY AND NON-POLICY DETERMINANTS OF ...

AN ANALYSIS OF POLICY AND NON-POLICY

DETERMINANTS OF FOREIGN DIRECT

INVESTMENT IN PAKISTAN

PHD DISSERTATION

Fiaz Hussain

Registration No. NDU-GPP/PhD-13/S-017

Supervisor

Dr. Shahzad Hussain

Co. Supervisor

Dr. Muhammad Bashir Khan

Department of Government and Public Policy

Faculty of Contemporary Studies

National Defence University

Islamabad

2017

ii

AN ANALYSIS OF POLICY AND NON-POLICY

DETERMINANTS OF FOREIGN DIRECT

INVESTMENT IN PAKISTAN

PHD DISSERTATION

Submitted by

Fiaz Hussain

Registration No. NDU-GPP/PhD-13/S-017

Supervisor

Dr. Shahzad Hussain

This Dissertation is submitted to National Defence University, Islamabad in

partial fulfillment for the degree of PhD in Government & Public Policy

Department of Government and Public Policy

Faculty of Contemporary Studies

National Defence University

Islamabad

2017

iii

To my school teachers

iv

ACKNOWLEDGEMENTS

First and foremost, I thank Almighty Allah for giving me the strength, knowledge,

ability, and opportunity to undertake this dissertation and to persevere and complete it

satisfactorily. Without His blessings, this achievement would not have been possible.

On this journey, the pivotal support came from my research supervisor, Dr. Shahzad

Hussain. He has given me invaluable guidance, inspiration, and suggestions in my quest for

knowledge. Most importantly, he has given me all the freedom to pursue my research while

silently and non-obtrusively ensuring that I stay on course and do not deviate from the core

of my research.

I am also grateful to Dr. Sarfraz Hussain Ansari for mentoring me on all academic

matters and encouraging me throughout the PhD work. I am also grateful to the

administration and faculty of National Defence University especially the Department of

Government and Public Policy for their kind cooperation. I am also thankful to Mr. Baqir

Hasnain, Ms. Saima Naurin, Mr Zaheer Uddin HEC, Islamabad for their support.

I take pride in acknowledging the insightful guidance provided by Dr. Ghulam

Behlol, Dr. Syed Bashir Hussain Shah, Dr. Muhammad Bashir Khan, Dr. Muhammad Bilal,

Dr.Tahir Mukhtar and Ms. Tanzeela during the research work. I also acknowledge and

appreciate my colleagues and other research fellows, Dr. Tahir Ul Mulk, Mr. Imranullah,

Ms. Naila, Mr. Waqas, Ms Mehwish, Mr. Muqeem ul Islam, Ms. Nadia Khan, Mr Abbasi,

Ms. Momenah Yousaf, Mr. Shahryar, Dr. Shoaib, Ms. Noor Ulain and Ms Anza Khan for

their support. I cannot forget to thank my friends Azfar, Zain, Nauman, Qaiser for

supporting and pushing me to complete the dissertation. The research is not possible without

data, so I am grateful to World Bank, United Nation Conference on Trade and Development,

State Bank of Pakistan, Federal Bureau of Statistics for providing me the required data.

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Nobody has been more important to me in the pursuit of this dissertation than the

members of my family. I would like to pay my sincere gratitude to my mother whose prayers,

love and affection have always been with me in pursuit of academic goals. Most importantly,

I wish to thank my affectionate brothers, supportive wife and my little son whose desirous

toy has always been my laptop and his innocent and cute smiles always brought joy and

relief during the hard time of dissertation compilation.

Lastly, I thank all those who supported, guided, encouraged, and motivated me to

accomplish this dissertation.

Fiaz Hussain

1

TABLE OF CONTENTS

Content Page

No.

List of Acronyms/Abbreviations 4

List of Figures 7

List of Tables 9

List of Annexures 11

Abstract 12

CHAPTER 1: INTRODUCTION 14

1.1 Context of the Research and Statement of the Problem 14

1.2 Purpose and Scope of the Study 19

1.3 Contribution to Literature 22

1.4 Definitions of Key Terms 25

1.4.1 Foreign Direct Investment Inflow 25

1.4.2 FDI Stock 26

1.4.3 Multinational Enterprises 27

1.4.4 Unit of Analysis 27

1.5 Dissertation Outline 27

CHAPTER 2: AN OVERVIEW OF FDI POLICY REGIME AND

BUSINESS ENVIRONMENT IN PAKISTAN

29

2.1 Introduction 29

2.2 Historical Overview of FDI Policies 29

2.2.1 1947-1971: Era of Private Sector Dominance and Focus on

Manufacturing Sector

30

2.2.2 1972-1977: Era of Nationalization 31

2.2.3 1984-1999: Liberalization, Deregulation and Privatization 33

2.2.4 2000-2008: FDI Growth Era with Service Sector Dominance 36

2.2.5 2009-onward: FDI Strategy and Policy Revision 38

2.2.5.1 The FDI Strategy 2013-17 38

2.2.5.2 The Investment Policy 2013 39

2.3 The Business Environment in Pakistan 42

2.3.1 Business Environment Indicators 42

2.3.2 FDI Specific Indicators 47

2.3.3 Governance Indicators 51

2.4 Conclusion 55

CHAPTER 3: AN OVERVIEW OF FDI INFLOWS TO PAKISTAN:

TRENDS, DIRECTION AND COMPOSITION

57

3.1 Global and the Regional Trends of FDI Inflows 57

3.2 Sectoral Distribution of Global FDI Inflows 62

3.3 The FDI Inflows to Pakistan: Trend, Sources and Sectoral Composition 63

3.3.1 The Sources of FDI Inflows to Pakistan 66

3.3.2 The Sectoral Distribution of FDI Inflows to Pakistan 69

3.3.3 The Structural Pattern of FDI in Pakistan 73

3.3.4 Repatriation of Profits and Dividends 74

3.4 Conclusion 75

CHAPTER 4: FOREIGN DIRECT INVESTMENT: THEORETICAL

FRAMEWORK AND REVIEW OF LITERATURE

78

4.1 Introduction 78

2

4.2 Literature Review Methodology 78

4.3 Theories of FDI 79

4.3.1 The OLI Paradigm 82

4.3.2 The New Theory of Trade 86

4.3.3 The Institutional Theory 86

4.4 FDI Motives 88

4.4.1 The Investment Development Path 91

4.5 FDI Determinants: Empirical Evidence 92

4.5.1 Classification of FDI Determinants 92

4.5.2 Determinants of FDI at Country Level 95

4.5.3 Determinants of FDI at Sectoral Level 102

4.5.4 Determinants of FDI at Firm Level 106

4.5.5 Synthesis of Literature 109

4.6 Gap in the Literature 112

4.7 Conclusion 113

CHAPTER 5: RESEARCH METHODOLOGY 115

5.1 Introduction 115

5.2 Research Methodologies on the Subject 115

5.3 Dissertation Research Methodology 116

5.3.1 Variables and Hypotheses Development 118

5.3.1.1 Policy Determinants of FDI in Pakistan 119

5.3.1.2 Non-Policy Determinants of FDI in Pakistan 122

5.3.2 The Estimation Technique 128

5.3.3 The World Bank’s Enterprise Survey Data 129

5.4 Ethical Considerations 130

5.5 Conclusion 130

CHAPTER 6: DETERMINANTS OF FDI: COUNTRY LEVEL

ANALYSIS

132

6.1 Introduction 132

6.2 Variable Selection, Data Sources, and Model Specification 132

6.3 ARDL Model Specifications 133

6.4 Estimation Results and Interpretations 135

6.4.1 Unit Root Test 135

6.4.2 Bounds Testing to Cointegration 136

6.4.3 Long Run Estimates 137

6.4.4 Short Run Estimates 139

6.4.5 Diagnostics and Stability Tests 140

6.5 Discussions 142

6.6 Conclusions 146

CHAPTER 7: DETERMINANTS OF FDI: SECTORAL LEVEL

ANALYSIS

148

7.1 Introduction 148

7.2 Variable Selection, Data Sources, and Model Specification 148

7.2.1 Primary Sector and FDI 148

7.2.2 Secondary Sector and FDI 149

7.2.3 Tertiary Sector and FDI 151

7.3 ARDL Model Specifications 151

7.4 Estimation Results and Interpretations 153

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7.4.1 Unit Root Test 153

7.4.2 Bounds Testing to Cointegration 155

7.4.3 Long Run Estimates 156

7.4.4 Short Run Estimates 158

7.5.5 Diagnostics and Stability Tests 160

7.5 Discussions 162

7.6 Conclusions 169

CHAPTER 8: INVESTMENT OBSTACLES: FIRM LEVEL ANALYSIS 170

8.1 Introduction 170

8.2 The World Bank’s Enterprise Survey 2013 Data Analysis 170

8.2.1 Sample Characteristics 171

8.2.2 Investment Obstacles faced by Firms 180

8.2.3 The Biggest Obstacle faced by Firms 189

8.3 Conclusion 190

CHAPTER 9: CONCLUSIONS, POLICY IMPLICATIONS,

LIMITATIONS AND FUTURE RESEARCH DIRECTION

193

9.1 Introduction 193

9.2 Research Objectives achieved 193

9.3 FDI Location Determinants 195

9.4 Key Contributions 199

9.5 Implications of Research 200

9.5.1 Implications for Policymakers 200

9.5.2 Implications for Firms 201

9.6 Research Limitations 202

9.7 Future Research Direction 202

References 204

Annexures 227

4

LIST OF ACRONYMS/ABBREVIATIONS

ADB: Asian Development Bank

ARCH: Autoregressive Conditional Heteroscedasticity

ARDL: Auto Regressive Distributed Lag

ARIMA: Auto Regressive Integrated Moving Average

ASEAN: Association of South East Asian Nations

BITs: Bilateral Investment Treaties

BOI: Board of Investment

BRICS: Brazil, Russia, India, China and South Africa

BVL: Business Visa List

CAF: Corporate Agriculture Farming

CCR: Combined Cumulative Index

CEEC: Central and East European Candidates

CPEC: China Pakistan Economic Corridor

CPI: Consumer Price Index

CPI: Corruption Perception Index

CPS: Commodity Producing Sector

DTF: Distance to Frontier

ECM: Error Correction Model

EO: Economic Openness

EPZ: Export Promotion Zone

ESP: Economic Survey of Pakistan

EU: European Union

FBR: Federal Board of Revenue

FDI: Foreign Direct Investment

FEM: Fixed Effects Model

FMLOS: Fully Modified Ordinary Least Square

FRDL: Fiscal Responsibility and Debt Limitation Act

FY: Fiscal Year

FYA: First Year Allowance

GARCH: Generalized Autoregressive Conditional Heteroscedasticity

GDP: Gross Domestic Product

5

GFCF: Gross Fixed Capital Formation

GMM: Generalized Method of Moments

HEC: Higher Education Commission

IB: International Business

ICI: Imperial Chemical Industries

ICRG: International Country Risk Guide

IMF: International Monetary Fund

IPR: Institute for Policy Reforms

IPR: Intellectual Property Rights

ISI: Import Substitution Industrialization

JV: Joint Venture

MDG: Millennium Development Goals

MENA: Middle East North Africa

MNE: Multi National Enterprise

MOF: Ministry of Finance

NOC: No Objection Certificate

OECD: Organization for Economic Cooperation and Development

OIC: Organization of Islamic Cooperation

OICCI: Overseas Chamber of Commerce and Industry

OLI: Ownership, Location and Internalization

OLS: Ordinary Least Square

PIA: Pakistan International Airlines

PME: Plant Machinery and Equipment

PRS: Political Risk Service

REM: Random Effects Model

SAARC: South Asian Association for Regional Cooperation

SAFTA: South Asian Free Trade Agreement

SBP: State Bank of Pakistan

SDG: Sustainable Development Goals

SEA: South East Asia

SEZ: Special Economic Zone

SIZ: Special Industrial Zone

SMEs: Small and Medium Sized Enterprises

SMI: Stock Market Index

6

TI: Transparency International

TO: Trade Openness

UAE: United Arab Emirates

UK: United Kingdom

UNCTAD: United Nations Conference on Trade and Development

US: United States

VAR: Vector Auto Regression

VECM: Vector Error Correction Model

WB: World Bank

WDIs: World Development Indicators

WIR: World Investment Report

WLS: Weight Least Square

7

LIST OF FIGURES

No. Title Page No.

2.1 Inward FDI Performance Score of Pakistan 48

2.2 Worldwide Governance Indicators-Percentile Rank (2014-

2015)

52

2.3 Comparison of Worldwide Governance Indicators-Percentile

Rank (2015)

53

2.4 The Problematic Factors of Doing Business in Pakistan 53

2.5 Corruption Perception Index Score (1995-2016) 55

3.1 Global FDI Inflows (1970-2014) 58

3.2 Regional Trend of FDI Inflows (1982-2014) 60

3.3 FDI Inflows to Asian Regions 61

3.4 Sectoral Distribution of Global FDI 62

3.5 FDI Inflows to Pakistan 63

3.6 Percentage Share of FDI by different Countries (1982-2015) 66

3.7 Major Source Countries investing in Pakistan (2016-2017) 69

3.8 Sectoral Share of FDI Inflows to Pakistan (1985-2015) 71

3.9 FDI Inflows to Major Sub Sectors (FY 2016-2017) (Million

US$)

72

3.10 Structural Pattern of FDI Inflows to Pakistan 73

3.11 Repatriation of Profits and Dividends (Million US$) 74

4.1 OLI Paradigm 83

5.1 Funnel Approach of Analysis 116

6.1 Graphs of CUSUM and CUSMSQ for Stability of Parameters 142

7.1 Graphs of CUSUM and CUSMSQ for Stability of Parameters

of Primary Sector

161

7.2 Graphs of CUSUM and CUSMSQ for Stability of Parameters

of Secondary Sector

161

7.3 Graphs of CUSUM and CUSMSQ for Stability of Parameters

of Tertiary Sector

162

8.1 Incidence of Corruption at Sub-Sectoral Level 178

8.2 Incidence of Corruption at different Regional Locations 179

8.3 Incidence of Corruption and Firms Ownership 179

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8.4 Biggest Obstacle affecting the Operations of the Firm 184

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LIST OF TABLES

S. No. Title Page No.

2.1 Ease of Doing Business Indicators 43

2.2 Ease of Doing Business Rankings (2016-2017) 44

2.3 World Competitiveness Rankings (2015-2016) 46

2.4 Matrix of Inward FDI Performance and Potential Indices 49

2.5 Inward FDI Performance and Potential Index Rankings (1990-

2010)

49

2.6 The Global Opportunity Index 2016 50

2.7 The Global Opportunity Index (2014-2015) 51

3.1 Major investing Countries in Pakistan (2008-2016) 68

3.2 FDI Economic Sectors 70

4.1 Classification of FDI Determinants 94

4.2 Theoretical Framework based on FDI Theories and Empirical

Literature

111

5.1 Policy and Non-Policy Variables 127

6.1 ADF Unit Root Test on Variables 136

6.2 ARDL Bounds Test Results 136

6.3 Estimated Long Run Coefficient using the ARDL approach 137

6.4 Estimated Short Run Coefficient using the ARDL approach 140

6.5 Results of Diagnostics Tests 140

7.1 ADF Unit Root Test on Sectoral Variables 154

7.2 ARDL Bounds Test Results on Sectoral Data 155

7.3 Estimated Long Run Coefficient using the ARDL approach

(Sectoral Models)

156

7.4 Estimated Short Run Coefficient using the ARDL approach

(Primary Sector)

158

7.5 Estimated Short Run Coefficient using the ARDL approach

(Secondary Sector)

159

7.6 Estimated Short Run Coefficient using the ARDL approach

(Tertiary Sector)

159

7.7 Results of Diagnostics Tests on Sectoral Data 160

8.1 Sample Characteristics of WB’s Enterprise Survey 2013 171

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8.2 Investment Obstacles faced by Firms 173

8.3 Electricity as an Obstacle 174

8.4 Corruption as an Obstacle 175

8.5 Corruption Indicators at Firms Level 176

8.6 Incidence of Corruption at Sectoral Level 177

8.7 Incidence of Corruption among different sizes of Firms 178

8.8 Transportation as an Obstacle 181

8.9 Customs and Trade Regulations as an Obstacle 182

8.10 Practices of competitors in informal sector as an obstacle 182

8.11 Access to Land as an Obstacle 183

8.12 Access to Finance as an Obstacle 183

8.13 Crime, Theft and Disorder as an Obstacle 184

8.14 Tax Rates as an Obstacle 185

8.15 Tax Administrations as an Obstacle 186

8.16 Business Licensing and Permits as an Obstacle 186

8.17 Political Instability as an Obstacle 188

8.18 Courts as an Obstacle 188

8.19 Labor Regulations as an Obstacle 188

8.20 Inadequately Educated Workforce as an Obstacle 188

8.21 Biggest Obstacle affecting the Operation of the Firm 189

9.1 Summary of Significant Determinants of Sectoral FDI Inflows 197

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LIST OF ANNEXURES

Title Page No.

A List of Countries /Organizations with which Pakistan has signed BITs 227

B List of Business-Friendly Countries (BVL) 228

C List of Countries with which Pakistan has signed the Avoidance of

Double Taxation Agreements

229

D List of Countries falling in three Categories of Developed, Developing

and Transition

230

E List of Countries falling in five Regions (Africa, America, Asia,

Europe and Oceanic)

233

F List of Countries falling in four Regions of Asia (Eastern, Southern,

South-Eastern and Western Asia)

236

G The Historical Data of FDI Inflows from different Countries 237

H Structural Pattern of FDI Inflows to Pakistan 239

J Summary of the Literature Review on the Determinants of FDI at the

Country Level

240

K Summary of the Literature Review on the Determinants of FDI in

Pakistan

242

L Summary of the Literature Review on the Determinants of FDI (Cross

Countries Studies)

246

M Summary of the Literature Review on the Sectoral Determinants of

FDI

251

N Summary of the Literature Review on the Determinants of FDI at Firm

level

257

O Details of Variables used in the Estimations, their Definitions, and

Data Sources

260

P Sample Frame of the World Bank’s Enterprise Survey 2013 262

Q Details about the Sample Units of the World Bank’s Enterprise Survey

2013

263

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ABSTRACT

Foreign Direct Investment (FDI) inflows are considered beneficial for host countries

as this kind of investment brings a complete package of benefits including capital inflows,

management, entrepreneurship, technological skills, a source of business competition and

innovation. Therefore, countries provide competitive incentives to attract foreign investors.

Pakistan has been offering several policy packages and incentives but has not been able to

attract a significant amount of FDI. The dissertation aims to examine the determinants of

FDI inflows to Pakistan. Having the knowledge of the factors that determine the inflow of

FDI is very crucial for the policymakers in devising FDI relevant policies. They will come

to know the factors that motivate or deter FDI inflows. It adds to the existing available

literature by providing an extended framework on the determinants adopting a funnel of

three different levels: country, sectors, and firms. The econometric technique of Auto

Regressive Distributed Lag (ARDL) bounds testing has been used to examine policy and

non-policy determinants of FDI inflows at the country and sectoral levels (primary,

secondary, tertiary) for the period 1984-2015. The results reveal that corporate tax rate,

inflation, and corruption are negatively associated with FDI inflows while infrastructure,

trade openness (TO), and GDP per capita (market size) are positively associated with FDI

at country level. At the sectoral level, results reveal that corporate tax rate is negatively

associated with primary FDI and availability of natural resources is positively associated

with primary FDI; inflation and exchange rate volatility have a negative associated with

secondary FDI whereas energy, TO, and infrastructure show positive association with

secondary FDI; existing services FDI stock, labor quality, infrastructure, and market size

are positively associated with tertiary FDI while corporate tax rate and inflation are

negatively associated with tertiary FDI. Lastly, at the firm level, the World Bank’s

Enterprise Survey data 2013 based on the responses from 1247 firms have been used. The

13

statistical findings reveal that electricity shortfall and corruption are the obstacles to doing

business in Pakistan. The manufacturing sector is facing more power outages. The

corruption in the provision of different public service is detrimental to investment in the

country, especially getting electricity connections. Further, the manufacturing sector

experiences more corruption than the services sector. Among sub-sectors of the

manufacturing and the services, the firms relating to the business of non-metallic and

mineral products experience more bribery, followed by the textile sector. The region, firms

operating in Baluchistan are experiencing more bribe. Lastly, the MNEs operating in

Pakistan experience more bribe than domestic firms. The research findings have some

implications for both policymakers and firms. Pakistan as an investment location has some

deterring factors such as corruption, higher tax rates, financial and economic instability, and

consistent electricity shortfall. These factors should be dealt with by relevant state

institutions to make Pakistan favorable investment location. The factors, corruption and

electricity shortage increase the cost of doing business in Pakistan while higher tax rates

reduce the profitability margin of the firms.

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CHAPTER 1

INTRODUCTION

1.1 Context of the Research and Statement of the Problem

The socio-economic indicators of Pakistan portray a lackluster picture of the country.

The economy of Pakistan is witnessing slow Gross Domestic Product (GDP) growth,

constantly high budget deficits, unsustainable public debt, huge trade deficits, low savings

& investment and stagnant revenue base. These macroeconomic imbalances have brought

about high unemployment and widespread poverty in the country and these issues have been

confronting the policy makers.

On average, Pakistan demonstrated 5.3 percent annual GDP growth rate from 1974-

2013 and in the most recent five-year period (2008-2013), the average growth dropped to

2.8 percent (Planning Commission, 2008). It was averaged 4.91 percent from 1952 to 2015.

It achieved a highest point of 10.22 percent in 1954 and lowest point of -1.80 percent in

1952 (Pakistan Bureau of Statistics, 2017). During the period 2015-2016, Pakistan economy

grew at 4.7 percent against the targeted growth of 5.5 percent. This growth can further be

analyzed to see how this compares with the neighboring South Asian countries. Pakistan

has been growing at a much lower rate. For instance, during 2016, India, Bangladesh, and

Sri Lanka achieved 7.5, 6.6 and 5 percent GDP growth rate respectively (Ministry of

Finance, 2016).

Pakistan has been experiencing consistent revenue-expenditure gap (fiscal deficit).

Fischer and Easterly (1990) point outs that every mode of financing this gap is associated

with macroeconomic imbalances. Its financing through different means has different effects.

One of the means of filling this gap is borrowing which is being used by the successive

governments of Pakistan. So, this borrowing has accumulated public debt which has

surpassed the sustainability threshold limits, 60 percent Debt to GDP, as prescribed in the

15

Fiscal Responsibility and Debt Limitation Act (FRDL), 2005. The public debt-to-GDP ratio

is 66.5 percent (State Bank of Pakistan, 2016). As a high level of debt hurts growth and

macroeconomic stability, it is often advised to keep it within safe limits (SBP, 2015). This

public debt consists of two main components, namely: domestic debt (which is used

primarily to finance the budget deficit) and external debt (which is mainly needed to finance

development costs and the balance of payments). Pakistan is one of the countries where

public debt is dominated by domestic debt and the commercial banks, with 47 percent share,

are the main source of financing the budget deficit in Pakistan (SBP, 2016). For commercial

banks, it would be less risky to lend to the government, rather than the private sector. But

such domestic borrowing creates a negative impact on local domestic financial sectors. The

funds which could be made available for the private sector are taken by the government

itself. The would badly affect the private sector especially the small and medium enterprises

(SMEs) which find hard to obtain capital from the commercial banks.

Apart from the revenue-expenditure gap, Pakistan has been facing saving-

investment gap. According to Economic Survey of Pakistan 2016-2017, there is a gap of Rs.

858 billion between savings and investment (MOF, 2017). Savings has long been considered

as a driving force behind the economic growth. It is an important factor for attaining a higher

level of investment in the country. But unfortunately, Pakistan has the lowest savings as a

percentage of its GDP (9.045 %) in the region, excluding Afghanistan. The rate of capital

formation is increased with the higher level of savings and investment (Khan, 2007). For

sustainable growth, it is important to fill the saving-investment gap rationally. There could

be two options for minimizing this gap. One of them is to increase savings, and another is

to decrease in investment. The option of reducing investment is unacceptable for any

growing country, as it has serious implications for economic growth and employment. It is

therefore not surprising that almost all the high growth periods in Pakistan witnessed huge

16

inflows of foreign savings (in the form of remittances, loans, and grants). Consequently,

whenever such inflows have dried up, economic growth has moved back, as domestic

savings and investment have never been sufficient to sustain the growth momentum.

Moreover, these inflows are volatile and unpredictable because of cyclical movements in

world economies, external shocks and exchange rates (Ali, 2016).

Apart from macroeconomic indicators, Pakistan’s social indicators do not present

favorable picture. The Human Development Report 2016 released by the United Nations

Development Program positions Pakistan at 147th out of 188 countries under its Human

Development Index (HDI). The HDI value ranges from 0 to 1 and Pakistan has scored 0.55

against the average HDI value of South Asia, 0.62 and the average HDI value of world, 0.72.

Moreover, in line with the United Nation Development Program (UNDP), Pakistan has

developed a new poverty measurement, Multidimensional Poverty Index1 (MPI). It places

nearly 39 percent of Pakistanis in multidimensional poverty. Four out of 10 Pakistanis are

living in acute poverty. The official statistics further show that 60.6 percent of the population

lacks access to cooking fuel, 48.5 percent of the population do not complete schooling, over

38 percent of the population resides in a single room and nearly one-third of the population

lacks proper medical facilities. End of poverty is the foremost goal of the United Nations

(UN)’s the Sustainable Development Goals (SDGs) which were adopted in September 2015.

Besides poverty, the country is also facing unemployment and its statistics are not

encouraging as well. The Institute for Policy Reforms (IPR), a Lahore based think tank, has

released a factsheet on the employment situation in Pakistan. It shows unemployment is

closer to 8.5 percent and the government claims it below 6 percent (MOF, 2017).

1 Government of Pakistan now uses the multidimensional approach in measuring poverty in the

country. It has a broader connotation as it measures education, health, and standard of living aspects

of human deprivation.

17

Above-narrated macroeconomic imbalances can be corrected and social problems

can be alleviated with the inflows of foreign capitals from abroad. There are two established

ways, FDI and Foreign Portfolio Investment (FPI) by which foreign investors seek

investment in an economy. FDI refers to an investment of foreign investors directly in

productive assets of another country, while FPI is an investment in financial assets such as

stocks and bonds in a different country (Salem & Baum, 2016). FDI is preferred over

portfolio investment because of its highly resilient nature. And there are rare chances of its

withdrawal even during the financial crisis (Singhania & Gupta, 2011). It is considered a

long term attachment with the host country as it helps developing countries in creating

capital formation (Cho, 2003).

It is broadly accepted that the benefits of FDI to the host nations are greater than its

costs (Janicki & Wunnava, 2004). It could be a source of funding for developing countries

especially during debt crisis (Ismail, 2009). It fills the gaps between domestically available

supplies of savings and desired level of investment and targeted tax revenues and

domestically raised taxes2. It supports in fulfilling foreign exchange requirements. It boosts

host country’s management, entrepreneurship, technological skills (Iqbal, 1997; Saeed,

2001; Todaro, 1994). It has the potential to increase productivity, create jobs opportunities,

provide capital stocks, and transfer skills and technology (Solomon & Ruiz, 2012). It is a

key source of business competition and innovation (Xaypanya, Rangkakulnuwat &

Paweenawat, 2015). FDI inflow provides a complete package of benefits that it brings to

2 Pakistan has been struggling to increase its Tax to GDP ratio. At the moment, it is unable to meet

its current expenditure (non-development) from its own indigenous revenue resources. Therefore,

there is a gap between the current expenditure and total revenue collected by the government

(primary deficit). The primary deficit was recorded 3.8 percent of GDP in 2013 and 0.3 percent of

GDP in 2016 (MOF, 2017).

18

the host economy (Sahoo, 2006). The integrative trade,3 a new international trade paradigm,

recognizes the significance of FDI inflows in boosting economic activities as it re-

engineered business processes, stimulate trade and enhancing profitability. Ultimately it

increases national wealth (The Conference Board of Canada, 2017).

With this realization, countries now compete to attract FDI to accrue benefits

attached with this kind of investment. Therefore, they have taken several policy initiatives

to open their economies and have liberalized their FDI regimes. They have removed trade

restrictions and have adopted liberal market-oriented reforms in order to appease foreign

direct investors (Toulaboe, Terry & Johansen, 2011). As a result, they could receive a

considerable volume of FDI. The global inflows of FDI increased by 38 percent in 2015 to

US$ 1,762 billion from US$ 1,277 billion in 2014. FDI flows to developed countries jumped

84 percent to reach their second highest level of US$ 962 billion. Strong growth of inflows

was reported in Europe with an increase of 65 percent. In the United States, FDI flows have

almost quadrupled. Developing economies, with an increase of 9 percent, achieved a new

high of US$ 765 billion. Developing Asia, with an inward FDI exceeding half a trillion

dollars, has attained the top position in the world. The top five host economies in Asia are

Turkey, India, Singapore, China, Hong Kong (China). These five countries are also placed

among top 20 host economies in the world (United Nation Conference on Trade and

Development, 2016).

Pakistan has not been left behind in the developing world in this competition to

receive FDI inflows. The subsequent governments in Pakistan have offered several

incentives to attract foreign direct investors. But the policies’ success can be appraised from

3 The integrative trade is a framework which uses value chains for cross-border trade of goods and

services. The framework is for trade-related investment, business relationships, and partnerships for

development of mutually beneficial value to stakeholders.

19

the magnitude of FDI inflows to a country. Pakistan has received FDI inflows mostly less

than 1 percent of its GDP except for a few good years of 2006, 2007 and 2008 (Figure 3.5).

Highest FDI inflows, 3.67 percent, were received in 2007 when the country ranked among

the 10 largest recipients of FDI in Asia. In comparison to two giant neighbors in attracting

FDI, China and India, Pakistan have been receiving far less FDI flows. Pakistan is a

strategically located country and it has the potential to provide a corridor for energy, trade,

and transport (Abbas, 2015; Sahoo, Natarnsportaj, & Dash, 2014). Moreover, its foreign

investment regime is considered as the attractive and the most liberal in the South Asia

region (Asian Development Bank, 2008). Despite these facts, Pakistan has been an under-

performer vis-à-vis FDI inflows. Against this background, having the knowledge of the

factors that determine the inflow of FDI is very crucial for the policymakers in devising FDI

relevant policies. They will come to know the factors that motivate or deter FDI inflows. So

that they can make relevant policies.

1.2 Purpose and Scope of the Study

This massive movement of international capital has invited the academics to

examine the motivations behind the decisions of multinational enterprises (MNEs) to locate

a location and invest therein. A clearer understanding of the way MNEs choose a particular

location for investment and identification of the key determinants of FDI location are

necessary for the formulation of adequate policies. This importance of finding out the

location determinants of FDI flows has generated an enormous amount of theoretical

explanations and empirical studies (Petrovic-Randelovic, Dencic-Mihajlov & Milenkovic-

Kerkovic, 2013). With the help of these works, the policy makers are enabled to find out the

factors that could actually increase the magnitude of FDI inflows and resultantly they can

manipulate these factors to lure more inward FDI to their own countries. Therefore, this

research has the purpose to identify those determinants of FDI inflows to Pakistan at

20

macro/aggregate level (i.e. country) as well as at sectoral level (primary, secondary, tertiary)

for the period 1984-2015 by using annual time series data and finally at the micro level (i.e.

firms) by using cross-section enterprise survey data. There is no shortage of empirical

studies on the subject. It has been a well-treaded topic for academics and researchers. But it

is difficult to find out any previous empirical research for any region or country that has

assessed the policy and non-policy determinants at more meticulously. The dissertation

attempts to find out these determinants at three levels to get more detailed insight into the

determinants of FDI inflows. With this evidence obtained, it could be possible to find out

suitable factors that contribute to attracting or deterring FDI inflows. The deterring factors

can be mitigated and motivating factors can be capitalized on for FDI inflows.

The underlying theme of the research can be summed up as the determinants of FDI

flows in Pakistan. The scope and the objectives of the research are based on the growing

interest in FDI among policymakers and investors, and the lack of academic research that

was conducted on this topic. Thus, the study conducted through this dissertation aims to

provide a deep insight into the understanding of FDI in Pakistan by addressing the following

research question:

What are the policy and the non-policy determinants of FDI in Pakistan?

These determinants are to be investigated at three levels: country, sectors, and firm.

There are considerable empirical studies available on the determinants of FDI inflows to a

host country (Anuchitworawong & Thampanishvong, 2015; Bekhet & Al-Smadi, 2015;

Pattayat, 2016). There are also studies which have examined the determinants of FDI at

sectoral level (Bellak, Leibrecht and Stehrer, 2008; Walsh & Yu, 2010; Ramasamy & Yeung,

2010; Yin et al. 2014; Alecsandru and Raluca, 2015). Moreover, studies have also examined

determinants of FDI at firm level and these studies have mostly used the surveys where the

respondents are the entrepreneurs (Ablov,2015; Garavito, Iregui, & Ramírez, 2014). This

21

dissertation adds to the existing available literature by providing an extended framework on

the determinants adopting a funnel of three different levels and with three different units of

analysis. Thus, the main research question can be subdivided into the following sub-

questions:

i. What are different policy and non-policy determinants of FDI inflows to Pakistan?

ii. What are policy and non-policy determinants of FDI inflows at sectoral level?

iii. What are different obstacles faced by firms while doing business in Pakistan?

Following five research objectives have been outlined to address the above-mentioned

research questions:

i. To overview the FDI related policies and business environment in Pakistan

ii. To analyze the trend, direction, and composition of FDI inflows in Pakistan

iii. To examine the policy and non-policy determinants of FDI inflows at country level

iv. To examine the policy and non-policy determinants of FDI inflows at sector level

v. To study the obstacles faced by the firms while doing business in Pakistan

While solving the research questions and achieving the research objectives, the theories

developed on the subject in other countries and regions can be tested and expanded. The

time period in the scope of the research ranges from 1984 to 2015 for country and sectoral

levels examination. This time span is long enough to cover 32 years with the period of low

and high FDI inflows. Moreover, the time period is also important in the sense that era of

the 1970s was of nationalization and damages to foreign investment done during this era

were corrected during 1980s4.

The empirical studies available about FDI inflows are generally based on three

approaches namely aggregate econometric analysis, econometric study at the industrial level

4 FDI inward regime was liberalized with the announcement of industrial policy of 1984. Chapter 2,

Section 2.2 highlights the FDI policies during different eras.

22

and survey appraisal of foreign investors’ opinion. But these studies have failed to reach

consensus (Singh & Jun, 1995). These diverse results in the empirical literature may be

attributed to the different time period covered, model specifications, proxies used for

variables and the countries included in the studies. The reason could also be that the

empirical studies have pooled structurally diverse countries in order to find out the

determinants. These limitations in the empirical studies have been addressed in the

dissertation by applying methodological pluralism with time series econometric analysis

(aggregate and sectoral) and enterprise survey data.

Moreover, the study is based on the notion of evidence-based policy making. The

objective is to advise and suggest government institutions to have the evidence required for

interventions and to consider reliable evidence as one of the primary factors in their

decisions. Evidence-based policy making is a rationalist concept that stresses the possibility

of objective information. It emphasizes the need for building the bridge between research

and policy. This approach is designed to help the policymakers to have well-informed

decisions by using the best information in the design and implementation of policies.

Policies are seldom based on research-based evidence (Almeida & Bascolo, 2006). With

these ideas, the current research qualifies to be the research that contributes to policy making

as it intends to provide useful information to decision-makers for resolving problems

associated with attracting FDI inflows. The type of policy research is secondary analysis

(empirical examination of data from different data bases).

1.3 Contribution to Literature

The significance of the topic, determinants of FDI, is evident from the vast array of

literature available on determinants of FDI inflows. Even the use of key word ‘determinants

of FDI’ in a search engine results into a vast empirical and theoretical literature

(Anuchitworawong & Thampanishvong, 2015; Choong & Lam, 2010; Dimitropoulou,

23

McCann & Burke, 2013; Dumludag, 2009; Kwon, 1999; Nahidi, NasimJaberikhosroshahi

& Norouzi, 2012; Moosa, 2009). On the classification of determinants of FDI, Tsai (1991)

categorizes the determinants into supply side and demand side factor. Loots (2000)

categorizes FDI determinants into macroeconomic, policy and business facilitation.

Nunnekamp (2002) classifies them into traditional and non-traditional determinants. Ahmed,

Arezki & Funke (2005) have grouped them into push and pull factors. This classification of

FDI determinants depends on the motivation behind the study. Apart from country level

aggregate studies, sectoral level determinants have also been found in the empirical studies

(Alecsandru & Raluca; 2015; Bellak, Leibrecht & Stehrer, 2008; Ramasamy & Yeung, 2010;

Walsh & Yu, 2010; Yin, Ye & Xu, 2014). But these empirical studies differ from variables

and their proxies used, research methods, characteristics of FDI and locations or regions.

Apart from the research articles on the subject, several dissertations/theses have also

been produced. The Higher Education Commission of Pakistan’s (HEC) research repository

hosts PhD theses produced in Pakistan. Saeed (2001) in his PhD dissertation analyses FDI

and its impact on Pakistan’s trade and growth. Awan (2011) examines major determinants

of FDI at aggregate/country level with annual frequency data ranging from 1971 to 2008

and at sectoral level (Services and Commodity-Producing sectors) by using quarterly data

with time period 1996Q-2008Q. Mahmood (2012) in his PhD dissertation analyzes the

nexus between human capital and FDI in Pakistan for the time period ranged from 1971 to

2005. The study examines the factors that hinder FDI inflows to Pakistan. Apart from the

HEC repository, Aqeel (2012) in his PhD dissertation at the University of Nottingham,

explores the relationship and the determinants of FDI, trade, and migration in three

empirical essays. In his first essay, he finds out the determinants at aggregate level and

sectoral level (Manufacturing, Mining, Construction, Transport and Communications,

Commerce Utility) by using Knowledge Capital Model for the period 1986-2007 and 2002-

24

2007 respectively. Among other researchers at different locations, Dumludag, Saridogan &

Kurt (2007) have applied mixed method (quantitative and qualitative) to examine the impact

of both macroeconomic and institutional variables on FDI inflows to the emerging markets

(Turkey, Poland, Brazil, Hungary, Mexico, and Argentina). But the study has not considered

policy variables and has not explored sectoral level determinants either. Rogmans (2011)

finds out the FDI determinants in the region of the Middle East North Africa (MENA). The

study employs mix research method. The econometric method with pooled data and

qualitative method for micro level (firms) analysis by using case study method. The sectoral

level analysis is missing in the study.

Above narrated glimpse of literature available on the topic establishes the

importance of the subject area and at the same time poses a challenge for the researcher to

contribute to the existing pool of literature. By accepting the challenge, this current research

attempts to contribute to the available empirical literature in several ways. First, the existing

FDI theories are tested in a country which strives hard in pursuing FDI and where these

theories have not been tested extensively before. As these theories fall under the discipline

of International Business (IB), they are amalgamated into the discipline of Public Policy.

The discipline of IB is interdisciplinary in nature, so does the discipline of Public Policy.

The evidence-based policy making with the rationalistic approach in policy making has been

devised. Secondly, the variables are classified into policy and non- policy variables and they

are examined as determinants in attracting or deterring FDI flows in Pakistan at the macro

(country and sectoral levels) as well as at the micro (firm) level. The study uses

methodological pluralism with time series aggregate and sectoral data, and survey data. The

different unit of analysis (country, sector, and firm) have been used. This systematic

research design embodies the funnel approach which is aimed at narrowing down the unit

25

of analysis. With such a detailed analysis, the results would deliver an inclusive picture to

understand the determinants of FDI inflows to Pakistan.

This dissertation has used the standard econometric method of Autoregressive

Distributed Lag (ARDL) for country and sectoral level data analysis and survey method for

firms’ level data analysis. But the framework of the study is unique encompassing analysis

at three levels (country, sector, firm) and provides a deeper insight facilitating the research

that contributes to policy making. Moreover, the dissertation has used data from reputable

sources (UNCTAD for FDI data, Political Risk Services (PRS) Group data on corruption &

terrorism and World Bank’s Enterprise Survey 2013 for firm’s level data).

The study has a broader scope and is more comprehensive in comparison to the

available empirical literature on the topic as far as its diversification and the number of

determinants included in the analysis are concerned. An endeavor has been made to produce

something different and original. Lastly, it would also provide a valuable reference for

academicians, policy makers, policy advocacy institutions (for example Board of

Investment, Pakistan) or any other relevant government department.

1.4 Definitions of Key Terms

There are some key terms used in the dissertation. Some of them are defined in the

section and the remaining have been defined in the relevant sections as and when required.

1.4.1 Foreign Direct Investment Inflow

The definition of FDI has been provided by the international organizations such as

UNCTAD, IMF (International Monetary Fund), OECD (Organization for Economic Co-

operation and Development). FDI is defined as “an investment involving a long-term

relationship and reflecting a lasting interest and control by a resident entity in one economy

(foreign direct investor or parent enterprise) in an enterprise resident in an economy other

than that of the foreign direct investor (FDI enterprise or affiliate enterprise or foreign

26

affiliate). FDI implies that the investor exerts a significant degree of influence on the

management of the enterprise resident in the other economy” (IMF 1993 & 2009; OECD

1996). “An equity capital stake of 10 percent or more of the ordinary shares or voting power

for an incorporated enterprise, or its equivalent for an unincorporated enterprise, is normally

considered as the threshold for the control of assets” (UNCTAD, 2016). FDI could appear

in the forms of equity capital, intra-company loans and reinvested earnings. If a foreign

investor purchases shares of an enterprise in another country is called equity capital. Intra-

company debt/loans are short or long-term borrowing and lending of funds between parent

enterprise and its affiliates. Reinvested earnings are the share of foreign investor’s direct

investment. They are retained for reinvestment. So, they are not paid out as dividends by

foreign affiliates and are not remitted to the parent enterprise (UNCTAD, 2016).

FDI outflows from one country can be considered as inflows for another country.

Although the empirical literature has analyzed FDI outflows, the scope of this dissertation

is delimited to FDI inflows to Pakistan. Therefore, the terms, FDI or FDI inflows or inward

FDI are used interchangeably in the dissertation.

1.4.2 FDI Stocks

FDI stock is the accumulated value of direct investment at a certain point of time

which is usually a year end (OECD, 2018). The difference between FDI flow and FDI stock

is that the former is the value of direct investment over a specified period of time while the

latter is overall value of foreign direct investment at certain point of time. In simple words,

FDI stock is the revaluation of accumulated past FDI flows while FDI flows is valuation of

current transactions.

Both FDI flows and FDI Stocks are actually characterized as direct investments.

However, in case of FDI stocks, both equity capital and reinvested income are finally

consolidated into equity capital holdings at the end of the period. The data on FDI stocks is

27

more problematic because it raises the question of valuation of accumulated assets of

multinationals which were acquired previously (Wacker, 2013).

The dissertation has used FDI inflows as dependent variable both for country and

sector level analysis.

1.4.2 Multinational Enterprises

MNEs or multinational companies are comprised of both parent enterprises and their

foreign affiliates. A parent enterprise or company controls the assets of other entities located

in foreign countries normally owning a certain amount of equity capital shares. For the

control of assets, an equity capital stake of 10 percent or more of the ordinary shares or

voting power is usually considered as the threshold. A foreign affiliate is also an enterprise

in which foreign investor possesses the minimum 10 percent stakes which allow a lasting

interest in the enterprise’ management.

1.4.4 Unit of Analysis

The research is carried out at three different levels. The econometric time series

analysis is done at the country and sector levels where the units of analysis are country and

sector respectively. For the survey data, the unit of analysis is the enterprise or firm

(investing entity). The objective of the research is to find out the location determinants of

FDI inflows to Pakistan. Therefore, the geographical location is Pakistan where investment

takes place.

The dissertation has used several abbreviations and acronyms. Each abbreviation

and acronym has been mentioned in full at first place and afterwards, it is used in abbreviated

form.

1.5 Dissertation Outline

This introductory chapter is followed by eight more chapters: Chapter 2 provides a

descriptive historical and current overview of FDI policy regimes in Pakistan and the

28

prevailing business environment in the country. Chapter 3 highlights the dynamics of FDI

inflows by focusing their trend, direction and composition particularly in Pakistan. Chapter

4 provides the theoretical explanations and reviews the empirical literature with intend to

provide a theoretical framework and to find the gap in the empirical literature. Chapter 5

outlines the research methods used for data analysis in the dissertation. Chapters 6 and 7

provide the results and discussion of econometric time series analysis of FDI data at country

and sectoral levels respectively. Chapter 8 provides the findings of survey data and provides

discussion on them. Lastly, Chapter 9 summarizes the overall findings and provides

conclusions as well as the implications for policymakers and firms. It also gives limitations

in the research and provides future research direction on the subject matter.

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CHAPTER 2

AN OVERVIEW OF FDI POLICY REGIME AND BUSINESS

ENVIRONMENT IN PAKISTAN

2.1 Introduction

This Chapter aims to provide the overview of FDI policies enacted during different

periods of time and to analyze the prevailing business environment in Pakistan. This will

help to understand the evolution of FDI relevant policies and how they have stimulated

foreign investment in the country. It illustrates chronologically the FDI policies that indicate

how Pakistan has changed its policy stances to seek FDI over time. The policies of the host

country exert influence on the investment decisions of MNEs. Through policies, foreign

direct investors can be attracted or they can also be restricted in several ways. Hence, the

policies are considered instrumental in the control and the management of FDI inflows

(Khan and Kim, 1999) and the success of the host country in receiving FDI is associated

with the conducive environment created through the investment policy (Khan, 1997). Apart

from the policies, the FDI flows from developed countries to developing countries depends

upon the governance environment of the host countries (Dunning, 2002; Rodriguez-Pose &

Cols, 2017). With this backdrop, Section 2.2 gives the historical overview of FDI policies

and Section 2.3 deals with the business environment in Pakistan. In this way, both policy

and non-policy aspects are described and analyzed.

2.2 Historical Overview of FDI Policies

To facilitate the understanding, the history of FDI relevant policies has been divided into

five-time periods. These periods are as follows:

I. 1947-1971: Era of Private Sector Dominance and Focus on Manufacturing Sector

30

II. 1972-1977: Era of Nationalization

III. 1984-1999: Liberalization, Deregulation and Privatization

IV. 2000-2008: FDI Growth Era with Privatization and Service Sector Dominance

V. 2009-onward: FDI Strategy and Policy Revision

All these above-mentioned time periods have their own unique FDI related features.

But these eras are not mutually exclusive in terms of policy features. Public policy making

is a process and it passes through evolutionary phases as policies are living documents. They

have influence on the environment and they are get influenced by the environment. FDI

related and stimulating policies will be discussed briefly now onwards.

2.2.1 1947-1971: Era of Private Sector Dominance and Focus on Manufacturing

Sector

Pakistan has gradually liberalized its economic policies to attract FDI. This gradual

liberalization has given enough time for the domestic industry to become more competitive.

From 1947 to 1958, Pakistan held tight control over FDI (Shuaib and Bandara, 2009). FDI

has originally been allowed in the manufacturing sector, and large investments were

required to be in the form of joint stock companies with local equity participation (Hamdani,

2013). Pakistan on its independence in 1947 inherited an agricultural economy. Therefore,

the country did not have the required industrial capacity at that time to process the locally

produced agricultural output. This phenomenon urged the successive governments to work

on enhancing the manufacturing capacity of the country. To materialize it, the industrial

policies were made accordingly. The private sector was the most important means of

industrial investment during the era (Khan, 1997).

Despite the fact that the annual inflows of FDI were less than 6 percent of total

private sector investment, FDI was required for the success of the import substitution and

infant industrial policies in the post-independence period. During the period, Pakistan was

31

mainly following the policies of import substitution industrialization (ISI) in order to

achieve self-reliance and the foreign assistance was the main source of filling the gap

between savings and investment in the country. Therefore, the private foreign investment

remained negligible (Khan, 2011). But Pakistani start-up companies and foreign companies

went through several joint ventures or licensing/franchising and distribution agreements.

Multinational companies chose Pakistan for their investment ventures even before

independence, for example, Imperial Chemical Industries (ICI) was the first MNE to set up

a soda ash plant in 1942. First bilateral investment treaty (BIT) was signed between Pakistan

and Germany in 1959 and that was the first in the world as well (Hamdani, 2013).

The hallmark of the 1960s is the rapid industrialization in the country. President

Ayub Khan’s government changed the policies of the 1950s. Pakistan got a constitutionally

functioning government which took steps to attain rapid economic growth in the country.

The then Government dismantled import controls of the 1950s through issuing licenses on

importable items (Butt & Bandara, 2009). In 1961, automatic licensing schemes were

introduced to allow for the automatic updating of the import license. More importantly, in

1962, a free list was introduced, which was considered a crucial step towards trade

liberalization (Butt & Bandara, 2009). In the late 1960s, the private sector was dominated

in key areas such as insurance, banking, some basic industries and international merchandise

trade. The foreign investors were not allowed to invest in the commerce, banking and

insurance sectors, and only the local investors were permitted to invest in the service sector

(Khan and Kim, 1999).

2.2.2 1972-1977: Era of Nationalization

The period of industrialization lasted 25 years in Pakistan (1947-1972). The era of

the 1950s and 1960s was the predominance of the private sector. But the decade of the 1970s

was a period of public sector dominance with the onset of nationalization. From 1972 to

32

1977, the government transferred the liberal policies of the 1960s and adopted the

nationalization policy with intending to enhance the role of the public sector in economic

activities (Khan, 2011; Butt & Bandara, 2009). On January 1, 1972, ten major categories of

industries, seven commercial banks, insurance companies and development financial

institutions were nationalized by the then government. Subsequently, the small-sized

agricultural processing units were also taken over in 1975 (Khan & Kim, 1999; Sahoo et al.

2013). In order to succeed in the nationalization program, the government gave concessions

on import of machinery. In addition, the import licensing system was also liberalized. The

emphasis of nationalization was on domestic firms and foreign investment was mainly

sparred (Hamdani, 2013).

The sudden shift to the nationalization of private industries shattered the confidence

of private investors. This 5-years nationalization wave exerted a lasting impact on the

economy of Pakistan. And foreign investors were discouraged by this nationalization policy

and excessive regulations by the government on trade and commerce (Malik, 2008). The

effects of nationalization policy were visible with the inflows of FDI, which became

negative in 1973 and 1974 and did not recover for a decade until 1982. To recover the

damage of nationalization, the government introduced the Foreign Investment (Promotion

and Protection) Act of 1976. The aim was to provide legal protection of the sovereign. The

law provides protection to foreign investments from nationalization and expropriation. It

secures the repatriation of capital and the payment of profits and dividends. It encourages

investment in the capital goods industries. However, these assurances had little impact on

FDI inflows to the country. Overall, this era was based on protectionist policies in Pakistan

(Abbas, 2015).

33

2.2.3 1984-1999: Liberalization, Deregulation, and Privatization

This period is earmarked as consolidation of the damages caused during the process

of nationalization. The disappointing performance of the nationalized institutions compelled

the government to relax its position on foreign investment. It started gradually to attract and

encourage foreign investors in the country. The first significant step towards liberalizing

FDI regime in the country was introduced in 1984 by promulgating the Industrial Policy

Statement. It gave the equal importance to both private and public sectors (Sahoo, 2006).

Foreign investors were motivated to invest in the country in the mode of joint ventures by

partnering with domestic investors particularly in the sectors where marketing expertise,

advanced technology, management and technical skills were required (Atique, Ahmad,

Azhar, & Khan, 2004; Sahoo, 2006). The emphasis was on strengthening the existing

public-sector enterprises and additional investment in the sector was firmly controlled. The

public sector encompasses the industries such as fertilizers, steel, cement, engineering &

automotive equipment and petroleum refining & petrochemicals, and these areas were out

of bound for the private sector (Khan, 1997).

To facilitate FDI in export based industries, the Export Processing Zone (EPZ) was

established in Karachi. Both foreign investors and overseas Pakistanis were invited to take

benefits from the initiative and invest there in industrial projects on non-repatriatable

investment basis.

The EPZ offered the incentives such as tax exemptions and duty-free imports and

exports of goods. Moreover, the overseas Pakistanis were relaxed from unveiling the origin

of their investments. They were also permitted to import the used machinery with the

surveying certificate. Despite these incentives, the country failed to attract FDI flows

because of the highly-regulated nature of the economy. The deterring factors to FDI include:

(i) significant public ownership, strict industrial licensing, and government’s price controls

34

policies (ii) inefficient financial sector with generally public ownership, directed credits,

and segmented markets and (iii) a non-competitive and distorting trade regime with import

licensing, bans, and high tariffs (Khan & Kim, 1999, p.5).

These issues were realized and Pakistan started to open its economy and liberalize

its FDI regime in the late 1980s with the announcement of the new industrial policy package

in 1989. The role and significance of the private sector were recognized. In order to attract

FDI, several regulatory steps were taken for the improvement of the business environment

in the country. The Board of Investment (BOI) was established with the aim to create

opportunities for foreign direct investors and to facilitate investment activities. In addition,

a “one-window facility” was launched to facilitate the establishment of new industries. All

the industrial sectors were opened for foreign investments and exempted from the

requirement of Government sanctions except arms & ammunition, high explosives,

radioactive, currency & mint and security printing. If the investment project falls in the area

earmarked negative by the government, then it was necessary for foreign investors to get

the No Objection Certificate (NOC) from the concerned provincial government. Outside of

these negative areas, the investors were free to set up their project with obtaining NOCs

(Saeed, 2006).

The successive Governments of the 1990s offered generous incentives to foreign

investors through fair trade policy, tax incentives and tariff reductions with intend to make

Pakistan the most attractive location for investment in the region. The foreign exchange

regime was liberalized by permitting foreigners to open an account and possess foreign

currency certificates and the clearance from the State Bank of Pakistan (SBP) was not

required for the remittance of principal and dividends from FDI. Several fiscal incentives

were provided to foreign investors such as tax holidays, customs duty and sales tax

concessions and tariff rate reductions. The visa policy of Pakistan was also relaxed as 3-

35

years multiple visa facility was available to foreign investors with substantial investment.

Special industrial zones (SIZs) with liberal fiscal incentives were established to attract FDI

in export-oriented industries (Khan & Kim, 1999).

The BOI, Pakistan promulgated its first investment policy in 1997 ‘New Investment

Policy 1997’. It opened infrastructure, social, services and agriculture sectors to all investors.

It is considered a significant measure for the economy of Pakistan to be integrated with the

global market as previously foreign investment was limited to the manufacturing sector only

(FDI Strategy 2013-17). Sahoo (2006) highlights some of the major policy initiatives

introduced in the New Investment Policy 1997.

The policy allowed foreign investment in all sectors with 100 percent foreign equity.

Government permission was not necessary to start a business except for a few areas like

radioactive substances, high explosives, arms and ammunition, currency and mint, security

printing, liquor and alcoholic beverages. It allowed 100 percent foreign equity in the

infrastructure, the service sector, and the social sector projects on a repatriable basis. Foreign

investors could set up their projects in any area of the country except the areas declared

negative by the government. Further, they were not required to get NOC from the provincial

governments. The minimum foreign equity requirement was US$ 0.15 million in services

and US$ 0.3 million for agriculture and social sectors. In the manufacturing sector, no

custom duty levied on imported raw materials used for production for exports, while imports

of plants, machinery and equipment (PME) not locally manufactured were charged a 5

percent custom duty. Government did not impose any restriction flows on repatriation of

profits, capital, returns on intellectual property, capital gains, debt service, or payments for

imported inputs. Government offered a First-Year Allowance (FYA) on new investment in

PME. Subsequently, the Government also gave the five-year tax holiday on loan-free

investments. In order to ease the visa process, BOI, Pakistan launched ‘online work visa

36

application system' and ‘online branch/liaison office application system’. Foreign

investment was legally protected through laws such as the Foreign Investment (Promotion

and Protection) Act 1976, the Protection of Economic Reforms Act, 1992, and the Foreign

Currency Accounts (Protection) Ordinance, 2001.

2.2.4 2000-2008: FDI Growth Era with Privatization and Service Sector Dominance

The important features of the period are privatization, deregulation, fiscal incentives

and liberalized repatriation of capital and profits regime in the country (Zakaria, 2008).

Foreign investment was allowed in all economic sectors including services sector.

Privatization, as it was the hallmark of the era, of the enterprise was fully protected under

the law. It cannot be nationalized and the Government cannot takeover any foreign-owned

business either (Khan, 2007). The period from 2000 to 2007 is considered to be the most

successful era of economic policies (Butt and Bandara, 2009). The government encouraged

FDI in the country. FDI policy not only promoted investment environment but also boosted

the opportunities for employment and production. To attract foreign private investment, a

number of incentives were offered. They included the issuance of a negative list of industrial

activities for private investment, removal of restrictions on maximum holding of equity by

foreigners, cancellation of the permission of the SBP for remittances of dividends and

disinvestment proceeds, removal of restrictions on the raising of advance from the domestic

market, permitting of foreign firms to raise equity capital from the local market on a

repatriable basis, permitting of investment in the stock exchange, removal of restrictions on

royalties and technical fees and provision of guarantees to foreign investors relating to

remittances of profit, capital, and appreciation of capital investment (MOF, 2005, p.32)

During this period, the main objective of the government’s trade policy was a rapid

reduction in anti-exports and import biases by opening the economy to international trade

and FDI. Tariff reduction became an important aspect of the trade policy. The maximum

37

tariff was reduced to 25 percent in 2003 from 92 percent a decade ago. The number of tariff

slabs was also reduced from 13 to 4, minimizing the use of excise duties in tariffs, the

announcement of anti-dumping legislation in accordance with the World Trade

Organization (WTO), the import liberalization measures introduced for agricultural and

petroleum products, and restrictions on the export of agricultural products were lifted.

Moreover, the government made progress in regional and bilateral trade liberalization as the

regional trade agreement, i.e. South Asian Free Trade Agreement (SAFTA) was signed in

2004 to promote regional trade (MOF, 2005).

The privatization program in Pakistan has passed through three distinct phases:

1988-1999, 2000-2008 and 2009-onwards. Starting with manufacturing units, followed by

the financial sector units, the program has moved into the capital-intensive sectors of

telecommunications and energy. In this time period, the privatization was an essential

industrial policy and massive privatization took place during 2000-085. Compared to the

hundred units sold for Rs 59 billion during 1988-1999, 60 units were sold in 2000-08 for Rs

416 billion. These included capital-intensive State-Owned Enterprises (SOEs) in the energy

sector, manufacturing industries like cement, fertilizers, major banks and large capital

market transactions. Most of the units were bought by the foreign investors including the

United Bank Limited (UBL), the Pakistan Telecommunications Limited (PTCL), the Habib

5 The successive governments of the 1990s adopted the policy of privatization. The first distinct

phase of privatization lasted from 1988 to 1999. In 1988, the government of Ms. Benazir Bhutto

(1988-1990) adopted the policy of deregulation, liberalization, and privatization. The Government

shortlisted seven large state-owned enterprises (SOEs), but could only privatize 10 percent shares of

the Pakistan International Airlines (PIA). A massive privatization program was started by the

government of Mian Nawaz Sharif (1990-1992). It set up the Privatization Commission in 1991

which privatized two banks while in 1992 further 47 units were sold (Tahir, 2014).

38

Bank Limited (HBL), Karachi Electric Supply Company (KESC) and National Refinery

(Tahir, 2014).

The largest privatization proceeds came from the sale of telecommunications

companies, especially PTCL, with a share of 39 percent in total proceeds. Privatization

generated Rs 418 billion during the period which is equivalent to 88 percent of the

cumulative proceeds since 1991, including almost US$ 6 billion of foreign exchange

receipts. This figure represents a share of 30 percent of the total foreign investment (both

FPI and FDI) during the period and 70 percent of the foreign exchange reserves in 2008. It

clearly shows that the government utilized privatization as a means of promoting FDI and

accumulating foreign exchange reserves (Pasha, 2014).

2.2.5 2009-onward: FDI Strategy and Policy Revision

The first draft of the FDI Strategy 2013-2017 was prepared in 2008 and after

thorough deliberations, the BOI, Pakistan approved it in-principle on May 18, 2010. It was

further revised after the promulgation of the SEZ Act, 2012 and then presented to the then

Federal Cabinet for its final approval/endorsement. In 2013, the BOI published the FDI

Strategy 2013-2017 and the FDI Policy 2013.

2.2.5.1 The FDI Strategy 2013-17

This 5-year FDI Strategy outlines a conceptual framework for cooperation of

economic sectors in Pakistan, public and private sectors, towards mobilizing the private

investments, (domestic and foreign) that are required to achieve Pakistan’s economic targets.

Following targets are highlighted by the Strategy namely average growth rate of some 7-8

percent per year, employment opportunities for increasing population (230-260 million by

2030), creating a knowledge-based economy and prioritizing the human development,

enhancing the global competitiveness of the Pakistani economy from the 2011-12 rank (118

out of 142) to rank 50 by 2030.

39

To achieve the above-mentioned targets, the strategy has outlined following

operational windows:

Policy Formulation and Public-Private Sector Dialogue,

FDI Promotion Campaign,

Investment Facilitation (one window),

Development of SEZs,

Coordination Networks with Stakeholders Ministries,

BOI’s Re-organization, Capacity Development and establishing it as self-Financing

Organization

2.2.5.2 Investment Policy 2013

The New Investment Policy 1997 culminated into the Investment Policy 2013. The

current policy reinforces the components of the old policy and has introduced further liberal

measures along with the futuristic strategic programs for its implementation. Some of the

prominent features of Investment Policy 2013 are:

I. Liberal Investment Regime:

a. Free Entry for Foreign Investment

Foreign investment is permitted in all the economic sectors of the country except a

few areas like radioactive substances, high explosives, arms and ammunition, currency and

mint, security printing, liquor and alcoholic beverages. There is no minimum foreign equity

requirement for any sector and there is no maximum limit on foreign equity investment

except in the areas of media, airline, agriculture, and banking. The government does not

impose any restriction on foreign investors to repatriate profits, dividends, and any other

funds of the currency of their origin.

40

b. Ease of Registration and Entry

The government has introduced the open admission system which does not entail any

pre-screening and approval. But foreign enterprises are required to fulfill corporate

registration conditions under the law (Companies Ordinance 1984). To facilitate foreign

companies’ operations, the BOI, Pakistan has introduced ‘online’ system of registration.

Moreover, the BOI takes seven weeks to approve the applications for the opening of foreign

companies’ branch, representative or liaison offices. But the approval for opening of

branches of foreign banks falls under the domain of the SBP. Foreign investors are entitled

to sell shares, transfer ownership and deregister under the law.

c. Flexibility in Financial Procedures

The State Bank of Pakistan and the Security and Exchange Commission of Pakistan

(SECP) have relaxed, to treat foreign and local investor equally, the equity caps for setting

up of banks and insurance companies. Exchange of local currency is freely permitted. There

are no restrictions on the use of foreign private loans and domestic borrowing is also allowed

to foreign investors in all sectors.

d. Flexibility in Land and Estate Procedures

Under the policy, foreign investors have the right to lease land without restriction

according to the rules and regulations of the relevant authority. Moreover, there is no

restriction on the transfer of any land owned by the foreign investor unless specified in the

contractual agreement. Real estate sector is now open for foreign investors and restrictions

have been removed.

e. Agriculture Policy

The agriculture sector is partially opened for foreign investors. They are permitted to have

60 percent stake in the agriculture projects and 100 percent equity in the Corporate

Agriculture Farming (CAF).

41

II. Investment Protection

a. Investor Rights

Foreign investment in the country is legally protected through laws such as the Foreign

Private Investment (Promotion & Protection) Act, 1976 and the Protection of Economic

Reforms Act, 1992. The policy assures that there will be no discrimination among local and

foreign investors and they shall receive fair and equitable treatment. Pakistan has signed the

BITs with 47 countries and 27 are being negotiations (Annexure A)6.

b. Right to due process of Law

The Commercial Arbitration Act, 2011 and the Recognition and Enforcement

(Arbitration Agreements and Foreign Arbitral Awards) Act, 2011 are in place and foreign

investors can pursue court of law in case of any dispute and after exhaustion of the local

remedies, the provision of international arbitration is also available.

c. Protection of Intellectual Property Rights (IPR)

The Intellectual Property Organization (Cabinet Division) has improved IPR policies

with enhancement of statutory penalties for the violations of copyright and patent

infringements. Foreign investors are facilitated in getting trademarks, copyrights and patents.

III. Establishment of SEZs

The SEZs Policy under the SEZ Act, 2012 encourages the establishment of industrial

clusterization. It offers special incentives for zone developer and enterprises like duty free

import of capital goods, ten years in income tax exemption, availability of all necessary

utilities and infrastructure, captive power generation is allowed, One-Window-Facilities by

the BOI, dry Ports facilities and security arrangements by the provincial governments.

6 List of countries/organizations with which Pakistan has signed BITs is annexed at ‘A’.

42

IV. Facilitation

Comprehensive and investor friendly visa policy is in place. Pakistan Missions abroad

have the authority to issue a five-year validity (multi-entry) visa to businessmen of 69

countries within 24 hours, and each stay is limited to three months upon the production of

the required documentation7. And Businessmen can also avail Visa-On-Arrival with 30 days

validity. Foreign technical and managerial personnel intended to impart technical education

to the local population can get work visas of one-year duration from the Pakistan Missions

abroad. It can be extended on yearly basis. Lastly, the Government of Pakistan has signed

the Avoidance of Double Taxation agreements with 52 countries (Annexure ‘C’).

2.3 The Business Environment in Pakistan

Prevailing business environment in the region in general and in Pakistan particularly

can be examined along multiple perspectives. For the matter, this section will present three

different types of measures: the business environment, the FDI-specific and governance

indicators.

2.3.1 Business Environment Indicators

The prevailing business environment is examined through two prominent measures,

the ‘Doing Business’ and the ‘Global Competitiveness’ indicators provided by the World

Bank (WB) and the ‘the World Economic Forum (WEF) respectively. The WB has been

publishing the Doing Business indicators since 1996. These indicators are regularly quoted

in the academic and non-academic literature. The Bank’s Doing Business measures the

regulations that flourish or hinder business activities. It measures ten areas affecting the life

of a business and these parameters provide the basis on which countries are examined for

the ease of doing business (Table 2.1).

7List of business-friendly countries is placed at Annexure ‘B’.

43

Table 2.1

Ease of Doing Business Indicators

Source: World Bank

The underlying philosophy of the Doing Business is that private sector development

as an economic activity benefits from clear and coherent rules. The rules that establish and

elucidate the property rights and facilitate the resolution of disputes. They improve the

predictability of economic interactions and contractual partners are provided with essential

safeguards against arbitrariness and abuse. Such rules are much more effective to generate

incentives for economic agents to promote growth and development when they are

sufficiently effective in design, are transparent and accessible to those for whom they are

designed and cost-efficient in implementation. The quality of the rules also has a decisive

influence on how societies distribute benefits and bear the costs of development strategies

and policies (WB, 2017). Table (2.2) presents the ease of doing business indicators in

Pakistan. It shows the comparison between 2015 and 2016 indicators and reports the

changes in the rank.

44

Table 2.2

Ease of Doing Business Rankings (2016-2017)

Source: Author’s compilation from the WB’s Doing Business Data

Pakistan has improved its the Doing Business 2017 ranking, placing at 144 out of

190 countries, with a Distance to Frontier (DTF) score of 51.77, which is marginally higher

than last year's score of 49.488. Countries are ranked on the basis of the DTF score which is

a composite measure of a country's progress along a series of indicators. Among the

members of the South Asian Association for Regional Cooperation (SAARC), Bhutan leads

at 73rd (down two), followed by Nepal (107th, down seven), Sri Lanka (110th, down one),

India (130th, up one) and Bangladesh (176th, up two). Pakistan, India, and Bangladesh have

improved their rankings.

8 The ranking of countries is based on their ease of doing business. High ranking means that the

regulatory environment is pro-business for starting and operations of the firms.

45

The Global Competitiveness Report is yearly published by the World Economic

Forum. It evaluates countries on 12 pillars of competitiveness: Business Sophistication,

Financial Market Development, Business Sophistication, Goods Market efficiency, Health

and Primary Education, Higher Education and Training, Infrastructure, Innovation,

Institutions, Labor Market Efficiency, Macroeconomic Stability, Market Size,

Technological Readiness. The Competitiveness scores are based on both qualitative and

quantitative data. The WEF has been publishing the report since 2002, but the components

of the 12 pillars have undergone changes from year to year. Table (2.3) shows the

comparison between 2015 and 2016 indicators and reports the change in the rank of Pakistan.

Table 2.3

World Competitiveness Rankings (2015-2016)

Source: Author’s compilation from the WEF’s the Global Competitiveness Reports

Topics GCI 2016 GCI 2015 Change in

Rank

Overall 126 129 3

Institutions 119 123 4

Infrastructure 117 119 2

Macroeconomic environment 128 137 9

Health and primary education 127 129 2

Higher education and training 124 127 3

Goods market efficiency 116 100 -16

Labor market efficiency 132 132 No change

Financial market development 99 72 -27

Technological readiness 113 114 1

Market size 28 30 2

Business sophistication 86 81 -5

Innovation 89 88 -1

46

Pakistan has achieved improvement by three ranks and attained the 126th position in

the Global Competitiveness Report 2015-16 compared to the 129th position of the last year

2014-15. Pakistan has shown improvement in seven pillars and degraded only in three pillars.

First place in the GCI rankings, for the seventh consecutive year, goes to Switzerland.

Among the members of the SAARC, India leads the way at 55th followed by Sri Lanka (68th,

up five). Nepal (100th, up two), Bhutan (105th, down two), Bangladesh (107th, up two), and

Pakistan (126th, up three). So, Pakistan is at the bottom among other south Asian countries.

2.3.2 FDI Specific Indicators

There are some business environment measures which are directly linked to the

prospects for FDI inflows to specific countries. The prominent scores are the FDI Potential

and the Performance indices provided by the UNCTAD, the FDI Confidence Index by the

AT Kearney9 and the Global Opportunity Index provided by the Milken Institute, USA.

The UNCTAD has been publishing inward FDI Potential Index since 1990. The

index covers 141 countries. It comprises of 12 economic and policy variables that could

affect a country’s attractiveness to foreign investors. These components are: per capita

commercial energy use, country risk, GDP growth rate, GDP per capita, infrastructure

(average number of telephone lines and mobile telephones per 1,000 inhabitants), share of

exports in GDP (proxy for openness and competitiveness), share of Research &

Development (R&D) expenditures in GDP, share of tertiary students in the population,

world market share in exports of natural resources (proxy for the availability of resources

for extractive FDI), world market share of exports of services, share of world FDI inward

stock (indicating the attractiveness and absorptive capacity for FDI and the investment

climate), world market share of imports of parts and components for automobiles and

9 Andrew Thomas (A.T) Kearney is a known global management consulting firm. It publishes

Foreign Direct Investment Confidence Index. The 2017 index ranks top 25 countries for FDI and

Pakistan is not included among these countries. Therefore, this measure has not been discussed here.

47

electronic products (UNCTAD, 2002). These elements are mentioned here as they are

considered to be the potential determinants of FDI by the UNCTAD. Countries are ranked

according to the FDI Potential Index.

The second index is the FDI Performance Index. It is the most straightforward. It is

the ratio of a country’s share in global FDI flows to its share in global GDP:

FDI Performance = (𝐹𝐷𝐼 𝐶𝑜𝑢𝑛𝑡𝑟𝑦

𝐹𝐷𝐼 𝑔𝑙𝑜𝑏𝑎𝑙) / (

𝐺𝐷𝑃 𝐶𝑜𝑢𝑛𝑡𝑟𝑦

𝐺𝐷𝑃 𝑔𝑙𝑜𝑏𝑎𝑙)

This index measures the relative success of a country in attracting global FDI flows.

If a country’s share of global FDI inflows matches its relative share in global GDP, then the

value of this index is one. A value greater than one shows that country is attracting a larger

share of FDI relative to its GDP while a value less than one point to a smaller share of FDI

relative to its GDP. A negative value indicates that the foreign investors disinvested in that

period. The index has been calculated and presented in Figure (2.1).

Figure 2.1. Inward FDI Performance Score of Pakistan

Source: Author’s compilation from the UNCTAD & the WDIs

As shown in Figure (2.1), Pakistan has achieved four times a value higher than one

during the period of 34 years. So, most of the times, Pakistan has received a smaller share

of FDI relative to its GDP. The countries with a value greater than one attract a larger

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

19

81

19

82

19

83

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

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94

19

95

19

96

19

97

19

98

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99

20

00

20

01

20

02

20

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20

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20

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08

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20

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11

20

12

20

13

20

14

Year

48

amount of FDI relative to their GDP sizes and in this category, many advanced industrial

economies are included. Their FDI performance reflects high incomes and technological

strengths. In other countries, the high scores reveal the end of political or economic crises,

transition to a market economy or substantial privatizations. The countries with low values

as a result of varying factors including instability, poor policy design and implementation

or competitive weaknesses (UNCTAD, 2002).

The UNCTAD compares these two indices and classifies countries into four categories

as follow:

Front-runners countries with both high FDI potential and performance

Above potential countries with low FDI potential but high performance

Below potential countries with high FDI potential but low performance

Under-performers countries with both low FDI potential and performance

Based on these two indices, a four-fold matrix of inward FDI performance and potential

can be drawn as shown in Table (2.4). Pakistan has been classified as the under-performer.

Table 2.4

Matrix of Inward FDI Performance and Potential Indices

Source: UNCTAD (2002)

Data on Pakistan’s FDI Potential and FDI Performance rankings are presented in Table (2.5).

This ranking is based on 141 countries data.

49

Table 2.5

Inward FDI Performance and Potential Index Rankings (1990-2010)

Year FDI Potential Index Ranking FDI Performance Index Ranking

1990 91 66

1995 111 93

2000 131 122

2005 125 92

2006 123 83

2007 121 84

2008 126 74

2009 121 100

2010 NA 110

Source: UNCTAD (2011)

The ranking shows that Pakistan has achieved better performance ranking contrary

to its potential. With these rankings, the UNCTAD has placed Pakistan in the fourth

category, under-performer, a country with low FDI potential and performance.

The Global Opportunity Index (GOI) also measures the country’s attractiveness to

foreign investors. For that matters, the index provides a systematic and data-rich framework.

Apart from economic variables, it also evaluates the important business, legal, and

regulatory policies that can drive the investment decisions. It examines the progress of a

country against five categories: Economic Fundamentals, Financial Services, Business

Perception, Institutional Framework and International Standard & Policy. Table (2.6)

provides the GOI 2016 ranking. This index has covered 124 countries.

Table 2.6

The Global Opportunity Index 2016

Source: The Milken Institute, USA

50

Other members of SAARC are positioned better than Pakistan as Sri Lanka leads the

way at 78th, followed by India at 79th, Nepal at 98th and Bangladesh at 106th.

Previously, the GOI benchmarked the countries in four categories: Ease of Doing

Business, Economic Fundamentals, Regulatory Quality, and Rule of Law. Table (2.7)

provides the comparison of the GOI of years 2014 and 2015. Pakistan showed a

deteriorating situation in all ranks.

Table 2.7

The Global Opportunity Index (2014-2015)

Source: The Milken Institute, United States

These above-mentioned three different FDI specific indices are presented with the

intention to present the business climate in Pakistan. They, obviously, do not provide a

comprehensive picture of FDI location or to examine the impact of FDI on host economies.

2.3.3 Governance Indicators

The governance environment of the host countries matters for the attraction of

foreign investment (Dunning, 2002; Rodriguez-Pose & Cols, 2017). It is argued that given

other factors, countries tend to attract more investment flows with established institutions

of rule of law, judiciary, and control of corruption (Mathur and Chaterjee, 2003). Good

governance practices of the host countries reduce the risk attached to the foreign investment.

The governance indicators are provided by different organizations such the WB, the

51

Transparency International (TI) and the PRS Group (Publisher of the International Country

Risk Guide, ICRG).

The World Bank’s Worldwide Governance Indicators (WGIs) project has been

publishing aggregate and individual governance indicators for countries since 1996 (The

World Bank, Worldwide Governance Indicators, 2016). The project reports six different

aspects of governance namely Control of Corruption, Government Effectiveness, Political

Stability and Absence of Violence, Regulatory Quality, Rule of Law and Voice and

Accountability. These indicators are now increasingly used in the academic literature10.

Figure (2.2) presents the percentile rank of Pakistan on these WGIs for 2014 and 2015. This

percentile rank shows the position of a country among all countries, where 0 denotes the

lowest position while 100 is the highest rank.

Figure 2.2. Worldwide Governance Indicators-Percentile Rank (2014-2015)

Source: Author’s compilation from World Bank’s WGIs data

10 For example, Zeshan & Talat (2014), Han, Khan & Zhuang (2014), Chaib & Siham (2014).

22.1223.08

3.33

28.36

25

27.09

21.63

27.88

1.43

28.84

24.52

27.09

0

5

10

15

20

25

30

35

Control ofCorruption

GovernmentEffectiveness

Political Stabilityand Absence of

Violence/Terrorism

Regulatory Quality Rule of Law:Percentile Rank

Voice andAccountability

2014 2015

52

Pakistan has shown improvement from 2014 to 2015 in all indicators except in

‘Political Stability and Absence of Violence/terrorism’. This indicator has got the lowest

percentile rank of 0.95. Pakistan does not show any improvement in ‘Rule of Law’ and

‘Voice and Accountability’. Overall the indicators do not paint good picture of Pakistan’s

governance environment. If the governance environment of Pakistan is compared with other

South Asian countries, Pakistan has performed better than Bangladesh only in areas of

‘regulatory quality’, government effectiveness’, and control of corruption’ (Figure 2.3).

Figure 2.3. Comparison of Worldwide Governance Indicators-Percentile Rank (2015)

Source: Author’s compilation from World Bank’s WGIs data

Among the governance indicators, the most widely discussed in the empirical

literature is the corruption. The Global Competitiveness Report 2015-16 declares

‘corruption’ as the most problematic factor of doing business in Pakistan (Figure 2.4).

0 10 20 30 40 50 60 70

Control of Corruption

Government Effectiveness

Political Stability and Absence ofViolence/Terrorism

Regulatory Quality

Rule of Law

Voice and Accountability

Sri Lanka

Pakistan

India

Bangladesh

53

Figure 2.4. The Problematic Factors of Doing Business in Pakistan

Source: Author’s compilation from WEF’s the Global Competitiveness Report 2015-16

The subsequent Global Competitiveness reports (2016-17 & 2017-18) have also

declared ‘corruption’ as the most problematic factor of doing business in Pakistan

There is another widely used measure of corruption, ICRG Corruption index. It is

coded in a 7-point scale, where 0 represents the most corrupt whereas 6 designates the least

corrupt. The ICRG claims that 80 percent of the largest companies in the world, which are

positioned by the Fortune magazine, have been using their information and data solutions

for their business ventures. It highlights that the most common manifestation of corruption

faced by the investors that is financial corruption. It manifests in the form of bribe for

obtaining import & export licences, tax assessments, exchange controls, police protection

and it is considered a detrimental for FDI. Corruption makes business operations difficult.

Sometimes it may force foreign investors to withdraw their investments or restrict their

businesses (PRS Group 2006). The data of the ICRG Corruption index has been obtained

54

from the PRS Group, Inc., New York, USA. The index average score from 1984 to 2010 is

2.0 which clearly shows the presence of corruption in Pakistan11.

Apart from the ICRG Corruption index, TI’s Corruption Perception Index (CPI) is

also used in the academic literature. It is also widely cited in business press to show the

country’s position in controlling corruption. The CPI 2016 ranks Pakistan at 116th among

176 countries of the world. Pakistan once, in 1995, was the second most corrupt country in

the world. The index ranks countries from 0 to 10 whereas 0 means highly corrupt and 10

means highly clean12. Denmark is the highly clean country with the score of 9 whereas

Somalia is the highly corrupt country with the score of 1. Figure (2.5) shows the trend of

corruption (the CPI as a measure of corruption) in Pakistan.

Figure 2.5. Corruption Perception Index Score (1995-2016)

Source: Author’s compilation from TI data

2.4 Conclusion

This Chapter has provided an overview of FDI policies and the prevailing business

environment in the country. Policies are considered to be instrumental in attracting or

11 This index has been further elaborated in Chapter 5 and Section 5.3. the same index has been used

as a measure of corruption in the empirical analysis carried out in the dissertation. 12 Since 2012, the CPI has changed its ranking from 0-10 to 0-100 (highly corrupt-highly clean) so

for such years, score is converted to the scale 0 to 10 in order to make the data and graph smooth.

2.25

1

2.532.7

2.2 2.32.6 2.5

2.1 2.1 2.12.4 2.5

2.9

2.32.5

2.7 2.8 2.9 33.2

0

0.5

1

1.5

2

2.5

3

3.5

Sco

re

Year

55

deterring FDI inflows to a country. During the first three decades, Pakistan pursued the

policies of the import substitution industrialization with the aim of achieving self-reliance.

And the country relied mostly on foreign assistance to fill the gap between the savings and

investment. Hence, in that period, the foreign investment remained negligible. As Pakistan

inherited an agricultural economy, so the industrial capacity was not enough to process the

locally produced agricultural raw materials. This phenomenon urged the subsequent

governments to emphasize on enhancing the manufacturing capacity of the country. To

materialize it, the industrial policies were made accordingly. The private sector was the most

important means of industrial investment during the era. Till 1971, the policies of the

successive governments were designed to encourage the private sector. But, the decade of

the 1970s witnessed the wave of nationalization and the focus was shifted from the private

sector towards the public sector. The role of the private sector was again emphasized in the

economy during the period of 1980-1990. During the 1990s, Pakistan started adopting the

liberalization focusing on the market-oriented policies, and the private sector was declared

the driving force of economic growth. Moreover, the foreign investors were offered

lucrative investment incentives.

The 5-years of nationalization has taken several years of governments to privatize

once nationalized industrial and other commercial units in the country. Pakistan has

gradually liberalized its economy. Starting from the only manufacturing sector, now all

economic sectors are open for the foreign direct investors except a few sectors. Currently

offered policy packages and incentives are encouraging for FDI. But apart from these policy

inducements, business environment also matters for foreign investors. Pakistan’s

governance environment also places the country at high risk. The most disturbing concerns

56

are political instability and prevailing violence/ terrorism, and corruption in the country13.

Corruption is rampant and Pakistan is not grouped among clean countries. The BOI,

Pakistan concedes that Pakistan is widely perceived as a high risks investment location.

Pakistan’s rankings on the Ease of Doing Business and Global Competitiveness do not

present it a favorable location for investors. The Investment Strategy 2013-2017 envisages

to enhance country’s positive image and outlines to achieve the rank of 50 by the year of

2030 on the global competitiveness. Presently it is positioned at 126th. So, the BOI needs to

revisit its strategy and come up with more realistic targets to be achieved.

13 These TWO indicators, terrorism & corruption, have also been empirically tested for their

relationship with FDI inflows to Pakistan (Chapter 6).

57

CHAPTER 3

AN OVERVIEW OF FDI INFLOWS TO PAKISTAN: TRENDS,

DIRECTION AND COMPOSITION

The preceding chapter has provided an overview of FDI relevant policies and the

prevailing business environment in Pakistan. The magnitude of FDI inflows depends on the

policies of the host country and the prevailing business environment in the country. The

objective of this chapter is to analyze the trend, direction, and composition of FDI inflows

at global, regional level but the focus is on Pakistan. It will help to comprehend the dynamics

of FDI inflows. Section 3.1 provides the discussion on FDI inflows at global and regional

levels. The main data source of FDI is the UNCTAD and its publications especially the

World Investment Report (WIR). The UNCTAD has been providing the data online on FDI

flows and stock since 1970 and 1980 respectively. Section 3.2 discusses the trend, direction,

and composition of FDI inflows to Pakistan. The main data sources on FDI in Pakistan are

the UNCTAD, the SBP and the BOI, Pakistan.

3.1 The Global and the Regional Trends of FDI Inflows

There has been a gradual increase in FDI inflow at the global level, with some

fluctuations, over the last three decades. Figure (3.1) shows the global trend of FDI.

According to the UNCTAD statistics, the global FDI inflow was US$ 329 billion in 1995

and it reached the level of US$ 1.4 trillion in 2000 (UNCTAD, 2003). In 2003, however,

these inflows decreased to the level of US$ 558 billion and then increased to US$ 916 billion

in 2005 (UNCTAD, 2006). Global FDI attained a new record of US$ 1.833 trillion in 2007

and showed the growth for the fourth consecutive year. The achievement set the new record

by breaking the 2000 record by some US$ 400 billion. All the three major groups of

58

economies (developed, developing and the transition) showed continued growth in FDI.

This sustained growth in FDI is the manifestation of relatively high economic growth and

strong economic performance showed by several countries (UNCTAD, 2008). The global

FDI inflows declined by 16 percent to US$1.23 trillion in 2014 from US$1.47 trillion in

2013. The reasons can be explained mainly because of the global economic instability, the

uncertainty of policies for investors and the increased geopolitical risks. But, in 2015 as

forecasted, the global flows rose by about 40 percent, to US$ 1.8 trillion and attained the

highest level since the beginning of global economic and financial in 2008 (UNCTAD,

2016). In 2016 again, the global inflows fell by about 2 percent, to US$ 1.75 trillion

(UNCTAD, 2017).

Figure 3.1. Global FDI Inflows (1970-2014)

Source: Author’s Compilation from UNCTAD data

The developed countries14 have been the largest recipient of FDI inflows since the

1970s. Although slowly, the share has been reduced over time. During the period 1982-86,

14 List of three major categories of countries, Developed, Developing, and Transition, is placed at

Annexure ‘D’.

59

the share of developed countries was 70 percent while the developing countries’ share was

30 percent in the global FDI (UNCTAD, 2008). The time period 1991-1992 was of global

FDI recession with inflows falling to US$ 115 billion in 1992 to US$ 111 billion in 1992.

From 1993, FDI inflows showed regular increase with more FDI inflows to the developed

countries and by 1994, the FDI share of the developed countries was 60 percent and

developing countries’ share was 40 percent (UNCTAD, 1995). In 2015, FDI flows to the

developed economies almost doubled to US$ 962 billion from US$ 522 billion in 2014 (up

84 percent) and their share constituted to 55 percent of the global FDI from 41 percent in

2014. The major factor is attributed to high cross-border Merger & Acquisitions (M&A)

among the developed economies and the strong growth in inflows was reported in Europe

and the United States. Similarly, the developing economies received FDI inflows to a new

height of US$ 765 billion, 9 percent higher than in 2014. Among the top 10 FDI recipient

countries in the world, 5 countries hail from the developing world. FDI inflows to the

transition economies fell by 38 percent to US$ 35 billion (UNCTAD, 2016).

The regions15 have also shown growth in FDI inflows. According to the UNCTAD

data of 2014, Asia is the leading region with 37.80 percent share, followed by America

(29.37 percent), Europe (24. 25 percent), Africa (4.13 percent) and Oceania (4.44 percent)

of world FDI inflows (Figure 3.2). Developing Asia16 remained the principal recipient of

FDI in the world with US$ 541 billion and showed 16 percent increase in FDI inflows from

the preceding year. The reason was the strong performance exhibited by the East and South

Asian economies. Among the top 20 largest recipients of FDI inflows, five are from

developing Asia (UNCTAD, 2016).

15 List of countries in the five regions namely Africa, America, Asia, Europe, and Oceanic is placed

at Annexure ‘E’. 16 According to UNCTAD, Israel and Japan are developed economies in Asia.

60

Figure 3.2. Regional Trend of FDI Inflows (1982-2014)

Source: Author’s compilation from UNCTAD data

Now if the Asia region is further dissected as per the UNCTAD classification into

four regions namely Eastern, Southern, South-Eastern and Western Asia17, the region of

Eastern Asia takes the lion’s share of FDI inflows to Asia with 53.34 percent. Total inflows

to this region increased by 25 percent to US$ 322 billion. Hong Kong (China) became the

second largest recipient of FDI in the world after the United States with US$ 175 billion in

inflows in 2015. It showed a 53 percent increase from the preceding year. This increase was

mainly attributed to a rise in equity investment (UNCTAD, 2016). FDI inflows to South-

East Asia (10 the Association of Southeast Asian Nations and Timor-Leste) increased

slightly, by 1 percent, to US$ 126 billion in 2015. Inflows to Singapore, the leading recipient

country in the ASEAN, dropped by 5 percent to US$ 65 billion. Because of rising FDI in

India, total inflows to South Asia increased by about 22 percent to US$ 50 billion. India

became the fourth largest recipient of FDI in developing Asia and the tenth largest in the

world, with inflows reaching US$ 44 billion. Overall FDI to West Asia decreased by 2

17 List of countries falling in four regions of Asia (Eastern, Southern, South-Eastern, and Western

Asia) is placed at Annexure ‘F’.

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percent to US$ 42 billion. Turkey was the largest FDI receiving country with US$ 17 billion

FDI inflows and showed 36 percent surge. This significant increase was attributed to a surge

in cross-border M&As which made Turkey the fifth largest FDI recipient in developing Asia

and twentieth in the world. The Financial services sector became the major recipient. The

decline in oil prices and geopolitical uncertainty constantly affected FDI to the oil-producing

region of West Asia (UNCTAD, 2016).

Figure 3.3. FDI inflows to Asian regions

Source: Author’s compilation from the UNCTAD data

According to the UNCTAD statistics 2015, in the region of South Asia, India is the

major recipient of FDI inflows with a share of 88 percent, followed by Bangladesh and Iran

with almost 4 percent each. Bangladesh attained a 44 percent increase to US$ 2.2 billion

and recorded a historically high level. However, inflows to Pakistan and Sri Lanka declined,

to US$ 865 million and US$ 681 million, respectively. In Nepal, FDI inflows increased by

74 percent to US$ 51 million in 2015.

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62

3.2 Sectoral Distribution of Global FDI

There are three main FDI recipient sectors namely primary, manufacturing and

services sectors. At the global level, the services sector dominates with 64 percent, followed

by the manufacturing sector with 27 percent and primary sector with 7 percent shares of

global FDI stock in 2014 and 2 percent share is classified as unspecified (Figure 3.4). The

share of the services sector in the developed, the developing and in the transition economies

account for 65 percent, 64 percent, and 70 percent respectively. The sectoral distribution in

the developed and the developing regions is almost same as seen in overall world

distribution. But, the variations are quite visible in the sectoral investment patterns in the

developing regions (Africa, Latin America & Caribbean, Developing Asia). The developing

Asian economies have 70 percent share in the services sector, 26 percent in the

manufacturing and just 2 percent in the primary sector. The primary sector has a significant

presence in Africa and Latin America & the Caribbean regions with 28 and 22 percent

respectively. It reflects the growth of extractive industries in these regions (UNCTAD,

2016).

Figure 3.4. Sectoral Distribution of Global FDI

Source: Author’s compilation from the WIR 2016 data

Services

64%

Manufacturing

27%

Primary

7%

Unspecified

2%

63

3.3 The FDI Inflows to Pakistan: Trend, Sources, and Sectoral Composition

This section highlights the historical trend, sources and sectoral composition of FDI

inflows to Pakistan. The data have been obtained from different Government agencies and

the UNCTAD.

Pakistan has traditionally not been a major FDI recipient country in comparison with

its neighboring countries, India and China. Recently, the country has shown an increase in

FDI inflows for a short period (Figure 3.5). FDI inflows constituted less than 1 percent of

GDP during the period of the 1970s and the 1980s, and in 1994 exceeded the level of 1

percent. And it reached the highest levels in the years of 2006, 2007 and 2008, attaining the

level of 3.1 percent, 3.7 percent, and 3.6 percent, respectively. Since 2011, there has been a

sharp drop in FDI and it constitutes again less than 1 percent of GDP.

Figure 3.5. FDI Inflows to Pakistan

Source: Author’s compilation from the UNCTAD data

The decade of the 1970s is remembered as the period of nationalization in Pakistan.

The country experienced negative net FDI in 1973 and 1974. Pakistan started taking FDI

initiatives since the mid-1980s. But since 1990, when liberalization programs began,

extensive efforts have been made, providing 100 percent foreign equity participation, tax

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64

incentives, credit facilities and liberalization of the currency regime. Due to the regulatory

policy framework, the country experienced negligible growth in FDI until 1990 (Khan,

2011).

Pakistan was striving hard to attract FDI and the government intensified its effort in

the late 1980s by introducing the policies of liberalization, deregulation, and privatization.

The net FDI rose from the negative value (US$ -4 million) in 1973 to US$ 108 million in

1981 to US$ 272 million in 1991. Since the start of the liberalization program (1991-92),

consistent growth has been seen in FDI inflows. It rose to US$ 789.34 million in 1994 and

US$ 711 million in 1997. This phenomenal growth is attributed to the investment in the

private power projects. The investment by the independent power producers (IPPs) was

attracted by an excessively generous incentive scheme that allowed them to purchase fuel

for generating electricity on preferential terms (duty-free import of plant and equipment,

low taxes, financing of capital costs, and foreign exchange risk-insurance on external loans)

and also guaranteed the purchase of the electricity on predetermined tariff structure for 15-

30 years (Hamdani, 2013). In 2000, FDI decreased to US$ 309 million in 2000. This decline

had several factors such as the sanctions imposed by the United States after the nuclear tests

conducted in May 1998, the East Asian financial crisis, and the political instability in the

country (Khan, 2007; Khan, 2011; Khan & Khan, 2011).

The flows of FDI picked up their pace after 2002 and showed increasing trend till

2007. The first-time net FDI inflows crossed the figure of US$ 1 billion in 2004. The reason

is mainly attributed to the privatization program of the government especially in the banking

and telecommunications sectors. The banking sector attracted FDI from the Arab countries

(Bahrain, Kuwait, Oman, and United Arab Emirates) and other countries (Malaysia, the

Netherlands, United Kingdom, United States, and Switzerland). These investments provided

fresh capital to the industry, managerial know-how and provided competition in the shape

65

of new entrants such as the Faysal Bank, the Bank Alfalah, and the Meezan Bank (Islamic

banking). The Telenor, a Norwegian telecommunications company, acquired the Global

System for Mobile communications (GSM) license in 2004 and has since made investments

over US$ 2.3 billion. The Emirates Telecommunications Corporation (branded name

‘Etisalat’), a UAE-based telecommunication company, acquired 26 percent equity share,

valuing US$ 2.6 billion, of PTCL in 2006. The China Mobile, a Chinese

telecommunications company, established its first overseas subsidiary, Zong, in 2008 with

an investment of US$ 1.7 billion. There was also FDI from other countries like Oman, Japan,

Singapore, and Qatar. Although these investments have generated dividends and profits

remitted abroad, a noteworthy improvement in availability, quality, and cost of

telecommunication services can also be seen across the country (Hamdani, 2013). Although

the increase in FDI in 2006 has significant share from the proceeds of the privatization, the

rise in the following years is mostly credited to the green field investment (Khan & Khan,

2011).

FDI inflows showed a downward trend during the period 2007-2012. This decrease

was mainly attributed to the worsening of the financial crisis, the falling of international

investors' profits, high risk and limited access to financial resources (SBP, 2009). Afza and

Mahmood (2009) attribute this decline to the bureaucratic red-tapism in the country, but

Bukhari (2011) considers the bureaucratic red-tapism along with deteriorating law & order

situation and energy crisis in the country. Since 2013, again FDI inflows have been showing

an increasing trend in the country. This growth is blessed with the development of the China-

Pakistan Economic Corridor (CPEC), a multi-billion-dollar mega project between China

and Pakistan. During the year 2017-2018, FDI amounted to US$ 2767.6 million compared

to US$ 2746.8 million during the same period last year. The major FDI inflows during the

period were from China.

66

3.3.1 The Sources of FDI Inflows to Pakistan

The USA has traditionally been the major direct investor in Pakistan. Figure (3.6)

presents percentage share of the FDI inflows to Pakistan by origin from 1982 to 2015. It

shows that as a single country, the USA with a share of 25.58 percent has been the main

source of FDI in Pakistan. The other two prominent countries are the UK with share 15.65

percent and the UAE 13.67 percent. The last category, others, shows the contribution of

28.12 percent. The prominent in this category are Singapore, China, Australia, Switzerland,

Norway, and Malaysia. China significantly started direct investment in Pakistan in 2007

with US$ 712.1 million and its net flows became negative in 2009 (US$ -101.41191 million)

and 2010 (US$-3.62346 million). Since 2011, the Chinese investment has been increasing.

Its inflows rose to US$ 695.8 million in 2014. During fiscal years 2016-2017 and 2017-

2018, FDI inflows from China constitute almost 44.11 percent and 57.3 percent of total FDI

inflows to the country respectively (BOI, 2017). The historical data of FDI inflows from

different countries are placed at Annexure ‘G’.

Figure 3.6. Percentage Share of FDI by different Countries (1982-2015)

Source: Author’s compilation from the SBP data

Chinese direct investment is rising in Pakistan and the countries that have invested

in past are now disinvesting (Table 3.1). The USA has been a leading source of direct

25.58

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67

investment in Pakistan, but this trend has changed now. The US investors withdrew from

Pakistan US$ 71.9 million during 2015-16. Beside the USA, other countries which have

withdrawn their investments are Saudi Arabia (US$ 91.6 million), Egypt (US$ 41.7 million)

and Germany (US$ 32.4 million) (“Other countries pulled out”, 2016).

68

Table 3.1

Major investing Countries in Pakistan (2008-2016) (Million US$)

Country 2008 2009 2010 2011 2012 2013 2014 2015 2016

USA 1309.30 869.9 468.3 238.1 227.7 227.1 212.1 208.9 40.5

UK 460.2 263.4 294.6 207.1 205.8 633 157 169.6 138.4

UAE 589.2 178.1 242.7 284.2 36.6 22.5 -47.1 218.8 138.6

Japan 131.2 74.3 26.8 3.2 29.7 30.1 30.1 71.1 35.2

Hong Kong 339.8 156.1 9.9 125.6 80.3 242.6 228.5 136.2 119.5

Switzerland 169.3 227.3 170.6 110.5 127.1 149 209.8 3.2 53.4

Saudi Arabia 46.2 -92.3 -133.8 6.5 -79.9 3.2 -40.1 -64.8 24

Germany 69.6 76.9 53 21.2 27.2 5.5 -5.7 -20.3 -11.6

South Korea 1.2 2.3 2.3 7.7 25.4 25.8 24.4 14.3 -2.3

Norway 274.9 101.1 0.4 -48 -275 -258.4 -21.6 2.7 172.5

China 13.7 -101.4 -3.6 47.4 126.1 90.6 695.8 256.8 626.2

Others 2,005.20 1,964.20 1,019.60 631.3 289.7 285.5 255.4 -73.6 566.8

Source: BOI, Pakistan

69

The statistics of major source countries investing in Pakistan are presented in Figure

(3.7). It indicates that China is the leading investor in Pakistan, followed by the USA, the

UK, the UAE, Japan, Hong Kong, Switzerland, Saudi Arabia and South Kora. Norway’s

direct investment was US$ 172.5 million in 2015-2016, but its net FDI flows are in the

negative (-US$ 12.6).

Figure 3.7. Major Source Countries investing in Pakistan (2016-2017)

Source: Author’s compilation from the BOI, Pakistan data

3.3.2 The Sectoral Distribution of FDI Inflows to Pakistan

After examining the trend of FDI at the country level, this section examines the

sectoral distribution patterns of FDI. As it is discussed in Chapter 2, the foreign investment

is permitted in all the economic sectors of the country except a few areas like radioactive

substances, high explosives, arms and ammunition, currency and mint, security printing,

liquor, and alcoholic beverages. There are around 36 sub-sectors where FDI inflows are

recorded. These sub-sectors can be categorized into three broader FDI sectors, primary,

secondary and tertiary18.

18 This categorization is aligned with the UNCTAD. But the researchers to meet their research

objectives have also classified these sub-sectors into manufacturing and non-manufacturing;

industry and services; and commodity producing sectors and services. The primary sector comprises

71.1 68.9 55.8 45.2 25 16.9 1.9 7.8

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Table 3.2

FDI Economic Sectors

Source: UNCTAD

The sectoral distribution of FDI inflows seems to be consistent with the global FDI

trends. Since 1985, the tertiary sector (services) has been dominant (Figure 3.8). During the

period, the primary sector has negative inflows, US$ -0.345 million, only once in 1999 while

the manufacturing and the services sectors experienced negatives inflows twice. The

manufacturing sector had negative inflows in 2000 and 2015 with values US$ -25.72 million

and US$ -14.59 million respectively. The net FDI inflows to the services sector recorded

negative in 2000 (US$ -10 million) and in 2012 (US$ -1.38 million). During the years 1987-

1989, the primary sector was dominant with significant shares of 57 percent, 49 percent,

and 56 percent. The mining, quarrying, and petroleum were the leading sectors during these

of mainly extractive industries; the secondary sector consists of manufacturing industries while the

tertiary sector constitutes of services groups.

71

years. Since 2002, this sector again has shown an upward trend. In recent years, the sector

has been the main attraction of foreign investors. Its share in net FDI inflows rose from 13

percent in 2008 to 55 percent in 2011. The government is aggressively awarding concessions

for oil and gas exploration. Several foreign companies have invested in the country,

including BHP Billiton (Australia), ENI (Italy), OMV (Austria), BP and Premier Oil (UK),

Petronas (Malaysia), and Petrobras (Brazil), which specifically intends to explore offshore

(Hadmani, 2014).

Figure 3.8. Sectoral Share of FDI Inflows to Pakistan (1985-2015)

Graph is based on net FDI inflows. The negative value in the concerned sector is considered as zero

share in the particular time.

Source: Author’s compilation from the UNCTAD data

The sectoral distribution of FDI has undergone a significant change. During the pre-

reform period (the 1980s), the primary sector seems to be dominant but it showed a declining

trend in the post-reform period (1990s). In contrast, the tertiary sector experienced

significant growth in the post-reform period. This shift towards services sector is because

of increasing liberalization in the sector, the increasing tradability of services and the growth

of global value chains in which services have a significant role. In 2012, the services

constituted 63 percent of the global FDI stock, more than twice the manufacturing share.

72

The primary sector amounted to less 10 percent (UNCTAD, 2015). Pakistan also achieved

highest FDI during the years from 2006 to 2008 because of the liberalization and the

privatization measures especially communications (including Telecommunication), and the

financial sectors. During 2012 and 2013, the tertiary sector experienced negative net FDI

inflows, US$ -86.207 and US$-18.374 respectively.

The Power generation was the main attraction of foreign investment during the fiscal

year 2016-2017 with investment of US$ 795.4 million, followed by the Construction (US$

467.7 million), the Oil & Gas (US$ 157.6 million), the Financial Business (US$ 64.3

million) and the Transport (US$ 53.5 million), the Trade (US$ 31.6 million), the

Communication (US$ 28.6 million), the Textiles (US$ 15.1 million) and the Chemicals

(US$ 12.6 million). Figure (3.8) presents the FDI inflows to major sub-sectors during 2016-

2017. The power and the construction sectors are the focus of the Chinese investment under

the CPEC projects.

Figure 3.9. FDI inflows to Major Sub Sectors (FY 2016-2017) (Million US$)

Source: Author’s compilation from BOI, Pakistan data

The Financial Businesses sector is the main attraction of foreign investment during the fiscal

year 2017-2018 with investment of US$ 885.3 million, followed by the Trade (US$ 707.3

million), the Textiles (US$ 276 million), the Oil & Gas (US$ 194.8) and the Construction

157.6

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73

(US$ 96.1 million), the Transport (US$ 94.2 million), the Power (US$ 74.2 million), the

Communication (IT & Telecom) (US$ 52.4 million), and the Chemicals (US$ 20.7 million).

3.3.3 The Structural Pattern of FDI in Pakistan

FDI comprises of three elements which are cash and capital equipment brought in,

and re-invested earnings. From 1980-1999, mainly cash brought in was the largest

component of FDI, followed by re-invested earnings and capital equipment brought in. Only

once in 1994, capital equipment brought in was the major component with 55.70 percent

share. The reason is attributed to the equipment brought in for the HUBCO Power Plant

(Khan and Kim, 1999). The re-invested earnings showed significant contribution in 2000

and 2002 with 57.40 percent and 57.10 percent shares respectively and overwhelming share

in 2003. The share of capital equipment brought in has been small except in 1992 and 1994

with 36 percent and 55.70 percent (Figure 3.10)19.

Figure 3.10. Structural Pattern of FDI Inflows to Pakistan

Source: Author’s compilation from the SBP data

19 The details are also provided in the tabulated form at Annexure ‘H’.

74

3.3.4 Repatriation of Profits and Dividends

FDI is considered to be a long-lasting attachment with the host location. It involves

the expansion of output, up-gradation of the production and increasing market share. That

is why it often warrants the reinvestment of short-term profits for the long-term benefits.

When opportunities are uncertain, most of the profits and dividends are repatriated to the

parent company, and sometimes investors disinvest and move their businesses to other

locations (Hamdani, 2013). The main purpose of MNEs is to maximize profits from their

investments. They are not interested in investing in countries that do not have or have limited

opportunities for profit (Khan, 2011). The good news is that companies are making profits

in Pakistan and the bad news is that these profits are being repatriated rather than reinvested.

This repatriation out of Pakistan has increased in recent years (Figure 3.10). In 2016, foreign

investors repatriated US$ 1511.9 million in profits and dividends. The Financial Business

is the leading sub-sector with (US$ 364.18 million), followed by the Telecommunications

(US$ 174.52 million), the Thermal Power (US$ 157.87 million), the Food (US$ 129.03

million), and the Oil & Gas Exploration (US$ 103.49 million).

Figure 3.11. Repatriation of Profits and Dividends (Million US$)

Source: Author’s compilation from SBP data

75

3.4 Conclusion

The FDI policies can be appraised from the magnitude of FDI inflows. Apart from

policies, the prevailing business environment also influences FDI inflows. This Chapter has

examined the trend, direction and composition of FDI inflows at world, regional and country

level. The aim was to understand the dynamics of FDI inflows. It will help to develop a link

between FDI inflows and policies and business environment. It will further set the platform

for finding out the determinants of these inflows.

The FDI inflows have been showing an increasing trend at the global level, with

some fluctuations for the past three decades. The highest level, US$ 1.8 trillion, was attained

in 2015 after economic crisis and the share of the developed and the developing economies

constituted 55 percent and 43 percent respectively. But, the FDI inflow has been the main

and consistent external source of finance for the developing economies since 2007. At the

regional level, Asia with 37.80 percent share is the top location of FDI inflows, followed by

America (29.37 percent) and Europe (24.25 percent). Now if the Asia region is further

dissected, the Eastern Asia region receives the major share of FDI inflows to Asia with

53.34 percent, followed by South-East Asia with FDI inflows of US$126 billion in 2015. In

the South Asia region, India is the major FDI location with a share of 88 percent, followed

by Bangladesh and Iran with almost 4 percent each.

Pakistan has, unfortunately, been receiving a small portion of FDI in comparison to

other Asian countries especially its two neighbors, China and India. In the last decade, the

country experienced a short surge in FDI inflows. Pakistan also achieved highest FDI during

the years from 2006-2008 because of the liberalization and privatization especially

communications (including Telecommunication), and the Financial subsectors. Pakistan

has not received a substantial amount of FDI. But, since 2013, again FDI inflow has been

showing an increasing trend in the country. This growth is blessed with the development of

76

the China-Pakistan Economic Corridor (CPEC), a mega bilateral project between Pakistan

and China. During the year 2017-2018, FDI amounted to US$ 2767.6 million compared to

US$ 2746.8 million during the preceding year. The major FDI inflows during the period

were from China.

There are three major recipient sectors of FDI. At the global level, the services sector

dominates with 64 percent share. The developing Asian economies have also 70 percent

share in their services. Similarly, the service sector has been the main recipient of FDI in

Pakistan. Even though all the economic sectors are open to the foreign investors except a

few (radioactive substances, security printing, high explosives, currency and mint, and arms

& ammunitions).

Pakistan has been striving hard to attract foreign investors. There are more than fifty

source countries that are investing in Pakistan and around 36 sub-sectors of the economy

are welcoming foreign investment with some fluctuations year to year basis. But the issue

is, China is the single most foreign investor country with almost 57 percent of total FDI

inflows share. Among sectors, the services sector has been the main recipient of FDI even

though the emphasis of Pakistan’s FDI related policies has been on the manufacturing sector.

Among subsectors, currently, the financial services and the trade sectors are the attraction

of foreign investment.

Pakistan has the liberal investment regime and it imposes no limitations on the

repatriation of profits and dividends. The good news is that companies investing in Pakistan

have been making profits; the bad news is that these profits are being repatriated rather than

reinvested. Repatriations out of Pakistan have increased in recent years. The government

needs to find out the ways and means to urge the multinational companies to re-invest in the

country.

77

Chapter 2 has examined policy and business environment in the country while

Chapter 3 reviews the FDI trends, direction and its composition. Now there is need to review

the academic literature to understand the motivations behind MNEs’ decisions of investing

abroad.

78

CHAPTER 4

FOREIGN DIRECT INVESTMENT: THEORETICAL FRAMEWORK

AND REVIEW OF LITERATURE

4.1 Introduction

The growing importance of multinational enterprises and FDI has provided impetus

to scholars to determine the behavior of firms and their motives of foreign investment. The

question that has been addressed extensively in theoretical and empirical literature is about

the decisions of MNEs in choosing a particular location and preferring it over the others.

With this backdrop to examine the locational determinants of FDI, this Chapter has been

organized accordingly. Section 4.3 describes the development of FDI theories of the

behavior of FDI vis-à-vis multinational companies, and reviews the selected theories.

Section 4.4 provides the motives behind FDI while Section 4.5 reviews the empirical

literature on the FDI determinants.

4.2 Literature Review Methodology

The review of literature started with a search of research articles relating to the main

theme of the dissertation, determinants of FDI. For that matter, main academic literature

repositories were explored. This literature review process begins from the HEC’s Digital

Library (http://www.digitallibrary.edu.pk) 20 which provides access to various known

research databases and repositories. Initially, the databases the Elsevier (Science Direct),

the Emerald, the JSTOR, the SpringerLink, the Taylor & Francis Journals, and the Wiley-

Blackwell Journals were explored with the specific key words. Apart from the Digital

20 The National Digital Library is a program by HEC that provides access to researchers at academic

and non-academic institutions in Pakistan to the international scientific literature on online. It

provides access to high-quality peer-reviewed journals, articles, databases, and e-books on a wide

range of disciplines.

79

Library, the HEC recognized national journals were also explored with a specific objective

to find out the material relevant to Pakistan. Each research article sequentially provided a

list of references which was further explored keeping in mind their relevance to the research

questions at hand. In addition to that, the papers published by the international agencies such

as the United Nations, the WB, the IMF, and the OECD that are pertinent to the topic and

provide their contributions in the development of theories and the insight into the FDI

determinants have also been considered. Books and reports published by the international

agencies, the UNCTAD, the WEF, the WB and the national institutions, the SBP, the

Ministry of Finance and other publications have also been included in the debate, but the

review of the empirical literature on the FDI determinants is exclusively based on the

research-based articles, and books and trade reports have been excluded.

4.3 Theories of FDI

The strong growth of FDI and international trade that has been observed during the

last few decades (Mohamed and Sidiropoulos, 2010) have enlivened broad research on the

conduct of MNEs and determinants of FDI (Faeth, 2006). The expansion of FDI was started

after the World War II when the forces of globalization emerged in the world. The increasing

significance of MNEs and FDI in the decades of 1950s and 1960s led several researchers to

study the behavior of MNEs and international production. Consequently, many theoretical

explanations about the international movement of capital have been articulated (Nayak &

Choudhury, 2014). But at the same time, it is also recognized that there is no single unified

theory that could comprehensively elucidate the phenomenon of FDI, the behavior of MNEs

and the international production (Jadhave, 2012; Moosa, 2015). This view has been

reinforced by many empirical works which have endorsed the complementary nature of the

FDI theories and they all play their part in explaining the determinants of FDI (Faeth, 2009).

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Therefore, several authors have deliberated about FDI determinants and have presented

several (and complementary) theoretical explanations (Assuncao, Forte & Teixeira, 2013).

According to Faeth (2009), the very first explanations of FDI were based on models

provided by Heckscher-Ohlin (1933), MacDougall (1960) and Kemp (1964) known as the

Heckscher-Ohlin Model /MacDougall-Kemp Model. These models explain that FDI is

attracted by a high return on investments and low labor cost and exchange risks (Assuncao

et al. 2013). In response to the failure of the Heckscher-Ohlin Model, Vernon (1966)

developed the product life-cycle theory while observing the investments in the

manufacturing industry of Western Europe by the US companies. Raymond Vernon, an

American economist, argued that the investment decision was a decision between the

exporting and the investing firms. In this way, he divided products into three categories in

the light of their stage in the product life cycle and how they carry on in the international

trade market: new, maturing and standardized (Faeth, 2009, p.168). The first phase of this

model addresses the introduction of innovation. New products are said to be invented,

produced and sold in higher-income countries. If the product achieves success there,

production increases and new markets are explored for exports. With this, the second phase,

maturity, begins. At this stage, the price elasticity of demand for the product is

comparatively low. Product demand in foreign markets increases and competitors are visible.

To cater product’s demand and compete for its rivals, production unit in the foreign country

is established. At the second stage, the company goes international. The last stage is

categorized by the standardization of the product. Production technique achieves perfection.

Resultantly, investment is ready to be moved to anywhere in the world where costs are as

low as possible. Finally, the product is exported back to the country of origin where the

product is eliminated in order to promote another product innovation (Nayak & Choudhury,

2014). Aharoni (1966) applied the behavioral theory to FDI research (Li et al. 2004) where

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the role of the management and decision-making process in explaining the

internationalization of the firm were introduced (Castro, 2000).

Caves (1971), Knickerbocker (1973) and Hymer (1976) propounded their theories

based on imperfect market assumptions. Markets are imperfect when there is an oligopoly

created by some firms, and customers do not have much information. This leads to

investment by some firms in the form of horizontal FDI through product differentiation

(Caves, 1971). Similarly, Knickerbocker (1973) considers market imperfections due to the

oligopolistic nature of the market in which some companies create the pool and eliminate

competition in the market. This character of the domestic market has compelled companies

to invest abroad. The essence of Hymer (1976)'s theory is that companies operating abroad

are competing with the local firms, which are in a privileged position in terms of language,

culture, consumer preferences and legal system. In addition, foreign companies are also

exposed to exchange risk. These disadvantages need to be compensated by some form of

market power to make the international investment profitable. This market power can be

attained from sources like patented-protected technology, marketing and management skills,

brand names, cheaper finance sources and economies of scale. According to Hymer,

technological superiority is a significant advantage, because it contributes to creating new

products. Besides, the possession of knowledge also helps in developing other skills, such

as marketing and improved production processes (Nayak & Choudhury, 2014).

Buckley and Casson (1976) provided another explanation of FDI by forwarding the

internalization theory where they stressed the factors that lead to the creation of MNEs.

They argued that companies prefer to internalize their operations through where the cost of

the transaction (such as information and trading costs, resulting from market processing) is

higher than the cost of internalization (related to internal communication and organization).

High market risk and uncertainty lead to higher transactions costs. In this case,

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internalization of business operations is the preferred strategy, and FDI is undertaken. They

also believe that in some markets (e.g. market for knowledge) there is a particularly strong

incentive to internalize. Since knowledge is a public good to the company and can, therefore,

be used within corporate divisions at no extra cost and is easily transferable from one

country to other (Assuncao et al. 2013)

As an extension of Hymer’s work, Dunning (1980, 1993) developed the Ownership-

Location-Internalization (OLI) paradigm. Besides, ‘a new theory of trade’ emerged which

determines FDI by analyzing OLI advantages with technological advancement and factor

endowments. Another explanation, the Institutional theory that stresses the importance of

institutions for FDI. These three theories are more relevant to this research and will be

discussed in detail in subsequent sections.

4.3.1 The OLI Paradigm

According to Dunning (1979, p.274), the eclectic paradigm resulted from his

dissatisfaction with the existing theories as they provided partial explanations of

international production (Castro, 2000). With the oligopolistic and internalization aspects

of MNEs’ decisions, he included a third dimension in the form of location to explain why a

firm opens a foreign subsidiary (Nayak & Choudhury, 2014). Henceforth, he proposed an

alternative line of development which tried to integrate the existing theories in a general and

‘eclectic’ model in which “the subject to be explained is the extent and pattern of

international production” (Dunning, 1991, p.124). This eclectic paradigm is considered

today the most comprehensive framework that explains the phenomenon of FDI. It is

frequently quoted in the FDI literature.

According to Dunning (1979, p.275), three conditions are necessary for a firm to involve in

FDI. They are:

i. It has ownership (O) advantages vis-à-vis firms from other countries;

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ii. It is useful to internalize those advantages rather than to use the market to transfer

them to foreign firms (I);

iii. To use firm’s ownership advantage, there are some location (L) advantages in a

foreign location (L).

A firm will engage itself in international production if there are above mentioned three

(Ownership, Location, and Internalization) advantages are available. Figure (4.1) illustrates

the OLI paradigm in a simple way.

Figure 4.1. OLI Paradigm

Image Source: Galan & Gonzalez-Benito (2001)

In the OLI eclectic paradigm, the second condition, location advantages, has a

significant value in attracting MNEs. If only the first condition (ownership) is met, firms

will depend on exports or licensing/franchising as opposed to FDI to serve a foreign market.

If both first and second conditions are met, the firms will utilize management contracts or

licensing/ franchising to benefit a foreign market and again FDI will not happen. FDI will

take place when the first and third conditions available. Among these three requirements for

FDI, it is the ‘location’ advantage which can be manipulated by the host governments

(UNCTAD, 2007). In simple words, wholly-owned entry or joint ventures in foreign

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locations are the only recognized modes of FDI as defined by the UNCTAD. So, exports,

licensing and franchising to foreign markets are not considered as FDI.

The ownership advantages (O) are considered to be the firm-specific advantages

(FSA). They turn to the question "why" about the decision of firms to go abroad (why do

firms invest in a foreign location?). These advantages permit firms to take benefits from

exploring investment opportunities in foreign locations. They are the intangible assets of the

firms which can be transferred within the firm at a low cost, for example, management and

technological skills, economies of the scale, brand name and reputation (Rugman, Verbeke

& Nguyen, 2011). These advantages either reduce costs or bring higher incomes to the firms.

Thus, they can compensate the operational costs of the firm in a foreign country. They may

change over time and will vary depending on the age and the experience of MNEs. These

advantages are the competitive advantages that a company possesses in its local country.

The higher the advantage is, the more likely the company is to engage in foreign countries

as it increases the company’s possibility to generate profit. If these ownership advantages

are strong, the company should exploit the opportunity to conduct an FDI. The company

can achieve ownership advantages by controlling specific assets which are not easily

available for competitors (Dunning and Lundan, 2008).

The location advantages (L) are country/location specific advantages. They answer

the question of where the firm should go as the host country must possess some locational

advantages. A firm gains these advantages by locating its production, wholly or partly, to

foreign locations (Mtigwe, 2006). The foreign country(s) with the highest location

advantages will add value to the MNEs by undertaking new activities and will, therefore, be

chosen as the host country for an FDI. The firm must assess the attractiveness of the target

market regarding its compliance with the business strategy (Gluckler, 2006). These

advantages are possessed by one potential FDI host country relative to another, for example,

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resource endowments, market size, cultural relations, social and political framework and

low taxes, wages and transportation costs, input prices (for example the labor quality and its

productivity), economic systems, investment incentives and policies of the host country

government. Dunning (1998) points out that the global economy has undergone dramatic

changes during the last few decades and it had affected the capabilities and strategies of

MNEs. Therefore, these enterprises increasingly find locations that provide the best

economic and institutional facilities for their core competencies to be used efficiently.

Finally, the internalization advantages (I) explain how a company should enter a

foreign country. In many cases, firms take the benefits from opening their own production

facilities in a foreign country rather than producing more indirectly through agreements such

as licensing (Mtigwe, 2006). Without the advantage of internalization, a lot of FDI is likely

to be replaced by contracts between the country of origin and a new foreign country. The

internalization is an alternative strategy to reduce the transaction costs. It is the firm that has

to decide to take the benefits of its ownership advantages by internalizing the foreign market

or serving the market externally (Gluckler, 2006).

The distinguished contribution of Dunning’s OLI Paradigm is to integrate numerous

complementary theories of FDI and to identify many factors that influence the MNEs

activities. That’s why, his model is more accepted than other imperfect market-based

theories (Nayak & Choudhury, 2014). But, his model is criticized on the grounds that it

includes several variables (Dunning, 2001) without much predictive power. In spite of this

criticism, the OLI paradigm has remained relevant as a framework to address the questions

regarding the activities of MNEs (Rogmans, 2011).

4.3.2 The New Theory of Trade

The New Theory of Trade (NTT) is based on Kindleberger’s theoretical models

(1969) along with those of Caves (1971) and Hymer (1976) and it has provided an

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alternative analytical framework for analyzing FDI and MNEs activities. It has emerged by

combining the ownership advantage (knowledge) and the location advantage (low

transaction costs and market size) with the technology and the intrinsic characteristics of a

country (factor endowments) (Assuncao et al. 2011; Faeth, 2009). This theory is an

extension of Dunning’s OLI paradigm in the sense that it aims to align the three OLI

variables (ownership, location, internalization) with technology and country characteristics

in a consistent manner. According to theory any firm not only evaluate market size, transport

cost but also analyzes the country’s internal characteristics like tariffs, trade barriers,

political and institutional factors (Markusen, 2002).

4.3.3 The Institutional Theory

The institutional theory submits that firms do their businesses in a complicated

environment. Therefore, the firm’s decisions rely on the institutional forces that impact on

its environment (Francis, Zheng, Mukherji, 2009). The companies’ decision regarding the

use of strategies mainly depend on the foreign countries’ policies and institutions (Peng,

2009). Policies that influence the companies’ strategic decision are tax regimes, subsidies,

access to capital and other fiscal and financial incentives. All these factors can influence the

company's choice of opting for export, license or FDI. Apart from policies, the institutional

factors of a host country have also been incorporated in empirical studies as the determinants

of FDI. Institutional factors for example regime type /democracy, the rule of law, the quality

of bureaucracy, corruption, political instability, intellectual property rights, judicial /legal

system and enforcement mechanisms have been studied in several empirical types of

research. Consequently, it is argued that the economic performance of a country depends on

the quality of domestic institutions (Acemoglu, Johnson, & Robinson, 2005; North, 1990).

In simplest and shortest form, the institutions are defined as the “rules of the games”

in a society (North, 1990). Rules are of two types, formal and informal. The formal rules

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are written and their implementation is ensured by the State. Laws enforcing contracts,

political systems, product information, the imposition of tariffs or quotas, the regulation of

banks, and so on are all formal institutions. The informal rules are unwritten and their

implementation is done through the groups within society. These include codes of conduct,

norms of behavior, and so on. (North, 1990) (cited in Dumludag et al. 2007). These

institutions play some important functions for the businesses. They minimized the

transaction costs resulting from incomplete information, which can be mitigated by

establishing relevant institutions. Moreover, they distinguish and ensure respect for property

rights, ultimately determine the degree of competition, and describe market entry conditions

(Azfar, 2006). Institutional factors have their own significance in the choice of MNEs’

decisions in locating their businesses and cross-country differences in economic

performance are the result of differences in institutions (Acemoglu et al. 2005; North 1990;

North and Thomas, 1973).

Therefore, the quality of national institutions exerts a significant influence in

attracting foreign investment. According to UNCTAD (2006), developing countries have

received an unevenly share of global FDI for a long time. And the reason is attributed to the

differences in the quality of institutions, which the researchers have in fact ignored. The role

of economic indicators has been emphasized alone in interpreting differences in FDI flows.

But now the importance of institutions has been recognized in attracting FDI flows as well

as their positive impact on the development of the host country (Dumlundag et al. 2007;

Rodriguez-Pose & Cols, 2017).

The strong growth in FDI over the past few decades has led to a broad study of the

determinants of this type of investment. A large number of theoretical and empirical

literature on FDI include a long list of determinants that attempt to explain direct investment

by MNEs in a particular place. There are several theories which explain the phenomenon of

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FDI. For the purposes of this research. Three theories namely the OLI Paradigm, the new

theory of trade and the institutional theory have been selected and tested for FDI inflows to

Pakistan. These theories should not be considered as highly specific theories. Instead, they

provide potentially useful insight into the determinants of FDI. All the variables outlined in

the research are location determinants. More specifically, they fall in these three theoretical

approaches, the OLI (infrastructure, economic stability, human capital and production costs),

the institutional approach (institutional quality, corruption, political instability, tax

incentives) and new trade theory (market size and market growth, TO) (Assuncao et al.

2011).

With this backdrop, the location factors of FDI inflows can be tested at country,

sector and firm levels in Pakistan. So here, both Ownership and Internalization advantages

are beyond the scope of the research. The former advantages are more difficult to be

examined since they require the comparison of competitive advantages possessed by

different firms while the latter have data availability issue. Therefore, this research has been

delimited to only location factors of FDI inflows to Pakistan.

4.4 FDI Motives

To determine the reasons of firms’ engagement in FDI, it is important to comprehend

the inspiration driving their decisions. Dunning (1993) found four main categories of FDI

motives: natural resource seekers, market seekers, efficiency seekers and strategic asset and

capability seekers. He argues the market and natural resource seekers primarily motivate

new FDI, while efficiency and asset seekers primarily motivate subsequent investments.

Today many MNEs might have more than one motivation to persuade an FDI. Separating

MNEs into categories is therefore not always feasible. To complicate matters further, MNEs

are today often involved in different types of markets, where the changes can be rapid. The

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motivation when planning an FDI is therefore not necessarily the same when the company

has come further in the development process in the foreign country.

The above-mentioned four main FDI motives have been presented in the following

paragraphs:

Natural resource seekers: It can further be divided into three classifications. The

first is those MNEs that look for physical resources, either in light of the fact that there is

an absence of the resources in their country of origin or on the grounds that it may be less

expensive to acquire them in different countries. These resources include natural resources

like metals, minerals and agriculture products. The second classification is those MNEs

looking for cheap and very well motivated unskilled or semi-skilled labor (Loots, 2000).

Such FDI more often than not includes a country with high labor costs putting investment

into locations with low labor costs. Finally, the MNEs seek expertise which they cannot find

in their own home countries or which might be less expensive in foreign locations for

example marketing, technology, and organizational expertise. The Africa region has the

plenty of natural resources, therefore more FDI in the primary sector is expected.

Market seekers: The market-seeking FDI aims to serve the domestic market. The

goods and services produced in the host locations are sold in the domestic market. Therefore,

this type of FDI is motivated by the domestic demand such as large markets and high income

in the host location (Asiedu, 2002). The essential characteristics of the host locations for the

market seeking FDI are market size, wage levels, and growth. This type of FDI is also

referred to as the horizontal FDI because it usually involves the establishment of similar

plants elsewhere to ensure the supply to that market (Lim, 2001).

Efficiency seekers: This type of FDI seeks to take the advantage of the economies

of scale and scope. It is also motivated to diversify risks. The advantage of economies of

scale and scope usually occurs between countries that have the same economic structures

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and income levels. For those looking for risk diversification, use differences in the relative

costs and availability in different countries’ traditional factor endowments. It provides

conducive cost bases to MNEs for their business operations. The aim is to reduce cost by

using factors of production at an international level. The emphasis is on decreasing the cost

through structural imperfections caused by the government such as tax differentials, or risk

reduction through production diversification. The focus is on the labor productivity, cost of

resources, input costs and participation of regional integration frameworks. Efficiency

seekers locate their businesses in countries where they can have skilled and disciplined

workforces and good technological and physical infrastructure (Hawkins and Lockwood,

2001).

Asset seekers: The aim is to promote long-term strategic objectives, particularly of

maintaining or advancing their global competitiveness. They do this by increasing a global

portfolio of human competencies and physical assets that they see as diminishing the

benefits of competitors or increasing their benefits. They take the advantages from

imperfections in the intermediate product market and may even add to these, particularly

highly-technological industries are motivated by acquiring assets, but FDI motivated by

assets are increasingly undertaken by MNEs from emerging economies.

It should, nonetheless, be noticed that FDI is not always beneficial for the host

country especially resource seeking FDI, since it less capital intensive with low value adding

activity except for extractive industries (Narula and Dunning 2000). Besides, the sort of FDI

a country might need to pull in depends much on its development stage. Therefore, MNEs

should along these lines be urged to put investment into high value-adding activities which

could come in the form of a market-seeking and other asset exploiting activities (Narula and

Dunning, 2000).

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4.4.1 The Investment Development Path

The Investment Development Path (IDP) theory propounded by Dunning & Narula

(1996) classifies countries into five stages of development.

At first stage, the location advantages of a country are insufficient to attract FDI

inflows, except for natural assets seeking FDI. The reasons could be attributed to very small

domestic markets, inadequate infrastructure, poorly educated labor and undeveloped

commercial and legal frameworks. On the other hand, domestic firms do not possess

necessary ownership advantages to engage in FDI outflows. Governments at this stage have

two courses of actions. They try to upgrade the basic infrastructure and human capital and

they introduce macroeconomic policies which could change the structure of domestic

markets and industries for example import protection and export promotion. In the second

stage, the level of FDI inflows begins expanding. There are some location advantages like

basic infrastructure that is an outcome of government policies and increased per capita

income. The FDI outflow is yet low reflecting the rare ownership advantages possessed by

the domestic firms. In the third stage, FDI inflow decreases as the domestic firms turn out

to be more competitive. While FDI outflow will steadily begin ascending since the domestic

firms have obtained ownership advantages over the period and they will now begin making

business ventures abroad. In the fourth stage, a country will turn into a net outward investor.

This change is ascribed to the improvement of ownership advantages accomplished by the

domestic firms that make them progressively competitive. In the last stage, the country's net

FDI position becomes zero, with an almost equal amount of FDI inflows and outflows. This

stage is seen in the present day’s developed countries.

According to the theory, first two stages (stages 1 and 2) comprise of developing

countries, stage (3) has newly industrialized countries while and in stages 4 &5) developed

countries fall. It predicts that resource seekers are attracted by developing countries, market

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seekers by the newly industrialized countries and strategic asset seekers are mainly attracted

by developed countries (Rogmans, 2011). In other words, with the development of the

country, the main factor of the location moves from the existence of natural resources to the

market attractiveness and then to the presence of country's infrastructure and the availability

of high-quality created assets (Narula & Dunning, 2000).

4.5 FDI Determinants: Empirical Evidence

The theories of FDI give a wide range of variables like economic factors, policy

variables, political, social, institutional and geographical and cost-related factors to

determine FDI and highlight different aspects through which a firm can decide why, where

and how to invest. The research is delimited to location factors in the light of the above-

quoted theories (Section 4.3). The review of empirical literature on the determinants of FDI

is presented in the subsequent sections.

4.5.1 Classification of Determinants of FDI

The researchers have classified the determinants of FDI based on the aims of their

studies. And these classifications are based on political, institutional, culture, social, policy

and geographical variables.

Nunnekamp (2002) classifies the determinants into traditional and non-traditional.

The nature of FDI is said to have changed over time because of the globalization. From this

point of view, determinants are classified according to traditional and non-traditional

determinants. Traditional determinants are usually linked to the resource-seeking FDI while

non-traditional determinants are related to efficiency-seeking FDI. The classification has

the motivation to seek whether the variables have undergone to changes particularly in the

context of developing countries. FDI has traditionally been natural resource seeking and

now it is transforming into efficiency-seeking FDI because of liberalization and

globalization.

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Tsai (1991) categorizes FDI determinants into supply-side and demand-side. It is

argued that this classification is valuable in the sense that identified demand side

determinants can be controlled to some extent by the country. The demand-side

determinants are empirically examined by using aggregate variables for example host

country GDP. The supply-side determinants are empirically tested by employing firm level

microeconomic data.

Loots (2000) is the view that location advantage is the only factor in Dunning’s OLI

paradigm which can be influenced by the host countries. Therefore, the location

determinants can be grouped into the national policy framework (trade and tax policies,

policy measures on entry, market structure), the business facilitation (corruption reduction,

promotion efforts, investment incentives) and the macroeconomic determinants (market size,

economic growth, skills, and openness). Fedderke and Romm (2006) have classified the

FDI determinants into policy and non-policy factors and the justification is to find out

variables that can be influenced by the government and the policies are correctly formulated.

The summary of classification of determinants of FDI is presented in Table (4.1). Of

all these classifications, it is important to note that a number of these determinants overlap

in different classifications. The motive behind presenting this classification is neither to

identify the overlapping classifications nor to work on the overlapping factors. Therefore,

this research does not work on the overlapping factors. Instead, these classifications have

provided further an understanding of the FDI determinants and with it, an effort has been

made to identify the policy and non-policy determinants appropriately and how they are

linked to FDI.

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Table 4.1

Classification of FDI Determinants

Classification Variables Evidence

Social

Human Capital. School Enrollment Azam and Khattak (2009)

Infant Death, Literacy Vadlamannati, Tamazian&Irala

(2009)

Political

Democracy, Political stability/instability Azam and Khattak (2009)

Political Stability/Instability Jadhav (2012)

Macroeconomic

Inflation, Exchange rate, Growth

Potential, Market size

Anuchitworawong

&Thampanishvong (2015)

Trade openness, Natural Resources Jadhav (2012)

Capital Account Convertibility, Labor

Union, Macroeconomic Risk

Vadlamannati et al. (2009)

Growth potential, infrastructure, trade

openness

Ali, Fiess and MacDonald (2013)

Demand Macroeconomic Stability Ismail (2009)

Supply Infrastructure, Skill Availability,

Technical Development

Ismail (2009)

Institution

Corruption, Government Effectiveness,

Voice & Accountability, Political

Stability/Instability, Rule of Law

Jadhav (2012)

Corruption, Economic Freedom,

Government Effectiveness, Voice &

Accountability, Political

Stability/Instability, Rule of Law,

Regulatory Control

Lucke & Eichler (2016)

Corruption, Democracy, Human Capital,

Social Tension, Property Rights,

Political Stability/Instability

Ali et al. (2013)

Civil Liberties, Economic Freedom,

State Fragility, Political Rights

Tintin (2013)

Corruption, Ethnic Diversity,

Government Effectiveness, Literary,

Urban Population, Voice &

Accountability, Political

Stability/Instability, Regular Quality,

Rule of Law

Naude and Krugell (2009)

Policy

Corporate Income Tax, Inflation Rate,

Tariff

Ali et al. (2013)

Inflation and Government Spending Mohamed & Sidiropoulos (2010)

Openness, Corporate tax, Trade barriers,

Product market regulations and

Infrastructure

Fedderke & Romm, (2006)

Non-Policy

Market size, Political & Economic

stability, Distance or Geographical

Fedderke & Romm, (2006)

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Location of Host Country, Factor

Endowments

Exports, Government Consumption,

Growth Potential, Inflation, Investment,

Infrastructure, Trade Openness, Money

Supply, Return

Naude and Krugell (2009)

Traditional

Telephone Mainline, GDP per capita,

Annual Real GDP, Growth rate,

Exchange Rate, Inflation, Annual Labor

Cost, Export

Kahai (2004)

Population, GDP per capita, GDP

Growth, Administrative Bottlenecks,

Entry Restrictions, Risk Factors

Nunnenkamp (2002)

Non-Traditional

Level of Corruption, Economic

Freedom, Trade Regulation

Kahai (2004)

Average Years of Schooling,

Complementary Factors of Production,

Cost Factors (taxes, labor market

regulations, employment conditions and

the leverage of trade unions)

Nunnenkamp (2002)

Source: Author’s compilation

4.5.2 Determinants of FDI at Country Level

There are considerable empirical studies available on the determinants of FDI. This

section reviews the most recent empirical literature that finds determinants of FDI inflows

to a host country. This review portion is aligned with the third objective of the research in

the dissertation.

Anuchitworawong & Thampanishvong (2015) investigate the determinants of FDI

inflow to Thailand by using the three-stage least squares (3SLS) method. It employs

variables, natural disaster; GDP per capita, exchange rate, CPI (consumer price index),

population, school enrolment at secondary and tertiary levels, financial market development

(domestic credit by banking sector as % of GDP), and TO. The main findings of the study

include that natural disasters negatively impact FDI inflow in Thailand. Bekhet & Al-Smadi

(2015) evaluate the long-run and short-run relation of GDP, CPI, EO (economic openness),

SMI (stock market index) and M2 (money supply) with FDI inflow to Jordan. The results

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of the Bounds Testing Approach and the Granger’s causality tests show that in both the

long-run and short-run all the variables have a positive relation with FDI except CPI in the

long-run which has a negative relation. The Granger’s causality test finds bidirectional

causal relationships between FDI and M2; unidirectional causality from FDI to GDP, SMI,

and EO whereas FDI and CPI has neutral causality. Ibrahim and Hassan (2013) explore FDI

determinants in Sudan for the period 1970-2010. The results of co-integration, Error

Correction Model (ECM) and Granger’s causality tests reveal that market size (real GDP

per capita), TO, and investment incentive policy are the factors that positively influence FDI

and inflation rate (proxied by CPI), indirect tax and exchange rate negatively impact FDI.

The study also finds unidirectional causality running from each of the exchange rate,

investment incentive policy and the market size to FDI.

Singhania and Gupta (2011) examine the FDI determinants in India for the time

period 1991-2008. The results from the autoregressive integrated moving average (ARIMA)

model reveal that GDP, inflation, and patents have a significant impact on FDI. Kaur and

Sharma (2013) find a positive association of FDI with GDP, external indebtedness,

openness, and foreign exchange reserves, and a negative association of FDI with exchange

rate and inflation rate. Pattayat (2016) also examines the determinants of FDI in India but

with different time span, 1980 to 2013. Co-integration tests are applied to check the long-

run relationship of GDP, TO, and exchange rate with FDI. Results reveal all variables have

a long-run relation with FDI. The results show a positive relation of GDP and TO and a

negative relation of exchange rate with FDI.

Boateng, Hua, Nisar & Wu (2015) examine the impact of macroeconomic

determinants of FDI inflow in Norway. By using quarterly data for the period 1986-2009,

the results of Fully Modified OLS (FMOLS) and Vector Error Correction Model

VECM/VAR Vector Auto Regression show real GDP, sector GDP, exchange rate, and TO

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are positively associated with FDI and money supply, inflation, unemployment, and interest

rate are negatively associated with FDI. Summary of the review of the determinants of FDI

inflows at the country level is placed at Annexure ‘J’.

The studies have also been found on the determinants of FDI in Pakistan. Shah and

Ahmed (2003) examine the FDI determinants for time period 1961-2000. The study finds a

positive association between FDI and per capita gross national product, expenditure on

transport, change in real GDP, communication, and tariff and a negative relation with the

cost of capital for foreign firms (CCFA). Dar, Persley, & Malik (2004) examine the

economic and socio-political determinants of FDI for the period of 1970–2002. The results

calculated from ARDL, Co-integration and the ECM conclude that FDI is influenced by

GDP, exchange rate, degree of openness of the economy, unemployment rate and political

risk index and there exists two-way causality of FDI with all the variables. Aqeel and Nishat

(2004) examine the FDI determinants for time period 1961–2002 and find that GDP per

capita, tax, credit, and exchange rate are positively associated with FDI whereas tariff has a

negative impact on FDI. Azam and Khattak (2009) examines the impact of political and

social factors on inflows of FDI for the time period 1971-2005 and conclude that FDI is

positively influenced by human capital whereas negatively affected by political instability.

Awan, Khan, uzZaman (2010) conclude that gross fixed capital formation (GFCF),

inflation, and TO are positively associated with FDI and current account balance has a

negative effect on FDI whereas results on debt servicing and GDP are statistically

insignificant. Azam & Lukman (2010) examine the impact of economic factors on FDI in

Pakistan, India and Indonesia during 1971 to 2005. Empirical results reveal that domestic

investment, market size, and infrastructure have a significant positive impact on FDI in both

India and Pakistan whereas in both the cases external debt has a significantly negative

association with FDI. In case of Pakistan, TO and return on investment positively impact

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FDI while in India, TO negatively impact FDI. Further, results on government consumption

and inflation rate are statistically insignificant in all the cases. In case of Indonesia, all the

results are statistically insignificant. Khan and Nawaz (2010) empirically investigate the

economic determinants of FDI for the period 1971 to 2005. By employing OLS model, the

results reveal that GDP growth rate used a proxy for market size, whole sale price index,

customs duty on imports, tariff, and volume of exports are positively associated with FDI

whereas exchange rate negatively influences FDI in Pakistan.

Rehman, Orangzeb & Raza (2011) find the major determinants of FDI which are

market size, quality of labor that positively influence FDI and TO negatively impacts FDI.

Market potential and communication facility show an insignificant impact on FDI.

Mohiuddin and Salam (2011) find a positive relation of FDI with real GDP, exchange rate,

interest rate, and TO and a negative relation with price and infrastructure. Mughal and

Akram (2011) find market size as the most dominating determinant of FDI and has a strong

positive impact on FDI. Exchange rate and corporate tax negatively impact FDI. The study

uses ARDL approach to co-integration for time period ranging from 1984 to 2008. Shahzad

and Zahid (2012) investigate the five economic factors that have an impact on FDI in

Pakistan over the period of 1991 to 2010. It is found that GDP, domestic investment, and

inflation rate have a significant positive impact on FDI whereas interest rate and tax rate

exert a negative but statistically insignificant impact on FDI. Ali, Chaudhary, Ali, Tasneem,

& Ali (2013) examine the effect of market size, TO, and human capital on FDI inflow to

Pakistan for the period 1975–2007. The results of OLS reveal that human capital and TO

have a positive relation with FDI whereas market size is negatively associated with FDI.

Zakaria and Shakoor (2013) evaluate the impact of TO as well other factors on FDI in

Pakistan by using quarterly data for the period 1972-2010. The results from general method

of moment (GMM) model show that TO, human capital, physical capital, capital returns,

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infrastructure development, terms of trade, and urbanization have a significant and positive

impact on FDI whereas domestic inflation rate and foreign debt have a significant negative

impact on FDI. The study also finds that in the year 1982 shift from the fixed exchange rate

to the flexible exchange rate leads to an increase in TO which has resulted in an increase in

FDI. Hakro and Ghumro (2016) examine determinants of FDI for the period 1970-2007

under four different categories namely cost related variables (wage rate, interest rate,

exchange rate), investment environment improving (TO, liberalization), political risk,

macroeconomic (output growth, infrastructure, human capital, savings, inflation rate,

exports, capital formation, employment/labor force and government expenditure on

education). The study finds significant results for exports and political risk with a negative

coefficient and a positive relation with wage rate, TO, human capital, savings, and

employment. The summary of the literature review on the determinants of FDI in Pakistan

is placed at Annexure ‘K’21.

There is a second group of empirical studies which have examined the regional

determinants of FDI. Though their approach does not correspond with the stated objectives

of this dissertation, they have been reviewed to get more in-depth insight into the locational

determinants of FDI.

The studies examine the decisions taken by MNEs to invest in the European Union

(EU), the OECD, ASEAN, South-East Asia (SEA), the MENA region, the SAARC, Brazil,

Russia, India, China, South Africa (BRICS), developing and emerging economies.

21 The studies reviewed dealing with determinants of FDI in Pakistan lack both theoretical and

empirical rigor except studies appeared in The Pakistan Development Review. None of the studies

appeared in the leading international business journals such as Journal of World Business,

Management International Review, and Journal of International Business Studies. Majority of them

are not from the HEC recognized journals either. So, they do not meet the literature review

methodology criteria outlined in Section 5.1. But these studies do provide somewhat insights into

the determinants of FDI and the author also faced the scarcity of the empirical literature on the topic

in case of Pakistan.

100

Janicki & Wunnava (2004) examine the determinants of FDI between 15 EU

countries and 8 Central and East European Candidates (CEEC) out of which 9 host countries

have been selected to apply model these include accession candidates of EU (Bulgaria,

Estonia, Czech Republic, Slovak Republic, Hungary, Poland, Slovenia, and Romania). The

cross-sectional data for the year 1997 has been used in the empirical model of the Weight

Least Squares (WLS). The results show that market size (GDP), TO, labor costs, and host

country risk (institutional investor country risk) exert a positive impact on FDI. Gondor and

Nistor (2012) find strong support for the fiscal policies (corporate tax rate) as the

determinant of FDI of 6 EU emerging European economies (Bulgaria, Hungary, Latvia,

Lithuania, Poland, and Romania) during the years 2000 to 2010. The study suggests that

government finds the friendly business environment more important instead of the corporate

tax rate. Low tax rates do not attract FDI if fiscal policy generates unfriendly business

environment due to unpredictability, lack of transparency, fiscal ambiguity, tax avoidance,

and tax frauds. Contrary to this, Hunady and Orviska (2014) find no support for corporate

tax rate as a determinant of FDI inflow in EU countries while they find labor cost, firing

costs, crisis, GDP per capita, TO, and public debt important factors of FDI inflow to the EU

countries during the period of 2004 to 2011.

Gast and Herrmann (2008) using gravity model on panel data set of 22 OECD

countries for the period of 1991 to 2001 find a positive relation of FDI with market size,

country size, country risk, bilateral investment treaties and a negative relation with distance

and economic freedom. Alam and Shah (2013) find market size, quality of infrastructure,

and labor costs the key determinants of FDI inflows to the OECD countries during 1985 to

2009.

In MENA countries, energy endowments, oil prices, TO, market size, R&D

expenditure, students in tertiary education, country risk, and domestic gross fixed capital

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formation are the factors which influence FDI inflows (Moosa, 2009; Rogmans and Ebbers,

2013). By using the pooled least squares regressions for the time period 1995-2004, Van

Wyk and Lal (2010) find TO, economic growth, business freedom, and current account

deficit as significant factors of FDI inflows to 18 MENA countries.

Sahoo (2006) examines the FDI determinants in five South Asian countries (Pakistan,

India, Sri Lanka, Nepal, and Bangladesh) for the time period of 1975-2003. Using panel co-

integration test and pooled Ordinary Least Squares (OLS) regression, the results find GDP,

TO, labor force growth, and infrastructure as significant determinants of FDI. During the

analysis of FDI determinants in ASEAN, it is found that in Cambodia, Laos, and Vietnam

infrastructure facility and TO are positively associated with FDI whereas inflation rate is

negatively associated with FDI. Further, in Indonesia, Malaysia, Philippines, Thailand, and

Singapore investors are not only attracted by large market size and good infrastructure but

they are still attracted to invest even with high inflation rates and low level of openness

(Xaypanya et al. 2015).

Reschenhofer et al. (2012) examine the potential determinants of FDI in 73

developing countries. The study finds that imports, gross capital formation and GDP per

capita are the major determinants of FDI having a positive relationship. Another major

determinant of FDI is net income from abroad that has a negative relation with FDI. Asiedu

& Lien (2011) examine the influence of democracy on FDI and also study whether the

relationship changes with the change in natural resources availability for 112 developing

countries with the time period ranging from 1982 to 2007. They find democracy exerts a

positive influence on FDI in countries where exports constitute a low share of natural

resources, but it shows a negative effect where exports are dominated by natural resources.

Through the application of fixed effects panel model and Generalized Method of Moments

(GMM) on the dataset of 28 developing countries for the time period 1985-2004, the results

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reveal that inflation rate, exchange rate uncertainty, and political risk show a significant and

negative impact on FDI, and investment and TO have a significant positive relation with

FDI (Solomon and Ruiz, 2012). The summary of the literature review of FDI determinants

of cross countries studies is placed at Annexure ‘L’.

4.5.3 Determinants of FDI at Sectoral Level

There is a third group of the studies which have examined the determinants of FDI

at sectoral level (primary, secondary and tertiary). This review portion is also aligned with

the objectives of the research in the dissertation, to construct a model of FDI determinants

at sectoral level in Pakistan.

There is a vast empirical literature available on the determinants of FDI but the

attention has recently been paid to the sectoral determinants of FDI. Foreign investors surely

consider different types of industries while planning for the possible investment abroad.

Here again, in most of the empirical studies, there is a lack of consensus on the importance

and direction of the impact of possible explanatory variables for FDI (Severiano,

2011). MNEs assess the sector where they have to invest, for that matter the sectoral

determinants of FDI are also important to be examined. A handful of empirical studies have

been undertaken to examine the factors of FDI inflows to three different economic sectors

of any economy: primary, secondary, tertiary.

The studies conducted in different economies find market size, labor cost, TO,

interest rate, export, import, exchange rate, human capital, institutional, qualitative and

political factors as the significant determinants of FDI for all the three sectors: primary,

secondary and tertiary (Bellak, Leibrecht and Stehrer, 2008; Walsh & Yu, 2010; Ramasamy

& Yeung, 2010; Yin et al. 2014; Alecsandru and Raluca, 2015).

Ho (2004) examines the factors of FDI in 13 sectors of China and 9 sectors of

Guangdong province by using pooled data for the period 1997 to 2002. The empirical results

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reveal that GDP by sector has a positive relation with FDI and labor cost measured by wage

rate and ownership level (no. of staff and workers) have a negative relation with FDI for

both the sectors and the province. Innovation level is significant and positively associated

with the FDI in 13 sectors of China and has insignificant relation with FDI in Guangdong

province. Blanco, Ruiz, Swayer & Wooster (2015) explore effect of crime (homicide rate,

crime victimization index, organized crime index) and institutional variables (governance,

bureaucratic quality, control of corruption, law and order, composite risk) on FDI in primary,

secondary and tertiary sectors of the Latin America and Caribbean countries during 1996 to

2010. Walsh & Yu (2010) examine the macroeconomic and institutional/qualitative

determinants of FDI in 27 developing and advanced economies in all the three sectors

(primary, secondary and tertiary) during the period from 1985 to 2008. Macroeconomic

variables include TO, real exchange rate, GDP growth, inflation, FDI stock and GDP per

capita and institutional/qualitative variables include financial depth, judiciary independence,

legal system efficiency, labor market flexibility, infrastructure quality, primary enrollment,

secondary enrollment, and tertiary enrollment. The GMM dynamic approach has been used

for the analysis. Results reveal that none of the variables is significant for the primary sector.

Among macroeconomic variables, FDI stock has a significant positive relation in the

secondary sector and real exchange rate, TO, GDP growth and FDI stock have a significant

positive relation with FDI in the tertiary sector. From institutional/qualitative variables in

the secondary sector, FDI has a positive relation with labor market flexibility, financial

depth, and infrastructure quality in developing countries whereas, in advanced economies,

FDI has a positive relation with labor market flexibility and infrastructure quality and has a

negative relation with judiciary independence, financial depth, and secondary enrollment.

From institutional/qualitative variables in the tertiary sector, FDI has a positive relation with

infrastructure quality in developing countries, whereas in advanced economies, judiciary

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independence and financial depth are positively associated with FDI and labor market

flexibility is negatively associated with FDI

Besides focusing on all three sectors, empirical studies have also examined

determinants of FDI at sector/industry specific level. Rashid, Bakar & Razak (2016) find

out the determinants of FDI in the agriculture sector of high income developing economies

(Malaysia, Oman and Brunei) of the Organization of Islamic Cooperation (OIC) countries

during the period from 2003 to 2012. The results of Pooled OLS, random and fixed effects

models show that market size has a positive and poverty has a negative impact on FDI of

the agriculture sector. Other variables inflation, exchange rate, and infrastructure are found

statistically insignificant.

Studies have also investigated determinants of FDI in the secondary (manufacturing)

sector. Karim, Winters, Coelli & Fleming (2003) and Tsen (2005) analyze determinants of

FDI in the manufacturing sector of Malaysia and find out labor productivity, imports,

production cost, infrastructure, human capital, market size and current account balance are

positively associated with FDI while GDP, interest rate, exports, inflation, and exchange

rate are negatively associated with FDI inflows. Karpaty and Poldahl (2006) find out that

energy intensity, capital intensity, and skills have a significant positive impact on

manufacturing FDI in Sweden. Bellak et al. (2008) examine market and efficiency related

determinants of FDI in the manufacturing sector of the US, 6-EU and 4-CEEC countries

during 1995-2003 and find that R&D expenditure, market potential, and lagged FDI stock

are positively associated with current FDI stock and GDP per capita, taxes, legal barriers to

FDI, unit labor cost, and share of low-skilled hours worked are negatively associated with

FDI. Polat and Payashoglu (2014) examine the determinants of FDI in the manufacturing

sectors of Turkey during 2007-2012. The results show that country risk for the US, price of

coking coal, and total tax rates have a negative association with FDI while the turnover

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index for the manufacturing sector and price of natural gas are positively associated with

FDI. Walsh and Yu (2010) show that GDP growth, TO, real effective exchange rate, and

clusters exert positive influence on the manufacturing FDI.

The services sector receives more foreign investment and countries face less fierce

competition in attracting foreign investors, contrary to the manufacturing sector. At the

global level, the services sector dominates with 64 percent share of the global FDI stock in

2014. The share of the services sector in developed, developing and in transition economies

account for 65 percent, 64 percent and 70 percent respectively (UNCTAD, 2016).

Ramasamy & Yeung (2010) examine the FDI determinants in both the

manufacturing and the services sectors of the OECD countries between the period of 1980

and 2003. The study uses the same set of independent variables for both the sectors but with

an addition of FDI of the manufacturing sector in the services sector. Results reveal same

results for both the sectors as TO, composite risk, GDP, GDP growth, education, and

infrastructure are positively associated with FDI while labor cost and interest rate have a

negative association. Moreover, FDI of the manufacturing sector has a positive and

significant impact on the attraction of FDI in the services sector. Yin et al. (2014) analyze

determinants of FDI in the service sector of China for the period 2000-2010. The Benchmark

dynamic panel data model has examined the demand-side factors (market size, market

potential, purchasing power measured by income, development level of service industry)

and supply-side factors (labor cost, human capital and infrastructure), agglomeration effects

(manufacturing FDI, urbanization), and institutional environmental factors (TO,

government intervention). The results reveal that all variables have a positive relation with

FDI except market size, labor cost and TO which have a negative relationship. Three

variables, government intervention, human capital, and infrastructure are found

insignificant.

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There is a scarcity of empirical studies found on the sectoral determinants of FDI in

Pakistan. Awan, Khan, uzZaman (2011) examine the economic determinants of FDI inflows

to the commodity producing sector (CPS) of Pakistan by employing quarterly data for years

1996 to 2008. The results show that GDP growth rate in commodity producing sector, GDP,

GFCF, foreign exchange reserves, per capita income, and TO exert significant impact on

FDI inflows to the sector. Hashim, Munir & Khan (2009) examine the determinants of FDI

in the telecommunication sector of Pakistan by using quarterly data (2000-2006). The results

show that market size, foreign trade, competition, literacy rate and per capita income have

a positive significant impact on FDI inflows to the telecommunication sector of Pakistan.

Summary of the literature review on the sectoral determinants of FDI is placed at Annexure

‘M’.

4.5.4 Determinants of FDI at Firm Level

There is a fourth group of the studies which have examined the firm-level

determinants of FDI. These studies have mostly used the surveys where the respondents are

the entrepreneurs. They are asked about the reasons for investing abroad (Garavito, Iregui,

& Ramírez, 2014) or the examination of investment climate (Ershova, 2017). This review

portion is also aligned with the objectives of the research in the dissertation to find out the

obstacles faced by the firms.

Garavito, Iregui, & Ramírez (2014) consider the factors that affect the FDI in

Colombia. For the purpose, they studied 5364 firms of different sizes over the period of

2000 to 2010. By employing panel probit, random effects and population average models,

they examined different variables, labor remuneration, capital intensity, labor productivity,

profitability, income tax, volatility in terms of trade, rule of law, that can influence the

decision of the firms regarding FDI. Not only these variables have an influence on FDI but

firm characteristics like firm size, location and its enlistment in national stock market effects

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investment to the firms. The study finds labor remuneration, capital intensity, labor

productivity, profitability, and rule of law have a positive association with FDI whereas,

income tax, and volatility in terms of trade show a negative influence on FDI.

Ablov (2015) studies the firm level determinants of FDI in Poland over the period

of 2003 to 2012. The sample comprises of 11064 manufacturing, 6718 services, and 4149

sales firms. The results from the panel data analysis reveal all variables, total assets of a host

firm, productivity of host firm, firm size, R&D expenditure, level of high-skilled workers,

age of a recipient firm, regional determinants (economic potential of a region in which a

firm operates, the road and railroad density of region and the location of a firm) show

significant positive relation with FDI except railroad density.

By conducting a survey in Kenya in 2007, Kinuthia (2010) finds political and

economic stability, market size, bilateral trade agreements and favourable climate factors of

attractiveness for Kenya firms to attract investment. On the other side, political instability,

corruption, crime, and insecurity creates hurdles in attracting FDI. Samuel, Gregory,

Maurice (2015) examine the relationship between FDI flows to the Kenyan manufacturing

sector and the governance indicators by performing the cross-sectional analysis for the

period 2009-2013. The results reveal a significant positive relationship between governance

and FDI growth. It implies that governance determines the FDI growth in the manufacturing

sector of Kenya.

Afza & Khan (2009) conducted a survey in Pakistan to ascertain the factors of FDI

affecting MNEs. The study classifies different factors into six categories: geographical,

political, social, business, legal and economic. The findings show that bureaucratic

administrative problems negatively impact MNEs and the law & order situation in Pakistan

is not a significant concern for the MNEs. Besides, the results reveal that level of natural

resources, natural climate, geographical structure & planning, infrastructure, fair dealing of

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bureaucracy, social norms and values, peace situation, the mindset of the politicians, fiscal

policy, and skilled labor are the factors that play the significant role in determining FDI.

Dumludag, Saridogan, & Kurt (2007) use the questionnaire-based survey on 52

executives of MNEs in Turkey to find out the impact of institutional factors on FDI. The

study finds the factors that attract FDI or deter FDI to Turkey. Finding of survey reveal that

growth of market is the most important factor that attracts FDI whereas political instability,

macroeconomic instability, and inflation badly affect FDI in Turkey. They also study

determinants of FDI in developing countries; Argentina, Brazil, Hungary, Mexico, Poland

and investigate, by employing panel data regression over the data set of the period from

1992 to 2004. The study finds the relationship of FDI with both macroeconomic and socio-

political factors. Results reveal that a negative association of FDI with GNI, inflation,

interest rate exists, while a positive association of FDI with government stability, investment

profile and corruption is found.

Ershova (2017) examine the factors which affect the decisions of the Russians firms

investing in Japanese firms. The study has used survey and interviews. While conducting

survey and interview, questions were asked regarding external, internal and non-economic

factors influencing FDI. The responses reveal that there are some factors such as market

demand potential, quality infrastructure, qualified labor force, high-quality production, high

profitability, and natural resources that help to attract investment and few factors such as

economic crisis, international relations, law & regulation, custom clearance, taxes, and labor

resources management deter the investment from the Russian firms in the Japanese firms.

By using the data of the Enterprise Surveys conducted by the World Bank for the

period of 2000 to 2006, Kinda (2010) examines the factors such as firm size and age,

agglomeration, telecommunication, electricity, transport, access to finance, skilled labor,

crime & disorder, property rights, labor regulation, corruption, custom and trade, tax rates,

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and wage that may promote FDI or hinders FDI in 77 developing countries. By employing

logit fixed-effect method and 2SLS, the study finds that FDI has a negative relationship with

firm size, age and agglomeration, physical and financial infrastructure, financing constraints,

lack of skilled labor force, corruption, and tax rate. Only custom and trade regulation

increases FDI.

Summary of the literature review on the determinants of FDI at the firm level is placed at

Annexure ‘N’.

4.5.5 Synthesis of Literature

There is a huge list of the potential determinants of FDI flows and these determinants

have been classified into several categories such as social, political, demand & supply, pull

& push, macroeconomic, institutional, traditional & non-traditional and policy & non-policy.

The variables in these classifications overlap with each other for example political factors

are also institutional factors. There are some macroeconomic variables which are also policy

variables. For synthesis, the possible classification could be policy and non-policy variables

where latter further comprise of institutional and macroeconomic variables.

The policies of the host country play an essential role in attracting foreign investors.

Fiscal policy is one of the factors that may affect FDI decisions (Simoes, Ventura & Coelho,

2014). Both tools of fiscal policy, taxes (Ali et al., 2013; Fedderke & Romm, 2006) and

expenditures (such as public investment on infrastructure) (Fedderke & Romm, 2006; Ismail,

2009; Naude and Krugell, 2009), have been used as factors of FDI inflows in empirical

studies. Monetary policy tools, interest rate (Boateng et al. 2015; Karim et al. 2003) and

money supply (Bekhet & Al-Smadi, 2015; Boateng et al., 2015; Naude and Krugell, 2009)

have also been considered as determinants of FDI inflows. The high inflation rate is an

indicator of macroeconomic instability and shows the ineptness of the central bank to

control it through appropriate monetary policy tools (Schneider and Frey,1985). Inflation

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finds its place in several empirical studies on the determinants of FDI inflows

(Anuchitworawong & Thampanishvong, 2015; Kahai, 2004; Ibrahim and Hassan, 2013).

The exchange rate is another policy variable used as a factor of FDI decisions (Kaur and

Sharma, 2013; Boateng et al., 2015). Trade openness is also a policy variable which

measures the degree of general trade restrictions of each country. It has been used as a

measure of FDI policy or liberalization policy of the government in attracting FDI

(Anuchitworawong & Thampanishvong, 2015; Fedderke & Romm, 2006; Pattayat, 2016;

Sahoo, 2006).

The quality of national institutions plays a crucial role in attracting foreign

investment. Several institutional measures, political (Ali et al. 2013; Azam and Khattak,

2009; Jadhav, 2012), social (Azam and Khattak, 2009), economic (Tintin, 2013) and

governance (Jadhav, 2012; Lucke and Eichler, 2016) have been utilized in empirical studies

as determinants of FDI. Charkrabarti (2001) views that market size is the most significant

determinants of FDI. It is widely employed in empirical studies (Ibrahim and Hassan, 2013;

Janicki and Wunnava, 2004).

Apart from variables, the empirical studies also differ in terms of the methodologies

used to ascertain the determinants of FDI. Generally, they are based on three approaches:

micro-oriented and aggregate econometric studies and survey data studies (Singh & Jun,

1995). Researchers have also used methodological pluralism to overcome the limitations in

one method (Dumlundag et al. 2007; Rogmans, 2011).

The theoretical framework based on the FDI theories and the empirical literature is

presented in Table (4.2).

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Table 4.2.

Theoretical Framework based on FDI Theories and Empirical Literature

Theory Variable Effect Empirical Evidence

OLI

Paradigm

Infrastructure

+

Xaypanya et al. (2015), Sahoo (2006), Tsen

(2005), Ramasamy and Yeung (2010),

Mohammadvandnahidi et al. (2012), Azam and

Lukman (2010)

- Botric and Skuflic (2006), Hussain and Hussain

(2016)

0 Mohamed and Sidiropoulos (2010)

Inflation

- Boateng et al. (2015), Kaur & Sharma (2013),

Bekhet and Al-Smadi (2015), Ibrahim and

Hassan (2013)

0 Mhlanga et al. (2010), Vijayakumar et al. (2010).

Exchange

Rate

Volatility

_ Sung and Lapan 2000; Bennassy-Quere,

Fontagné, & Lahrèche-Révil, 2001; Lemi and

Asefa 2003

Corporate

Taxation

-

Hartman, 1985, Hines, 2005; Desai, Foley and

Hines, 2004; Demirhan and Masca 2008; Mughal

and Akram, 2011).

0 Haberly and Wójcik 2014; Kinda, 2014; Hunady

and Orviska, 2014

New Trade

Theory

Market Size

+

Ibrahim and Hassan, 2013; Pattayat, 2016;

Janicki and Wunnava, 2004; Rehman, Orangzeb

and Raza, 2011; Gast and Herrmann, 2008

0 Mohamed and Sidiropoulos (2010)

Trade

Openness

+

Chakrabarti, 2001, Anuchitworawong and

Thampanishvong, 2015; Ismail, 2009; Bekhetand

Al-Smadi, 2015, Rogmans and Ebbers (2013),

Boateng et al. (2015), Xaypanya et al. (2015),

Hunady and Orviska (2014), Bekhet and Al-

Smadi (2015)

0 Vijayakumar, Sridharan, & Rao (2010)

Institutional

Theory

Corruption

-

Al-Sadig, 2009; Mauro 1995; Wei, 1997; Abed

and Davoodi, 2000; Caetano and Caelaro, 2005;

Canare, 2017; Woo and Heo, 2009

+ Bellos and Subasat, 2010; Egger and Winner,

2005

0 Akcay (2001), Alesina and Weder (1999)

Note: + positive and statistically significant effect, - negative and statistically significant effect, 0

insignificant effect.

Source: Author’s compilation

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The theoretical link between the variables and FDI inflows will be discussed in Chapter 5.

4.6 Gap in the Literature

The importance of the subject, determinants of FDI, is evident from the vast array

of empirical and theoretical literature. So, it has posed a challenge to the researcher to find

a gap in the literature and to ensure a legitimate contribution by filling it. An exhaustive

review of empirical studies has been attempted to cover all the possible aspects of the subject.

As research is a continuous and never-ending process, so it leaves the gaps for researchers

to attempt on. Studies whether at the country or the regional level lack the framework which

comprehensively examines the location determinants of FDI. Moreover, the studies have

seldom used methodological pluralism. There are handful studies which have used this

pluralistic approach in their research designs. Further, there has been a scarcity of research

on FDI determinants in three broader economic sectors (Wei & Liu, 2001). Though the

cross-country analysis provides useful insight into the potential determinants of FDI, yet it

is believed here that it may not be beneficial research contribution for policy makers. The

reason could be that most empirical studies have examined the FDI determinants by pooling

of structurally diverse countries (Singh and Jun, 1995). Besides, these empirical researches

lack methodological pluralism. There would hardly be any research that has utilized an

extended framework incorporating econometric time series analysis of aggregate (country),

disaggregate (sectoral) and firms’ level survey data analysis.

This gap in the literature has provided the researcher opportunity to come up with

more in-depth analysis of FDI determinants and produce a research which contributes to the

rationale policy making on the subject. Three relevant theories, the OLI Paradigm, the New

Trade Theory, the Institutional Theory, on the location determinants of inward FDI has been

has been selected. New variables like terrorism, corruption, and energy as determinants of

FDI have been included in the datasets. The other specific contributions of this research

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have been discussed in Chapter 1, Section 1.3. The further contribution will be highlighted

in the subsequent chapters.

4.7 Conclusion

Strong growth in FDI over the past few decades and the positive spillover effects of

this type of investment have invited many researchers to find out its determinants. Knowing

the factors that attract or deter FDI inflows is useful for policy makers in designing

appropriate policies. There is a considerable theoretical and empirical literature available on

the subject. This vast literature, on one hand, establishes the significance of the subject and

on other hand provides a broad list of determinants of FDI. But these studies have failed to

reach consensus. These diverse results in the empirical literature may be attributed to the

different time period covered, model specifications, proxies used for variables and the

countries included in the studies. Though these empirical studies provide useful analysis on

the subject, but hardly recognized as research that contributes to policy making. To

overcome these limitations, this dissertation chooses the determinants which are associated

with the location dimension of the OLI paradigm, the institutional approach and the New

Theory of Trade.

Academic research on the determinants of FDI in Pakistan is not scarce at the

country level, but the quality and in-depth analysis are lacking. And the research on the

sectoral determinants of FDI in Pakistan is scarce. Furthermore, there is no comprehensive

study on the determinants of FDI in Pakistan on the basis of above-mentioned three

theoretical approaches. With this backdrop, the research carried out in this dissertation

encompasses an extended framework where location determinants of FDI have been

examined at three levels, country, sector and firm. In addition to that, the empirical testing

of the location determinants of FDI in Pakistan can be carried out by incorporating both

previously tested parameters and some new parameters at the aggregate (country level) and

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disaggregate (sectoral level) by applying appropriate econometric methods. Moreover, the

survey data have been used to find out the obstacles faced by the firms commercially

operating in Pakistan. In this way, this study has used methodological pluralism to find out

the location determinants of FDI inflows to Pakistan.

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CHAPTER 5

RESEARCH METHODOLOGY

5.1 Introduction

This chapter is aimed at presenting the research methodology of the dissertation. The

research design addresses the topic of determinants of FDI in Pakistan at three different

levels and uses econometric techniques and analysis of survey data to achieve its objectives.

Section 5.2 briefs about prevailing research methods in the literature about FDI determinants.

Section 5.3 outlines the dissertation research methodology. It defines the variables and

specifies the hypotheses that have been tested in the subsequent chapters of analysis. It also

describes the estimation techniques used in the analysis.

5.2 Research Methodologies on the Subject

The empirical studies have used a variety of research design to ascertain the

determinants of FDI. They differ in terms of the variables, research methods, the

characteristics of FDI and the host locations. The studies have pooled different countries to

examine the location determinants of FDI (Ismail, 2009; Jadhav, 2012; Reschenofer et.al.

2012; Xaypanya et al. 2015). To find out the determinants, the research could also be carried

out by focusing a specific country which can either be FDI host country or source country

(Anuchitworawong & Thampanishvong, 2015; Bekhet & Al-Smadi, 2015, Boateng et al.

2015; Ibrahim and Hassan, 2013). Apart from regional or country level studies, the sectoral

determinants of FDI have also been examined. Rashid, Bakar & Razak (2016) examine the

determinants of FDI in the agriculture sector, services and manufacturing sectors

(Ramasamy & Yeung (2010), and Ho (2004) works to find the factors of FDI in China at

sectoral level (primary, secondary, tertiary). For the analysis, both econometric and survey

methods (Afza and Mahmood, 2009; Bitzenis and Marangos, 2008; Dumludag, 2009) have

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been reported in the literature. The survey studies (questionnaire-based) have provided a

valuable contribution in analyzing FDI determinants especially by incorporating less

quantitative variables effectively (Pearce, Islam, Sauvant, 1992).

5.3 Dissertation Research Methodology

The research uses a systematic research design embodying the funnel approach

(Figure 5.1), aimed at narrowing down the unit of analysis (country-sector-firm). The

approach provides a deep and comprehensive insight into the determinants of FDI.

Methodologically, time series econometric techniques have been used at country and

sectoral levels (primary, secondary, tertiary) with appropriate variables. Lastly at the firm

level, micro data obtained from the World Bank’s Enterprise Survey 2013 has been used

and statistical analysis has been done to examine the investment obstacles faced by firms in

Pakistan.

Figure 5.1. Funnel Approach of Analysis

The foremost aim of the research is to construct a framework on the determinants of

FDI by incorporating both macro (country and sectors) and micro (firm) level data into

consideration. Given the context of the research in Chapter 1, FDI policy and business

environment in Chapter 2, FDI profile in Chapter 3, and the lack of research that contributes

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to policy making on the subject, it is expected here that this research design can provide a

useful model to be tested in other FDI locations.

Based on the theoretical approaches and the empirical works, the most potential

factors explaining FDI inflows to Pakistan are outlined below:

Policy Factors

Corporate Tax Rate

Trade Openness

Inflation

Exchange Rate Volatility

Infrastructure

Non-Policy Factors

Corruption

Terrorism

Market Size

The selection of these policy and non-policy variables and their relevance in explaining

FDI inflows have been highlighted in the subsequent sections. The significance of these

location factors of FDI inflows has been tested through the formulation of specific

hypotheses, and the appropriate econometric model, described in Section 5.3.1, has been

used to test the hypotheses empirically. Besides the econometric model, a survey data has

also been used to find out the obstacles faced by the firms doing business in Pakistan. The

details about survey data are presented in Section 5.3.3. The dissertation has used both time

series econometric and survey data analysis to answer the research questions. The

econometric technique has been used at country and sectoral levels while the survey data

analysis has been done at the firm level.

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5.3.1 Variables and Hypotheses Development

Based on the empirical literature reviewed in Chapter 4 and assessment of business

environment in Pakistan provided in Chapter 3, the development of specific hypotheses on

the determinants of FDI into Pakistan has been achieved in this section and these hypotheses

will subsequently be tested through appropriate econometric method. The hypotheses relate

to policy and non-policy determinants of FDI in Pakistan are presented in subsequent

paragraphs.

5.3.1.1 Policy Determinants of FDI in Pakistan

The theoretical link between five policy variables and FDI inflows and the

development of hypotheses have been outlined in following paragraphs:

Corporate Tax Rate and FDI

The literature reviewed reveal that the influence of taxes on FDI inflow is

inconclusive. But the dominant view is that the FDI inflows react negatively to the increase

in corporate income tax (Demirhan and Masca 2008; Desai, Foley and Hines, 2004;

Hartman, 1985, Hines, 2005; Mughal and Akram, 2011). Simoes, Ventura & Coelho (2014)

survey the literature to find the relation between FDI and fiscal policies. Majority studies

find that corporate tax rate has an inverse relationship with FDI. In the OLI paradigm, the

favorable tax treatment is one of the location advantages that motivates MNEs for producing

abroad (Faeth, 2009). These fiscal incentives have a positive association with the FDI

(Babatunde and Adepeju, 2012). Studies also find an insignificant relationship of corporate

tax with the FDI (Haberly and Wojcik 2014; Hunady and Orviska, 2014; Kinda, 2014). This

mixed response has justified the requirement for further empirical work to better

comprehend the role of corporate tax among other factors that influence the location

decisions of foreign investors.

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In fiscal policy, taxation is very important for foreign investors as corporate tax rate

affects the profitability of the firms. The main purpose of MNEs is to maximize profits from

their investments. They are not interested in investing in countries that do not have or have

limited opportunities for profit (Khan, 2011). Therefore, foreign investors seek locations

where taxes are low.

Hypothesis 1a: Higher the corporate income tax rate, lower the FDI inflows.

Infrastructure and FDI

The quality infrastructure reduces the cost of doing business because it increases the

effective working hours and leads to operational efficiency for foreign investors. The

movement of industrial inputs relies on the infrastructural system (Yin et al. 2014). It

facilitates this movement from a source location to plant and to port. Therefore, foreign

investors favor economies with a reliable infrastructure in the form of road networks,

airports, uninterrupted water & power supply, internet and telephones access. The

operational cost of business increases with poor infrastructures and profit margin of firms

also decreases. Other things constant, production costs are usually lower in countries with

advanced infrastructure than those with weak infrastructure. It implies that countries with

good infrastructure are expected to attract more inward FDI (ShahAbadi and Mahmoodi,

2006). A significant and positive association is found between infrastructure and FDI

inflows in empirical studies (Azam and Lukman, 2010; Mohammadvandnahidi et al. 2012;

Ramasamy and Yeung, 2010; Sahoo, 2006; Tsen, 2005).

Hypothesis 1b: Host location with quality infrastructure attracts greater FDI inflows.

Infrastructure covers many aspects such as roads and railways, telecommunications

systems, even institutional development. In this research, the paved roads as a percentage

of total roads (INFRA) have been used as a proxy for infrastructure. This measure is in line

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with empirical literature (Alavinasab, 2013; Hoang & Goujon, 2014). The development of

road-related infrastructure is crucial for the country's economy and socio-economic

development, as the growth of other sectors of the economy depends on a better road

network for the timely flow and delivery of goods and services. Road infrastructure creates

not only a demand for primary resources, including the labor movement but also stimulates

the structure of the consumer society (MOF, 2017).

Trade Openness and FDI

The openness of an economy reduces trade costs. It will lead to a greater likelihood

of international vertical integration of MNEs that are involved in export-oriented

investments. Efficiency-seeking or cost reducing FDI is attracted as this offers to foreign

companies to import cheaper input materials and export finished goods to other countries

(Goldar and Banga, 2007). Therefore, a country that has fewer restrictions on cross-border

trading activities would be a more lucrative destination for foreign investors (Chakrabarti,

2001; Pistoresi, 2000). The level of international trade indicates the degree of openness of

any country. Increase in the degree of openness implies a decrease in the level of restrictions

imposed on the international trade by the host country. Eventually, it reduces the cost of

doing business in the host country. The cross borders trade and investments frequently

happen in more open economies. High degree of openness enhances the host country's

economic relations with other countries and makes them international markets, thus

preparing the conditions for several countries to invest in these countries

(Mohammadvandnahidi et al. 2012). The empirical literature is dominant with the positive

relationship between TO and FDI flows (Anuchitworawong and Thampanishvong, 2015;

Bekhetand Al-Smadi, 2015; Chakrabarti, 2001, Ismail, 2009).

Hypothesis 1c: Higher the degree of openness, greater the amount of FDI inflows.

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To measure the degree of trade openness in Pakistan, (TO), the trade ratio (export

plus import values divided by GDP) has been used. The use of the measure is supported by

the empirical studies (Ali et al. 2010; Anuchitworawong and Thampanishvong, 2015;

Choong and Lam, 2010).

Inflation and FDI

The high inflation rate is an indicator of macroeconomic instability and shows the

ineptness of the central bank to control it through appropriate monetary policy tools

(Schneider and Frey,1985). High inflation could adversely impact FDI as investors have to

devote additional money, time and energy to adapt to this inflationary environment. A

history of low or manageable inflation in the host country establishes the credibility and

commitment of the government, as it indicates the internal economic stability. This would

attract the foreign investors (Azam 2010; Khan & Mitra, 2014). A high inflation rate also

impacts the capital preservation of foreign investment. It affects the profitability of the

investors as higher prices can result in increased costs and lower profits. So, the stable

inflation rate is desirable to attract foreign capital (Aijaz, Siddiqui, & Aumeboonsuke, 2014).

Empirical literature finds an adverse impact of inflation on FDI flows (Bekhet & Al-Smadi,

2015; Ibrahim and Hassan, 2013; Kaur and Sharma, 2013).

Hypothesis 1d: An increase in inflation leads to a decrease in FDI inflows.

Annual percentage change in Consumer Price Index (CPI) has been used as a

measure of inflation. Its use here is also supported by the empirical literature on the subject

(Anuchitworawong & Thampanishvong, 2015; Bekhet & Al-Smadi, 2015).

Exchange Rate Volatility and FDI

The exchange rate is an important macroeconomic policy variable. It is policy

variable because central banks or governments decide about its regimes (fixed or floating)

(Bashir & Luqman, 2014). Therefore, exchange rates are among the most watched, analyzed

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and governmentally manipulated economic policy measures (Shah, Hussain, Hussain,

2015). Exchange rate instability is considered to be a risk factor for MNEs because it creates

an uncertain environment about the future benefits and costs of irreversible investment

projects. The stability of exchange rate enhances certainty in domestic economy hence it

increases investment probability in the current time and future as well. Expanded exchange

rate fluctuations make expanded changes in assets value, so projects appraisal becomes

difficult (ShahAbadi and Mahmoodi, 2006). Studies have found an adverse impact of

exchange rates volatility on FDI flows (Lemi and Asefa 2003; Bennassy-Quere et al. 2001;

Sung and Lapan 2000).

Hypothesis 1e: Higher the volatility in host country’s exchange rate, lower the FDI inflows.

Following formula is used to get real exchange rate from nominal exchange rate:

)(PAK

USAPAKPAK

CPI

CPINERRER

Where RER is the real exchange rate while NER is the nominal exchange rate.

CPIUSA and CPIPAK are the price levels in the USA and in Pakistan respectively. After

obtaining the variable of the real exchange rate, volatility has been calculated by applying

ARCH (autoregressive conditional heteroscedasticity) and GARCH (generalized

autoregressive conditional heteroscedasticity) techniques.

5.3.1.2 Non-Policy Determinants of FDI in Pakistan

The theoretical link between three non-policy variables and FDI inflows and

thereafter development of hypotheses has been outlined in following paragraphs:

Corruption and FDI

In the literature, there are two competing views on the relationship between

corruption and FDI inflow namely “sand the wheels” and “grease the wheels”. The former

views corruption as negative where it reduces FDI inflows. Corruption deters FDI inflows

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as it implies that the government is malfunctioning which adds costs to foreign investment.

It can deter FDI in several ways. It increases the direct costs through bribery. It encourages

the bureaucratic red-tapism and corrupts the institutions of contract enforcement and

property rights protection. It reduces the quality of government infrastructure and services

and contaminates its functions (for example provision of public utilities, import-export

permits, contacts awards). It also creates an uneven playing field in favor of the domestic

firms (Bellos and Subasat, 2012). Studies have empirically found that high levels of

corruption deter FDI inflows (Abed and Davoodi, 2000; Al-Sadig, 2009; Caetano and

Caelaro, 2005; Canare, 2017; Mauro 1995; Samimi and Monfared, 2011; Wei, 1997; Woo

and Heo, 2009).

Corruption can also grease the wheels of FDI in a host country. It is empirically

tested that corruption does not deter FDI inflows (Bellos and Subasat, 2010; Egger and

Winner, 2005). Bribery, a manifestation of corruption, may help foreign investors

particularly in emerging economies with huge bureaucracies. In this scenario, MNEs can

weigh the cost of bribery with its benefits. Bribery is used as a speed money (Williams,

Martinez-Perez & Kedir, 2016) and it helps foreign investors to break the bureaucratic red-

tapism.

There are some studies which remain inconclusive on finding the relationship

between corruption and FDI (Van Wyk and Lal, 2010; Okeahalam and Bah, 1998; Alesina

and Weder, 2002; Akcay, 2001). So, these inconclusive and mixed results reveal that the

relationship between corruption and FDI is an open-ended question.

Hypothesis 2a: Corruption deters FDI inflows. It does not grease the wheel of FDI.

The ICRG Corruption Index has been used as a measure of corruption. It records the

views of analysts on each country regarding the extent to which “high government officials

are likely to demand special payments” and “illegal payments are generally expected

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throughout lower levels of government” in the form of “bribes connected with import and

export licenses, exchange controls, tax assessment, policy protection or loans” (Knack and

Keefer 1995). The Index is composed of a 7-point scale starting from 0 to 6, where “0”

means most corrupt and “6” means least corrupt. The Index has been rescaled by subtracting

6 in order to make the interpretation of corruption coefficient relatively easier. Hence, it is

re-shaped to 0 (least corrupt) to 6 (most corrupt). This Index has widely been employed in

empirical studies as a measure of corruption (Al-Sadig, 2009; Wei, 2000; Woo and Heo,

2009)

Terrorism and FDI

Terrorism is rated by the corporate investors as import factor which influences their

foreign investment decisions (Global Business Policy Council, 2004). Abadie and

Gardeazabal (2008) reveal that the risk of terrorism decreases the expected returns on

investment. Economic growth showing a negative relationship with terrorism, where

terrorism exerts a negative influence on private sector more including investment from the

foreign investor (Barth, Li, McCarthy, Phumiwasana, & Yago, 2006). The terrorism has

dreadful consequences for investments as it threatens human lives and destroys property.

Moreover, the uncertain political and economic conditions resulting from the terrorist

attacks could harm the investment and it is an indirect cost to the host country’s economy

(Abadie and Gardeazabal, 2008). These terrorist incidents result in redirecting the public

investments to improving the security situation (Collier, 2003). Pakistan as a country has

lost a lot of finances in its fight against terrorism. Being a front-line State against terrorism,

it has cost the country heavily as the economy has lost US$ 118.32 billion during last 15

years (MOF, 2016). Further, Pakistan’s performance on ‘Political Stability and Absence of

Violence/terrorism’ has been poor as revealed by the Worldwide Governance Indicators

(Figure 2.2). Studies have found a negative impact of terrorism on FDI inflows (Abadie

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and Gardeazabal, 2008; Filer and Stanisic, 2016; Frey, Llusa and Tavares, 2011 and

Luechinger, and Stutzer, 2007).

Hypothesis 2b: Terrorism deters FDI inflows.

The PRS-ICRG provides two measures of violence - the internal conflict and the

external conflict. The former indicator is based on the components of civil war,

terrorism/political violence, and civil disorder while the latter is based on components such

as war, cross-border conflict and foreign pressures. Each indicator is coded on a 12-point

scale (very high risk = 1 and very low risk = 12). The simple average of these two indicators

has been taken as a proxy for terrorism. To make the interpretation terrorism coefficient

easier, this index has also been transformed where a score of 12 indicates a very high

incidence of terrorism and 1 indicates a very low incidence of terrorism.

These two indices are also used in the empirical literature as a measure of violence

(Witte, Burger, Ianchovichina, Pennings, 2016)

Market Size and FDI

There are four reasons which motivate multinationals to commence international

production activities namely market seeking, resource seeking, efficiency seeking and

strategic asset seeking (Chapter 4, Section 4.4). MNEs that are market seeking emphasize

the host location’s market size. FDI literature highlights the significance of market size for

the foreign investors. The larger market size means the increased demands of goods and

services. Charkrabarti (2001), while examining cross countries regressions on FDI

determinants, considers market size as the most reliable indicator of attracting foreign

investors to a host location. The rationale for the positive association between market size

and FDI flows is that a reduction in input costs through economies of scale can be exploited

in larger markets. At the same time, an increase in purchasing power makes it possible to

further differentiate the products, which can lead to the localization of the product or service

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(Ramasamy & Yeung, 2010). Empirically, studies have found a positive and significant

impact of market size in attracting FDI (Gast and Herrmann, 2008; Ibrahim and Hassan,

2013; Janicki & Wunnava, 2004; Pattayat, 2016; Rehman, Orangzeb & Raza, 2011).

Hypothesis 2c: Large market size is an attraction for foreign direct investors.

Market size is proxied by GDP per capita. The use of this proxy is in line with

empirical literature (Ibrahim and Hassan, 2013; Ali et al. 2010; Kahai, 2004; Reschenofer

et.al. 2012). In the sectoral analysis, GDP per capita is not considered an appropriate

indicator for market size for a specific industry or sector (Stobaugh, 1969). Instead of it, the

ratio of manufacturing output to GDP has been used for secondary/manufacturing sector

(Ali et al. 2010; Root and Ahmed, 1979) and the services value added as a share in GDP for

tertiary/services sector (Ali et al., 2010) as proxies for market size.

In the sectoral analysis, some location determinants of FDI such as FDI stock,

availability of natural resources, energy, human capital, have been added. Their description

along with justification has been provided in the respective sections.

The summary of policy and non-policy variables and their hypothesized relationship

with FDI inflows is given in Table (5.1).

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Table 5.1

Policy and Non-Policy Variables

Details of variables used in the estimations, their definitions and data sources are presented

at Annexure ‘O’.

All hypotheses have been tested using ARDL (Auto Regressive Distributed Lag)

estimation technique for the period 1984-2015 for the country and the sectoral analysis. The

results of the testing of the hypotheses are presented in Chapter 6 and Chapter 7.

5.3.2 The Estimation Technique

The ARDL co-integration / Bounds Testing (Pesaran and Shin, 1999; Pesaran, Shin,

Smith, 2001) econometric tool has been used to estimate empirically the short and long-run

relationships among the variables of interest. This technique has several features that have

distinguished it over other traditional cointegration testing methods. Firstly, it is simple to

execute. ARDL technique unlike other multivariate co-integration techniques, for example,

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Johansen and Juselius (1990), allows the cointegration relationship to be estimated by OLS

once the lag order of the model is identified. Secondly, the variables included in the model

do not require to be pre-tested for stationarity22. The technique does not require to have the

same order of integration of variables. It accepts a mixture of an order of integration I(1)

and I(0) variables as regressors. While other techniques such as the Johansen approach

require all variables to be stationary at first difference i.e. I(1). Hence, this technique does

not require a particular order of integration of the variables. Finally, it is comparatively more

efficient in small or finite data samples.

In the end, the ARDL approach has also been sanctioned by the empirical literature

on determinants of FDI inflows (Bekhet & Al-Smadi, 2015; Dar, Persley, & Malik, 2004;

Mohammadvandnahidi et al. 2012).

The details regarding the variables selection and estimation models have been

elaborated in the respective analysis chapters, chapter 6 for the country level analysis and

chapter 7 for the sectoral analysis.

5.3.3 World Bank’s Survey Data

At firm level, the dissertation has used the World Bank’s the Enterprise Survey 2013

data23 . The Enterprise Survey has used questionnaires for manufacturing and services

22Pre-testing of variables is done to ensure that none of the variables is I(2) because it will invalidate

the methodology. 23 The inclusion of the third tier in the research design gave rise to the issue of appropriate

methodology. Both survey based questionnaires and interviews have been used to get the micro data

at the firm level. The interview method was initially considered but in the end, it was dropped.

Because the qualitative data raises the issue of generalization. The questionnaire-based method has

also a wide literature support. The researcher prepared a draft questionnaire. Again, it was dropped

on three grounds. Firstly, a self-constructed questionnaire faces the issue of reliability and validity

and therefore it has to pass through several pre-testing processes. Secondly, researchers have faced

low response rate in case of both postal or web-based surveys. Thirdly, it may not be true

representative in nature because of resource constraints. These issues provided the justification for

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sectors where the respondents are the business owners and top managers of the firms. The

data were collected between May 2013 and May 2015 in Pakistan24. The Survey’s sample

frame comprises of 2667 firms. The details about the sample frame are provided in

Annexure ‘P’ and the sample size comprises of 1247 firms. Details about the sample units

are placed at Annexure ‘Q’. The Survey has used the stratified random sampling technique

with three levels of stratification: firm size, business sector, and geographical region of

Pakistan. There are three levels of firms: small firms (5-19 employees), medium-sized firms

(20-99 employees), and large-sized firms (100+ employees) The business sector has been

divided into manufacturing (Food, Textiles, Garments, Chemicals, Non‐metallic Minerals,

Motor Vehicles, other Manufacturing) and services (Retail and other services). Regional

stratification for Pakistan has been defined in five regions: Punjab, Sindh, Khyber

Pakhtunkhwa (KPK), Baluchistan, and Islamabad.

The World Bank’s Enterprise Survey is the culmination of 20 years of

implementation and testing. It has been used in 139 countries (J.Wimpey 25 , personal

communication, September 8, 2017).

The researcher downloaded the Enterprise Survey 2013 data from the World Bank

Enterprise Survey Portal (http://www.enterprisesurveys.org) by creating the log-in account.

The acquired data was in both Excel and Stata forms. The descriptive statistics, using SPSS

version 20, have been applied to analyze the data. Details on data analysis tools and

discussion on the results are presented in the Chapter 8.

the use of the World Bank’s Enterprise Survey data which is representative, reliable, accessible and

widely used in the empirical literature.

24 The survey is named after the year in which the data collection began as this most accurately

describes the data within.

25 Joshua Wimpey is private sector development specialist at Global Indicators Group, World Bank.

Email: [email protected]

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5.4 Ethical Considerations

The empirical research does not have any particular ethical issues. The data on FDI

(country and regional levels) are freely accessible and downloadable from the official

website of the UNCTAD (http://unctad.org/en/Pages/Home.aspx ). But the sectoral FDI data

have been obtained through email correspondence with the Division on Investment and

Enterprise (DIAE), UNCTAD, Geneva, Switzerland. The terrorism and corruption data

have been purchased from the PRS Group, Inc. USA (https://www.prsgroup.com ). So, in

this case, the data usage follows the licensing agreement. The firm-level data have been

obtained from the World Bank. It is also available free of charge but the membership from

the World Bank Enterprise Survey Portal is prerequisite. The sources of the acquired data

have been duly acknowledged and referred in this dissertation.

5.5 Conclusion

The research design is comprehensive in the sense that it encompasses three levels

of analysis (country, sectoral, firm). For the matter, the dissertation has used the

methodological pluralism by examining three different unit of analysis (country, sector, and

firm). This systematic research design embodies the funnel approach which is aimed at

narrowing down the unit of analysis. Previous researchers have examined the determinants

of FDI inflows to a host country and at sectoral level. There are a few studies which have

incorporated both host country and sectoral determinants. But the dissertation has

incorporated all three levels in its research design. Terrorism variable has been constructed

with the data from the PRS-ICRG. The contribution to literature has also been made by

including this variable in the analysis. Moreover, the research design has used sector-

specific variables. For example, in the sectoral analysis, GDP per capita is not considered

an appropriate indicator for market size for a specific industry or sector (Stobaugh, 1969).

Instead of it, the ratio of manufacturing output to GDP has been used for

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secondary/manufacturing sector (Ali et al. 2010; Root and Ahmed, 1979) and the services

value added as a share in GDP for tertiary/services sector (Ali et al., 2010) as proxies for

market size. The same set of variables for both country and sectors has not been used as

every sector has its unique characteristics.

The econometric technique of ARDL bounds testing has been selected to examine

the location determinants of FDI at country and sectoral levels. This technique is supported

by the empirical literature on the FDI determinants and it is also justified on empirical

grounds. The World Bank’s Enterprise Survey data has been used to examine the obstacles

faced by the firms operating in Pakistan. It can be said that data from credible institutions

(PRS Group, UNCTAD, WB) have been utilized in the research. Subsequent chapters will

provide the analysis at country (Chapter 6), sectoral (Chapter 7) and firm-level (Chapter 8).

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CHAPTER 6

DETERMINANTS OF FDI: COUNTRY LEVEL ANALYSIS

6.1 Introduction

The objective of this chapter is to analyze and discuss the determinants of FDI at the

country level. It empirically tests the hypotheses 1 and 2 relating to policy and non-policy

determinants of FDI described in Chapter 5. The empirical model and the relevant data

sources are presented in Section 6.2. Section 6.3 contains the results while Section 6.4

provides discussion on the results. The last Section 6.5 gives the conclusions.

6.2 Variable Selection, Data Sources, and Model Specification

The dependent variable is the FDI as a percentage of GDP (FDI/GDP). The use of

this measure is in line with the empirical literature on the determinants of FDI (Asiedu &

Lien, 2011; Hunady & Orviska, 2014). It is considered a better indicator for a single country

time series, but not for cross-country analysis as it does not control for the size of a country

(Vadlamannati, Tamazian & Irala, 2009). The selection of the independent variables is

inspired by the empirical literature reviewed, prevailing business environment and the

availability of data. There are five policy variables, Corporate Tax Rate (TAX), Inflation

(INF), Trade Openness (TO) and Exchange Rate Volatility (EXC), and three non-policy

variables, Corruption (CORR), Terrorism (TERR) and GDP per capita (GDPPC).

Chapter 5, Section (5.3.1) has explained the theoretical link between the dependent

variable, FDI, and independent variables (policy and non-policy). The equation (1) presents

the functional form of policy and non-policy variables included in the model:

FDI/GDP = f (TAX, TO, INF, EXC, INFRA, CORR, TERR, GDPPC) (1)

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In equation (1), FDI/GDP is the dependent variable taken as net FDI inflows

expressed in percentage of GDP. TAX is the corporate tax rate, INF is the inflation rate

measured by the annual percentage change in the consumer prices index (CPI) and is used

as a proxy for the macroeconomic instability in the country. EXR is the exchange rate

volatility. INFRA is the infrastructure and the percentage of paved roads to total roads is

used as a proxy for infrastructure. CORR is the corruption and ICRG corruption index is

used as a measure of corruption. TERR is the terrorism and average value of ICRG indices

of internal and external conflict is used as a proxy for terrorism and GDPPC is the GDP per

capita proxied for market size.

The data in annual time series for the period 1984-2015 have been obtained from the

UNCTAD, the WDIs database, the PRS-ICRG Group, the World Tax Database and the

Federal Bureau of Statistics, Government of Pakistan. Details of variables used in the

estimations, their definitions and data sources are placed at Annexure ‘O’.

6.3 ARDL Model Specifications

The following baseline model has been used to empirically investigate the

relationship between policy and non-policy variables and FDI:

𝐹𝐷𝐼𝑗 = 𝛽𝑋𝑗 + 𝜀𝑗 (2)

Where𝐹𝐷𝐼𝑗Foreign Direct Investment is to country i.e Pakistan, 𝑋𝑗 is a vector of

potential explanatory variables, and 𝜀𝑗is an error term. Based on equation (1), the equation

(2) is transformed into:

𝐿𝐹𝐷𝐼/𝐺𝐷𝑃𝑡 = 𝛽° + 𝛽1𝐿𝑇𝐴𝑋𝑡 + 𝛽2𝐿𝑇𝑂𝑡 + +𝛽3𝐿𝐼𝑁𝐹𝑡 + 𝛽4𝐸𝑋𝑅𝑡 + 𝛽5𝐿𝐼𝑁𝐹𝑅𝐴𝑡 +

𝛽6𝐿𝐶𝑂𝑅𝑅𝑡 + 𝛽7𝐿𝑇𝐸𝑅𝑅𝑡 + 𝛽8𝐿𝐺𝐷𝑃𝑃𝐶𝑡 + 𝜀𝑡 (3)

where the symbol “L” denotes logarithm form of the variable. All the variables are

in log form except exchange rate volatility (EXC). Because it contains negative values so

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that log was not possible. The log transformation decreases the possibility of

hetroskedasticity in the data (Cho & Park, 2006) and provides better estimation results.

Further, it also helps to reduce data variance over the years, limiting data to a small range.

The ARDL Bounds testing approach has been used to estimate the results. Following

equation (3), the representation of ARDL equation (4) is obtained as:

∆𝐿𝐹𝐷𝐼/𝐺𝐷𝑃 = 𝛽0 + 𝛽1𝐿𝑇𝐴𝑋𝑡−1 + 𝛽2𝐿𝑇𝑂𝑡−1 + 𝛽3𝐿𝐼𝑁𝐹𝑡−1 + 𝛽4𝐸𝑋𝐶𝑡−1 +

𝛽5𝐿𝐼𝑁𝐹𝑅𝐴𝑡−1 + 𝛽6𝐿𝐶𝑂𝑅𝑅𝑡−1 + 𝛽7𝐿𝑇𝐸𝑅𝑅𝑡−1 + 𝛽8𝐿𝐺𝐷𝑃𝑃𝐶𝑡−1 + ∑ 𝛽9∆𝐿𝑇𝐴𝑋𝑡−𝑖𝑘𝑖=0 +

∑ 𝛽10∆𝐿𝑇𝑂𝑡−𝑖𝑘𝑖=0 + ∑ 𝛽11∆𝐿𝐼𝑁𝐹𝑡−𝑖

𝑘𝑖=0 + ∑ 𝛽12∆𝐿𝐸𝑋𝐶𝑡−𝑖 +𝑘

𝑖=0 ∑ 𝛽13∆𝐿𝐼𝑁𝐹𝑅𝐴𝑡−𝑖𝑘𝑖=0 +

∑ 𝛽14𝐿𝐶𝑂𝑅𝑅𝑡−𝑖 + ∑ 𝛽15∆𝑇∆𝐸𝑅𝑅𝑡−𝑖 +𝑘𝑖−0 ∑ 𝛽16𝐿𝐺𝐷𝑃𝑃𝐶𝑡−𝑖

𝑘𝑖=0

𝑘𝑖=0 + 𝜀𝑡 (4)

Where β0 is intercept, ∆ is the operator for difference and 𝜖𝑡 is error term. To

estimate the ARDL equation, the maximum selected lag length is 1 for difference variable.

The Bounds testing technique has been used to test the existence of the long run

relationship between dependent and independent variables by following Pesaran, et al.

(2001). The null hypothesis is tested to implement bounds test by considering the

unrestricted error correction (UECM) for LFDI/GDP along with other variables. For this, a

joint significance test is performed as follows:

The null and alternative hypotheses are as follows:

0: 876543210 H (no long-run relationship)

Against the alternative hypothesis

0: 876543211 H (a long-run relationship exists)

The computed F-statistic value is assessed against the critical values. According to

Pesaran, et al. (2001), the lower bound critical values assumed that the explanatory variables

jx are integrated of order zero or I(0), while the upper bound critical values assumed that

jx are integrated of order one or I(1). The computed F-statistic could result in three different

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scenarios. If its value is smaller than the lower bound value, in this case, the null hypothesis

is not rejected and it is concluded that there exists no long-run relation between FDI and its

determinants. On the other hand, if the value of F-statistic is more than the upper bound

value, at that point the null hypothesis is rejected and it is inferred that FDI and its

determinants have a long-run relation. Finally, the results are inconclusive if the value lies

between the lower and upper bound values.

6.4 Estimation Results and Interpretations

6.4.1 Unit Root Test

Before using the ARDL approach to cointegration, the unit root of variables has been

tested. For that matter, the Augmented Dickey Fuller (ADF) test has been performed at level

and at first difference. The results of the unit root test have been presented in Table (6.1).

The results show that the dependent variable, FDI/GDP, is non-stationary at level but it is

stationary at 1 percent level of significance after first differencing. Among policy variables,

TAX, TO, and INFRA are stationary at 1 percent level of significance at first difference

while INF and EXC are stationary at level at 1 percent and 5 percent level of significance

respectively. The non-policy variables, CORR and TERR are stationary at level at 5 percent

and 10 percent respectively while GDPPC is stationary at first difference at 1 percent level

of significance.

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Table 6.1

ADF Unit Root Test on Variables

Variables At Level At First Difference Decision

FDI/GDP -2.09 -5.01* I(1)

TAX -2.48 -8.01* I(1)

INF -4.35* - I(0)

TO -2.51 -6.66* I(1)

EXC -4.26** - I(0)

INFRA -2.19 -2.07** I(1)

CORR -3.21** - I(0)

TERR -2.76*** -3.91* I(0)

GDPPC -0.52 -3.60* I(1)

Note: *, ** and *** indicate the rejection of the null hypothesis of non-stationary at 1%, 5% and

10% significant level, respectively

The unit root findings validate the ARDL model selection. There is no variable

stationary at second difference i.e I (2). The values of F-statistics provided by Pesaran et al.

(2001) cannot be interpreted in the presence of variables integrated of order two. Moreover,

there is a mixture of order of integration of variables, I(0) and I(1), and this phenomenon

further suits ARDL estimation technique. The next step is the estimation of the F-statistic

that tests the joint null hypothesis that the coefficients of the lagged level variables are zero

(i.e. no long-run relationship exists between them).

6.4.2 Bounds Testing to Cointegration

Table (6.2) reports the ARDL Bounds test results.

Table 6.2

ARDL Bounds Test Results

F-statistics 4.03

Significance level (%) Lower Bound Value Upper Bound Value

5 2.62 3.79

The computed F-statistics, 4.03, of the model exceed the upper critical bound values at 5

percent significance level. The result of the bounds co-integration test shows that the null

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hypothesis ( 0H ) is rejected against (1H ) at 5 percent significance level. It establishes the

presence of long-run relationship among variables. After confirming the existence of the

long run relationship, the next step is to find out both short run as well as long-run estimates

of the model.

6.4.3 Long Run Estimates

Following ARDL (1, 0, 0, 1, 0, 0, 0, 0, 1) specification has been estimated. The long run

estimates are reported in Table (6.3).

Table 6.3

Estimated Long Run Coefficient using the ARDL approach

Selected Model: ARDL (1, 0, 0, 1, 0, 0, 0, 0, 1) based on Schwarz criterion (SIC)

Variable Coefficient Std. Error t-Statistic

C -120.38* 33.26 -3.62

LOG(TAX) -1.21*** 0.72 -1.68

LOG(TO) 1.98** 0.94 2.10

LOG(INF) -1.09* 0.36 -3.05

EXC -0.74 2.26 -0.33

LOG(INFRA) 0.77* 0.23 3.34

LOG(CORR) -0.30*** 0.95 -0.32

LOG(TERR) -0.24 0.56 -0.43

LOG(GDPPC) 18.49* 5.68 3.26

Note: *, ** and *** indicate leve1 of significance at 1%, 5% and 10% respectively.

Fiscal policy (Taxation & Expenditures) turned to be the significant determinants of

FDI inflows to Pakistan. The coefficient of TAX is significant and has a negative association

with FDI. It implies that reducing corporate tax rate would positively affect the inflows of

FDI in Pakistan. The result is in line with empirical literature (Aqeel & Nishat, 2004; Ali et

al. 2010; Ibrahim and Hassan, 2013). Another tool of fiscal policy, INFRA is also significant

and positively linked to FDI. Empirical literature supports that quality infrastructure attracts

greater FDI inflows (Azam & Lukman, 2010; Alam & Shah, 2013; Lucke & Eichler, 2016;

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Xaypanya et al. 2015). Inflation (INF) has a negative and significant association with FDI.

Inflation as macroeconomic instability discourages foreign investors. A vast empirical

literature endorses this finding (Boateng et al. 2015; Ibrahim & Hassan, 2013; Ismail, 2009;

Kaur & Sharma, 2013; Lucke & Eichler, 2016; Naude & Krugell, 2009, Solomon & Ruiz,

2012; Xaypanya et al. 2015; Zakaria & Shakoor, 2013). Trade openness (TO) is positively

and significantly linked to FDI. It implies that a more open economy is an attraction for

foreign investors and empirical literature support this notion (Anuchitworawong &

Thampanishvong, 2015; Boateng et al., 2015; de Castro, Fernandes, Campos, 2015; Ibrahim

& Hassan, 2013; Ismail, 2009; Lucke & Eichler, 2016; Zakaria & Shakoor, 2013). The

coefficient of GDPPC proxied as market size positively and significantly associated with

FDI, supporting the hypothesis that large market size is an attraction for foreign investors

(Dumludag, 2009; Ibrahim and Hassan, 2013; Gast and Herrmann, 2008; Janicki &

Wunnava, 2004; Pattayat, 2016; Rehman et al. 2011).

The coefficient of CORR is negatively and significantly associated with FDI. The

result shows that corruption sands the wheels of FDI in Pakistan. Many researchers have

empirically tested that high levels of corruption deter FDI inflows (Abed and Davoodi, 2000;

Al-Sadig, 2009; Caetano and Caelaro, 2005; Dahlstrom and Johnson, 2007; Hussain, 2012;

Habib and Zurawicki, 2002; Kardesler and Yetkiner, 2009; Mauro 1995; Samimi and

Monfared, 2011; Wei, 1997; Woo and Heo, 2009).

The coefficients of EXC and TERR are negatively but insignificantly associated

with FDI. Literature finds a negative relationship between exchange rates uncertainty and

FDI inflows (Bennassy-Quere et al. 2001; Lemi and Asefa 2003; Sung and Lapan 2000).

The point is that a high degree of exchange rate volatility deters firms from making their

investment decisions. The insignificant results might imply that this variable is regulated

and critically monitored by the SBP. It is one of the core functions of the Bank to manage

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the exchange rate fluctuations and keeping the rate at an appropriate level (“Core Functions

of State Bank of Pakistan”, 2016). Terrorism creates uncertain political and economic

environ in the country. Studies have found a negative impact of terrorism on FDI inflows

(Abadie and Gardeazabal, 2008; Llusa and Tavares, 2011; Luechinger, and Stutzer, 2007).

The negative but insignificant result of terrorism variable, in the long run, might imply that

high risk brings high returns on investments. The data show that Pakistan received the

highest FDI inflows during the last decade when terrorism wave was at the peak in the

country.

Based on the long run estimated results, it can be concluded here that variables such

as corporate tax rate, infrastructure, inflation, trade openness, GDP per capita and corruption

are the major determinants of FDI inflows to Pakistan.

6.4.4 Short Run Estimates

The short run estimates are reported in Table (6.4). The equilibrium correction coefficient

estimated has the required negative signs (-0.85) and is highly significant at 1 percent level

of significance. It demonstrates a fairly-high speed of adjustment to equilibrium after a

shock. The results imply that the model is corrected from the short-run towards the long-run

equilibrium by approximately 85 percent, an annual rate of adjustment towards equilibrium.

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Table 6.4

Estimated Short Run Coefficient using the ARDL approach

Selected Model: ARDL(1, 0, 0, 1, 0, 0, 0, 0, 1) based on Schwarz criterion (SIC)

Variable Coefficient Std. Error t-Statistic

DLOG(TAX) -1.03 0.69 -1.49

DLOG(TO) 1.68** 0.67 2.51

DLOG(INF) -0.22 0.32 -0.68

D(EXC) -0.63 1.87 -0.33

DLOG(INFRA) 0.65** 0.25 2.65

DLOG(CORR) -0.25 0.78 -0.32

DLOG(TERR) -0.21 0.46 -0.45

DLOG(GDPPC) 1.66 4.87 0.34

CointEq(-1) -0.85* 0.12 -6.89

Note: *, ** and *** indicate leve1 of significance at 1%, 5% and 10% respectively.

6.4.5 Diagnostics and Stability Tests

The ARDL model has passed all the necessary diagnostic tests such as serial

correlation (Breusch-Godfrey Test), normality (Jarque-Bera Test), heteroskedasticity

(ARCH Test) and functional form misspecification (Ramsey RESET Test). The literature

on the application of ARDL for the determinants of FDI inflows mention these four tests to

confirm the robustness of the model used (Mohammadvandnahidi et al. 2012;

Ravinthirakumaran, Selvanathan, Selvanathan & Singh, 2015). Results of the diagnostics

tests are presented in Table (6.5).

Table 6.5

Results of Diagnostics Tests

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Normality Test (Jarque-Bera)

The result shows that the Jarque-Bera (JB) test statistics for normality is not

significant. Therefore, it does not reject the normality hypothesis of errors. It establishes

that the residuals are normally distributed.

Serial Correlation

Breusch–Godfrey test for serial correlation in residuals is not significant. So, the null

hypothesis of no serial correlation in the residuals cannot be rejected. It suggests that the lag

structure used in the model is appropriate.

ARCH Test

The ARCH test for heteroskedasticity in residuals is not significant. So, the null

hypothesis of no heteroskedasticity in the residuals cannot be rejected. It suggests that the

residuals are not heteroskedastic.

Model Specification Test

Ramsey’s RESET test has been performed to check the model specifications. It does

not reject the null hypothesis of linearity in the parameters. Thus, it provides the evidence

of linearity in the model specification. The same test is also applicable to the omitted

variables and incorrect functional form. The result indicates that the model specification is

appropriate and the parameters of the model are stable.

All the above-mentioned tests and their results establish that the model has the

aspiration of econometric properties; the model’s residuals have no serial correlation issue,

they are normally distributed and homoskedastic. Hence, the reliable interpretations can be

generated from the results.

Furthermore, the cumulative sum of recursive residuals (CUSUM) and the

cumulative sum of squares residuals (CUSUMSQ) tests are performed to assess the

parameter stability (Pesaran and Pesaran, 1997). Figure (6.1) plots the results for CUSUM

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and CUSUMSQ tests. The results show that the plot of the CUSUM and CUSUMSQ

statistic fall inside the critical bands of the 5 percent confidence interval of parameter

stability, thus indicating the absence of any instability of the coefficients. Therefore, it

establishes that stability in the coefficients exists for the sample period.

6.5 Discussions

The policies of the host country exert influence on the investment decisions of MNEs.

Through policies, foreign direct investors can be attracted or they can also be restricted in

several ways. Hence, the policies are considered instrumental in the control and the

management of FDI inflows (Khan and Kim, 1999). As FDI decisions are a strategic choice

by firms choosing among alternative locations, so it is considered a game between two

players, the host government and multinationals or a competition between countries to

attract FDI inflows (Faeth, 2008).

In fiscal policy, taxation is very important for foreign investors as corporate tax rate

affects the profitability of the firms. The main purpose of MNEs is to maximize profits from

their investments. They are not interested in investing in countries that do not have or have

limited opportunities for profit (Khan, 2011). Therefore, foreign investors seek locations

where taxes are low. The negative sign of corporate income tax states that higher tax rate

deters foreign direct investors in Pakistan. Pakistan’s corporate income tax rate is the third

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highest in the world with 34 percent. The main reason for higher tax rate is attributed to

narrow tax base (Reva, 2015). According to the State Bank of Pakistan, the country’s tax-

to-GDP ratio has remained in the range of 8.5 percent to 9.5 percent over the last 10 years.

The Overseas Chamber of Commerce and Industry (OICCI), Pakistan, a representing body

of foreign and MNEs in the country, has demanded the corporate income tax should be cut

down to 25 percent (Bukhari & Ikramul-Haq 2018, April 13). The worldwide average

corporate tax rate has declined since 2003 from 30 percent to 22.5 percent (Pomerleau and

Potosky, 2016). Corporate tax rate adversely affects the FDI inflows to Pakistan, providing

an evidence that country should lower its tax rate to attract greater inflows of FDI.

The quality infrastructure makes it possible to attract greater FDI inflows. For this

reason, countries with good infrastructures expect more direct foreign investments

(Mohammadvandnahidi et al. 2012, ShahAbadi, 2006). Infrastructure is the means that

facilitate the reliability of services, affordable, reduction in delivery time of goods and

ultimately, in combination with these factors, results in increased production and profits of

the companies in any country. The availability of infrastructures promotes vertical and

horizontal FDI, with a relatively more efficient way for vertical FDI, because it reduces

operational costs (Rehman, Ilyas, Alam, & Akram, 2011).

Pakistan and China have been working on the China-Pakistan Economic Corridor, a

multi-billion-dollar mega project. The power and construction sectors are the focus of

Chinese investment under the CPEC. Power generation was the main attraction of foreign

investment during the period 2016-2017 with US$ 1299.3 million, followed by Construction

(US$ 455million) 26 . The CPEC envisages an extensive overhaul of the existing

transportation infrastructure in Pakistan and laying out of new routes for the facilitation of

26 Chapter 3, Section 3.3.2

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transit trade and enhancement of market accessibility. Currently, the CPEC projects worth

Rs.700 billion related to road infrastructure are ongoing (MOF, 2017). The positive sign of

infrastructure endorses the government’s initiatives in developing and expanding the

infrastructure facilities in the country. The availability of quality infrastructure would attract

more foreign investors.

Trade Openness is one of the policy variables. It measures the degree of general

trade restrictions of each country. In the category of efficiency-seeking FDI, foreign capital

moves to countries which are more open to international trade. Greater is the degree of

openness, lower is the level of restrictions enforced by the host location on international

trade. Thus, it ultimately leads to the lower cost of doing business. Therefore, a country that

has fewer restrictions on cross-border trading activities would be a more lucrative

destination for foreign investors (Chakrabarti, 2001; Pistoresi, 2000). Amiti and Wakelin

(2003) are of the view that the presence of vertical FDI or horizontal FDI can be judged

from the sign of the trade openness variable in empirical studies. The vertical FDI is likely

to enhance trade where multinationals geographically split the production process while in

the case of horizontal FDI, MNEs produce finished goods in several locations and this

replaces the trade. It is inferred from the positive sign of trade openness variable, the vertical

FDI is dominant over the horizontal FDI in Pakistan, where the majority of investors hail

from the developed countries.

Price stability is considered to be one of the indicators manifesting the stable

macroeconomic environment of a country. Generally, a high rate of inflation in a country

can reduce the profit margin of the firms. It is also considered an indicator of

macroeconomic instability in the country as it points out internal economic tension, the

government’s unwillingness in balancing its budget and failure of the central bank for

having an effective monetary policy (Schneider and Frey,1985). Therefore, the result of the

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current study indicates that the relationship between FDI and monetary policy needs to be

adjusted. Inflation is negative in the long run and it has basically confirmed the efficiency-

seeking framework of Dunning (1980). These results mean that an increase of inflation rate

would lead to decreasing the inward FDI in Pakistan. High inflation would mean that the

government could not balance its budget and the persistent high budget deficits are financed

by the SBP. Inflation also affects FDI in terms of capital preservation. This is both an

internal and an external factor. If the investor wants to invest in the country, he would like

to invest where inflation is low and his return on investment should be higher than the

inflation rate to obtain net profit. Therefore, higher inflation with an uneven increase in

profitability will discourage the foreign investor and it leads to a loss of FDI (Singhania &

Gupta, 2011).

The market size of Pakistan shows a positive link with inward FDI. It endorses the

market-seeking framework of Dunning (1980). Market size has been recognized as the most

reliable determinant of FDI (Chakrabarti, 2001). Large markets have always remained a

source of attraction for foreign investors. Pakistan has a very vibrant market and provides a

considerable large consumer base of more than 207.8 million people 27 . The sectoral

distribution of FDI inflows shows that since 1985, the tertiary sector (services) has been the

dominant recipient of FDI (Chapter 3, Section 3.3)28. Moreover, the regionalization extends

the market and it provides a further attraction for the foreign investors (Asiedu 2006).

Pakistan’s regional cooperation especially with China under the CPEC would further

enhance the market attractiveness of Pakistan. It would ease the trade through corridors and

would ensure better regional connectivity.

27 According to the 6th Population Census 2017 conducted by Federal Bureau of Statistics, Ministry

of Statistics, Government of Pakistan. 28 Empirical results on sectoral analysis also confirm the market seeking hypothesis (Chapter 7)

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Corruption is a much talked about issue in Pakistan. There has been a lot of academic

and non-academic literature available on corruption. Chapter 2, section 2.3 the business

environment in Pakistan highlights the pervasiveness of corruption in Pakistan. Now the

empirical result reveals that corruption deters FDI inflows to Pakistan. Different measures

of corruption indicate the pervasiveness of corruption in the country. Last three reports of

the Global Competitiveness have declared ‘corruption’ as the most problematic factor of

doing business in Pakistan. Moreover, TI’s Corruption Perception Index (CPI) 2016 ranks

Pakistan at 116th among 176 countries of the world. Pakistan once, in 1995, was the second

most corrupt country in the world. Corruption is a major impediment to business in Pakistan,

and businesses expect to regularly face bribes or other corrupt practices29.

In the end, both variables, exchange rate volatility and terrorism show negative signs

but they are not significant determinants of FDI inflows to Pakistan. High level of exchange

rate volatility poses financial instability or risk in the country. But as said earlier, this

variable is regulated and critically monitored by the SBP as it falls in Bank’s core functions.

Moreover, terrorism also shows the political and economic uncertainty in the country. It

deters FDI inflows. But those who are the risk takers, it may not create an impediment for

them. The FDI inflows data shows that Pakistan received highest FDI inflows during the

last decade when the country was facing incidents of terrorism (Chapter 3, Section 3.3).

6.6 Conclusions

The current Chapter examines the long-run and short-run relationships among FDI

and five policy & three non-policy variables in Pakistan for the (1984–2015) period.

Methodologically, it utilizes ADF test for testing stationarity level. The ARDL bound

testing approach has been employed to analyze the long-run and short-run relationships

29 Chapter 8 provides a detailed insight into this variable.

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among variables of interest. The estimations indicate that corporate tax rate, infrastructure,

inflation, trade openness, corruption and GDP per capita yield significant coefficients in

relation to FDI. So, it is inferred that FDI inflows depend on both policy and non-policy

factors. Government’s macroeconomic policies provide location advantages to MNEs and

friendly business environment and higher institutional quality manifested as low levels of

corruption would be added advantages to the host location.

The research findings have a number of policy implications for Pakistan. Fiscal

policy in the form of tax and monetary policy vis-à-vis inflation seem to affect the FDI

decision on the part of investors; therefore, relevant policy institutions (the Ministry of

Finance and the State Bank of Pakistan) should reduce the corporate tax rate and control

inflation in the country. Lowering corporate tax would imply lower tax/revenue collection.

The government has already been relying on domestic debt to finance its the budget deficit.

So, prudent decision would be to enlarge the tax base. It would also relax the SBP as

domestic borrowing is inflationary in nature. Lastly, corruption is deterring the FDI inflows.

It should be curbed to make the location (Pakistan) attractive for foreign investors as it is a

hurdle in doing business in the country.

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CHAPTER 7

DETERMINANTS OF FDI: SECTORAL LEVEL ANALYSIS

7.1 Introduction

The objective of this chapter is to provide the analysis and discussion on FDI

determinants at the sectoral level. Three main sectors (primary, secondary, tertiary) of the

economy have been included. The empirical models and the relevant data sources are

presented in Section 7.2. Section 7.3 contains the results while Section 7.4 provides

discussion on the results. The last Section 7.5 gives the conclusions.

7.2 Variable Selection, Data Sources, and Model Specification

7.2.1 Primary Sector and FDI

According to the UNCTAD classification, the primary sector consists of agriculture,

forestry, fishing, hunting, mining, quarrying, and petroleum (Table 3.2). This sector is a

resource-driven. Therefore, its relationship with macroeconomic variables is minimal

(Nauwelaerts and Beveren, 2005; Walsh & Yu, 2010). According to Dunning (1993), the

natural resource seeking FDI depends on the availability of resources, the quality of

transport infrastructure and the level of taxation. Host economy’s market size and its

economic performance are perceived less important (Ali et al., 2010). This sector especially

mining and quarrying has been hit by terrorism30. From 2005 to 2017, there are 232 terrorist

attacks on gas pipelines alone in Baluchistan province consuming 16 lives (South Asia

Terrorism Portal, 2017). This sector especially its sub-sectors Mining & Quarrying, Oil &

Gas Explorations are more sensitive to the negative effects of terrorist attacks than others.

30 Mining and quarrying includes the extraction of minerals found naturally in the forms of solids

(coal and ores), liquids (petroleum) and gases (natural gas).

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Therefore, the model of primary FDI as a function of availability of natural resources

measured by the ratio of primary exports to GDP (RESO), the corporate tax rate (TAX),

infrastructure (INFRA), terrorism (TERR), quality of labor proxied by the secondary school

enrolment (QL), and existing primary FDI stock as percentage of primary GDP (PFDIS) as

agglomeration effect. The equation (5) presents the functional form of policy and non-policy

variables of the primary FDI:

Primary FDI = f (RESO, TAX, INFRA, TERR, QL, PFDIS) 5

The data are in annual time series for the period 1984-2015 and have been obtained

from the UNCTAD, the WDIs database, the PRS-ICRG Group, the World Tax Database

and the Federal Bureau of Statistics, Government of Pakistan. Details of variables used in

the estimations, their definitions and data sources are presented at Annexure ‘O’.

7.2.2 Secondary Sector and FDI

The FDI inflows to the secondary and tertiary sectors explain more association with

macroeconomic and qualitative variables than with direct investments in the primary sector.

However, the responsiveness of these two sectors may differ according to each factor

responsible for explaining FDI flows. The secondary sector (Manufacturing) FDI is

influenced by a number of factors. Keeping determinants of FDI at country level (Chapter

6, equation 1) as baseline model, the model secondary FDI as a function of the corporate

tax rate (TAX), infrastructure (INFRA), existing secondary FDI stock as percentage of

manufacturing GDP (SFDIS) as agglomeration effect, quality of labor proxied by the

secondary school enrolment (QL), trade openness (TO), inflation (INF), exchange rate

volatility (EXC), energy (ENR) and market size as proxied by manufacturing share of GDP

(MANUF). The equation (6) presents the functional form of policy and non-policy variables

of the secondary FDI:

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Secondary FDI = f (TAX, INFRA, SFDIS, QL, TO, INF, EXC, ENR, MANUF) 6

GDP per capita is not considered an appropriate indicator for market size for a

specific industry or sector (Stobaugh, 1969). Following (Root and Ahmed, 1979; Ali et

al.,2010), the ratio of manufacturing output to GDP (MANUF) has been used for secondary

/manufacturing sector as a proxy for market size instead of GDP per capita. The effect of

the exchange rate fluctuations on FDI inflows varies from one industry to another because

of the specific characteristics of each industry. The manufacturing sector is considered to be

more closely linked to exchange rate fluctuations than the services sector, as FDI in this

sector is predominantly export-oriented in nature. FDI in this sector is mostly linked with

capital imports and exports of output on the international market, while the services sector

is mainly targeted at the domestic market. Therefore, FDI in this sector is heavily exposed

to exchange rate uncertainty (Polat and Payaslıoglu, 2014). The major issue confronting the

manufacturing sector in Pakistan is the power shortage (MOF, 2016), and companies are

disinvesting and locating their businesses in other countries (Kiran & Kiran, 2016). With

this backdrop, commercial energy use (ENR) as a proxy for the availability and use of

energy is included in the model. Energy is considered vital for the efficiency-seeking FDI

(Moosa, 2009). Availability of the skilled labor is also significant for attracting the

efficiency-seeking FDI. Manufacturing industry needs skilled talent. With the use of the

qualified labor, MNEs can strengthen their ownership advantages which they own and adapt

to the host location environment by using available local talent. This permits them to expand

the market within host countries and in the region as well (Ramasamy & Yeung, 2010). The

variable, number of secondary school enrolment as a proportion of the population (QL) as

an indicator of labor quality (Ramasamy & Yeung, 2010; Sakali, 2013; Toulaboe et al. 2011)

is included in the model.

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7.2.3 Tertiary Sector and FDI

Like the secondary sector, FDI inflow to tertiary sector (services) is also influenced

by several factors. Keeping equation (1) and equation (6) in mind, changes have been made

in the services FDI model because of specific nature of services. The provisions of services

usually require the physical presence in a host country, therefore market size should have

greater importance. Hence, services value added as a share in GDP (SERV) is included as a

proxy for market size (Ali et al. 2012; Ramasamy & Yeung, 2010). The empirical literature

reveals that both local and foreign investors, particularly from the manufacturing sector,

benefit from services FDI. The services FDI especially in sectors of transport,

communications and finance often follow manufacturing FDI in order to provide necessary

support to global supply chain (Kolstad and Villanger, 2004). Therefore, it is positively and

significantly associated with the manufacturing FDI. With this view, manufacturing FDI

stock (SFDIS) is included. The model of tertiary FDI is formulated as a function of the

corporate tax rate (TAX), infrastructure (INFRA), existing tertiary FDI stock as percentage

of tertiary GDP (SFDIS) as agglomeration effect, quality of labor proxied by the secondary

school enrolment (QL), trade openness (TO), inflation (INF) and market size as proxied by

services value added as share in GDP (SERV). The equation (7) presents the functional form

of policy and non-policy variables of the tertiary FDI:

Tertiary FDI = f (TAX, INFRA, SFDIS, QL, TO, INF, TFDIS, SERV) 7

7.3 ARDL Model Specifications

Based on equation (2), the sectoral equations (8, 14, 18), have been transformed.

Primary Sector:

𝑃𝐹𝐷𝐼/𝑃𝐺𝐷𝑃𝑡 = 𝛽° + 𝛽1𝐿𝑅𝐸𝑆𝑂𝑡+ 𝛽2𝐿𝑇𝐴𝑋𝑡 + 𝛽3𝐿𝐼𝑁𝐹𝑅𝐴𝑡 + 𝛽4𝐿𝑇𝐸𝑅𝑅𝑡 + 𝛽5𝐿𝑄𝐿𝑡 +

𝛽6𝑃𝐹𝐷𝐼𝑆𝑡 + 𝜀𝑡 (8)

Where, PFDI/GDP is the dependent variable. ‘L’ denotes lag form of the variables.

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Independent variables include LRESO, LTAX, LINFRA, LTERR, LQL, PFDIS, and 𝜀𝑡

represents error term.

ARDL equation of the model of primary sector is as follows:

𝑃𝐹𝐷𝐼/𝑃𝐺𝐷𝑃 = 𝛽0 + 𝛽1𝐿𝑅𝐸𝑆𝑂𝑡−1 + 𝛽2𝐿𝑇𝐴𝑋𝑡−1 + 𝛽3𝐿𝐼𝑁𝐹𝑅𝐴𝑡−1 + 𝛽4𝐿𝐿𝑇𝐸𝑅𝑅𝑡−1 +

𝛽5𝐿𝑄𝐿𝑡−1 + 𝛽6𝑃𝐹𝐷𝐼𝑆𝑡−1 + ∑ 𝛽7∆𝐿𝑅𝐸𝑆𝑂𝑡−𝑖𝑘𝑖=0 + ∑ 𝛽8∆𝐿𝑇𝐴𝑋𝑡−𝑖

𝑘𝑖=0 +

∑ 𝛽9∆𝐿𝐼𝑁𝐹𝑅𝐴𝑡−𝑖𝑘𝑖=0 + ∑ 𝛽10∆LTERR𝑡−𝑖 +𝑘

𝑖=0 ∑ 𝛽11∆𝐿𝑄𝐿𝑡−𝑖𝑘𝑖=0 + ∑ 𝛽12𝑃𝐹𝐷𝐼𝑆𝑡−𝑖

𝑘𝑖=0 + 𝜀𝑡

(9)

The null and alternative hypotheses are as follows:

The null hypotheses:

0: 6543210 H (no long-run relationship) (10)

Against the alternative hypothesis

0: 6543211 H (a long-run relationship exists) (11)

Secondary Sector:

𝐿𝑆𝐹𝐷𝐼/𝑆𝐺𝐷𝑃𝑡 = 𝛽° + 𝛽1𝐿𝑇𝐴𝑋𝑡 + 𝛽2𝐿𝐼𝑁𝐹𝑅𝐴𝑡 + 𝛽3𝑆𝐹𝐷𝐼𝑆𝑡 + 𝛽4𝐿𝑄𝐿𝑡 + 𝛽5𝐿𝑂𝑃𝐸𝑁𝑡 +

+𝛽6𝐿𝐼𝑁𝐹𝑡 + 𝛽7𝐸𝑋𝐶𝑡 + 𝛽8𝐿𝐸𝑁𝑅𝑡 + 𝛽9𝐿𝑀𝐴𝑁𝑈𝐹𝑡 + 𝜀𝑡 (14)

ARDL equation of the model of the Secondary sector is as follows:

𝐿𝑆𝐹𝐷𝐼/𝑆𝐺𝐷𝑃 = 𝛽0 + 𝛽1𝐿𝑇𝐴𝑋𝑡−1 + 𝛽2𝐿𝐼𝑁𝐹𝑅𝐴𝑡−1 + 𝛽3𝑆𝐹𝐷𝐼𝑆𝑡−1 + 𝛽4𝐿𝑄𝐿𝑡−1 +

𝛽5𝐿𝑂𝑃𝐸𝑁𝑡−1 + 𝛽6𝐿𝐼𝑁𝐹𝑡−1 + 𝛽7𝐸𝑋𝐶𝑡−1 + 𝛽8𝐿𝐸𝑁𝑅𝑡−1 + 𝛽9𝐿𝑀𝐴𝑁𝑈𝐹𝑡−1 +

∑ 𝛽8∆𝐿𝑇𝐴𝑋𝑡−𝑖𝑘𝑖=0 + ∑ 𝛽9∆LINFRAt−1

𝑘𝑖=0 + ∑ 𝛽10∆SFDIS𝑡−𝑖

𝑘𝑖=0 +

∑ 𝛽11∆LQL𝑡−𝑖 +𝑘𝑖=0 ∑ 𝛽12∆𝐿𝑂𝑃𝐸𝑁𝑡−𝑖

𝑘𝑖=0 + ∑ 𝛽13∆𝐿𝐼𝑁𝐹𝑡−𝑖

𝑘𝑖=0 + ∑ 𝛽14

𝑘𝑖=0 ∆EXC𝑡−1 +

∑ 𝛽15∆𝐿𝐸𝑁𝑅𝑡−1 + ∑ 𝛽16∆𝐿𝑀𝐴𝑁𝑈𝐹𝑡−𝑖𝑘𝑖=0

𝑘𝑖=0 + 𝜀𝑡 (15)

To perform the bound testing, the null and alternative hypotheses are as follows:

The null hypotheses:

0: 876543210 H (no long-run relationship) (16)

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Against the alternative hypothesis

0: 876543211 H (a long-run relationship exists) (17)

Tertiary Sector:

𝐿𝑇𝐹𝐷𝐼/𝑇𝐺𝐷𝑃𝑡 = 𝛽° + 𝛽1𝐿𝑇𝐴𝑋𝑡 + 𝛽2𝐿𝐼𝑁𝐹𝑅𝐴𝑡 + 𝛽3𝑆𝐹𝐷𝐼𝑆𝑡 + 𝛽4𝐿𝑄𝐿𝑡 + 𝛽5𝐿𝑂𝑃𝐸𝑁𝑡 +

𝛽6𝐿𝐼𝑁𝐹𝑡 + 𝛽7𝐿𝑆𝐸𝑅𝑉𝑡 + 𝛽8𝐿𝑇𝐹𝐷𝐼𝑆𝑡 + 𝜀𝑡 (18)

ARDL equation of the model of the Tertiary sector is as follows:

𝐿𝑇𝐹𝐷𝐼/𝑇𝐺𝐷𝑃 = 𝛽0 + 𝛽1𝐿𝑇𝐴𝑋𝑡−1 + 𝛽2𝐿𝐼𝑁𝐹𝑅𝐴𝑡−1 + 𝛽3𝑆𝐹𝐷𝐼𝑆𝑡−1 + 𝛽4𝐿𝑄𝐿𝑡−1 +

𝛽5𝐿𝑂𝑃𝐸𝑁𝑡−1 + 𝛽6𝐿𝐼𝑁𝐹𝑡−1 + 𝛽7𝐿𝑆𝐸𝑅𝑉𝑡−1 + 𝛽8𝐿𝑇𝐹𝐷𝐼𝑆𝑡−1 + ∑ 𝛽9∆𝐿𝑇𝐴𝑋𝑡−𝑖𝑘𝑖=0 +

∑ 𝛽10∆LINFRAt−1𝑘𝑖=0 + ∑ 𝛽11∆SFDIS𝑡−𝑖

𝑘𝑖=0 + ∑ 𝛽12L∆QL𝑡−𝑖 +𝑘

𝑖=0 ∑ 𝛽13∆𝐿𝑂𝑃𝐸𝑁𝑡−𝑖𝑘𝑖=0 +

∑ 𝛽14∆𝐿𝐼𝑁𝐹𝑡−𝑖𝑘𝑖=0 + ∑ 𝛽15

𝑘𝑖=0 ∆LSERV𝑡−1 + ∑ 𝛽16∆𝐿𝑇𝐹𝐷𝐼𝑆𝑡−1

𝑘𝑖=0 + 𝜀𝑡 (19)

To perform the bound testing, the null and alternative hypotheses are as follows:

0: 876543210 H (no long-run relationship) (21)

Against the alternative hypothesis

0: 876543211 H (a long-run relationship exists) (22)

7.4 Estimation Results and Interpretation

7.4.1 Unit Root Test

Before applying the ARDL approach to cointegration, the variables in the models

are tested for the unit root. Results of unit root under the ADF of all three models (primary,

secondary and tertiary) are summarized in Table 7.1. It shows the result of the unit root test

at level and at first difference.

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Table 7.1

ADF Unit Root Test on Sectoral Variables

Primary Sector

Variables At Level At First Difference Decision

PFDI -1.69 -2.21** I(1)

RESO -5.11* - I(0)

TAX -1.72 -8.13* I(1)

INFRA -1.92** - I(0)

TERR -2.65*** -4.03* I(0)

QL 0.54 -3.49* I(1)

PFDIS -1.67 -6.12* I(1)

Secondary Sector

SFDI -3.15 -6.84* I(1)

TAX -1.72 -8.13* I(1)

INFRA -1.92** - I(0)

SFDIS -0.78 -7.67* I(1)

QL 0.54 -3.49* I(1)

TO -1.92 -6.99* I(1)

INF -2.36 -6.50* I(1)

EXC -4.26* - I(0)

ENR -0.66 -4.95** I(1)

MUNUF (Market

Size) -3.70** - I(0)

Tertiary Sector

TFDI 1.90 -4.51* I(1)

TAX -1.72 -8.13* I(1)

INFRA -1.92** - I(0)

SFDIS -0.78 -7.67* I(1)

QL 0.54 -3.49* I(1)

TO -1.92 -6.99* I(1)

INF -2.36 -6.50* I(1)

TFDIS 0.06 -6.51* I(1)

SERV (Market size) -0.39 -4.19* I(1)

Note: *, ** and *** indicate the rejection of the null hypothesis of non-stationary at 1%,

5% and 10% significant level, respectively

The unit root findings of all three models (primary, secondary, tertiary) validate the

ARDL model selection. There is no variable stationary at second difference i.e I (2). The

values of F-statistics provided by Pesaran et al. (2001) cannot be interpreted in the presence

155

of variables integrated of order two. Moreover, there is a mixture of order of integration of

variables, I(0) and I(1), and this phenomenon further suits ARDL estimation technique. The

next step is the computation of the F-statistic.

7.4.2 Bounds Testing to Cointegration

Table (7.2) presents the ARDL Bounds test results of all three models estimated.

Table 7.2

ARDL Bounds Test Results on Sectoral Data

Primary Sector

Secondary Sector

Tertiary Sector

F-statistics 10.79 3.69 3.55

Significance

level (%)

Lower

Bound

Value

Upper

Bound

Value

Lower

Bound

Value

Upper

Bound

Value

Lower

Bound

Value

Upper

Bound

Value

5 2.87 4 2.14 3.3 2.22 3.39

Source: Author’s calculation

The computed F-statistics of the primary (10.79), the secondary (3.69) and the

tertiary (3.55) models exceed the upper critical bound values at 5 percent significance level.

The result of the bound co-integration test shows that the null hypothesis ( 0H ) is rejected

against (1H ) at 5 percent significance level. It establishes the presence of long-run relations

among variables. After confirming the existence of the long run relationship, the next step

is to find out both short run as well as long run estimates of all three models.

Following ARDL model specifications for the primary sector (1, 2, 2, 2, 2, 0, 2) the

secondary sector (1, 0, 0, 1, 0, 1, 0, 0, 1, 0) and the tertiary sector (1, 1, 0, 1, 0, 0, 1, 0, 1)

have been estimated.

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7.4.3 Long Term Estimates

The long run estimates are reported in Table (7.3).

Table 7.3

Estimated Long Run Coefficient using the ARDL approach (Sectoral Models)

Regressor Primary Sector

Secondary Sector

Tertiary Sector

Constant -858.04(124.72)* -97.79 (27.78)* -52.18(16.07)*

TAX -77.64(16.01)* -1.67(1.94) -6.43(3.21)***

FDI Stock 0.0027(0.0017) -0.02(0.23) 1.26(0.44)**

INFRA 20.89(14.97) 1.87 (1.30)***

1.79(0.69)**

QL -7.07(6.11)

0.67(1.23) 8.50(2.06)*

RESO 86.12(16.82)*

- -

TERR -2.46(2.83) - -

INF - -0.61(0.32)*** -3.13(0.66)*

TO - 6.67(2.24)* 3.96(0.15)

ENR - 10.75(3.48)* -

EXC - -6.77(2.72)** -

MANUF

(Market Size)

0.58(1.41)

SERV

(Market Size)

- - 1.48(0.35)*

SFDIS - - -0.0051(0.33)

Note: *, ** and *** indicate leve1 of significance at 1%, 5% and 10% respectively. Standard errors

are reported in parentheses.

Primary Sector: Column 1 of Table (7.3) shows the determinants of FDI inflows

to the primary sector in Pakistan. The results reveal that the primary FDI is positively and

significantly associated with the availability of natural resources (RESO), so justifying the

resource-seeking FDI which is driven by the availability of natural resources in host

countries. The findings are in line with the empirical studies (Aseidu, 2006; Dupasquier &

Osakwe, 2006; Deichmann, Eshghi, Haughton, Ayek, & Teebagy, 2003). Primary FDI is

negatively and significantly related to corporate tax rate (TAX). The impact of the quality

157

of infrastructure (INFRA), as measured by the ratio of paved roads to total roads, is positive

but insignificant. Terrorism (TERR) is negatively related to primary FDI but it is

insignificant. Secondary school enrolment, a measure of quality of labor (QL), is negative

but insignificant. The existing FDI stock in the primary sector (PFDIS) is negative but

results are statistically insignificant.

Secondary Sector: Column 2 of Table (7.3) shows the determinants of the

secondary sector FDI in Pakistan. Corporate tax rate, labor quality and existing FDI stock

and market size appear not to influence manufacturing FDI, but inflation and exchange rate

volatility appear to discourage manufacturing FDI. Energy, trade openness and quality of

infrastructure positively influence the manufacturing FDI.

Tertiary Sector: Column 3 of Table (7.3) shows the determinants of the tertiary

sector FDI in Pakistan. Existing services FDI stock (TFDIS), labor quality (QL),

infrastructure (INFRA) and market size (SERV) positively and significantly influence

services FDI. Corporate tax rate (TAX) and inflation rate (INF) are negatively and

significantly associated with services FDI. Trade openness (TO) is positively and existing

manufacturing stock (SFDIS) is negatively associated with services FDI, but both results

are insignificant.

Based on the long run estimated results, it can be concluded here that variables such

as corporate tax rate and availability of natural resources are significant determinants in the

primary sector. In the secondary sector, inflation, exchange rate volatility, energy, trade

openness, and quality of infrastructure are the major determinants whereas existing services

FDI stock, labor quality, infrastructure, market size, corporate tax rate, and inflation are the

significant determinants of the services FDI.

158

7.4.4 Short Run Estimates

The short run estimates of the primary sector model are reported in Table (7.4).

Table 7.4

Estimated Short Run Coefficient using the ARDL approach (Primary Sector)

ARDL (1, 2, 2, 2, 2, 0, 2) based on Schwarz criterion (SIC)

Variable Coefficient Std. Error t-Statistic

DLOG(RESO) -2.93 22.76 -0.13

DLOG(RESO(-1)) 49.56** 20.36 2.43

DLOG(TAX) 14.04** 5.51 2.55

DLOG(TAX(-1)) -30.72** 10.44 -2.94

DLOG(INFRA) 4.64 4.84 0.96

DLOG (INFRA(-1)) -8.51*** 4.70 -1.81

DLOG(TERR) -20.52* 5.15 -3.99

DLOG(TERR(-1)) -9.60** 4.06 -2.37

DLOG(QL) -5.96 4.59 -1.30

D(PFDIS) -0.001456 0.001012 -1.44

D(PFDIS(-1)) -0.004765** 0.001575 -3.04

CointEq(-1) -0.84* 0.14 -6.23

Note: *, ** and *** indicate leve1 of significance at 1%, 5% and 10% respectively.

The equilibrium correction coefficient estimated has the required negative signs (-

0.84) and is highly significant at 1 percent level of significance. It demonstrates a fairly-

high speed of adjustment to equilibrium after a shock. The results imply that the model is

corrected from the short-run towards the long-run equilibrium by approximately 84 percent,

an annual rate of adjustment to equilibrium.

The short run estimates of the secondary sector model are reported in Table (7.5).

The equilibrium correction coefficient estimated has the required negative signs (-0.76) and

is highly significant at 1 percent level of significance. It demonstrates a fairly-high speed of

adjustment to equilibrium after a shock. The results imply that the model is corrected from

159

the short-run towards the long-run equilibrium by approximately 76 percent, an annual rate

of adjustment to equilibrium.

Table 7.5

Estimated Short Run Coefficient using the ARDL approach (Secondary Sector)

ARDL(1, 0, 0, 1, 0, 1, 0, 0, 1, 0) based on Schwarz criterion (SIC)

Variable Coefficient Std. Error t-Statistic

DLOG(TAX) 1.27 1.45 0.88

DLOG(INFRA) 1.43 1.07 1.34

DLOG(SFDIS) 0.93* 0.24 3.89

DLOG(QL) 0.51 0.90 0.57

DLOG(TO) 0.51 1.11 0.46

DLOG(INF) -0.47** 0.22 -2.08

D(EXC) -5.17*** 2.61 -1.98

DLOG(ENR) 10.01** 4.77 2.10

DLOG(MANUF) -0.45 1.04 -0.43

CointEq(-1) -0.76* 0.14 -5.60

Note: *, ** and *** indicate leve1 of significance at 1%, 5% and 10% respectively.

The short run estimates of the tertiary sector model are reported in Table (7.6).

Table 7.6

Estimated Short Run Coefficient using the ARDL approach (Tertiary Sector)

ARDL(1, 1, 0, 1, 0, 0, 1, 0, 1) based on Schwarz criterion (SIC)

Variable Coefficient Std. Error t-Statistic

DLOG(TAX) -0.18 0.53 -0.34

DLOG(INFRA) 1.27* 0.38 3.37

DLOG(SFDIS) 0.34 0.24 1.43

DLOG(QL) 6.00* 0.93 6.48

DLOG(TO) 2.80 1.74 1.61

DLOG(INF) 0.89 0.55 1.62

DLOG(TFDIS) 0.89** 0.42 2.09

DLOG(SERV) 0.03 0.26 0.11

CointEq(-1) -0.71* 0.13 -5.24

Note: *, ** and *** indicate leve1 of significance at 1%, 5% and 10% respectively.

160

The equilibrium correction coefficient estimated has the required negative signs (-0.71) and

is highly significant at 1 percent level of significance. It demonstrates a fairly-high speed of

adjustment to equilibrium after a shock. The results imply that the model is corrected from

the short-run towards the long-run equilibrium by approximately 71 percent, an annual rate

of adjustment to equilibrium.

7.4.5 Diagnostics and Stability Tests

The ARDL models have passed all the necessary diagnostic tests such as serial

correlation (Breusch-Godfrey Test), normality (Jarque-Bera Test), heteroskedasticity

(ARCH Test) and functional form misspecification (Ramsey RESET Test). Results are

presented in Table (7.7). All the above-mentioned tests and their results establish that all

three models have the aspiration of econometric properties; the model’s residuals have no

serial correlation issue, they are normally distributed and homoskedastic. So, reliable

interpretations can be drawn from the results.

Table 7.7

Results of Diagnostics Tests on Sectoral Data

Note: p-values are in parenthesis

161

Furthermore, the cumulative sum of recursive residuals (CUSUM) and the

cumulative sum of squares residuals (CUSUMSQ) tests are performed to assess the

parameter stability (Pesaran and Pesaran, 1997). Figure (7.1), (7.2) and (7.3) present the

graphs of CUSUM and CUSMSQ for stability of parameters of primary, secondary and

tertiary sectors respectively. The results show that the plots of the CUSUM and CUSUMSQ

statistic fall inside the critical bands of the 5 percent confidence interval of parameter

stability, thus indicating the absence of any instability of the coefficients. Therefore, it

establishes that stability exists in the coefficients over the sample period.

162

7.5 Discussions

Primary Sector:

The primary sector comprises of agriculture, hunting, forestry and fishing mining,

quarrying, and petroleum. According to the Economic Survey of Pakistan (2015-2016), the

mining and quarrying contain 14.19 percent share of the industrial sector in Pakistan. This

sub-sector witnessed a growth of 6.80 percent as compared to 4.81 percent for the last year.

The agriculture sector recorded a negative growth of -0.19 percent against the growth of

2.53 percent for the last year. The decline in growth was due to drop in the production of

cotton, rice, maize and other minor crops due to extreme weather. The agriculture sector has

some restriction for foreign direct investors as 100 percent foreign equity is allowed only in

the Corporate Agriculture Farming (CAF) on the case-by-case basis. Foreign investment in

this sector is subject to a minimum of US$ 0.3 million. There are no foreign firms currently

operating in this sector (The World Bank, 2017). The primary sector is labor intensive and

is natural resource driven. Resource-seeking FDI is motivated by the availability of natural

resources in host countries. The result shows that the proxy of natural resources is positively

and significantly related to FDI inflow to this sector. The finding is in line with empirical

studies. Previously, studies (Aseidu, 2006; Dupasquier & Osakwe, 2006) show that natural

resources in African countries attract more FDI. The availability of natural resources in

163

transition economies of Euro-Asia has also been a great attraction for foreign investors

(Deichmann et al. 2003).

The relationship of the existing FDI stock with FDI in the sector is positive but

statistically insignificant. According to the UNCTAD statistics, net primary FDI stock is in

negative in Pakistan. Pakistan has abundant natural resources but they are somewhat

untapped. One of the reason could be infrastructure. Natural resources are located in remote

and far-flung areas and there is a lack of road infrastructure. The terrorism proxy is

insignificant in the long run but significant and negative in the short run. It implies that

terrorist incidents pose a high risk to the foreign investors but as FDI is a long-term

investment, so terrorism does not deter foreign investors. Historical data also show the

primary sector has been receiving considerable inflows of FDI since 2000 (Chapter 3,

Section 3.3.2).

Secondary Sector:

Manufacturing is reckoned as the second largest sector of Pakistan’s economy,

accounting for 13.6 percent GDP. It is also the second largest sector of FDI recipient. Power

shortage has been hampering the growth of this sector (MOF, 2016). The result also shows

that energy is positively and significantly related to FDI in the sector. Electricity is the major

input for the manufacturing industry. Its shortage is considered to be the greatest obstacle

in doing business in Pakistan31. There is a critical need for sufficient, reliable and affordable

energy supply. Further, a higher degree of openness attracts more FDI into the

manufacturing sector. In the category of efficiency-seeking FDI, foreign capital moves to

countries which are more open to international trade. Greater is the degree of openness,

lower is the level of restrictions enforced by the host location on international trade. Thus,

31 For details, see Chapter 8, Section 8.2.2.

164

it ultimately leads to the lower cost of doing business. Therefore, a country that imposes

fewer restrictions on international trade is considered more lucrative destination for foreign

investors (Chakrabarti, 2001; Pistoresi, 2000). The manufacturing sector is export-oriented

in nature and it is more concerned with the openness of the economy. It imports essential

inputs for industrial productions from abroad and after necessary processing, it tends to

export the finished goods to other foreign locations. Previous studies have found the similar

results. Awan, Khan, and Zaman (2011) find trade openness as significant determinants of

FDI inflows to the commodity-producing sector of Pakistan. Ramasamy & Yeung (2010)

also find a positive association between degree of openness and manufacturing FDI for the

OECD countries.

Infrastructure has been found as positive and significant determinants of

manufacturing FDI. Finding has got an endorsement from other empirical studies on the

topic (Ramasamy & Yeung, 2010; Tsen, 2005). A quality infrastructure available in the

form of roads networks facilitates the movements of input from the sea or dry ports to the

industrial plant locations and subsequently finished goods to the sea or dry ports for delivery

to other locations. The positive and significant association between manufacturing FDI and

infrastructure prove that countries with an established infrastructure would receive a higher

amount of FDI inflows to the manufacturing sector.

The manufacturing/secondary sector is primarily an export-oriented in nature that’s

why it is more prone to exchange rate fluctuations than the services sector. FDI in the

manufacturing sector is mainly associated with capital imports and exports of output to the

international market, while services sector is mainly targeted at the domestic market.

Therefore, FDI in this sector is heavily exposed to exchange rate uncertainty (Polat and

Payaslıoglu, 2014). It is confirmed by the results with a negative and significant relationship

between manufacturing FDI and exchange rate volatility. The results are in line with other

165

empirical studies (Kandilov & Leblebicioglu, 2011). Exchange rate instability is considered

to be a risk factor for foreign investors because it creates an uncertain environment about

the future benefits and costs of irreversible investment projects. The stability of exchange

rate enhances certainty in domestic economy hence it increases investment probability in

the current time and future as well. Expanded exchange rate fluctuations make expanded

changes in assets value, so projects appraisal becomes difficult (ShahAbadi and Mahmoodi,

2006). Dixit and Pindil (1994) provide the justification of the negative effect of exchange

rate volatility on FDI. A country with more exchange rate volatility would generate a riskier

stream of profit.

Inflation, as a measure of macroeconomic stability, appears to discourage foreign

investors in the manufacturing sector. The finding is in line with other empirical studies

(Tsen, 2005). Inflation points out internal economic tension and the government’s

unwillingness in balancing its budget and failure of the central bank for having an effective

monetary policy (Schneider and Frey,1985). Likewise, inflation negatively impacts

manufacturing output as its increase leads to decrease in manufacturing productivity (Odior,

2013). With the reduction in production, employment opportunities are also reduced and

may lead to shutting down of manufacturing plants during the time of high inflation (Gumbe

& Kaseke, 2011). High inflation is not only stemmed from instruments of monetary policy

(money supply and interest rate) but comes from instruments of fiscal policy (government

revenue and expenditure) (Van Bon, 2015). The fiscal deficit is one of the determinants of

high inflation (Fischer, Sahay & Vegh, 2002). As discussed earlier, the Government of

Pakistan has been relying on domestic debt to finance its budget deficits. This mode of

financing is inflationary in nature.

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Tertiary Sector:

The share of the services sector in GDP of Pakistan is 60 percent and it has witnessed

a growth of 6 percent in the year 2016-2017 (MOF, 2017). At the global level, the services

sector dominates with 64 percent share of the global FDI stock in 2014 (Figure 3.4). The

contribution of FDI into the service sector in Pakistan is well witnessed from the magnitude

of inflows to this sector (Chapter 3, Section 3.3.2). The empirical results reveal that

corporate tax rate, existing services FDI stock, labor quality, infrastructure, market size, and

inflation are the significant determinants of the services FDI.

Corporate tax rate is deterring FDI inflows to the services sector of the country.

Taxation falls in the domain of the fiscal policy. Recently, the Government of Pakistan

compromised tax equity by imposing minimum 8 percent tax on services (Anjum, 2018,

April 15).

Existing services FDI stock positively and significantly influence the services FDI.

The comparative advantage of a particular sector increases with the presence of

agglomeration and more FDI flows are attracted to that sector. This effect has been found

for both the manufacturing and services sectors (Gross, Raff, and Ryan, 2005). Foreign

investors in a host location confront more uncertainties in comparison to local firms and

along these lines have a solid motivating force to follow past investors whose presence

might be viewed as a sign of trustworthiness in that particular location (Barry, Gorg, &

Strobl, 2003). It is considered less risky and less costly for investors to add to the existing

stock of any specific location (Billington, 1999). As a result, there is a positive connection

between investment in a market and the probability of further investment in the same market.

Once the firm is set up in a specific foreign market, lower transaction costs, learning benefits,

and reduced uncertainty from the existing investments can be acknowledged through other

projects in the same location. In addition, existing investors could offer opportunities to the

167

potential investors to have forward as well as backward links with them. This would provide

an additional attraction to foreign investors (Yin et al. 2014).

Labor quality is the significant determinant of the services FDI inflows to Pakistan.

The services sector is mainly a labor-intensive industry and hence the quality of labor is a

determining factor for profitability (Ramasamy & Yeung, 2010). The service sector in

contrast to the manufacturing sector usually has higher prerequisites on human capital with

some highly skilled and experienced employees; this is particularly the case in areas, for

example, insurance, banking, IT services and consultancy. Despite the fact that Pakistan is

an agricultural country, however, because of urbanization, employment opportunities are

diminishing and peoples are exploring other sectors for better employment. The services

sector offers diverse jobs where unskilled, semi-skilled, and highly skilled people are

accommodated. The share of this sector has increased from 27 percent in 1973 to 34.5

percent in 2009 (Ahmed & Ahsan, 2011).

Infrastructure is also the significant determinant of the services FDI inflows to

Pakistan. It reduces the operational costs of the firms (Rehman et al. 2011). Therefore, the

efficiency-seeking FDI is attracted by the presence of the quality infrastructural system in

the host location. Host locations with established infrastructural systems receive a higher

flow of services FDI (Ramasamy and Yeung, 2010). This sector depends on the

infrastructural networks to serve its customers. Therefore, an efficient transportation and

communication system is essential for the attraction of FDI flows to this sector.

While openness matters for the secondary sector FDI, it does not matter for the

services FDI in Pakistan as the empirical result revealed. Some empirical studies

demonstrate that degree of bilateral trade is the significant determinant of services FDI

especially for financial services (Buch and Lipponer, 2004; Moshirian, 2001). But Kolstad

and Villanger (2008) view differently and argue that services FDI is market seeking in

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nature and it is seldom affected by the trade openness. As a result of their characteristics,

several services are costly to trade or non-tradable. The sector whose products are not

exposed to cross-border trade or it is restricted to serve the local market, the host country's

trade openness might have less impact on the FDI flow to that sector. That’s why the effect

of trade openness is a matter of an open inquiry (Yin et al. 2014). FDI in the service sector

is horizontal that targets the market where investment is made. It is not vertical that is export-

oriented in nature (Walsh and Yu, 2010).

The services sector is also affected by inflation in the country. As discussed in the

case of the manufacturing sector, inflation, as a measure of macroeconomic stability, also

appears to discourage foreign investors in the service sector. Generally, a high rate of

inflation in a country can reduce the profit margin of the firms. It is also considered an

indicator of macroeconomic instability in the country as it points out internal economic

tension, the government’s unwillingness in balancing its budget and failure of the central

bank for having effective monetary policy (Schneider and Frey,1985)

Market size also clearly matters for the services FDI. Services GDP as a measure of

market size matters for FDI inflows to the sector. The results are in line with empirical

studies (Ali et al. 2013; Ramasamy and Yeung, 2010). A large consumer base is considered

to be an attraction for the services FDI (Raff and von der Ruhr, 2001). Moreover, the market

seeking FDI is attracted by the presence of a large market. The services are non-tradable or

costly to trade and they are usually aimed at serving the local market. Therefore,

insignificant empirical results of trade openness (TO) variable complement the market size

(SERV) variable in the analysis.

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7.6 Conclusions

The current Chapter examines the long-run and short-run relationships among the

sectoral FDI inflows (primary, secondary, tertiary) and sector specific variables in Pakistan

for the (1984–2015) period. Methodologically, it utilizes the ADF test for testing stationarity

level. The ARDL bound testing approach has been employed to analyze the long-run and

short-run relationships among variables of interest at sectoral levels. The estimations

indicate that variables such as corporate tax rate and availability of natural resources are

significant determinants in the primary sector. In the secondary sector, inflation, exchange

rate volatility, energy, trade openness and quality of infrastructure are the major

determinants whereas existing services FDI stock, labor quality, quality of infrastructure,

market size, corporate tax rate, and inflation are the significant determinants of the services

FDI. Government’s macroeconomic policies seem to be major location advantages to MNEs.

The research findings have a number of policy implications for Pakistan. The role

of the State Bank of Pakistan as an institution responsible to control inflation and

maintaining the stability of the currency is significant. The instability in these two indicators

highlights the macroeconomic and financial vulnerability. Corporate tax rate falls in the

domain of the fiscal policy. It is deterring FDI inflows to the service sector which is the

largest contributor to GDP of the country. Recently, the Government of Pakistan

compromised tax equity by imposing minimum 8 percent tax on services. Lowering

corporate tax rate would imply lower tax/revenue collection. Government has already been

relying on domestic debt to finance its the budget deficit. So, prudent decision would be to

enlarge the tax base. It would also relax SBP as domestic borrowing is inflationary in nature.

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CHAPTER 8

INVESTMENT OBSTACLES: A FIRM LEVEL ANALYSIS

8.1 Introduction

The objective of this chapter is to find out the obstacles faced by the firms in Pakistan.

The chapter uses the World Bank’s Enterprise Survey data 2013. It evaluates the business

environment on fifteen different factors which impact the businesses by influencing

investors’ decision in choosing investment locations. Section 8.2 provides the WB’s

Enterprise Survey 2013 data analysis and Section 8.3 presents conclusion.

8.2 The World Bank Enterprise Survey 2013 Data Analysis

At firm level, the dissertation has used the World Bank’s the Enterprise Survey 2013.

8.2.1 Sample Characteristics

The World Bank has collected data from firms operating in Pakistan. Table (8.1)

presents frequencies of demographics of the selected sample. Firm size includes small,

medium and large firms. The small firms which participated are 509, medium-sized firms

are 471 while large firms are 267. There are two major sectors of the economy covered in

the survey, manufacturing and services. The manufacturing sector comprises of 1086 firms

while the services sector consists of 161 firms in the sample. Domestically owned firms are

1208 and 38 are the foreign-owned firms that have been included in the sample. From the

type of firms, there are 159 exporters while 1088 are non-exporters.

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Table 8.1

Sample Characteristics of WB’s Enterprise Survey 2013

Total 1247

Micro/Small (<20 employees) 509

Medium (20-99 employees) 471

Large (100+ employees) 267

Manufacture 1086

Services 161

Domestically owned 1208

Foreign owned 38

Exporters 159

Non-Exporters 1088

Location

Punjab (668)

Sindh (215)

Khyber-Pakhtunkhwa (212)

Islamabad (91)

Baluchistan (61)

Source: Enterprise Survey 2013, the World Bank

8.2.2 Investment Obstacles faced by Firms

The WB’s Enterprise Survey 2013 seeks the responses from 1247 firms regarding

different factors as obstacles in doing business in Pakistan. It has a number of indicators to

assess the business environment in the country. But to meet the objective of dissertation,

two measures “how much of an obstacle” and “the biggest obstacle” have been selected for

the analysis. Because these two measures will assess the business environment and provide

insight into factors which are obstructing investment in the country. For that matter, fifteen

different factors which impact the businesses by influencing investors’ decision in choosing

investment have been selected. To collect data of the first measure, the World Bank has

used a 5-point Likert scale to ascertain the answers from the representatives of firms

operating in Pakistan. Each item has been given a specific value as given below:

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Very Severe Obstacle =4

Major Obstacle =3

Moderate Obstacle =2

Minor Obstacle =1

No obstacle =0

Table (8.2) shows the frequencies, mean and standard deviation against each factor.

Mean of only two factors ‘electricity’ and ‘corruption’ is greater than 2. Electricity with the

highest mean score (3.28) is the obstacle that affects the operation of establishments. 59.3

percent firms consider electricity as a very severe obstacle. Next is corruption, the results

reveal that corruption with mean score (2.09) is also the obstacle. All other factors have less

than 2 mean score, ‘tax rates’ (1.85), ‘crime, theft and disorder’ (1.57), ‘political instability’

and ‘transport’ with mean score (1.52). The remaining factors have less than 1 mean score.

173

Table 8.2

Investment Obstacles faced by Firms

174

The World Bank Enterprise Survey 2013 considers shortage of electricity as the

major obstacle in doing business in Pakistan. Moreover, poor electricity supply has been

shown to have adverse effects on firms’ productivity and the investments they make in their

productive capacity, thereby hindering the local economy (Geginat & Ramalho, 2015). By

calculating frequencies of responses, for electricity as an obstacle for firms in operating

business, Table (8.3) finds 740 firms considering electricity as a very severe obstacle while

a small number finds electricity as no obstacle in the operations of a firm.

Table 8.3

Electricity as an Obstacle

The World Bank Enterprise Survey 2007 also revealed electricity as a major

constraint for firms in Pakistan. The findings of the WB’s Enterprise Survey 2013 further

reveal that the bureaucratic efficiency has improved in awarding electricity connection to

firms from 106.3 days to 82.8 days in 2013. Electric power outages cost Pakistani firms an

average of 21.2 percent of their annual sales, and 65.4 percent firms owned generators as

alternate for power supply. Electricity outage has been worst in 2013 against the findings of

the previous survey. Firms face an average of 13.2 hours of outage and the number electrical

outage in a month reaches to 75.2 in 2013 against 31.7 of 2007.

At the sectoral level, both manufacturing and services sectors consider electricity

shortfall as the major obstacle in doing business in Pakistan. It is the manufacturing sector

that is facing more power outages and the situation has become the worst in 2013 with 96.4

percent electrical outages in a month. As a consequence of electrical outages, an extra cost

175

has been incurred on firms to tackle the issue of electricity supply by using generators; in

manufacturing sector, 59.3 percent whereas 71.6 percent firms in service sector own or share

generators. Because of electricity shortage, firms face losses upto 18.8 percent and 23.6

percent as of annual sales, in the manufacturing and the service sector respectively.

The second obstacle faced by the firms is corruption. Corruption increases the cost

for firms as they have to pay bribes to public officials for their works to be done. 376 firms

consider corruption as a severe obstacle in running a business (Table 8.4).

Table 8.4

Corruption as an Obstacle

Corruption of public officials can represent a serious financial and administrative

burden on firms. It creates an environment where the operational efficiency of the firms is

undermined as it increased the costs and risks attached to businesses. Firms have to pay

bribes to public officials to get things done. It creates an impediment to the growth of firms.

The World Bank has constructed a corruption indicator, the Graft Index which is a

composite index of corruption that reflects the proportion of instances in which firms have

either been expected or requested to bribe public officials while applying for six different

public services (The World Bank, 2013). These services along with the Graft Index are

mentioned in Table (8.5). The Index is based on the Enterprise Survey 2013 data. The results

have been obtained from the website (http://www.enterprisesurveys.org) by generating

customized enquiries. The Index reflects the experience of the firms regarding the payment

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of bribe in getting essential six services for the business performance. It is more appropriate

in comparison to other measures of corruption. Most notably, the index is based on the

experiences of the firms, not on the perception of the managers about the level of corruption.

Further, it relies on the primary data which have been gathered through a nationally

representative survey (Gonzalez, Ernesto Lopez-Cordova & Valladares, 2007).

Table 8.5

Corruption Indicators at Firms Level

Pakistani firms have experienced more corruption in comparison to the regional

average of South Asia. The Index strongly indicates that firms in Pakistan are vulnerable to

graft. As shown in Table (8.5), entrepreneurs in Pakistan experience bribe on average 30.8

percent and it is higher in the world (17.8 %) and in the region of South Asia (24.8 %).

According to Enterprise Survey data, the same graft index was recorded 60.2 percent in

2007. So, firms experience less bribe now in comparison to 2007. But the performance is

still poorer in comparison to other four countries namely Bhutan (0.9 %), India (22.7 %),

Nepal (14.4 %) and Sri Lanka (10.0 %) and better than Afghanistan (46.8%) and Bangladesh

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(47.7 %). The results also show that bribery is more common when requesting for the

electricity connection (57.8 %).

The sectoral data reveal that bribery is more common in the manufacturing sector

than in the services sector as shown in Table (8.6). Bribery is frequently demanded while

firms request for the electricity connection (76.6 %). Electricity is the major input in

industrial products.

Table 8.6

Incidence of Corruption at Sectoral Level

Among sub-sectors of the manufacturing and the services, the firms relating to the

business of non-metallic and mineral products have experienced more bribery (47.1%) and

textile sector (42.7%) is the next in experiencing bribe (Figure 8.1).

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Figure 8.1. Incidence of Corruption at Sub-Sectoral Level

Source: The World Bank’s Enterprise Surveys Portal (http://www.enterprisesurveys.org)

The results show that medium-sized firms (with 20-99 employees) have experienced

more bribe in comparison to small and large-sized firms (Table 8.7). Among small and large

sized firms bribe is very common while getting the electrical connections (78.6 %) and

(71 %) and operating license (61.4 %) and (69.7 %).

Table 8.7

Incidence of Corruption among different sizes of firms

35.8

42.7

14.5

29.7

47.1

10.5

30.6

5.4

37.4

0.05.0

10.015.020.025.030.035.040.045.050.0

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Firms operating in the province of Baluchistan are more vulnerable to bribe than to

other locations of Pakistan, followed by Islamabad (39.5 %), Punjab (34.2 %), Sindh (28 %)

and KPK (227.7 %) (Figure 8.2).

Figure 8.2. Incidence of Corruption at different Regional Locations

Source: The World Bank’s Enterprise Surveys Portal (http://www.enterprisesurveys.org)

The results further reveal that the foreign firms operating in Pakistan experience

more bribe than the domestic firms (Figure 8.3).

Figure 8.3. Incidence of Corruption and Firms Ownership

Source: The World Bank’s Enterprise Surveys Portal (http://www.enterprisesurveys.org)

The results can be summed up as the graft incidence has decreased in Pakistan from

60.2 percent in 2007 to 30.8 in 2013. But it is still higher in comparison to the world and

72.0

39.5

27.734.2

28.0

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

Balochistan Islamabad Khyber-Pakhtunkhwa Punjab Sindh

30.4

52.5

0.0

10.0

20.0

30.0

40.0

50.0

60.0

Domestic 10% or more foreign ownership

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the South Asian regional averages. Among different public services, getting the electricity

connection is more problematic as the incidence of the bribe is higher. Further, the

manufacturing sector has experienced more bribe than the services sector. Again, the

electricity connection is the vulnerable area for bribery. Among sub-sectors of the

manufacturing and the services, the firms relating to the business of non-metallic and

mineral products experience more bribery, followed by the textile sector. The region,

Baluchistan is experiencing more bribe. Lastly, the MNEs operating in Pakistan experience

more bribe than the domestic firms.

Different components of corruption mentioned in the graft index fall in the category

of “petty corruption”. The other form of corruption is “grand corruption” which consists of

public trust in politician, legal political donation and bureaucratic red-tapism. It is petty

corruption which is detrimental for foreign investment in the country (Lambsdorff, 2004).

Furthermore, the control of corruption, an indicator among six Worldwide Governance

Indicators, consists of both petty and grand forms of corruption. And the performance of

Pakistan in controlling corruption is poor among the South Asian countries and it is only

better than Bangladesh. The Global Competitiveness Report 2015-16 declares ‘corruption’

as the most problematic factor of doing business in Pakistan (Chapter 2, Section 2.3.3). This

Graft Index is more valuable as it gives the hard data of firms experiencing bribe in Pakistan.

It is not based on the perception of the respondents.

Among 15 obstacles, 13 have got mean less than 2. These factors have been

described and discussed in the subsequent paragraphs.

Transportation is also an important factor in the operations of a business and it is.

284 firms do not find it as an obstacle for businesses and a very small number of firms face

it as a severe obstacle whereas 321 firms find it as a minor obstacle, 301 as a moderate

obstacle and 234 as a major obstacle (Table 8.8).

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Table 8.8

Transportation as an Obstacle

An efficient infrastructure in place is the requisite for the smooth movement of input

⁄ output from source to plant and to port (Yin et al. 2014). A strong infrastructure increases

the competitiveness of the economy and creates a conducive business environment

necessary for the growth and development of firms. It effectively connects companies with

their customers and suppliers and allows the use of modern production technology. On the

contrary, infrastructure gaps create barriers to opportunities and higher production costs for

all firms, from small firms to large MNEs. Good infrastructure positively affects

investments decisions. Poor transportation conditions and more cost results as a major

constraint for firms. The results obtained from the WB’s Enterprise Surveys website further

reveal that infrastructure condition has slightly deteriorated in Pakistan from 14.2 percent

identifying transportation as a major constraint in 2007 to 25.5 percent in 2013. While

comparing the scenario with other countries in South Asia and in the world, the condition

was better in 2007 (14.2%).

To promote, operate a business, and to attract foreign investment firms focus on

customs and trade regulations and need a favorable regulation but sometimes these

regulations become hurdles for firms depending on the nature of regulations. When firms

were asked about customs and trade regulations as an obstacle, a majority of the firms (395)

responded as these regulations are not an obstacle in doing business and only 107 firms

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considered it as a severe obstacle (Table 8.9). The results obtained from the WB’s Enterprise

Surveys website further reveal that the average days to clear exports through customs take

11.4 days in Pakistan whereas firms take 8.7 days, on average, in South Asia and 7.7 days

in the world.

Table 8.9

Customs and Trade Regulations as an Obstacle

Table (8.10) shows the findings on the obstacle ‘practices of competitors in informal

sector’. It is not considered as an obstacle by 379 firms and only 55 firms out of 1247 firms

find it as an obstacle for their business.

Table 8.10

Practices of competitors in informal sector as an obstacle

The accessibility and availability of land is an important factor for performing

business. 547 firms do not find access to land as an obstacle and is considered as a minor

obstacle for businesses while it is a severe obstacle for only 65 firms (Table 8.11).

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Table 8.11

Access to Land as an Obstacle

Frequency Percent Cumulative Percent

Don’t Know 13 1.0 1.0

Does Not Apply 9 .7 1.8

No Obstacle 547 43.9 45.6

Minor Obstacle 250 20.0 65.7

Moderate Obstacle 225 18.0 83.7

Major Obstacle 138 11.1 94.8

Severe Obstacle 65 5.2 100.0

Total 1247 100.0

Source: Author’s computation from the WB’s Enterprise Survey 2013 data

Finance is a significant factor in pursuing and operating a business. Its access in

Pakistan has not been considered as a severe obstacle by most of the firms (392) and only

72 firms consider it as a severe obstacle (Table 8.12).

Table 8.12

Access to Finance as an Obstacle

Frequency Percent Cumulative Percent

Don’t Know 33 2.6 2.6

Does Not Apply 9 .7 3.4

No Obstacle 392 31.4 34.8

Minor Obstacle 313 25.1 59.9

Moderate Obstacle 311 24.9 84.8

Major Obstacle 117 9.4 94.2

Severe Obstacle 72 5.8 100.0

Total 1247 100.0

Source: Author’s computation from the WB’s Enterprise Survey 2013 data

Responses regarding crime, theft, and disorder as a hurdle to business are presented

in Table (8.13). Majority of the firms (386) consider crime, theft, and disorder as no obstacle

to their business. Only 170 firms consider this factor as a severe obstacle. The results based

on the weighted average estimates extracted from the WB’s Enterprise Surveys website

further reveal that the crime situation has improved slightly from 35.2 percent in 2007 to

34.1 percent in 2013. But crime is still a higher constraint for doing business in Pakistan in

comparison to the South Asian region (17.7%) and the world average (20.4%). Due to crime,

184

theft and disorder firms not only experience losses but also have to pay an opportunity cost

in form of security; 51.7% firms have to pay for securities whereas the situation was slightly

better in 2007 with 48.7%. Statistics show that firms operating in Pakistan experience fewer

losses due to theft and vandalism in comparison to the average of South Asia region and the

World.

Table 8.13

Crime, Theft, and Disorder as an Obstacle

Frequency Percent Cumulative Percent

Don’t Know 1 .1 .1

Does Not Apply 3 .2 .3

No Obstacle 386 31.0 31.3

Minor Obstacle 283 22.7 54.0

Moderate Obstacle 191 15.3 69.3

Major Obstacle 213 17.1 86.4

Severe Obstacle 170 13.6 100.0

Total 1247 100.0

Source: Author’s computation from the WB’s Enterprise Survey 2013 data

Table (8.14) shows responses of firms for tax rate as an obstacle and 313 firms

consider tax rates as a major obstacle in operating a business and 229 firms find tax rate as

a severe obstacle.

Table 8.14

Tax Rates as an Obstacle

Frequency Percent Cumulative Percent

Don’t Know 12 1.0 1.0

Does Not Apply 17 1.4 2.3

No Obstacle 223 17.9 20.2

Minor Obstacle 222 17.8 38.0

Moderate Obstacle 231 18.5 56.5

Major Obstacle 313 25.1 81.6

Severe Obstacle 229 18.4 100.0

Total 1247 100.0

Source: Author’s computation from the WB’s Enterprise Survey 2013 data

Table (8.15) represents that tax administration is not an obstacle for most of the firms

(342) and only 132 consider it as a severe obstacle in doing business and 297 firms consider

185

tax administration as a minor obstacle. The results obtained from the WB’s website further

reveal that this factor has been observed as a major constraint in case of Pakistan (54.1%)

in comparison to the regional average of South Asia (26.4%) and the world (30.3 %). And

the situation has worsened from 2007 (40.1%) to 2013 ((54.1%). At sectoral and further at

sub-sector levels, tax rates make hurdles as well. But it is the services sector (63.1 %) than

the manufacturing sector (46.9%) that considers tax rates as a major constraint in doing

business in Pakistan. In the manufacturing sector, it is the garments industries (66 %) that

identify tax rates as a major impediment. These results based on weighted average estimates

are in line with the sectoral analysis conducted in Chapter 7 where the corporate tax rate is

negatively associated with FDI inflows. And it is the service sectors where corporate tax

rate discourages foreign investments.

Table 8.15

Tax Administrations as an Obstacle

Frequency Percent Cumulative Percent

Don’t Know 37 3.0 3.0

Does Not Apply 18 1.4 4.4

No Obstacle 342 27.4 31.8

Minor Obstacle 297 23.8 55.7

Moderate Obstacle 235 18.8 74.5

Major Obstacle 186 14.9 89.4

Severe Obstacle 132 10.6 100.0

Total 1247 100.0

Source: Author’s computation from the WB’s Enterprise Survey 2013 data

Business licensing and permits, a key to operate a business, is not considered as an

obstacle by most of the firms (417), according to the findings of the survey (Table 8.16).

Only 116 firms consider it a severe obstacle.

186

Table 8.16

Business Licensing and Permits as an Obstacle

Frequency Percent Cumulative Percent

Don’t Know 38 3.0 3.0

Does Not Apply 19 1.5 4.6

No Obstacle 417 33.4 38.0

Minor Obstacle 268 21.5 59.5

Moderate Obstacle 226 18.1 77.6

Major Obstacle 163 13.1 90.7

Severe Obstacle 116 9.3 100.0

Total 1247 100.0

Source: Author’s computation from the WB’s Enterprise Survey 2013 data

The findings on the Political instability reveal that majority of the firms (270) do not

consider it obstacle (Table 8.17). 247 firms find political instability as a severe obstacle.

Table 8.17

Political Instability as an Obstacle

Frequency Percent Cumulative Percent

Don’t Know 27 2.2 2.2

Does Not Apply 29 2.3 4.5

No Obstacle 270 21.7 26.1

Minor Obstacle 233 18.7 44.8

Moderate Obstacle 207 16.6 61.4

Major Obstacle 234 18.8 80.2

Severe Obstacle 247 19.8 100.0

Total 1247 100.0

Source: Author’s computation from the WB’s Enterprise Survey 2013 data

Table (8.18) shows frequencies of responses relating to courts as an obstacle.

Findings show 395 firms do not consider this factor as an obstacle while 234 find it a minor

obstacle and only 115 firms consider it a severe obstacle in their businesses.

187

Table 8.18

Courts as an Obstacle

Frequency Percent Cumulative Percent

Don’t Know 42 3.4 3.4

Does Not Apply 56 4.5 7.9

No Obstacle 395 31.7 39.5

Minor Obstacle 234 18.8 58.3

Moderate Obstacle 207 16.6 74.9

Major Obstacle 198 15.9 90.8

Severe Obstacle 115 9.2 100.0

Total 1247 100.0

Source: Author’s computation from the WB’s Enterprise Survey 2013 data

Table (8.19) shows frequencies of responses relating to labor regulations as an

obstacle. Majority of the firms (416) do not find it as an obstacle for business, 324 consider

it as a minor obstacle and only 90 of the total sample find labor regulations as a severe

obstacle in the operations of a business.

Table 8.19

Labor Regulations as an Obstacle

The workforce is the main component in business which brings innovation and ways

of efficiency. More the workforce is educated more the business will progress. Majority of

the firms (399) do not consider inadequately educated workforce as an obstacle. Only 72

firms consider it obstacle (Table 8.20). Though availability of the educated workforce does

not seem to be worse, the situation has deteriorated from the statistics of 2007. The results

obtained from the WB’s Enterprise Surveys website reveal that 24.2 percent firms have

188

identified inadequately educated workforce as the major constraint and in 2007 it was just

8.1 percent firms which reckoned it a major constraint in Pakistan.

Table 8.20

Inadequately Educated Workforce as an Obstacle

8.2.3 The Biggest Obstacle faced by Firms

The Enterprise Survey 2013 asked the firms about the biggest obstacle in their

businesses. From all of the fifteen factors, electricity is the biggest obstacle affecting the

operation of the establishments by getting highest frequencies as 735, next to it is corruption

with 169 out of 1247 (Table 8.21). The results can be validated through mean scores and

frequencies presented in Table (8.2).

189

Table 8.21

Biggest Obstacle affecting the Operations of the Firm

Figure (8.4) shows highest percentage and frequency of responses for the electricity

as the biggest obstacle in operating businesses. The Global Competitiveness report (2017-

18) ranks Pakistan at 121 position among 138 economies on the quality of electricity supply.

The analysis of data (Table 8.20 & Figure 8.4) places corruption as the second biggest

obstacles. The Global Competitiveness reports (2015-16, 2016-17 & 2017-18) have

declared ‘corruption’ as the most problematic factor of doing business in Pakistan ( Section

2.3.3).

190

Figure 8.4. Biggest Obstacle affecting the Operations of the Firm

Source: Author’s computation from the Enterprise Survey 2013 data

8.3 Conclusion

The chapter is based on the World Bank’s Enterprise Survey data 2013. It has

examined the business environment through different factors influencing firms in Pakistan.

These location factors may impact on investors’ decision in choosing investment locations.

The enterprise survey data have been analyzed through statistical tools. The WB

Enterprise Survey 2013 was conducted to seek the responses from 1247 firms regarding

different factors as obstacles in doing business. The analysis of the survey data reveal that

electricity shortfall and corruption are the obstacles in doing business in Pakistan. Both have

got the mean score greater than 2. Electricity has got the highest mean score (3.28) and it is

a severe obstacle that affects the operation of firms in Pakistan. 59.3 percent firms consider

electricity as a very severe obstacle. Next is corruption, the results reveal that corruption

with the mean score (2.09) is also the obstacle. All other factors have got less than 2 mean

score, ‘tax rates’ (1.85), ‘crime, theft and disorder’ (1.57), ‘political instability’ and

‘transport’ with mean score (1.52). The factors such as ‘access to finance’ ‘access to land’,

191

‘business licensing and permits’, ‘courts’, ‘customs and trade regulations’, ‘labor

regulation’, ‘practices of competitors in the informal sector’ have less than 1 mean score.

The Survey data analysis further reveal that electricity has consistently been the

major constraint of doing business in Pakistan. Firms have to face delays in getting

electricity connections and power outages cost firms. They have to acquire generators as

alternate for power supply. Though the electricity shortfall is an issue for both the

manufacturing and the services sectors, it is the manufacturing sector that is facing more

power outages and the situation has become the worst in 2013.

The incidence of graft has decreased; the situation is not better than other South

Asian countries. The bribe in different public services is detrimental to investment in the

country, especially the getting electricity connection is more problematic as the incidence

of the bribe is higher. Further, the manufacturing sector experienced more bribe than the

services sector. Again, the electricity connection is a vulnerable area for bribery. Among

sub-sectors of the manufacturing and the services, the firms relating to the business of non-

metallic and mineral products experience more bribery, followed by the textile sector. The

region, firms operating in Baluchistan are experiencing more bribe. Lastly, the MNEs

operating in Pakistan experience more bribe than domestic firms.

The favorable business environment requires flexible and good economic control of

regulations, licensing and tax rates. Tax rates are one of the factors that influence the choice

of investors while selecting the location for investment. Like other obstacles; tax rate is also

observed as a major constraint by firms and this factor has been observed more critical in

case of Pakistan and the situation has worsened from 2007 to 2013. It is the services sector

which is affected more by the tax rate.

Pakistan as an investment location has some deterring factors such as rampant

corruption, consistent electricity shortfall, and higher tax rates. Both corruption and power

192

outages escalate the firms’ business and higher tax rates reduce the profitability margin of

the companies. Moreover, reduction in crime incidence shows the government’s

commitment to providing a peaceful environment to companies’ operations. Bureaucratic

efficiency and transparency in offering public services and in trading procedures are

necessary for creating a favorable location for investment.

193

CHAPTER 9

CONCLUSIONS, POLICY IMPLICATIONS, LIMITATIONS, AND

FUTURE RESEARCH DIRECTION

9.1 Introduction

This Chapter provides conclusions, implications, limitations in the research and

provides direction for future research. Section 9.2 summarizes the achievement of the

research objectives outlined in the introductory chapter. Section 9.3 provides the summary

and conclusion regarding the main theme of the research i.e Determinants of FDI inflows.

Although research contributions have been highlighted in the introductory chapter and in

Chapter 4, here again Section 9.4 reiterates the key contributions made by the dissertation.

Section 9.5 brings out some of the implications for the policy makers and firms. Section 9.6

highlights some of the limitations of the research and finally, Section 9.7 provides future

research direction.

9.2 Research Objectives achieved

The dissertation has outlined five research objectives in the introductory chapter

(Section 1.2). The first research objective is to overview the FDI related policies and

business environment in Pakistan. Chapter 2 has addressed the first research objective by

providing the overview of FDI policies enacted during different periods of time and

analyzing the prevailing business environment in Pakistan. The history of FDI relevant

policies has been divided into five-time periods starting from 1947. Policies are considered

to be instrumental in attracting or deterring FDI inflows to a country. Starting from the only

manufacturing sector, now all economic sectors are for foreign investment except a few.

But apart from policy inducements, business environment also matters for foreign investors.

The Chapter has analyzed the prevailing business environment in the region in general and

194

in Pakistan particularly through multiple perspectives. For the matter, it provides the

evidence of measures and indicators developed by different international agencies like WB,

WEF, UNCTAD, Milken Institute, United States, TI and the PRS Group, USA. The analysis

reveals that Pakistan’s governance environment places the country at high risk. The most

disturbing concerns are political instability and prevailing violence/ terrorism, and

corruption in the country.

The second research objective is to analyze the trends, direction and composition

FDI inflows to Pakistan. Chapter 3 has focused the second objective as it provides an

overview of the trends, direction, and composition of FDI inflows at the global and regional

levels but the focus has been on Pakistan. It helps in understanding the dynamics of FDI

inflows. For the matter, the secondary data of FDI have been obtained from the UNCTAD

and its publications especially the World Investment Report, the SBP and the BOI, Pakistan.

The analysis reveals that the FDI inflows at global level have been showing an increasing

trend, with some fluctuations for the past three decades. The highest level, US$ 1.8 trillion,

was attained in 2015 and the share of the developed and the developing economies

constituted 55 percent and 43 percent respectively. At the regional level, Asia is the leading

destination of FDI inflows with 37.80 percent share. Within Asia, the Eastern Asia region

receives the major share of FDI inflows to Asia with 53.34 percent. In the South Asia region,

India is the major recipient of FDI inflows with a share of 88 percent, followed by

Bangladesh and Iran with almost 4 percent each. Pakistan has been striving hard to attract

foreign investors. There are more than fifty source countries that are investing in Pakistan

and around 36 sub-sectors of the economy are welcoming foreign investment with some

fluctuations year to year basis. But the issue is, China is the single largest foreign investor

country with almost 57 percent of total FDI inflows share. Among sectors, the services

sector has been the main recipient of FDI even though the emphasis of Pakistan’s FDI

195

related policies has been on the manufacturing sector. Among subsectors, currently, the

power sector is the main attraction of foreign investment, followed by construction. And the

reason is the Chinese investment under the CPEC.

The third research objective is to examine the policy and non-policy determinants of

FDI inflows at the country level. Chapter 6 has addressed the third research objective by

empirically finding out the policy and non-policy determinants of FDI inflows to Pakistan.

The fourth research objective is to find out the policy and non-policy determinants of FDI

inflows at the sectoral level. To achieve this research objective, Chapter 7 empirically

examines the FDI determinants at the sectoral level. Three main sectors (primary, secondary,

tertiary) of the economy have been analyzed. The research objectives 3 and 4 have been

achieved by applying the econometric methodology of ARDL bound testing. The findings

on FDI locations determinants have been presented in the next section (9.3).

Last research objective is to study the obstacles faced by the firms doing business

in Pakistan. Chapter 8 has addressed this research objective by statistically examining the

fifteen different obstacles faced by the firms operating in Pakistan. For the matter, the

Enterprise Survey 2013 data have been acquired from the World Bank. The descriptive

statistics have been used to analyze the data. The findings on the obstacles faced by the

firms operating in Pakistan have been summarized and presented in the next section (9.3).

9.3 FDI Location Determinants

The underlying theme of the research can be summed up as the determinants of FDI

flows in Pakistan. Thus, the study conducted through this dissertation aims to provide a deep

insight into the understanding of FDI in Pakistan by addressing the research question “What

are the policy and the non-policy determinants of FDI in Pakistan?”. To answer this research

questions empirically, the dissertation has applied methodological pluralism with time series

econometric analysis (aggregate and sectoral) and firm level survey data.

196

At the country level with time series econometric analysis, the dissertation has

examined the long-run and short-run relationships among FDI inflows and five policy &

three non-policy variables in Pakistan for the (1984–2015) period. The estimations indicate

that corporate tax rate, infrastructure, inflation, trade openness, corruption and GDP per

capita (market size) yield significant coefficients in relation to FDI. FDI inflows depend on

both policy and non-policy factors. Government’s macroeconomic policies provide location

advantages to MNEs and friendly business environment and higher institutional quality

manifested as low levels of corruption would be added advantages.

In summary, the significant determinants of FDI in Pakistan are found to be:

Corporate Tax Rate (-ve)

Infrastructure (+ve)

Inflation (-ve)

Trade Openness (+ve)

Corruption (-ve)

Market Size (+ve)

At the sectoral level with time series econometric analysis, the dissertation has also

examined the relationships among sectoral FDI inflows (primary, secondary, tertiary) and

sector specific variables in Pakistan for the (1984–2015) period. The estimations have

indicated that variables such as corporate tax rate and availability of natural resources are

significant determinants in the primary sector. In the secondary sector, inflation, exchange

rate volatility, energy, trade openness and quality of infrastructure are the major

determinants whereas existing services FDI stock, labor quality, quality of infrastructure,

market size, corporate tax rate, and inflation are the significant determinants of services FDI.

The summary of determinants of the sectoral FDI inflows is presented in Table (9.1).

197

Table 9.1

Summary of Significant Determinants of Sectoral FDI Inflows

Primary Sector Secondary Sector Tertiary Sector

Corporate Tax Rate (-ve) Exchange Rate Volatility

(-ve)

Corporate Tax Rate (-ve)

Availability of Natural

Resources (+ve)

Inflation (-ve) Inflation (-ve)

Energy (+ve) Agglomeration Effect (+ve)

Trade Openness (+ve) Labor Quality (+ve)

Infrastructure (+ve) Market Size (+ve)

Infrastructure (+ve)

At the firm level, the dissertation has used the World Bank’s Enterprise Survey data

2013. It has examined the business environment through fifteen different factors influencing

firms in Pakistan. The Survey data is based on the responses from 1247 firms. The statistical

findings reveal that electricity shortfall and corruption are the obstacles in doing business in

Pakistan. Both have got the mean score greater than 2. Electricity has got the highest mean

score (3.28) and it is a severe obstacle that affects the operations of firms in Pakistan. Next

is corruption, the results reveal that corruption with mean score (2.09) is also the obstacle.

Corruption has also been found negatively influencing FDI inflows in Pakistan. So, it can

be inferred here that corruption is pervasive and has been creating hurdle in the way of

investments. All other factors have got less than 2 mean score, ‘tax rates’ (1.85), ‘crime,

theft and disorder’ (1.57), ‘political instability’ and ‘transport’ with mean score (1.52). The

remaining six factors have less than 1 mean score.

The survey data analysis reveals that electricity has consistently been the major

constraint of doing business in Pakistan. Firms have to face delays in getting electricity

connections and the power outages cost firms. They have to own generators as alternate

source for power supply. Though the electricity shortfall is an issue for both manufacturing

and services sector, it is the manufacturing sector that is facing more power outages and the

situation has become the worst in 2013.

198

The incidence of graft has decreased; the situation is not better than other South

Asian countries. The bribe in different public services is detrimental to investment in the

country, especially the getting electricity connection is more problematic as the incidence

of bribe is higher. Further, the manufacturing sector experiences more bribe than the

services sector. Again, the getting electricity connection is a vulnerable area for bribery.

Among sub-sectors of the manufacturing and the services, the firms relating to the business

of non-metallic and mineral products experience more bribery, followed by the textile sector.

The firms doing business in the region, Baluchistan, experience more bribe. Lastly, the

MNEs operating in Pakistan experience more bribe than domestic firms.

The favorable business environment requires flexible and good economic control of

regulations, licensing and tax rates. Tax rate is one of the factors that influences the choice

of investors while selecting the location for investment. Like other obstacles; tax rate is also

observed as a major constraint by firms and this factor has been observed more critical in

case of Pakistan and the situation has worsened from 2007 to 2013. It is the services sector

which is affected more by the tax rate.

Pakistan as an investment location has some deterring factors such as rampant and

pervasive corruption, consistent electricity shortfall, and higher tax rates. Both corruption

and power outages escalate the cost of doing business and higher tax rates reduce the

profitability margin of the companies. Moreover, reduction in crime incidence shows the

government’s commitment to providing a peaceful environment to companies’ operations.

Bureaucratic efficiency and transparency in offering public services and in trading

procedures are necessary for creating a favorable location for investment.

199

9.4 Key Contributions

Although there is abundant empirical and theoretical literature available on the

subject, the research has made an attempt to provide a deep analysis of the FDI determinants.

It adds to the existing available literature by providing an extended framework on the

determinants adopting a funnel of three different levels: country, sectors and firms. It has

three different units of analysis. This framework requires the methodological pluralism.

Therefore, the study has utilized econometric time series analysis at the country and the

sectoral levels and the survey data at the firm level. With this pluralistic approach, the

dissertation has been able to provide a holistic view on FDI determinants in Pakistan.

Three relevant theories, the OLI electric Paradigm, the New Trade Theory, the

Institutional Theory, on the location determinants of inward FDI have been selected and

tested in Pakistan. The subject of FDI falls under the discipline of International Business

(Rogmans, 2011) which is an interdisciplinary in nature as it relies on other many social

science disciplines, for instance, Economics, History, Political Science, Anthropology,

Sociology and Psychology (Shenkar, 2004). But the scholarly debate has been dominated

by political scientists and economists (Meyer, 2004). Similarly, public policy is also an

interdisciplinary subject which draws upon multiple disciplines (Miller & McTavish, 2013).

So, the dissertation attempts to examine the determinants of FDI from the perspective of

public policy and it has been intended to produce a research which contributes to policy

making. The framework in the form of funnel embedded in the dissertation can be replicated

and tested in other FDI locations. The framework could be used as a policy intervention

model as it covers different aspects of the problem and provides details necessary for

meaningful policy making.

200

9.5 Implications of Research

The research findings have generated some important implications for policymakers and

firms.

9.5.1 Implications for Policymakers

The findings point out to the specific areas where the policymakers should focus

their efforts. Pakistan as an investment location has some deterring factors such as rampant

corruption, higher tax rates, financial and economic instability, consistent electricity

shortfall. Both corruption and power outages escalate the cost of doing business, higher tax

rates reduce the profitability margin of the companies and financial and economic instability

emerging from higher exchange rate volatility and inflation create the uncertainty and

instability in the macroeconomic environment of the country. On the other hand, there are

attractions for foreign investors available in the form of opened economy, large market size,

workforce and improved physical infrastructure.

The policymakers aim to design and implement policies that attract foreign investors

should focus on the alignment of FDI relevant policies and actions. Corruption being a

problematic factor for doing business should be controlled especially in the provision of

public utilities. Higher tax rates made the location less attractive, so the tax rate, especially

on the services sector, should be reduced or be made equitable with other sectors. Though

Pakistan is an energy-starved country and the Government has been striving hard to plug

the issue of the energy crisis. Electricity is an essential input for industrial production. So,

a reliable and uninterrupted supply of electricity should be ensured. The State Bank of

Pakistan should control inflation through appropriate monetary tools and maintain the

stability of the currency being its core function. The Bank’s measures should be in align

with other state institutions especially the Ministry of Finance.

201

There are some other factors such as the opened economy, large market size,

workforce and improved physical infrastructure which are attractions for foreign investors.

These factors should be capitalized on inviting foreign investors. Both market and efficiency

seeking FDI can be targeted. Finally, policymakers should diversify FDI sources and the

Chinese investment in Pakistan should be used as a bait for other potential investors. They

should dismantle any favorable treatment awarded to the Chinese companies. Other

investors should not feel threatened and start disinvesting in the country. An equitable policy

incentive and treatment should be offered to all potential investors.

9.5.2 Implications for Firms

The multinationals which are not currently investing in Pakistan, the research

demonstrates that FDI inflows to Pakistan have been showing increasing trend since 2013.

This growth is blessed with the development of the China-Pakistan Economic Corridor

(CPEC), a multi-billion-dollar mega project between China and Pakistan. Since 2011, the

Chinese investment in Pakistan has been increasing. Its inflows rose to US$ 695.8 million

in 2014. Currently, it is the single largest investing country in Pakistan (MOF, 2017). China

is the major player as far as FDI is concerned. Currently, it is the second largest FDI

receiving economy with FDI inflows amounting to $128.50 billion in 2014, and is the third

largest FDI provider in the world (UNCTAD, 2016). The emergence of China as a global

economic player and its economic interests in Pakistan would attract and a provide platform

for the investors from other countries. China as the major FDI player in the world showing

confidence in the location of Pakistan. The policymakers need to benefit from China’s quest

for regional integration and globalization (Hussain and Hussain, 2016).

The research further demonstrates that firms operating in Pakistan have been facing

obstacles in the form of widespread corruption and consistent electricity shortfall. These

two factors rise the cost of doing business in Pakistan while higher tax rates reduce the

202

profitability margin of the companies. These factors may discourage the companies to

expand their business and may deter potential new entrant in the market. But at the same

time, reduction in crime incidence shows the government’s commitment to providing a

peaceful business environment.

9.6 Research Limitations

Although the research has made contributions to the literature and it exhibits several

merits, yet there are still a few specific limitations in this dissertation that should be noted.

Since the theme of the dissertation is to find out FDI determinants in Pakistan and

for that matter the factors like ownership and internalization of firms have been taken

constant and the emphasis has been only on location advantages of Pakistan. The MNEs use

a number of means while internalizing their businesses in a location such as wholly owned

subsidiary and joint venture. Therefore, why do companies select a particular mode of entry

while investing in any location (like Pakistan) has not been investigated in this research.

At the firms’ level, the study has used the WB Enterprise Survey data 2013. There

are only 38 foreign-owned firms in the data sample. And the sample is dominated by the

domestic firms. The justification could be provided as the investment policy and incentives

are the same for both foreign and local investors.

9.7 Future Research Direction

Based on the research limitations discussed in Section 9.6, this section has presented

some possible future research areas.

This research has taken both ownership and internalization of firms as constant and

has examined the location factors of investment in Pakistan. As said in the previous section,

the multinationals use different modes of entry while internalizing their businesses such as

wholly owned subsidiary and joint venture. Therefore, reasons behind the selection of a

203

particular mode of entry can be investigated. For that matter, a survey method based on

questionnaire or case study method can be utilized.

Currently, China is the single largest direct investor in Pakistan. So, future study can

be done on the motivations of the Chinese investment in Pakistan. The Chinese direct

investment constitutes almost 57 percent of total FDI inflows in the country. Pakistan is

strengthening its investment and trade link with China through the CPEC project. Therefore,

its significance for Pakistan in particular and for the region, in general, cannot be overlooked.

The factors that have been attracting Chinese companies need to be investigated.

204

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ANNEXURES

Annexure A: List of Countries/Organizations with which Pakistan has Bilateral

Investment Treaties

S.No

. Country Year S.No. Country Year

1 Sweden 1981 25 Belarus 1997

2 France 1983 26 Mauritius 1997

3 Netherlands 1988 27 Oman 1997

4 South Korea 1988 28 Sri Lanka 1997

5 China 1989 29 Japan 1998

6 Uzbekistan 1992 30

Belgo-Luxemburg and

Economic Union 1998

7 Spain 1994 31 Australia 1998

8 Turkmenistan 1994 32 Czech Republic 1999

9 United Kingdom 1994 33 Philippines 1999

10

Kyrgyz

Republic 1995 34 Qatar 1999

11 Iran 1995 35 Yemen 1999

12 Bangladesh 1995 36 Egypt 2000

13 Azerbaijan 1995 37 Lebanon 2001

14 Malaysia 1995 38 Bosnia 2001

15 Portugal 1995 39 Morocco 2001

16 Romania 1995 40 Bulgaria 2002

17 Singapore 1995 41 Kazakhstan 2003

18 Switzerland 1995 42 Cambodia 2004

19 Syria 1995 43 Tajikistan 2004

20 U.A.E. 1995 44 Germany 2009

21 Indonesia 1996 45 Kuwait 2011

22 Denmark 1996 46 Turkey 2012

23 Tunisia 1996 47 Bahrain 2014

24 Italy 1997

Source: Board of Investment, Pakistan

228

Annexure B: List of Business-Friendly Countries

S.No. Country S.No. Country S.No. Country

1 Argentina 24 Indonesia 47 Portugal

2 Australia 25 Iceland 48 Poland

3 Austria 26 Iran 49 Qatar

4 Belgium 27 Ireland 50 Romania

5 Singapore 28 South Korea 51 Bosnia

6 Saudi Arabia 29 South Africa 52 Bahrain

7 Russia 30 Malta 53 Azerbaijan

8 Switzerland 31 Lithonia 54 Iceland

9 Sweden 32 Latvia 55 Thailand

10 Spain 33 Kuwait 56 Sri Lanka

11 Brazil 34 Kazakhstan 57 Philippine

12 Brunei 35 Jordan 58 Oman

13 Czech Republic 36 Japan 59 Norway

14 Denmark 37 Italy 60 Turkey

15 Netherlands 38 Luxembourg 61 Turkmenistan

16 Estonia 39 Malaysia 62 UAE

17 Finland 40 Egypt 63 Slovenia

18 France 41 Mauritius 64 Ukraine

19 USA 42 Germany 65 Slovakia Republic

20 Greece 43 New Zealand 66 UK

21 Hungary 44 Canada 67 Morocco

22 Vietnam 45 Chile 68 Cyprus

23 Mexico 46 China

Source: Directorate General of Immigration & Passports, Ministry of Interior, Pakistan

229

Annexure C: List of countries with Pakistan has signed the Avoidance of Double

Taxation agreements

S.No. Country S.No. Country

1 Libyan Arab Republic 27 Azerbaijan

2 Austria 28 Malta

3 Bangladesh 29 Mauritius

4 Belarus 30 Netherlands

5 Belgium 31 Nigeria

6 Canada 32 Norway

7 China 33 Oman

8 Denmark 34 Philippines

9 Finland 35 Poland

10 France 36 Qatar

11 Germany 37 Romania

12 Greece 38 Sri Lanka

13 Hungary 39 South Africa

14 UAE 40 USA

15 India 41 Singapore

16 UK 42 Uzbekistan

17 Lebanon 43 Malaysia

18 Indonesia 44 Saudi Arabia

19 Iran 45 Syria

20 Ireland 46 Switzerland

21 Italy 47 Sweden

22 Japan 48 Thailand

23 Jordan 49 Tunisia

24 Kazakhstan 50 Turkey

25 Kenya 51 Turkmenistan

26 Republic of Korea 52 Kuwait

Source: Board of Investment, Pakistan

230

Annexure D: List of Developed, Developing, and Transition Countries

Developing Countries Developed Countries Transition Countries

S.N

o.

S.N

o.

S.N

o.

S.N

o.

1 Algeria 53 Guyana 1 Bermuda 1 Albania

2 Angola 54 Haiti 2 Canada 2 Armenia

3 Anguilla 55 Honduras 3 Greenland 3 Azerbaijan

4 Antigua and Barbuda 56 Jamaica 4

Saint Pierre and

Miquelon 4

Belarus

5 Argentina 57 Kenya 5 United States 5 Bosnia and Herzegovina

6 Aruba 58 Lesotho 6 Israel 6 Georgia

7 Bahamas 59 Liberia 7 Japan 7 Kazakhstan

8 Barbados 60 Libya 8 Andorra 8 Kyrgyzstan

9 Belize 61 Madagascar 9 Austria 9 Montenegro

10 Benin 62 Malawi 10 Belgium 10 Republic of Moldova

11

Bolivia (Plurinational

State of) 63 Mali 11 Bulgaria 11

Russian Federation

12

Bonaire, Sint Eustatius

and Saba 64 Mauritania 12 Croatia 12

Serbia

13 Botswana 65 Mauritius 13 Cyprus 13 Serbia and Montenegro

14 Brazil 66 Mexico 14 Czechia 14

Socialist Federal Republic of

Yugoslavia

15 British Virgin Islands 67 Montserrat 15 Czechoslovakia 15 Tajikistan

16 Burkina Faso 68 Morocco 16 Denmark 16 TFYR of Macedonia

17 Burundi 69 Mozambique 17 Estonia 17 Turkmenistan

18 Cabo Verde 70 Namibia 18 Faeroe Islands 18 Ukraine

231

19 Cameroon 71 Netherlands Antilles 19 Finland 19

Union of Soviet Socialist

Republics

20 Cayman Islands 72 Nicaragua 20 France 20 Uzbekistan

21 Central African Republic 73 Niger 21 Germany

22 Chad 74 Nigeria 22

Germany, Democratic

Republic of

23 Chile 75 Panama 23

Germany, Federal

Republic of

24 Colombia 76 Panama, Canal Zone 24 Gibraltar

25 Comoros 77

Panama, excluding Canal

Zone 25 Greece

26 Congo 78 Paraguay 26 Holy See

27 Costa Rica 79 Peru 27 Hungary

28 Côte d'Ivoire 80 Rwanda 28 Iceland

29 Cuba 81 Saint Helena 29 Ireland

30 Curaçao 82 Saint Kitts and Nevis 30 Italy

31 Dem. Rep. of the Congo 83 Saint Lucia 31 Latvia

32 Djibouti 84

Saint Vincent and the

Grenadines 32 Lithuania

33 Dominica 85 Sao Tome and Principe 33 Luxembourg

34 Dominican Republic 86 Senegal 34 Malta

35 Ecuador 87 Seychelles 35 Netherlands

36 Egypt 88 Sierra Leone 36 Norway

37 El Salvador 89 Sint Maarten (Dutch part) 37 Poland

38 Equatorial Guinea 90 Somalia 38 Portugal

39 Eritrea 91 South Africa 39 Romania

40 Ethiopia 92 South Sudan 40 San Marino

41 Ethiopia (...1991) 93 Sudan 41 Slovakia

232

42 Falkland Islands (Malvinas) 94 Sudan (...2011) 42 Slovenia

43 Gabon 95 Suriname 43 Spain

44 Gambia 96 Swaziland 44 Sweden

45 Ghana 97 Togo 45 Switzerland

46 Grenada 98 Trinidad and Tobago 46 United Kingdom

47 Guatemala 99 Tunisia 47 Australia

48 Guinea 100 Turks and Caicos Islands 48 New Zealand

49 Guinea-Bissau 101 Uganda

50 United Republic of Tanzania 102

Venezuela (Bolivarian

Rep. of)

51 Uruguay 103 Western Sahara

52 Zambia 104 Zimbabwe Source: UNCTAD

233

Annexure E: List of countries falling in five Regions (Africa, America, Asia, Europe, and Oceanic)

S.N

o. Africa

S.N

o. America

S.N

o. Asia

S.N

o. Europe

S.N

o. Oceanic

1 Burundi 1 Anguilla 1 China 1 Andorra 1 American Samoa

2 Comoros 2 Antigua and Barbuda 2

China, Hong Kong

SAR 2 Austria 2 Cook Islands

3 Djibouti 3 Aruba 3 China, Macao SAR 3 Belgium 3 Fiji

4 Eritrea 4 Bahamas 4

China, Taiwan

Province of 4 Bulgaria 4 French Polynesia

5 Ethiopia 5 Barbados 5

Korea, Dem.

People's Rep. of 5 Croatia 5 Guam

6 Ethiopia (...1991) 6

Bonaire, Sint Eustatius

and Saba 6 Korea, Republic of 6 Cyprus 6 Kiribati

7 Kenya 7 British Virgin Islands 7 Mongolia 7 Czechia 7 Marshall Islands

8 Madagascar 8 Cayman Islands 8 Afghanistan 8 Czechoslovakia 8

Micronesia (Federated

States of)

9 Malawi 9 Cuba 9 Bangladesh 9 Denmark 9 Nauru

10 Mauritius 10 Curaçao 10 Bhutan 10 Estonia 10 New Caledonia

11 Mozambique 11 Dominica 11 India 11 Faeroe Islands 11 Niue

12 Rwanda 12 Dominican Republic 12

Iran (Islamic

Republic of) 12 Finland 12

Northern Mariana

Islands

13 Seychelles 13 Grenada 13 Maldives 13 France 13

Pacific Islands, Trust

Territory

14 Somalia 14 Haiti 14 Nepal 14 Germany 14 Palau

15 South Sudan 15 Jamaica 15 Pakistan 15

Germany, Democratic

Republic of 15 Papua New Guinea

16 Uganda 16 Montserrat 16 Sri Lanka 16

Germany, Federal

Republic of 16 Samoa

234

17

United Republic of

Tanzania 17 Netherlands Antilles 17 Cambodia 17 Gibraltar 17 Solomon Islands

18 Zambia 18 Saint Kitts and Nevis 18 Indonesia 18 Greece 18 Tokelau

19 Zimbabwe 19 Saint Lucia 19 Indonesia (...2002) 19 Holy See 19 Tonga

20 Angola 20

Saint Vincent and the

Grenadines 20

Lao People's Dem.

Rep. 20 Hungary 20 Tuvalu

21 Cameroon 21

Sint Maarten (Dutch

part) 21 Malaysia 21 Iceland 21 Vanuatu

22

Central African

Republic 22 Trinidad and Tobago 22 Myanmar 22 Ireland 22

Wallis and Futuna

Islands

23 Chad 23

Turks and Caicos

Islands 23 Philippines 23 Italy 23 Australia

24 Congo 24 Belize 24 Singapore 24 Latvia 24 New Zealand

25

Dem. Rep. of the

Congo 25 Costa Rica 25 Thailand 25 Lithuania

26 Equatorial Guinea 26 El Salvador 26 Timor-Leste 26 Luxembourg

27 Gabon 27 Guatemala 27 Viet Nam 27 Malta

28

Sao Tome and

Principe 28 Honduras 28 Bahrain 28 Netherlands

29 Algeria 29 Mexico 29 Iraq 29 Norway

30 Egypt 30 Nicaragua 30 Jordan 30 Poland

31 Libya 31 Panama 31 Kuwait 31 Portugal

32 Morocco 32 Panama, Canal Zone 32 Lebanon 32 Romania

33 Sudan 33

Panama, excluding

Canal Zone 33 Oman 33 San Marino

34 Sudan (...2011) 34 Argentina 34 Qatar 34 Slovakia

35 Tunisia 35

Bolivia (Plurinational

State of) 35 Saudi Arabia 35 Slovenia

36 Western Sahara 36 Brazil 36 State of Palestine 36 Spain

37 Botswana 37 Chile 37

Syrian Arab

Republic 37 Sweden

235

38 Lesotho 38 Colombia 38 Turkey 38 Switzerland

39 Namibia 39 Ecuador 39

United Arab

Emirates 39 United Kingdom

40 South Africa 40

Falkland Islands

(Malvinas) 40 Yemen

41 Swaziland 41 Guyana 41

Yemen, Arab

Republic

42 Benin 42 Paraguay 42 Yemen, Democratic

43 Burkina Faso 43 Peru 43 Israel

44 Cabo Verde 44 Suriname 44 Japan

45 Ghana 45 Bermuda

46 Guinea 46 Canada

47 Guinea-Bissau 47 Greenland

48 Liberia 48

Saint Pierre and

Miquelon

49 Mali 49 United States

50 Côte d'Ivoire 50 Uruguay

51

Gambia

51

Venezuela (Bolivarian

Rep. of)

52 Mauritania

53 Niger

54 Nigeria

55 Saint Helena

56 Senegal

57 Sierra Leone

58 Togo

Source: UNCTAD

236

Annexure F: List of Countries falling in four Regions of Asia (Eastern, Southern, South-Eastern, and Western Asia)

S.No. Eastern Asia S.No. Southern Asia S.No. South Eastern Asia S.No. Western Asia

1 China 1 Afghanistan 1 Cambodia 1 Bahrain

2 China, Hong Kong SAR 2 Bangladesh 2 Indonesia 2 Iraq

3 China, Macao SAR 3 Bhutan 3 Indonesia (...2002) 3 Jordan

4 China, Taiwan Province of 4 India 4 Lao People's Dem. Rep. 4 Kuwait

5 Korea, Dem. People's Rep. of 5 Iran (Islamic Republic of) 5 Malaysia 5 Lebanon

6 Korea, Republic of 6 Maldives 6 Myanmar 6 Oman

7 Mongolia 7 Nepal 7 Philippines 7 Qatar

8 Pakistan 8 Singapore 8 Saudi Arabia

9 Sri Lanka 9 Thailand 9 State of Palestine

10 Timor-Leste 10 Syrian Arab Republic

11 Viet Nam 11 Turkey

12 United Arab Emirates

13 Yemen

14 Yemen, Arab Republic

15 Yemen, Democratic Source: UNCTAD

237

Annexure G: Historical Data of FDI Inflows from different Countries (Million US Dollar)

Year USA UK UAE Germany France Hong Kong Italy Japan Saudi Arabia Canada Netherlands

1982 15.2 19.5 8.2 3.5 0.19 0.2 0.0 0.4 0.2 0.3 1.5

1983 4.9 7.1 4.2 1.4 0.10 0.0 0.0 0.2 1.1 0.1 1.4

1984 4.2 7.3 3.9 2.3 0.05 0.2 0.2 1.2 0.1 0.7

1985 17.2 8.9 11.9 6.4 1.20 0.6 0.1 6.7 3.8 0.3 0.5

1986 35.2 12.5 69.5 4.3 0.80 2.8 0.4 6.3 -7.3 0.0 1.3

1987 42.9 5.1 25.6 5.4 1.50 6.7 0.4 9.4 1.0 0.8 0.6

1988 45.8 25.5 24.4 18.3 5.00 5.5 1.1 13.6 0.9 1.0 0.4

1989 94.4 22.6 12.9 10.0 7.70 6.3 1.2 16.7 0.5 0.9 1.7

1990 93.9 22.8 15.9 11.2 6.00 0.9 3.8 16.1 1.1 0.9 5.3

1991 130.0 33.8 9.0 12.5 7.10 3.3 2.9 26.2 0.9 1.9 2.3

1992 213.4 20.8 10.5 21.4 8.50 0.0 2.0 17.7 0.1 3.0 0.8

1993 136.9 25.7 9.5 36.2 5.70 12.4 0.6 22.0 8.2 0.3 5.6

1994 114.5 32.0 7.5 9.1 11.10 1.2 0.3 29.7 1.9 1.2 -0.1

1995 176.4 38.7 46.8 17.6 13.50 2.2 0.3 16.3 0.9 0.4 4.5

1996 319.8 331.7 52.8 26.0 14.00 33.9 0.5 82.1 26.9 0.8 11.9

1997 246.2 240.1 54.9 17.6 10.20 7.5 1.8 36.6 -17.0 1.7 7.7

1998 256.6 135.3 19.2 24.0 4.90 2.1 0.9 17.8 1.2 0.5 26.9

1999 214.6 89.3 6.9 19.8 9.80 3.0 0.2 59.0 22.8 0.3 5.9

2000 166.9 169.0 5.7 10.5 1.60 0.8 0.5 17.7 28.6 0.2 10.7

2001 92.7 90.5 5.2 15.5 0.70 3.6 1.3 9.1 56.6 0.1 4.8

2002 326.4 30.3 21.5 11.2 -6.88 2.8 0.1 6.5 1.3 3.5 -5.1

2003 211.5 219.4 119.6 3.7 2.59 5.6 0.2 14.1 43.5 0.5 3.0

2004 238.4 64.9 134.6 7.0 -5.58 6.3 1.9 15.1 7.2 0.5 14.0

2005 326.0 181.5 367.5 13.1 -3.63 32.4 0.4 45.2 18.4 1.9 36.7

238

2006 516.7 244.0 1424.5 28.6 3.17 24.0 57.0 277.8 4.8 121.1

2007 913.3 860.0 662.2 78.9 -0.08 32.6 64.4 104.9 10.7 771.8

2008 1309.7 460.2 588.6 69.6 8.41 339.8 131.2 46.2 13.3 121.6

2009 869.9 263.4 178.1 76.9 6.37 156.1 74.3 -92.3 2.4 41.8

2010 468.3 294.6 242.7 53.0 8.00 9.9 26.8 -133.8 1.1 278.6

2011 238.1 207.0 284.2 21.2 17.85 125.6 3.2 6.5 3.0 -44.3

2012 227.6 205.8 37.1 27.2 -0.53 80.3 200.5 29.7 -79.9 10.8 22.1

2013 227.1 633.0 22.5 5.5 27.00 242.6 199.4 30.1 3.2 -12.5 -118.7

2014 212.1 157.0 -47.1 -5.7 96.30 228.5 97.6 30.1 -40.1 -20.9 5.5

2015 209.0 174.3 216.4 -20.3 -58.00 83.4 105.7 71.1 -64.8 -27.4 -32.4

Source: State Bank of Pakistan

239

Annexure H: Structural Pattern of FDI Inflows to Pakistan

As % of Total Assets

Year Cash brought Capital Equipment brought in Re-invested Earnings

1980 42.90 31.00 26.10

1981 57.23 19.34 23.43

1982 45.06 23.11 31.83.

1983 73.30 02.90 23.80

1984 53.60 01.90 44.50

1985 65.14 01.45 33.41

1986 74.20 01.30 24.50

1987 47.90 01.00 51.10

1988 56.13 13.15 30.72

1989 52.76 16.11 31.13

1990 66.76 08.16 25.08

1991 63.55 05.93 30.52

1992 40.46 33.06 26.48

1993 64.70 11.60 23.70

1994 32.40 55.70 11.90

1995 69.60 09.70 20.70

1996 81.47 08.76 9.77

1997 85.30 00.90 13.80

1998 85.83 02.43 11.74

1999 50.10 01.10 48.80

2000 40.50 02.10 57.40

2002 42.60 00.30 57.10

2003 -0.70 00.80 99.90

2004 69.44 02.02 28.54

2005 78.85 00.43 20.72

Source: State Bank of Pakistan

240

Annexure J: Summary of the literature review on the determinants of FDI at the

country level

Study Locatio

n

Time

Period Method Variables

Significant

Determinants

Anuchitworawon

g &

Thampanishvong

(2015)

Thailand 1971-

2012

3SLS Natural

Disaster;

GDP per

capita,

exchange rate,

CPI,

population,

school

enrolment at

secondary and

tertiary level,

financial

market

development

(domestic

credit by

banking sector

as % of GDP),

TO

All variables

except CPI are

significant

and have

positive and

hence FDI

except CPI

which has

insignificant

negative

relation.

Natural

disasters have

a negative

impact on FDI

inflow

Bekhet & Al-

Smadi (2015)

Jordan 1978-

2012

Bounds

Testing

Approach,

Granger’s

Causality

Test

GDP,

M2(money

supply), EO,

SMI, CPI

All variables

are +ve except

CPI in the

long run.

Boateng et al.

(2015)

Norway Quarterl

y data

1986 to

2009

FMOLS,

VECM/VA

R

Real GDP,

sector GDP,

exchange rate,

TO, Money

supply,

inflation,

unemploymen

t, interest rate

Real GDP,

sector GDP,

exchange rate,

TO (+ve);

Money

supply,

inflation,

unemploymen

t, interest rate

(–ve)

Singhania &

Gupta (2011)

India 1991 to

2008

ARIMA Adjusted

GDP, TO,

inflation –

GDP, inflation

and patents

have a

241

CPI, interest

rate, money

growth,

patents

significant

impact on

FDI.

Kaur & Sharma

(2013)

India Quarterl

y data

(1991-

2011)

Co-

integration

Exchange

rate, GDP,

inflation,

openness,

foreign

exchange

reserves,

external

indebtedness

Exchange

rate, inflation

(–ve)

GDP,

openness,

Foreign

exchange

reserves,

external

indebtedness

(+ve)

Pattayat (2016) India 1980-

2013

Co-

integration,

Error

correction

Log of GDP,

TO, exchange

rate

Long-run

relationship

exists with all

variables

GDP to FDI is

highest

Source: Author’s compilation

242

Annexure K: Summary of the Literature Review on the Determinants of FDI in

Pakistan

Study Time Period Method Variables Findings

Aqeel and

Nishat

(2004)

1961-2002

Cointegation GDP, wage, tax,

tariff, credit,

exchange rate,

general price

share

GDP, tax credit

and exchange

rate are

positively

associated with

FDI; tariff has

a negative

association

with FDI

Ali et al.

(2013)

1975-2007 OLS HDI, market size

(GDP/capita), TO

HDI and TO

are positively

while market

size is

negatively

associated with

FDI

Awan et al.

(2011)

Quarterly data

(1996- 2008)