PAKISTAN’S POTENTIAL FOR FREE TRADE

PAKISTAN’S FREE TRADE POTENTIAL WITH NEIGHBOURS

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PAKISTAN’S POTENTIAL FOR FREE TRADE

WITH NEIGHBORING COUNTRIES

Author

MUHAMMAD SALAHUDDIN AYYUBI

L1F10PCOM0006

FACULTY OF MANAGEMENT STUDIES

UNIVERSITY OF CENTRAL PUNJAB

LAHORE

November 2017


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Pakistan’s Potential for Free Trade with

Neighboring Countries

Author

Muhammad Salahuddin Ayyubi

L1F10PCOM0006

A thesis submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy in Commerce

Thesis Supervisor:

Dr. Qais Aslam

Professor, School of Accounting & Finance (SAF)

External Examiner’s Signature: ________________________________________

Thesis Supervisor’s Signature: ________________________________________

Faculty of Management Studies

School of Accounting and Finance

UNIVERSITY OF CENTRAL PUNJAB, LAHORE

November, 2017


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Dedication

To my past, present and future students, as they have always been; and (hopefully)

shall always be the ultimate source of my inspirations.


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Table of Contents

Abstract ……………………………………………………………………………vii

Undertaking ………………………………………………………………………viii

Acknowledgements ………………………………………………………………. ix

List of Figures ……………………………………………………………………. xi

List of Tables …………………………………………………………………….. xii

Abbreviations …………………………………………………………………… xviii

Chapter I: Introduction ………………………………………………………... .1

1.1. Contextual Background …………………………………………… 1

1.2. History of Pakistan’s Trade with the World ………………………. 3

1.2.1. The beginning – in pursuit of survival …………………….. 4

1.2.2. The 50s and 60s – in search of self-reliance ……………....5

1.2.3. 1970s and 80s ……………………………………………...6

1.2.4. 1990 and onwards ………………………………………… 8

1.3. Continental Preference of Pakistan’s Trade ……………………... 10

1.3.1. Pakistan’s trade with Asian countries ……………………. 10

1.3.2. Pakistan’s trade with Western Europe ……………………. 11

1.3.3. Pakistan’s trade with North America …………………….. 12

1.3.4. Pakistan’s trade with rest of the world …………………… 13

1.4. Pakistan’s Trade with Neighbors …………………………………. 14

1.4.1. Pakistan’s trade with China……………………………….. 16

1.4.2. Pakistan’s trade with India ……………………………….. 17

1.4.3. Pakistan’s trade with Afghanistan… ……………………... 18

1.4.4. Pakistan’s trade with Iran ………………………………… 19

1.5. Objectives …………………………………………………………. 20

1.6. Significance ……………………………………………………… 21

1.7. Research Gap …………………………………………………….. 25

1.8. Organization ………………………………………………………. 26

Chapter II: Literature Review …………………………………………………. 28


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Chapter III: Theoretical Framework …………………………………………… 51

3.1 Historical Background …………………………………………….. 51

3.2 Gravity Model of Trade ………………………………………… 53

3.3 Disaggregate Trade Indices …………………………………… 57

3.4 The Link between Aggregate and Disaggregate Trade ………… 65

Chapter IV: Methodoly ……………………………………………………… 70

4.1 Estimation Techniques ……………………………………… 70

4.1.1 Aggregate trade ……………………………………… 71

4.1.2 Disaggregated trade ………………………………… 77

4.2 Model Justification …………………………………………… 81

Chapter V: Descriptive Statistics …………………………………………… 84

5.1 Pakistan’s Aggregate Trade with Neighbours …………………… 84

5.2 Determinants of Pakistan’s Aggregate Trade with Neighbous.…. 86

5.3 Pakistan’s Disaggregated Trade …………………………………. 90

5.3.1 Pakistan’s Exports to World ………………………………. 90

5.3.2 Pakistan’s Imports from World ……………………………. 92

5.3.3 Pakistan’s Trade with its Neighbors ………………………. 93

5.4 Leading Categories Traded in World and Pakistan’s Leading Trade

Categories …………………………………………………………. 106

5.5 Summary of Critical Trends in Descriptive Statistics ……………...108

Chapter VI: Estimation and Interpretation of Results ………………………….. 111

6.1 Pakistan’s Aggregate Trade Potential……………………………… 111

6.1.1 Pakistan’s bilateral trade potential with neighboring countries

……………………………………………………………....112

6.1.2 Pakistan’s export potential with the neighboring countries.. 121

6.2 Pakistan’s Disaggregated Trade Potential ………………………... 129

6.2.1 Pakistan’s trade with China ……………………………….. 130

6.2.2 Pakistan’s trade with India…………………………………. 138

6.2.3 Pakistan’s Trade with Afghanistan ………………………... 150

6.2.4 Pakistan’s Trade with Iran ………………………………… 153

6.3 Collaborated Findings of the Study……………………………….. 160


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Chapter VII: Conclusions and Recommendations ……………………………… 174

7.1 Conclusions………………………………………………………… 174

7.2 Recommendations………………………………………………… 177

7.3 Future Research…………………………………………………… 181

References………………………………………………………………………… 183

Appendices………………………………………………………………………… 197


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ABSTRACT

The study used augmented gravity model of trade to identify Pakistan’s free trade

potential with its neighbouring countries China, India, Iran and Afghanistan at the

aggregate level and At disaggregate level using TCI, RCA, GLI, EII and III. The results

show that Pakistan possessed the largest free trade potential of over $ 5 billion with

China, by about $ 2 billion with India, around $ 1.5 billion with Iran and nearly $ 1.4

billion with Afghanistan. The study also showed that Pakistan possessed almost twice

as much complementarity for imports from its neighbours than the complementarity

that neighbours had for Pakistani exports in to their respective countries. Moreover,

Iran had the highest bilateral complementarity with Pakistan among the four neighbours

(on the basis of RCA Index). India showed the smallest potential for inter industry trade

with Pakistan (on the basis of RCA index) but the highest prospect of intra industry

trade (on the basis of GLI) among the neighbouring countries. Pakistan possessed intra

industry trade potential in edible fruits (08), wool (51), knitted fabric (60) and copper

articles (74) with India; food preparations (20), staple fibers (55) and optical apparatus

(90) with China; and insignificant potential with Iran and almost no potential with

Afghanistan in that regard. Pakistan’s exports were more than their potential by the

greatest margin with Afghanistan (on the basis of EII), while, ironically, imports were

more than their potential in the same manner with India and Iran. Among the

recommendations are that trade policy with neighbouring countries should pay

attention to Pakistan’s leading imports and exports and the reversal of trade in some

products from India and Iran to China. The study recommended that trade policy with

any neighbouring country should not ignore four categories that were simultaneously

in Pakistan’s leading exports to and imports from each neighbouring country – mineral

fuels (27), articles of plastic (39), iron and steel (72) and Machinery, nuclear reactors

and boilers (84). Moreover, it recommended the removal of trade diversion from India

and Iran to China for four categories – organic chemicals (29), iron and steel (72),

articles of iron and steel (73) and Fertilizer (31). Last, but not the least, the research

recommended to follow economic rationale in the process of trade liberalization with

a partner country rather than political preferences.

Keywords: Trade potential, Trade complementarity, RCA, EII, GLI, FTA


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UNDERTAKING

I certify that research work titled “Pakistan’s Potential for Free Trade with Neighboring

Countries” is my own work. The work has not been presented elsewhere for

assessment. Wherever material has been used from other sources in the thesis, it has

been properly acknowledged / referred to the original author.

Muhammad Salahuddin Ayyubi

L1F10PCOM0006


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ACKNOWLEDGEMENTS

First of all credit must be given to Whom all credit is due, therefore, I would begin by

thanking Allah Almighty for giving me the strength to achieve this academic milestone

in my life. After that I must thank my late father Professor Abdul Jabbar Shakir who

taught me the art of learning, with an end of earning wisdom, in order to share it with

humanity, while maintaining inner as well as outer humility, in speech, as well as, in

actions.

Professor Dr. Ather Azim Khan, Associate Dean, Faculty of Management Studies and

Director, School of Accountancy and Finance, is a source of inspiration for many

people around him. I want to thank him for his characteristic graciousness ever since I

met him for the first time in 2006. He has constantly reminded me, to get my work done

as soon as possible, every time he came across, especially in the last two years. I cannot

thank him enough for his kindness and affection and would always remember him for

his kind gestures. Professor Azhar Ikram Ahmad, former Dean, Faculty of Commerce,

was a fatherly figure and always encouraged me with his words of appreciation. I thank

him for encouraging me, whenever he met.

Professor Dr. Qais Aslam, who has supervised my dissertation, has been my teacher

since college days in mid and late 1990s at Government College Lahore. I have the

privilege of his attention and encouragement since college days. He has been the only

teacher who has taught me in my M.A., M.Phil. and Ph.D. and has added a treasure to

my learning. He kept patience for my delays; rendered support whenever needed; and

lent vision whenever, he found me disoriented. I have no words to express my gratitude

for all that he has done for me over the years.

I was very fortunate to have a brilliant company of my batch mates in the Ph.D.

program, some of whom have already completed their Ph.D. like Dr. Ather Azim Khan,

Dr. Muhammad Ahmad and Dr. Zafar Ahmad. I thank all of them for making the


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coursework exciting for me and congratulate those who have completed their Ph. D.

degrees.

In the process of model building and estimation many people helped me with kind and

constant support. I wish to thank my respected teacher Dr. Zahid Ahmad, my dearest

friend and colleague Dr. Zahid Iqbal and a talented young colleague Dr. Suleman

Abdiah in particular, for their help in this regard. I also wish to thank my other

colleagues at the department of Economics at FC College Dr. Akmal Hussain, Dr.

Roger Philip Martin, Dr. Muhammad Ali Bhatti, Dr. Shabib Haider Syed, Dr. Uzma

Haneef, Dr. Hafiz Rizwan Ahmad, Dr. Rabia Aziz, Mrs. Azma Asif, Dr. Tanveer

Ahmad, Dr. Ghulam Shabbir, Dr. Abdul Jalil Khan and Mr. Luqman Saeed for their

occasional help and advice in the last three years. I must also thank my head of

department Dr. Babar Aziz who always accommodated me at the department as a

younger brother which helped a great deal in completing my work. I have a very special

thanks to a very dear friend Mr. Iqbal Mehmood for his words of wisdom and

motivation to complete my thesis.

I also wish thank the esteemed external reviewers Dr. Samuel K. Andoh, Dean,

Business School, Southern Connecticut State University, USA, ; Dr. Safdar Khan,

Senior faculty at Bond University, Australia; and Dr. Muhammad Omer Chaudhry,

Associate Professor, Bahauddin Zakria University, Multan, for their detailed and

candid comments after a thorough review of the dissertation. The incorporation of

valuable comments of the reviewers have added the quality of the dissertation and

enhanced the appeal of earlier draft dissertation. I thank them all for taking their

precious time out to review this dissertation.

Generally, one needs to make sacrifices to achieve something in life, but sometimes

others make sacrifices, to make someone accomplish something in life. I think, I belong

to the latter category where my family members made all the sacrifices, for me to

complete my work. I sincerely thank my mother Safia Sultana, my wife Samara, my

daughter Zarmeen, my son Aizaz and all other members of my family who never

hesitated to sacrifice their preferences to accommodate mine. I thank them all for their

continuous and selfless affection.


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LIST OF FIGURES

Number Caption Page

Fig 1.1 Share of Pakistan’s Trade with Neighbours in its Total Trade

(2004-14) ……………………..……………………..…………….. 2

Fig 3.1 Gravity Based Explanation of Pakistan’s Trade with Partner

Countries ……………………..……………………..…………….. 56

Fig 3.2 Pakistan’s Export Intensity Index of Cotton for India

(Thematic Explanation) ……………………..…………………….. 61

Fig 3.3 Pakistan’s Import Intensity Index of Crude Oil from Iran

(Thematic Explanation)………………….……………..……….… 62

Fig 3.4 Thematic Explanation of Trade Complementarity……………...… 64

Fig 3.5 Thematic Link between Aggregate and Disaggregate

Analysis in the Study……………………………………………… 66

Fig 6.1 Comparison of Gap between Actual and Potential Bilateral Trade

of Pakistan with Neighbours ……………………...………………..162


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LIST OF TABLES

Number Caption Page

Table 1.1 Pakistan’s Exports and Imports 1950-1969………………………... 6

Table 1.2 Pakistan's Average Trade Aggregates 1970s and 80s……………… 7

Table 1.3 Pakistan's Average Trade Aggregates 1990s onwards ………….… 9

Table 1.4 Pakistan’s Bilateral Trade with Neighbours……………………….. 15

Table 1.5 A Dynamic View of Pakistan’s Trade with China……………….…16

Table 1.6 A Dynamic View of Pakistan’s Trade with India………………..… 17

Table 1.7 A Dynamic View of Pakistan’s Trade with Afghanistan………...…19

Table 1.8 A Dynamic View of Pakistan’s Trade with Iran…………………… 20

Table 4.1 Pattern of Pakistan's Concentration of Trade with its Neighbours

on Average (2004-14) ……..………………………………………. 78

Table 5.1 Descriptive Statistics of Pakistan’s Bilateral Trade with

Neighbouring Country …………..……………………………….... 85

Table 5.2 Descriptive Statistics of Pakistan’s Exports to Neighbouring

Countries ………..……………………..………………………….. 86

Table 5.3 Descriptive Statistics of GDP of Pakistan’s Neighbouring

Countries……..……………………..……………………………... 87

Table 5.4 Descriptive Statistics of Weighted Mean Tariff Rates in Pakistan’s

Neighbouring Countries ……………………..……………………..87

Table 5.5 Descriptive Statistics of Documents Required to Import in Pakistan’s



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Table 5.6 Descriptive Statistics of Cost per Container on the Ports of Pakistan’s

Neighbouring Countries ……………………..…………………….. 89

Table 5.7 Descriptive Statistics of Time Taken to Import in Pakistan’s


Table 5.8 Pakistan's Exports to the World as Per cent of Total Exports…….. 92

Table 5.9 Pakistan's Imports from the World as a Percentage of Total

Imports ……..……………………..……………………..………… 93

Table 5.10 Pakistan’s Leading Exports to India ……………………..……...… 95

Table 5.11 Pakistan’s Leading Imports from India……………………..………96

Table 5.12 Pakistan’s Leading Exports from China………..……………..…… 98

Table 5.13 Pakistan’s Leading Imports from China…………..…………..…… 99

Table 5.14 Pakistan’s Leading Exports to Afghanistan………….…………….. 101

Table 5.15 Pakistan’s Leading Imports from Afghanistan…………………….. 102

Table 5.16 Pakistan’s Leading Exports to Iran……………………....………… 104

Table 5.17 Pakistan’s Leading Imports from Iran ……………………..……… 106

Table 5.18 Top Ten Traded Categories in World (Amount in billions of $ and

Average is that of % of total World Trade – 2004-14) …….……… 107

Table 6.1 Results of Estimated Augmented Gravity Model of Bilateral

Trade ……..……………………..…………………………………. 113

Table 6.2 Results of Estimation Bilateral Trade Potential with Neighbouring

Countries……………………..……………..……………………… 115

Table 6.3 Pakistan's Ratio of Estimated Potential to Actual Bilateral Trade

with Neighbours ………………..……………………..…………… 116


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Table 6.4 Pakistan's Bilateral Trade with China (in Thousands of $)………... 117

Table 6.5 Pakistan's Bilateral Trade with India (in Thousands of $)……….… 118

Table 6.6 Pakistan's Bilateral Trade with Afghanistan (in Thousands of $).… 119

Table 6.7 Pakistan's Bilateral Trade with Iran (in Thousands of $)………...… 120

Table 6.8 Estimation of Pakistan’s Export Determinants with Neighbouring

Countries ……………………..……………………..……………... 122

Table 6.9 Estimation of Pakistan’s Export Potential with Neighbouring

Countries ……………………..……………………..……………...123

Table 6.10 Ratio of Estimated Potential to Actual Pakistan's Exports to

Neighbours ……………………..……………………..…………… 124

Table 6.11 Pakistan's Exports to China (in Thousands of $)…………………... 125

Table 6.12 Pakistan's Exports to India (in Thousands of $)………………….... 126

Table 6.13 Pakistan's Exports to Afghanistan (in Thousands of $)…………….127

Table 6.14 Pakistan's Exports to Iran (in Thousands of $)……………………..128

Table 6.15 Pakistan’s Trade Complementarity with China………………….… 130

Table 6.16 Pakistan’s Average Leading Exports to China and their Respective

Features ……………………..……………………..…………….… 132

Table 6.17 Pakistan’s EII and RCA of Top Ten Exports to China (2004-14)…134

Table 6.18 Pakistan’s Average Leading Imports from China and their

Respective Features…………..……………………..…………...… 135

Table 6.19 China’s RCA and Pakistan’s III for Top Ten Imports from China

(2004-14) ……………………..……………………..…………..… 137

Table 6.20 Pakistan’s Trade Complementarity with India ……..……………... 138


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Table 6.21 Pakistan’s Average Leading Exports to India and their Respective

Features ……………………..……………………..……………… 140

Table 6.22 Pakistan’s EII and RCA of Top Ten Exports to India (2004-14)…. 142

Table 6.23 Pakistan’s Average Leading Imports from India and their Respective

Features……………………..……………………..……………..… 143

Table 6.24 India’s RCA and Pakistan’s III for Top Ten Imports from India (2004-

14) ……………………..……………………..………………….… 145

Table 6.25 Pakistan’s Trade Complementarity with Afghanistan…………...… 146

Table 6.26 Pakistan’s Average Leading Exports to Afghanistan and their

Respective Features……………………..…………………………. 148

Table 6.27 Pakistan’s EII and RCA of Top Ten Exports to Afghanistan

(2008-14) …..……………………..………………………………...149

Table 6.28 Pakistan’s Average Leading Imports from Afghanistan and their

Respective Features……………………..………………………….151

Table 6.29 Afghanistan’s RCA and Pakistan’s III for Top Ten Imports from

Afghanistan (2008-14) ……………………..…………………….. 152

Table 6.30 Pakistan’s Trade Complementarity with Iran ……………………...154

Table 6.31 Pakistan’s Average Leading Exports to Iran and their Respective

Features…………….……………………..………………………...155

Table 6.32 Pakistan’s EII and RCA of Top Ten Exports to Iran (2004-14)…… 156

Table 6.33 Pakistan’s Average Leading Imports from Iran and their

Respective Features…………..……………………..……………..158

Table 6.34 Iran’s RCA and Pakistan’s III for Top Ten Imports from Iran

(2004-06 and 2010-14) ……………………..……………………. 160


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Appendix A List of 2-digit HS Classification Codes and Titles………………… 197

Appendix B Pakistan’s Revealed Comparative Advantage (2004-14) ………….198

Appendix C Pakistan’s Grubel and Lloyd Intra Industry Trade Index (2004-14)

………………………………………..……………………..………………200

Appendix D Pakistan’s Trade Intensity Index with China……………………… 202

Appendix E Pakistan’s Trade Intensity Index with India………………………..204

Appendix F Pakistan’s Trade Intensity Index with Afghanistan…………..…… 206

Appendix G Pakistan’s Trade Intensity Index with Iran………………………... 208

Appendix H Pakistan’s Export Intensity Index with China…………………….. 210

Appendix I Pakistan’s Export Intensity Index with India…………………….... 212

Appendix J Pakistan’s Export Intensity Index with Afghanistan……………… 214

Appendix K Pakistan’s Export Intensity Index with Iran……………………..…216

Appendix L Pakistan’s Import Intensity Index with China…………………….. 218

Appendix M Pakistan’s Import Intensity Index with India…………………….. 220

Appendix N Pakistan’s Import Intensity Index with Afghanistan……………… 222

Appendix O Pakistan’s Import Intensity Index with Iran………………………..224

Appendix P Average GLI of Pakistan and its Neighbouring Countries…………226

Appendix Q Average Revealed Comparative Advantage of Pakistan and its

Neighbours……………..………………………………………….. 227


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ABBREVIATIONS

AFTA ASEAN Free Trade Area

APTMA All Pakistan Textile Mills Association

APTTA Afghanistan Pakistan Transit Trade Agreement

ASEAN Association of South East Asian Nations

BoP Balance of Payments

BVS Bonus Voucher Scheme

CBM

CASA

Confidence Building Measure

Central Asia-South Asia

CGE Computable General Equilibrium

CPC

CPEC

Central Product Classification

China Pakistan Economic Corridor

CPGS Centre for Peace and Gulf Studies

D-8 Developing Eight (Islamic Countries)

EBS Export Bonus Scheme

ECM Error Components Model

ECO Economic Cooperation Organization

ECOTA

ESCAP

ECO Trade Agreement

Economic and Social Commission for Asia and the Pacific

EII Exports Intensity Index

EU European Union

FEM Fixed Effect Model

FICCI Federation of Indian Chambers of Commerce and Industries

FTA Free Trade Agreement

GATT General Agreement on Trade and Tariff

GDP Gross Domestic Product

GLI Grubel and Lloyd Index

GLS Generalized Least Squares


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GSP Generalized System of Preference

H-O Heckscher Ohlin

HS

IAI

Harmonized System

India-Afghanistan-Iran

III Import Intensity Index

IIT

ILO

Intra-Industry Trade

International Labour Organization

IMF International Monetary Fund

IPI Iran Pakistan India

IPRI Islamabad Policy Research Institute

ISI Import Substitution Industrialization

LoC Line of Control

LSDV Least-Squares Dummy Variable

MERCOSUR Mercado Común del Sur (Southern Common Market)

MFN

MNC

Most Favored Nation

Multi National Corporation

NAFTA North Atlantic Free Trade Agreement

NAICS North American Industrial Classification System

NATO North Atlantic Treaty Organization

NDMA Non Discriminatory Market Access`

NL Negative List

NPA Non-discriminatory Parallel Access

NRCA Normalized Revealed Comparative Advantage

NTB Non-Tariff Barrier

OLS

PCI

Ordinary Least Squares

Pakistan China Institute

PIA Pakistan International Airline

PILDAT Pakistan Institute of Legislative Development And Transparency

PL Positive List

PPP Purchasing Power Parity

PTA Preferential Trade Agreement


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QIZ Qualifying Industrial Zones

RCA Revealed Comparative Advantage

REM

RoW

Random Effect Model

Rest of World

RTA Regional Trade Agreement

SAARC South Asian Association for Regional Cooperation

SAFTA

SAP

South Asian Free Trade Agreement

Structural Adjustment Program

SAPTA South Asian Preferential Trade Agreement

SCO Shanghai Cooperation Organization

SIC Standard Industrial Classification

SITC Standard International Trade Classification

SL

TAPI

Sensitive List

Turkmenistan-Afghanistan-Pakistan-India

TCI Trade Complementarity Index

TDAP

ToT

TPP

Trade Development Authority of Pakistan

Terms of Trade

Trans Pacific Partnership

UAE United Arab Emirates

UK United Kingdom

UNCTAD United Nations Conference on Trade and Development

UNO United Nations Organization

USA United States of America

USAID

VER

United States Agency for International Development

Voluntary Export Restraint

WAPDA Water And Power Development Authority

WCO World Customs Organization

WDI World Development Indicators

WTO World Trade Organization


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CHAPTER 1

INTRODUCTION

1.1 Contextual Background

Global Economic integration of a country truly succeeds when it flourishes with its

neighbours too, besides succeeding elsewhere, especially, when seen in the context of

countries in Western Europe and North America. Pakistan’s economy was not as

integrated with its neighbouring countries as it was with rest of the world at the start of

the 21st century but this trend has gradually been changing since then in a manner that

the share of neighbour’s in Pakistan’s exports has been increasing while that of rest of

the world is decreasing (Hamid & Hayat, 2012). According to Economic Survey of

Pakistan 2014-15 more than a quarter of Pakistan’s imports1 and around 18% of its

exports2 have been with the neighbouring countries while China became the largest

trading partner of Pakistan in 2014-15.

Pakistan signed Free Trade Agreement (FTA) with China in 2006 and its exports to

China as a percentage of its total exports more than tripled while imports from China

have more than doubled from 2006 to 2014. While, there were extraordinary political

circumstances in Afghanistan until 2014 (before withdrawal of NATO forces) and in

Iran until 2015 (before the withdrawal of American and European sanctions), shaping

the bilateral trade with each country, trade with India was affected more because of

bilateral factors. The success of bilateral trade with China as a result of signing an FTA

suggest that if trade barriers are lowered with the other three neighbours too, and more

1 Imports of RS 776 billion (23%) from China and RS 134.1 billion (4%) from India while those from Iran and Afghanistan were not significant and therefore not mentioned in the Survey. 2 Exports of RS 169.9 billion (9%) to China and RS 143.6 billion (8%) to Afghanistan while those to Iran and India were not significant and therefore not mentioned in the Survey.


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importantly with India, because of the size of its economy and a long contiguous border

with Pakistan, there would also be improvement in bilateral trade. This study therefore

attempted to explore Pakistan’s free trade potential with its neighbouring countries.

Figure 1.1 is constructed by using the data from State Bank of Pakistan that compares

trade with neighbours and Rest of the World (RoW).

Figure 1.1 Share of Pakistan’s Trade with Neighbours in its Total Trade (2004-14)

The inner most circle of the doughnut informs about 2004 while the outermost circle

describes the distribution for 2014 in Figure 1.1. It is clearly visible that the share of

neighbouring countries has increased considerably during the period under review from

around 12% of world trade in 2004 to around 22% in 2014. However, it is important to

note that the share of India, Iran and Afghanistan did not change much as a percentage

of Pakistan’s total world trade while there was an extraordinary growth in Pakistan’s

trade with China especially after the agreement of free trade between the two countries

so much so that the share of China in Pakistan’s total trade increased from around 6%

in 2004 to nearly 16% in 2014.

Pakistan’s involvement in international trade (exports + imports) grew from less than

a billion dollars in 1947 to around $70 billion by 2014. However Pakistan’s

India China Iran Afghan RoW


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involvement in world trade grew in such a way that its trade with neighbouring

countries was not a significant volume of its world trade till the beginning of the current

century (shown in the inner most circle of figure 1.1) but gradually increased in the

subsequent years (shown in the outer circles of the figure 1.1). Situated in South Asia,

Pakistan shares its border in the North with Peoples Republic of China, in the East with

India, in the West with Afghanistan and Islamic Republic of Iran while in the South it

meets the Arabian Sea. It is an extremely important group of countries as (IMF, World

Economic Outlook database) over forty per cent of world population lives here while

over 23% of world’s GDP (on Purchasing Power Parity (PPP) basis) is being produced

by these countries. Although, population of these countries has historically been a

leading fraction of the world population its collective GDP was a little over six per cent

of the world in 1980 (IMF, World Economic Outlook database). Afghanistan is the

smallest economy, followed by Pakistan, Iran, India and China. India and China, the

two of the fastest growing economies in the world, comprised around 3/4th of the of the

five countries’ GDP in 1980 while by 2014, it was 9/10th of their GDP at PPP.

1.2 History of Pakistan’s Trade with the World

Pakistan’s trade with the world had passed through many phases in the last sixty seven

years from the birth of the country in 1947 to its partition in 1971; from short phases

of democratic governments to long tenures of dictatorships; from inward looking trade

policies in the early period after Pakistan came in to being to increased trade

liberalization in the latter periods of 1990s and afterwards; and from fixed exchange

rate systems to control pegged system and managed float exchange rates to market

based exchange rate system in the last few years. Khan (1998) provided a review of

Pakistan’s trade strategies in the latter half of the twentieth century from import

substitution in 1950s to export promotion in 1960s through the famous Bonus Voucher

Schemes; devaluation led export promotion in 1970s to one of the most restrictive

phases in Pakistan’s history in the 1980s; and eventually in post 1988 period most of

the non-tariff barriers were replaced with the tariff barriers.


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The following section attempts to distribute Pakistan’s trade with rest of the world in a

plain classification of decade wise performance of exports, imports and overall trade.

1.2.1 The Beginning – in Pursuit of Survival

Pakistan’s problems regarding potential involvement in international trade started even

before the August 14, 1947. Most of the traders, in the expected territory of Pakistan,

were Hindus and out of sheer uncertainty of the future lying ahead for them, they started

to relocate themselves in most likely Hindu dominated locations. This situation

deprived commercial banks of some their largest customers and eventually a substantial

number of bank branches were shut in just a few months preceding the already

proposed partition of India. Moreover, the newly established state of Pakistan was

being denied the payments due to be disbursed by the Government of India in time,

thus causing issues of financial solvency for the country. The long shadows, caused by

these initial acts of mistrust and misdemeanor between the two states, can even be seen

today.

The newly established state of Pakistan was desperate to establish its ability to survive

independently without any external crutches. Pakistan was a member of Common

Wealth of Nations under the leadership of the United Kingdom (UK) and as per

convention of that time all the member nations linked the external value of their

domestic currency to Pound Sterling. When the UK government devalued its currency

in late 1940s, the ambitious administration of newly born Pakistan chose not follow the

tradition. After the Indian decision to devalue its currency, the odds grew bigger for

Pakistan as the few potential exports of Pakistan to the world became more expensive

than the rival India’s world exports, especially in the context of jute and cotton crops.

But, apparently foolish3 policy choice proved a blessing in disguise when Korean War

created an opportunity for Pakistan to realize greater export earnings due to overvalued

exchange rate and a shorter supply of jute and cotton in international market.

3 It was apparently “foolish” policy choice for Pakistan not to comply with the devaluation drive by the Commonwealth countries because that would have made Pakistan’s exports expensive at a comparatively over-valued exchange rate.


24

1.2.2 The 50s and 60s – in Search of Self-reliance

The two most dominant policies of 1950s and 60s were Import Substitution

Industrialization (ISI) and export promotion policies respectively. As the country faced

Balance of Payments (BoP) related challenges in the very beginning, lavish support

was offered in 1950s to the industries producing import substitutes to save the scarce

foreign exchange. The policy package of ISI included cheaper access to credit, higher

tariffs on substitute import items, favourable tax treatment and administrative

guarantees not to withdraw the support offered to those industries for some time. The

typical excuse of that time, in order to extend support to an industry was called “infant

industry argument”, whereby an industry is declared an infant and is offered support

against its foreign competitors as long as it grows strong enough face global

competition. Unfortunately, most of these protected industries operate even today with

support and protection and have registered a case of grown up “infants”.

The decade of 1950s saw the highest average annual growth rate of 14.24% in exports

than any of the following decades (Table 1.1). Moreover, 1950s is the only decade that

produced an average annual trade surplus of $ 852004.89 whereas all the following

decades witnessed a continuous hike in the trade deficits with an exception of 1990s

when the average annual deficit was $16.42 million which in 1980s had reached around

$27 million.

The most significant feature of the trade policy in the 1960s was export Bonus Voucher

Scheme (BVS) that offered a preferential exchange rate to the exporters. The official

exchange rate was RS 4.76/$ while the voucher recipients were enjoying a preferential

exchange rate of RS 6.19/$ to RS 7.62/$. This policy allowed exporters to become more

competitive internationally. Moreover, an extraordinary emphasis was laid on the

export of manufactured goods by introducing an Export Bonus Scheme (EBS). As a

result of BVS and EBS, the share of manufactured exports increased and that of primary

goods decreased drastically by the end of 1960s (Zaidi, 2015).

Table 1.1: Pakistan’s Exports and Imports 1950-1969

1950-59 1960-69


25

Average Volumes (in

Mln $)

Exports 354.20 513.32

Imports 323.60 868.34

Trade Balance 30.60 -355.02

Average Percentage

Change

Exports 14.24% 9.91%

Imports 6.02% 13.97%

Total Trade 6.94% 11.91%

Average Per Capita

Volumes (in $)

Exports 9.677 10.132

Imports 8.825 17.161

Trade Balance 0.852 -7.029

Source: Author’s Calculation from Annual Reports of State Bank of Pakistan Various

Issues (Trade Data) and Economic Survey of Pakistan, Various Issues (Population Data

for Per Capita Measures).

1.2.3 The 1970s and 80s

These two decades can be distributed in to three different phases from the economic

perspective. The first phase started with the separation of Eastern and Western wings

of Pakistan and coming into power of Zulfiqar Ali Bhutto; and ended with the end of

his rule in 1977. The second phase is spread over eleven years Zia ul Huq from 1977

to 1988, when he was killed in a plane crash. After the general elections of 1988, there

started a period of deep political divide in the country and increased role of World Bank

and IMF in introducing and implementing the structural adjustment programs.

The 1970s was different from that of 1960s in almost every respect. The decade of

1960s had a Pakistan consisting of both the Eastern and Western wings, with high GDP

growth rates, where trade was governed through a comprehensive policy framework of

import licensing and BVS under overvalued exchange rate; whereas that of 1970s was

Pakistan that has lost its Eastern wing; growth rates remained low because of a host of

factors like domestic floods and international oil price hike; with abandoned import

licensing and BVS, while exchange rate was devalued by 56% and the import of all

luxury items was banned. However, when Zia assumed power in 1977, he gradually

reversed many of the policies introduced by the successive governments of Bhutto and

liberalized the trade by reducing the number of banned goods and removing the non-


26

tariff barriers on imported goods. Moreover, Rupee was delinked from dollar to

introduce a flexible exchange rate system whereby exporters were given concessionary

credit facility for competitiveness in the international market. In 1988, the first

structural adjustment program was launched to further liberalize Pakistan’s trade.

Table 1.2: Pakistan's Average Trade Aggregates 1970s and 80s

1970-79 1980-89

Average volumes

(in Mln $)

Exports 1017.65 3170.62

Imports 1796.28 5723.69

Trade Balance -778.63 -2553.07

Average

Percentage

Change

Exports 10.37% 11.79%

Imports 17.54% 7.26%

Total Trade 14.08% 8.61%

Average Per

Capita Volumes

(in $)

Exports 14.163 33.307

Imports 24.629 60.756

Trade Balance -10.312 -27.035



for Per Capita Measures)

If we compare the two decades of 70s and 80s, it can be seen clearly from the aggregates

that exports grew at higher average rate of 11.79% in 80s than at 10.37% in the

seventies while imports grew at a much higher average annual percentage of 17.54%

in 1970s than at 7.26% in the 80s. As a result the deficits that grew in both the decades,

grew at an average annual percentage of 14.08% in 70s than at 8.61% in the 80s. When

we look at average annual per capita terms exports, imports and trade deficits all grew

in this period from 1970s to 1980s but as growth in per capita exports was less than

that of imports, trade deficits grew bigger in the two decades.

1.2.4 The 1990 and Onwards

Eleven years of Zia (1977-88) period were followed by eleven years of alternating

administrations of Benazir Bhutto and Nawaz Sharif (1988-99) when in 1999 Pervez


27

Musharraf assumed power. When Nawaz Sharif went for the atomic option in 1998, he

chose an option foreign exchange control by fixing the official rate of exchange for

currencies and carried out the policy of exchange rationing that unleashed a wave of

uncertainty in Pakistan’s trading stakeholders both at home and abroad. After 9/11

incident, Pakistan was in the eye of the storm but when USA chose to attack

Afghanistan, Pakistan became its strategic partner and gradually the economic revival

was seen but not without huge costs associated with this revival.

When the first tenure of Benazir government started in 1988-90, it was preceded by

two years of strong export growth of (over 20% in each year) and negative import

growth (over 4% in each year) but the two years term that followed saw reversal of the

trends. The period offered a modest export growth of around 5% and imports grew in

excess of 10% in 1989, touching seven billion dollar mark for the first time but fell

slightly in the following year. Benazir’s government was succeeded by Nawaz Sharif

(1990-93) in whose term in office liberalization that had started under Structural

Adjustment Program (SAP) started to pay off with strong export and import growth but

the budget deficits that had generally been falling during 1985-91 started to rise sharply

in this period. In the second term of Benazir (1993-96) despite showing growth in

exports, the growth in imports was higher than that of exports and deficit hovered

around $ 3 billion mark.

The period from 1997 to 2004 was marked with falling deficits for Pakistan. Budget

deficit in the country was highest (till then) of more than $ 3.5 billion but on average

remained less than $ 1.4 billion for the next six years due to a number of significant

developments both at home and abroad. In May 1988, Pakistan carried out atomic test

causing capital fight and trade sanctions on Pakistan for the next few years; and latter

in 2001, the collapse of twin towers added to the already existing compulsions for

Pakistan. The aforementioned reasons in general and other specific policy and market

condition in particular kept export earnings in excess of import payments to keep the

monster of trade deficit at leash. From 2004 onwards there started a period of

extraordinary deficits whereby deficits that had never been four billion dollar got $6


28

billion in 2005, then doubled in the following year and then reached $20 billion in the

next two years.

Table 1.3: Pakistan's Average Trade Aggregates 1990s Onwards

1990-99 2000-09 2010-13

Average volumes (in

Mln $)

Exports 7324.76 13493.63 23046.00

Imports 9599.36 21369.69 41246.50

Trade Balance -2274.60 -7876.06 -18200.50

Average Percentage

Change

Exports 5.65% 8.88% 9.11%

Imports 3.70% 15.12% 6.83%

Total Trade 4.40% 12.52% 7.49%

Average Per Capita

Volumes (in $)

Exports 59.306 86.196 128.669

Imports 77.844 127.369 230.151

Trade Balance -16.421 -48.155 -101.482



for Per Capita Measures)

The volume of deficit saw an astronomical increase since the start of 21st century when

in a period of 2000-09 average annual deficits increased almost three times more than

the preceding decade. Later on, during the period of 2010-13, average deficits doubled

from that of 2000-09, thus causing huge balance of payments compulsions for the

country. It was precisely this extraordinary development that forced the country to seek

short term borrowing from IMF under stiff conditional terms and high mark up after

exhausting most of domestic borrowing options. The huge domestic borrowing, in the

face of insufficient revenue collection, had extreme domestic implications of

inflationary burden, higher cost of domestic borrowing, and in consequence, poor

investment possibilities eventually causing low growth rate of the economy.

1.3 Continental Preference of Pakistan’s Trade


29

Regionally, Pakistan’s trade is linked with the world in such a way that most of its trade

takes place with the three regions of Asia, Western Europe and North America.

Historically, from early 1950s till 2005, on average, nearly 4/5th of Pakistan’s total trade

was with these regions. Due to greater diversity in Pakistan’s trading destinations, the

fraction of Pakistan’s total trade with these regions has dropped a little, but is still

around 3/4th of Pakistan’s total trade. Pakistan’s import requirements were met over 90

% from these three regions until 2000 but then gradually this dependence started

declining and in 2014, less than 80% imports came from the three regions. As far as

exports are concerned nearly 3/4th of Pakistan’s total exports have reached these three

regions throughout our history, though there have been phases when this ratio has stood

significantly higher than the average. An attempt is being made here to observe the

Pakistan’s pattern of trade with its leading regional trade partners.

1.3.1 Pakistan’s Trade with Asian Countries

Pakistan’s trade with Asian countries has traditionally been dominating Pakistan’s

trade in recent times. Countries of Western Europe were dominating Pakistan’s trade

both in terms of exports and imports until 1960s, but then Asia became the largest

trading partner of Pakistan. Since the start of 21st century, on average, more than half

of Pakistan’s overall trade is with Asian countries. In the recent years, on average,

nearly 60% of Pakistan’s overall trade was with Asian countries.

Pakistan’s trade with Asian countries can be distributed in two distinct phases – the

first being the one during which exports to Asian countries were more than imports as

a percentage of total exports and total imports, respectively; and the second being the

one where the trend of the former period was reversed. Until 1970s exports to Asian

economies as a percentage of Pakistan’s total exports remained higher than the imports

from Asian countries as a percentage of its total imports. However, during early 1980s,

Pakistan’s reliance on Asian economies for its overall imports exceeded Pakistan’s

preference to export to Asian economies as a percentage of its overall exports – a trend

that never reversed subsequently. By the middle of 1970s, more than half of Pakistan’s

exports were from Asian countries, and by the end of 1970s, more than half of its

imports were also from the Asian countries, but the dominance of Asia in terms exports


30

was rather short lived from 1972 to 1984, with an exception of 1979 when it was 48.5%.

It is, however, important to note that in spite of the fact that Pakistan’s export to Asian

economies fell below 50% after the middle of 1980s, it still dominates all other regions

regarding exports.

Therefore, it would be fair to say that Pakistan’s trading relations with Asian countries

dominates its trade with any other region in world. Though it was Western Europe that

dominated Pakistan’s trade till 1970s but then it returned in favour of Asia.

1.3.2 Pakistan’s Trade with Western Europe

The sources of early trading dependence of Pakistan with Western Europe may be

linked to the colonial period when the British ruled over India. In the beginning, nearly

half of Pakistan’s total trade was with the countries in Western Europe but it gradually

declined and now stands less than a tenth of its total trade. There was a surge in this

declining trend from mid 1980s to the end of twentieth century but it sharply declined

afterwards. The strongest trade partners of Pakistan in Western Europe included

countries like UK, Germany and France.

It is important to note that the decline in the proportion of imports to Western Europe

vis-à-vis Pakistan’s total imports was sharper than the case of Pakistani exports to the

same region. Although the proportion of trade with Western Europe has been declining,

the absolute volume of trade with region continued to escalate with exceptions of early

1970s and the period of 2000-05. The absolute volume of trade that was a little over

three hundred million dollar was nearly six billion by 2013. Imports and exports that

were roughly $150 million each in late 1950s have on average exceeded $ 3.2 and $

2.6 billion respectively during 2011-13. The period of 2001-05 showed that both

exports to and imports from Western Europe almost halved than the comparable

preceding period’s absolute values.

1.3.3 Pakistan’s Trade with North America

Pakistan’s trade with North America was primarily focused on United States of

America (USA) and Canada, of which USA is a leading partner. Pakistan’s total trade


31

with the region that was less than 5% of its total trade in early 1950s reached its peak

value of around 1/3rd of its total trade in 1964. During 1957-71, on average, nearly

quarter of Pakistan’s total trade was with North America. It is sometimes argued that

this early trading relationship with North America in general and USA in particular was

the result of political ties between USA and Pakistan – first as a result of Liaqat Ali

khan’s visit in May 1950 and then Ayub Khan’s visit in 1961 to USA thus contributing

to diplomatic, political and economic ties.

Pakistan’s imports from the region increased quickly during the Ayub period, so much

so that during the period 1961-65, North America was the largest importing region for

Pakistan with almost 40% of Pakistan’s overall imports arising from North America.

However, this import reliance as percentage of total imports continued to wane in the

following periods in such a way that during 2011-13, Pakistan’s imports from the

region were around 3% of its total imports. In absolute terms, Pakistan’s imports from

the region that were on average less than $100million during 1951-60, increased three

times during 1960s; Later on, imports on average were over $400million during 1971-

80 and almost doubled; while the similar growth was not seen after 1990s.

Ironically, while Pakistan was picking up its trade volumes with North America,

especially USA, it could not manage to find markets for its exports in that region.

Pakistan’s exports to the region never crossed $100 million till 1979 which marks the

beginning of continuous surge in Pakistan’s exports to the region. Pakistan’s exports

exceeded one billion dollar at the beginning of 1990s and more than doubled by the

end of the decade. Pakistan’s export earnings have been in excess of four billion dollar

during 2006-13 and is around 1/5th of Pakistan’s overall export earnings since mid-

1990s.

The pattern of Pakistan’s trading relationship with North America was such that in the

beginning, Pakistan had increasing import dependence on North American region, and

in the latter period, Pakistan realized increased export preference with the region, thus

making trade ties with the region more comprehensive.

1.3.4 Pakistan’s Trade with Rest of the World


32

Earlier, Pakistan’s trade with Asia, Western Europe and South America was discussed

and all the other regions combined have been termed as rest of the world in present

discussion. The regions that constitute this residual category include Central and South

America, Eastern Europe, Africa and Oceania. The major countries in the region

Oceania are Australia and New Zealand.

Pakistan’s trade progressed in such way that bulk of its overall trade was focused in the

three regions discussed earlier, however at the beginning of twenty first century this

trend changed significantly and the category called rest of the world became the second

largest regional group after that of Asia and nearly 1/5th of Pakistan’s overall trade was

with these regions, called rest of the world since the start of 21st century.

If Pakistan’s trade with rest of the world is taken as a proxy for trade diversification, it

offers an interesting analysis. The reason for taking trade with rest of the world as a

proxy for trade diversification is that if the number of trading destinations increased, it

is termed as trade diversification; hence it is argued that increased regional coverage

may also be treated as trade diversification. Pakistan’s diversification in terms of

imports increased with the start of twenty first century when Pakistan’s imports to

Asian countries as a percentage of its total imports became quite significant, exceeding

that of North America and Western Europe. Historically, Pakistan’s imports from rest

of the world mostly did not significantly exceed 1/10th of its total trade until 1998 and

the exception to this trend was seen in 1958 (11.01%), 1967 (13.79%), 1969-73 (on

average 12.06%) and 1975 (12.52%). The trend that started from 1999 onwards showed

that by 2007 nearly a quarter of all the imports were coming from the regions

comprising “rest of the world”.

The case of exports was more interesting as it offered cyclical trend in terms of exports

to the region as a percentage of total exports. The period of 1959-71 showed that on

average more than 1/5th of Pakistan’s exports were to the region, which during 1972-

86 on average was around 1/8th, and during 1987-01 dropped further to less than 1/10th.

However this trend reversed significantly in the period following 2001 when on

average Pakistan’s exports to the region exceeded a quarter of its total exports. Thus,

thirteen years of greater export diversification (1959-71) was followed by a gradual


33

decline in the following thirty years (1972-2001) to see a continuing upswing in the

following years (2002-13).

The start of twenty first century marks the beginning of World Trade Organization

(WTO) regime, so it would not be unrealistic to expect that one of the reasons of greater

trade diversification, both in terms of exports as well as imports, was the consequence

of trade liberalization ushered by WTO regime.

1.4 Pakistan’s Trade with Neighbours

Pakistan’s trade with its neighbours increased in every year since 2009 when exports

as well as imports with the neighbouring countries fell; but in that year it was a part of

a bigger trend when both imports and exports of Pakistan with the world also fell

significantly. Pakistan’s overall trade with the neighbouring countries grew both in

absolute value as well as the proportion of the total world trade in each year during

2004-14 with an exception 2009. There was more than five times growth of bilateral

trade with neighbours from a little above $ 3 billion in 2004 to a little below $17 billion

in 2014. Despite all the challenges confronted in bilateral trade with the neighbouring

countries, collectively, bilateral trade as a proportion of world trade improved in each

year since 2004-14 and more than doubled from 10.5% in 2004 to 23.3% in 2014.

Pakistan’s exports with its neighbours grew in a manner that it outpaced Pakistan’s

overall exports performance in each year from 2007 to 2012. However, there has been

a decline in Pakistan’s exports both in terms of total value and proportion of total

exports during 2013-14 with a joint decline of around $ 641 million and proportionate

decline of around 3% respectively in the two years. Pakistan’s exports to the neighbours

almost doubled from 2004 to 2005 in one year which was followed by a decline in each

of the two following years and a strong recovery of over a billion dollars increase in

exports to the neighbours both in 2008 and 2011. In percentage terms, Pakistan’s

exports to the neighbouring countries increased almost three times during 2004-2012.

However, these trends of export performance were not the same with all the

neighbouring countries alike.


34

Table 1.4: Pakistan’s Bilateral Trade with Neighbours vis-à-vis World

Years

Neighbours (In Million $) World (In Million $) Neighbours as % of World

Imports Exports

Total

Trade Imports Exports

Total


Total

Trade

2004 2262.9 1026.4 328. 17948 13379 31327.6 12.6% 7.7% 10.5%

2005 3342.4 2016.1 5358 25096 16050.2 41146.8 13.3% 12.6% 13.0%

2006 4538.0 2003.6 6541 29825 16932.9 46758.6 15.2% 11.8% 14.0%

2007 5956.7 1889.4 7846 32593 17838.4 50432.3 18.3% 10.6% 15.6%

2008 7252.6 2955.1 10208 42326 20279 62605.6 17.1% 14.6% 16.3%

2009 5937.2 2859.2 8796 31583 17554.7 49138.4 18.8% 16.3% 17.9%

2010 7829.6 3577.8 11407 37537 21413.1 58950.1 20.9% 16.7% 19.4%

2011 8581.3 4765.4 13347 43578 25343.8 68922 19.7% 18.8% 19.4%

2012 8615.5 5209.2 13825 43813 24613.7 68426.9 19.7% 21.2% 20.2%

2013 8975.76 5115.7 14091 43775 25120.9 68896.1 20.5% 20.4% 20.5%

2014 12271.1 4567.3 16838 47544 24722.2 72267.1 25.8% 18.5% 23.3%

Source: Author’s Calculation from UNCOMTRADE

Pakistan’s imports from the neighbouring countries showed a very strong trend from

2004 to 2008 when the imports increased in excess of one billion dollars each year such

that there was an increase of almost $ 5 billion in this duration. The highest increase in

imports from the neighbours in a single year came in 2014 with an increase of $ 3.295

billion from the previous year’s import bill. Nearly, a quarter of Pakistan’s imports

from the world now come from its neighbours while more than 3/4th of this quarter

comes from China alone, which is analyzed in detail separately in the following section.

1.4.1 Pakistan’s Trade with China

Historically China has been a dominant shareholder of Pakistan’s trade with its

neighbours in the last twenty five years in general and last fifteen years in particular.

In 2004 more than half of Pakistan’s total trade with neighbours was with China and

by 2014 significantly more than 2/3rd of the same was with China. Pakistan’s bilateral

trade volume increased, almost by a billion dollar per year during 2004-14, from $ 1.78

billion to $ 11.84 billion. Pakistan’s bilateral trade volume as percentage of world trade

increased almost three times from less than 6% in 2004 to well above 16% in 2014.

Table 1.5: A Dynamic View of Pakistan’s Trade with China


35

Year

s

China (In Million $) China as % of World Trade China as % of Neighbours

Imports Exports Total

Trade

Import

s

Export

s

Total

Trade

Import

s

Export

s

Total

Trade

2004 1488.7

7

300.58

1

1789.

4 8.3% 2.2% 5.7% 65.8% 29.3% 54.4%

2005 2349.4 435.68

2

2785.

1 9.4% 2.7% 6.8% 70.3% 21.6% 52.0%

2006 2914.9

3

506.64

2

3421.

6 9.8% 3.0% 7.3% 64.2% 25.3% 52.3%

2007 4164.2

3

613.75

9 4778 12.8% 3.4% 9.5% 69.9% 32.5% 60.9%

2008 4738.0

6

726.71

1

5464.

8 11.2% 3.6% 8.7% 65.3% 24.6% 53.5%

2009 3779.7

7

997.85

4

4777.

6 12.0% 5.7% 9.7% 63.7% 34.9% 54.3%

2010 5247.7

1

1435.9

4

6683.

7 14.0% 6.7% 11.3% 67.0% 40.1% 58.6%

2011 6470.6

5

1678.9

6

8149.

6 14.8% 6.6% 11.8% 75.4% 35.2% 61.1%

2012 6687.5

7

2619.9

4

9307.

5 15.3% 10.6% 13.6% 77.6% 50.3% 67.3%

2013 6626.3

2

2652.2

2

9278.

5 15.1% 10.6% 13.5% 73.8% 51.8% 65.8%

2014 9588.4

2 2252.9 11841 20.2% 9.1% 16.4% 78.1% 49.3% 70.3%

Source: Author’s Calculation from UNCOMTRADE4

Most of Pakistan’s bilateral trade with China was in the form of Pakistan’s imports

from China that also increased persistently in each year during 2004-14, with an

exception of 2013. There was an increase of almost $ 3 billion in 2014. The share of

China in Pakistan’s total imports more than doubled during 2004-14. Moreover, the

share of China in Pakistan’s imports from neighbours also increased from less than

2/3rd in 2004 to significantly more than 3/4th in 2014.

4 UNCOMTRADE is a pseudonym for United Nations International Trade Statistics Database.


36

Traditionally, Pakistan’s exports to China had not been as impressive as were its

imports from China. Although Pakistan’s exports to China remained below $ 1 billion

mark till 2009 yet the volume of exports almost tripled from 2004 to 2009. Latter on,

there was an increase of almost $ 1 billion, in one year, from 2011 to 2012. Pakistan’s

exports, on average, were above $ 2.5 billion that was almost 10% of total exports and

50% of its exports to the neighbours during 2012-14.

1.4.2 Pakistan’s Trade with India

India was the largest trade partner of Pakistan in late 1940s and early 1950s but the

entire structure of Pakistan’s economic relations underwent a change because of

political developments over the years and such political and institutional factors

continue to determine the pattern of Indo-Pak trade today.

Table 1.6: A Dynamic View of Pakistan’s Trade with India

India (In Million $) India as % of World Trade India as % of Neighbours

Years Imports Exports Total


Total


Total

Trade

2004 454.41 158.5 612.9 2.53% 1.18% 1.96% 20.08% 15.44% 18.63%

2005 576.7 337.22 913.9 2.30% 2.10% 2.22% 17.25% 16.73% 17.06%

2006 1115 326.7 1442 3.74% 1.93% 3.08% 24.57% 16.31% 22.04%

2007 1266.2 291.7 1558 3.88% 1.64% 3.09% 21.26% 15.44% 19.86%

2008 1691.5 354.64 2046 4.00% 1.75% 3.27% 23.32% 12.00% 20.04%

2009 1080.4 235.32 1316 3.42% 1.34% 2.68% 18.20% 8.23% 14.96%

2010 1559.9 274.98 1835 4.16% 1.28% 3.11% 19.92% 7.69% 16.09%

2011 1607.3 272.86 1880 3.69% 1.08% 2.73% 18.73% 5.73% 14.09%

2012 1572.6 347.99 1921 3.59% 1.41% 2.81% 18.25% 6.68% 13.89%

2013 1874.1 402.75 2277 4.28% 1.60% 3.30% 20.88% 7.87% 16.16%

2014 2104.8 392.21 2497 4.43% 1.59% 3.46% 17.15% 8.59% 14.83%


Pakistan’s bilateral trade with India increased more than three times in four years from

2004-08 from $ 612 million to over $ 2 billion but remained below that value till 2012.

Although, Pakistan’s bilateral trade with India as a percentage of world trade has

modestly increased during 2004-14 but it has decreased as a proportion of its trade with


37

neighbouring countries in the same period. Most of Pakistan’s bilateral trade with

India was in the form of imports from India despite the fact that India extended the

Most Favoured Nation (MFN) status to Pakistan while Pakistan has yet to return this

favour.

Pakistan’s imports from India increased from 2004-08 from less than half a billion

dollars to about $ 1.6 billion and went drastically down in 2009, most probably because

of Mumbai attacks of 2008. Imports started growing again during 2010-14 with a value

of $2.1 billion in 2014. There was a general increase in Pakistan’s imports from India

as a percentage of its total imports from 2.5 % in 2004 to 4.4% in 2014 while the same

as a percentage of neighbours generally declined from 24.5% in 2006 to 17.1% in 2014.

Pakistan’s exports to India have been very low, stagnant and insignificant (considering

the size of Indian economy) with the highest export value of $ 402 million in 2013.

While Pakistan’s exports to India as a percentage of its total exports have been rather

stagnant, the same has fallen drastically as a percentage of its exports to neighbours

from more than 15 % in 2004 to less than 6% in 2011.

1.4.3 Pakistan’s Trade with Afghanistan

Pakistan’s bilateral Trade with Afghanistan has historically been dominated by its

exports to Afghanistan rather than imports from there. However, this trend was

changing slowly with only about 5% of bilateral trade in the form of imports from

Pakistan in 2005 that later increased to more than 17% by 2014. Pakistan’s bilateral

trade with Afghanistan that generally increased from 2004-11, latter on reversed in the

next three years.

Pakistan’s imports from Afghanistan have not been even 1% of Pakistan’s total imports

but volume of the imports continued increasing consistently during 2004-14, with an

exception of 2008, when the imports fell by around $ 4 million. However, broadly

speaking, imports from Afghanistan increased from less than $ 50 million in 2004 to $

392 million by 2014.

Table 1.7: A Dynamic View of Pakistan’s Trade with Afghanistan


38

Afghanistan (In Million $) Afghanistan as % of World Trade Afghanistan as % of Neighbours

Years Imports Exports

Total


Total

Trade Imports Exports Total Trade

2004 48.79 464.57 513.36 0.27% 3.47% 1.64% 2.16% 45.26% 15.61%

2005 53.218 1064.7 1118 0.21% 6.63% 2.72% 1.59% 52.81% 20.86%

2006 64.944 991.5 1056.4 0.22% 5.86% 2.26% 1.43% 49.49% 16.15%

2007 89.493 837.68 927.17 0.27% 4.70% 1.84% 1.50% 44.34% 11.82%

2008 85.545 1447.6 1533.2 0.20% 7.14% 2.45% 1.18% 48.99% 15.02%

2009 121.16 1373.9 1495 0.38% 7.83% 3.04% 2.04% 48.05% 17.00%

2010 138.38 1684.7 1823 0.37% 7.87% 3.09% 1.77% 47.09% 15.98%

2011 199.53 2660.3 2859.8 0.46% 10.50% 4.15% 2.33% 55.83% 21.43%

2012 235.09 2099.3 2334.4 0.54% 8.53% 3.41% 2.73% 40.30% 16.89%

2013 307.6 1998.1 2305.7 0.70% 7.95% 3.35% 3.43% 39.06% 16.36%

2014 392.17 1879.1 2271.3 0.82% 7.60% 3.14% 3.20% 41.14% 13.49%


Pakistan’s exports to Afghanistan in terms of value and percentages generally increased

from 2004 to 2011 and in the following three years there was a decline both in value as

well as percentage. There was nearly six fold increase in the value of Pakistan’s exports

to Afghanistan, more than three times increase in percentage of its total exports and

about 10% increase in the percentage of its exports to neighbours from 2004 to 2011.

The decline in Pakistan’s exports to Afghanistan in all the three aspects could be due

to gradual withdrawal of NATO forces from Afghanistan during 2012-14 bringing a

decline in NATO supplies to Afghanistan through Pakistan.

1.4.4 Pakistan’s Trade with Iran

The share of Iran in Pakistan’s bilateral trade with the neighbours was the smallest,

among all the neighbours during 2004-14. However, in absolute terms the value of

bilateral trade exceeded $ 1 billion `during 2008-10. The value of the bilateral trade

kept constantly shrinking since 2009 in each year. Pakistan’s imports from Iran fell

drastically from $977 million in 2009 to $ 185 million in 2014. Similarly Pakistan’s

exports to Iran also declined drastically from $ 426 million in 2008 to $ 43 million in

2014. The decline in Pakistan’s bilateral trade with Iran could be the result of increased

sanctions on Iran’s participation in international trade at that time. Therefore recent


39

withdrawal of sanction on Iran in 2015 could be expected to bring an improvement in

the bilateral trade between the two countries.

Table 1.8: A Dynamic View of Pakistan’s Trade with Iran

Iran (In Million $)

Iran as % of World

Trade Iran as % of Neighbours

Years Imports Exports

Total


Total


Total

Trade

2004 270.951 102.78 373.7 1.51% 0.77% 1.19% 11.97% 10.01% 11.36%

2005 363.168 178.43 541.6 1.45% 1.11% 1.32% 10.87% 8.85% 10.11%

2006 443.177 178.78 622 1.49% 1.06% 1.33% 9.77% 8.92% 9.51%

2007 436.803 146.24 583 1.34% 0.82% 1.16% 7.33% 7.74% 7.43%

2008 737.6 426.15 1164 1.74% 2.10% 1.86% 10.17% 14.42% 11.40%

2009 955.906 252.16 1208 3.03% 1.44% 2.46% 16.10% 8.82% 13.73%

2010 883.591 182.19 1066 2.35% 0.85% 1.81% 11.29% 5.09% 9.34%

2011 303.785 153.27 457.1 0.70% 0.60% 0.66% 3.54% 3.22% 3.42%

2012 120.338 141.95 262.3 0.27% 0.58% 0.38% 1.40% 2.73% 1.90%

2013 167.777 62.635 230.4 0.38% 0.25% 0.33% 1.87% 1.22% 1.64%

2014 185.731 43.049 228.8 0.39% 0.17% 0.32% 1.51% 0.94% 1.36%


The table 1.8 clearly shows that the decline in Pakistan’s trade with Iran was drastic in

the context of neighbouring countries where the share of Iran in bilateral trade was

nearly 10% from 2004-06 while it was only fraction above 1% by 2014. Moreover, the

decline was relatively more in Pakistan’s exports to Iran than imports from there.

1.5 Objectives

This dissertation attempts to assess and explore Pakistan’s free trade potential, at

aggregate and disaggregate levels respectively, with each of its four neighbouring

countries of China, India, Iran and Afghanistan. The dissertation sets following specific

objectives in this regard.


40

To find out key determinants of Pakistan’s bilateral trade with and exports to

the partner countries in order to determine the sensitivity of bilateral trade with

the partner countries

To estimate Pakistan’s bilateral and export potential with each of its

neighbouring countries at aggregate level and an assessment of trade gap and

export gap from their respective potentials

To estimate Pakistan’s bilateral trade complementarity with each of the

neighbouring countries and changes in bilateral complementarity from 2004 to

2014

Economic assessment of Pakistan’s leading export and imports with each

neighbour on the basis of indices derived from disaggregated trade statistics

The first two objectives serve the aggregate trade considerations while the last two

objectives furnish the disaggregated trade considerations.

1.6 Significance

The significance of this study cannot be understated in the cotemporary local as well

as global circumstances. South Asia was argued to be the least integrated region

(Hussain, 2012) in an otherwise fast integrating world. Political and ideological

imperatives that used to determine economic relations during the periods world wars,

cold war and even after the cold war were being shaped by the economic opportunities

in the twenty first century. The fruits of European cooperation have attracted the

attention of politicians and policy makers alike all around the world.

India and China have seen tremendous growth in their bilateral trade in the last fifteen

years; America resumed diplomatic relations with Cuba and approved the withdrawal

of sanctions on Iran in 2016; Western Europe started gradually engaging with East

European nations; and China is getting along with Hong Kong under the slogan of “one

country – two systems”. Similarly, at a local level, Pakistan and India, the most hostile

regional enemies, have not been in the best of terms with each other but became


41

members of Shanghai Cooperation Organization (SCO) in July 2015; granting of 10-

Year Generalized System of Preference (GSP) plus status to Pakistan by the European

Union (EU) in 2013; Iran Pakistan India (IPI) gas pipeline despite the delays over the

years was agreed; and despite all the challenges Pakistan’s trade with its neighbours

increased both in volumes as well as in percentages in the last ten years. Moreover,

Pakistan entered in to an FTA with Sri Lanka, China and Malaysia in 2002, 2006 and

2007 respectively besides signing South Asian Free Trade Agreement (SAFTA) in

2004 and a Preferential Trade Agreement (PTA) with Iran in the same year.

At the time of submission of the synopsis in 2012, the author hoped that the significance

of this study would increase in the following few years. However, the extraordinary

developments that have taken place, made this study a lot much more significant in

2016, were not really anticipated. The study would discuss the importance of Pakistan’s

trade with each of its neighbour separately.

China has become Pakistan’s largest trading partner, the largest shareholder in its

imports from the world and the second largest export destination of Pakistan after USA

(Pakistan, 2015). There has been a phenomenal growth in Pakistan’s trade with China

ever since the two countries entered in to an FTA with each other in 2006. Pakistan’s

imports from China have more than doubled as a percentage of its overall imports and

its exports have more than tripled as a percentage of overall exports from their

respective levels from 2006 to 2014. There are greater ramifications of the FTA in the

form of a much larger bilateral economic cooperation between the two countries in the

shape of China-Pakistan Economic Corridor (CPEC). The end of the infrastructural

projects of CFEC, would mark the beginning an era of greater economic relations with

Central Asia both for Pakistan as well as China.

At the time of Pakistan’s independence, for the first few years, nearly two third of

Pakistan’s world trade was with India (Zaidi, 2015), however, the four wars in 1948,

1965, 1971 and 1999 along with longstanding issue of Kashmir between the two

countries eroded much of that bilateral economic cooperation between the two

countries, being attained in the earlier period. Nearly, half of Pakistan’s imports were

from India in 1948-49 which was at its lowest point of 0.06% in 1975-76 (Pakistan,


42

2013). However, there were some positive developments in the last twenty years also.

India extended the MFN status to Pakistan though the favour was not returned by

Pakistan. The real trouble in that regard, perhaps, was because special interest groups

in Pakistan made the term MFN sound much more than what it actually meant.

Pakistani government also took steps to promote bilateral economic relations by

replacing the shallow trading opportunities emanating from the so called “positive list”

(of less than 2000 items) with relatively larger trading opportunities for India with the

so called “negative list” (of 1200 prohibited items). The government of Pakistan

committed in November 2011 to extend MFN to India by the end of 2012 but could

meet its pledge due to adverse domestic atmosphere in that regard; therefore the

commerce minister decided to peruse the move for granting MFN to India with a new

expression of “Non Discriminatory Market Access (NDMA)” on reciprocal basis

(Pakistan, 2014) which was also the true essence of MFN concept in its true nature and

scope.

Pakistan has had historic trade relations with Iran. The two countries were founder

members of Economic Cooperation Organization (ECO) along with Turkey when it

was launched in 1991 but a new turn came in the bilateral relations between the two

countries when they signed PTA on March 4, 2004 that became officially operational

on September 1, 2006 with an average concession in tariffs of about 18% and the

bilateral trade between the two countries remained above $ 1 billion during 2008-10.

However, it is argued that due to international sanctions on Iran and Iran’s reluctance

in fully implementing the PTA rates caused trust deficit between the two countries and

the bilateral trade came to around a quarter (on average during 2012-14) of what it was

during 2008-10 (Pakistan, 2013). In the context of withdrawal of large proportion of

international sanctions on Iran in 2015, it could be hoped that Pakistan’s bilateral trade

with Iran could flourish again soon after the withdrawal of international sanctions.

Afghanistan is a land locked country and historically it relies mostly for its trade

through Pakistan and Iran. Trade between Pakistan and Afghanistan took place without

any formal agreement until 1965 (PILDAT, 2011a) when a formal arrangement of

Afghanistan Pakistan Transit Trade Agreement (APTTA) was negotiated which was


43

last revised on June 12, 2011. Pakistan is member of South Asian Association of

Regional Cooperation (SAARC) and ECO with Afghanistan where the two countries

are signatories of SAFTA and ECO Trade Agreement (ECOTA). Afghanistan

traditionally remained one the leading export destination of Pakistan both in times

peace and war. During the period when NATO forces were present in Afghanistan,

there was an extraordinary growth in Pakistan’s export to Afghanistan from less than

half a billion dollars in 2004 to its peak value of more than $ 2.6 billion in 2011 due to

NATO supplies through APTTA.

Although China is the only neighbour with which Pakistan has formally entered in to

an FTA but strong formal commitments with India, Iran and Afghanistan too for free

trade through SAFTA and ECOTA. Pakistan is partner with India and Afghanistan in

SAFTA and with Afghanistan and Iran in ECOTA. The SAFTA member countries

agreed on implementing zero duties by 2015 where India and Pakistan ratified the treaty

in 2009 while Afghanistan did the same in 2011. Similarly, ECO countries signed

ECOTA in 2003 and it formally came in to force in 2008 when Turkey and Pakistan

took lead in submitting the Positive List (PL), Negative List (NL) and Sensitive List

(SL) in that regard. These lists were requested by the member countries in trade

negotiations and Pakistan has been argued to have entered in these agreements without

due preparations. The SL is usually included in the NL and it (SL) is usually small. PL

is usually very large. For example ECOTA asked for a maximum of 1% tariff lines in

SL and 20% in NL and a minimum of 80% of 6-digit tariff lines in the PL. Under Izmir

Treaty, the ECO member countries (including Iran and Afghanistan) were committed

to establish an FTA within the region by 2015 but there were failures in the full

implementation of ECOTA regarding noncompliance of Iran in particular (Pakistan,

2013). Din and Ghani (2011) argued that liberal trade policies between ECO member

countries could bring benefits like lower prices, more product variety and higher quality

and improved incentives for innovation.

1.7 Research Gap

Pakistan’s multilateral trade relations have mostly been investigated in the context of

SAARC (Hassan, Fatima, Aysha and Muhammad, 2011; Exim, 2008; and Ali &


44

Talukder, 2009), SAFTA (Mukherji, 2004; Rehman, Shadat and Das, 2006; and

Bandara & Yu, 2003) or regional trade in general (Kemal, 2004; Pitigala, 2005; and

Dorosh, 2008). There was very little interest in probing Pakistan’s trade relations with

its neighbours. Hamid and Hayat (2012) investigated Pakistan’s trade with its

neighbours who did explore aggregate and disaggregated trade volumes but with the

help of averages and percentages of Pakistan’s trade with its neighbours. There were a

number of reasons that propelled to undertake the present research. The research gap

that was addressed in the present study is being laid out in this section.

Firstly, the present study employed the gravity model of trade for analyzing the

aggregate volumes of trade which is methodological workhorse of studies on trade.

There were a number of studies that were being carried out with the help of gravity

model in Pakistan like Gul and Yasin (2011) who worked out Pakistan’s trade potential

with the help of gravity model for a panel data of 42 countries and time period of 1981-

2005. The present study carried out gravity model for a panel of 177 countries and the

reference period of 2005-2013. Therefore, the present study enriched the data on one

hand, and used updated information to determine trade potential, on the other.

Secondly, the present study made use of five different trade indices to analyze

Pakistan’s trade with neighbouring countries at disaggregated level. Although, carrying

out disaggregated analysis of bilateral trade has become increasingly fashionable

(Helpman, 1987; Kim, 2009; and Chandran, 2010) yet this form of analysis could not

gain much popularity in Pakistani context. Akram (2013) probed disaggregated trade

performance but on a narrower scale of probing the intra industry trade. The present

study has worked out five major indices of Pakistan’s trade with each of its neighbours

from 2004 to 2014, a task that was hardly ever taken up at this scale for the specific

scope of the present study, thus extending the boundaries of investigation of Pakistan’s

trade with the neighbours at disaggregated level.

Lastly, gravity modeling offer a wide variety of influences that can be probed with

augmented gravity model techniques. The present study made use of the data that was

not previously available to see their respective role in Pakistan’s trade with partner


45

countries. Thus, the present study attempted to bridge the research gap at three different

levels.

1.8 Organization

This dissertation was organized in such a way that the first chapter was dedicated to

Introduction, which, apart from introducing the topic and its background, offered a brief

history Pakistan’s trade with the world in general, and with neighbouring countries, in

particular, with a special reference to past ten years (2005-14). Moreover, the

introduction attempted to develop a good case for carrying out this study by outlining

the significance of this study besides phrasing out the specific objectives of the study

and the research gap.

The second chapter offer a comprehensive literature review for the study covering

aspects like Pakistan’s trade within SAARC, ECO in general and with its four

neighbours in particular. Moreover, another important issue relevant to the study was

determination of free trade potential with the help of gravity modeling and that too was

being discussed in the second chapter.

The third chapter described the theoretical framework of carrying out the study. In

order to explain the conceptual framework of the study, theoretical framework of

studying international trade was traced from mercantilism in the 16th and 17th centuries

to augmented gravity model based explanations of trade in the 21st century. Similarly,

the theoretical framework of studying international trade at disaggregate level has also

been discussed comprehensively.

After the comprehensive overview of the theoretical framework both at aggregate and

disaggregate levels, chapter four explained the estimation techniques being adopted in

the study to fulfill the objectives laid down in the first chapter. The chapter offered

specific models and techniques employed in the study along with necessary information

about the data and its sources.


46

Chapter five was about the descriptive statistics that determined the scope of

disaggregate analysis of Pakistan’s bilateral trade with its neighbours as well as those

of determinants of bilateral aggregate trade. Pakistan’s leading exports and import

categories and their proportion in total trade were being analyzed for their trend during

2004-14. Similarly, Pakistan’s leading exports and imports from each of its neighbour;

and their respective proportions in Pakistan’s total exports and imports respectively,

were also being analyzed for their trend during the same period.

Chapter six discussed the results and findings of the study, both, at aggregate and

disaggregated levels. The results for aggregate free trade potential were drawn from the

augmented gravity model for each of the neighbouring countries. Similarly five indices

namely, Revealed Comparative Advantage (RCA), Exports Intensity Index (EII),

Import Intensity Index (III), Grubel and Lloyd Index (GLI) and Trade Complementarity

Index (TCI) were being discussed to explain trade potential as well as performance at

disaggregated levels. The last section of Chapter six presented collaborated findings

that were being reported in the earlier sections for each country. An effort was made to

present the collaborated findings with the help of foot notes that referred to the findings

reported in the earlier section of the chapter.

The last chapter (seven) was dedicated for conclusions and recommendations based on

the findings of the study (provided in chapter six, in the context of the objectives

outlined in the first chapter. The conclusions and recommendations were prepared with

a view to inform academia as well as policy makers about the strengths and

shortcomings of Pakistan’s trade with its neighbours. The conclusions and

recommendations were based solely on economic criteria and therefore should be

viewed accordingly.


47

CHAPTER 2

LITERATURE REVIEW

The dissertation is aimed at analyzing Pakistan’s potential for free trade with its

neighbours, primarily, with the help of gravity model of trade, and keeping in view the

scope of this topic the literature has been chosen to cover the breadth of this topic in an

orderly manner. There are a total of eighty two studies that have been included in the

literature review covering the aspects of Pakistan’s regional trade, the issues pertaining

to free trade and a wide range of studies about Pakistan’s trade with each of its

neighbouring countries. The composition of the literature review is such that out of all

the studies being reviewed, nearly half are about Pakistan’s regional trade issues,

directly or indirectly; potential free trade matters have been raised and analyzed in

twenty studies; twenty studies were about Pakistan’s trade with India, thirteen about

that with China and Afghanistan each; and twelve about Pakistan’s trade with Iran. The

proportion of studies, for each of the neighbouring countries, is not reflective of the

importance Pakistan’s trade with that country but the researchers’ interest in Pakistan’s

trade with the respective countries.

There was a lot of interest among academic researchers about Pakistan’s relations with

India because of the obvious reasons and very little academic interest regarding trade

with Iran, because of not so obvious reasons. As regards Pakistan’s economic relations

in general and trade relations in particular with China and Afghanistan, a growing

amount of literature was found about that and it seemed that this interest would grow

in the time to come, especially in the context of Pakistan’s economic relations with

China, keeping in view the developments at the point of writing these lines like

developments on CPEC and Pakistan’s inclusion in SCO in July 2015.

As regards the temporal distribution of the studies included in the literature review,

fifty eight of the studies were most recent (2010 or later) that was nearly 71% of the


48

total literature review. There were sixteen studies that were published during 2005-09

in the review of literature that becomes nearly 20% of all the studies. Thus more than

90% of all the studies (74 studies) were from the last ten years’ time. The remaining

studies were published after 2000, except the one by Williamson (1998), which was

included for carrying a description of the critical dimensions of Pakistan’s regional

trade dynamics, making it indispensable to be included in the review, while more than

ninety per cent studies are from the last ten years.

The review is designed in manner that it begins with a review of Pakistan’s regional

trade dynamics in the context of Asia in general and South Asia in Particular. It duly

analyzed the regional trade bodies that Pakistan has been associated with like SAARC

and ECO. After that part, the nature, scope and significance of free trade, as a global

phenomenon, has been analyzed through the studies included in the review. After

analyzing Pakistan’s regional trade and free trade issues, Pakistan’s trade with each of

its neighbouring country has been reviewed for India, China, Afghanistan and Iran

respectively.

South Asia has been making efforts to integrate just like most other regions in the world

through efforts of promoting bilateral and multilateral trade among the SAARC

member countries but it was still considered as one of the least integrated regions in

the world (Hussain, 2012). Researchers have shown a lot of interest in SAARC region

and came up with mixed results about its success. Most researchers do not seem to be

convinced about the promotion of bilateral and multilateral trade among the SAARC

member countries (Kemal, 2004; Pitigala, 2005; Coulibaly, 2007; and Ali & Talukder,

2009) while there were some who expected greater promotion of international trade

among SAARC member countries in the long run despite low trade volumes in the

short run (Bandara & Yu, 2003; Dorosh, 2008; and Akhtar & Ghani, 2010). Some

researchers argued that benefits of SAFTA in SAARC region were awkwardly

distributed for smaller member countries because the large economies were more likely

to benefit and smaller countries were likely to lose as a result of successful

implementation of SAFTA (Bandara & Yu, 2003; Rehman et al., 2006; and Ali &

Talukder, 2009).


49

Different researchers used different methodologies and different reasons to reach at

their respective conclusions about the prospects of regional trade among the SAARC

member countries. Gravity model of trade and its augmented variations however

remained the preferred choice in most of the researches (though with different spread

of countries and different lengths of time) in forming their conclusions which included

Rehman et al. (2006), Exim (2008), Kien (2009), Akhtar and Ghani (2010), Gul and

Yasin (2011) and Tash, Jajri and Tash (2012). The other methodological option being

used to study SAARC regional trade was CGE modeling and was being adopted by

Shaikh, Naimatullah, Shafiq and Jamali (2011) and Hassan et al. (2011), however,

Kemal (2004) warned about greater sensitivity of CGE models to the assumptions of

the data and therefore recommended gravity modeling instead for its ability in terms of

incorporating the data assumption while studying the SAARC regional trade. Pitigala

(2005) and Akram (2013) used disaggregated data to explain regional trade potential

in the SAARC region with the help of indices being constructed for this purpose.

As most of the studies expected relatively lesser potential of trade among the SAARC

member countries, the reasons that they extended in this regard included lack of trade

complementarity (Kemal, 2004; Mehta & Kumar, 2004; Mukherji, 2004; and Pitigala,

2005), adverse political situation between in the member countries (Williamson, 1998;

Exim, 2008; Ali & Talukder, 2009; and Hassan et al. 2011), besides some other factors

identified by individual studies for low intra-regional trade among the SAARC member

countries. It was interesting to note that most of the earlier studies hint at lack of

complementarity while most of the latter studies considered adverse political factors

for poor performance of intra-regional trade among the SAARC member countries,

though implicit in the pattern of the findings could be the pattern of ground realities in

the SAARC region. Exim (2008) concluded that improved economic relations among

SAARC member countries could also contribute towards improved political relations

between member countries too, as economic and political relations were considered

mutually reinforcing. Shaikh et al. (2011) concluded that SAFTA can bring economies

of scale through specialization across the member countries and also diversification of

export basket for individual participating countries.


50

As Pakistan is also a member of D-8 group of countries and ECO, there were studies

in the context of trade potential within D-8 countries by Tash et al. (2012) and ECO

countries by Kemal (2004) and Gul and Yasin (2011). Hamid and Hayat (2012) studied

critical challenges and opportunities of Pakistan’s trade with its neighbouring countries

(including Oman as the nearest overseas partner country) while Kien (2009) captured

the comparative performance of various FTAs of different regional blocks for the

period of 1988-2002.

It was noteed that some of the studies argued the central role of India and Pakistan for

success of SAFTA among the SAARC member countries (Williamson, 1998;

Mukherji, 2004). Coulibaly (2007) emphasized that countries that join a regional block

earlier stand a better chance of yielding benefits of trade liberalization greater than the

ones that join latter. Williamson (1998) further emphasized that India and Pakistan face

similar challenges in the success of SAARC that France and Germany faced for the

successful foundation of the EU and hoped that Indian prime minister Vajpai’s visit to

Pakistan (in 1999 following tense period after atomic explosions by both India and

Pakistan) to resume diplomatic relations could be as critically important as was Nixon’s

visit to China to resume diplomatic ties between USA and China. However, the

sentiment built by Vajpai’s visit in February 1999 were reversed by Kargil war a few

months later in May just like the positive sentiment built by Indian Prime Minister

Narindra Modi’s surprise visit to Pakistan in December 2015 was reversed by

Pathankot attack in India in January 2016. Asghar and Nazuk (2015) and Jalil (2011)

also pointed out the potential benefits of positive political developments between India

and Pakistan on regional economic prospects.

One of the major challenges of trade liberalization is the potential trade diversion to a

trading partner with whom trade has been liberalized from a partner that did not possess

a similar privilege, but was otherwise competitive. On the other hand, trade creation is

positive outcome of trade liberalization when new room is created for trade between

the partners that have removed the barriers to trade. These issues have duly been

investigated in the body of literature. Rehman et al. (2006) used the criterion adopted

by Coulibaly (2004) to study the trade creation and trade diversion as a result of RTAs


51

as there was contradictory evidence coming from earlier studies due to the application

of inappropriate methodologies and followed the tradition of two stage process of

estimation, where gravity equation was estimated in the first stage and the effect of

standard dummy variables was seen in the second stage, which led them to conclusion

that Bangladesh, India and Pakistan were likely to gain by joining SAFTA whereas

Maldives and Sri Lanka were likely to be negatively affected as a result of participating

in it.

Kien (2009) provided evidence of trade creation under AFTA while trade diversion

under EU during 1988-2002. Ali and Talukder (2009) and Akhtar and Ghani (2010)

showed their respective concerns about the possibility of trade diversion as a result of

successful implementation of SAFTA, not only among the SAARC member countries

but also for other countries, which was consistent with the earlier findings of Hassan

(2001) for the data of 1996. Akhtar and Ghani (2010) further argued that SAFTA

arrangements may not be beneficial in the short run, due to possible trade diversion,

but in the long run, as a result of improved and simplified tariff structure, ease in foreign

exchange controls, better banking and financing facilities in the countries of the region,

there could be greater potential trade among South Asian countries. Exim (2008), on

the contrary, argued that increased openness among SAARC countries could boost

intra-regional trade and also reported absence of trade diversion in consequence of this

increased openness and hoped that with greater intra-regional trade; South Asian region

could remove its trade deficit with the rest of the world. Tash et al. (2012) used trade

diversion and trade creation indices to explain the incidence of this phenomenon in the

D-8 group of countries.

Trade possibilities between two countries for inter industry trade tend to be limited

when there was less trade complementarity, though the scope for intra industry trade

can still be there. Akram (2013) explored the potential of Intra-Industry Trade (IIT)

between Pakistan and other SAARC countries by analyzing the vertical and horizontal

prospects of IIT separately. Moreover, vertical IIT was separately analyzed for

high/low quality IIT prospects and the results for average number of industrial

establishments impacting IIT were contrary to Turkcan (2005). Turkcan (2005), while


52

investigating the same issue for Turkey, concluded that the results for average number

of industrial establishments in developing countries tend to be different from those of

developed countries. Moreover, Akram (2013) found that an overwhelming majority

(82.5%) of IIT between Pakistan and the selected SAARC member countries was in

vertical IIT while the remaining pertained to horizontal IIT, whereas competitive nature

of most SAARC economies created hurdles in the growth of IIT among the member

countries. Moreover, Kemal (2004) argued the importance of intra-industry trade,

vertical specialization and joint export marketing of competing regional exports for the

success of mutual trade benefit and recommended joint industrial ventures, cooperation

in financial and monetary fields and deepening trade liberalizing for promoting intra-

regional trade among the member countries of SAARC.

Pitigala (2005) reported RCA indices of trade competition and trade concentration

profiles of the member countries and argued that intra region trade among the member

nations may not be facilitated in the context of the structure of South Asian trade that

has developed over the years. Mehta and Kumar (2004), however, were of the opinion

that although there was low trade complementarity across SAARC member countries

in goods trade (because of relatively similar resource endowment and environment) yet

there was better trade complementarity for trade in services, in particular health and

education across SAARC member countries. Dorosh (2008) recommended greater

private sector role in South Asian food markets than that of the public sector on the

premise that the private sector carried a better ability to react to price shocks than the

inefficiencies and slow mechanisms of the public sector. Akhtar and Ghani (2010)

concluded that SAFTA arrangements may not be beneficial in the short run due to

possible trade diversion; but in the long run, as a result of improved and simplified

tariff structure, ease in foreign exchange controls and better banking & financing

facilities in the countries of the region, there will be greater potential trade for South

Asian countries.

Trade liberalization as a force shaping the world has had a long history and Beary

(2013) presented the chronology of this phenomenon since the freedom of American

colonies in 1776 to the twenty first century, with a more detailed analysis of the period


53

following the Second World War USA has been the greatest economic might for more

than a century and it has placed trade liberalization at the heart of its economic strategy

(Guerin et al., 2007). However, the group of twenty eight nations of the EU,

collectively, became the largest proportion of world trade as well as the world

economy, with nearly 17.2% of the world’s GDP, surpassing USA with 16.5%, because

the EU accomplished this elevation through the policies of trade liberalization with rest

of the world in general and amongst the EU member countries in particular (European

Commission, 2014). The strongest contemporary force in world trade in twenty first

century is China and Song and Yuan (2012) analyzed the Trans Pacific Partnership

(TPP) between USA and China (latter accommodating other partners too) and

explained how that partnership will impact, not only trade between the USA and China

but also the pattern of overall world trade.

Agreements of free trade were considered critical milestones in the process of trade

liberalization between two partner countries or regions and there were a number of

studies that identified the consequences of FTAs on trade diversion (Francois &

Manchin, 2009; and Urata & Okabe, 2007); domestic welfare in the context of

environmental concerns (Gulati, 2008); outsourcing and off-shoring (Beary, 2013);

regulatory protectionism (Watson & James, 2013); terms of trade and volumes of trade

(Kowalczyk & Reizman, 2009); laws governing the labour market in the trading sectors

(ILO, 2015).

Just like there were studies on the implications of free trade, there were also studies

that explained the precautions, preconditions and sensitivities in the process of

negotiating and finalizing FTA between the two aspiring partners. Pyne (2008) argued

that the traditional H-O theorem assumed separate ownership of resources in the two

countries while it was more realistic to assume that the ownership of capital was shared

between the two trading countries (unlike ownership of labour), especially with

increasing tendency of Multi-National Corporations (MNCs) in the twenty first

century, and therefore, suggested the negotiating countries to take precautions in that

regard. Guerin et al. (2007), while analyzing the prospects of EU-South Korea FTA,

stressed the importance of removing Non-Tariff Barriers (NTBs) before entering in to


54

FTA aimed negotiations as it was one of preconditions of FTA. ILO (2015) urged

negotiating countries to pay attention to the potential sensitivities confronted by their

respective labour markets in the wake of liberalizing trade through FTAs.

The cost of trade liberalization can be studied in a variety of circumstances, however,

when partners that liberalized trade bilaterally, increase the bilateral volume of their

trade, at the cost of trade with other trading countries, the phenomenon is termed as

trade diversion. Urata and Okabe (2007) classified trade diversion of two different

types in their study of EU member countries – one where the exports of FTA member

to non FTA members declined; and the other, where the exports of non FTA members

to FTA members declined; and they also detected the former type of trade diversion

regarding electrical machinery; and the latter type for all the other products in the

sample except apparel.

Pakistan’s trade with India gathered lot of academic and organizational interest on both

sides of the border especially since the beginning of composite dialogue between the

two countries since 1999 (PILDAT, 2015). There were studies that considered that

improved bilateral trading relations could contribute to improved political relations

(Ali, Mujahid & Rehman, 2015; Kugelman & Hathaway, 2013; Avula, Devashish &

Imtisal, 2013, Mehta, 2011; and Williamson, 1998). Mehta (2011) and Williamson

(1998) both argued a case for promoting bilateral trade between India and Pakistan in

the context of the way France and Germany managed to utilize economic relations to

neutralize their political difference and built comprehensive cooperative relations with

each other, despite bitter memories of the two world wars and a baggage of history

spread over centuries. Mehta (2011) also provided examples from East Asia where

Thailand, Vietnam, Cambodia, China and Laos removed their political differences or

lessened their impact through the forum of ASEAN; and Argentina and Brazil in South

America eased their historic rivalry through regional integration with the help of trade;

and recommended India and Pakistan to follow the foot prints of countries in Europe,

East Asia and South America for their shared welfare. Ali et al. (2015) argued a case

that bilateral trade benefits can promote peace between India and Pakistan. Avula et al.

(2013) proposed a case for potential cuts in defense expenditure of the two countries,


55

if they seek to build trade relations with each other while Kugelman and Hathaway

(2013) went on to suggest that economic stability and trade do not compromise national

security (as was usually perceived especially in the context India Pakistan trade) but

strengthen it through improved internal economic security.

There were also researchers who believed that political relations contributed towards

the economic relations contrary to earlier studies that believed in reverse chain of

reaction especially in the context of Pakistan and India. Coulibaly (2007) argued that

bilateral political differences between the two countries have not allowed the fruits of

economic integration to mature, not only for India and Pakistan but for the entire South

Asian region at large, and has considered that slow progress on SAPTA primarily was

due to adverse political relations between the two countries. Similarly TRTA (2015)

and Khan (2012c) believed that bilateral trade between India and Pakistan to be

historically influenced by the political factors while PILDAT (2011b) noted that

political influence on economic relations of the two countries increased after the war

of 1965.

There were wide ranging benefits of trade liberalization that were being pointed out by

the researchers for both India and Pakistan. Hussain (2014) envisaged prospects of

economic growth in the two countries if they transform their adversarial relations in to

bilateral cooperation as that would help in removing the structural constraints being

faced in the process of economic growth in Pakistan. TRTA (2015) identified the

benefits of improved utilization of resources, enhanced bilateral access to larger

markets, lower prices for the customers in the two countries and development of value

chains in the two countries as a result of smoother trading relations. Pasha, Burki and

Imran (2012) considered that Pakistani industries could benefit from increased bilateral

trade whereas Raihan and De (2013) argued that benefits of trade facilitation would not

accrue to one country at the cost of another but shall be shared across border between

the two countries. Mehta (2012) pointed out potential for IIT between India and

Pakistan as a result of removing trade barriers between the two countries.

While there were many studies that identified and explored the potential benefits of

stronger trading relations between India and Pakistan, there were also some that


56

identified the challenges of liberalizing trade between the two countries especially in

the context of agriculture (Khan & Hussain, 2014; Ahmad, Nadia & Sohain, 2012;

Dorosh, 2008); presence of tariff and non-tariff barriers (PILDAT, 2015; Avula et al.

2013; Raihan & De, 2013; and Pasha et al., 2012); the lost revenues to the governments

of the two countries as a result of informal trade and the trade routed through the third

country ports (TRTA, 2015; Acharya & Marwaha, 2012; and Mehta, 2011).

Khan and Hussain (2014) argued that while Pakistan removed most of its price support

and subsidies over its agriculture, there could be serious challenges and implications of

trade liberalization with India on Pakistan’s agriculture, as India heavily protected its

agriculture through price supports and subsidies for agricultural crops. Ahmad et al.

(2012) identified heavily protected Indian agriculture and warned of negative

implications on Pakistan’s agricultural crops in the event of easing agricultural trade

with India. Dorosh (2008) however believed that both India and Pakistan managed food

prices to avoid adverse domestic implications and this policy kept real food prices in

the two countries quite different from their respective competitive international prices

of such crops.

NTBs were the most cited challenge in the way of ensuring freer trade between India

and Pakistan. PILDAT (2015) pointed out the expensive and unfairly time consuming

certificates being issued by the Indian authorities to Pakistani exporters which were

required to be renewed annually as well as the delays on the Indian side of check posts

for Pakistani exporters to be extremely destructive for realizing greater Pakistani access

to Indian markets despite the MFN status accorded to Pakistan since 1996. Pasha et al.

(2012) and Batra (2004) also hinted about the mistrust of Pakistani exporters to India

over the use of NTBs while Avula et al. (2013) identified the problems faced by the

traders of perishable goods on both sides by the respective Indian and Pakistani check

posts due un-necessary delays in clearance processes. Raihan and De (2013) explained

how quality control measures and other technical barriers to trade compromised a large

proportion of trade potential between India and Pakistan.

TRTA (2015) explained how tariff and non-tariff obstructions in India-Pakistan trade

caused a large proportion of bilateral trade between India and Pakistan to rout through


57

third countries’ ports, thus making the bilaterally traded goods more expensive and

depriving the two respective governments to lose trade related revenues in this process.

Acharya and Marwaha (2012) hinted at large volumes of informal trade between the

two countries and considered the volumes of informal trade to be as big as the formal

volume of trade between the two countries while Mehta (2011) was of the opinion that

the volume of informal trade was nearly four times as much as the formal trade volume.

Nearly all the studies have recommended steps of policies to improve bilateral trade

relations between India and Pakistan. TRTA (2015), PILDAT (2015), Acharya and

Marwaha (2012) and PILDAT (2011b) all pointed out the need for improving the entry-

exit infrastructure in both the countries with multiple operating counters and

warehousing facilities to promote bilateral trade between India and Pakistan. Ahmad et

al. (2012) and Avula et al. (2013) stressed the importance of easing visa restrictions

and granting multiple visas for multiple cities for business visits to let the trading

interactions contribute to the cause of greater bilateral trade. Gopalana, Ammar and

Kenneth (2013) and Mehta (2011) stressed the importance of not conceding to the

monopoly interests of the few at the cost of larger public interest and identified how

the monopoly of sugar cartel in Pakistan and cement cartel in India managed to block

trade both through tariff and non-tariff barriers and thereby recommended the

respective governments to review their policies of guarding such narrow monopoly

interests of such like cartels in their respective countries. Avula et al. (2013) also asked

for central bank coordination to allow commercial banks to open their branches in the

other country to facilitate financial flows of trade.

There were other specific recommendations also being made by these studies as well.

Kugelman and Hatahway (2013) recommended India to exercise Voluntary Export

Restraint (VER) with Pakistan and other smaller trading partners to win goodwill from

these partner countries and empower the role of private sector in designing the

commercial policy. Mehta (2011) suggested the development of Qualifying Industrial

Zones (QIZs) between India and Pakistan (like the ones between Jordon and Israel)

through collaborative productive mechanisms whereas Mehta (2012) asked for the

foundation of harmonized quality standards between the two countries, especially in


58

pharmaceutical, textile, cement and food processing sectors to promote bilateral trade

in these sectors. PILDAT (2015) recommended a minimal role of NL, holding a joint

forum in resolving problems related to NTBs, and establishing bilateral dispute

settlement network to enhance trade between India and Pakistan. Akram (2013)

identified Pakistan’s potential for IIT with SAARC countries in general and with India

in particular.

India granted MFN status to Pakistan since 1996 while Pakistan did not return the

favour to India due to host of factors being discussed by researchers. Gopalana et al.

(2013) considered that the reason for this delay in according MFN status to India was

based upon Pakistan’s fear of negative implications on its manufacturing industry as a

result. Mehta (2012) considered that one of the reasons involved in the delay was the

reason how “most favoured nation (sub se pasand-deedah mulk)” sounded for Pakistani

people in the context of a long standing adverse baggage of history. Perhaps it is this

reason that Pakistani government opted for an alternative expression for MFN in this

regard that was called Non-Discriminatory Market Access (NDMA) being discussed

by PILDAT (2015).

Pakistan enjoyed a long and time tested relationship with China that dates back to 1959,

when Pakistan signed Air Transportation Agreement with China; and then in 1963

when both nations accorded MFN status to each other and also signed a Border Trade

Agreement. Khan, Manzoor and Omair (2013) considered that besides the

aforementioned milestones in the bilateral economic relations between China and

Pakistan, another huge accomplishment was the construction of Karakoram Highway

in the 1970s that completed in 1979, followed up by the establishment of a Commission

on Economy Trade and Technology in 1982. IPRI (2013), however, argued that the

bilateral relations between the two countries remained dominated by the military

hardware transfers until 1990s, but latter, according to Jamil (2015) within a span of

four years from 1998 to 2002, the bilateral volume of trade, more than doubled from

one billion dollars to more than $ 2.4 billion. Rahman (2011) termed the period of

2000-10 as a decade of institutionalization in Pakistan-China economic relations,

especially since the two countries signed an FTA with each other in 2006. Jamil (2015)


59

also informed about another milestone, where China obtained the control of Gawader

Port in 2013, while Elahi (2015) provided the detailed analysis of CPEC and termed it

as a “game-changer” in the bilateral relations of the two countries.

Song and Yuan (2012) quoted an old Chinese proverb that meant “close neighbours are

better than the distant relatives”, to emphasize the warmth and depth of China’s relation

with Pakistan. However, Beckley (2012) questioned the idea that the two countries

have been “all weather friends” and termed the bilateral relationship of the two

countries as a “marriage of convenience” where each country contributed and

benefitted bilaterally to get the relationship going for over half a century. Hamid and

Hayat (2012) viewed that China and Pakistan historically kept the security perspective

in their bilateral relations more active than the economic perspective but that seemed

to be the case just till late 1990s (IPRI, 2013) while the twenty first century witnessed

the signing of FTA and CPEC that potentially laid the foundations of a comprehensive

economic relationship between the two countries. Khan et al. (2013) have discussed

the huge potential for China-Pakistan relations in the context of FTA while Elahi (2015)

expressed his hopes about the CPEC.

Kataria and Naveed (2014) have held the opinion that Pakistan was the strongest ally

of China in South Asia as their relations were based on sovereign equality where

Pakistan supported China’s stance on Tibet and Taiwan and China supported Pakistan’s

efforts in fighting terrorism in the region. The foundations of strong economic relations

in the twenty first century could be the result of strong political alignment in the latter

half of 20th century as Song and Yuan (2012) argued that political factors played as

important a role in developing China’s relations with other countries as did the

economic factors.

China’s integration in to the world trade started and matured in an institutional manner

in the 21st century, when China became a member of WTO according to Irshad and Xin

(2015) and then it entered in to Trans Pacific Partnership (TPP) in 2005. Song and Yuan

(2012) provided a comprehensive account of how TPP started with four member

countries and its membership increased to nine countries by 2011 and further explained

the critical position of China in TPP.


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There was a lot of comparison about China’s integration with South Asia vis-à-vis the

same in the South East Asia and Rahman (2011) compared China’s FTA with ASEAN

and Pakistan to explain the shallowness of the agreement in case of Pakistan. Abraham

and Hove (2005), however, established that the benefits of China’s participation in

AFTA would accrue to China first and to the other member countries, only later on.

Despite the blame of shallow coverage of free trade in case of Pakistan as argued by

Rahman (2011), there were arguments offered by Hamid and Hayat (2012) and Kataria

and Naveed (2014) about the critical preference that China extended to its trade with

Pakistan in the region. Hamid and Hayat (2012) built an elaborate case of outsourcing

from Chinese industries to Pakistan (by using the flying geese model of development)

due the fact that Chinese labour may have become expensive over the years, and

therefore argued the superiority of Pakistan in South Asia over other South East Asian

nations in the region because of shallow labour market dynamics in South East Asian

countries; while the presence of a large, cheap and young labour market in Pakistan.

Kataria and Naveed (2014) have identified that Pakistan was the first country in South

Asia that China entered in with an FTA and considered that event as an agreement of

great strategic importance for Pakistan.

Trade liberalization between two countries was considered successful if it brought

bilateral prosperity in both the countries which becomes a difficult proposition to be

fulfilled in case the nations in question possessed extremely unequal economic sizes,

as is the case between China and Pakistan. There were a number of studies that

identified the unequal scope of prosperity for China and Pakistan as a result of the FTA

signed in 2006 with a special focus on very shallow base of Pakistan’s exports to China

(Irshad & Xin, 2015; PCI, 2013; Hamid & Hayat, 2012; Rahman, 2011; and Din, Ejaz

& Usman, 2013). Irshad and Xin (2015) argued that China’s dominance in its trade

with Pakistan was not a unique phenomenon but a global trend of its trade with other

partner countries also, and reported that Pakistan generally exported labour intensive

and raw material based goods to China. PCI (2013) also confirmed as narrow base of

Pakistani exports to China and that too in low value primary products whereas most

Chinese exports to Pakistan were of higher value that did contribute to growing trade

deficit for Pakistan in the bilateral trade between the two countries. Din et al. (2013)


61

also reported a very narrow range (though they also reported that bulk of Pakistani

exports to China to have shifted from primary goods in 1990s to semi manufactured

goods by 2004) of Pakistani exports while a gradually diversifying range of China’s

exports to Pakistan, because of the fact that duty free and lowest tariff range items were

more favourable to Chinese exports than to the exports of Pakistan. Rahman (2011)

also blamed the narrow coverage of items in the FTA to be responsible for a narrow

range of Pakistani exports to China.

Hamid and Hayat (2012) identified three critical dimensions in the unequal trading

relationship between the two countries that included presence of NTBs in China against

Pakistani exports (especially in towels and textile); mis-specification and under

invoicing of Chinese exporters to avoid duties, collaboration with unscrupulous

Pakistani importers; and export of poor quality cheap imitations of established domestic

brands, all being made possible due to insufficient detection mechanism in Pakistan.

Researchers recommended steps to optimize the fruits of FTA between the two

countries. Irshad and Xin (2015) argued that an FTA covering only goods was not

enough and asked an extended FTA, covering services also, under the argument that an

FTA of goods would bring benefit to China while that covering services could be

fruitful for Pakistan. IPRI (2013) asked for greater strategic alignment between China

and Pakistan especially after US withdrawal from Afghanistan in 2014 in key

infrastructural projects. Khan et al. (2013) identified the importance of comprehensive

collaboration, not only between the governments of the two countries, but also, between

the business community and research organizations in the two countries; and expected

such collaborations to serve as catalyst to their bilateral relations also. Hamid and Hayat

(2012) appreciated the role of China in establishing the cellular phone and motorcycle

assemblies in Pakistan but warned against the failed traditional policy of seeking

foreign investment (from China) in import substituting industries, and therefore

recommended the role of Chinese investment in export oriented industries for a more

fruitful foreign investment from China.

IPRI (2013) and PCI (2013) identified the challenges confronted by Chinese investment

in Pakistan before CPEC while Elahi (2015) and Jamil (2015) explained the challenges


62

and opportunities of Chinese investment in Pakistan through the projects in CPEC. IPRI

(2013) especially mentioned the delay on all important energy related Thar Coal Project

and urged the Pakistani government to satisfy the Chinese investors in that regard. PCI

(2013) identified five major challenges being faced by the Chinese investors in Pakistan

which included poor law and order situation, high tax rates, excessive tariff rates, high

rates of domestic inflation, and last but not the least, bureaucratic red tape causing

difficulties in smooth execution of investment in Pakistan by the Chinese foreign

investors.

Elahi (2015) termed CPEC as a “game changer” for Pakistan by highlighting the details

of $ 46 billion agreement of Chinese investment for construction of almost 3000 km

road network between Gawader port and Chinese province Xinjiang, involving 8112

Chinese workers on 210 projects in Pakistan. Jamil (2015) explained CPEC in the

context of largest province of Pakistan, Baluchistan for its critical role in the CPEC and

asked for taking Confidence Building Measures (CBMs) for Baluch people by

engaging and integrating them in CPEC projects and Gawader port operations in a

sufficient manner. Elahi (2015) further argued that CPEC faced both internal and

external threats, such that the biggest internal threat was terrorism while the most

challenging external threat was argued to be from India.

Afghanistan is a landlocked country that shares its border with six countries including

Pakistan, but according to PILDAT (2012) no other country possessed a unique

position whereby most Afghan markets, seemingly, appeared almost a corresponding

extension of Pakistani markets. However, both the countries have shared a history of

uncomfortable bilateral relations (Durrani & Khan, 2009; Iqbal, 2010; PILDAT, 2012).

Durrani and Khan (2009) lamented that despite a shared border (of 2240 km), ethnicity

and faith, the relations between the two countries have never been “smooth” with an

exception of four years of Taliban rule (1997-2001). Iqbal (2010) argued that bilateral

relations historically suffered from “mutual suspicion”, despite the Pakistani wish for

building “friendly” relations with Afghanistan. PILDAT (2012) considered the

relations between the two countries, historically, that can be termed as “patchy,

inconsistent, erratic and prone to violence” across borders. Akhtar and Sarkar (2015),


63

however, observed a strategic shift in Pakistan’s Afghan Policy due to three important

reasons – rise of domestic terrorism in Pakistan, growing Indian influence in

Afghanistan and the threat of Pukhtun nationalism in the bordering province of Khyber

Pukhtunkhwa.

Pakistan started to extend the trade facilitation to Afghanistan in 1947 under Article V

of General Agreement on Trade and Tariff (GATT), however, a more formal bilateral

mechanism of their trade was put in to place in the form of APTTA in 1965. PILDAT

(2011a) argued that as a result of significant political, geographic and institutional

changes, APTTA was renegotiated and this new framework became operative on 12th

of June, 2011, with continuous US involvement in the process of renegotiations. Akhtar

and Sarkar (2015) analyzed how the new APTTA framework contributed to Afghan

trade by giving Afghan goods the permission to move in trucks to India and China

moving through Pakistan.

The bilateral trade between the two countries being governed by APTTA, repeatedly,

came under question by the researchers, both from Pakistani as well as Afghan

perspectives. Sharif, Farooq and Bashir (2000) reported a variety of agricultural and

non-agricultural goods being traded illegally from Pak-Afghan and Pak-Iran borders,

in connivance with the local customs officials who contributed to a huge revenue loss

to the government in Pakistan as a result of supporting this illegal trade. PILDAT

(2011a) also reported this illicit trade through the abuse of APTTA and identified that

tyre and tube industry was worst hit by this abuse. Parto, Jos, Ehsan, Mohsin, and

Anastasiyz (2012) reported the grievance of Afghan authorities who blamed the

complacent attitude of Pakistani authorities in checking the abuse of APTTA. Hussain,

Asmat and Bashir (2014) identified the increasing incidence of re-exports in

Afghanistan under the smoke screen of APTTA and termed it as the biggest “smuggling

racket” in the world and termed the money earned in this process as “Norco-dollars”

due to the illicit drug trade. PILDAT (2011a) noted the significant decline in the trend

of transit trade in 2006-07, when strict actions were taken to check the trend of illicit

drug trade between the two countries, substantiating the flow of Norco-dollars in this

process.


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Akhtar and Sarkar (2015) examined the strategic future of South Asia after US

withdrawal from Afghanistan and considered Pak-Afghan bilateral trade as mutually

beneficial with an expectation for it to grow further in future. Shabbir and Ahmed

(2015), however, argued negative implications of NATO withdrawal from Afghanistan

for Pakistan, especially for the employment and earning opportunities in trade,

transport, warehousing and communication sectors, in particular, for the two provinces

of Khyber Pukhtunkhwa and Baluchistan, and also recommended the formation of

suitable commercial policies to integrate the people working in these affected sectors

under the new APTTA framework in the post US withdrawal period. PILDAT (2011a)

warned that peace in the region to be the ultimate pre-requisite for both countries to

benefit from trade, business and investment opportunities. The silk rout is considered

the critical infrastructure facilitating Afghan trade, and it has been damaged over the

years of war in the region, Aziz (2007), therefore emphasized the importance of “new

silk rout” to realize the full potential of Afghan trade in general and Pak-Afghan trade

in particular.

Pakistan’s trade with Afghanistan is a component of broader Pakistani interest in

Central Asian markets whereas Afghanistan aims at access to China and India through

its trade with Pakistan but PILDAT (2012) analyzed that the “trust deficit” between

two countries to be responsible for not realizing this potential by both the trade partners.

Bohr and Price (2015) identified the importance of two multilateral development

projects of extraordinary importance for linking South Asia with Central Asia – Central

Asia-South Asia (CASA) electricity transmission and trade project and Turkmenistan-

Afghanistan-Pakistan-India (TAPI) pipeline to seek regional connectivity through

these projects. Ahmad (2013) argued the importance of Afghanistan for the cross-

regional integration between South Asia and Central Asia, calling Afghanistan a

potential “pivot” between the two regions. Parto et al. (2012) and Aziz (2007) also

emphasized to revive the centuries old relations between Afghanistan and Central Asia

because Afghanistan becomes the corridor of access to Central Asian markets while

ESCAP (2015) considered Afghanistan a “land bridge” between South Asia and

Central Asia.


65

Pakistan shares its water sources with India in the East and Afghanistan in the West

and there is no water treaty with Afghanistan. PILDAT (2012) noted that although there

was no water dispute between Pakistan and Afghanistan but Pakistan needed a treaty

more than Afghanistan as Pakistan is a lower riparian country while Afghanistan is an

upper riparian, therefore Pakistan potentially stands to lose more in case of a potential

water dispute. Akhtar and Sarkar (2015) considered Kunar Hydroelectric Dam as an

important collaborative project and urged to build more coordination in that regard.

Iran was the first country to recognize Pakistan internationally as a newly established

independent state. Pakistan shares a 909 km border with Iran that is called Goldsmith

line in its South Western province of Baluchistan. Sial (2015) argued both countries

remained in the capitalist block and had reasonably warm relations until late 1970s

when Iran experienced its religious revolution and the Soviet forces attacked

Afghanistan, the two events that later transformed bilateral relations between the two

countries till the beginning of the twenty first century. CPGS (2014) however analyzed

that the pendulum of political and economic relations between the two countries kept

swinging throughout the history.

One of the most investigated areas in the bilateral relations of Iran and Pakistan was

Iran-Pakistan-India (IPI) gas pipeline project. Khan (2012a) investigated that the whole

idea of gas pipeline started when India planned to acquire Iran’s gas in 1980s through

an underwater pipeline through the Persian Gulf that failed due to technical problems

and an alternative idea of IPI was conceived in mid 1990s but was finalized in 2005.

Meibodi, Meibodi and Rad (2009) considered it a very useful project for political and

economic restructuring in the region whereas Asghar and Nazuk (2007) considered the

project as the most credible CBM for Indo-Pak trade in a game theoretic setting with

the conclusion that coordination and cooperation would be fruitful for each of the three

countries involved in the project, under two different frames of analysis. Khan (2012a)

was of the opinion that US hostility to Iran caused delay in the completion of the project

which according to her led to introduction of trade barriers affecting the overall bilateral

trade between the two countries.


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Meibodi et al. (2009) argued that Iran’s objective through the construction of IPI was

to seek greater access to the Asian oil and gas markets. Asghar and Nazuk (2007)

informed that India was 50% larger recipient of IPI than Pakistan in the proposed

project but India withdrew from the project in 2009. Sial (2015) and Katzman (2016)

argued that Indian exit from the deal was the result of US intervention that offered Civil

Nuclear Deal to the Indians in late 2008 and a few months later India came out of the

IPI project. On the other hand, despite the completion of pipeline by Iran on its side,

Pakistan did not show any serious progress on project till 2015. Sial (2015) informed

about Iran’s sentiment over delay from Pakistan’s side as being under the influence of

USA and Saudi Arabia on Pakistan’s government, especially in the Nawaz Sharif

government since 2013.

Iran and Pakistan signed a PTA in 2004 but its implementation started in 2006 and

Aftab (2012) considered this agreement did influence the bilateral trade between two

countries favourably, whereas Sial (2015) was of the opinion that despite the PTA

bilateral trade between the two countries remained much below the potential. CPGS

(2014) however, analyzed that the two countries have shown resolve to sign an FTA

with each other in 2014 but that FTA was not being signed till the end of January 2016.

Aftab (2012) analyzed that despite significant complementarity between the two

countries for each other’s leading trade categories, the share of Pakistan in Iran’s total

imports was less than one per cent. Khan (2012b), CPGS (2014) and Sial (2015)

informed about the presence of informal and illegal trade between Iran and Pakistan.

Khan (2012b) informed how Iran checked the incidence of bilateral informal trade by

establishing common markets with Turkey, Turkmenistan and Azerbaijan and also that

both Iran and Pakistan have expressed bilateral resolve to establish common markets

on their border also to create disincentive to indulge in the informal trade. CPGS (2014)

was of the view that the volume of informal bilateral trade between the two countries

was much higher than the formal trade; while a lot formal trade was in barter terms,

thus masking the true size of bilateral trade between the two countries. CPGS (2014)

and Mustafa (2015) identified that the potential for bilateral trade between the two

countries was around $ 5 billion.


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Khan (2012b) identified the importance of enhancing bilateral cooperation in three

areas, namely, extended facilitation at Hub Oil Refinery, coordination on Chabahar

Port and cooperation in power supply while Sial (2015) identified that Pakistan was in

the process of finalizing a 1000 MWe power supply deal with Iran thus enhancing the

earlier deal of 100 MWe as it was found cheaper to provide electricity in Baluchistan

from Iran than through Pakistani national grid being managed by WAPDA.

The year 2016 brought new opportunities for Pakistan in building trade relations with

Iran as Mustafa (2015) informed about Pakistan’s anticipation of the removal of

sanctions on Iran and rebuild trade through a revival of financial linkages between the

two countries; deepening the PTA signed in 2004; seeking joint investment in agro

food processing and infrastructure; up-gradation of border trade posts; and greater

cooperation in the energy sector. As a result of a joint comprehensive plan of action

being finalized among the US, UN and EU expecting lifting sanctions on Iran’s energy,

financial, shipping, automotive and other sectors, that was eventually adopted on

January 16, 2016 after Iran agreed to the nuclear disarmament commitments, a new era

of Iran’s importance in the international trade started (Katzman, 2016). Mustafa (2015)

earlier warned that once the sanctions would be lifted, Pakistan would have very little

reaction time and therefore recommended preparation of plans and interdepartmental

coordination in Pakistan to launch efforts on economic and trade projects of bilateral

importance with Iran. The post-sanctions phase of greater cooperation will promote

trade between the two neighbouring countries in future.

The exercise of carrying out literature review helped a great deal in completing this

study at each step on the way. Although each of the four countries that border Pakistan

is a neighbouring country of Pakistan but the political, diplomatic and economic

relationship is different for each of these neighbouring countries. Therefore, the key

learning was that the bilateral heterogeneity of Pakistan’s bilateral relations with each

neighbouring countries matters more than the homogeneity of considering each

neighbor like the others. Pakistan’s bilateral trade with each of its neighbours cannot

be explained effectively as long as one is not aware of the pattern and progression of

trade with each of the neighbouring country. Moreover, literature also helped in


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identifying various different methods and measures of analyzing bilateral trade

between the countries both at aggregate as well as disaggregated levels. There were a

lot of studies that were reviewed in the last four years from the beginning of the

preparation for the synopsis to the end of composing this dissertation and less than half

of these studies were selected cautiously to be added in the literature review to help the

reader in understanding perspective behind carrying out this research.

There was no lack of studies on Pakistan’s overall trade with countries and regions with

which Pakistan had greater value of bilateral trade but there were very few that

explained and explored disaggregated trade. There was a need to carry out studies on

Pakistan’s disaggregated trade performance at a greater level of disaggregation. Most

of the trade negotiations benchmark PLs, NLs and SLs at 6-digit HS classification;

therefore more studies should be made at that level of disaggregation.

Although Pakistan’s trade with India was a very small part of bilateral trade in both the

countries and while China became the largest trading partner of Pakistan yet the

academic research in the field of Economics was much more focused on trade with

India than on China. The focus of studying trade with India should be in the realm of

political economy and Politics as the true contributors to the present state of Indo-Pak

were political rather than economic. The finding of this research established that trade

with India in the leading categories mostly was above expectations, therefore factors

that contributed to this situation, where overall trade was significantly below

expectations while leading categories of trade were mostly above expectations, should

be explained in future studies. On the contrary, the extraordinary growth in trade with

China was speculated to be the outcome of the political facilitation rather than

economic, therefore the economic rationale or lack of it should be probed in the context

of Pakistan’s trade with China.

As far as trade with Iran and Afghanistan was concerned, there was a great significance

of studying the trade with these two neighbours in the context of withdrawal of

sanctions on Iran in 2015 and post US withdrawal from Afghanistan in 2014. While

the withdrawal of sanctions from Iran could be a harbinger of opportunities for trade,


69

withdrawal of US forces from Afghanistan has already sent signals of diminished

volume of trade potential after the withdrawal, if not, as a result of it.


70

CHAPTER 3

THEORETICAL FRAMEWORK

This chapter was intended to provide the theoretical framework of the study both for

aggregate as well disaggregate analysis. Aggregate analysis is done with the help of

gravity model of trade whereas disaggregated analysis has adopted five different

indices meant for analyzing bilateral trade between Pakistan and each of its

neighbouring country. Gravity model of trade has been relatively a recent framework

for analyzing trade between partner countries; while the indices adopted for

disaggregate analysis are also relatively recent phenomenon. The chapter begins by

presenting a quick historical development in explaining trade that has evolved from

seventeenth century mercantilism to eighteenth century absolute advantage and from

there to comparative advantage theory in the nineteenth century and its further

explanation by Heckscher-Ohlin theorem in the twentieth century. The latter sections

of the chapter duly introduce the theoretical frameworks being adopted to carry out the

study.

3.1 Historical Background

The first formal explanation of international trade was termed as mercantilism, and it

suggested that a country benefits from export of goods as it adds to its national wealth

(gold and silver) from rest of the world in to the country and loses from imports as it

causes national wealth to decrease as a result. It came in a period when currencies were

gold backed and exchange rate of currencies too was primarily determined on the basis

of gold/silver content of the respective currencies. The idea of mercantilism stressed

that a nation benefits from trade only when the value of its exports was more than that

of its imports. The theory of international trade under mercantilism had a serious


71

limitation that though it explained why a country chooses to export, but not why it

imports. The first explanation of bilateral trade benefit came from Adam Smith in 1776

in a book titled “An enquiry in to the nature and causes of wealth of nations”.

Adam Smith introduced the absolute advantage theory of international trade. He argued

if a country specializes and exports a good that it produces more efficiently than the

other country and in return imports another good (rather than producing it) from the

other country that produced that good more efficiently; it is mutually beneficial for both

the countries, conditional to the fact that there are no barriers to trade among these

countries. Adam Smith’s idea was challenged in a situation where one country is less

efficient in producing all things than another country and the question was raised as

how should such an inefficient country benefit through trade. The response to such

limitation came from David Ricardo in a theory called Comparative Advantage theory.

Ricardo based his theory on weak foundations of labour theory of value. However, the

theory was soon revised for its weak foundations on labour theory of value with the

help of alternative explanation through opportunity cost theory by Haberler (1936).

Comparative advantage theory in its modified form has remained the most popular

explanation of international trade for a long period of time.

The comparative advantage theory suggests that even if a country has less efficiency in

production of two goods (for simplicity) than another country (and there are only two

countries again for the sake of simplicity), then the less efficient country should

specialize and export a good in which the margin of its inefficiency is less and in return

should import the good in which the margin of its inefficiency is more, rather than

choosing to produce both the goods domestically with its own resources. Similarly a

country that is more efficient in producing both the goods should specialize and export

a good in which it is relatively more efficient; in return of importing the other good

from the other country, rather than producing and consuming both the goods with its

own resources. The theory argues that in the absence of any barriers to trade both

countries can benefit by following the recommendations of the theory.

The basic focus of the comparative advantage theory has been on effective utilization

of resources. The theory suggests that a country should dedicate its resources to the


72

production of goods that are most efficiently produced vis-à-vis the productive

efficiency of another country. The allocation of resources to the production of goods

for which the resources are most suitable brings the benefit of increased production

which brings the possibility of increased consumption, thereby increasing the standard

of living of the people across the trading countries. Most of these fruits of indulging in

international trade are expected under very restrictive assumptions and the critique of

the comparative advantage theory is less over its implications and more over its

unrealistic assumptions.

The next theoretical pursuit of international trade theory was to explain the foundations

of comparative advantage. Two Swedish economists Eli Heckscher and his student

Betil Ohlin offered a resource based explanation of comparative advantage that

eventually led them to win the Nobel Prize in Economics in 1977. They argued that a

country realizes the comparative advantage in the production of a good that requires an

intensive use of a resource that is available in relative abundance and at a relatively

cheaper rate than the ones in the rest of the world. Therefore, a country specializes and

exports a good in which it has comparative advantage. They further argued that a

country chooses to import a good because that good intensively uses a resource that is

relatively scarce and relatively expensive domestically and that country imports it from

another country where the same resource is relatively abundant and cheap. The

explanation of comparative advantage offered by Heckscher and Ohlin is popularly

called the H-O theorem. There have been extensions and reinterpretations of the H-O

theorem, but it has remained the only mainstream explanation of the comparative

advantage theory.

3.2 Gravity Model of Trade

In early 1960s, a new model of trade emerged that was called the gravity model of

trade. Jan Tinbergen in 1962 used the analogy of Newtonian Law of gravitation to

explain the aggregate trade flows between any two countries. Newton’s law of

gravitation says that there is a force of attraction between any two objects that is directly


73

proportional to the product of their masses and inversely proportional to the square of

distances between them. The Newton’s law is expressed as

𝐹 =𝑀1×𝑀2

𝐷2 ----------------------------------------------- (3.1)

Where F stands for the force of attraction, M1 and M2 are the masses of the two objects

and D expresses the distance between the objects. The traditional gravity model was

expressed in a similar manner as

𝑇 =𝐺𝐷𝑃𝐴𝐺𝐷𝑃𝐵

𝐷 --------------------------------------------- (3.2)

In equation 3.2, T stands for bilateral trade volume between the two countries A and B,

whereas D stands for the geographical distance between them. Just like the Newton’s

law it was argued that bilateral trade between the countries A and B is directly

proportional to their GDPs and inversely proportional to the distance between them. In

its earliest empirical framework, the model simply suggested that at a given point in

time, a country is likely to have greater trade volume with a country that has higher

GDP and is situated at a lesser distance from it than with the one that has lower GDP

and is situated at a greater distance. Despite strong empirical results in the works carried

out by Tinbergen (1962) and Poyhonen (1963), there was a lack of theoretical

justification for the model.

The interesting feature of the gravity model was that it offered an explanation of

international trade in a manner that was entirely different from legacy that was set by

the absolute advantage theory introduced almost two hundred years earlier. All the

mainstream explanations of international trade (that came since Adam Smith’s

explanation with the help absolute advantage theory) were either an extension or

reinterpretation within the framework offered by Adam Smith. The real strength of the

initial gravity models was the model fitness and the real shortcoming was the lack of

theoretical and logical foundation, however, latter efforts offered sound theoretical base

to the gravity model and today it has become a workhorse for most investigations (Wall,

2000) in the field of international trade.


74

In the beginning gravity model was considered an empirical fit without any theoretical

foundations to justify its empirical strength. Chaney (2011) argues that the gravity

model has been one the most stable and robust empirical regularities in the field of

Economics and not just in the area of international economics. The model produced

stable coefficients in a wide variety of circumstances and over long time durations. The

gravity model has also been used to test trade at firm level and still produced consistent

results. Therefore, it was not the model fitness that ever challenged the empirical

gravity model in a wide variety of studies being carried out in this framework over the

years since 1960s, but the theoretical explanation for its strong empirical regularity.

There have been numerous efforts made to develop the theoretical foundations of the

gravity model of trade and a pioneering work in this regard was done by Anderson

(1979). The theoretical justification of the gravity model was not all that much

challenging for the GDP of the countries, as it was regarding the distance. GDP of a

country in its dual composition is both output as well as income, each of which has

direct relevance with the forces that affect bilateral trade between countries. A

country’s output performance (measured in GDP) has direct implications on the

performance of its exports in a stable setting. Similarly, a country’s income (also

measured in GDP) directly and indirectly determines that country’s import demand.

There have been a number of different explanations for the role of distance in affecting

trade each of which tried to explain the role of distance in their own way. Distance was

taken as a proxy of economic cost but when the Meta analysis carried out by Disdier

and Head (2008) found that there have been very stable coefficients of the Distance

variable over the years, across studies, for around half a century, despite extraordinary

developments in transportation infrastructure. There were again doubts over the earlier

explanations offered by Anderson (1979), Bergstrand (1989), Anderson & Wincoop

(2001). However Chaney (2011) extended yet another explanation of the distance in

the context of the underlying mechanism in carrying out trade contracts that have not

changed much around most parts of the world causing suspicion about the significance

of the distance variable.


75

Figure 3.1 Gravity Based Explanation of Pakistan’s Trade with Partner Countries

Figure 3.1 attempts to explain Pakistan’s trade with its partner countries with the sizes

of all the flags showing the relative size of economy while BT standing for bilateral

trade of Pakistan with China (BT-1), USA (BT-2), Afghanistan (BT-3), Iran (BT-4)

and India (BT-5).

USA and China possess nearly same size of the economy, but the USA is at a great

distance from Pakistan while China is a neighbour, therefore, the model suggests that

there would be greater bilateral trade of Pakistan with China than with the USA. On

the other hand, Pakistan shares its Eastern border with India while the Western border

is being shared with Afghanistan. Thus with nearly equal distance with both India and

Afghanistan, gravity model suggests that there will be more bilateral trade of Pakistan

with India than with Afghanistan because India is a much larger economy than

Afghanistan. Similarly, Pakistan shares its Western border both with Iran and

Afghanistan, but bilateral trade with Iran (according to gravity model) is expected to

be larger than that with Afghanistan as Iran is considerably larger economy than

Afghanistan.

There have been a number of new regressors that have been added to the gravity model

and these modified models were collectively given the name augmented gravity model.

The new regressors that were included to the gravity equation were population, shared

official language, shared border, per capita GDP, exchange rate, tariff rate, institutional

quality (a host of variables like government stability, government effectiveness,

regulatory quality, rule of law etc.), trade openness, territorial area etc.. These variables

BT-4

BT-2 BT-1

BT-3 BT-5


76

were incorporated over a long period and the list of such new variables goes on. The

tradition of incorporating new variables in the gravity model started from Linnerman

(1967) and other notable studies in this tradition included Anderson (1979), Bergstrand

(1985), Deardorff (1995), Anderson and Wincoop (2001), Frankel and Rose (2002),

Anderson and Wincoop (2003), Rose and Spiegel (2004), Portes and Rey (2005),

Melitz (2007), Warin, Wunnava, Tengia and Wandscheider (2009) and Baier,

Bergstrand and Feng (2014). Despite many differences in these models there were some

fundamental similarities along all these models as well.

The gravity model specification has been modified to study a variety of circumstances

as well. Helpman (1987) studied the intra industry trade under monopolistic

competition; Wall (2000) applied gravity model to study trade under assumptions of

H-O model, Helpman and Krugman (1985) studied the model under increasing returns

to scale. These examples explain the ability of gravity model to accommodate variety

of circumstances under alternative set of assumptions, thus making it a prime choice

for carrying out a wide range of investigations in the area of international trade.

3.3 Disaggregate Trade Indices

While most the studies until 1980s were focused on studying aggregate bilateral trade,

Helpman (1987) studied intra industry trade. Conventional trade theory explained inter

industry trade which takes place when there is Complementarity (one country needs

what the other country offers and vice a versa) between the two countries in question.

However, complementarity does not exist across all countries for a particular type of

traded good or between two countries for all type of traded goods. Intra industry trade

may take place between two countries even when there is lack of complementarity.

Such kind of studies can be carried out with disaggregated data (as is the case in present

study).

The availability of disaggregated data did increase with time but there were problems

of multiple classifications and inconsistency across countries in compiling and

reporting disaggregated data internationally that needed to be consistent across


77

countries in terms of units of measurement and coverage for a given classification etc.

There has been increasing consistency in data reporting for all the recognized countries

and increasing disaggregation of reported data in the last twenty five years. The most

commonly administered classification of disaggregated data is Harmonized System

(HS) of classification that was last revised in 2007.

There have been a number of classifications developed by United Nations Organization

(UNO) over the years. Board of Economic Classification at UNO classified all goods

in four categories on the basis of their end use in 1970. UNO adopted Standard

International Trade Classification (SITC) in 2007 which has a different pattern of

classification than that of HS classification. UNO earlier adopted Central Product

Classification (CPC) in 1990 that could cover all economic activities of production

which it latter on revised in 2008. There is yet another classification named Standard

Industrial Classification (SIC) also released by UNO.

Every classification is being done with a distinct purpose, and therefore each of these

classifications follow a pattern of classification that is somewhat different from the

other classifications. Some classifications are very broad with just a few categories

(UNO’s classification of all goods in to four categories on the basis of their end use in

1970 – Capital Goods, intermediate goods, consumer goods and Others) while others

are very detailed with thousands of categories (10-digit HS classification). It is,

therefore, not always easy to reconcile data across many different classifications

despite a number of concordance tables being developed for this purpose (beyond 6-

digit level HS classification is not even harmonized) (WTO & UNCTAD, 2012). A list

of 2-digit HS classification list of categories is given in appendix-I. The administrative

adoption of a particular classification may not be universal in nature. For example USA,

Canada and Mexico have abandoned the use of SIC since 1997 and chose to operate

with North American Industrial Classification System (NAICS), after signing NAFTA

for the convenience of member nations of the FTA.

The availability of disaggregated trade data has allowed a number of studies that were

not possible in the absence of it. Researchers developed many indices that could be

worked out for a given point in time or over a period of time. Some of these indices


78

include Trade Intensity Index (TII), Export Intensity Index (EII), Import Intensity Index

(III), Grubel and Lloyd Index (GLI), Trade Complementarity Index (TCI), Revealed

Comparative Advantage (RCA) Index and host of other indices as well.

Yamazawa (1970) first developed TII and then many others made use of the index and

introduced developments, especially Kim (2009) and Chandran (2010). Kim (2013)

estimated trade index of South Korea with its major trading partners and Chandran

(2010) has studied trade similarity and complimentarily between India and ASEAN

countries. According to Chandran (2010) and Kim (2013) TII of a country is the ratio

of, the proportion that country’s export to the partner country in its total exports, to

proportion of partner country’s import in total world trade. For example, Pakistan’s TII

with India would be

Pakistan’s export to India/Pakistan’s total exports (Numerator)

Total Import of India /total import of the world (denominator)

TII index may be worked out both for aggregate trade or disaggregated trade. For

overall trade between any two countries i and j, TII of country i for j (for aggregate

trade) is

𝑇𝐼𝐼𝑖𝑗 =

(𝑋+𝑀)𝑖𝑗(𝑋+𝑀)𝑖

⁄

(𝑋+𝑀)𝑗𝑊

⁄ -------------------------------------------- (3.3)

Where (X+M)ij are exports and imports of country i to/from country j and total exports

of country i respectively while W is total world trade.

Similarly, TII for disaggregated trade between countries i and j can be worked out as

𝑇𝐼𝐼𝑖𝑗𝐻 =

(𝑋+𝑀)𝑖𝑗𝐻

(𝑋+𝑀)𝑖𝐻⁄

(𝑋+𝑀)𝐽𝐻

𝑊𝐻⁄

------------------------------------ (3.4)

Where all the components of formula in equation 3.4 for disaggregated TII are the same

as the components of equation 3.3, except, that each component appears with a

superscript H, denoting TII for a particular classification. Therefore, we can work out


79

TII between any two countries for their overall bilateral trade as well as the bilateral

trade in a particular sector or industry with homogenous classification across the two

countries.

Chandran (2010) further explored TII between any two countries in to EII and III. EII

can be determined as

𝐸𝐼𝐼𝑖𝑗 =𝑋𝑖𝑗

𝑋𝑖⁄

𝑀𝑗(𝑊−𝑀𝑖)

⁄

---------------------------------------- (3.5)

Where the numerator is the proportion of country i’s exports to the partner country j

(Xij) in its total exports (Xi) while the denominator is the proportion of imports of

country j (Mj) taken from the difference of the world trade (W) and imports of country

i (Mi). Similarly, III can be determined as

𝐼𝐼𝐼𝑖𝑗 =𝑀𝑖𝑗

𝑀𝑖⁄

𝑋𝑗(𝑊−𝑋𝑖)

⁄

------------------------------------------ (3.6)

Where the numerator is proportion of country i’s imports from country j (Mij) in its

total imports (Mi) while the denominator is the proportion of country j’s exports (Xj)

taken from the difference of world trade (W) and country i’s exports (Xi).

Just like TII can be worked out for any category/classification of goods with the help

of equation 3.4, EII and III can also be worked out both at aggregate and disaggregate

levels for bilateral trade between any two countries for given (homogeneous) sectors

or industries. The disaggregated export and import indices can be determined for any

HS classification with the help of equations 3.7 and 3.8 respectively.

𝐸𝐼𝐼𝑖𝑗𝐻 =

𝑋𝑖𝑗𝐻

𝑋𝑖𝐻⁄

𝑀𝑗𝐻

(𝑊𝐻−𝑀𝑖𝐻)

⁄

------------------------------------------- (3.7)

Thematically, export intensity index of Pakistan to India, for the export category of

cotton, can be expressed with help of figure 3.2 that provides the explanations of

numerator and denominator of equation 3.7 where the ratio of the two specific shares


80

of cotton trade in the two countries provides EII for a particular country, in a particular

category at given point in time duration.

Figure 3.2 Pakistan’s Export Intensity Index of Cotton for India (Thematic

Explanation)

The III for a given classification (regarding a given category) can also be expressed in

the shape of a formula in equation 3.8

𝐼𝐼𝐼𝑖𝑗𝐻 =

𝑀𝑖𝑗𝐻

𝑀𝑖𝐻⁄

𝑋𝑗𝐻

(𝑊𝐻−𝑋𝑖𝐻)

⁄

------------------------------------- (3.8)

The ratio of Pakistan’s share in the partner country’s export to its share in the world

trade (excluding partner country’s volume in world trade volume) of a given category

is considered Pakistan’s III with the partner country in that particular category.


81

Figure 3.3 Pakistan’s Import Intensity Index of Crude Oil from Iran (Thematic

Explanation)

An index of TII, EII or III that is greater than one means that trade flow (overall

bilateral, export or import) is greater than expected for a given partner country in world

trade while value of these indices below one mean that the trade flow is less than

expected (Chandran, 2010 ). Any country’s exports to and imports from world for any

particular good is an outcome of its comparative advantage/disadvantage with rest of

the world, when the good is homogeneous, and there are no transportation costs or other

trade barriers in the trade (Kim, 2013).

Intra industry trade can be assessed in a variety of ways. It is instinctively believed that

most developing countries indulge in inter industry trade for most of the goods,

exporting the goods for which they have comparative advantage and importing the ones

for which they have comparative disadvantage. On the contrary, in most of the highly

developed countries there is usually IIT taking place regarding most of

goods/industries. However, practically most of the countries carry out both inter

industry trade for some goods/industries while IIT for the others. It is important for the

strategic formulation of commercial policy for a country to identify the areas of inter

industry trade as well as IIT in the wake of effective participation in world trade. The

GLI measures IIT of a particular product. It was introduced by Herb Grubel and Peter

Lloyd in 1971 and has been a popular measure of IIT ever since. It is measured as


82

𝐺𝐿𝐼𝑖 = 1 − [|𝑋𝑖−𝑀𝑖|

(𝑋𝑖+𝑀𝑖)] ----------------------------------- (3.9)

where Xi denotes total export and Mi denotes total import of good i for the country. The

numerator of the ratio has the absolute difference of total export and total import of a

particular category in the country while the denominator has sum of total export and

total imports for the same category. The ratio thus arrived at is being subtracted from

one to arrive at the GLI for a particular category of good.

In case the value of the index is 1, there is only IIT taking place in that industry. On the

contrary, if the value of index is zero, it means there no IIT taking place in the country

for that category, which means that the country either only exports or only imports that

category of good for which the index has been constructed. An exporter only or

importer only makes the numerator close to zero and hence the index equal to zero. The

index, however, should be interpreted cautiously as the GLI value tends to be higher

for greater aggregation and lower when there is greater disaggregation.

Comparative Advantage determines the direction of trade in the theory of international

trade. As the overall trade between any two parts of the word comprises of thousands

of things, it is not possible to determine comparative advantage with aggregate trade

volumes and hence disaggregated trade volumes are used to explore comparative

advantage of any country with its partner for different types of traded things between

them. Such a kind of comparative advantage is called RCA and it can be measured for

any level of disaggregation.

The traditional measure of RCA was developed by Balassa (1965) where it is measured

by taking a ratio of, proportion of export of a given category “H” in a country’s overall

exports; to the proportion of trade of the same category in the overall world trade.

RCAiH =

XiH/Xi

WH

W⁄ ----------------------------------------- (3.10)

If this ratio in equation 3.10 is greater than one for the country in question for a given

category of traded goods, that country is considered to have RCA in that category. The

problem with RCA index is that there are incredibly higher values for categories with


83

RCA and values close to zero for the ones with no RCA. Therefore Laursen (2000)

introduced a change in estimating RCA and he called it Normalized RCA or NRCA.

𝑁𝑅𝐶𝐴 =𝑅𝐶𝐴−1

𝑅𝐶𝐴+1 ----------------------------------------------- (3.11)

The value of NRCA lies between zero and one and thus the distribution of RCA across

the categories become more symmetric as a result.

Trade complementarity is a matter between two countries. A measure of assessment of

complementarity between any two countries over the entire range of all the categories

within any given classification at a given point of time is called Trade Complementarity

Index (TCI).

Figure 3.4 Thematic Explanation of Trade Complementarity

Thematically TCI can be expressed with a help of figure 3.4 where all traded goods are

categorized in twenty six categories (each shown with an alphabet) which are being

reflected to express the leading exports and imports of India, Pakistan and Iran.

Pakistani export and import categories are listed in the center columns while the Indian

categories are placed on the right and those of Iran on the left. Pakistani imports that

correspond to Indian and Iranian imports (Pakistani demand for Indian and Iranian

exports) were shaded light blue and were stretched out of the three respective lists.

Iran's Exports

C

D

E

F

G

H

I

J

K

L

Iran's Imports

W

X

Y

Z

A

B

C

D

E

F

Pakistani Imports

A

B

C

D

E

F

G

H

I

J

Pakistani exports

Q

R

S

T

U

V

W

X

Y

Z

Indian Exports

I

J

K

L

M

N

O

P

Q

R

Indian imports

M

N

O

P

Q

R

W

X

Y

Z


84

Similarly Pakistani exports that match Indian and Iranian Imports (demand for

Pakistani exports in India and Iran) were shaded green and stretched out from the three

respective lists.

Figure 3.4 shows that Pakistan has more complementarity for imports from Iran than

imports from India while India has more complementarity for Pakistani exports than

Iran. These conclusions were drawn from straightforward correspondence of the

categories. In a more formal manner, TCI was introduced by Michaely (1996) while

investigating the possibilities of natural trading partners between two given countries.

TCI is measured as

𝑇𝐶𝐼𝑖𝑗 = 100[1 − ∑ |𝑚𝑖𝐻 − 𝑥𝑗

𝐻|/2𝑛𝐻=1 ] -------------------- (3.12)

𝑚𝑖𝐻and 𝑥𝑗

𝐻stand for the proportion of import of category H in country i’s total import

(demand of country j) and proportion of export in the same category in the total exports

of country j (offer of country j). The absolute difference in the offer of country j for

any category H (𝑋𝑗𝐻) and the demand of country i for the same category H (𝑀𝑖

𝐻) is

taken for all the possible categories in a given classification. After dividing each of

these absolute differences for each category from 1 to n, the sum of these divided

absolute differences provide the entire scale of complementarity of country i’s demand

to the offer of country j. In order to make the TCI easy to interpret, it is then subtracted

from 1 and multiplied with 100. Higher the value of TCI of any country “i” greater is

the complementarity of that country with a partner country “j’s” exports and vice a

versa.

3.4 The Link between Aggregate and Disaggregate Analysis

Historically, most of the trade studies were being done on aggregate trade basis until

the beginning of the second half of the twentieth century with the help of comparative

advantage theory framework. The development of gravity trade model of trade started

a new manner of carrying out trade research and has since then become the preferred

choice of the researchers. However, availability of the classified data of trade in the last


85

quarter of the twentieth century allowed researchers to explore the underlying patterns

of disaggregated trade that stayed obscure in the aggregate level studies.

The figure 3.5 has been designed to express and explain the present study being carried

out at both for aggregate and disaggregate trade.

Figure 3.5 Thematic Link between Aggregate and Disaggregate Analysis in the Study

This study made use of both aggregate and disaggregate data, to explore the specific

dynamics of different categories in Pakistan’s trade with its neighbouring countries

besides identifying the potential for Pakistan’s exports to; and bilateral trade with the

four neighbouring countries. The thematic link between aggregate and disaggregate

PAKISTAN TRADE WITH PARTNER

COUNTRY

Disaggregated Categories

Category-Cotton

If Pakistan possess RCA it is likely to export more (espacially if

the partner country does not possess RCA)

Pakistan's EII is likely to me more than one with the

partner country

Category-Crude oil

If Pakistan does not possess RCA it is likely to import from a

country that possess RCA

Pakistan's III is likely to be more than one with the

partner country

Category-Copper

If GLI is high in both the countries, there is scope for

IIT

The possibility of IIT is more if both the countries are relatively

developed

Pakistan's total exports to

partner country

Pakistan is likely to export more to

the partner country if TCI of the

partner country is high for Pakistan

Pakistan is likely to export

more to the partner country if

TCI of the partner country is

high for Pakistan

Documents required to clear a trade

consignment in the partner country is

likely to decrease Pakistani exports to

that partner

Increase in distance between

Pakistan and Partner country

decreases Pakistani exports to

the Partner

Pakistan's total trade with the

partner country

An increase in Pakistani GDP is likely to increase the supply of

Pakistani exports and demand for Pakistani imports

Increase in Partner country's GDP is likely to increase the demand for Pakistani exports

Signing an FTA with partner country is likely to boost the

bilateral trade

Distance is likely to negatively affect bilateral trade


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analyses was explained with the help of figure 3.5 that presents a link how the two

different analyses support and complement each other in an organized way for this

study.

The aggregate level analysis was done separately for bilateral trade with and exports to

the neighbouring countries. The sensitivities of both aggregate measures were

explained in the context four different kinds of influences in each case. At

disaggregated level, three distinct categories were presented to explain the

disaggregated indices being used for the analysis. There were five indices being used

for analysis which were thematically explained in the context of the three categories

mentioned in the figure 3.5. However, the use of indices were not specific to the chosen

categories but could conveniently be used to analyze any given category for the

disaggregated analysis.

Trade theory evolved over the years and researchers made use of the theoretical

breakthroughs in their study of international trade. The traditional theory of

international trade (absolute and comparative advantage) assumed no barriers to trade

while in reality that was an important factor affecting the flow of trade between any

two countries. Gravity model of trade incorporated that omitted dimension with the

help of bilateral geographical distance between the two countries to serve as a proxy

variable for barrier/cost to trade. It was later realized that distance alone does not

incorporate the host of factors responsible for causing barriers to trade. The realization

of this limitation (of distance as the sole factor representing trade barriers) led to the

development of a whole range of augmented gravity models. The introduction of new

variables to augment the basic gravity model depends upon the availability of data and

model specifications as well as diagnostic constraints of the estimated models. This

study incorporated the newly available data of processing cost per container and

documents required to process a trade consignment in the importing country to enrich

the scope of costs/barriers to trade.

Disaggregated trade analysis has been in practice for the last half a century. There were

new developments besides improvements in traditional measures of analysis. This

study estimated five of the most popular indices of disaggregated bilateral trade for


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Pakistan’s trade with each of its four neighbours. The disaggregated trade analysis

complemented the aggregate analysis being done with gravity model. The thematic

framework of two-tier analysis was explained with figure 3.5 which also explain the

scope of the research.

There was a two-fold focus of the study, wherein, aggregate free trade potential of

Pakistan with neighbouring countries was estimated with the help of augmented gravity

model, while disaggregated potential was worked out with the help of five

representative indices of Pakistan’s trade with each neighbouring country.

The effect of FTA was studied through gravity type models by a number of researchers.

Roberts (2004) carried out a study on the potential of free trade between China and

ASEAN member countries through gravity model with the help of OLS estimation.

Peridy (2005) and Abedini and Peridy (2008) assessed the potential for free trade in the

context of Pan Arab free trade area through OLS estimation for a panel of 42 countries

with the help of gravity type model. Similarly, Kepaptsoglou et al. (2009) studied the

effect of FTAs in the Mediterranean region through gravity based modeling in a panel

framework.

This study followed the footprints of Roberts (2004), Peridy (2005), Abidini and Peridy

(2008) and Kepaptsoglou et al. (2009) to determine free trade potential through gravity

type modeling in a panel framework. An alternative choice in that regard could have

been Computable Generalized Equilibrium (CGE) model that has been used for similar

investigations. However, CGE models were criticized for not having strong

econometric foundations (Hertel et al., 2007) and need for making excessive parametric

assumptions Filippini and Molini, 2003). Therefore, this study preferred gravity model

over CGE modeling technique. Moreover, basic gravity model can be augmented to

accommodate a host of explanatory dimensions of cost/barriers to bilateral trade, thus

enriching the range of explanation of a study.

With regard to the choice of indices, for disaggregated trade analysis, international

trade department of the World Bank published a comprehensive guide for trade data

analysis, explaining each of these five indices of disaggregated trade volume, besides


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discussing many other tools and techniques (World Bank, 2008). Helpman (1987)

estimated IIT potential for fourteen industrialized countries at disaggregate level. Kim

(2013) estimated and analyzed trade complementarity and trade intensity of South

Korea with its major trading partners after the entry of South Korea in an FTA with

them in 2012. Similarly, Chandran (2010) analyzed disaggregated trade between India

and ASEAN member countries when they entered in to an FTA in 2009 with the help

of RCA, EII and III. Bernatonyte and Normantiene (2009) explored the patter of trade

specialization in the Baltic states by estimating indices of inter and intra industry trade.

Thus, the present study has followed the footprints of Helpman (1987), Bernatonyte

and Normantiene (2009), Chandran (2010) and Kim (2013) to carry out its analysis at

disaggregate level.


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CHAPTER 4

METHODOLOGY

As the present research carried out an analysis both at aggregate and disaggregate

levels, therefore, estimation techniques adopted at both the levels have elaborately been

discussed in this chapter. The rationale behind the choice of estimation techniques have

been established by discussing the theoretical foundations of the estimation technique

by citing the earlier established researchers who adopted similar techniques in finding

the answers of similar questions. The chapter also presents data sources and sampling

strategy to facilitate future researchers in similar contexts.

4.1 Estimation Techniques

In this dissertation Pakistan’s free trade potential was assessed at aggregate level as

well as for disaggregated trade. The framework that explained the factors responsible

for governing aggregate trade were different from those of disaggregated trade;

therefore, separate techniques were being adopted at aggregate and disaggregated

levels. Augmented gravity model has been used to determine Pakistan’s aggregate free

trade potential with the neighbouring countries whereas five different disaggregated

indices were being prepared including EII, III, RCA, GLI and TCI for each of the four

neighbouring countries of Pakistan to draw inferences on the bases of these indices for

Pakistan’s bilateral trade with each of its neighbouring countries. The indices EII and

III identified the categories for which there is free trade potential between two

countries; while TCI and RCA can provide economic justification or lack of it

regarding trade between two countries for a particular category; and GLI can guide

about the potential for IIT between two counties for a particular category in their

respective trade profiles at any given point in time.


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4.1.1 Aggregate Trade

The study estimated the trade potential with neighbouring countries both for the total

volume of bilateral trade (exports plus imports) as well as for Pakistan’s export

potential to each neighbour. There were separate gravity model specifications being

used for Pakistan’s export potential with neighbouring countries and the bilateral trade

potential at aggregate level. This section also discusses the panel data estimation

choices of fixed and random effect modeling and then explains the critical features of

the aggregate data that was being used to determine Pakistan’s export and bilateral

trade potential with each of its neighbouring countries.

4.1.1.1 Functional specification

Most of the estimation of gravity models until 1990s was being done with the use of

cross-sectional data. However, gravity modeling in the last fifteen years has

increasingly been done with the help of panel data because panel data modeling offered

more information, greater variability across cross-sections over time and lesser

correlation among the explanatory variables (Gujrati, 2004).

Pakistan’s exports to the neighbouring countries have been probed through the

following functional form of the model

Xpct = f (GDPpt GDPct , Dispc , Tarct , Timct , Docct, Cpcct) -------- (4.1)

Where the subscript “p” stands for Pakistan, “c” for the partner country (1, 2,

3,........,177) and “t” for the time period (2005, 2006 ........, 2013) such that Xpct stands

for Pakistan’s exports to a partner country in time period t; GDPpt stands for Pakistan’s

aggregate output/income in time period “t”; GDPct stands for the aggregate

output/income of the partner country in time period “t”; Dispc stands for the distance

between Islamabad (Pakistan’s capital) and the capital of the partner country “c”; Tarct

is the average proportional tariff in the partner country in time period “t”; Timct stands

for the average time (in number of days) it takes to process a trading transaction in the

partner country in a particular year “t”; Docct stands for the average number of

documents that are needed to process a trading transaction in the partner country in


91

time period “t”, and CPCct stands for cost per container in the partner country in time

period “t”.

In order to estimate this functional equation with panel data, the study transformed

bilateral trade and the respective GDPs were transformed in their natural logs according

to the tradition of gravity modeling for better specification of the model. Moreover, In

order to see the impact of free trade agreement with a partner country dummy variable

was introduced in the estimated equation. In this manner, the impact of FTA was also

studied with a dummy variable (DFT) for the countries with which Pakistan has signed

an FTA.

𝑋𝑝𝑐𝑡 = ∝ + 𝛽1𝐺𝐷𝑃𝑝𝑡 + 𝛽2 𝐺𝐷𝑃𝑐𝑡 − 𝛽3𝐷𝑖𝑠𝑝𝑐 − 𝛽4𝑇𝑎𝑟𝑐𝑡 − 𝛽5𝑇𝑖𝑚𝑐𝑡 − 𝛽6𝐷𝑜𝑐𝑐𝑡 −

𝛽7𝐶𝑝𝑐𝑐𝑡 + 𝛽8𝐷𝐹𝑇 ---------------------------- (4.2)

Where, DFT stands for dummy variable for free trade. The expected impact of the GDP

of both the countries and signing of FTA with partner countries on Pakistan’s exports

was positive. On the other hand, the impacts of distance between the capitals of

Pakistan and its partner countries, average tariff rates, time taken to import and number

of documents needed to import and the cost per container in the partner countries on

Pakistan’s exports to partner countries was expected to be negative.

It was difficult to accommodate all the variables in a single model because of a variety

of challenges involved in the process of estimation. Cost per container did not produce

theoretically consistent effect in any of the estimated models, therefore it was dropped

from the final selected model. While accommodating the GDP of Pakistan and its

partner countries in the model, most of the other variables became statistically

insignificant, despite trying many different specifications which led to dropping the

two from this model to see the critical role played by other variables on Pakistan’s

exports to partner countries. Thus four critical determinants of Pakistani export to

partner countries were being estimated – three of which were reflecting the costs

confronting Pakistani exports to the partner countries while the fourth was the impact

of an agreement of free trade signed between Pakistan and its partner country. By using

natural log specification of Pakistan’s export to the partner countries, the results


92

became more significant and this change turned out a more appropriate specification of

the model. Thus the selected model for estimating the determinants of Pakistan’s

exports turned out to be in the following specification.

𝑙𝑛𝑋𝑝𝑐𝑡 = ∝ −𝛽1𝐷𝑖𝑠𝑝𝑐 − 𝛽2𝑇𝑎𝑟𝑐𝑡 − 𝛽3𝑇𝑖𝑚𝑐𝑡 + 𝛽9𝐷𝐹𝑇 ------------- (4.3)

In order to determine the export potential with the neighbouring countries another

model was estimated that could be used to compare the actual exports to the

neighbouring country with the potential determined through the estimated equation for

measuring the export gap with the neighbouring countries.

𝑋𝑝𝑐𝑡 = ∝ + 𝛽1𝐺𝐷𝑃𝑝𝑡 + 𝛽2 𝐺𝐷𝑃𝑐𝑡 − 𝛽3𝐷𝑖𝑠𝑝𝑐 + 𝛽4(1/𝐷𝑜𝑐𝑐𝑡) ----- (4.4)

The variables selected in the equation 4.4 can be classified in to three categories. The

first category included GDP of Pakistan which explained the role of domestic

conditions affecting the overall supply of exports to the world. The second category

included the GDP of the partner country that explained the role of partner country’s

income to generate demand for Pakistani exports. The third category included the

distance between Pakistan and the partner country and the number of documents

required to clear a consignment on the port of import of the partner country. By using

inverse of the documents to import, the results became statistically significant.

The bilateral trade of Pakistan was measured with standard gravity specification putting

bilateral trade and the GDP of the two countries in natural logs along with the distance.

The augmented gravity model attempted to see the impact of other variables too. This

study augmented the standard gravity model with the effects of shared border and FTA

with the partner countries to estimate the determinants of Pakistan’s bilateral trade with

the partner countries. Following functional specification was being used for estimating

Pakistan’s bilateral trade with the partner countries.

𝑙𝑛𝐵𝑇𝑝𝑐𝑡 = ∝ + 𝛽1𝑙𝑛𝐺𝐷𝑃𝑝𝑡 + 𝛽2 𝑙𝑛𝐺𝐷𝑃𝑐𝑡 − 𝛽3𝐷𝑖𝑠𝑝𝑐 + 𝛽4𝐷𝑁 + 𝛽5𝐷𝐹𝑇 ----- (4.5)

In the equation 4.5, BTpct stands for Pakistan’s bilateral trade (sum of exports and

imports) with partner country “c” in time period “t”. Moreover, besides dummy


93

variable for FTA (DFT), this equation measured the effect of shared border with dummy

variable DN. In order to measure the potential of bilateral trade with the neighbouring

country, a different specification of the same variables was being used to determine the

trade gap between Pakistan and each of its neighbouring country by using variables in

their original form rather than their natural logs in order to carry out the comparison of

potential trade vis-à-vis actual trade. The impact of determinants of bilateral trade was

seen through double log model as it offered better results. However, in order to estimate

the potential for bilateral trade with neighbouring countries, natural logs were removed

and estimation was done through original values of the same variables.

Despite the benefits that were mentioned in the literature for using enriched panel data,

it has its own challenges as well. A panel data set where information about the variable

in each cross-section is available for all the time periods during the study is called a

balanced panel and if this condition is not being met, it is called unbalanced panel. The

panel data set in this particular study was an unbalanced panel because the information

about some variables for a few countries in all the years and that for a few countries in

some years was not available in the world development Indicators data set.

4.1.1.2 Fixed effects and random effects

There are different ways estimating panel data under different assumptions but these

possibilities could be broadly classified in to two categories – Fixed Effect Model

(FEM) and Random Effect Model (REM). FEM is also called Least-Squares Dummy

Variable (LSDV) regression model because of the technique that is used to analyze

such models. Similarly, REM is also called Error Components Model (ECM) because

of the two separate components in the model that are considered for the model

estimation.

There were four different possibilities in estimating FEM. The simplest and perhaps

the most naive approach was to consider all cross-sections as alike and to expect these

cross-sections to progress over time in the same manner while FEM measured one

intercept and slope coefficient for all the cross-sections over time. In another

framework FEM model assumed and estimated same slope coefficients but different


94

intercepts for each cross-section in the model expecting the intercepts to be different

across cross-sections as well as over time. In yet another possible FEM, slope

coefficients were considered to be constant and intercept was considered to vary across-

sections but stable over time. The last possibility in FEM was where both intercept as

well as slope coefficients were assumed to vary in each cross-section, in which case the

whole idea of panel data modeling failed because if both intercept and slope

coefficients vary across cross-sections over time, the whole point of putting different

cross-sections in a single panel was lost. The two FEM possibilities where intercepts

vary but the slope coefficients remain constant were being estimated with the help of

dummy variables for each cross-section or each time period called LSDV regression

model. The LSDV model, however, has a cost in terms of lost degrees of freedom in

estimating more and more dummies that decreases the wealth of information in the

panel data set.

The panel data sets can also be measured with REM or the so called ECM with a view

that the intercept of the model is not constant but a random variable (αi) such that

αi = α + єi ------------------------------------(4.6)

where α is the average intercept and єi is a random error in the intercept with zero mean

and constant variance. Moreover, it is assumed that the error term of each cross-section

unit at different points in time must not be correlated, no matter how far apart the two

time period may be. In case, there is a correlation in the error terms, the REM estimation

with Ordinary Least Squares (OLS) produces inefficient estimates and the appropriate

estimation technique is Generalized Least Squares (GLS).

The ultimate criteria to choose between FEM or REM for panel data analysis and

estimation depends upon the underlying assumptions of the estimation and significance

of the model statistics and diagnostic tests. However, a test was developed by Hausman

in 1978 that is called the Hasusman test after his name to choose between FEM and

REM. The null hypothesis of the Hausman test is that there is no substantial difference

between FEM and REM. If the null hypothesis is rejected, the more appropriate

technique is selected after comparing the results of the diagnostic tests of the two


95

models. Gujrati (2004) offered general conclusion that if there were few cross-sections

comprising long periods of time the parameters estimated by FEM and REM would not

be different significantly and FEM would be more appropriate; whereas, if there are a

large number of cross-sections and the information is available for relatively smaller

time period, the estimates of FEM and REM would differ significantly and REM would

be more appropriate (conditional to the fact that the conditions of ECM were being

fulfilled). He further argued that if the error component of the intercept (Ɛi) was found

to be correlated with one or more regressors, then estimates obtained from REM were

biased while those obtained from FEM were unbiased.

As the fixed effect models do not measure the impact of time invariant attributes in a

panel data, the study adopted REM in order to estimate the effects of distance, shared

borders and FTA, all of which were time invariant in nature. The standard gravity

model measured the impact of distance and therefore REM was a natural choice for

estimating gravity model. This study, therefore, measured the both bilateral trade and

export potential with the neighbouring countries with the help REM.

4.1.1.3 Characteristics of variables used for aggregate analysis

A panel data set with nine variables of 177 countries from 2005 to 2013 was being used

for the gravity model. The variables included in the panel were Pakistan’s exports to

and total trade (exports + imports) with each of the 177 countries for the duration and

has been taken from Sate Bank of Pakistan. The values of exports to and total trade

with these partner countries is measured in thousands of current US $. The data for the

distance between Islamabad and Partner countries’ capital city (measured in

kilometers) was taken from timeanddate.com. The expected impact of distance was

negative on exports as well as overall bilateral trade. The data for the remaining six

variables was taken from World Development Indicators (WDI).

The study included GDP of Pakistan and the 177 partner countries, in constant US $

of 2005, for estimating the impact of domestic and international demand for and supply

of traded goods on Pakistan’s exports and bilateral trade of Pakistan with its partner

countries. The likely impact, of both the GDPs, was positive on Pakistan’s export to


96

and trade with the partner countries. The data of tariff was also taken from WDI which

was applied weighted mean of all products for each country over the years and

weighted for the share of goods of that country with its trading partners. The impact of

increase in tariff rate was expected to affect Pakistan’s exports to the partner country

negatively.

Trade of goods across countries involve two distinct types of costs namely the time it

takes in the process and the number of documents required to be furnished for

completing a trading activity across countries. There were two different variables of

time related to international trade that were available in WDIs. Both the time variables

were measured in number of days. The data for lead time to import was the median

time taken in days for a shipment to arrive at the consignee from the port of discharge.

The data for lead time to import were the exponentiated averages of the logarithm of

single value responses and of midpoint values of range responses for the median case.

The data available for time to import in the WDIs was the time in calendar days for the

procedure of import from start to end in the importing country. It was expected that a

decrease in the time taken for the trading activity shall promote trade and vice a versa.

The data for number of documents needed for import in any country was also available

in WDI. The documents in this regards included all those documents that required

clearance from the relevant ministries, port/terminal & customs authorities, and health

& safety agencies and last but not the least banks and financial institutions. The

expected impact of a smaller number of documents was that it would facilitate trade

enhancement.

4.1.2 Disaggregated Trade

The analysis of disaggregated data involved indices and the process of construction of

each of these indices is being explained along with the rationale of interpreting trade in

any given category at disaggregate levels in chapter three. The next two sections

present the method of analysis adopted in this study and characteristics of the data used

in this analysis. First section presents the methodology of analysis being adopted in this

study while the second section presents the important characteristics of the data that

was being used for the construction of these indices. World Bank (2008) gave a


97

comprehensive review of the ways in which indices could be built to explain the pattern

of disaggregate trade between the partner countries.

4.1.2.1 Indices and their interpretation

The disaggregated analysis of bilateral trade with the neighbouring countries was being

done on the basis of the five selected indices TCI, EII, III, RCA and GLI.

UNCOMTRADE data was being used to construct the five indices for five countries

Pakistan, India, China, Iran and Afghanistan for each year during 2005-14. Each of the

five indices was being developed for ninety eight categories at 2-digit HS classification

for the selected period. The method of constructing each of the five indicators and its

interpretation has been explained in the theoretical framework.

Table 4.1: Pattern of Pakistan's Concentration of Trade with its Neighbours on

Average (2004-14)

Proportion of Top-25

Categories in

Proportion of Top-10

Categories in

Proportion of Top-5

Categories in

Total

Trade

Exports Import Total

Trade

Export Import Total

Trade

Export Import

China 86.7% 98.0% 89.0% 69.3% 92.4% 70.0% 55.1% 84.9% 55.4%

India 92.6% 96.2% 95.0% 72.5% 81.0% 78.5% 57.3% 66.4% 64.6%

Afghanistan 95.0% 95.2% 99.4% 74.7% 77.8% 97.4% 51.4% 54.7% 87.7%

Iran 96.0% 96.3% 98.6% 85.5% 87.0% 91.4% 74.2% 77.1% 83.2%


The size of the information obtained through the indices was incredibly enormous to

obtain a meaningful, discernable and comprehensive analysis of these indices for all

the countries involved, in all the years selected and for every single category.

Therefore, a method was adopted to work out the average of all the five indices (being

developed for this study for every year) for every single category. Although, working

out the averages did economize the huge volume of the data being collected through

the indices, yet it was observed that the actual bilateral volume of trade of Pakistan

with each of its neighbours was quite concentrated around a few categories. Table 4.1

provides a quick view of the degree of concentration of Pakistan’s trade with its

neighbours.


98

For all the neighbouring countries, Pakistan’s top 25 average traded categories with

these countries were identified besides determining Pakistan top 25 exports to and

imports from each of the neighbouring country. It was then analyzed whether categories

in these top 25 lists (on average) are being traded/exported or imported according to

expectations or not (on average) just to identity the average behaviour of these top

categories. Moreover, the behaviour of EII and III over the years was tracked for

Pakistan’s top ten categories traded with, exported to and imported from each of its

neighbouring country respectively. Table 4.1 gives a description of how concentrated

Pakistan’s trade with its neighbouring countries has been on average during 2004-14.

It can be clearly seen that more than 95% of exports and imports are concentrated in

top-25 categories with each of the neighbouring country except China where Proportion

of imports in top 25 categories was 89%. Similarly more than 70% of exports and

imports are in top-10 categories and more than 50% in top-5 categories for all the

neighbours. Since, around 70% of Pakistan’s total trade, exports and imports on

average with its neighbours are in the top-10 categories an in depth over time analysis

of TII, EII and III has been done for the top-10 categories.

The trade Complementarity of Pakistan with its neighbouring countries and that of

neighbouring countries with Pakistan has been analyzed at two levels. First the

comparison of average RCA of leading categories of Pakistan with each of the

neighbouring countries carried out to identify the categories having comparative

advantage in all the five countries. TCI of Pakistan with each of its neighbours and that

of its neighbours with Pakistan for each year during 2004-14 was worked out to view

an overall complementarity or lack of it over the years and the changes that have taken

place in that regard.

Subsequently, on average top 25 exports and imports categories with each

neighbouring country were analyzed in the context of the respective RCAs of Pakistan

and its partner neighbouring countries with a view to explore whether in the bilateral

trade of Pakistan with its neighbouring countries, exporting country possessed an

average RCA for the categories in these lists. Categories in Pakistan’s bilateral trade


99

with its neighbour where the exporter possessed an RCA were considered to have

economic justification for being the exporting category.

The export and import intensity indices of Pakistan with each of its neighbouring

country were constructed with a view to analyze export/import with the neighbour for

a given category is more than expected or less. Average EIIs and IIIs for the top 25

categories were being analyzed to identify categories where exports to and imports

from the neighbouring country respectively were more than expected or less.

The average GLI values for the top 25 traded categories of Pakistan and each of its

neighbours were used identify those categories where Pakistan had prospects of intra

industry trade with its neighbouring countries. The categories in the top 25 lists of

bilateral trade where the GLIs of Pakistan and its neighbour were more than 0.5 were

considered to offer the prospects of bilateral trade between the two countries.

The analysis of top-25 exports and imports to each neighbouring country in the context

of average values of RCAs and GLIs of the two countries as well as average values of

Pakistan’s EII and III of those categories does not explain the variation that takes place

in those categories over the year. Therefore, a second round of analysis for the top-10

exports and imports of Pakistan to each neighbour is also being done that focuses on

the variation in RCA and EII/III over the years for these to ten categories.

Pakistan’s RCA and EII with each neighbour for its top ten export categories for their

changes over the years. If Pakistan possessed RCA in a category but its EII was less

than expected then two countries in question were expected to experience barriers in

their bilateral trade causing such a disruption. Pakistan’s III and each neighbouring

country’s RCA for top ten Pakistani import categories were analyzed for their

respective variations over the years. If neighbour country possessed RCA in one

category but Pakistan’s III was less than expected then two countries in question were

expected to experience barriers to their trade for such a category.

4.1.2.2 Characteristics of variables used for disaggregate analysis


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The study used disaggregated data of Pakistan with its neighbour countries from 2004-

14 in HS classification because all the member countries of World Customs

Organization (WCO) report their data to UNCTAD in HS classification. The Data on

HS classification is available at 8-digit and 10-digit classifications as well but that are

not harmonized at that higher level of disaggregation (WTO & UNCTAD, 2012). The

study therefore has opted for 2-digit HS classification. All traded goods are classified

in to 98 categories (called chapters) at 2 digit classification (Appendix I). The original

purpose of HS classification was not that of classifying trade but was developed to

organize the collection of tariff in a systematic and standardized manner all over the

world. However, subsequent revisions in HS classifications have addressed the issue

of classification of traded material as well.

The study collected HS 2-digit exports and imports of Pakistan, China, India, Iran and

Afghanistan for 2004-14. Moreover, Pakistan’s export to and import from each of the

neighbouring countries were also collected for all the 98 categories for the same

duration. The total traded volume of each category around the world was also gathered

for this duration. In case of Iran and Afghanistan, data was missing in a few years. The

data of Afghanistan’s trade statistics from 2004 to 2007 were not available. Similarly

the data for Iran’s trade was not available from 2007 to 2009. The whole data was in

thousands of US $ and was retrieved from UNCOMTRADE data portal.

4.2 Model Justification

GDP of the countries in question and the distance between them have remained

standard variables of gravity type models. GDP of each country explained both demand

(income) and supply (output) dimensions of the macroeconomy of any country to

indulge in international trade. With increase in GDP, a country may increase its imports

due to increase in its national income; whereas, it is also expected to export more as a

result of more output. GDP, therefore, remained a standard and stable component of

gravity models. Distance variable was added in gravity model as a proxy for

cost/barriers to trade between two countries. Head and Mayer (2013) noticed


101

questionable (relative) stability of coefficient for distance while barriers to trade did

fall significantly over the years. As a result of his finding researchers started looking

for more variables that could indicate other relevant barriers to trade. Therefore, the

effort to probe new variables received justifications for augmented gravity models to

introduce new factors affecting bilateral trade. For example, Melitz (2007), Siliverstovs

& Schumacher (2008), Kepaptsoglou et al. (2009) studied the impact of FTA in their

respective models and found positive impact of FTA on trade between countries. The

effect of common border was investigated by Melitz (2007),Lampe (2008), Papazoglou

(2007), and Lee and Park (2007). There were other interesting explanatory variables

being included in published research like tariff level (Lampe, 2008); historical ties

(Siliverstovs & Schumacher, 2008); tariff (Kepaptsoglou et al. 2009; Lampe, 2008);

and common language (Melitz, 2007) to name a few.

Researchers have a wealth of new variables that inform them about many different

types of barriers to trade. The study, therefore, picked up some new variables like

documents required for a trading transaction to execute; $ cost per container in the

country of import; and time taken (in days) to complete a given consignment, in order

to enrich the augmented gravity model. The data, for these dimensions, was publically

available since 2005-06, for a few countries but gradually there was increased

availability of this data for most of the countries.

In the model of bilateral trade, potential effect of FTA and shared border was

incorporated besides traditional explanatory variables of GDPs and bilateral distance.

The specification of this model was adopted because of more efficient values of

coefficients of explanatory variables. The effort to add another variable yielded

coefficients that were not significant and were incapable to add any great explanatory

power of the model.

The model, meant for Pakistan’s export potential, estimated the impact of tariff (proxy

for fiscal barriers), geographical distance (proxy for transportation cost), time taken to

complete the legal process of import in the partner country (proxy for administrative

cost), and FTA (proxy of joint effort to remove trade barriers). Another explanatory

variable of documents required to process a trade order was dropped because its


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coefficient was not significant and was hiving high correlation with the variable of time

taken to import. Similarly, many efforts were being made to accommodate the variable

of cost per container in the estimated model, but in almost all the cases, the opportunity

cost of having that variable in the equation was high standard errors of remaining

variables as well as unstable and counter-intuitive coefficient of tariff. Therefore, these

two variables were dropped in the final estimated equation.


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CHAPTER 5

DESCRIPTIVE STATISTICS

This chapter furnishes the descriptive statistics of Pakistan’s trade with the

neighbouring countries for aggregate volume of trade and the determinants of this

bilateral trade volume were being explained and interpreted in the following chapter.

In the first section the chapter the critical aspects of Pakistan’s disaggregated trade with

the world in general and the leading disaggregated traded categories with the

neighbouring countries in particular are being discussed in detail. In the second section

of the chapter, Pakistan’s trade with world in general and with neighbours in particular

at 2-digit HS classification are being explored and explained. The criterion for selection

of a leading category in bilateral trade was set as the top ten categories of trade with a

volume of at least one million dollar per year.

5.1 Pakistan’s Aggregate Trade with the Neighbours

Pakistan’s aggregate trade has been increasingly getting aligned with its neighbours.

China has become the largest trading partner of Pakistan in 2015, a position earlier held

by USA. However, there was a lot of diversity in the pattern and composition of

aggregate trade with the neighbouring countries.

Pakistan’s bilateral trade on average was above $1 billion with all the neighbours

except Iran where it was less than half a billion per annum. The standard deviation is

highest in case of China where the volume of bilateral trade went up from less than two

billion dollars in 2005 to $ 7.4 billion in 2013. The bilateral trade with India has also

seen improvement from around half a billion dollars in 2005 to over two billion dollars

in 2013. While Pakistan’s trade with China followed a relatively stable growth its

bilateral trade with India has fluctuated with political developments over the years. The


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bilateral trade with Afghanistan has also seen growth, though there has been a general

decline since 2011. As a landlocked country, Afghanistan relied abundantly on Pakistan

for its trade with rest of the world.

Table 5.1: Descriptive Statistics of Pakistan’s Bilateral Trade with Neighbouring

Countries

Pakistan's

Average

bilateral

trade to

(000$)

Standard

Deviation Range (000$)

Afghanista

n

1093191.01

4

370400.4

5 682056.5 (2007) - 1875174 (2011)

China

4249689.43

9

1912105.

7 1810256 (2005) - 7425006 (2013)

India

1433766.53

8

398816.4

3 676027 (2005) - 2006336 (2013)

Iran

481852.068

1

326379.4

1 116097 (2013) - 1220945 (2010)

Source: Author’s calculations from State Bank of Pakistan (Direction of Trade

Statistics)

Iran was the first country that accepted Pakistan diplomatically but due to a host of

bilateral and international issues the volume of bilateral trade squeezed in recent years

significantly from over a billion dollar in 2010 to around $ 116 million in 2013. During

international sanctions on Iran, a large proportion of Pakistan’s bilateral trade with Iran

was in barter terms, however, after removal of sanctions, earlier in 2016, the bilateral

trade volumes could be expected to grow in future.

Pakistan’s bilateral trade with China, on average, was nearly four times higher than its

bilateral trade with Afghanistan (as seen in Table 5.1) however, Pakistan’s exports to

both the countries, on average were almost of same size (as seen in Table 5.2).

However, while looking at the way Pakistan’s exports have progressed with China


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(especially after signing of the FTA between Pakistan and China), Pakistan’s exports

to China were more than a billion dollar more than its exports to Afghanistan.

If the range of Pakistan’s exports to the neighbouring countries is seen in table 5.2,

there emerges an interesting feature in Pakistan’s exports to each of its neighbours.

Each year during 2010 to 2013 increased. Pakistan’s exports to Iran Peaked in 2010;

exports to Afghanistan peaked in 2011; exports to India peaked in 2012; and Pakistan’s

Exports to China were the highest in 2013.

Table 5.2: Descriptive Statistics of Pakistan’s Exports to Neighbouring Countries

Pakistan's

Average

Exports to

(000$)

Standard

Deviation Range (000$)

Afghanistan 1084382.412 367016.36 680299(2007) - 1864928 (2011)

China 1135185.092 840172.69 281981.8 (2005) - 2698910 (2013)

India 283724.9351 45391.918 190398.5 (2005) - 333457.3 (2012)

Iran 146389.4386 39926.941 94454 (2013) - 203822 (2010)

Source: Author’s calculations from State Bank of Pakistan (Direction of Trade

Statistics)

It can be seen clearly in table 5.3 that on average Pakistan’s exports to India were nearly

twice as much as were its exports to Afghanistan. However, the standard deviation of

exports to India was relatively more than that of exports to Iran. Similarly, although

Pakistan’s exports to Afghanistan’s were just a fraction less than its exports to China

on average, but the standard deviation for exports to China was significantly higher

than that to Afghanistan.

5.2 Determinants of Pakistan’s Aggregate Trade with Neighbours

It is clearly visible from the statistics in the table that China, India and Iran have

distinctly higher GDPs than Afghanistan. However, China appeared to be nearly three


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times bigger economy with over $ 3.53 trillion than India with an average GDP of $1.14

trillion. Iran is a fairly large economy but appeared to be around a quarter of the size of

India’s economy with an average volume of $ 228.5 billion. Afghanistan with an

average volume of $ 9.32 billion is the smallest economy of the four neighbours of

Pakistan. The standard deviation of Iran is the smallest of all the neighbouring countries

and it was the only neighbouring country whose GDP was less in 2013 than its peak

value in 2012.

Table 5.3: Descriptive Statistics of GDP of Pakistan’s Neighbouring Countries

Average GDP

(Constant billion $

of 2005)

Standard

Deviation Range (Constant billion $ of 2005)

Afghanistan 9.323 2.405 6.275 (2005) to 12.679 (2013)

China 3531.013 894.817 2256.902 (2005) to 4864.002 (2013)

India 1147.996 219.017 834.215 (2005) to 1458.737 (2013)

Iran 228.558 21.837 192.014 (2005) to 257.482 (2012)

Source: Author’s calculations from World Development Indicators

It can be seen in table 5.4 that China has the most liberalized trading system (with an

average of the weighted mean tariff rates of 4.35%) while Iran has the most illiberal

trade system amongst Pakistan’s neighbours (with an average of the weighted mean

tariff rates of 19.65%). It can be observed, over time, the weighted mean tariff rates

have come down in in China and India while it increased in Afghanistan and Iran. Mean

Tariff rates nearly halved from their peak value of 13.36% in 2005 to 6.59% in 2008

which was a remarkable effort towards trade liberalization. On the contrary, Iran,

already having a significantly high weighted mean tariff rate of 17.55%, increased it

even further to 21.77% in 2011.

Table 5.4: Descriptive Statistics of Weighted Mean Tariff Rates in Pakistan’s

Neighbouring Countries

Average weighted mean

applied Tariff (%)

Standard

Deviation Range (%)

Afghanistan 6.19 0.526 5.61 (2006) - 6.79 (2012)


107

China 4.35 0.294 4.04 (2010) - 4.83 (2005)

India 9.3767 3.540 6.59 (2008) - 13.36 (2005)

Iran 19.653 2.110 17.55 (2007) - 21.77 (2011)


The low weighted mean tariff rates and low standard deviation of China (Table5.4)

expressed China’s continued and persistent commitment to the cause of free trade. The

standard deviations of tariff rate are relatively high for Iran and India but for entire

opposite policy options – the tariff rates declined in case of India and increased in case

of Iran. There was an increasing trend of the tariff rates in Afghanistan as well, but

quite significantly slower in pace and much smaller in size than Iran.

An important non-tariff barrier to trade is the number of documents that are required

for processing the trading consignment in the importing country. Table 5.5 provides

the descriptive statistics of the document required to import in each of the four

neighbouring countries of Pakistan. It can be seen through table 5.5 that there was no

change in the number of documents required to import in any neighbouring country

except China where the number of documents required has gone up from five

documents to six since 2008. That shows an interesting feature of Chinese liberalization

where tariff barriers have fallen while non-tariff barriers (number of documents) have

increased, though quite modestly.

Table 5.5: Descriptive Statistics of Documents Required to Import in Pakistan’s


Average number of documents

required to import (Number)

Standard

Deviation Range (Number)

Afghanistan 10 0

China 5.44 0.527 5 (2005-8) to 6 (2008-13)

India 10 0

Iran 11 0



108

There were roughly twice as many documents required in each of the other three

neighbours than in China with Iran having the highest 11 documents, followed up by

India and Afghanistan each requiring ten documents to clear a trade consignment there.

It also interesting to note that there was no change in the number of documents required

in Iran, India and Afghanistan during 2005-13, showing the stability in the behaviour

this non-tariff barrier to trade in all the neighbouring countries.

Table 5.6: Descriptive Statistics of Cost per Container on the Ports of Pakistan’s


Average cost per

container on port in ($)

Standard

Deviation Range ($)

Afghanistan 3174.44 1068.59 2100 (2007) to 5180 (2013)

China 542.78 117.02 430 (2005) to 800 (2013)

India 1181.67 159.37 990 (2007) to 1462 (2013)

Iran 1685.78 309.49 1330 (2005) to 2100 (2012)


The efficiency of the port facilities contributed towards growth in trade and table 5.6

provides the descriptive statistics of cost incurred per container in Pakistan’s

neighbouring countries. Not surprisingly, on average, Afghanistan turns out to be least

efficient ($ 3174.44) while China ($ 542.78) turned out to be most efficient

neighbouring country in that regard. Cost per container generally went up in all the

neighbouring countries, though some did better in that regard than others. Afghanistan

did poorly in that regard with the highest standard deviation, where the cost per

container more than doubled from $ 2100 in 2007 to $ 5180 in 2013. Although, Cost

per container nearly doubled in China from 2005 to 2013 which was not the case with

Iran and India yet the competitive edge that China possessed in terms of efficiency was

not lost during this period. India too showed promise in keeping the cost under control

and showd some efficiency vis-à-vis China over the years. The cost per container in

India was more than twice as much as it was in China in 2007 and the relative gap

between the two large economies and Pakistan’s neighbours declined in 2013.


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The time taken to import in a particular country was considered a cost or barrier to trade

that affects its trade with the partner countries. Table 5.7 provides the administrative

cost of trade in Pakistan’s neighbouring countries. Just like table 5.6, this aspect of cost

too was found highest in Afghanistan. While all the other neighbouring countries

showed a general improvement in the time taken to import, there was deterioration in

this cost in the case of Afghanistan where the number of days taken to clear a trade

consignment increased from 71 days in 2006 to 85 days by 2013.

Table 5.7: Descriptive Statistics of Time Taken to Import in Pakistan’s Neighbouring

Countries

Average time taken

to import (days)

Standard

Deviation Range (days)

Afghanistan 76.88 4.25 71 (2006) to 85 (2013)

China 24.22 0.66 24 (2006 onwards) from 26 (2005)

India 24.9 9.13 20 (2012) from 41 (2005)

Iran 40.77 3.59 37 (2013) from 44 (2005)


China showed its dominance in relatively less cost per container than India on average

(as shown in table 5.6) but, on average there was nearly same time taken in China as

well as India in clearing a trade consignment. India, showed an extraordinary

improvement by nearly half as many days taken to import in 2012 than in 2005. China

showed a modest improvement by improving its efficiency by two days in 2006 and

has maintained that level since then. Iran, however, showed a considerable

improvement by bring the cost down to 37 days in 2013 from 44 days in 2005.

5.3 Pakistan’s Disaggregated Trade

5.3.1 Pakistan’s Exports to World

Pakistan’s overall exports have been quite concentrated around a few categories in such

a way that nearly three forth of all the exports are concentrated in top ten exports


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categories in Pakistan. The table of top ten exports presents the performance of cereals

(10), Salt, sulphur, earth, stone, plaster, lime and cement (25), Mineral fuels, oils,

distillation products, etc (27), Raw hides and skins (other than furskins) and leather

(41), Articles of leather, animal gut, harness, travel goods (42), Cotton (52), Articles of

apparel, accessories, knit or crochet (61), Articles of apparel, accessories, not knit or

crochet (62), Other made textile articles, sets, worn clothing etc. (63) and Pearls,

precious stones, metals, coins, etc. (71) for a period of 2004-14.

Table 5.8: Pakistan's Exports to the World as Percent of Total Exports

'52 '63 '61 '10 '62 '27 '42 '25 '41 '71

2004 22.26 17.58 12.45 5.11 6.97 2.71 3.71 0.33 2.14 0.22

2005 21.36 19.13 10.31 6.86 8.28 4.20 4.32 0.70 1.91 0.13

2006 21.27 19.15 11.23 6.81 7.96 4.97 4.02 0.75 1.88 0.14

2007 19.28 17.82 10.38 6.97 7.69 5.57 3.88 1.41 2.20 0.67

2008 17.73 15.51 9.31 12.37 6.71 6.06 3.78 2.96 1.89 1.18

2009 18.25 16.62 9.57 10.39 6.87 4.07 3.29 3.21 1.54 2.73

2010 18.74 15.34 9.26 10.65 6.83 5.62 2.89 2.40 1.94 2.76

2011 20.11 14.09 8.83 11.08 7.00 5.18 2.69 2.25 1.85 1.85

2012 21.23 13.35 8.15 8.37 6.88 1.34 2.74 2.90 1.86 6.64

2013 21.23 14.67 8.38 8.68 7.38 2.10 2.96 2.88 2.11 1.74

2014 19.14 15.80 9.72 8.94 8.03 2.62 3.00 2.81 3.21 0.52

Average 20.05 16.28 9.78 8.75 7.33 4.04 3.39 2.05 2.05 1.69

Source: Author’s Compilation from UNCOMTRADE

When we look at Pakistan’s leading exports they have mainly been dominated by cotton

(52) based exports with nearly one fifth of total exports coming from this sector alone.

Collectively cotton, textile, apparel and clothing account for more than half of

Pakistan’s total exports on average. All the cotton textile and related sectors (52, 61,

62 and 63) have seen a gradual decline since 2004 with an exception of articles of

apparel, accessories, not knit or crochet (62) that managed to recover the decline in the

same duration. Although the cotton and related sectors seemingly show a declining

trend, over the years, in terms overall trade, yet, each of these sectors have had higher

export earnings than the previous year, throughout the time period under analysis, with

cotton (52) exports registering an average increase in export value of around $214

million and clothing (63) earned over $121 million per year.

Leather and Leather based products (41 and 42), were roughly five percent of overall

exports, on average, such that leather based products (42) have generally remained a


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greater share of exports than raw leather (41). Cereal exports have shown excellent

performance with a little over half a billion dollar exports in 2004 to over two billion

dollar exports in 2014. It was interesting to note that two of Pakistan’s top ten exports

were also in the list of top ten traded goods in the world (27and 71). However, there

was twice as much category in Pakistan’s leading imports that were top traded world

categories too.

5.3.2 Pakistan’s Imports from World

Just like exports, imports in Pakistan are also concentrated around a few categories,

such that more than 72 percent of Pakistan’s overall imports were in its top ten imports

with nearly one third of its imports regarding Mineral fuels, oils, distillation products,

etc. (27).

Table 5.9: Pakistan's Imports from the World as a Percentage of Total Imports

'27 '84 '85 '29 '72 '15 '87 '39 '52 '31 2004 21.75 12.67 5.71 7.21 3.89 4.44 4.28 3.98 3.57 1.98 2005 21.12 12.38 10.04 5.05 5.75 3.44 5.95 3.98 2.06 2.61 2006 25.75 11.21 10.33 3.95 4.67 2.95 5.81 3.78 1.44 1.52 2007 25.62 10.01 10.58 4.82 4.73 3.99 4.35 3.93 2.82 2.29 2008 33.20 9.27 8.94 4.16 3.85 4.44 2.80 3.21 2.86 1.72 2009 27.98 9.88 8.29 4.81 5.33 4.47 3.07 3.75 1.58 2.55 2010 30.37 7.87 6.50 4.59 4.52 4.93 3.48 3.88 2.21 1.73 2011 34.10 6.77 5.58 5.08 3.86 5.94 3.45 3.85 2.09 2.36 2012 36.40 6.99 6.28 4.65 4.22 5.28 3.64 3.43 1.56 2.07 2013 34.83 6.99 6.13 4.60 4.21 4.52 2.83 3.59 2.39 1.38 2014 31.17 8.26 7.04 4.13 4.84 4.52 2.76 1.50 1.56 1.71 Average 29.30 9.30 7.77 4.82 4.53 4.45 3.86 3.53 2.19 1.99


The top ten imports of Pakistan were Animal, vegetable fats and oils, cleavage

products, etc (15), Mineral fuels, oils, distillation products, etc (27), Organic chemicals

(29), Fertilizers (31), Plastics and articles thereof (39), Cotton (52), Iron and steel (72),

Machinery, nuclear reactors, boilers, etc. (84), Electrical, electronic equipment (85) and

Vehicles other than railway, tramway (87).

Most of Pakistan’s imports were regular imports for any growing economy that needs

energy, machinery, electrical inputs, chemicals and fertilizers for productive growth in


112

the economy. It was interesting to note that seven of our top ten import categories were

in the top ten traded categories in the world (27, 29, 39, 72, 84, 85 and 87).

As regards the biggest import category of mineral fuels there were variations in the

import bill for that category due to huge fluctuations of the prices in the reference

period of the study. Therefore, despite greater import in terms of volumes, the value of

import of this category did not increase that much. Pakistan has also been importing

cotton that was the biggest category of Pakistan’s export to furnish the needs of high

value exports in that category. Cotton imports crossed a billion dollar mark in 2014.

5.3.3 Pakistan’s Trade with its Neighbours

Pakistan’s trade with its neighbouring countries showed tremendous growth at the end

of reference period both in exports as well as imports. There was over four times

increase in Pakistan’s exports and more than five times increase in its imports with the

neighbours in absolute value terms. Moreover, Pakistan’s trade with neighbouring

countries as a percentage of its total trade with world more than doubled from their

respective imports and total trade in 2004. What was most encouraging, in that regard

was the extraordinary growth in Pakistan’s exports to its neighbours from less than 8

percent of its total exports in 2004 to more than 18 percent in 2014. Similarly Pakistan’s

imports from its neighbours more than doubled in terms percentage of its total world

trade.

Pakistan became more integrated with its neighbours both in absolute as well as relative

terms vis-à-vis its overall trade with the world. However, there were great differences

in the scale and direction of trade integration within the neighbouring countries.

Following sections would reveal the differences in Pakistan trade within its neighbours

because of economic, political, social and geographical factors.

Pakistan’s trade with its neighbouring countries was also quite concentrated around a

few top categories just like its overall trade. In the following section the study explored

Pakistan’s leading exports and imports from each of its neighbours during 2004-14.

However, there were four categories in Pakistan’s leading exports (61, 62, 63 and 71)

which were not in the list of Pakistan’s leading exports with any of the neighbouring


113

countries whereas there were two categories 15 and 87 in the list of Pakistan’s leading

imports that were not in the list of leading imports from any of its neighbour.

Pakistan’s bilateral trade volume was more than one billion dollars with all the

neighbouring countries except Iran. China became the largest importer in Pakistan

since 2010 crossing USA that had been the largest destination Pakistani imports before

2010.

5.3.3.1 Pakistan’s leading exports to and imports from India

Pakistan’s exports to India remained rather stable in terms of percentage of its exports

to the world while exports nearly halved in terms of percentage of its exports to the

neighbours since 2004. It was a very interesting trend that showed although Pakistan’s

export to India did not increase much vis-à-vis its exports to the world but this trend

almost reversed when viewed in the context other neighbours. This trend suggested that

lack of penetration of Pakistan’s exports in India was divorced from the trend of

Pakistan’s export penetration with the other neighbouring countries.

Pakistan’s import from India showed persistent growth since 2006 with an average

growth of over a hundred million dollars in terms of value. This trend was kick started

when Pakistan’s imports from India nearly doubled from 2005 to 2006 in such a way

that total imports increased from a little over half a billion to well over a billion dollar

in that one year’s time.

5.3.3.1.1 Pakistan’s leading exports to India

Over three quarters of Pakistan’s exports to India were in the sectors of Edible

vegetables and certain roots and tubers (07), Edible fruit, nuts, peel of citrus fruit,

melons (08), Salt, sulphur, earth, stone, plaster, lime and cement (25), Mineral fuels,

oils, distillation products, etc (27), Organic chemicals (29), Raw hides and skins (other

than furskins) and leather (41), Cotton (52) and Copper and articles thereof (74). There

were four categories in the list of Pakistan’s leading exports to India that were also in

the list of Pakistan’s overall leading exports (25, 27, 41 and 52).


114

This list was formed on the basis of averages; however, actually, there were huge

fluctuations in the performance of these categories over the years. On average Pakistan

exported over $126 million worth of exports in mineral fuels category from 2004 to

2008 but this average drastically fell down to around $ 15 million from 2009 to 2014.

On the contrary, the export of Salt, sulphur, earth, stone, plaster, lime and cement (27)

registered an extraordinary growth with less than a quarter of million dollars exports in

2004 to around $ 54 million in 2014 with an average increase of around $ 5 million per

year in that period.

Table 5.10: Pakistan’s Leading Exports to India (In millions of $)

'27 '52 '08 '25 '29 '41 '74 '07 2004 86.0 13.0 24.4 0.2 1.2 1.3 0.1 5.5 2005 151.3 37.8 28.8 0.4 10.4 3.6 1.6 69.6 2006 162.0 56.3 32.2 0.4 30.4 3.0 3.0 3.3 2007 94.1 56.2 34.3 10.3 1.6 11.9 2.8 1.9 2008 137.7 52.8 36.3 65.8 4.8 13.5 3.2 0.1 2009 22.6 44.5 45.0 32.3 25.9 8.4 4.0 0.1 2010 26.4 38.3 45.3 32.3 25.7 12.9 8.7 0.1 2011 9.3 30.5 47.9 48.0 26.0 13.0 11.3 3.4 2012 3.1 81.7 67.2 52.8 9.4 9.8 21.4 0.9 2013 21.4 42.4 74.0 45.1 30.4 20.6 34.5 0.5 2014 7.9 60.1 64.3 54.0 12.6 36.2 33.6 0.0 Average 65.6 46.7 45.4 31.0 16.2 12.2 11.3 7.8


Pakistan’s cotton exports to India, on average, accounted for nearly 15% of Pakistan’s

total exports to India and that value generally kept rising during the reference period of

the study, with an occasional decline in few years. Pakistan’s fruit exports to India were

almost constantly rising since 2004 with an average increase of $ 3.6 million annually

during 2004-14. Similarly, Pakistan’s Copper related exports showed huge growth with

less than a hundred thousand dollar worth of exports in 2004 to over $ 33 million in

2014.

5.3.3.1.2 Pakistan’s leading imports from India

Just like Pakistan’s exports to India, its import from India was also quite concentrated

around a few top traded categories. Nearly, three quarters of Pakistan’s imports from

India were in the selected few categories of Edible vegetables and certain roots and


115

tubers (07), Coffee, tea, mate and spices (09), Sugars and sugar confectionery (17),

Residues, wastes of food industry, animal fodder (23), Organic chemicals (29),

Tanning, dyeing extracts, tannins, derives, pigments etc. (32), Plastics and articles

thereof (39) and Cotton. There were three categories in the list of Pakistan’s leading

imports from India that were also in Pakistan’s overall leading imports as well (29, 39

and 52). Vegetables, Chemicals and Cotton were in Pakistan’s top exports as well as

top imports with India in such a way that bilateral traded volume on average (2004-14)

of chemicals was above $ 300 million while that of Cotton was a little below $ 300

million and vegetables accounted for about $ 125 million between India and Pakistan.

Table 5.11: Pakistan’s Leading Imports from India (In millions of $)

'29 '52 '23 '07 '39 '17 '32 '09 2004 196.7 63.3 17.9 2.0 52.8 0.0 12.5 7.5 2005 163.1 32.5 57.3 32.4 55.9 0.7 20.0 16.6 2006 209.4 73.1 102.2 33.1 103.1 324.3 23.3 22.3 2007 410.9 281.8 83.8 42.9 96.5 3.5 30.6 15.2 2008 455.6 448.3 116.8 74.0 81.8 0.0 37.2 40.6 2009 338.5 139.3 82.0 137.2 42.9 0.0 35.8 18.4 2010 260.7 338.3 131.8 123.3 37.5 156.7 42.0 51.5 2011 373.3 305.5 186.3 141.1 67.5 51.5 41.2 56.7 2012 308.2 190.6 268.3 201.5 85.8 0.4 45.5 58.5 2013 259.1 408.9 297.4 230.7 149.5 0.3 57.1 35.6 2014 237.8 381.2 209.9 252.0 157.1 2.6 89.4 57.1 Average 292.1 242.1 141.2 115.5 84.6 49.1 39.5 34.5


Organic chemicals that were in Pakistan’s leading exports to India were Pakistan’s

largest category of import from India also. However, Pakistan’s chemical imports from

India were much more than its exports to India. Pakistan’s average exports of chemicals

was a little over $ 16 million while imports were a little below $ 300 million for the

reference period making it a very important category of trade. Pakistan’s reliance on

Indian sugar import was sporadic in nature where Pakistan plugged its domestic

shortages with sugar imports from India. Pakistan had significant imports of sugar from

India in three years with highest import of over $ 324 million in 2006 and $ 156 million

in 2010 and $ 51 million in 2011.


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5.3.3.2 Pakistan’s leading exports to and imports from China

China became the biggest trade partner of Pakistan with over 16% of Pakistan’s total

trade being done with China while USA that previously was the biggest trade partner

of Pakistan stood at less than 8% of total trade of Pakistan in 2014. The share of

Pakistan’s imports from China more than doubled in 2014 from where it was ten years

earlier. More than one fifth of Pakistan’s imports from the world come from China.

However, USA continued to remain the largest market of Pakistani exports with around

15% of total exports (Pakistan, 2015).

While Pakistan realized tremendous growth in imports from China, there was a more

remarkable growth in its exports to China. Pakistani exports to China rose from a

modest level of around $ 300 million in 2004 to over $ 2.25 billion in 2014. This

improvement was not just in $ terms but also in terms of share in total Pakistani exports

from a little over 2% of total exports in 2004 to well over 10% by 2013.

China has historically been the friendliest neighbour of Pakistan both politically and

economically. The proof of this argument can be seen in trade statistics of Pakistan’s

trade with China as a percentage of its trade with neighbouring countries. Nearly, half

of Pakistan’s exports and more than 3/4th of its import with the neighbouring countries

came from China. Thus China was the most important trade partner of Pakistan, not

just regionally but also internationally both in terms of exports and imports. After Pak-

China free trade agreement that became fully operational and CPEC that is in progress,

the future seems to offer greater prospects for trade between the two countries.

5.3.3.2.1 Pakistan’s leading exports to China`

Although Pakistan’s exports to China showed tremendous growth in the first fifteen

years of the 21st century, yet its structure remained heavily tilted in favour of cotton

that historically remained nearly 2/3rd or more of its total exports to China. China, India

and Pakistan are three of the top four cotton producing countries in the world with

China as the largest producer of Cotton followed up by India, USA and Pakistan.

Pakistan exported less than $50 million worth of cotton to India (second largest export

to India) annually on average during 2004-14 while the same for China was more than


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$ 875 million on average in the same period. Pakistan had over a billion dollar worth

of cotton exports to China each year from 2011 to 2014.

While cotton exports dominated Pakistan’s exports to China, there were other sectors

worth mentioning also. Pakistan’s second largest export to China was Ores, slag and

ash ('26) that constituted around 7% of its exports to China on average. Cereal ('10)

was the third largest category that showed excellent growth since 2004 from an export

of $ 28 thousand to over a million dollar in 2010 and around $ 257 million in 2012.

There were four categories in the list of Pakistan’s leading exports to China that were

also in the list of Pakistan’s overall leading exports (10, 25, 41 and 52).

Table 5.12: Pakistan’s Leading Exports from China (In millions of $)

'52 '26 '10 '41 '03 '39 '25 '74 '23 2004 203.4 15.1 0.03 22.6 23.2 8.4 1.4 0.1 0.00 2005 271.8 25.6 0.25 29.3 28.3 2.5 1.4 3.5 0.00 2006 358.2 27.8 0.06 31.4 24.6 5.2 1.9 7.5 1.27 2007 376.8 87.7 0.31 38.2 29.7 12.3 5.0 6.6 0.01 2008 382.3 158.6 0.53 43.1 40.3 14.2 7.5 11.0 2.07 2009 701.4 74.9 0.43 34.5 47.0 19.5 10.9 21.3 4.74 2010 910.8 149.8 1.49 47.0 63.1 40.2 21.0 25.7 26.5 2011 1134.2 112.7 11.2 47.9 41.3 47.3 38.2 30.1 14.6 2012 1833.6 120.9 256.9 62.0 41.6 35.9 44.2 41.3 25.9 2013 1936.0 129.2 144.1 57.1 35.8 43.1 63.5 36.6 37.8 2014 1525.3 91.7 137.8 55.4 55.2 33.2 52.3 34.5 64.7 Average 875.8 90.4 50.3 42.6 39.1 23.8 22.5 19.8 16.1


There were sectors in which Pakistan’s share in total exports to China fell despite

increase in export earning over the years as well as those where absolute export

earnings as well proportion of that category’s export increased. Pakistan’s share in

exports of Fish, crustaceans, molluscs, aquatic invertebrates ('03), Plastics and articles

thereof ('39) and Raw hides and skins (other than furskins) and leather ('41) decreased

from their respective levels in 2004 despite increase in value of absolute export

earnings in those categories. On the contrary, Pakistan’s share in its exports to China

as well as its absolute export earnings from China increased in the sectors of Residues,

wastes of food industry, animal fodder ('23), Salt, sulphur, earth, stone, plaster, lime

and cement ('25) and Copper and articles thereof ('74).


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5.3.3.2.2 Pakistan’s leading imports from China

More than two thirds of Pakistan’s imports from China come from the sectors of

Organic chemicals ('29), Fertilizers ('31), Plastics and articles thereof ('39), Manmade

filaments ('54), Manmade staple fibres ('55), Iron and steel ('72), Machinery, nuclear

reactors, boilers, etc. ('84) and Electrical, electronic equipment ('85). There were six

categories in the list of Pakistan’s leading imports from China that were also in its

overall leading imports as well (29, 31, 39, 72, 84 and 85).

Table 5.13: Pakistan’s Leading Imports from China (In millions of $)

'85 '84 '29 '54 '72 '31 '39 '73 '55

2004 221.8 369.6 99.5 33.3 23.1 23.9 32.0 23.3 7.8

2005 428.9 539.9 118.6 93.1 40.6 22.3 65.9 44.3 6.9

2006 568.1 691.5 127.4 137.5 129.6 1.6 85.8 76.5 20.5

2007 1066.9 695.6 160.5 176.2 235.1 228.2 100.6 95.6 91.1

2008 1392.0 851.6 221.9 162.7 215.7 88.1 106.5 166.8 96.2

2009 989.7 599.3 276.0 190.0 104.3 10.4 98.4 150.3 86.9

2010 1245.3 800.1 321.0 327.4 243.2 140.4 135.6 149.7 186.3

2011 1366.2 851.1 394.9 483.7 267.9 409.3 202.0 136.2 303.6

2012 1741.4 868.6 374.1 373.5 357.7 339.5 203.0 181.0 177.0

2013 1756.3 836.8 378.3 367.8 324.8 229.4 232.4 252.4 170.7

2014 2265.0 1370.0 490.8 479.4 712.7 551.7 335.8 292.9 310.9

Average 1185.6 770.4 269.4 256.8 241.3 185.9 145.2 142.6 132.5


However, during 2004-14 shares of the two categories in total imports from China

reversed. In 2004, the share of Machinery in Chinese imports was 25% while that of

Electrical equipment was 15% which got reversed when we look at the average of the

two sectors for the reference duration because there was much greater growth in import

of electrical equipment than that of mechanical equipment in That duration.

There were three sectors that showed extraordinary growth in Pakistan’s import from

China namely fertilizer (31, iron and steel (72) and Manmade filaments (54) which

constituted about 15% of imports from China. In 2004, their collective contribution

was less than 6% of imports from China and in absolute terms these sectors involved

an import bill of around $ 700 million in 2014 from less than $ 100 million in 2004.

Another sector in which Pakistani imports from China increased tremendously was

manmade staple fibres (55) in which case Pakistan was importing less than ten million


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in 2004 from China which increased to over $ 300 million by 2014 increasing its share

from around 1% in 2004 to around 3% on average by 2014. It was interesting to note

that in 2004 Pakistan’s import of this category was around 5% of its total import from

the world which jumped to more than 40% by 2014, thus suggesting a lot of trade

diversion in this category over the years towards China.

5.3.3.3 Pakistan’s leading exports to and imports from Afghanistan

Afghanistan is a landlocked country sharing its long eastern border with Pakistan;

North western border with central Asian states and southern border with Iran and

relying heavily on its imports through/from Pakistan. However, this reliance has been

decreasing over the years as in 2008 nearly half of Afghanistan’s imports were coming

from Pakistan but by 2014 less than 1/3rd of it was from Pakistan. Pakistan witnessed

significant growth both in export and imports with Afghanistan since 2004 as its

exports increased from less than half a billion dollar in 2004 to over two billion dollars

in 2014 and its imports grew from less than $ 50 million in 2004 to around $ 400 million

in 2014. Though Pakistan’s imports from Afghanistan never touched even one percent

of its total imports from world its exports to Afghanistan were more than ten percent

of its world exports in 2011. As regards neighbours more than 90 % of its exports to

neighbours went to China and Afghanistan since 2010-11 leaving the rest was to be

traded with India and Iran.

5.3.3.3.1 Pakistan’s Leading Exports to Afghanistan

Pakistan’s exports to Afghanistan if focussed around a few top categories, listed in the

leading exports constituted nearly 3/4th of its total exports to Afghanistan. These

categories include Edible vegetables and certain roots and tubers ('07), Cereals ('10),

Milling products, malt, starches, insulin, wheat gluten ('11), Animal, vegetable fats and

oils, cleavage products, etc ('15), Sugars and sugar confectionery ('17), Salt, sulphur,

earth, stone, plaster, lime and cement ('25), Mineral fuels, oils, distillation products,

etc. ('27), Plastics and articles thereof ('39) and Articles of iron or steel (73). There were

three categories in the list of Pakistan’s leading exports to Afghanistan that were also

in the list of Pakistan’s overall leading exports (10, 25 and 27).


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Table 5.14: Pakistan’s Leading Exports to Afghanistan (In millions of $)

'27 '25 '15 '11 '10 '73 '17 '39 '07 2004 83.1 28.0 55.3 51.0 28.4 22.1 21.4 22.8 9.5 2005 253.6 84.3 96.8 101.5 37.5 62.5 38.4 104.0 7.7 2006 278.5 85.1 99.4 120.9 50.1 34.3 28.5 72.7 10.6 2007 248.8 98.9 105.5 94.1 22.7 56.5 10.3 33.2 5.5 2008 467.1 151.5 164.5 4.4 234.6 70.9 72.8 48.8 8.3 2009 394.5 149.4 93.9 3.3 148.6 85.1 18.3 69.2 43.8 2010 662.7 185.3 81.7 38.8 96.1 103.2 18.9 62.9 60.9 2011 776.3 244.5 190.4 228.5 184.3 152.2 28.3 96.5 165.3 2012 5.1 322.1 218.6 227.3 171.0 195.7 75.9 112.9 136.6 2013 25.5 282.7 152.5 196.9 145.6 108.4 202.4 92.8 143.0 2014 108.3 240.2 115.1 195.4 98.0 111.7 250.0 47.4 95.7 Average 300.3 170.2 124.9 114.7 110.6 91.1 69.6 69.4 62.4


Pakistan’s leading export of Afghanistan was mineral fuels which is around 1/5th of

Afghanistan’s total imports, and Pakistan, at its peak exports in 2008-11, served nearly

half of Afghanistan’s total need but it slipped drastically in the following years when

in 2012 and 2013 this contribution was roughly around 1%. Salt, sulphur, earth, stone,

plaster, lime and cement that is the second largest export of Pakistan to Afghanistan

which was ironical to find that Pakistan’s export of these to Afghanistan exceed

Afghanistan’s total import of these items from the world as reported by the

UNCOMTRADE data of both the countries. The war devastated country of

Afghanistan needed these materials for construction purposes but it seemed lack of data

diligence in that respect. The cases of iron/steel and articles of plastics were no different

from those of cement etc where the value of Pakistan’s exports to Afghanistan was

greater than total world imports of Afghanistan in these categories according to data.

Therefore, although the earlier discussed categories, not only, cater for almost 1/5th of

Pakistan’s exports to Afghanistan, those could be an overwhelming volume of overall

imports of Pakistan from world in these categories.

The remaining leading exports of Pakistan to Afghanistan mentioned in the table

pertained to food groups and constituted nearly 1/3rd of Pakistani exports to

Afghanistan. The export earning of Pakistan have increased in these categories from $

165 million in 2004 to around half a billion dollars by 2014.


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5.3.3.3.2 Pakistan’s leading imports from Afghanistan

Although Pakistan’s imports from Afghanistan were quite insignificant part of

Pakistan’s total imports from world both at aggregate and disaggregate levels and there

absolute value too was rather insignificant as no category during 2004-14 touched a $

100 million mark except Cotton once in 2013. Moreover, leaving the top three

categories of Cotton, Iron & steel and mineral fuels no other category ever registered

an import value of even $ 50 million in the selected period. However, here were three

categories in the list of Pakistan’s leading imports from Afghanistan that are also in its

overall leading imports as well (27, 52 and 72).

Table 5.15: Pakistan’s Leading Imports from Afghanistan (In millions of $)


The two top categories of Cotton (52) and iron & steel (72) constituted nearly half of

Pakistan total imports from Afghanistan. However, Pakistan imported a significant

proportion of (around 10-12%) of its total imports from Afghanistan in those two

categories. There were three food related categories in the list of Pakistan’s leading

imports from Afghanistan which included the categories of Edible vegetables and

certain roots and tubers ('07), Edible fruit, nuts, peel of citrus fruit, melons ('08) and

Oil seed, oleagic fruits, grain, seed, fruit, etc. ('12). Nearly a quarter of Pakistan’s total

imports from Afghanistan were in those categories. The import of these three categories

'52 '72 '08 '27 '07 '25 '44 '41 '12

2004 9.2 10.7 11.2 0.14 5.7 0.11 5.40 0.83 1.46

2005 1.2 22.5 11.1 1.16 6.2 0.40 1.80 1.14 0.83

2006 9.5 8.2 16.6 0.16 14.5 0.58 4.73 1.63 1.03

2007 12.9 22.4 19.5 0.01 16.5 0.86 8.43 1.78 2.30

2008 9.3 23.9 12.8 8.69 20.5 1.48 4.17 2.03 0.64

2009 40.7 29.6 17.0 4.13 16.7 2.56 3.28 3.01 0.48

2010 60.6 30.4 13.3 1.91 12.4 6.07 3.96 3.14 0.48

2011 30.6 69.3 22.9 36.8 12.1 15.5 3.44 2.47 1.12

2012 68.5 46.8 31.9 59.6 17.6 2.02 1.47 2.88 0.49

2013 126.7 41.8 34.1 55.7 25.2 16.6 0.62 2.68 0.21

2014 69.6 84.5 72.6 88.5 26.0 37.9 0.42 4.96 1.47

Average 39.9 35.5 23.9 23.3 15.7 7.63 3.43 2.41 0.95


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showed remarkable growth form less than $ 20 million in 2004 to over $160 million in

2014.

5.3.3.4 Pakistan’s leading exports to and imports from Iran

Iran is the only neighbouring country of Pakistan with which the volume as well as the

proportion of trade has shrunk over the years. While overall trade (exports and imports)

of Iran was growing at over $5 billion annually from 2008-14, Pakistan’s volume of

overall trade with Iran was shrinking not just as proportion to its overall trade with

world but also in absolute terms. Pakistan’s exports to Iran fell from over $426 million

in 2008 to $ 43 million in 2014 and its imports from Iran fell from around a billion

dollar in 2009 to less than $185 million 2014. Despite the fact that Iran is a

resource rich, leading economy of the world and also a neighbouring country, the

declining volumes of Pakistan’s trade with Iran cannot be explained with economic

arguments alone. Therefore the reason of this declining trend can be sought through

political economy based explanations. As Iran has continuously been in anti-American

camp since 1979 (with latest development post 2014 suggesting a reversal of many of

these sanctions) and Pakistan has remained an American Ally mostly therefore

Pakistan’s volumes of trade much smaller than those could otherwise have been

expected.

5.3.3.4.1 Pakistan’s leading exports to Iran

Pakistan’s exports to Iran were hugely dominated by one category of cereals that on

average accounted for 62% of Pakistan’s export to Iran during 2004-14. However, there

was a declining trend in Pakistan’s exports to Iran, later and it stood only 14% of

exports to Iran in 2014. The export of cereals was at its peak in 2008 when its exports

were $ 367 million which dropped to less than $ 6 million 2014. It is quite interesting

to note that the total size of Iran’s import market for cereals in 2014 was around five

billion dollar and its size grew half a billion dollar each year on average during 2010-

14. Moreover, it was surprising that Pakistan’s share in the import market of Iran was

around 40% in 2006 and it dropped down 0.1% in 2014 while cereal export of Pakistan

was nearly 1/10th of Pakistan’s cereal export and the absolute value of its export from


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Pakistan constantly grew from around half a billion dollars in 2004 to over two billion

dollars in 2014. There were three categories in the list of Pakistan’s leading exports to

Iran that were also in the list of Pakistan’s overall leading exports (10, 42 and 52).

Table 5.16: Pakistan’s Leading Exports to Iran (In millions of $)

'10 '08 '02 '48 '55 '52 '42 '53 '89 2004 43.36 0.009 0.01 3.13 23.17 9.20 0.43 4.20 1.63 2005 80.10 0.17 0 0.08 16.84 17.41 21.48 7.23 1.30 2006 111.22 5.29 0 1.47 8.95 6.19 12.69 3.98 1.56 2007 72.38 8.55 0 0.47 10.64 5.97 0.20 5.74 2.72 2008 367.52 15.76 0 0.06 3.99 4.93 0.22 2.26 4.37 2009 208.45 10.06 0 1.35 0.17 4.12 0.26 2.60 6.03 2010 124.77 11.19 1.33 0.35 0.25 3.81 0.53 2.54 4.31 2011 75.17 11.80 23.42 4.31 0.62 6.72 0.44 2.44 4.21 2012 68.86 5.00 31.52 8.02 0.24 2.37 0.24 0.29 2.39 2013 15.64 1.22 5.31 23.11 0.01 1.24 0.04 0.08 0.68 2014 5.97 0.03 5.22 23.90 0.02 0.39 0.04 0 0.69 Average 106.67 6.28 6.07 6.02 5.90 5.67 3.32 2.85 2.72


Pakistan’s export of Paper and paperboard, articles of pulp, paper and board ('48) was

also a very interesting case among Pakistan’s leading exports to Iran. Although on

average (2004-14) it contributed 3.5% of Pakistan’s exports to Iran yet during 2013-14

while Pakistan’s total exports to Iran fell drastically the share of this category in total

exports to Iran jumped to 56% in 2014. From 2012-13, when total exports to Iran more

than halved ($142 to 62 million), the export of paper and paper board (48) exports

almost tripled ($8 to 23 million). Pakistan’s exports continued to fall in 2014 as well

but the export of this category (48) to Iran managed to register an increase. The import

market of paper in Iran (with around 2% share in Iran’s paper import market, on

average) increased from half a billion dollars in 2004 to $1.36 billion in 2014.

Pakistan’s export of Meat and edible meat offal ('02) was another category of Pakistan’s

leading exports to Iran. The trend of this category was quite similar to that of Paper and

paper board case. The differences in these two cases were that latter’s share in the

import market was smaller; the market actually shrank in its absolute value from 2004

to 2014 unlike paper and board category. Pakistan did not hold a significant share Iran

import of Meat.


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The other categories in which Pakistan had leading exports to Iran included Edible fruit,

nuts, peel of citrus fruit, melons ('08), Articles of leather, animal gut, harness, travel

goods ('42), Cotton ('52), Vegetable textile fibres, paper yarn, woven fabric ('53),

Manmade staple fibres ('55) and Ships, boats and other floating structures ('89).

Although all these categories registered almost 16% of Pakistan’s total exports to Iran

on average the collective volume of export in all these categories was less than $ 2

million in 2014.

5.3.3.4.2 Pakistan’s leading imports from Iran

The average import volumes of 2004-14 from Iran showed that the import of mineral

fuels by Pakistan was more than half the total imports from Iran. However, there was a

decline in overall imports from Iran since 2011 but the decline in the largest category

of imports was larger than the decline in overall trade thus decreasing its share to nearly

1/3rd of the total imports. The case of Ores, slag and ash ('26) was quite similar to that

of mineral fuels except for the fact that the decline in this case was much more drastic

as its imports declined from $ 90 million in 2008 to $ 6 thousand in 2014. It was

interesting to note that there were four categories in the list of Pakistan’s leading

imports from Iran that were also in its overall leading imports as well (27, 29, 39 and

72).

The category of Iron and steel ('72) was also significant in the list of imports from Iran.

It was second largest import category from Iran with average percentage of around

11%. There were fluctuations in the import of Iron and steel, both in terms of value and

percentage. The import of this category was at its peak 2006 with an absolute value of

import $ at 121.5 million and a percentage of 27%. There was no other persistent trend

seen with regards to iron and steel imports from Iran. The other important categories

of import from Iran included Edible vegetables and certain roots and tubers ('07),

Edible fruit, nuts, peel of citrus fruit, melons ('08), Organic chemicals ('29), Plastics

and articles thereof ('39), Raw hides and skins (other than furskins) and leather ('41)

and Ships, boats and other floating structures ('89)`.

Table 5.17: Pakistan’s Leading Imports from Iran (In millions of $)


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'27 '72 '29 '39 '26 '41 '07 '08 '89

2004 138.8 15.4 24.1 14.4 6.4 2.2 14.2 12.5 0

2005 123.8 83.2 39.2 15.6 19.7 5.1 13.9 11.2 6.1

2006 167.4 121.5 39.5 15.7 19.3 9.5 7.3 11.8 0

2007 213.9 72.6 26.8 23.5 18.4 7.2 2.6 13.0 0

2008 443.6 63.3 61.4 7.4 90.6 15.4 6.1 5.0 0

2009 653.1 33.4 57.2 56.9 61.8 12.1 8.0 5.7 6.3

2010 528.3 56.1 122.5 86.3 11.4 7.7 2.7 6.0 2.6

2011 36.6 31.1 75.6 65.4 38.2 9.0 4.0 4.8 0.4

2012 35.1 19.4 17.5 5.4 8.8 8.2 5.7 3.9 0

2013 59.3 23.2 4.7 3.0 5.6 29.6 5.0 2.3 14.3

2014 58.1 21.1 3.8 4.8 0.0 13.8 15.6 7.1 28.0

Average 223.4 49.1 42.9 27.1 25.5 10.9 7.7 7.6 5.2


Though Pakistan’s imports from Iran declined for nearly all the categories, the rare

exception was the import of ships and boats. There was no import in that category

where there was either no or very insignificant volume of import from Iran during

2004-2012 but then in 2013 the import in this category was over 8% of trade for that

year and in the following year the value as well as proportion of imports doubled, thus

making it an exceptional import item from Iran. Another category that progressed like

ships and boats was Electrical equipment ('85). Although electrical equipment was not

listed in the leading categories of imports from Iran on average yet import in this

category was rising while Pakistan’s overall imports value from Iran were falling

during 2008-14.

5.4 Leading Categories Traded in the World and Pakistan’s

Leading Trade Categories

The top ten traded categories around the world for reference period include Mineral

fuels (27), Electrical, electronic equipment (85), Machinery, nuclear reactors, boilers,

etc (84), Vehicles other than railway, tramway (87), Plastics and articles thereof (39),

Optical, photo, technical, medical, etc. apparatus (90), Pearls, precious stones, metals,

coins, etc (71), Pharmaceutical products (30), Organic chemicals (29) and Iron and

steel (72) in order the average leading percentage of world trade.


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Table 5.18: Top Ten Traded Categories in World (Amount in Billions of $ and

Average is that of % of Total World Trade – 2004-14)

'27 '85 '84 '87 '39 '90 '71 '30 '29 '72

2004 1022.11 1253.333 1262.731 838.664 295.68 288.863 180.112 224.158 243.1047 250.21

2005 1428.36 1397.756 1395.523 912.356 340.308 326.428 205.101 248.933 272.1663 283.242

2006 1767.31 1633.64 1575.235 1007.75 385.328 372.035 244.29 288.01 298.1138 329.384

2007 1955.75 1806.614 1791.191 1182.98 446.733 397.109 294.528 342.144 344.2021 423.454

2008 2824 1916.814 1945.951 1239.34 478.498 439.444 370.199 398.791 367.8633 519.077

2009 1763.33 1603.213 1508.366 847.1 389.881 395.716 324.031 419.782 305.9689 274.963

2010 2339.62 1986.297 1816.564 1094.04 492.214 477.174 438.166 443.419 374.6062 389.678

2011 3277.75 2155.053 2078.817 1280.01 572.725 529.623 634.104 468.161 447.4199 480.297

2012 3421.5 2172.256 2069.078 1303.43 568.587 551.64 654.037 466.499 444.3096 428.642

2013 3311.84 2303.544 2081.673 1346.95 600.425 559.897 709.08 484.161 445.2655 396.159

2014 3052.05 2382.31 2145.616 1390.72 621.624 571.497 615.024 513.803 437.0324 411.026

Average % 15.67% 12.82% 12.30% 7.79% 3.20% 3.04% 2.74% 2.64% 2.47% 2.61%


Although on average mineral fuel (27) was the leading trade category in the world but

in 2004 and earlier years Machinery (84) was the largest category traded while fuel was

the third largest traded category Machinery (84) and electrical equipment (85). Fuel

has become the largest trading category since 2005. Vehicles (87) was the fourth largest

category traded around the world with around 7.8% of world trade on average (2004-

14), though at its peak it was 9.6% of world trade in 2002 and was at its lowest 6.9%

in 2009 during 2001-14. The top four categories on average were nearly half of total

world trade and all the other categories contributed the remaining trade.

The remaining six categories of Plastics articles, Optical apparatus , Pearls and

precious stones, Pharmaceutical products, Organic chemicals and Iron and steel

contribute around 16.7% of world trade on average with each category contributing 2-

3%.

It was interesting to note that there were only two categories of Pakistan’s leading

exports (27 and 71) that were also in the top ten traded categories in the world while

there were as many as seven categories in the list of Pakistan’s leading imports that are

also in the list of top ten traded categories. These statistics speak the nature of

Pakistan’s preferences in international trade in terms of its exports and potential, on


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one hand, and export performance on the other its exports in the context of top trading

categories in the world.

5.5 Summary of Critical Trends in Descriptive Statistics

In summary, the descriptive statistics are summed up at the end of chapter for a quick

and broad view of the descriptive statistics being discussed throughout the chapter.

Therefore, critical aspects of descriptive statistics are presented in the end of chapter.

Pakistan’s bilateral trade volumes were seen to be growing with China and declining

with Iran and Afghanistan. China as the largest trade partner of Pakistan among

neighbours was followed up by India, Afghanistan and Iran, on average during the

reference period of the study.

The barriers to trade were highest in Iran and Afghanistan and lowest in China, with

India showing moderate improvements, over the years. There were four types of

barriers to trade that were analyzed for each neighbour. Tariffs were found lowest in

China (4.35%), followed up by Afghanistan (6.19%), India (9.37%) and Iran (19.65%).

There were 10-11 documents required for import in Iran, Afghanistan and India while

only 5-6 in China throughout the reference period, on average. The cost per container

was highest in Afghanistan ($3174.44) and lowest in China ($542.78). The average

time taken to import was lowest in China and India (24-25 days), almost twice that time

in Iran (40.77 days) and the highest in Afghanistan (76-88 days).

Nearly, half of Pakistan’s total exports were in four categories (52, 61, 62, 63)

pertaining to cotton and textile with nearly 20% in cotton alone. The profile of

Pakistan’s trade vis-à-vis world trade was such that only two categories in Pakistan’s

top ten exports (27, 71) while seven categories in Pakistan’s top ten imports (27, 29,

39, 72, 84, 85, 87) were in top ten traded goods in the world on average, during the

reference period.


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There was an increasing trend in Pakistan’s exports and imports with neighbours as a

percentage of its total exports and imports. The share of Pakistan’s exports with

neighbours increased from 8% in 2004 to 18% in 2014.

There were three categories (29, 07, 52) that were in both the lists of Pakistan’s top ten

exports and imports with India with an average bilateral trade volume of $ 700 million

per year. Pakistan’s exports to India as a percentage of total exports remained relatively

stable, but were almost halved as a percentage of trade with neighbours. Cotton (52),

fruits (08) and cement (25) were the most stable and significant categories of Pakistan’s

exports to India with a joint share of nearly 40% in total exports. There was an

increasing share in Pakistan’s exports of fruits (08) and declining share in that of

vegetables (07) to India. Nearly half of Pakistan’s imports from India were in three

categories chemicals (29), Cotton (52) and animal fodder (23). However, the most

extraordinary increase was seen in the import of vegetables from India from $ 2 million

in 2004 to $ 200 million during 2012-14.

There was an extraordinary growth in Pakistan’s trade with China making it the largest

trading partner of Pakistan replacing USA. However, relatively speaking, growth in

Pakistan’s export to China was more spectacular (from around $ 300 million in 2004

to $ 2.25 billion in 2014) than its imports from China (which is more in absolute

volume). Cotton dominates Pakistan’s export to China (67% of total exports to China,

on average) with more than one billion dollar export of cotton each year during 2011-

14. On the other hand, nearly 40% of Pakistan’s imports from China were in two

categories of machinery (84) and electrical equipment (85), which were two of the top

three Pakistani imports from the world.

Pakistan’s exports to Afghanistan were almost ten times more than its imports from

Afghanistan. However, there was nearly 8 times increase in the imports while 4 times

increase in the exports during the reference period. Afghanistan’s reliance on its

imports from/through Pakistan has decreased from nearly half in 2008 to one third in

2014.


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Iran was only country for which the volume as well as share of Pakistan’s bilateral

trade has shrunk over the years. Pakistan’s exports to Iran decreased $426 million in

2008 to 43 million in 2014 whereas its imports from Iran fell from around a billion

dollar in 2009 to less than $ 185 million in 2014. Cereal (10) was the largest Pakistani

export to Iran with 63% share in the total exports to Iran.


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CHAPTER 6

ESTIMATION AND INTERPRETATION OF RESULTS

This chapter was dedicated to the estimation and interpretation of the results of the

study. As the focus of the study was two-fold, both at aggregate and disaggregate levels,

therefore the results were discussed separately for aggregate and disaggregate trade

potential of Pakistan with its neighbours. The first section (and all its sub sections)

discussed the results of aggregate trade analysis of Pakistan with its neighbours on the

basis of augmented gravity model technique, for a panel data of 177 countries for nine

years. The second section (and all its sub sections) discussed disaggregated trade

analysis with a wide range of indices being worked out at HS-2 classification for

Pakistan and its neighbouring countries. The final section presented the collaborated

results and findings to facilitate the reader in order to link the findings spread over the

first two sections of this chapter.

6.1 Pakistan’s Aggregate Trade Potential

Pakistan’s aggregate trade was heavily biased towards its neighbour in the first few

years after its independence and that too with India but the political developments in

the subsequent periods changed this scenario latter on. There were great developments

in Pakistan’s trade with its neighbouring countries in the 21st century, with its imports

from the neighbouring countries constituting more than 1/4th of its total imports, and

nearly 1/5th of its total exports also were to the neighbours in 2014. However, despite

the extraordinary growth in trade, most analysts believe that the volumes of bilateral

trade were much less than the potential to these neighbours.

Gravity model was employed to determine this potential in an extended context of panel

data set for determining the free trade potential of Pakistan’s trade with its neighbouring


131

countries. The study used augmented gravity model by determining the impact of tariff

rate, number of documents needed to import, effect of entering in to an FTA and sharing

border besides estimating the impact of traditional variables of gravity model – GDPs

of the trading countries and the distance between them.

There were two separate equations that were estimated for the purpose of determining

the trade potential with the neighbours, one explaining the bilateral trade volumes while

the other explaining Pakistan’s exports potential to the neighbouring countries. The

results of both the equations are explained separately.

6.1.1 Pakistan’s Bilateral Trade Potential with Neighbouring Countries

Although, panel data modelling can be done both for fixed effects as well as random

effects modelling, this study used random effect model rather than fixed effects model

because of the data characteristics and the standard specification of the gravity model

equation that covered the impact of time invariant role of physical distance between the

trading partners (Dis) which could not be estimated through the fixed effect models.

This study used augmented gravity model to see the impact of shared border as well as

the effect of FTA on trade with the partner country by means of dummy variables (DN

and DF respectively), which were also time invariant attributes, impossible to be

estimated through fixed effect model. The study attempted many different models for

the estimation of determinants of bilateral trade of Pakistan with its partner countries

and eventually selected following function.

𝑙𝑛𝐵𝑇 = 𝑓(ln 𝐺𝐷𝑃𝐶 , ln 𝐺𝐷𝑃𝑃 , 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒, 𝐷𝑁, 𝐷𝐹) ----------- (6.1)

Where

LnBT stands for natural log of Pakistan’s bilateral trade with the partner country

Ln GDPC stands for the natural log of the GDP of the partner country

LnGDPP stands for the natural log of the GDP of Pakistan

Dis stands for the distance between the capitals of Pakistan and its partner

countries

DN stands for the dummy variable for neighbouring countries


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DF stands for dummy variable for FTA being signed between Pakistan and its

partner country

The model therefore used double log feature to see the impact of GDP of the two

countries on their bilateral trade which explained the growth in bilateral trade of the

two countries as a result of growth in their GDPs; and log-linear feature was used to

see the impact of time invariant attributes of geographical distance, shared border and

FTA on bilateral trade which explained growth in bilateral trade as a result of all the

discussed characteristics. The results of the estimation are presented in table 6.1.

Table 6.1: Results of Estimated Augmented Gravity Model of Bilateral Trade

LnBT Coefficient S.E Z-

Value

P> ⃓

Z⃓

95% Confidence

Interval LnGDPC 0.816546 0.04404 18.54 0.000 0.7302296 0.9028633

LnGDPP 2.495929 0.2432636 10.26 0.000 2.019141 2.972717

Dis -0.0000858 0.0000261 -3.29 0.001 -0.000136 -

0.0000347 Dn 1.310891 0.723733 1.81 0.070 -0.107599 2.729382

DF 1.306578 0.7141346 1.83 0.067 -0.0931 2.706256

Constant -72.8594 6.090087 -1196 0.000 -84.79581 -60.92311

R2

Within 0.1377

Between 0.7161

Overall 0.6684

Wald Ch2 636.93

Prob>Chi2 0.0000

Observations 1587 & Groups 177

Source: Authors Estimation

The estimated equation offered theoretically understandable and econometrically

significant results where growth in both the domestic GDP and that of the partner

country’s GDP contributed positively towards the growth in bilateral trade between

Pakistan and its partner countries while increase in distance (a proxy of trade cost

between the partner countries) affected the bilateral trade negatively at highly

significant values of the estimated coefficients. The effect of shared border and FTA

was also theoretically sound with positive signs of the coefficients showing a positive

influence of both on bilateral trade volumes at reasonably significant values at 7% and

6.7% respectively. The value of Wald Chi2 was also highly significant suggesting that

the model was good with an overall R2 of 0.6684 showing that 66.84% variation in the


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bilateral trade between Pakistan and its neighbouring countries (177 countries of the

panel) came from the variables selected in the model.

For interpretation of the model, the study estimated the elasticity of bilateral trade with

respect to bilateral supply and demand (GDP of Pakistan and its partner country –

GDPP and GDPC respectively) conditions. Moreover, the percentage change in

bilateral trade with increase in bilateral distance between Pakistan and its partner

country as well as due to the shared border or signing of an FTA was also estimated.

The results showed that with 1% increase in Pakistan’s GDP there was nearly 2.5%

increase in Pakistan’s bilateral trade with the partner country with no change in other

circumstances which was by far the strongest impact on the growth in Pakistan’s

bilateral trade with its partner countries. The growth in GDP added to both the supply

of exports to the partner country as well as the demand for imports for domestic growth.

On the other hand, with one percent growth in the GDP of the partner country there

was less than one percent (0.816%) growth in the bilateral trade of Pakistan with that

country. This aspect showed that Pakistan’s export and/or import elasticity with growth

in the income/output of the partner country was less than one. However, the value of

this coefficient of elasticity must not be understated for two reasons – the positive sign

was realized in the context of a fairly large group of partner countries (177) and that

was not very low either. Moreover, with 100 KM increase in economic distance

between Pakistan and its partner country there was 0.85% decrease in the bilateral

volume of trade with no change in the rest of the factors affecting bilateral trade, thus

suggesting cost of transportation (measured with the proxy of bilateral geographical

distance between the capitals of the two countries concerned) also affected bilateral

trade significantly (the shortest distance in the sample was of 371 KM with Afghanistan

and the longest distance was 16677 KM with Chile).

The effect of FTA and shared border was estimated with the help of dummy variable

technique and the results showed, as expected theoretically that both impact bilateral

trade positively. The impact of shared border was that there was nearly 1.31% more

bilateral trade with neighbouring countries than with other partner countries. The


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impact of signing an FTA with the partner country was 1.306% growth in the bilateral

trade with that country.

Table 6.2: Results of Estimation Bilateral Trade Potential with Neighbouring

Countries

BT Coefficient S.E Z-Value P> ⃓ Z⃓ 95% confidence Interval

GDPC 0.00000071 0.000

00003

68

19.39 0.000 0.0000006

41

0.00000078

6 GDPP 0.00000426 0.000

00056

0

7.60 0.000 0.0000031

6

0.00000536

Dis -35.84296 12.24

547

-2.93 0.003 -59.84364 -11.84229

Dn 535139.9 34521

3.7

1.55 0.121 -141466.6 1211746

DF 610760.4 33902

2.3

1.80 0.072 -53711.03 1275232

Constant -229306.7 12574

0.1

1.82 0.068 -475752.8 -17139.34

R2

Within 0.2949

between 0.4337

Overall 0.4140

Wald Ch2 507.10

Prob>Chi2 0.0000

Observations 1587 & Groups177

Source: Authors Estimation

Pakistan signed FTA with only four countries (Sri Lanka, China, Malaysia and

Indonesia) and the results showed an impact almost equivalent to turning that country

as your neighbour in terms of growth in bilateral trade (at least in the context of the

panel). China was the only neighbour with which Pakistan signed an FTA and it was

perhaps this reason that China later stood as the largest trading partner of Pakistan

surpassing USA. Pakistan’s bilateral trade Potential with each of the neighbouring

countries was estimated with the help of estimation presented in table 6.2.

By using the coefficients of the estimated equation, Pakistan’s bilateral trade potential

was worked with each of its neighbouring countries for the duration of the study. A

ratio of estimated trade to actual trade was worked out for each of the neighbours to

see whether the volume of bilateral trade was more than its potential or less than it.

Table 6.3 shows this ratio for each of the neighbours during 2005-13.

The table 6.3 shows that on average, Pakistan’s volume of bilateral trade, with each of

its neighbours was less than its potential for the duration of 2005-13. On average,

Pakistan’s bilateral trade was closest to its potential with China followed up by


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Afghanistan, India and Iran. The degree of unrealized trade potential was highest with

Iran in all the neighbouring countries. However, it can be clearly seen in table 6.3 that

the trends over the years have undergone significant changes. In order to capture the

issue more closely, the trend of each country was studied separately.

Table 6.3: Pakistan's Ratio of Estimated Potential to Actual Bilateral Trade with

Neighbours

China India Afghanistan Iran

2005 1.830 2.665 1.666 5.973

2006 1.463 1.823 1.499 3.120

2007 1.334 1.318 1.871 2.791

2008 1.089 1.178 1.243 2.845

2009 1.264 1.551 1.333 2.016

2010 1.007 1.649 1.085 1.161

2011 0.828 1.301 0.707 3.337

2012 0.793 1.460 0.966 5.736

2013 0.716 1.193 1.244 12.738

Average 1.011 1.465 1.188 2.90

Source: Author’s Calculations Based on Estimation of Table 6.2

6.1.1.1 Pakistan’s potential bilateral trade with China

Pakistan signed an FTA with China, making it the only neighbour with an agreement

on free trade. Although on average the volume of Pakistan’s estimated potential was

slightly above the actual bilateral volume of trade but since 2011 Pakistan’s actual

volume of bilateral trade with China was above the potential and this gap was

increasing over the years. Table 6.4 offers the detailed comparison potential and actual

trade with China, presenting the year on year comparison of actual and potential trade,

the ratio of potential trade to actual trade and the gap between the two.

In 2005, the bilateral volume of trade with China was more than $ 1.5 billion that was

less than the potential. The gap, however, continued to decline in each of the following

year with an exception of 2009 when the gap increased because of a decline in actual


136

volume of trade from $ 3.7 billion in 2008 to $ 3.3 billion. During 2010-11, Pakistan

met with its potential for bilateral trade with China. Since 2011 Pakistan’s actual

volume of bilateral trade was more than its potential and that positive gap increased at

an incredible pace. The actual trade was nearly one billion dollar more in 2011 and by

2013 this positive gap had more than doubled exceeding two billion dollars by 2013.

Table 6.4: Pakistan's Bilateral Trade with China (in millions of $)

Actual Potential Gap Ratio

2005 1810.256 3313.75 1503.5 1.83

2006 2422.851 3546.847 1123.99 1.463

2007 2868.828 3827.930 959.102 1.334

2008 3703.649 4036.477 332.82 1.089

2009 3369.152 4260.828 891.67 1.264

2010 4494.607 4528.843 34.23 1.007

2011 5789.927 4798.940 -990.98 0.828

2012 6362.927 5048.106 -1314.82 0.793

2013 7425.006 5321.408 -2103.59 0.716

Average 4249.68 4298.126 48.43 1.011


As this extraordinary growth, in Pakistan’s bilateral trade appeared in the years

following the free trade agreement between the two countries, to an extent that actual

volume of trade exceeded the potential. Such a dramatic change in bilateral trade could

be the outcome of trade diversion in Pakistan’s trade away from one or more countries

towards China. A possibility of trade diversion could be responsible for distortions in

the bilateral trade with a number of member countries.

6.1.1.2 Pakistan’s potential bilateral trade with India

Pakistan’s bilateral trade with India historically remained below its potential though in

the early period after the creation of Pakistan India was the largest trade partner of

Pakistan. But this episode of trading harmony did not last long. However the


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comparison of actual and potential bilateral trade in more recent times can be seen

through table 6.5.

Table 6.5: Pakistan's Bilateral Trade with India (in millions of $)


2005 676.02 1801.82 1125.79 2.66

2006 1033.95 1885.81 851.86 1.82

2007 1496.27 1973.54 477.26 1.31

2008 1705.68 2010.18 304.50 1.17

2009 1345.99 2088.09 742.09 1.55

2010 1320.90 2179.44 858.53 1.64

2011 1731.85 2253.55 521.69 1.30

2012 1586.86 2318.28 731.42 1.46

2013 2006.33 2393.91 387.57 1.19

Average 1433.76 2100.51 666.75 1.46


The politico-economic climate between India and Pakistan relations fluctuated more

frequently than the physical climate in South Asia, and that is evident in table 6.5.

Although, the volume of bilateral trade crossed $ 2 billion mark in 2013 but it remained

less than its potential by $ 387.578 million less than its potential, which was the

smallest gap since 2005 with the average gap of $ 666.751 million during 2005-13.

In 2005, the bilateral trade volume was more than a billion dollar less than the potential,

which kept declining till 2008 when it was $304 million. Perhaps, the Mumbai attacks

of 2008 contributed to the declining trade volume in the following two years such that

by 2010, the gap had risen to the same level in 2006. The improvement in 2011 was

followed by a decline in 2012 once again. These summersaults in the pattern of bilateral

trade between the two countries could be viewed both through the gap as well as the

ratio of potential trade to actual trade between India and Pakistan.

6.1.1.3 Pakistan’s potential bilateral trade with Afghanistan


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Afghanistan is a landlocked neighbouring country and relies heavily for its trade

through Pakistan both in times of peace and war. Pakistan shares a long border with

Afghanistan and the official estimates of trade could be far less than the actual trade

that takes places both through formal and informal channels. The estimates in table 6.6

help in understanding the comparison between actual and potential trade between the

two countries.

Table 6.6: Pakistan's Bilateral Trade with Afghanistan (in millions of $)


2005 733.256 1222.108 488.851 1.666

2006 834.153 1251.174 417.020 1.499

2007 682.056 1275.760 593.704 1.870

2008 1033.884 1284.789 250.904 1.242

2009 975.968 1300.913 324.945 1.332

2010 1207.631 1310.20 102.575 1.084

2011 1875.174 1325.818 -549.355 0.707

2012 1393.071 1346.820 -46.250 0.966

2013 1103.521 1372.862 269.340 1.244

Average 1093.191 1298.939 205.748 1.188


Pakistan’s average potential estimated bilateral trade with Afghanistan was around $

1.3 billion for the entire period with an average actual bilateral trade of nearly $ 1.09

billion for the same period. There were two years 2011 and 2012 when the actual

volume of trade exceeded the potential which was perfectly normal for a landlocked

country. Since there was an on-going competition between Iran and Pakistan to offer

trading access to Afghanistan and from there onwards seeks an entry in to the Central

Asia, a bilateral volume of trade below the potential for Pakistan could be alarming in

the future. The actual bilateral volume declined during 2011-13 from $ 1.875 billion to

$ 1.103 billion thus pushing the ratio up from 0.707 to 1.244 in the same duration.


139

Afghanistan relies mostly for its imports from/through Pakistan but does not possess

much to offer in return – both because of its modest productive base as well as due to

lack of peace since the time of Soviet invasion in Afghanistan. The real picture of

bilateral trade emerged when the study latter explored Pakistan’s export potential of

Afghanistan (discussed in the latter sections). On average, there was around $ 205

million worth of unrealized trade between the two countries.

6.1.1.4 Pakistan’s potential bilateral trade with Iran

Iran is the neighbour with whom Pakistan’s bilateral trade volume remained drastically

below its potential both due to the international sanctions imposed against Iran as well

as due to lack of bilateral trade diplomacy and appeasement between the two countries.

A clearer comparison of potential and actual bilateral trade between the two countries

was done with the help of table 6.7.

Table 6.7: Pakistan's Bilateral Trade with Iran (in millions of $)


2005 217.077 1296.660 1079.582 5.97

2006 427.356 1333.558 906.201 3.12

2007 490.451 1368.855 878.403 2.79

2008 484.514 1378.597 894.082 2.84

2009 694.043 1399.753 705.710 2.01

2010 1220.94 1418.117 197.171 1.16

2011 431.018 1438.480 1007.46 3.33

2012 255.164 1463.716 1208.552 5.73

2013 116.097 1478.923 1362.826 12.73

Average 481.852 1397.407 915.554 2.90


The table 6.7 clearly shows that the actual bilateral trade between the two countries was

merely 1/3rd of the potential on average during 2005-13. The average potential volume

of trade was $ 1.4 billion while the actual volume went above a billion dollar only once


140

in 2010 when the ratio of potential to actual bilateral trade was lowest in the reference

period.

In 2005, Pakistan’s bilateral trade potential with Iran was nearly six times more than

its performance (with a gap of over a billion dollars) but then the situation kept

improving till 2010 (with a gap of a little over $ 197 million). The recovery from 2005

to 2010 got reversed so abruptly that by 2013 actual trade between the two countries

was drastically below the bilateral trade potential with as much as $ 1.362 billion worth

of unrealized trade potential between the two countries. In each year during 2011-13

there was an unrealized trade potential between the two countries in excess of one

billion dollars that should worry the policy makers of both neighbouring countries.

6.1.2 Pakistan’s Export Potential with the Neighbouring Countries

Pakistan experienced chronic trade deficits that were in excess of $ 20 billion every

year since 2012. Despite growth in exports, imports increased much more than the

exports. Pakistan’s exports to the neighbours were not an exception in that regard

either, as it struggled to realize export growth with neighbouring countries also.

Pakistan’s export potential with the partner countries was estimated with augmented

gravity model in the context of tariff rates, the time taken to complete the legal process

of import and the geographical distance between Islamabad and the capital of each

partner country (proxy for the transportation cost) besides determining the impact of

FTA with the help of dummy variable technique. Thus the estimated equation measured

the impact of three types of cost confronting Pakistan’s exports to the partner countries

namely, transportation cost (measured with proxy of geographical distance);

administrative cost (measured with the time taken to complete the legal requirements

of import); and the economic cost (measured with tariff rates). Besides the three types

of cost confronting Pakistan’s exports the impact of trade liberalization as a result of

signing an FTA was estimated with the help of dummy variable.

Table 6.8: Estimation of Pakistan’s Export Determinants with Neighbouring

Countries

LnXPC Coefficient S.E Z-

Value

P> ⃓

Z⃓

95% Confidence

Interval


141

Tariff -0.05275 0.0139915 -3.76 0.000 -0.07999 -0.02515

Time -0.0318601 0.0039495 -8.07 0.000 -

0.0396009

-0.0241193

Distance -0.0001608 0.0000417 -3.85 0.000 -

0.0002426

-0.0000791

DF 2.098773 1.097573 1.91 0.056 -

0.0524304

4.249976

Constant 11.53199 0.3711954 31.07 0.000 10.80446 12.25952

R2

Within 0.0891

Between 0.2327

Overall 0.2245

Wald Ch2 124.61

Prob>Chi2 0.0000

Observations 954 & Groups164

Source: Author’s Estimation

Although there was a choice in estimating the panel data to use fixed effect model or

random effect model being decided on the basis of Hausman Test, the study persisted

with Random Effect model because of the inability of fixed effect model to estimate

the impact of time invariant attributes in panel data like FTA and bilateral distance on

exports to the partner countries.

The estimated equation offered theoretically consistent and statistically significant

results whereby increase in each of the three costs (transportation, administrative and

economic) affected Pakistan’s export to the partner countries negatively while signing

of an FTA with the partner country affected Pakistan’s export positively. The detailed

results of the estimation through stata are given in table 6.8.

The estimated equation showed that with a decrease in weighted mean applied tariff

rates by one per cent in the partner country Pakistan’s exports were likely to increase

by 5.25% while there was no change in the other factors affecting Pakistan’s exports to

the partner country. The results further explained that at 95% confidence, there was at

least 2.5% increase in Pakistani exports to the partner country as a result of 1% decline

in the tariff rate in the partner country which could go on to be as high as almost 8%

on the other end of the extreme.

Similarly, if the there was an increase in physical distance by 100KM for the destination

of our exports there was reduction of 1.6% in our exports to that destination, all else

being unchanged. The interval estimates, explained at 95% confidence suggested that


142

at the most there was 2.42% decrease in export to the partner country if that destination

of Pakistan’s exports was situated 100 Km further away. On the other extreme of the

interval, there was 0.7 % reduction in Pakistan’s export to partner country if it was

located a hundred Km away.

Table 6.9: Estimation of Pakistan’s Export Potential with Neighbouring Countries

XPC Coefficient S.E Z-

Value

P> ⃓

Z⃓

95% Confidence Interval

GDPC 0.000000384 0.0000000117 32.89 0.000 0.000000361 0.000000407

GDPP 0.00000135 0.00000018 7.53 0.000 0.00000100 0.00000171

Distance -11.121 3.929654 -2.83 0.005 -18.82299 -3.41903

Inv Doc 148307.3 83656.96 1.77 0.076 -15657.31 -312271.9

Constant -117093.9 40250.64 -2.91 0.004 -195983.7 -38204.06

R2

Within 0.5176

Between 0.7421

Overall 0.7207

Wald Ch2 1248.47

Prob>Chi2 0.0000

Observations 1514 & Groups 172

Source: Author’s Estimation

As far as time taken in clearing a trade consignment on average by fulfilling the legal

requirements (from the start to the end including unloading) was concerned, the longest

time taken was 117 days for Uzbekistan and the shortest time is that of 4 days for

Singapore. As far as Pakistan’s neighbours were concerned the longest time taken to

import was for Afghanistan 71-85 days, followed up by Iran 37-44 days, 41-20 days in

India and 26-24 day in China during 2005-13. India showed an extraordinary

improvement in terms of this feature where the time taken nearly halved from 41 days

in 2006 to 21 days in the following year. According to the estimated equation on

average if it took one more day in the partner country for the consignment to clear,

Pakistan’s exports decreased by 3.186%. The negative impact of time taken in clearing

a consignment at 95% confidence ranged from 2.41% to 3.96% for one more days (on

average) in the process of clearance in the partner country of Pakistan.

As a result of impact of signing an FTA with a partner country there was 2.098 times

increase in Pakistan’s exports to that country. However, when it was looked at 95%


143

interval there was a wide range of a possible impact of signing an FTA from as little as

5.24% to as much as more than four times increase in Pakistan’s exports to that country.

In order compare the pattern of Pakistan’s exports to its neighbours, another estimation

through regression, was being done for Pakistan’s exports to its neighbours with results

shown through table 6.9. In order to compare actual performance and estimated

potential, following functional form was being used for the purpose of estimation.

𝑋𝑃𝐶 = 𝑓 (𝐺𝐷𝑃𝑃, 𝐺𝐷𝑃𝐶,1

𝐷𝑜𝑐, 𝐷𝑖𝑠) ------------------------- (6.2)

The estimated coefficients were then used to determine Pakistan’s export

potential with neighbouring countries in a competitive global trade system. After the

determination of this potential a ratio of estimated potential exports with actual export

to each neighbouring country was worked out as presented in the table 6.10.

Table 6.10: Ratio of Estimated Potential to Actual Pakistan's Exports to Neighbours

China India Afghanistan Iran

2005 3.116 1.881 0.059 0.824

2006 2.420 1.505 0.063 0.627

2007 2.088 1.394 0.089 0.690

2008 1.860 1.737 0.062 1.225

2009 2.082 1.582 0.071 0.718

2010 1.254 2.090 0.060 0.698

2011 1.009 2.019 0.041 1.162

2012 0.858 1.827 0.061 1.210

2013 0.715 1.959 0.087 1.710

Average 1.228 1.770 0.063 0.912


Although the Pakistan’s export potential was more than its actual exports with both

China and India on average these volumes fluctuated over the years. On the contrary,

Pakistan’s actual exports to Afghanistan and Iran were more than potential on average

but that too fluctuated over the years. In order to explore this pattern more closely


144

export potential with each of the neighbour was discussed separately in the following

sections of this chapter of the study.

6.1.2.1 Pakistan’s export potential to China

China was the largest trading partner of Pakistan primarily because of Pakistan’s

imports from China and not because of its exports to China. On average, Pakistan’s

exports to China were below the potential but the situation got reversed since 2012

when the negative gap and less than unity ratio was realized, as seen in the table 6.11.

Table 6.11: Pakistan's Exports to China (in millions of $)


2005 281.98 878.86 596.88 3.116

2006 412.21 997.85 585.64 2.420

2007 547.56 1143.74 596.18 2.088

2008 674.12 1253.95 579.82 1.860

2009 660.80 1376.25 715.44 2.082

2010 1210.76 1518.46 307.69 1.254

2011 1645.0 1660.38 15.37 1.009

2012 2085.29 1789.99 -295.29 0.858

2013 2698.91 1931.26 -767.64 0.715

Average 1135.18 1394.31 259.12 1.228


On average, Pakistan could not realize its export potential to China until 2011 with an

average unrealized potential exports of almost half a billion dollars per year during

2005-11. The situation reversed so quickly that during 2012-13 Pakistan’s exports to

China were more than their potential with an average export volume in excess of the

potential by more than half a billion dollars.

In 2005, Pakistan’s exports to China were less than 1/3rd of its potential, however,

despite gradual improvement in the ratio during 2005-09, the unrealized export

potential to China remained in excess of half a billion dollars throughout the period.


145

Pakistan’s exports to China exceeded $ 1 billion for the first time in 2010 which crossed

$ 2 billion in 2012 thus producing a huge growth in Pakistan’s exports to China, turning

their volume more than the potential during 2012-13.

6.1.2.2 Pakistan’s export potential to India

India was only neighbouring country of Pakistan with whom it has consistently

performed less than its potential for exports during 2005-13. The average export

potential of Pakistan to India was more than half a billion dollars and Pakistan was not

realizing its export potential of more than $ 218 million per year on average during

2005-13.

Table 6.12: Pakistan's Exports to India (in millions of $)


2005 190.39 358.23 167.84 1.881

2006 263.72 397.04 133.32 1.505

2007 314.87 438.94 124.06 1.394

2008 262.87 456.69 193.81 1.737

2009 313.06 495.29 182.22 1.582

2010 259.51 542.49 282.98 2.090

2011 286.72 579.00 292.28 2.019

2012 333.45 609.43 275.97 1.827

2013 328.88 644.38 315.50 1.959

Average 283.72 502.39 218.67 1.770


There had been some improvements in Pakistan’s exports to India from 2005 to

2007and since then there was a general deterioration in exports. Pakistan did not realize

nearly half of its potential exports to India during 2010-13. Despite impressive

economic growth in India after 2005, there were three years (2008, 2010 and 2013)

when the absolute volume of Pakistan’s exports to India decreased in value. Although

India had accorded Pakistan with MFN status since 1996 but Pakistan did not possess


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a huge export potential with India, therefore, it could not realize its export potential to

India over the years.

6.1.2.3 Pakistan’s export potential to Afghanistan

Afghanistan is a landlocked country and as Pakistan provides a major trading access to

Afghanistan, Pakistan has exported to Afghanistan more than its potential throughout

the period. The average export potential of Pakistan to Afghanistan was around $69

million while the actual exports on average were in access of one billion dollars.

Table 6.13: Pakistan's Exports to Afghanistan (in millions of $)


2005 732.18 43.86 -688.31 0.059

2006 831.55 53.13 -778.42 0.063

2007 680.29 61.07 -619.22 0.089

2008 1031.68 63.97 -967.70 0.062

2009 975.59 69.34 -906.25 0.071

2010 1204.59 72.41 -1132.18 0.060

2011 1864.92 77.46 -1787.46 0.041

2012 1379.98 84.36 -1295.61 0.061

2013 1058.61 92.66 -965.95 0.087

Average 1084.38 68.70 -1015.67 0.063


There was a great competition between Iran and Pakistan over trading access to

Afghanistan in order to extend their access further in to the landlocked countries of

central Asia. However, Pakistan shares a long border with Afghanistan that keeps its

superiority in being a preferred route to Afghan trade. Pakistan’s exports to Afghanistan

were nearly fifteen times more than the potential.

6.1.2.4 Pakistan’s export potential to Iran

Pakistan shares its border with Iran in the South West of the country and had mixed

history of exports to Iran. On average, Pakistan’s exports were in excess of the potential


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to Iran. The average export potential of Pakistan to Iran was around $ 133.5 million

while the actual exports to Iran were on average around $146.4 million with around

$12.8 million worth of exports more than the potential. However, that trend varied over

the years from 2005 to 2013 with significant fluctuations.

Table 6.14: Pakistan's Exports to Iran (in millions of $)


2005 116.18 95.83 -20.34 0.824

2006 174.32 109.31 -65.00 0.627

2007 178.10 123.01 -55.08 0.690

2008 103.08 126.30 23.21 1.225

2009 187.10 134.37 -52.72 0.718

2010 203.82 142.32 -61.49 0.698

2011 128.93 149.93 20.99 1.162

2012 131.49 159.11 27.62 1.210

2013 94.45 161.57 67.12 1.710

Average 146.38 133.53 -12.85 0.912


The ratio of potential to actual exports declined during 2005-06 and then started

increasing in the following two years such that actual exports were less than potential

in 2008. The ratio again declined during 2009-10 showing that Pakistan exported more

than potential in that period. There was a persistent increase in the ratio from 2011-13

showing a period where Pakistan’s exports to Iran were again less than the potential

during 2011-13. The volume of unrealized export potential to Iran was more than $ 67

million in 2013.

6.2 Pakistan’s Disaggregated Trade Potential

As the previous entire section has been dedicated to explore and explain Pakistan’s

bilateral trade and export potential with each of the neighbouring countries’ at


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aggregate level, this section and all its subsections are dedicated to explore and explain

the export and import potential of Pakistan with each neighbouring country at

disaggregate level.

There were five different indices that were used in the analysis at disaggregate level

which include TCI, RCA, GLI, EII and III. The study estimated GLI and RCA for

Pakistan and each of its neighbouring countries for each year during 2004-14 at HS-2

classification. Moreover, EII and III of Pakistan were worked out for Pakistan with

each of its neighbouring country in the same period. Bilateral TCI of Pakistan with its

neighbours was estimated for all the years in which data was available in the reference

period.

The analysis was carried out in such a way that export and import potential and

performance were compared with each neighbour at disaggregate level. The first

subsection explained the pattern of TCI of Pakistan and its neighbour for each other’s

exports over the years to assess the compatibility of both partners’ export to other’s

demand. The second subsection discussed Pakistan’s exports to the neighbouring

countries’ top 25 and top-10 exports on average with the partner country while the third

section did the same for Pakistan’s imports from the neighbours. The analysis of top

25 categories was made on the basis of RCA and GLI of both the countries and the

export/import intensity of Pakistan with the neighbours by using average values of

these indices. The analysis of top 10 categories however was done in the context of

developments in RCA and export/ import intensity indices over the years.

The analysis was distributed in such a way that complementarity, exports and imports

were being done for each country one by one. The benefit of such methodology to

analyze allowed a comprehensive analysis of Pakistan’s trade with each country for

aspects covered through the indices.

6.2.1 Pakistan’s Trade with China


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China is the largest trading partner of Pakistan with a bilateral aggregate potential of

over $ 5 billion since 2012 with a fast growing (although relatively modest as compared

to imports) export potential of over $1.5 billion. In the following sections leading

exports to and imports from China would be explained thoroughly.

6.2.1.1 Trade Complementarity with China

The complementarity of trade between Pakistan and China can be clearly seen through

the TCI of the two countries for each other in all the years during 2004-14. Pakistan’s

complementarity for China’s exports was almost twice as much as that of China’s

complementarity for that of Pakistan.

Table 6.15: Pakistan’s Trade Complementarity with China

Years

TCI of China's offer for

Pakistan's demand

TCI of Pakistan's offer

to China's demand

2004 45.10% 17.25%

2005 49.38% 19.15%

2006 48.32% 18.92%

2007 48.55% 19.81%

2008 45.74% 21.72%

2009 44.12% 20.03%

2010 42.19% 21.45%

2011 40.30% 20.98%

2012 40.18% 18.33%

2013 40.59% 18.63%

2014 44.07% 18.89%


The correspondence of Pakistan’s demand with China’s offer peaked in 2005 when

China’s TCI improved by more than 4% and then continued a generally declining trend

in the latter years, though with a strong revival in 2014. On the other hand the

correspondence of China’s demand for Pakistani exports was highest during 2007-11,

when it was on average 20.8%. The TCI of Pakistan’s offer declined more than two


150

percent in 2012 while almost a similar decline was seen in China’s TCI in 2010 and

2011. It was interesting to note that even with a modest complementarity of less than

19% for Pakistan’s offer, China became the second largest market for Pakistani exports

(Pakistan, 2015) and with less than 50% complementarity with China’s offer in

Pakistan’s demand, China emerged as the largest importer in Pakistan due to enormous

size of China’s second largest economy in the world. If Pakistan needed to enhance its

export base in Chinese market, it also needed to increase its complementarity with

China’s demand profile spread across the categories.

6.2.1.2 Pakistan’s exports to China

Pakistan’s leading exports to China were so concentrated that 98% of Pakistan’s

exports to China were on average (2004-14) accounted for top 25 categories while 92%

it was in the top 10 categories and 85 % in the top 5 categories on average.

6.2.1.2.1 Pakistan’s top 25 exports to China

When we look at top 25 categories of Pakistan’s leading exports to China, there were

12 categories (03, 10, 23, 25, 26, 39, 41, 52, 63 and 74) where Pakistan on average

possessed RCA while there were 11 categories (14, 16, 52, 55, 61, 62, 63, 68, 84, 89

and 90) leading exports where China possessed RCA. It was interesting to note that

there were five categories (16, 68, 84, 89 and 90) in Pakistan’s top-25 exports to China

where Pakistan did not possess RCA while China did and six categories (14, 52, 55,

61, 62 and 63) where both China and Pakistan had RCA. In Pakistan’s EII with China

on average, there were 11 categories (3, 8, 10, 12, 13, 14, 16, 23, 25, 26 and 68) in the

top 25 exports where exports were more than expected; whereas the exports to China

were less than expected in the remaining categories. Moreover, there were three

categories (12, 25 and 26) where exports were fractionally more than expected and two

categories (41, 52) where exports were fractionally less than expected. In all the

remaining categories the exports were significantly less than expectations in the list of

top 25 categories that can be seen in the table 6.16.

As far as intra industry trade is concerned there were three categories (08, 55 and 90)

in the list of top 25 exports where the average GLI of Pakistan and China on average


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was above 0.5 simultaneously. As far as manmade staple fibre (55) was concerned,

both Pakistan and China had RCA in that category with average GLI in excess of 0.6.

However, in the other two categories one country had RCA while the other did not.

Table 6.16: Pakistan’s Average Leading Exports to China and their Respective

Features

Level Code Title Exports

RCA-

PK

RCA-

CH EII

GLI

CH

GLI

PK

1 '52 Cotton 875813.4 51.40 2.11 0.83 0.91 0.31

2 '26 Ores, slag and ash 90362.5 0.44 0.07 1.39 0.02 0.58

3 '10 Cereals 50289.1 15.23 0.14 2.12 0.55 0.26

4 '41 Raw hides and skins 42579.7 9.51 0.33 0.9 0.23 0.36

5 '03 Fish, crustaceans etc 39096.7 2.0 0.99 83.7 0.72 0.03

6 '39 Plastics and articles thereof 23807.1 0.50 0.74 0.07 0.77 0.39

7 '25 Salt, sulphurand cement 22472 7.88 0.82 1.28 0.83 0.40

8 '74 Copper and articles thereof 19828.8 0.41 0.40 0.13 0.29 0.62

9 '23 Residues, wastes and fodder 16136 0.32 0.29 14.4 0.80 0.23

10 '63 Other made textile articles 13576.8 51.38 3.90 0.37 0.02 0.08

11 '13 Lac, gums, resins etc 13413.4 6.382 0.83 360 0.47 0.32

12 '29 Organic chemicals 11927.6 0.09 0.75 0.46 0.74 0.05

13 '72 Iron and steel 6380.27 0.05 0.84 0.15 0.73 0.03

14 '12 Oil seed, grain, seed, fruit, etc 6249.45 0.65 0.37 1.7 0.18 0.20

15 '84 Machinery, reactors, boilers, 5854.36 0.07 1.53 0 0.70 0.11

16 '08 Edible fruit, nuts, etc 5652.81 2.18 0.33 2.86 0.85 0.70

17 '27 Mineral fuels and products 5234 0.26 0.12 0 0.28 0.15

18 '90 Optical apparatus etc 4951.27 0.42 1.05 0 0.73 0.76

19 '14 Vegetable plaiting materials 3713.36 6.42 1.04 566 0.69 0.41

20 '68 Stone, plaster, cement articles 3626.54 0.52 1.49 3.08 0.29 0.81

21 '89 Ships, boats etc 3528.63 0.63 1.56 0.45 0.15 0.25

22 '62 Apparel-not knit or crochet 3258.36 6.01 3.08 0.03 0.05 0.02

23 '55 Manmade staple fibres 3225.81 7.08 2.28 0.11 0.60 0.85

24 '61 Apparel-knit or crochet 2706.54 8.05 3.58 0.04 0.03 0.02

25 '16 Meat, fish and seafood 2673.81 0.50 1.81 194 0.03 0.29

Source: Author’s Calculation from UNCOMTRADE (Exports in 000 $, All others are

Indices)


152

The prospects of intra industry trade in the stated categories were significant and

institutional support in that regard may develop greater prospects in the context of FTA

being signed between the two countries.

6.2.1.2.2 Pakistan’s top 10 exports to China (RCA and EII – 2004-14)

There were six categories (03, 10, 25, 41, 52 and 63) in the top ten Pakistani exports

(on average) to China where Pakistan maintained its RCA in all the years and in the

remaining four categories (23, 26, 39 and 74) Pakistan never had RCA in any year. On

the other hand, there was only one category Fish (03) which is also the largest exporting

category where exports were consistently and considerably above expectations while

in all the remaining categories exports were either always below expectations (39, 63

and 74) or at least in some years (10, 23, 25, 26, 41 and 52).

Although the RCA of residue (23) in Pakistan was absent during 2004-14, yet

Pakistan’s export in this category remained above expectations in all the years after

2006 while there were no exports of this category to China in 2004-05. Similarly,

Pakistan did not possess RCA in Ores (26) in any of the years but its exports to China

remained more than expectations during 2004-09 and below expectation in the

following years with a generally declining trend of EII during 2004-14.

Despite continuous RCA of Pakistan in other made textile (63) in all the years, its

export to China remained below expectations in all those years, however, there in an

increasing trend of EII in that category since 2009. On the other hand, it was not

surprising to find that in the two other categories where Pakistan never possessed RCA

during 2004-14, its exports to China also remained below expectations throughout the

period.

In the categories where Pakistan had RCA in all the years, RCA was highest for cotton

(52) with a generally rising trend over the years while that of fish etc. (03) had the

lowest RCA, also with a slowly rising trend in RCA values over the years. The two

categories of raw hides (41) and salt and sulphur etc. (25) also reflected RCA in all the

years and did show an excellent growth in RCA over the years. The remaining two

categories where Pakistan had RCA in all the Years were textile articles (63) and cereal


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(10) in such a way that former had a falling trend (despite high value) from 2006

onwards and the latter kept fluctuating throughout the period.

Table 6.17: Pakistan’s EII and RCA of Top Ten Exports to China (2004-14)

Code 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Average

Pakistan's RCA

'03 1.5 1.6 1.9 1.9 2.4 1.9 2.0 1.9 2.2 2.3 2.4 2.0053

'10 10.4 16.0 16.3 13.3 18.9 16.5 19.1 17.2 12.7 13.2 14.2 15.2350

'23 0.1 0.1 0.1 0.2 0.2 0.2 0.5 0.4 0.5 0.6 0.7 0.3275

'25 1.3 2.8 3.0 5.8 9.9 12.0 9.3 8.8 11.2 11.5 11.1 7.8839

'26 0.3 0.3 0.2 0.6 0.9 0.5 0.6 0.3 0.4 0.4 0.3 0.4481

'39 0.4 0.5 0.4 0.3 0.5 0.6 0.6 0.7 0.7 0.6 0.4 0.5084

'41 7.3 7.4 7.7 9.6 10.4 9.1 9.7 10.3 10.6 11.0 11.4 9.5196

'52 41.6 46.8 49.8 51.3 53.4 52.6 48.5 51.5 57.8 55.5 56.6 51.4034

'63 54.7 58.7 62.5 60.5 55.1 48.2 47.7 45.6 43.3 43.6 45.4 51.3860

'74 0.1 0.1 0.2 0.2 0.3 0.4 0.4 0.5 0.8 0.8 0.7 0.4097

Pakistan EII with China

'03 109.1 93.6 76.1 96.6 97.3 108.4 124.3 61.1 56.4 41.3 56.1 83.6522

'10 0.0 0.0 0.0 0.2 0.1 0.1 0.2 0.9 11.7 5.8 4.4 2.1237

'23 0.0 0.0 25.3 0.1 12.5 13.5 23.7 15.6 18.9 19.9 28.7 14.3760

'25 1.1 0.4 0.9 1.0 0.3 0.7 1.5 2.2 1.7 2.4 1.9 1.2777

'26 2.3 2.2 2.2 1.7 1.4 1.1 1.0 0.9 0.9 0.8 0.8 1.3940

'39 0.1 0.0 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0652

'41 0.6 0.8 0.8 0.8 1.1 1.2 1.0 0.9 1.2 0.9 0.8 0.9028

'52 0.3 0.3 0.4 0.5 0.6 1.5 1.0 0.8 1.0 1.2 1.5 0.8325

'63 0.4 0.1 0.1 0.1 0.3 0.3 0.5 0.6 0.6 0.6 0.7 0.3717

'74 0.0 0.3 0.2 0.1 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1346


6.2.1.3 Pakistan’s imports from China

Pakistan’s imports from China were quite concentrated around a few categories though

not as much as its exports were. The top 25 import categories from China on average,

account for 89% of all the imports from China while 70% of imports from China were

in top 10 categories of imports and as much as 55% correspond to top 5 categories on

average.


154

6.2.1.3.1 Pakistan’s top 25 imports from China

There were 15 categories (7, 28, 52, 54, 55, 64. 69, 70, 73, 84, 85, 86, 89, 90 and 96)

in Pakistan’s leading imports from China where it had an average RCA of greater than

one while there were four categories (7, 52, 54 and 55) where Pakistan also possessed

RCA along with China. In three of the four categories (7, 52 and 55) where both China

and Pakistan possessed RCA on average, Pakistan’s RCA was more than that of China.

Table 6.18: Pakistan’s Average Leading Imports from China and their Respective

Features

Level Codes Title Imports RCA CH RCA PK III GLI CH GLI PK

1 '85 Electrical equipment 1185585.9 1.87 0.04 0.01 0.90 0.08

2 '84 Machinery, reactors, boilers 770367.45 1.53 0.07 0.01 0.70 0.11


4 '54 Manmade filaments 256777.73 2.36 1.83 2.20 0.57 0.36

5 '72 Iron and steel 241336.18 0.84 0.05 0.09 0.73 0.03

6 '31 Fertilizers 185905.64 0.80 0.04 3.96 0.70 0.00

7 '39 Plastics articles 145246.82 0.74 0.50 0.16 0.77 0.39

8 '73 Articles of iron or steel 142627.27 1.57 0.39 0.15 0.37 0.50


10 '40 Rubber and articles thereof 112118.55 0.83 0.11 1.35 0.94 0.10

11 '38 Misc. chemical products 107959.45 0.55 0.06 1.92 0.83 0.05

12 '87 Vehicles other than railway 107101 0.32 0.04 0.01 0.87 0.09

13 '28 Inorganic chemicals, isotopes 84626.091 1.09 0.13 1.98 0.84 0.09

14 '32 Tanning, dyeing extracts 72938 0.61 0.32 4.89 0.89 0.19

15 '90 Optical apparatus 67737.364 1.05 0.42 0.01 0.73 0.76

16 '69 Ceramic products 62896.455 2.58 0.23 2 0.10 0.23

17 '48 Paper and paperboard 59629.182 0.56 0.12 0.68 0.66 0.12

18 '07 Edible vegetables 58626.545 1.12 1.69 14.7 0.36 0.46

19 '27 Mineral fuels and products 53618.182 0.12 0.26 0.02 0.28 0.15

20 '86 Railway and equipment 45388.818 2.83 0.01 0.57 0.26 0.06

21 '96 Misc. manufactured articles 44306.909 2.78 0.80 0.12 0.22 0.44

22 '64 Footwear and parts thereof 42232.636 3.55 0.96 0.88 0.06 0.57

23 '52 Cotton 41737.636 2.11 51.40 0.16 0.91 0.31

24 '70 Glass and glassware 41358.182 1.53 0.20 1.39 0.65 0.39

25 '89 Ships, boats etc 36710.636 1.56 0.63 0.02 0.15 0.25

Source: Author’s Calculation from UNCOMTRADE (Exports in 000 $, all others are

Indices)


155

There were only ten categories (7, 28, 31, 32, 38, 40, 54, 55, 69 and 70) in the top 25

list of imports from China where average III was more than unity suggesting that

imports were more than expected. In half of the categories (28, 38, 40, 55 and 70) where

imports from China were more than expected and average III was less than two. In half

of the top 25 imports from China, III was less than 0.5 suggesting considerably less

than expected imports.

As far as the prospects of intra industry trade were concerned in the top 25 Pakistani

imports from China, there were only two categories (55 and 90) where the GLI of both

the countries were more than 0.5 to hint the possibility of IIT. In case of optical

apparatus (90) China possessed RCA on average but not Pakistan and the volume of

imports on average were less than expected. On the other hand, in case of manmade

staple fibres (55), both China and Pakistan possessed RCA on average and the volume

of imports were more than expected. Moreover, both of these categories were present

on both the lists of Pakistan’s top 25 exports to China as well as Pakistan’s top 25

imports from China.

6.2.1.3.2 Pakistan’s top 10 imports from China

There were five categories (54, 55, 73, 84 and 85) in the list of top ten imports of

Pakistan from China where China had RCA in all the years; three categories (29, 39

and 40) where it did not possess RCA in any year; and two categories (31, 72) where it

had RCA in some years but not in the others.

As far as Pakistan’s III with China was concerned there were only two categories in

the list (40 and 54) where imports were more than expected in all the years; six

categories (29, 39, 72, 73, 84, 85) where imports remained less than expectations in all

the years; and two categories (31, 55) where the imports were more than expected in

some years and less than expected in the others during 2004-14.

There was only one category manmade filaments (54) in the list of top 10 imports from

China where China possessed RCA and its imports in to Pakistan were more than

expectations in all the years. On the other hand there were two categories Organic


156

Chemicals (29) and Plastic articles (39) where China had RCA and Pakistan had III

values below unity in all the years.

Table 6.19: China’s RCA and Pakistan’s III for Top Ten Imports from China (2004-

14)

Code 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Average

China's RCA

'29 0.6 0.6 0.6 0.7 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.753

'31 0.9 0.5 0.5 1.0 0.7 0.6 0.9 1.0 0.9 0.8 1.1 0.803

'39 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.8 0.9 0.9 0.9 0.742

'40 0.7 0.7 0.8 0.8 0.8 0.9 0.8 0.9 0.9 0.9 1.0 0.838

'54 2.1 2.1 2.1 2.1 2.3 2.2 2.3 2.7 2.7 2.7 2.7 2.368

'55 2.0 2.1 2.3 2.2 2.4 2.3 2.2 2.5 2.4 2.3 2.4 2.286

'72 0.7 0.7 0.9 1.1 1.2 0.5 0.7 0.8 0.8 0.8 1.1 0.843

'73 1.5 1.5 1.6 1.6 1.8 1.5 1.5 1.6 1.6 1.6 1.5 1.574

'84 1.4 1.5 1.5 1.4 1.5 1.6 1.6 1.6 1.6 1.6 1.5 1.534

'85 1.6 1.7 1.7 1.9 2.0 1.9 1.9 2.0 2.0 2.1 1.9 1.873

Pakistan's III with China

'29 0.5 0.5 0.5 0.4 0.4 0.6 0.5 0.5 0.5 0.5 0.6 0.490

'31 2.8 2.0 0.2 6.3 2.4 0.4 3.6 5.1 5.3 6.7 8.7 3.960

'39 0.2 0.2 0.2 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.162

'40 1.7 1.4 1.1 1.0 1.1 1.4 1.3 1.2 1.4 1.5 1.7 1.354

'54 1.2 1.8 2.3 2.6 2.4 2.5 2.5 2.3 2.3 2.2 2.3 2.202

'55 0.4 0.2 0.4 1.9 1.7 1.3 2.1 2.0 1.4 1.3 1.5 1.271

'72 0.0 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.091

'73 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.2 0.2 0.2 0.156

'84 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.010

'85 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.011


The category of rubber articles (40) where China did not possess RCA throughout the

period was still imported more than expectations throughout the period. On the

contrary, the three categories of articles of Iron and Steel (73), Machinery (84) and

Electronics (85) where China maintained RCA throughout the period, the imports of

these categories remained below expectations throughout the period.


157

6.2.2 Pakistan’s Trade with India

Pakistan possessed a bilateral trade potential with India of more than $ 2 billion since

2008 and the bilateral actual volume of trade crossed this figure in 2013, still $ 387.5

million below its potential in 2012. Similarly, Pakistan possessed an export potential

with India that was more than half a billion dollars much of which remained unutilized.

In the following sections, the study explained Pakistan’s trade with India at

disaggregated level for complementarity, export and import performance in their

bilateral trade.

6.2.2.1 Trade Complementarity with India

The adversity of Pakistan’s trade performance vis-à-vis that of India in their bilateral

trade was evident in their respective TCI for each other. India’s TCI for Pakistan’s

demand was about three times more than that of Pakistan’s TCI for Indian demand.

Despite the fact that Pakistan did not grant MFN to India, which India had granted to

Pakistan in 1996, Pakistan’s exports to India were very modest as compared to India’s

exports to Pakistan.

Table 6.20: Pakistan’s Trade Complementarity with India

Years TCI of India's offer

for Pakistan's

demand

TCI of Pakistan's offer to

India's demand

2004 52.31% 15.56%

2005 53.26% 17.32%

2006 56.55% 16.79%

2007 58.31% 19.85%

2008 60.95% 20.57%

2009 55.78% 20.66%

2010 59.95% 22.27%

2011 58.69% 19.86%

2012 59.26% 21.92%

2013 61.90% 17.17%

2014 59.91% 16.99%


India’s TCI for Pakistan’s demand consistently increased each year from 2004 to 2008

and declined drastically in 2009 by about 5%, much of which was recovered in 2010.


158

The TCI of India remained nearly 60% in line with Pakistan’s demand profile during

2010-14. On the other hand, Pakistan’s TCI to India’s demand also showed

improvement generally from 2004 to 2010. However, Pakistan’s TCI witnessed a

declining trend since 2012 in such a way that it almost reached a level in 2014 where

it was in 2005-06.

It was interesting to note that despite nearly more than 10% higher complementarity

with Pakistan’s demand in India than in China, Pakistan’s imports from India were just

a fraction of what it imported from China. There were credible signs of trade diversion

as a result of FTA between Pakistan and China diverting the direction of trade towards

China from that of India. Another, interesting feature of comparing TCI of Pakistan

with India and China was that Pakistan’s TCI with China’s demand was generally more

than that with India’s demand (there were seven years from 2004-14 when Pakistan’s

TCI with China was more than the same with India), therefore Pakistani exports were

(relatively speaking) greater prospects in China’s market than that with India’s.

6.2.2.2 Pakistan’s exports to India

Pakistan’s leading exports to India were so concentrated that more than 96% of

Pakistan’s export to India was accounted for on average (2004-14) in top 25 categories

while 81% in the top 10 categories and more than 66% in the top 5 categories on

average. However, Pakistan’s exports to India are not as concentrated as they are with

China.

6.2.2.2.1 Pakistan’s top 25 exports to India

As regards Pakistan’s top 25 exports to India on average (2004-14), there were 12

categories (7, 8, 10, 12, 22, 25, 27, 29, 39, 41, 52 and 74) where Pakistan had an average

RCA in those categories. However, there were 15 categories (7, 8, 9, 10, 12, 17, 25, 27,

29, 41, 52, 61, 63, 72 and 74) in the same list where India possessed RCA. It can be

noticed that there were ten categories in the list of top 25 (on average) exports to India

where both India and Pakistan simultaneously possessed RCA on average – nine out of

these ten categories were present in the list of Pakistan’s top ten exports to India.


159

Table 6.21: Pakistan’s Average Leading Exports to India and their Respective

Features

Level Codes Title Exports

RCA-

PK

RCA-

IN EII

GLI

PK

GLI

IN

1 '27 Mineral fuels and products 65619.8 0.26 1.01 0.11 0.15 0.46

2 '52 Cotton 46689.2 51.4 7.19 0.92 0.31 0.19

3 '08 Edible fruit, nuts etc 45418.7 2.18 1.27 66.5 0.70 0.90

4 '25 Salt, sulphur, cement etc 31041.3 7.88 2.75 3.67 0.40 0.81


6 '41 Raw hides and skins etc 12193.5 9.51 2.23 3.08 0.36 0.67

7 '74 Copper and articles thereof 11281.3 0.40 1.48 1.14 0.62 0.77


9 '39 Plastics and articles thereof 7178.36 0.50 0.57 0.18 0.39 0.78

10 '12 Oil seed, grain, seed, fruit, etc 6674.82 0.65 1.32 186 0.20 0.26

11 '28 Inorganic chemicals, isotopes 6644 0.13 0.88 5.13 0.09 0.48

12 '51 Wool, animal hair, etc 5744.36 0.47 0.70 83.8 0.69 0.56

13 '78 Lead and articles thereof 5249.18 1.10 0.92 18.3 0.32 0.30

14 '72 Iron and steel 4975.82 0.05 1.33 0.3 0.03 0.84


16 '17 Sugars and sugar confectionery 4445.64 3.68 1.84 11.2 0.35 0.38


18 '70 Glass and glassware 3077.64 0.20 0.51 3.92 0.39 0.91

19 '10 Cereals 1622.27 15.2 3.82 7.07 0.26 0.10

20 '84 Machinery, reactors, boilers, etc 1366.55 0.07 0.32 0 0.11 0.48

21 '20 Vegetable, fruit, nut, etc 1233.27 0.51 0.43 109 0.82 0.29

22 '60 Knitted or crocheted fabric 1079.18 2.02 0.37 3.8 0.51 0.79

23 '22 Beverages, spirits and vinegar 1030.91 1.48 0.13 4.39 0.10 0.73

24 '09 Coffee, tea, mate and spices 958.727 0.89 4.24 34.7 0.21 0.29



Indices)

It was interesting to note that despite the fact there is a general perception of trade

barriers that exist between India Pakistan trade, there were only eight categories (27,

39, 52, 61, 63. 72, 84 and 90) in the top 25 list where Pakistan’s exports to India were

less than expected. However, there was only one category (74) where the export on

average was fractionally more than expected one category (52) where it was

fractionally less than expectations.


160

As far as intra industry trade prospects between the two countries were concerned, on

the basis of average GLI in India and Pakistan in the top 25 Pakistani exports to India,

there were four categories (8, 51, 60, 74) in that list where GLI in both the countries

was found to be in excess of 0.5. There was one category edible fruits (08) where both

India as well as Pakistan possessed RCA; while in case of Wool (51) neither India nor

Pakistan possessed RCA. In the other two categories one country possessed RCA while

the other did not in each case.

6.2.2.2.2 Pakistan’s top 10 exports to India (RCA and EII – 2004-14)

There were four categories (8, 25, 41 and 52) in the list of Pakistan’s top ten exports to

India where Pakistan had RCA in all the years; five categories (12, 27, 29, 39 and 74)

where it did not possess RCA in any year; and one category (07) where it had RCA in

some years and missed it in the others.

As far as Pakistan’s EII for India in the top exports was concerned there were only two

categories (8 and 12) in which the exports were more than expectations in all the years;

two categories (27 and 39) where exports were less than expectations in all the years;

and six categories (7, 25, 29, 41, 52 and 74) with fluctuating trend in EII around unity.

There was only one category Edible fruit (8) in the top 10 export list to India where

Pakistan possessed RCA in all the years and its exports to India were also more than

expected. On the contrary, Oil seed (12) where Pakistan did not possess RCA in any

year during 2004-14, its exports to India remained more than expectations in all the

years. However, both the categories (27 and 39) where Pakistan’s exports were less

than expectations in all the years were among those where Pakistan never possessed

RCA in any years from 2004 to 2014.


161

Table 6.22: Pakistan’s EII and RCA of Top Ten Exports to India (2004-14)

Codes 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Average

Pakistan's RCA

'07 0.9 2.1 0.8 1.3 0.7 1.2 1.5 3.0 2.4 2.8 2.0 1.699

'08 1.7 1.4 1.6 1.6 1.6 2.1 2.3 2.5 2.9 3.3 3.1 2.189

'12 0.8 0.5 0.6 0.8 0.7 0.7 0.3 0.5 0.6 0.8 0.9 0.655

'25 1.3 2.8 3.0 5.8 9.9 12.0 9.3 8.8 11.2 11.5 11.1 7.884

'27 0.2 0.3 0.3 0.4 0.3 0.3 0.4 0.3 0.1 0.1 0.2 0.264

'29 0.1 0.2 0.1 0.0 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.096

'39 0.4 0.5 0.4 0.3 0.5 0.6 0.6 0.7 0.7 0.6 0.4 0.508

'41 7.3 7.4 7.7 9.6 10.4 9.1 9.7 10.3 10.6 11.0 11.4 9.520

'52 41.6 46.8 49.8 51.3 53.4 52.6 48.5 51.5 57.8 55.5 56.6 51.403

'74 0.1 0.1 0.2 0.2 0.3 0.4 0.4 0.5 0.8 0.8 0.7 0.410

Pakistan's EII with India

'07 82.9 348.0 32.2 7.9 0.4 0.3 0.2 3.7 1.1 0.5 0.0 43.378

'08 67.6 70.4 86.2 96.8 79.1 65.0 59.3 46.7 68.0 55.8 36.3 66.459

'12 249.0 369.8 205 184 177 250 198 100 88 121 106 186.402

'25 0.5 0.3 0.5 5.3 6.4 3.8 5.6 5.5 3.5 4.3 4.5 3.671

'27 0.4 0.3 0.2 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.108

'29 0.5 1.3 5.7 1.3 3.7 4.5 4.4 2.5 2.1 5.0 1.4 2.935

'39 0.1 0.2 0.1 0.2 0.1 0.2 0.4 0.1 0.1 0.1 0.2 0.180

'41 0.6 1.5 1.2 3.5 3.9 3.2 3.3 3.3 2.7 4.5 6.2 3.081

'52 0.3 0.8 1.1 1.2 0.9 1.4 1.0 0.7 1.1 0.6 1.0 0.921

'74 0.2 1.8 1.3 0.8 0.8 1.2 1.2 0.8 0.8 1.7 1.8 1.140

Source: Author’s Calculation from UNCOMTRADE)

6.2.2.3 Pakistan’s imports from India

Pakistan’s imports from India were quite concentrated around a few categories. The

top 25 import categories from India on average, accounted for 95% of all the imports

from India while 78% of imports from India were in top 10 categories of imports and

as much as 64% corresponded to top 5 categories. However, Pakistan’s imports from

India were relatively less concentrated around few top categories than the imports from

China are.


162

6.2.2.3.1 Pakistan’s top 25 imports from India

There were 17 categories (7, 9, 10, 12, 17, 23, 26, 27, 29, 30, 32, , 52, 55, 71, 72, 73

and 89) in Pakistan’s top 25 imports from India where India possessed RCA.

Interestingly, Pakistan also possessed RCA on average in 5 categories (7, 10, 17, 52

and 55) present in the top 25 imports from India, with a higher RCA in Pakistan than

in India, in all these shared RCA categories.

Table 6.23: Pakistan’s Average Leading Imports from India and their Respective Features

Leve

l

Cod

e

Title Imports RCA

IN

RCA

PK

III GLI

PK

GLI

IN 1 '29 Organic chemicals 292121.0

9

1.69 0.09 1.98 0.05 0.90

2 '52 Cotton 242052 7.19 51.40 2.16 0.31 0.19

3 '23 Residues, wastes and fodder 141243.0

9

2.96 0.32 52.1

6

0.23 0.16

4 '07 Edible vegetables 115461.4

5

1.23 1.69 130.

4

0.46 0.70

5 '39 Plastics and articles thereof 84580.45

5

0.57 0.50 1.07 0.39 0.78

6 '17 Sugars and sugar

confectionery

49099.09

1

1.84 3.68 18.3

9

0.35 0.38

7 '32 Tanning, dyeing extracts 39502.90

9

1.67 0.32 6.75 0.19 0.77

8 '09 Coffee, tea, mate and spices 34544.90

9

4.24 0.89 19.7

2

0.21 0.29

9 '40 Rubber and articles thereof 33456.72

7

0.83 0.11 3.29 0.10 0.88

10 '38 Miscellaneous chemicals 31947.45

5

0.98 0.06 1.73 0.05 0.87

11 '72 Iron and steel 29093.27

3

1.33 0.05 0.06 0.03 0.84

12 '12 Oil seed, grain, seed, fruit,

etc

28405.81

8

1.32 0.65 13.0

6

0.20 0.26

13 '26 Ores, slag and ash 28390.18

2

3.23 0.44 5.97 0.58 0.69

14 '84 Machinery reactors, boilers,

etc

26461.63

6

0.32 0.07 0.01 0.11 0.48

15 '55 Manmade staple fibres 18099.36

4

3.26 7.08 0.78 0.85 0.37

16 '30 Pharmaceutical products 14465.45

5

1.09 0.23 0.40 0.45 0.29

17 '89 Ships, boats floating

structures

13162.63

6

1.43 0.63 0.04 0.25 0.68

18 '27 Mineral fuels and products,

etc

11658.36

4

1.01 0.26 0.00 0.15 0.46

19 '73 Articles of iron or steel 9318.454

5

1.54 0.39 0.07 0.50 0.72

20 '28 Inorganic chemicals,

isotopes

8492.272

7

0.88 0.13 1.74 0.09 0.48

21 '34 Soaps, lubricants, waxes 8412.909

1

0.46 0.26 12.5

1

0.20 0.85

22 '10 Cereals 8041.727

3

3.82 15.23 3.80 0.26 0.10

23 '85 Electrical equipment 6512.454

5

0.27 0.04 0.00

2

0.08 0.47

24 '71 Pearls, precious stones etc 6282.363

6

5.65 0.55 0.03 0.49 0.77

25 '76 Aluminium and articles

thereof

6135.181

8

0.62 0.13 0.53 0.23 0.81


Indices)

There was no such category on the list where Pakistan possessed RCA while India did

not. As far as the volume of Pakistan’s imports from India were concerned, it was

generally believed that imports from India were much less than expected due to the fact

that Pakistan had not extended MFN status to India and host of other economic and


163

political barriers to trade were still there. However, in 15 out of top 25 imports

categories (7, 9, 10 12, 17, 23, 26, 28, 29, 32, 34, 38, 39, 40 and 52), imports were more

than expected. Interestingly, there were 5 categories (28, 34, 38, 39 and 40) where

Pakistan’s imports from India were more than expected through India did not possess

the RCA in those categories whereas there were seven categories (27, 30, 55, 71, 72,

73 and 89) where India did possess RCA but imports were more than expected.

There were two categories (26 and 73) in the list of top 25 imports from India where

the GLIs of both countries were above 0.5. In both the cases India possessed RCA but

Pakistan did not however, in case of ores (26) imports were more than expected while

in the case of articles of iron and steel (73), imports were less than expectations.

In the case of fruit (8) both countries possessed RCA while in wool (51), neither country

possessed RCA but in both of the cases imports were more than expected. In the other

two cases one country possessed RCA while the other did not but imports still were

more than expected.

6.2.2.3.2 Pakistan’s top 10 imports from India

There were five categories (9, 23, 29, 32 and 52) in the list of Pakistan’s top 10 imports

from India where India had RCA in all the years; two categories (39 and 40) where it

did not possess RCA in any year; and three categories (7, 17 and 38) where India

possessed RCA in some years and missed it in the others.

Pakistan’s import from India in the top 10 categories were more than expectations in

six categories (7, 9, 23, 29, 32 and 40) for all the years and there was not a single

category where Pakistan’s imports from India were less than expectations in all the

years. However, there were four categories (17, 38, 39 and 52) where imports from

India were less than expectations in some years and more than expectations in the other

years.

There were five categories in Pakistan’s top ten imports from India where India

maintained RCA in all the years and in four of those categories (9, 23, 29 and 32)

Pakistan’s imports from India remained more than expectations in all the years while


164

for the fifth category of cotton (52), imports remained more than expectations in all the

years during 2004-14 except for 2005. On the other hand, rubber articles (40) where

India never possessed RCA during 2004-14, the imports remained more than

expectations in all the years during that period.

Table 6.24: India’s RCA and Pakistan’s III for Top Ten Imports from India (2004-14)

Code 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

India’s RCA

'07 1.4 1.7 1.6 1.3 1.2 1.1 1.1 1.0 0.9 1.2 1.0

'09 6.1 4.9 5.0 4.9 4.7 3.5 3.7 3.5 3.5 3.5 3.4

'17 0.4 0.3 2.3 3.4 4.2 0.2 1.6 2.3 2.6 1.3 1.7

'23 3.8 2.8 3.9 3.8 4.6 2.3 2.5 2.5 2.3 2.5 1.5

'29 1.6 1.7 1.9 1.8 1.9 1.6 1.6 1.5 1.8 1.7 1.6

'32 1.7 1.6 1.7 1.8 1.9 1.4 1.6 1.4 1.6 1.8 2.1

'38 0.9 1.2 1.0 1.0 1.1 0.9 0.9 0.8 1.0 1.0 1.0

'39 0.8 0.7 0.7 0.6 0.5 0.4 0.5 0.6 0.5 0.6 0.5

'40 1.0 1.0 1.0 0.8 0.9 0.7 0.7 0.7 0.8 0.8 0.8

'52 6.1 5.7 6.8 8.0 7.5 5.2 8.1 6.6 8.1 8.8 8.3

Pakistan's III with India

'07 14.9 96.9 69.8 93.4 155 158.3 120.6 131.7 217 184 191.6

'09 8.4 16.4 20.5 14.7 29.2 17.1 28.3 19.8 22.7 15.5 24.3

'17 1.5 2.7 98.5 13.6 0.0 0.3 30.7 44.1 1.1 1.6 8.2

'23 68.0 110. 80.1 66.0 37.6 40.9 42.1 36.2 38.3 28.3 26.0

'29 2.5 1.7 2.1 3.3 3.0 2.5 1.5 1.6 1.3 1.1 1.1

'32 5.5 6.8 6.5 6.5 5.9 7.7 6.8 6.3 6.7 7.0 8.7

'38 0.5 0.6 0.8 1.4 2.1 2.2 2.8 2.4 2.6 1.7 2.0

'39 1.9 1.2 1.7 1.5 1.1 0.7 0.4 0.5 0.8 1.1 0.9

'40 5.0 4.0 3.4 4.1 3.8 3.7 2.7 2.4 2.0 2.3 2.8

'52 1.0 0.6 1.5 2.5 3.5 3.4 2.6 2.2 1.6 1.8 3.2

Source: Author’s Calculation from UNCOMTRADE)

It was very interesting to note that Pakistan’s imports were more than expectations on

average for all the top ten categories being imported from India while the general

perception was that Pakistan’s imports from India were less than their potential.

6.2.3 Pakistan’s Trade with Afghanistan


165

Pakistan’s trading relations with Afghanistan were primarily determined by

Afghanistan’s imports from/through Pakistan (as Afghanistan being a landlocked

country relies on Pakistan for its trade with world). As it was established earlier been

found that Pakistan’s exports to Afghanistan were almost 15 times more than

expectations, it would be interesting to see the categories that dominate Pak-Afghan

bilateral trade with each other.

6.2.3.1 Trade Complementarity with Afghanistan

Afghanistan is a landlocked country and despite shared borders between the two

countries there was very little complementarity in the TCIs of these countries.

However, Pakistan’s TCI for Afghanistan’s demand has remained significantly above

that of Afghanistan’s TCI for Pakistan’s demand in all the years from 2008 to 2014.

Table 6.25: Pakistan’s Trade Complementarity with Afghanistan

Years

TCI of Afghanistan’s offer for

Pakistan's demand

TCI of Pakistan's offer to

Afghanistan's demand

2008 2.98% 14.07%

2009 5.13% 14.92%

2010 7.35% 16.58%

2011 7.19% 17.35%

2012 3.10% 6.65%

2013 2.62% 6.95%

2014 26.04% 29.87%


There was an interesting coincidence in the fluctuations of bilateral TCIs of the two

countries for each other in all the years. In most of the seven years’ period the rise and

fall in the TCI of one country was matched with that of the other’s TCI. There was a

significant decline in the TCIs of both the countries for each other during 2012-13 when

the complementarity in both the countries dropped to 1/3rd of their respective levels in

the earlier years in both the cases simultaneously. Moreover, there was a stronger

recovery in 2014 in the TCIs of both countries for each other than the decline during


166

2012-13 when the TCIs of these countries reached their respective highest levels since

2008. The strong revival in the TCI of the two countries was a positive sign for the

bilateral trade relations between the two countries. The strong correlation between the

TCI of both countries with each other’s offer could be the result of some common factor

that jointly affected the complementarity of both the countries for each country’s

exports to the other.

6.2.3.2 Pakistan’s exports to Afghanistan

Pakistan’s leading exports to Afghanistan were so concentrated that 95% of Pakistan’s

export to Afghanistan was accounted for (2008-14) top 25 categories (on average)

while 78% in the top 10 categories and more than 55% in the top 5 categories on

average.

6.2.3.2.1 Pakistan’s top 25 exports to Afghanistan

There were nine categories (7, 8, 10, 11, 15, 17, 22, 25 and 63) in the list of top 25

exports to Afghanistan where Pakistan possessed RCA. On the other hand, there were

three categories (7, 8 and 25) in the same list where Afghanistan also possessed RCA.

However, it was worth noticing that in each of these three categories Pakistan also had

RCA (on average) during the reference period.

Afghanistan, being a landlocked country, relied heavily on its trade through Pakistan

and Iran, it was not surprising to find that every category in the list of top 25 Pakistani

exports to Afghanistan, on average (2008-14), the exports were found more than

expectations on the basis of average EII of Pakistan for Afghanistan. There was only

one category of vehicles other than railway (87) where exports were more than

expectations by a modest margin, otherwise in all the other categories exports were

found significantly more than expectations.

As far as the prospects of intra industry trade with Afghanistan were concerned, there

was not a single category in the list where average GLI in the two countries was in

excess of 0.5 to suggest the prospects intra industry trade were poor. There was only

one category of edible vegetables (07) in Pakistan’s top 25 exports to Afghanistan


167

where the value of GLI in Afghanistan was more than 0.5 on average but that of

Pakistan was much modest to offer the prospects bilaterally.

Table 6.26: : Pakistan’s Average Leading Exports to Afghanistan and their

Respective Features

Leve

l

Code

s Title Exports

RCA-

PK

RCA-

AF EII

GLI

PK GLI AF

1 '27 Mineral fuels, products

300311.

9 0.26 0.09 190.66 0.15 0.04

2 '25 Salt, sulphurand cement

170184.

2 7.88 2.98

15805

8 0.40 0.20

3 '15 Animal,vegetable fats

124887.

9 1.24 0.11 1309 0.14 0.005

4 '11 Milling products

114730.

1 6.90 0.04 1041.5 0.36 0.0001

5 '10 Cereals

110622.

2 15.23 0.03 598.9 0.26 0.003

6 '73 Articles of iron or steel

91143.0

9 0.39 0.005 20605 0.50 0.001

7 '17 Sugars & confectionery

69563.2

7 3.68 0.01 2027.5 0.35 0.0001

8 '39 Plastics and articles thereof

69380.7

3 0.50 0.007 630.5 0.39 0.005

9 '07 Edible vegetables

62444.6

4 1.69 5.32 10655 0.46 0.52

10 '08 Edible fruit, nuts

54246.4

5 2.188 72.03 20774 0.70 0.10

11 '04

Dairy products, eggs,

honey

46824.7

3 0.52 0.04 35376 0.78 0.006

12 '30 Pharmaceutical products

28028.7

3 0.23 0.006 612.51 0.45 0.001

13 '84

Machinery, reactors,

boilers

27694.8

2 0.07 0.009 5.15 0.11 0.005

14 '44 Wood and articles of wood

24781.5

5 0.18 0.01 582.84 0.43 0.001

15 '32 Tanning, dyeing extracts

22944.2

7 0.32 0.0007 4523 0.19 0.0001

16 '85 Electrical equipment

17307.2

7 0.04 0.007 1700 0.08 0.003

17 '19 Cereal, milk preparations

17288.2

7 0.45 0.0005 277.27 0.63

5.8E-

05

18 '22 Beverages and vinegar

13320.9

1 1.48 0.004 622.7 0.105 0.001

19 '34 Soaps, lubricants, waxes

12362.4

5 0.26 0.001 2226.5 0.20 0.0001

20 '20 Vegetable, fruit, nut

12297.3

6 0.51 0.245 257.6 0.82 0.042

21 '87 Vehicles other than railway

10176.7

3 0.04 0.002 1.46 0.09 0.0007

22 '72 Iron and steel

7324.90

9 0.05 0.649 16.01 0.03 0.12

23 '01 Live animals

6786.18

2 0.38 0.008 4E+06 0.51 0.006

24 '76 Aluminium and articles

6558.81

8 0.13 0.008 203.37 0.23 0.008

25 '63 Other made textile articles

6378.54

5 51.38 0.004 2.54 0.08 0.0005


168


Indices)

6.2.3.2.2 Pakistan’s top 10 exports to Afghanistan (RCA and EII – 2008-14)

There were four categories (8, 10, 11 and 25) in Pakistan’s to 10 exports to Afghanistan

where Pakistan possessed RCA in all the years; three categories (27, 39 and 73) where

it did not possess RCA in any year; and three categories (7, 15 and 17) where RCA

kept fluctuating during 2008-14.

Table 6.27: Pakistan’s EII and RCA of Top Ten Exports to Afghanistan (2008-14)

Cod

e

2008 2009 2010 2011 2012 2013 2014 Averag

e Pakistan's RCA

'07 0.7 1.2 1.5 3.0 2.4 2.8 2.0 1.7

'08 1.6 2.1 2.3 2.5 2.9 3.3 3.1 2.2

'10 18.9 16.5 19.1 17.2 12.7 13.2 14.2 15.2

'11 1.1 1.1 2.3 14.4 10.6 8.7 8.6 6.9

'15 1.5 1.0 0.7 1.2 1.5 1.2 0.9 1.2

'17 5.8 2.3 1.4 0.9 3.6 9.1 7.2 3.7

'25 9.9 12.0 9.3 8.8 11.2 11.5 11.1 7.9

'27 0.3 0.3 0.4 0.3 0.1 0.1 0.2 0.3

'39 0.5 0.6 0.6 0.7 0.7 0.6 0.4 0.5

'73 0.3 0.4 0.4 0.5 0.7 0.4 0.5 0.4

Pakistan's EII with Afghanistan

'07 14008.

3

21580.3 16365.5 19373.3 3259.

2

10655.2

'08 21788.

8

19671.2 38012.7 64507.5 1440.

6

20774.4

'10 1328.2 1329.0 628.4 750.7 156.5 599.0

'11 591.2 112.7 1413.1 1357.0 2109.

1

800.

2

907.5 1041.5

'15 1079.6 3504.2 1332.9 1142.9 350.3 941.

8

811.1 1309.0

'17 2310.8 755.5 532.6 2212.1 7208.

7

889.

7

283.1 2027.5

'25 1282.9 761597.

1

169246.

4

174053.

9

223.8 158057.

7 '27 1165.5 51.2 43.8 26.4 1.0 3.7 42.9 190.7

'39 967.1 2147.5 595.9 599.0 104.5 630.6

'73 55862.

3

14804.1 23241.6 50235.1 93.6 20605.2


On the basis of EII, it was observed that most of Pakistan’s exports to Afghanistan

remained more than expected. As it was seen earlier in the average based analysis of

top 25 categories that Pakistan’s exports to Afghanistan on average were more than


169

expectations for all the categories, the exports were found more than expectations in

each year during 2008-14 for every category in the list of top 10 exports to Afghanistan.

There were four categories (8, 10, 11 and 25) in Pakistan’s top 10 exports to

Afghanistan where it possessed RCA in all the years and its exports were also more

than expectations in all those years. The empty cells show the years when Pakistan did

not export to Afghanistan in the corresponding year for a given category.

In almost all the categories of Pakistan’s top 10 exports to Afghanistan, there were huge

fluctuations in Pakistan’s EII with Afghanistan, despite the fact that the values

remained exceptionally above unity for some categories at some times. Therefore, it

was difficult to draw any stable signals from such fluctuating situations of trade

between the two countries.

6.2.3.3 Pakistan’s imports from Afghanistan

Pakistan’s imports from Afghanistan were quite concentrated around a few categories.

The top 25 import categories from Afghanistan (on average), accounted for 99% of all

the imports from Afghanistan while 97% of imports from Afghanistan were in top 10

categories of imports and as much as 88% corresponded to top 5 categories on average.

6.2.3.3.1 Pakistan’s top 25 imports from Afghanistan

Pakistan did not import much from Afghanistan which was also evident from the list

of top 25 imports where the annual average imports were less than $ 100,000 for two

categories. It was interesting to notice that there were ten categories (7, 8, 9, 12, 14, 25,

`30, 51, 52 and 57) in the list of top 25 imports from Afghanistan where Afghanistan

possessed average RCA above unity, and there were 10 categories (7, 8, 10, 14, 15, 25,

41, 52, 55 and 57) on the same list where Pakistan possessed RCA, while there were

seven categories (07, 08, 14, 25, 41, 52, 57) where both countries possessed RCA.

Pakistan’s imports were more than expected in only six categories (8, 9, 12, 41, 51 and

57) on the list and in each of these categories Afghanistan possessed RCA with three

categories (8, 41 and 57) where both Pakistan and Afghanistan possessed RCA on

average simultaneously. As regards intra industry trade, there were two categories (7


170

and 9) in Afghanistan and four categories (8, 26, 51 and 55) in Pakistan in the top 25

list where GLI was more than 0.5 but there was no category where both the countries

had average GLI above 0.5 in both the countries. There were five categories (8, 12, 27,

41 and 57) in Pakistan’s top 10 imports from Afghanistan where Afghanistan possessed

RCA in all the years; one category (44) where it did not possess RCA in any year; and

four categories (7, 25, 52 and 72) where Afghanistan had RCA in some years and

missed it in the others.

Table 6.28: Pakistan’s Average Leading Imports from Afghanistan and their

Respective Features

Level Code Title Imports RCA AF RCA PK III GLI PK GLI AF

1 '52 Cotton 39887.45 5.70 51.40 0.81 0.31 0.26

2 '72 Iron and steel 35450.18 0.001 0.057 0.0002 0.39 0.0007

3 '08 Edible fruit, nuts 23913.55 72.03 2.188 10.29 0.70 0.10

4 '27 Mineral fuels, products 23348.64 0.09 0.26 0.013 0.15 0.04


6 '25 Salt, sulphur, and cement 7631.818 2.98 7.88 0.427 0.40 0.20

7 '44 Wood and articles of wood 3428.636 0.01 0.18 0.001 0.43 0.001


9 '12 Oil seed, grain, seed, fruit 954.1818 11.64 0.65 1.664 0.20 0.12

10 '57 Carpets and floor coverings 876.6364 175.27 9.77 25.03 0.21 0.19

11 '84 Machinery, reactors, boilers 754 0.009 0.07 0.001 0.11 0.005

12 '15 Animal,vegetable fats oils 361.5455 0.119 1.24 0.01 0.14 0.005

13 '09 Coffee, tea 262.4545 19.48 0.89 2.78 0.21 0.55

14 '51 Wool, yarn and fabric 216.8182 25.61 0.47 3.65 0.69 0.03

15 '85 Electrical, equipment 199.7273 0.007 0.04 0.001 0.08 0.003


17 '76 Aluminium and articles 182.3636 0.008 0.13 0.001 0.23 0.008


19 '30 Pharmaceutical products 157.0909 0.006 0.23 0.0009 0.45 0.001

20 '80 Tin and articles thereof 148.7273 0 0.01 0 0.07 0

21 '87 Vehicles other than railway 137.1818 0.002 0.04 0.0004 0.09 0.0007

22 '26 Ores, slag and ash 130.0909 0.007 0.44 0.001 0.58 0.071

23 '10 Cereals 104 0.030 15.23 0.004 0.26 0.003

24 '14 Vegetable plaiting materials 93.45455 3.04 6.42 0.434 0.41 0.103

25 '86 Railway equipment 93.45455 0.004 0.012 0.0006 0.06 0.008

Source: Author’s Calculation from UNCOMTRADE (Exports in 000 $, all others are

Indices)


171

6.2.3.3.2 Pakistan’s top 10 imports from Afghanistan

The table 6.29 shows Pakistan’s top 10 imports from Afghanistan in the context of their

respective RCAs and IIIs during the reference period.

Table 6.29: Afghanistan’s RCA and Pakistan’s III for Top Ten Imports from

Afghanistan (2008-14)

Code 2008 2009 2010 2011 2012 2013 2014 Average

Afghanistan's RCA

'07 2.0 7.4 11.1 6.1 0.0 0.0 10.7 5.33

'08 118.3 93.0 68.6 69.8 46.4 52.8 55.3 72.03

'12 5.1 21.3 19.1 17.1 9.4 3.3 6.2 11.65

'25 0.0 0.5 1.1 1.2 0.0 0.0 18.1 2.99

'27 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.10

'41 21.3 8.8 19.0 24.1 12.0 15.1 8.2 15.46

'44 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.01

'52 0.0 0.2 11.8 3.4 0.0 0.0 24.5 5.71

'57 313.3 175.6 192.3 152.6 209.0 169.3 14.7 175.27

'72 0.0 0.0 0.0 0.0 0.0 0.0 4.5 0.65

Pakistan's III with Afghanistan

'07 8696.3 1294.6 715.8 1453.1 701.0 1837.28

'08 163.0 234.6 365.9 944.4 1541.5 1261.9 841.0 764.63

'12 63.9 6.8 5.5 15.7 8.1 10.5 19.6 18.57

'25 7290.5 10338.7 22508.4 1389.9 5932.51

'27 96.5 116.8 9.7 31.85

'41 44.8 283.3 140.9 84.1 228.7 103.4 219.5 157.82

'44 472.8 67.54

'52 29851.7 10370.5 178.0 335.3 73.1 5829.80

'57 6.7 17.2 23.1 41.6 19.0 18.0 136.6 37.45

'72 12.9 1.85


Pakistan’s imports from Afghanistan were more than expectations for four categories

in the list of top ten imports in all the years but there was no category on the list where


172

imports were consistently below expectations in all the years during the reference

period. However, there were six categories (7, 25, 27, 44, 52 and 72) in the top ten list

where Pakistan’s III for Afghanistan remained below unity for some years and above

in the others.

The value zero in the column corresponding to RCA of Afghanistan showed the

absence of exports from Afghanistan to the world in those categories for the

corresponding years. On the other hand, the empty cells corresponding to Pakistan’s III

with Afghanistan meant the absence of Pakistan’s imports from Afghanistan for those

categories in the corresponding years.

6.2.4 Pakistan’s Trade with Iran

Pakistan’s bilateral trade with Iran remained persistently less than its potential with

almost a billion dollar worth of lost bilateral trade (on average) in each year during

2005-13. The study earlier identified that the true potential of bilateral trade was almost

twelve times more than the actual bilateral trade in 2013. The following sections

explain the disaggregated pattern of trade between the two countries.

6.2.4.1 Trade Complementarity with Iran

Pakistan’s imports from Iran were even lesser than that from Afghanistan in each year

during 2012-14 despite the fact that Iran’s TCI for Pakistan’s demand was higher than

any other neighbour throughout reference period in the years for which Iran’s TCI for

Pakistan’s demand was available. While the cause of less than expected imports from

India (in the context of India’s TCI for Pakistan’s demand) could be attributed to

politically adversarial positions in the two countries for each other; and highest imports

from China could be attributed to historical friendship between the two countries, the

reason marginal Pakistani imports from Iran was primarily not their bilateral relations

but the adverse international structure for Iran to participate in the international trade

because of sanctions. As a result removal of a large part of sanctions from Iran in 2015,

a strong revival in Pakistan’s imports from Iran could be expected in the following

years.


173

It was interesting to note that despite low levels of bilateral trade and a decline in those

levels over the years there was a general improvement in the TCIs of both countries for

each other’s demand. Pakistan’s TCI for Iran’s demand had almost doubled in 2014

from where it was in 2004 and similarly there was a considerable improvement in Iran’s

TCI for Pakistan’s demand also. There was a thorough increase in the TCI of Iran (in

the selected period) disrupted by a decline in 2014.

Table 6.30: Pakistan’s Trade Complementarity with Iran

Years

TCI of Iran's offer for Pakistan's

demand

TCI of Pakistan's offer to Iran's

demand

2004 65.20% 16.80%

2005 65.31% 19.66%

2006 68.91% 12.67%

2010 74.13% 23.57%

2011 74.18% 24.47%

2012 74.55% 33.83%

2013 74.84% 30.10%

2014 72.40% 30.47%


6.2.4.2 Pakistan’s exports to Iran

Pakistan’s leading exports to Iran were so concentrated that more than 96% of

Pakistan’s export to Iran was accounted for (on average) top 25 categories while 87%

in the top 10 categories and more than 77% in the top 5 categories on average.

6.2.4.2.1 Pakistan’s top 25 exports to Iran

In the list of Pakistan’s leading exports to Iran, Pakistan possessed RCA in thirteen

categories (7, 8, 10, 25, 42, 52, 53, 54, 55, 61, 62, 63 and 82) where the value of average

RCA was more than unity in Pakistan.

There were three categories (7, 8 and 25) in the same list where Iran possessed RCA

on average. It was interesting to note, all such categories where Iran had RCA, Pakistan


174

also possessed RCA in those categories in the list. When we analyzed Pakistan’s

leading exports to Iran, in the context of average EII of Pakistan for Iran, there were

only 6 categories (27, 72, 74, 84, 85 and 87), where the exports were less than

expectations. In the 19 categories where Pakistan’s exports to Iran were more than

expectations, there were four categories (28, 29, 38 and 39) where exports were just

fractionally more than expectation.

Table 6.31: Pakistan’s Average Leading Exports to Iran and their Respective Features

Level

Code

s Title Exports

RCA-

PK

RCA-

IR EII GLI PK GLI IR

1 '10 Cereals 106674.2 15.23 0.04 20.00 0.26 0.01

2 '08 Edible fruit & nuts 6279 2.18 3.95 35.61 0.70 0.24

3 '02 Meat & edible meat 6073 0.72 0.03 210.8 0.15 0.18

4 '48 Paper and paperboard 6022.273 0.12 0.01 14.43 0.12 0.08


6 '52 Cotton 5668.182 51.40 0.03 1.65 0.31 0.32

7 '42 Articles of leather 3324.091 9.73 0.01 39.39 0.04 0.38

8 '53 Vegetable textile fibres 2850.091 1.01 0.17 550.3 0.18 0.08

9 '89 Ships & boats 2717.455 0.63 0.02 30.92 0.25 0.26

10 '39 Plastics and articles 2260.818 0.50 0.80 0.17 0.39 0.67


12 '12 Oil seed, grain, seed, fruit 2005.545 0.65 0.06 28.30 0.20 0.13


14 '54 Manmade filaments 1229.364 1.83 0.05 1.70 0.36 0.13


16 '35 Albuminoids, enzymes 1182.545 0.24 0.19 141.7 0.39 0.44


18 '17 Sugars & confectionery 849.5455 3.68 0.29 3.83 0.35 0.30

19 '20 Vegetable, fruit, nut 820 0.51 0.50 190.5 0.82 0.53

20 '82 Tools, implements, cutlery 780.5455 1.008 0.01 1.03 0.84 0.05

21 '19 Cereal, flour, starch 736.2727 0.457 0.54 5960 0.63 0.36

22 '62 Apparel-not knit or crochet 646.3636 6.01 0.04 1.93 0.02 0.25

23 '72 Iron and steel 581.5455 0.05 0.44 0.61 0.03 0.36


25 '25 Salt, sulphur and cement 495.9091 7.88 2.44 3.40 0.40 0.39


Indices)


175

As far as intra industry trade prospects between the two countries were concerned, there

was no category in the list where both the countries had GLI above 0.5 for any category

in both the countries. There were 5 categories (9, 28, 29, 34 and 39) in Iran and four

categories (4, 8, 26 and 73) in Pakistan where average GLI in respective countries was

0.5 or more but there was no category where both countries shared above the

benchmark GLI simultaneously.

6.2.4.2.2 Pakistan’s top 10 exports to Iran (RCA and EII – 2004-06 and 2010-14)

There were five categories (8, 10, 42, 52 and 55) in Pakistan’s top 10 exports to Iran

where it maintained RCA in all the years; two categories (39 and 48) where there was

absence of RCA in all the years; and three categories (2, 53 and 89) where RCA was

present in some years and absent in the others.

Table 6.32: Pakistan’s EII and RCA of Top Ten Exports to Iran (2004-06 and 2010-14)

Code 2004 2005 2006 2010 2011 2012 2013 2014 Average

Pakistan's RCA

'02 0.2 0.2 0.3 0.9 1.0 1.3 1.3 1.2 0.729

'08 1.7 1.4 1.6 2.3 2.5 2.9 3.3 3.1 2.189

'10 10.4 16.0 16.3 19.1 17.2 12.7 13.2 14.2 15.235

'39 0.4 0.5 0.4 0.6 0.7 0.7 0.6 0.4 0.508

'42 10.5 12.6 12.5 8.4 7.4 7.3 7.4 7.3 9.736

'48 0.1 0.0 0.1 0.1 0.1 0.2 0.2 0.3 0.126

'52 41.6 46.8 49.8 48.5 51.5 57.8 55.5 56.6 51.403

'53 1.3 1.9 1.5 0.8 0.7 1.1 0.4 0.2 1.017

'55 2.8 1.8 5.6 10.4 10.2 8.4 7.6 7.5 7.090

'89 3.1 0.2 0.1 0.3 0.0 0.0 0.1 0.0 0.632

Pakistan's EII with Iran

'02 6.1 0.0 0.0 28.2 464.5 796.9 174.0 217.2 210.869

'08 0.2 1.9 110.8 33.8 38.5 72.3 27.1 0.4 35.613

'10 19.1 20.6 102.4 9.0 4.7 3.4 0.6 0.2 20.009

'39 0.4 0.1 0.3 0.2 0.1 0.1 0.2 0.1 0.174

'42 6.6 139.6 165.4 1.7 1.5 0.3 0.0 0.0 39.390

'48 15.0 0.6 46.8 0.6 6.2 12.2 18.2 15.9 14.430

'52 2.2 3.3 6.1 0.4 0.5 0.4 0.1 0.1 1.650

'53 344.5 311.8 2323.7 356.9 437.9 171.4 456.6 0.0 550.355

'55 15.0 17.3 10.3 0.0 0.1 0.0 0.0 0.0 5.355

'89 0.1 0.4 28.5 9.6 85.0 7.0 1.4 115.3 30.929


As far as performance of exports was concerned, there was only one category plastic

articles (39) where exports were less than expectations in all the years and Pakistan


176

never had RCA in that category during the reference period. There was no other

category in the top-10 list where exports were more than expectation or less than

expectations in all the years. Each category (except 39), in the list of top ten exports to

Iran, presented a situation where exports were more than expectations in some years

and less than expectations in the others. However, Pakistan’s EII to Iran had an

increasing trend in two categories (02 and 89) and decreasing in four categories (10,

42, 52 and 55) in the list of top ten Pakistani exports to Iran and the trend of these

increasing and decreasing patterns could be viewed in table 6.32.

The detailed picture of Pakistan’s EII with Iran showed that Pakistan did not export at

all in two categories – meat (2) in two years (2005 and 2006), and vegetable textile (53)

in 2014, while in all the other years export of meat and vegetable textile was more than

expected. Pakistan never had RCA in paper and paper board in any year but its exports

to Iran in that category were more than expectations in all the years, except 2005 and

2010, in the reference period.

6.2.4.3 Pakistan’s imports from Iran

Pakistan’s imports from Iran were quite concentrated around a few categories. The top

25 import categories from Iran (on average) account for 98.6% of all the imports from

Iran while 91.4% of imports from Iran were in top 10 categories and as much as 83.2%

corresponded to top 5 categories.

6.2.4.3.1 Pakistan’s top 25 imports from Iran

There were ten categories (07, 08, 09, 25, 26, 27, 29, 31, 57 and 79) in the list of top

twenty five imports from Iran, where Iran possessed RCA (on average). On the other

hand, there were seven categories (07, 08, 10, 25, 41, 52 and 57) in the same list where

Pakistan, on average, possessed RCA, with four categories (07, 08, 25, 57) where both

Iran and Pakistan shared RCA in those particular categories in the list. It was interesting

to note that there were three categories (10, 41, and 52) in the list of top 25 imports

from Iran where Iran did not possess RCA whereas Pakistan possessed RCA.


177

Most of the categories in the list of top 25 imports from Iran posed a situation (on the

basis of IIIs of these imports) whereby Pakistan’s imports from Iran regarding those

categories were more than expected. Contrary to the situation mentioned earlier, there

were only six categories (27, 72, 73, 84, 85 and 87) where Pakistan’s imports were less

than expectations in the top 25 imports. There was only one category mineral fuels (27)

where Iran had RCA but Pakistan’s import from Iran in that category were less than

expectations (on average). There were only four categories (28, 29, 38 and 39) where

Pakistan’s imports were more than expectations by a little margin (1<III<2) while in

the other 15 categories imports were considerably more than expectations.

Table 6.33: Pakistan’s Average Leading Imports from Iran and their Respective Features

Level Code Title Imports RCA IR RCA PK III GLI PK GLI IR

1 '27 Mineral fuels, products 223439.5 5.32 0.26 0.02 0.15 0.07

2 '72 Iron and steel 49128.55 0.44 0.05 0.75 0.03 0.36


4 '39 Plastics and articles 27125.27 0.80 0.50 1.29 0.39 0.67

5 '26 Ores, slag and ash 25484.27 1.40 0.44 54.9 0.58 0.21



8 '08 Edible fruit, nuts 7564.545 3.95 2.18 21.0 0.70 0.24

9 '89 Ships and boats 5235 0.02 0.63 120.5 0.25 0.26

10 '25 Salt, sulphur, cement 4951.455 2.44 7.88 27.2 0.40 0.39

11 '85 Electrical equipment 4947.818 0.01 0.04 0.28 0.08 0.10

12 '31 Fertilizers 4294.727 1.46 0.04 18.29 0.007 0.35


14 '52 Cotton 3262.455 0.03 51.40 19.81 0.31 0.32

15 '79 Zinc and articles thereof 3031.091 1.84 0.032 12.70 0.03 0.30

16 '38 Misc. chemical products 2418.455 0.23 0.068 1.51 0.05 0.46

17 '09 Coffee and tea 2294.182 1.02 0.893 18.04 0.21 0.58

18 '10 Cereals 1757.727 0.04 15.23 78.65 0.26 0.01

19 '57 Carpets and floor coverings 1312.545 7.28 9.77 7.47 0.21 0.13

20 '87 Vehicles other than railway 1289.909 0.03 0.04 0.006 0.09 0.23

21 '40 Rubber and articles thereof 845.4545 0.06 0.11 4.001 0.10 0.18

22 '04 Dairy products, eggs, honey 819.6364 0.25 0.52 472.3 0.78 0.47

23 '28 Inorganic chemicals, isotopes 804.9091 0.75 0.13 1.13 0.09 0.76


25 '34 Soaps, lubricants, waxes 739.5455 0.36 0.26 5.60 0.20 0.56


178


Indices)

With regard to the prospects intra industry trade between Pakistan and Iran, there was

not even a single category in the top 25 list where the average GLI of both Iran and

Pakistan was more than 0.5. However, there were five categories (9, 28, 29, 34 and 39)

in the list where the average GLI of Iran was more than 0.5 and four categories (4, 8,

26 and 73) where average GLI of Pakistan was more than 0.5. Thus the GLI brought

home a fact that there was very low or almost insignificant prospects of intra industry

trade between the two countries.

6.2.4.3.2 Pakistan’s top 10 imports from Iran

There were only three categories (8, 25, 27) in Pakistan’s top 10 imports from Iran

where Iran possessed RCA in all the years; three categories (41, 72, 89) where Iran did

not possess RCA in any of the selected years; and four categories (7, 26, 29 and 39)

where Iran’s RCA in those categories was present in some years and absent in the

others.

There were four categories in the list where Pakistan’s imports from Iran were more

than expectations in all the years; one category where the import was less than

expectations in all the years; and five categories where imports were more than

expectations in some years and less than expectations in the others.

There were two interesting anomalies in Pakistan’s top ten imports from Iran regarding

mineral fuels (27) and raw hides and skins (41). Pakistan’s imports remained less than

expectations in mineral fuels for all the years while Iran had RCA in this category in

all the years as well. On the other hand, Iran did not possess RCA in raw hides and

skins for any year in the given time period but Pakistan’s import in that category

remained more than expectations for all the years in the selected time period.

There were two categories edible fruit (08) and salt and sulphur (25) where the pattern

of trade followed economic rationale. Iran possessed RCA in the two categories for all


179

the years and Pakistan’s imports from Iran also remained more than expectations in all

the years during the selected time period.

Table 6.34: Iran’s RCA and Pakistan’s III for Top Ten Imports from Iran (2004-06 and

2010-14)

Code 2004 2005 2006 2010 2011 2012 2013 2014 Average

Iran's RCA

'07 0.6 0.7 1.3 2.3 1.3 0.5 0.7 0.8 1.028

'08 3.9 4.4 5.4 5.4 3.2 2.5 3.2 3.7 3.956

'25 1.2 1.1 1.1 3.8 2.5 2.7 3.5 3.6 2.444

'26 0.3 0.4 0.6 1.1 0.5 2.3 3.2 2.8 1.408

'27 7.8 6.3 5.6 4.8 4.5 4.4 4.3 4.8 5.330

'29 0.3 0.5 0.4 1.4 1.1 1.5 1.9 1.6 1.087

'39 0.1 0.2 0.4 1.0 0.8 1.0 1.4 1.5 0.809

'41 0.7 0.6 0.7 0.9 0.7 0.8 0.9 0.7 0.753

'72 0.6 0.6 0.6 0.3 0.2 0.2 0.5 0.4 0.440

'89 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.024

Pakistan's III with Iran

'07 420.1 176.4 36.0 3.5 6.5 37.7 34.0 70.3 98.067

'08 55.3 23.3 22.8 9.7 12.5 17.7 11.4 16.2 21.084

'25 20.8 47.5 82.5 5.1 11.6 10.5 17.7 21.9 27.223

'26 87.6 87.8 60.6 62.2 116.2 18.7 6.2 0.0 54.915

'27 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.027

'29 3.1 2.5 3.5 2.1 1.0 0.3 0.1 0.1 1.588

'39 5.2 2.0 0.9 1.2 0.9 0.1 0.0 0.0 1.294

'41 16.6 27.3 46.2 32.0 30.9 51.5 145.3 99.8 56.206

'72 0.5 0.9 1.3 1.2 0.6 0.6 0.5 0.4 0.756

'89 0.0 941.9 0.0 0.3 0.5 0.0 7.8 14.2 120.586


6.3 Collaborated Findings of the Study

Keeping in view the wide ranging scope of the study, it was realized to structure the

findings of aggregate and disaggregate trade patterns with each neighbouring country

in such a way that the reader can manage to understand and appreciate the link that runs

through the findings being reported in the earlier sections of this chapter. The

interpretations of results were documented country wise in the earlier sections of

chapter six, both at aggregate and disaggregate levels of trade. The analysis of trade


180

potential at disaggregate level is being done with the help of five different indices (TCI,

GLI, RCA, EII and III) that were being constructed by the author for Pakistan and all

its neighbouring countries from 2005 to 2014. The averages of all these indices (for

the selected period) have been reflected in the Appendices at the end.

Pakistan exceeded its free trade potential with China5 and Afghanistan6 (though for

different reasons7 and by different volumes8) in 2010 while Pakistan possessed the

largest9 free trade potential with China and the smallest10 with Afghanistan among all

the neighbours. The Pattern of Pakistan’s bilateral trade with Iran 11 and India 12

consistently remained below their respective potentials. It was very interesting to

compare China with the other three neighbouring countries of Pakistan collectively in

terms of their respective performance in bilateral trade with Pakistan. In 2005

Pakistan’s actual bilateral trade with all neighbouring countries was less than their free

trade potential which can be gauged with the help of gap between actual and potential

bilateral trade. The gap for China was compared with the collective gap of India,

Afghanistan and Iran (IAI) with the help of figure 6.1. to express a special finding.

5 China’s trade exceeded its potential in 2011 and then continued to stay above the potential 6 Afghanistan’s trade exceeded its potential in 2011 and then reversed such that its trade was

again less than potential in 2013 7 The growth in Pakistan’s bilateral trade with China was the result of an FTA signed

between the two countries in 2006 while the same with Afghanistan was because it was a

landlocked country and relied on Pakistan for much of its trade with rest of the world. 8 In 2013, China exceeded the potential bilateral trade with India by more than two billion

dollars that actually was more than Pakistan’s total bilateral free trade volume with

Afghanistan in 2013 that was $ 1.37 billion. 9 Over $ 3.31 billion in 2005 that increased to $5.32 billion by 2013, the largest among

neighbouring countries followed up by India that increased from $ 1.8 billion to $ 2.39 billion

in the same duration 10 Over $ 1.22 billion in 2005 that increased to $1.37 billion by 2013 with Iran slightly more

potential that increased from $ 1.29 billion to $ 3.31 billion to $ 1.47 billion in the same

duration. 11 The average volume of unrealized trade was almost a billion dollars per year. The smallest

volume of unrealized trade was over $ 197 million in 2010 but it was more than a billion

dollars in each of the following years. 12 The average volume of unrealized trade was more than $ 666 million per year though with

fluctuations over the years between $ 304 million to $1.12 billion worth of lost trade between

the two countries.


181

The gap continued to fall symmetrically during 2005-08 but increased in 2009 both for

China as well as IAI. The downward trend resumed in 2010 for China as well as IAI

and continued to fall for China in all the following years, and then started to rise again

for IAI after 2011(the year China’s actual bilateral trade with Pakistan exceeded its

potential). The total volume of China’s trade above its potential in three years (2011-

13) was less than the total volume of lost trade by IAI in the same period13. Pakistan’s

effort to promote bilateral trade with China came at a cost of losing bilateral trade with

the other neighbouring countries.

Figure 6.1 Comparison of Gap between Actual and Potential Trade of Pakistan with

Neighbours

The analysis of Pakistan’s complementarity with each neighbouring countries revealed

that Pakistan possessed more complementarity for imports coming from neighbours

than the complementarity that each of the neighbouring country possessed for Pakistani

exports with an exception of Afghanistan14. Despite changes in the profile of Pakistan’s

complementarity for neighbours’ exports it consistently maintained highest

13 Pakistan’s bilateral trade with China was $ 4.41 billion more than its potential during 2011-

13 while the total volume of lost bilateral trade of Pakistan with India, Afghanistan and Iran

was $ 4.89 billion in the same period. 14 Afghanistan consistently had more complementarity for Pakistani exports than Pakistan

had for Afghan exports.

-3000000

-2000000

-1000000

0

1000000

2000000

3000000

2005 2006 2007 2008 2009 2010 2011 2012 2013

000 of $IAI

China


182

complementarity to the imports from Iran followed up by India and then China15. The

order of complementarity, however, was not the same across neighbours for Pakistani

exports and also it was not consistent over the years either. Iran possessed the highest

complementarity16, followed up by China, India17 and Afghanistan at the bottom18. The

profile of complementarity did not seem to govern the pattern of Pakistan’s bilateral

trade with any of the neighbouring country that reflected a fact that actual bilateral trade

in the region followed geo-political preferences more than the economic foundations

presented through their global trading pattern vis-à-vis each other’s TCI.

Pakistan’s trade with Iran was facing the most serious distortions among all the

neighbouring countries because of lack of volumes of trade despite economic rationale

for its growth, so much so, that the volume of trade with Iran (the country with highest

bilateral complementarity) was even less than that of Afghanistan (the country with

lowest bilateral complementarity)19. Similarly, Pakistan’s complementarity to imports

from India was consistently above the complementarity for imports from China but

Pakistan’s imports from China consistently remained extraordinarily more than those

from India, while the broader case of bilateral India-Pakistan trade puts up an even

worse scenario20.

15 Ironically despite the consistently maintained order of this complementarity for Iran, India

and China, the pattern of Pakistan’s import consistently followed a contrary course where

Pakistan imported more from China than India or Iran. 16 With an exception 2004 and 2006, when China showed the highest TCI for Pakistani

exports 17 Contrary to the case that Pakistan consistently had more TCI for Indian exports than for the

Chinese; China consistently showed higher TCI for Pakistan’s exports than did India. 18 Despite the lowest TCI for Pakistani exports, Pakistan generally exported more to

Afghanistan than to India or Iran because it was a landlocked country and relied on Pakistan’s

rout for bulk its import requirements. 19 It was amazing to note that Pakistan’s imports from Iran were even less than its imports

from Afghanistan during 2012-14 while Pakistan’s compatibility to Afghan exports was less

than 8% on average during 2010-14 compared to around 74% on average with Iran for the

same duration. 20 It was ironic that although the gap between Pakistan’s bilateral Complementarity with India

and China was increasing (in favour of India) over the years from around 5% in 2004 to

around 20% on average during 2012-13 yet the volume of bilateral India-Pakistan trade was

shrinking while that with China was increasing in Pakistan’s global trade.


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The study revealed that the pattern of India-Pakistan trade can be interpreted through

the complementarity of the two countries for each other’s exports in to their respective

countries. The profile of complementarities21 also explained why Pakistan failed to

enhance its exports to India despite receiving an MFN status since 1996 while

Pakistan’s imports from India constantly rising despite the fact that Pakistan did not

return the “favour”. Hence it could be stated that higher Pakistan’s imports from India

with less Pakistani exports to India was the result of their respective complementarity

for each other’s exports into their countries.

Pakistan possessed relatively more potential for imports from the neighbouring

countries than potential for its exports to the neighbours22. For example, in the nine

categories of Pakistan’s top ten imports all except category 31 were on the list of two

neighbouring countries’ leading exports23. India possessed the most categories in its

leading exports that match with Pakistan’s leading imports while Afghanistan

possessed the least number of matching categories in the respective two lists24. On the

other hand, only three of the top ten categories of Pakistan’s exports (10, 27 and 71)

were in the leading top ten imports of one or more of the neighbouring countries25.

Thus it was concluded that Pakistan possessed a potential to furnish its leading imports

from India whereas it’s leading exports to Iran.

The results of determinants of aggregate bilateral trade suggested that impact of signing

an FTA with a partner country on its bilateral trade was almost equivalent to turning

that country’s status in to a neighbouring country for Pakistan’s bilateral trade potential

21 Traditionally, Pakistan had relatively high complementarity to Indian exports (52%-61%

during 2004-14) than India had for Pakistani exports (15%-23% during 2004-14). 22 In Pakistan’s top-ten imports from the world except for category 15, all other categories

were present in the list of top ten exports of one or more neighbouring countries. 23 Categories 84, 85 and 87 that were the leading imports of Pakistan happened to be leading

exports of both India and China also; and three particular categories 27, 29 and 72 were

leading exports of both India and Iran. 24 India had seven categories in its top exports to the world that were on the list of top ten

Pakistani imports from the world; followed up by Iran with five categories, China with four

and Afghanistan with only one. 25 Iran had all the three in its leading imports, India has two categories (27 and 71 – the top

two imports of India from the world also) and China and Afghanistan with one category each

(27) in this regard.


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with such a partner 26 . Therefore, the study found that signing an FTA with a

neighbouring country could offer a great boost to the bilateral trade of Pakistan with

that neighbour (especially when Pakistan could not reach the same milestone with other

neighbours)27.

Inter industry trade potential was explained on the basis of RCA. Pakistan’s inter

industry trade was explored at three levels – Pakistan’s bilateral inter industry trade

potential with neighbouring country, the potential for inter industry trade for Pakistan’s

exports to the neighbours and the same for its imports from the neighbours.

The study, therefore, established that Pakistan’s inter industry bilateral trade potential

was the highest with China and Iran28 at narrow (top-5), intermediate (Top 10) and

broad (top-25) levels; and was the smallest at narrow and intermediate levels with

India29 whereas it was smallest with Afghanistan30 at broad level. The inter industry

export potential of Pakistan was the smallest with India at all the three levels31.

On the other extreme, Pakistan’s export potential, for inter industry trade, was the

highest with Iran at broad and intermediate levels32 and with China and Afghanistan at

26 The results of panel regression pointed out that there could be 1.31% more trade with a

neighbouring country than with any other partner country in Pakistan’s world trade.

Moreover, it showed that as a result of signing an FTA with a partner country Pakistan can

boost its bilateral trade with the partner by 1.306%. 27 The results were applied to compare Pakistan’s trade performance and potential with all the

neighbours and it was seen that Pakistan’s bilateral trade with China exceeded its potential

within years of signing the FTA. 28 There were 17 categories in Pakistan’s top-25 exports to and imports from China and Iran

where the exporting country had RCA while the importing country did not; 7 categories in

top-10; and 5 categories in top-5 in Pakistan’s bilateral trade with both countries. 29 There was not a single category in Pakistan’s top five exports to and imports from India

where the exporting country possessed RCA while the importing country did not and only

four categories at intermediate level. 30 There were just nine categories in Pakistan’s top 25 exports to and imports from

Afghanistan where the exporting country possessed RCA while the importing country did not

providing an economic rationale for trade. 31 There were only three categories in Pakistan’s top-25 exports to India where Pakistan

possessed RCA while India did not but not even a single such category in top-10 or top-5

exports to India. 32 There were eleven categories in Pakistan’s top-25 exports to Iran where Pakistan’s

possessed RCA while Iran did not and five categories in top ten exports to Iran with same

qualification (which was the highest among all the neighbours at both levels).


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narrow level33. With regard to Pakistan’s inter industry potential for its imports from

neighbours; Pakistan had the smallest potential with Afghanistan at all the three levels34

while the potential for same was the highest with India at broad and intermediate

levels35 and it was the highest with Iran at narrow level36. Pakistan’s top ten RCA

categories37 included largest number of categories where Iran38 did not have RCA and

smallest in case of India39. Moreover, there were the most extended prospects of inter

industry trade with all the neighbours for Cereals (10)40 and milling products (11)41.

The shallowest potential for inter industry trade with neighbouring countries in leading

RCA categories was for Salt, sulphur, earth stone plaster, lime and cement (25)42 and

cotton (52)43. Moreover, it was observed that Pakistan’s leading imports44 from the

world had a stronger rationale for inter industry trade than its leading world exports45,

33 There were three categories each in Pakistan’s top-5 exports to both Afghanistan and China

where Pakistan possessed RCA while each of the importing countries did not (which was the

highest among all the neighbours at narrow level). 34 There were only three categories in top-25 imports; one in top-10 and none in top-5

Pakistani imports from Afghanistan where Afghanistan possessed RCA but Pakistan did not

(which was the smallest among all the neighbours at all the three levels). 35 There were twelve categories in Pakistan’s top-25 imports from India where it possessed

RCA while Pakistan did not; and there were four similar categories in top-10 imports from

India as well (which was the highest among all the neighbours at those levels). 36 There were three categories in Pakistan’s top-5 imports from Iran where it possessed RCA

while Pakistan did not (which was the highest among all the neighbours at that level). 37 Categories included 10, 11, 14, 25, 41, 42, 52, 55, 61 and 63 with two categories (52 and

63) on average possessing distinctly more RCA than the rest 38 With an exception of one category (25) Pakistan had inter industry trade potential in all the

top RCA categories as Iran did not have RCA in those categories. 39 With an exception of one category (11) Pakistan did not have inter industry trade prospects

in the leading RCA categories as India also possessed RCA in all the other categories.

Afghanistan had the similar potential for five of the categories (10, 11, 42, 55 and 61) while

China had it for four categories (10, 14, 25, 41) on the same list. 40 Pakistan possessed inter industry trade in this category with all the neighbours except India

and there was RCA in this category in Pakistan for all the years during 2004-14. 41 Pakistan possessed inter industry trade in this category with all the neighbours except

China and there was RCA in this category in Pakistan for all the years during 2004-14 42 Pakistan possessed inter industry trade potential for this category only with China 43 Pakistan possessed inter industry trade potential for this category only with Iran 44 There were only five categories (07, 09, 10, 52 and 55) in top 25 imports of Pakistan from

the world where Pakistan possessed RCA but there were relatively larger imports from the

world. However, there was one category in Pakistan’s top 10 imports where it had the highest

RCA on average. 45 There were nine categories (02, 05, 09, 13, 14, 36, 56, 58 and 82) in Pakistan’s top 25

exports to the world on average where it did not possess RCA. However all the categories in

Pakistan’s top 10 world exports were the ones where Pakistan possessed RCA.


186

though most of its world exports and imports generally followed the rationale of inter

industry trade on the basis of average RCA46. Although there were 29 categories in

which Pakistan possessed RCA but there were fewer 47 categories where Pakistan

consistently possessed RCA during 2004-14. The largest segment of categories where

Pakistan possessed average RCA belonged to the cotton and textile sectors48 with as

many as eleven categories related to the this sector. However, only half of these

categories49 consistently exhibited RCA for Pakistan during 2004-14; while Pakistan

missed the potential for one or more years for all the remaining categories.

Just as RCA explained the prospects of inter industry trade; GLI explained the

prospects of IIT. Pakistan possessed the highest IIT potential with China50 at all three

levels of analysis. The potential for IIT with India was slightly less than that of China

at all the three levels51.

There was significantly lesser IIT potential of Pakistan with Iran and Afghanistan as

compared to that of India and China. The smallest IIT potential, among all the

neighbours, was with Afghanistan52. While the study identified overall bilateral IIT

potential with each neighbouring country, it also identified the prospects of bilateral

46 There were 20 leading imports and sixteen leading imports with a rationale for inter

industry trade. 47 There were eleven categories (07, 15, 17, 22, 53, 54, 56, 58, 60, 78 and 82) where Pakistan

lost its RCA in one or more years during 2004-14. Thus there were only eighteen categories

(03, 05, 08, 10, 11, 13, 14, 25, 36, 41, 42, 52, 55, 57, 61, 62, 63, and 95) where Pakistan

consistently possessed RCA for inter industry trade 48 These categories included (52, 53, 54, 55, 56, 57, 58, 60, 61, 62 and 63). There was only

one category (in this stretch of HS classification) of Impregnated, coated or laminated textile

fabric (59) where Pakistan did not possess RCA. 49 The consistently portrayed RCA was for six categories (52, 55, 57, 61, 62 and 63) 50 There were 31 categories in Pakistan’s top-25 exports to and imports from China where

one or both the countries have GLI greater than the cut off value of 0.5. In the similar

analysis there were 17 categories in top ten traded goods and nine categories in top five

traded goods in the two countries. 51 There were 30 categories in Pakistan’s top-25 exports to and imports from India where one

or both the countries have GLI greater than the cut off value of 0.5. In the similar analysis

there were twelve categories in top ten traded goods and six categories in top five traded

goods in the two countries. 52 There were just eleven categories in Pakistan’s top-25 exports to and imports from

Afghanistan where one or both the countries have GLI greater than the cut off value of 0.5. In

the similar analysis there were only five categories in top ten traded goods and four categories

in top five traded goods in the two countries.


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IIT for leading categories in that regard. India possessed the most categories for its

shared prospects of bilateral trade with Pakistan53 while China stood next to India for

similar prospects with Pakistan54. There was a sheer absence of any such prospects with

Iran55.

Although there were twenty nine categories offering prospects of inter industry trade

and twenty five categories offering opportunity of IIT, there were only few categories

where Pakistan enjoyed the potential for both inter industry as well as intra industry

trade with the neighbouring countries56.

EII was used to determine whether the export of a given category from one country to

another was more than potential or less than that. Afghanistan (being a landlocked

country) was ahead of all the neighbouring countries in terms of Pakistan’s exports

exceeding their potential57 whereas China showed the poorest performance in that

regard58 at all the three levels (for both countries). It was interesting to note that despite

the fact that Pakistan’s overall exports to both India and Iran were less than their

respective potentials, Pakistan’s export performance at disaggregate levels were quite

53 There were four categories (08, 51, 60 and 74) in Pakistan’s top-25 exports to India where

both India and Pakistan has GLI more than the cutoff point (categories 08 and 74 were also in

the top 10 exports while category 08 was included in top 5 exports too) whereas there were

two categories (26 and 73) in Pakistan’s top-25 imports from India where both the countries

had GLI above the cut off (these categories were not in the top 10 imports from India). 54 There were three categories (08, 55 and 90) in Pakistan’s top-25 exports to China where

both China and Pakistan has GLI more than the cutoff point (categories 55 and 90 were also

in the top 10 exports while category 55 was included in top 5 exports too) whereas there were

two categories (55and 90) in Pakistan’s top-25 imports from China where both the countries

had GLI above the cut off (these categories were not in the top 10 imports from China). 55 There was only one category (20) in Pakistan’s top 25 exports to Iran where the GLI of

both the countries was more than the cut off value though this category was not amongst the

top 10 Pakistani exports to Iran. On the other hand, there was no category with similar

credentials in Pakistan’s top 25 imports from Iran. 56 There were six categories (08, 55, 56, 58, 60 and 82) in Pakistan where it had prospects of

both inter and intra industry trade. However there were only two categories (08 and 55)

where potential for inter industry trade were consistent over the years. 57 Pakistan’s exports to Afghanistan in all the top 25 categories were more than their potential

(on average). 58 There were fourteen categories in Pakistan’s top 25 exports to China where exports were

less than potential. Same was the case for half of the top 10 exports and two of the top 5

exports as well.


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contrary to what was being found at aggregate level59. The reason for this divergence

between aggregate trade analysis of Pakistan’s exports performance with India and Iran

and analysis at disaggregate level could be the result of domestic compulsions60 or

institutional biases61 rather than any economic rationale for it.

Just as EII was used to determine the performance of export in the context of its

potential; III was used to determine the performance of imports in the context of their

respective potential. The imports of Pakistan from Afghanistan were less than their

potential by the greatest margin among all the neighbouring countries of Pakistan62.

Pakistan’s imports from India and Iran exceeded their potential by the greatest margin

among all the neighbouring countries63. Pakistan’s imports from China, at aggregate

level, dominated not only among Pakistan’s neighbouring countries, but also all other

countries from where Pakistan’s imports64 came. However, at disaggregated level, the

59 Pakistan’s exports to India have consistently remained below their potential at aggregate

level during 2005-13 with the ratio deteriorating multiple times in this duration but at

disaggregate level Pakistan’s exports to India were more than expectations on average for

seventeen of the top 25 categories (with seven in the top 10 and three in the top 5 exports).

Similarly, Pakistan’s aggregate exports to Iran have remained below their potential in many

years during 2005-13 with the ratio continuously deteriorating since 2010 but at disaggregate

level Pakistan’s exports to Iran were more than expectations on average for twenty one of the

top 25 categories (with nine in the top 10 and all the categories in the top 5 exports). 60 This can be properly explored at 4 or 6 digit HS classification, however, in the context this

study it could be stated as a result of dumping domestic produce in excess of domestic needs

– for example as a result of a given increment in the support price of wheat or sugarcane if

the production exceeds the expectations as well as domestic absorption it is off loaded to a

partner country. This was a case regarding the two crops in a number of years in recent past

in Pakistan and Edible vegetable (07) is the 9th largest export to India and 11th largest to Iran. 61 For example the presence of negative list for imports from India that was later replaced

with a positive list could be pertinent examples of institutional bias in international trade. 62 There were only six categories in top 25 imports from Afghanistan where Imports were

more than expectations (with four in top 10 and only one in top 5 imports from Afghanistan). 63 For the top 25 Pakistani imports, each from India and Iran, there were nineteen categories

where imports from Iran were more than expectations and 15 categories where imports from

India were more than expectations. However, Pakistan’s imports from India were more than

expectation for all the top ten imports while there were two categories each in top 10 and top

5 Pakistani imports from Iran where imports were less than expectations. 64 Imports from China became the largest proportion of its total import in 2014 (Pakistan,

2015).


189

pattern of import performance was not consistent with the aggregate pattern though it

was not contrary either65.

Pakistan’s trade with each neighbour was found extremely concentrated around a few

categories of HS classification. Nearly 90% or more of Pakistan’s total trade, exports

and imports with each of the four neighbours (on average) during 2004-14 was found

to be concentrated around top-25 categories. However, Pakistan’s total bilateral trade

was most concentrated with Iran66, its exports were most concentrated with China67

while its imports were most concentrated with Afghanistan 68 . On the other hand,

Pakistan’s bilateral trade, at large, was most diversified with China69; exports were

most diversified to Afghanistan70; and its imports were most diversified from China71.

Thus Pakistan’s exports to Afghanistan were most diversified while imports were most

concentrated among all the neighbouring countries at broader as well as narrower base

of analysis. On the other hand, Pakistan’s bilateral trade, as well as imports, were most

diversified while its exports were most concentrated in its trade with China among all

the neighbouring countries.

Pakistan’s imports from as well as exports to the neighbouring countries were quite

concentrated around the top 25 traded categories with all the neighbouring countries.

65 There were only nine categories in top 25 Pakistani imports from China where imports

were more than expectations (with four in top 10 and only one in top 5 categories). 66 96% of Pakistan’s bilateral trade with Iran was in the top 25 categories which was 85.5%

for top ten categories and 74.2% for the top five traded categories 67 98% of Pakistan’s total exports to china were in the top 25 categories which was 92.4 % for

top ten categories and 84.9 % for the top five traded categories 68 99.4% of Pakistan’s total imports from Afghanistan were in the top 25 categories which

was 97.4 % for top ten categories and 87.7 % for the top five traded categories. 69 86.7 % of Pakistan’s bilateral trade with China was in the top 25 categories which was

69.3% for top ten categories and 55.1% for the top five traded categories (However,

Afghanistan was most diversified for top five categories with 51.4% of bilateral trade 70 95.2% of Pakistan’s total exports to Afghanistan were in the top 25 categories which was

77.8% for top ten categories and 54.7% for the top five traded categories 71 89% of Pakistan’s total imports from China were in the top 25 categories which was 70%

for top ten categories and 55.4% for the top five traded categories.


190

However, Pakistan’s imports72 to the neighbouring countries were more concentrated

than its exports73 to the neighbours. Moreover, it was interesting to note that Iron and

Steel (72) and Machinery, nuclear reactors and boilers (84) were in Pakistan’s leading

exports to and imports from all the neighbouring simultaneously74. Similarly, there

were two other categories mineral fuels, oils and distillation products (27) as well as

Plastic and articles thereof (39) that were in Pakistan’s leading exports to and imports

from almost all the other countries with an exception of one75. These four categories

(27, 39, 72 and 84), therefore, were most important for any trade policy development

in the context of Pakistan’s trade with its neighbouring countries.

There was a lot of potential competition between India and China in their trade with

Pakistan both in terms of exports to as well as import from the two countries. However,

72 There were seven categories (07, 27, 52, 72, 73, 84 and 85) that were simultaneously in

Pakistan’s top 25 imports from all the neighbouring countries with two categories (27 and 72)

that were in its top ten Pakistani imports from the world. 73 There were six categories (08, 25, 39, 63, 72 and 84) that were simultaneously in Pakistan’s

top 25 exports to all the neighbouring countries though there was not a single category that

was in its top ten exports to world also. 74 It was interesting to note that Pakistan did not have RCA in both the categories. There was

only one country China that had RCA in category 84 and only one country India that had

RCA in 72. Afghanistan was the only country for which Pakistan’s exports of both the goods

were more than expectations however both Pakistan’s exports and imports of both the

categories were less than expectations in all the other cases. 75 Mineral fuels (27) were in the top 25 exports and imports of all except top 25 exports to

Iran whereas plastics (39) also had the same credentials with an exception of top 25 imports

from Afghanistan.


191

there was greater competition between the two countries for the share in Pakistani

imports76 than for accommodating Pakistan’s exports77 in their countries.

There were some categories that were found extremely central to both exports as well

as imports of Pakistan with both India and China78. Thus any trade policy that was

aimed at Pakistan’s trade with India or China should focus on its implications on the

trade with both the countries because of the competitive nature of Pakistan’s trade with

both India and China especially in the context of mineral fuels (27) and cotton (52).

Efforts of bilateral trade liberalization to promote trade may result in trade creation or

trade diversion while the former is a privilege; latter is one of the most challenging

aspects of such an exercise. As Pakistan signed an FTA with China in 2006, it

confronted Pakistan with the challenge of (potential) trade diversion, evident both at

76 There were fifteen categories (07, 27, 28, 29, 32, 38, 39, 40, 52, 55, 72, 73, 84, 85 and 89)

in Pakistan’s top 25 imports from India as well as China that were similar. Moreover, there

were as many as seven categories (27, 29, 39, 52, 72, 84 and 85) that were in the top 10

Pakistani imports from the world three categories (29, 39 and 40) in the list of Pakistan’s top

ten imports from the two countries. There were two categories (73 and 89) where both the

countries possessed RCA but Pakistan’s imports from each neighbour were less than

expectations while two other categories (38 and 40) where both countries lacked RCA but the

imports were more than expectations from both the neighbours. Moreover, there were three

categories (07, 52 and 55) where both the countries possessed RCA but imports were more

than expectations from both the countries in case of category 07whereas imports were more

than expectations from India and less than expectations from China for category 52; and

imports were less than expectations from India and more than expectations from China for

category 55. There were four categories (27, 29, 32 and 72) where India possessed RCA

while China did not and three categories (28, 84 and 85) where China possessed RCA while

India did not. There were only three categories (07, 29 and 52) in Pakistan’s top 25 imports

from China where value of import from India was more than that from China while there

were sixteen categories in Pakistan’s top 25 imports from India where value of imports from

China was more than that from India. 77 There were fourteen categories (08, 10, 12, 25, 27, 29, 39, 41, 52, 61, 63, 72, 74, 84 and

90) in Pakistan’s top 25 exports to India as well as China that were similar. Moreover, there

were as many as seven categories (10, 25, 27, 41, 52, 61 and 63) that were in the top 10

Pakistani exports to world. 78 There were six categories (27, 29, 39, 52, 72 and 84) that were simultaneously in Pakistan’s

top 25 exports to and imports from both India and China while two of these categories (27

and 52) were in the top 10 Pakistani exports to the world.


192

aggregate79 as well as disaggregated80 levels of analysis. There was trade diversion

from India to China in the import of Iron and steel (72)81 and Articles of iron and steel

(73) 82 in Pakistan as a result of trade liberalization between Pakistan and China.

Moreover, there was trade diversion from Iran to China regarding import of fertilizers

(31)83 in Pakistan due to this liberalization. Pakistan’s import of Organic chemicals

(29)84 was diverted away from both India and Iran to China, as result of the FTA

between Pakistan and China.

79 It was found that Pakistan’s bilateral trade with China gradually improved in the years

following the signing of FTA between the two countries and eventually exceeded its potential

in 2011 and the ratio of potential bilateral trade to the actual was consistently deteriorating

since then. The volume by which the actual bilateral trade exceeded the potential was more

than $ 2 billion in 2013 while it was fractionally less one billion dollars gap in 2011. 80 There were four categories (29, 31, 72 and 73) that were identified in Pakistan’s top 25

imports from China where trade diversion could have taken place from India or Iran or both

to China. 81 The import of Iron and Steel (72) is the 5th largest import of Pakistan and the 2nd largest

import from both Iran and Afghanistan. There was a consistent increase in its share from the

neighbours from 9% in 2004 to 37% in 2014 in such a way that import from all the other

neighbours as a percentage of world import generally declined while that of China continued

to increase. It was interesting to note that there was only one neighbouring country India that

possessed RCA in this category but its share did not exceed 3% of Pakistan’s total import of

Iron and Steel while that of China increased from 3% of Pakistan world import in 2004 to

31% by 2014. 82 The import of articles of Iron and steel (73) was in the top 25 Pakistani imports from each

of its neighbouring country which was 8th largest import from China, 6th largest from

Afghanistan and 19th largest from India. India and China both possessed RCA in that category

on average (and the values of of their respective RCAs were also quite close). However, the

share of India in total Pakistani import of this category (on average) was less than 1% (it

declined sharply from 8% in 2011 to less than 1% in 2014) while that of China consistently

increased from 15% in 2004 to 52% in 2014. 83 Fertilizer (31) was another category where China dominated trade within the neighbours

and also Pakistan’s global import from less than 1% in 2006 to around 68% by 2014 despite

the fact that China did not possess RCA in that category while Iran possessed and its share

decreased over the years. 84 Organic chemicals (29) were the fourth largest import of Pakistan and it was Pakistan’s

largest import from India, 3rd largest import from China as well as Iran. Organic Chemicals

(29) was a category for which Pakistan has generally relied abundantly with the neighbours.

There seemed to be a case of trade diversion from Iran and India (that have RCA) to China

(that does not have RCA) because while China’s share in Pakistani import of organic

chemicals increased from 8% in 2004 to 25% in 2014, the share of India fell from 26% in

2007 to 12% in 2014 and that of Iran fell from 7% in 2010 to less than 1% in 2014. The share

of both India and Iran in Pakistan’s world import of organic chemicals fell in the years

following the signing of FTA between Pakistan and China.


193


194

CHAPTER 7

CONCLUSIONS AND RECOMMENDATIONS

This chapter was composed with a view to funnel the findings and results of this study,

being carried out to determine Pakistan’s free trade potential with the neighbouring

countries, both at aggregate and disaggregate levels, being documented in the previous

chapter. All the conclusions were made from the collaborated results and findings,

which were substantiated lavishly by means of footnotes to help the reader

contextualize the background of these conclusions in last section of the previous

chapter. The recommendations were made in the context of stakeholder to whom these

were being addressed.

7.1 Conclusions

The importance of conclusions demanded due substantiation of their basis. Therefore,

the conclusions were based on the interpretation of results and collaborated findings

presented in chapter six.

The most crucial conclusion for aggregate trade with neighboring countries was in the

context of extraordinary growth in bilateral trade with China after the signing of FTA

between China and Pakistan in 2006. China became the largest trading partner of

Pakistan and Pakistan’s volume of bilateral trade with China remained above the

potential since 2010, which appeared to have been realized at the opportunity cost of

lost bilateral trade volumes with the other three neighbours, especially India and Iran.

The collective volume of lost bilateral trade with India, Afghanistan and Iran (the

negative difference between actual and potential bilateral trade) was found to be larger

than the surplus volume of Pakistan’s bilateral trade with China (the positive difference

between actual and potential bilateral trade) for the period of 2010-13.


195

It was further concluded that trade liberalization with China has caused trade diversion

both at aggregate and disaggregate levels. Pakistan experienced trade diversion from

India to China regarding its imports of two categories Iron & Steel and Articles of Iron

& steel; and from Iran to China regarding the import of fertilizer, after the FTA signed

between Pakistan and China.

Pakistan’s bilateral trade always remained below its potential with India and Iran

whereas bilateral volume of trade with China and Afghanistan exceed their respective

potential after 2010. Coincidently, Pakistan’s bilateral trade deteriorated with Iran and

improved with China in an exaggerated manner after 2010, though logical connection

can be drawn for one being a result of the other.

Negative response of Pakistani exports to increase in trading costs was observed for

increase in time taken at the port of the partner country, the distance from the partner

country as well the average tariff rates there, but the effect of tariff rate was found most

glaring. Pakistan can expect more than five per cent increase in its exports to the partner

country for an average one percent decrease in the tariff rates, suggesting a high tariff

elasticity of Pakistani exports to a partner country. Realization of an FTA with the

partner country can be expected to double Pakistani exports to that partner country.

Pakistan’s exports to Afghanistan have always remained more than the potential,

primarily, because of landlocked nature of the country whereas the exports to India

always remained below potential despite the fact that India had accorded the MFN

status to Pakistan since 1996. It could be concluded that India could be using NTBs to

block Pakistani exports to India as after MFN India cannot discriminate against

Pakistani exports regarding tariff after. Pakistan’s exports generally remained more

than expectations to Iran before 2010 and to China afterwards.

The study also concluded that Pakistan’s exports did not enjoy as much potential

demand in its neighbouring countries as did the exports of its neighbours enjoyed

demand in Pakistan, with the sole exception of Afghanistan. Although Pakistan

possessed the largest prospects of bilateral trade with Iran based on their respective

complementarity for each other’s exports yet Pakistan’s volume of bilateral trade was


196

the lowest among all the neighbours. The study therefore concluded that the removal

of sanctions could take the volume of Pakistan-Iran trade to the volume indicated by

the profile of their complementarity for each other.

On the basis of RCA indices of Pakistan and its neighbouring countries, it was

concluded that Pakistan’s inter-industry trade potential was the highest with China and

the lowest with India among all its neighbours. There were two categories in Pakistan’s

leading exports, namely, cereals and milling products for which Pakistan possessed

export potential to all its neighbouring countries. On the other hand, there were two

products in Pakistan’s leading exports, namely, cement and cotton, where Pakistan

possessed lowest trade potential with all the neighbours.

Pakistan revealed its comparative advantage in twenty nine categories but more than

1/3rd of these categories pertain to cotton & textile related categories (eleven

categories). Another significant feature of comparative advantage depicted by the

cotton and textile related categories, was the fact that Pakistan consistently exhibited

RCA in these categories, unlike many others in the list of twenty nine categories.

Therefore, it is concluded that Pakistan possesses significant and persistent

comparative advantage in cotton and textile sectors of its economy. Moreover, there

were six such categories where Pakistan possessed potential for both inter-industry and

intra-industry trade and four of these categories represent the textile sector. The study

concluded that Pakistan showed significant potential of unilateral exports as well as

bilateral trade in the textile sector with world in general and with the neighbours in

particular.

The GLI index of Pakistan and its neighbouring countries explained the IIT potential.

On the basis of GLI indices of Pakistan and its neighbours, it was concluded that

Pakistan possessed reasonable IIT potential with China and India, whereas the potential

for IIT was insignificant in case of Iran and absent in case of Afghanistan.

Pakistan’s exports to Afghanistan were more than their potential by the greatest margin

whereas its imports from Afghanistan were less than their potential by the greatest

margin among all the neighbouring countries. Thus the pattern of trade with


197

Afghanistan was consistent both at aggregate and disaggregate levels. However, the

pattern of trade with the other three countries were different both at aggregate and

disaggregate levels. It is therefore concluded that political and institutional factors

contributed more to Pakistan’s trade with Iran, China and India than the economic

factors – domestic compulsions and institutional preferences determine the pattern of

Pakistan’s bilateral trade with these three countries especially at disaggregate levels.

The pattern of Pakistan’s leading exports and imports were most concentrated with

Afghanistan and most diversified in case of China at disaggregate level among the

neighbouring countries. However, in general, Pakistan’s exports as well as imports

were significantly concentrated around few top traded categories. There were four

categories that were present in Pakistan’s leading exports as well as leading imports of

almost all the neighbours, which included Iron & Steel (72), Machinery & Boilers (84),

Mineral fuels (27) and Plastic articles (39).

7.2 Recommendations

All bilateral and multi-lateral trade negotiations are based on “give and take” principle

where a country gains access to the partner countries by giving them access to domestic

markets. The policy makers in Pakistan must bear in mind while negotiating trade with

any of its neighbouring countries that Pakistan has more to offer and less to receive as

a result of an equal reduction in barriers with any of our neighbouring countries. Trade

negotiations with any of Pakistan’s neighbours must incorporate this dimension in their

frameworks before embarking upon any kind of trade liberalization with them.

Pakistan’s trade with India and China should be seen contextually for a wholesome

analysis in matters like bilateral complementarity and leading exports and imports of

Pakistan to and from both India and China. The sum of Pakistan’s complementarity of

trade with India was consistently more than it was with China but the bilateral volume

of trade of Pakistan with India was just a fraction of what it was with China. However,

when we split the bilateral complementarity for exports and imports, Pakistan


198

possessed more complementarity for Indian imports than to Chinese imports; while

China possessed more complementarity for Pakistani exports than India possessed;

therefore Pakistan should welcome trade talks with India in granting them greater

access to our markets and seek greater access for Pakistani exports in China. Moreover,

since 2012-13 both Pakistan’s bilateral trade and export with China exceeded their

respective potential while its trade with India remained below the potential for both

aspects, therefore Pakistan needs to identify and eliminate the potential presence of

trade diversion of Pakistan’s trade from India to China.

Pakistan should seek to eliminate instances of trade diversion and seek opportunities of

trade creation while signing an FTA with a partner country as the study identified

potential trade diversion in the categories of organic chemicals (29) from India and Iran

to China while it was also one of the leading Pakistani import categories. Similarly the

trade diversion in the categories of Iron and steel (72) and Articles of Iron and steel

(73) should be removed to allow for greater import in these categories from India rather

than China. Moreover, trade diversion in fertilizer (31) from Iran to China may also be

reverted to Iran through prudent policy actions. There might be instances of similar

diversion in minor categories of trade, as well, as the minor categories of trade have

not been the focus of this study. Therefore trading bodies and Chambers of commerce

and in industries in Pakistan must identify and block the path of such like trade

diversions.

Although China was the largest trade partner of Pakistan in 2014 in aggregate manner,

yet at disaggregate level most of Pakistan’s leading imports from China were much less

than their true potential. Similarly at aggregate level, Pakistan’s trade with India and

Iran was less than their potential but at disaggregate level most of the leading traded

items of Pakistan with India and Iran were above their potential. The difference

between trade performance at aggregate and disaggregate levels hint at political/special

stakeholders’ influence, through institutional bias, in affecting disaggregated trade

flows between the partner countries. The presence of PLs and NLs were examples of

such influence. Therefore the study recommended the removal of NLs and PLs (or


199

keeping NLs as short as possible and PLs as wholesome as possible) to remove these

distortions.

There were six categories (08, 55, 56, 58, 60 and 82) where Pakistan had prospects for

both inter and intra industry trade and four of these categories pertained to the textile

sector. The textile sector should be provided with knowledge base for its domestic

growth and connectivity support for its international outreach to realize the dual

benefits of trade in these sectors.

Although Pakistan’s imports were concentrated with all the neighbouring countries in

general but it was found that its imports were most concentrated from Iran and

Afghanistan where more than 80% of total imports from these two countries were in

the top five categories being imported in Pakistan from both the countries. There were

two categories in Pakistan’s top five imports from Iran in which Iran did not possess

RCA (39 and 72) and two categories in top five imports from Afghanistan where

Afghanistan did not possess RCA (27 and 72). The study recommends that Pakistan

should realign its imports in the context of RCA of the countries from where Pakistan

imports. Pakistan imports Iron and Steel (72) from Iran and Afghanistan more than it

imports the same from India (India possessed RCA in Iron and Steel while Iran and

Afghanistan did not), therefore the study recommends that Pakistan should import

greater amounts of Iron and Steel from India than from Iran or Afghanistan because

there is an economic justification for this rearrangement and efforts should be made to

engender this rearrangement. On average the import of Iron and Steel was less than

expectations from India, Iran and Afghanistan (III was less than unity for all), trade

facilitation with India in this regard may bring the import of Iron and steel from India

close to its economic potential. The process of trade facilitation, however, should be

carried out after consultations with the leading traders of Iron and steel in Pakistan.

The complementarity of each neighbouring country for Pakistan’s exports (on average)

was less than 25% but there was extraordinary growth in the TCI of Afghanistan and

Iran for Pakistani exports when it reached around 30% in each of the two countries in

2014. Therefore stakeholders in exporting sectors should look at extraordinary


200

opportunities for export growth in these two countries especially when the

complementarity was generally falling in China and India since 2010.

Pakistan possessed lowest inter industry trade potential with India but the potential for

IIT was maximum with India amongst all the neighbouring countries. The categories

where Pakistan possessed IIT potential with India included edible fruits and nuts (08),

wool etc. (51), knitted or crocheted fabric (60) and copper articles (74). Similarly

Pakistan possessed IIT potential with China in three categories of vegetable, fruit and

nuts based food preparations (20) (the only category where Pakistan had potential for

IIT with Iran), manmade staple fibres (55) and optical apparatus (90) therefore the

ministries of commerce, industries and agriculture and textile should work together in

liaison with concerned stakeholders in those sectors to optimize the benefits of IIT with

India.

Since Pakistan’s trade was most concentrated with Iran and Afghanistan steps must be

taken to engender trade diversification because a diversified trade relations provide a

more solid foundations between trading countries than a concentrated trade relation

with a partner country does.

There is generally a perception in Pakistan that as India exports much more to Pakistan

than Pakistan exports to India, therefore offering greater access to India in to Pakistan

was not worth considering. The study recommends that this pattern of trade emanates

from the economic justification of trade complementarities of the two countries export

for each other and therefore Pakistan should fix the destinations of its imports on

economic considerations rather than political preferences since economic realities are

more durable than political perceptions.

Pakistan needs to enhance its bilateral trade with Iran which was the weakest link in

Pakistan’s trade with its neighbours. Pakistan did not import much from Iran despite

the fact that Iran’s offer of disaggregated exports was consistently most compatible

with Pakistan’s demand among all the neighbouring countries. Mineral fuels (27)

category was the most important in that regard which was the largest export of Iran and

the largest import category of Pakistan for so many years in the past. It was ironical


201

that (on average) Pakistan imported less than 4% of its mineral fuels from all the

neighbours collectively. Although the greatest share of neighbours import in crude oil

comes from Iran yet it was less than 3% on average of Pakistan’s total world import.

In the context of extraordinary, development of lifting of US and EU sanctions on Iran

in 2015, Pakistan should seek to build its bilateral trade, with Iran in general, and in the

context of mineral fuels in particular.

Fertilizer (31) was one of the leading Pakistani imports that was also the leading export

of Iran but Pakistan tend to import more than 2/3rd of its need from China that did not

possess RCA in it and less than 1% from Iran that possessed RCA in fertilizer, therefore

the study recommends ministry of commerce and ministry of industries to work jointly

for furnishing institutional mechanism to promote import of fertilizer from one

neighbour (Iran) that qualified on economic grounds than from another neighbour

(China) that did not, especially in the context of post sanctions Iran after 2015.

Pakistan has started to lose its traditional edge in cotton and textile sectors as Pakistan

lost RCA in many of the categories pertaining to cotton and textile sectors. Moreover,

there was only one category impregnated, coated or laminated textile fabric (59) in

cotton and textile categories where Pakistan did not possess RCA on average. Ministry

of textile and APTMA must make efforts to secure the backbone of Pakistan’s export

earnings through institutional support for enhancing the scale and scope of value

addition in the textile sector I order to strengthen and broaden Pakistan’s RCA for the

textile sector in Pakistan.

6.3 Future Research

It is hoped that this research would invite the attention of more researchers on the issue

being investigated through the present study. However, there are some areas that this

research would propose to be taken up by future researchers in the light of findings and

conclusions of the current study.


202

Pakistan’s trade performance with China that was mostly studied and discussed in the

context of its own progression and performance is needed to be viewed in the context

of Pakistan’s trade with world, in general, and with other neighbouring countries, in

particular. The more specific questions could be a formal probe in to the possibilities

of Pakistan’s trade diversion from other trade partners, in general and India, in

particular in favour of China.

The present study concluded the presence of possible NTBs against Pakistan’s exports

to India. A focused research on identifying the specific NTBs and their corresponding

impact on Pakistan’s exports to India could be very useful for Pakistani policy makers

in carrying out trade negotiations with India. Similarly, poor bilateral trade between

Pakistan and Iran, despite a strong economic rationale for much better trade volumes

between the two countries, was a key finding of the present study. The present study

proposes to the future researchers to investigate this phenomenon from the perspective

of political economy, as economic arguments, alone, do not seem sufficient to explain

the low bilateral trade volumes between the two countries. The significance of such a

study would be greater in the context of withdrawal of international sanctions on Iran

in 2015.

Last, but, not the least, the present study invites research attention to work on trade

potential through disaggregated data because of the extent of its ability to analyze the

chemistry of bilateral trade between two countries. In order to create this appeal, the

present study made, five different indices being estimated for this research, public

(present in the Appendix B to Q) for future researchers. The study recommends to the

future researchers to extend the scope of disaggregation to four or six digit HS

classifications, for deeper and better insights in bilateral trade.


203

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217

Appendix A: List of 2-digit HS Classification Codes and Titles

Code

s Titles

'01 Live animals

'02 Meat and edible meat offal

'03 Fish, crustaceans, molluscs, aquatic invertebrates nes '04 Dairy products, eggs, honey, edible animal product nes '05 Products of animal origin, nes

'06 Live trees, plants, bulbs, roots, cut flowers etc

'07 Edible vegetables and certain roots and tubers

'08 Edible fruit, nuts, peel of citrus fruit, melons

'09 Coffee, tea, mate and spices

'10 Cereals

'11 Milling products, malt, starches, inulin, wheat gluten '12 Oil seed, oleagic fruits, grain, seed, fruit, etc, nes '13 Lac, gums, resins, vegetable saps and extracts nes '14 Vegetable plaiting materials, vegetable

products nes '15 Animal,vegetable fats and oils, cleavage products, etc '16 Meat, fish and seafood food preparations nes

'17 Sugars and sugar confectionery

'18 Cocoa and cocoa preparations

'19 Cereal, flour, starch, milk preparations and products '20 Vegetable, fruit, nut, etc food preparations

'21 Miscellaneous edible preparations

'22 Beverages, spirits and vinegar

'23 Residues, wastes of food industry, animal fodder '24 Tobacco and manufactured tobacco substitutes

'25 Salt, sulphur, earth, stone, plaster, lime and cement '26 Ores, slag and ash

'27 Mineral fuels, oils, distillation products, etc

'28 Inorganic chemicals, precious metal compound, '29 Organic chemicals

'30 Pharmaceutical products

'31 Fertilizers

'32 Tanning, dyeing extracts, tannins, derivs,pigments etc '33 Essential oils, perfumes, cosmetics, toiletries

'34 Soaps, lubricants, waxes, candles, modelling pastes '35 Albuminoids, modified starches, glues, enzymes '36 Explosives, pyrotechnics, matches, pyrophorics, etc '37 Photographic or cinematographic goods

'38 Miscellaneous chemical products

'39 Plastics and articles thereof

'40 Rubber and articles thereof

'41 Raw hides and skins (other than furskins) and leather '42 Articles of leather, animal gut, harness, travel goods '43 Furskins and artificial fur, manufactures thereof

'44 Wood and articles of wood, wood charcoal

'45 Cork and articles of cork

'46 Manufactures of plaiting material, basketwork, etc. '47 Pulp of wood, fibrous cellulosic material, waste etc '48 Paper and paperboard, articles of pulp, paper and board '49 Printed books, newspapers, pictures etc '50 Silk

Code

s Titles

'51 Wool, animal hair, horsehair yarn and fabric thereof '52 Cotton

'53 Vegetable textile fibres nes, paper yarn, woven fabric '54 Manmade filaments

'55 Manmade staple fibres

'56 Wadding, felt, nonwovens, yarns, twine, cordage, etc '57 Carpets and other textile floor coverings

'58 Special woven or tufted fabric, lace, tapestry etc

'59 Impregnated, coated or laminated textile fabric

'60 Knitted or crocheted fabric

'61 Articles of apparel, accessories, knit or crochet

'62 Articles of apparel, accessories, not knit or crochet '63 Other made textile articles, sets, worn clothing etc '64 Footwear, gaiters and the like, parts thereof

'65 Headgear and parts thereof

'66 Umbrellas, walking-sticks, seat-sticks, whips, etc '67 Bird skin, feathers, artificial flowers, human hair '68 Stone, plaster, cement, asbestos, mica, etc articles '69 Ceramic products

'70 Glass and glassware

'71 Pearls, precious stones, metals, coins, etc

'72 Iron and steel

73 Articles of iron or steel

'74 Copper and articles thereof

'75 Nickel and articles thereof

'76 Aluminium and articles thereof

'78 Lead and articles thereof

'79 Zinc and articles thereof

'80 Tin and articles thereof

'81 Other base metals, cermets, articles thereof

'82 Tools, implements, cutlery, etc of base metal

'83 Miscellaneous articles of base metal

'84 Machinery, nuclear reactors, boilers, etc

'85 Electrical, electronic equipment

'86 Railway, tramway locomotives, rolling stock

'87 Vehicles other than railway, tramway

'88 Aircraft, spacecraft, and parts thereof

'89 Ships, boats and other floating structures

'90 Optical, photo, technical, medical, etc apparatus '91 Clocks and watches and parts thereof

'92 Musical instruments, parts and accessories

'93 Arms and ammunition, parts and accessories thereof '94 Furniture, lighting, signs, prefabricated buildings '95 Toys, games, sports requisites

'96 Miscellaneous manufactured articles

'97 Works of art, collectors pieces and antiques

'99 Commodities not elsewhere specified

Source: UN COMTRADE


218

Appendix B: Pakistan’s Revealed Comparative Advantage (2004-14) Codes 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Average

'01 0.404 0.159 0.026 0.013 0.005 0.572 0.991 0.772 0.652 0.564 0.12 0.3884

'02 0.193 0.192 0.3 0.459 0.494 0.645 0.89 1.024 1.318 1.269 1.24 0.7295

'03 1.465 1.645 1.888 1.854 2.375 1.923 1.975 1.939 2.248 2.35 2.4 2.0053

'04 0.184 0.329 0.427 0.424 0.385 0.532 0.461 0.646 0.838 0.86 0.73 0.5285

'05 2.618 1.998 2.083 2.235 2.496 3.03 3.027 4.391 4.58 3.473 2.99 2.9927

'06 0.03 0.032 0.027 0.023 0.039 0.023 0.027 0.038 0.051 0.066 0.05 0.0369

'07 0.905 2.133 0.808 1.263 0.73 1.243 1.47 2.981 2.394 2.805 1.96 1.6994

'08 1.706 1.396 1.571 1.585 1.622 2.127 2.327 2.54 2.862 3.258 3.08 2.1888

'09 0.699 0.724 0.741 0.764 0.759 0.931 0.894 0.788 1.084 1.263 1.18 0.8932

'10 10.38 15.98 16.3 13.3 18.87 16.5 19.06 17.17 12.7 13.15 14.2 15.235

'11 4.561 8.089 9.691 6.793 1.146 1.12 2.298 14.39 10.55 8.702 8.56 6.9007

'12 0.815 0.459 0.557 0.775 0.685 0.701 0.333 0.532 0.631 0.85 0.87 0.6551

'13 5.695 4.795 5.482 6.5 6.269 3.328 3.829 6.87 10.91 9.734 6.79 6.382

'14 2.332 2.136 2.517 4.239 4.645 3.212 5.573 4.501 6.013 6.926 28.6 6.4254

'15 1.017 1.628 1.575 1.38 1.475 1.033 0.744 1.244 1.53 1.158 0.91 1.2449

'16 0.886 0.351 0.643 0.497 0.345 0.561 0.564 0.892 0.522 0.187 0.16 0.51

'17 3.634 2.755 2.332 1.568 5.768 2.286 1.384 0.883 3.572 9.102 7.21 3.6817

'18 0.036 0.038 0.031 0.017 0.008 0.003 7E-04 0.002 0.005 0.004 0 0.0133

'19 0.139 0.314 0.324 0.294 0.431 0.34 0.355 0.639 0.61 0.857 0.73 0.4579

'20 0.27 0.287 0.335 0.388 0.403 0.382 0.453 0.705 0.84 0.841 0.73 0.5119

'21 0.248 0.222 0.161 0.217 0.242 0.216 0.21 0.247 0.334 0.297 0.28 0.2429

'22 0.363 0.798 1.215 1.486 1.997 1.073 1.511 1.871 1.193 2.398 2.38 1.4804

'23 0.121 0.083 0.094 0.153 0.156 0.239 0.454 0.397 0.54 0.63 0.74 0.3275

'24 0.359 0.262 0.219 0.27 0.185 0.232 0.354 0.593 0.445 0.457 0.41 0.3445

'25 1.287 2.786 3.046 5.759 9.909 11.97 9.344 8.776 11.23 11.55 11.1 7.8839

'26 0.318 0.281 0.234 0.626 0.918 0.483 0.596 0.325 0.401 0.405 0.34 0.4481

'27 0.241 0.305 0.337 0.394 0.343 0.284 0.363 0.287 0.071 0.118 0.16 0.264

'28 0.074 0.073 0.053 0.092 0.082 0.202 0.181 0.189 0.198 0.196 0.17 0.1377

'29 0.091 0.225 0.148 0.031 0.028 0.121 0.079 0.12 0.053 0.065 0.09 0.096

'30 0.174 0.201 0.213 0.249 0.235 0.263 0.216 0.231 0.266 0.259 0.29 0.2363

'31 0.256 0.178 0.037 0.002 0.01 0.009 0.005 0.002 0.002 6E-04 0 0.0456

'32 0.137 0.708 0.257 0.172 0.236 0.249 0.285 0.324 0.408 0.397 0.39 0.3235

'33 0.096 0.06 0.085 0.107 0.124 0.139 0.081 0.073 0.091 0.135 0.12 0.1009

'34 0.201 0.118 0.061 0.086 0.299 0.357 0.222 0.221 0.349 0.532 0.42 0.2606

'35 0.152 0.219 0.28 0.254 0.298 0.295 0.293 0.247 0.269 0.192 0.19 0.2442

'36 5.317 4.858 5.903 6.361 5.961 4.03 3.761 3.608 4.556 4.057 2.95 4.6696

'37 0.065 0.037 0.033 0.017 0.019 0.089 0.051 0.138 0.105 0.015 0 0.0518

'38 0.058 0.075 0.076 0.049 0.081 0.072 0.073 0.056 0.059 0.079 0.08 0.0686

'39 0.379 0.52 0.392 0.324 0.488 0.55 0.586 0.682 0.677 0.555 0.44 0.5084

'40 0.064 0.211 0.506 0.089 0.05 0.049 0.042 0.046 0.048 0.06 0.06 0.1114

'41 7.301 7.427 7.728 9.635 10.39 9.118 9.738 10.31 10.61 11.03 11.4 9.5196

'42 10.53 12.63 12.46 12.07 12.14 9.436 8.388 7.423 7.27 7.423 7.32 9.7356

'43 0.026 0.002 0.008 0.018 0.002 0.066 0.032 0.043 0.034 0.015 0.05 0.0268

'44 0.093 0.09 0.083 0.082 0.12 0.189 0.199 0.305 0.298 0.294 0.29 0.1856

'45 0.004 0 0.009 0.009 0.002 4E-04 0 0.015 4E-04 0.004 0 0.0041

'46 0.099 0.248 0.175 0.036 0.019 0.05 0.038 0.091 0.265 0.229 0.37 0.1474

'47 0.013 0.013 0.038 0.014 0.025 0.078 0.017 0.004 0.011 0.004 0 0.0199

'48 0.061 0.029 0.063 0.068 0.132 0.102 0.099 0.113 0.179 0.248 0.29 0.1259

'49 0.097 0.169 0.092 0.062 0.051 0.065 0.071 0.071 0.093 0.071 0.07 0.0831

'50 0.288 0.117 0.401 0.19 0.391 0.05 0.186 0.085 0.163 0.438 0.37 0.2432

'51 0.247 0.254 0.337 0.332 0.331 0.338 0.647 0.499 0.625 0.825 0.75 0.4712

'52 41.61 46.84 49.82 51.3 53.36 52.57 48.48 51.51 57.82 55.53 56.6 51.403

'53 1.251 1.908 1.542 1.797 0.595 0.911 0.83 0.69 1.075 0.379 0.21 1.017

'54 5.6 4.122 2.708 2.459 0.645 1.579 1.076 0.548 0.536 0.457 0.5 1.8391


219

'55 2.78 1.763 5.644 9.145 6.884 7.74 10.4 10.17 8.376 7.6 7.49 7.0899

'56 2.218 2.028 1.444 1.219 1.196 0.934 1.032 0.891 0.548 0.845 2.62 1.3615

'57 16.19 16.06 13.98 12.48 10.49 7.856 6.445 6.041 6.075 6.082 5.82 9.7749

'58 4.816 2.097 2.172 2.008 0.882 1.479 1.486 0.973 1.527 1.226 1.23 1.8087

'59 0.372 0.2 0.266 0.295 0.17 0.25 0.307 0.293 0.401 0.37 0.3 0.2928

'60 6.192 2.165 1.759 2.127 2.176 1.971 2.222 1.248 0.883 0.737 0.81 2.0261

'61 9.873 8.604 9.29 8.338 8.344 7.407 7.796 7.68 6.99 6.696 7.6 8.0561

'62 4.719 5.899 6.025 6.241 5.884 5.405 6.107 6.429 6.52 6.569 6.39 6.0171

'63 54.73 58.73 62.47 60.54 55.05 48.18 47.7 45.56 43.26 43.62 45.4 51.386

'64 1.192 1.5 1.299 1.06 1.14 1.003 0.675 0.707 0.653 0.638 0.69 0.9601

'65 0.142 0.155 0.143 0.221 0.257 0.233 0.19 0.16 0.126 0.131 0.18 0.1758

'66 0.029 0.041 0.032 0.057 0.066 0.068 0.06 0.065 0.042 0.056 0.05 0.0514

'67 0.009 0.01 0.017 0.022 0.017 0.003 0.002 0.003 0.004 4E-04 0 0.0082

'68 0.591 0.557 0.644 0.511 0.53 0.512 0.393 0.442 0.533 0.55 0.47 0.5213

'69 0.31 0.342 0.254 0.246 0.275 0.232 0.174 0.187 0.203 0.173 0.15 0.2318

'70 0.13 0.135 0.227 0.262 0.269 0.276 0.173 0.162 0.155 0.167 0.3 0.2051

'71 0.11 0.065 0.07 0.317 0.51 1.037 0.952 0.531 1.847 0.457 0.16 0.5502

'72 0.021 0.035 0.053 0.045 0.057 0.047 0.057 0.07 0.065 0.078 0.1 0.0573

'73 0.259 0.375 0.252 0.274 0.307 0.396 0.421 0.476 0.656 0.43 0.47 0.3928

'74 0.124 0.122 0.178 0.241 0.279 0.378 0.393 0.485 0.843 0.773 0.69 0.4097

'75 0.001 0.001 0.002 2E-04 0.008 0.013 0.016 0.001 0.003 7E-04 0 0.0045

'76 0.062 0.125 0.093 0.087 0.141 0.228 0.167 0.131 0.157 0.152 0.16 0.1366

'78 0.042 0.42 0.889 2.757 0.349 0.951 1.452 1.546 2.048 1.108 0.54 1.1001

'79 0.001 0.029 0.033 0.034 0.014 0.01 0.023 0.046 0.048 0.045 0.08 0.0328

'80 0.006 0.002 0.042 0.037 0.01 0.006 0.005 0.012 0.023 0.026 0.01 0.0163

'81 0.004 0.005 0.022 0.032 0.004 0.005 0.019 0.006 0.012 0.007 0.02 0.012

'82 0.862 0.789 0.705 1.036 0.9 1.112 1.274 1.111 1.105 1.073 1.13 1.0088

83 0.018 0.039 0.021 0.033 0.038 0.015 0.015 0.02 0.016 0.015 0.03 0.0232

'84 0.044 0.051 0.051 0.067 0.128 0.116 0.097 0.081 0.066 0.064 0.09 0.0778

'85 0.036 0.048 0.052 0.057 0.053 0.031 0.051 0.038 0.036 0.037 0.03 0.0427

'86 0.006 0.021 0.004 0.027 0.009 0.007 0.014 0.036 0.003 0.002 0.01 0.0124

'87 0.03 0.026 0.059 0.032 0.063 0.055 0.054 0.046 0.041 0.041 0.03 0.0436

'88 0.219 0.086 0.039 0.182 0.059 0.016 0.036 0.016 0.009 0.006 0.05 0.065

'89 3.053 0.214 0.051 3.049 0.057 0.078 0.302 0.039 0.04 0.063 0.01 0.6325

'90 0.385 0.367 0.324 0.47 0.502 0.456 0.386 0.41 0.428 0.461 0.48 0.4246

'91 0.013 0.045 0.05 0.006 0.002 0.002 0.002 0.001 9E-04 0.019 0.05 0.0173

'92 0.389 0.38 0.61 0.473 0.523 0.479 0.428 0.393 0.361 0.366 0.39 0.4361

'93 0.203 0.19 0.108 0.077 0.633 0.39 0.345 0.288 0.235 0.157 0.09 0.2469

'94 1.115 0.217 0.343 0.17 0.251 0.467 0.411 0.366 0.329 0.331 0.3 0.3909

'95 3.349 2.998 3.146 1.949 1.823 1.464 1.747 1.708 1.776 1.826 2.11 2.1719

'96 1.166 1.518 1.044 0.86 1.101 0.654 0.516 0.526 0.347 0.512 0.6 0.8042

'97 0.012 0.007 0.012 0.001 1.153 0.002 0.005 0.427 0.003 0.004 0.01 0.1482

'99 0.012 0.025 0.031 0.002 0.002 0.003 0.003 8E-04 0.415 8E-04 0 0.0454

Source: Author’s Calculation of RCA indices from UNCOMTRADE


220

Appendix C: Pakistan’s Grubel and Lloyd Intra Industry Trade Index (2004-14)

Code 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Average

'01 0.191 0.85 0.296 0.058 0.019 0.711 0.64 0.821 0.975 0.714 0.406 0.517

'02 0.054 0.27 0.554 0.362 0.139 0.076 0.066 0.079 0.036 0.042 0.047 0.157

'03 0.013 0.02 0.019 0.021 0.013 0.01 0.022 0.029 0.038 0.067 0.078 0.03

'04 0.811 0.81 0.705 0.678 0.854 0.847 0.67 0.755 0.845 0.938 0.768 0.789

'05 0.058 0.12 0.083 0.13 0.122 0.109 0.12 0.106 0.092 0.141 0.191 0.115

'06 0.505 0.89 0.973 0.556 0.948 0.667 0.76 0.958 0.94 0.848 0.859 0.81

'07 0.551 0.7 0.263 0.403 0.26 0.334 0.382 0.631 0.481 0.665 0.407 0.462

'08 0.639 0.85 0.839 0.96 0.831 0.72 0.632 0.561 0.567 0.469 0.648 0.701

'09 0.121 0.14 0.156 0.186 0.18 0.239 0.209 0.223 0.271 0.332 0.295 0.214

'10 0.542 0.26 0.206 0.164 0.805 0.307 0.062 0.066 0.085 0.193 0.229 0.265

'11 0.418 0.4 0.072 0.093 0.613 0.858 0.869 0.143 0.236 0.16 0.141 0.364

'12 0.235 0.12 0.117 0.159 0.256 0.226 0.097 0.162 0.23 0.388 0.257 0.204

'13 0.274 0.39 0.365 0.357 0.331 0.533 0.489 0.233 0.121 0.148 0.289 0.321

'14 0.223 0.19 0.215 0.366 0.343 0.325 0.665 0.516 0.392 0.461 0.907 0.418

'15 0.13 0.2 0.203 0.155 0.164 0.129 0.089 0.138 0.174 0.145 0.103 0.148

'16 0.131 0.24 0.194 0.26 0.411 0.238 0.207 0.092 0.179 0.588 0.704 0.295

'17 0.184 0.36 0.238 0.791 0.207 0.715 0.191 0.769 0.206 0.09 0.175 0.357

'18 0.268 0.26 0.26 0.107 0.058 0.022 0.004 0.008 0.028 0.016 0.01 0.095

'19 0.35 0.61 0.485 0.464 0.743 0.592 0.52 0.723 0.742 0.98 0.789 0.636

'20 0.788 0.88 0.93 0.858 0.933 0.889 0.872 0.725 0.632 0.684 0.874 0.824

'21 0.809 0.66 0.486 0.525 0.597 0.603 0.489 0.523 0.703 0.64 0.439 0.588

'22 0.318 0.13 0.077 0.137 0.052 0.097 0.083 0.073 0.106 0.038 0.044 0.105

'23 0.317 0.11 0.074 0.151 0.147 0.28 0.371 0.277 0.289 0.309 0.25 0.234

'24 0.176 0.71 0.997 0.925 0.885 0.708 0.906 0.75 0.74 0.712 0.895 0.764

'25 0.88 0.62 0.594 0.384 0.344 0.211 0.292 0.327 0.25 0.242 0.27 0.402

'26 0.561 0.48 0.54 0.978 0.942 0.974 0.19 0.557 0.33 0.469 0.427 0.586

'27 0.17 0.23 0.197 0.213 0.161 0.15 0.191 0.162 0.041 0.067 0.084 0.151

'28 0.067 0.06 0.05 0.087 0.038 0.128 0.132 0.138 0.131 0.143 0.113 0.098

'29 0.049 0.14 0.1 0.017 0.015 0.067 0.048 0.066 0.031 0.038 0.054 0.057

'30 0.501 0.51 0.524 0.455 0.431 0.452 0.429 0.44 0.438 0.402 0.421 0.455

'31 0.048 0.02 0.007 .0004 0.003 0.001 0.001 .0004 .0004 .0.04 0 0.008

'32 0.105 0.44 0.164 0.103 0.124 0.14 0.165 0.203 0.24 0.235 0.194 0.192

'33 0.287 0.18 0.212 0.232 0.292 0.331 0.203 0.171 0.213 0.306 0.213 0.24

'34 0.163 0.09 0.048 0.057 0.165 0.255 0.175 0.206 0.309 0.441 0.299 0.201

'35 0.353 0.46 0.502 0.397 0.415 0.436 0.418 0.361 0.398 0.304 0.256 0.391

'36 0.119 0.14 0.127 0.238 0.309 0.384 0.357 0.226 0.193 0.193 0.256 0.231

'37 0.131 0.09 0.078 0.034 0.036 0.165 0.094 0.242 0.165 0.025 .0004 0.096

'38 0.041 0.05 0.06 0.039 0.068 0.05 0.055 0.048 0.051 0.065 0.053 0.053

'39 0.375 0.43 0.318 0.254 0.358 0.41 0.437 0.489 0.515 0.445 0.312 0.395

'40 0.073 0.2 0.429 0.09 0.053 0.058 0.048 0.054 0.06 0.071 0.058 0.109

'41 0.304 0.41 0.385 0.317 0.468 0.427 0.316 0.385 0.319 0.348 0.341 0.365

'42 0.021 0.02 0.025 0.033 0.032 0.057 0.056 0.075 0.068 0.061 0.071 0.047

'43 0.874 0.06 0.164 0.308 0.073 0.569 0.323 0.592 0.641 0.487 0.986 0.461

'44 0.388 0.37 0.274 0.242 0.314 0.406 0.465 0.603 0.582 0.646 0.536 0.438

'45 0.075 0 0.193 0.209 0.042 0.008 0 0.249 0.007 0.066 0 0.077

'46 0.489 0.31 0.381 0.641 0.45 0.944 0.501 0.804 0.286 0.357 0.303 0.497

'47 0.021 0.02 0.055 0.019 0.026 0.075 0.024 0.007 0.014 0.004 0.002 0.024

'48 0.099 0.04 0.07 0.071 0.128 0.111 0.103 0.107 0.162 0.23 0.228 0.123

'49 0.581 0.52 0.24 0.159 0.079 0.173 0.185 0.265 0.079 0.01 0.023 0.21


221

'50 0.338 0.1 0.17 0.055 0.097 0.012 0.041 0.022 0.04 0.138 0.068 0.098

'51 0.465 0.53 0.59 0.583 0.625 0.598 0.921 0.742 0.936 0.841 0.834 0.697

'52 0.354 0.26 0.213 0.421 0.503 0.269 0.343 0.303 0.231 0.328 0.271 0.318

'53 0.36 0.42 0.283 0.29 0.073 0.113 0.114 0.105 0.191 0.081 0.052 0.189

'54 0.676 0.95 0.64 0.573 0.201 0.366 0.219 0.103 0.121 0.109 0.094 0.368

'55 0.855 0.54 0.903 0.841 0.913 0.93 0.994 0.942 0.909 0.88 0.706 0.855

'56 0.51 0.6 0.808 0.895 0.898 0.81 0.839 0.708 0.497 0.658 0.994 0.747

'57 0.076 0.1 0.131 0.172 0.204 0.239 0.294 0.344 0.335 0.217 0.249 0.214

'58 0.29 0.75 0.803 0.778 0.813 0.991 0.782 0.558 0.725 0.656 0.648 0.708

'59 0.437 0.2 0.242 0.267 0.166 0.24 0.242 0.234 0.32 0.336 0.223 0.264

'60 0.068 0.24 0.387 0.342 0.362 0.41 0.431 0.892 0.961 0.885 0.649 0.512

'61 0.006 0.01 0.015 0.028 0.027 0.02 0.028 0.026 0.023 0.023 0.027 0.021

'62 0.01 0.01 0.016 0.036 0.028 0.028 0.038 0.03 0.031 0.024 0.033 0.026

'63 0.035 0.05 0.051 0.057 0.074 0.083 0.106 0.106 0.113 0.102 0.111 0.08

'64 0.316 0.27 0.288 0.468 0.473 0.511 0.788 0.8 0.876 0.76 0.776 0.575

'65 0.888 0.5 0.921 0.859 0.617 0.861 0.879 0.229 0.792 0.752 0.876 0.744

'66 0.386 0.54 0.369 0.347 0.646 0.94 0.618 0.737 0.677 0.519 0.37 0.559

'67 0.139 0.13 0.182 0.174 0.209 0.028 0.019 0.024 0.048 0.007 0.032 0.09

'68 0.787 0.97 0.988 0.499 0.509 0.893 0.767 0.882 0.955 0.966 0.72 0.812

'69 0.344 0.28 0.215 0.206 0.221 0.23 0.231 0.23 0.244 0.212 0.135 0.231

'70 0.296 0.28 0.448 0.482 0.522 0.548 0.367 0.307 0.306 0.339 0.421 0.392

'71 0.152 0.09 0.096 0.91 0.481 0.413 0.473 0.835 0.251 0.976 0.731 0.491

'72 0.022 0.02 0.035 0.032 0.045 0.021 0.036 0.055 0.04 0.044 0.047 0.036

'73 0.522 0.59 0.296 0.379 0.323 0.42 0.517 0.682 0.797 0.542 0.521 0.508

'74 0.244 0.22 0.33 0.461 0.578 0.633 0.791 0.939 0.832 0.901 0.957 0.626

'75 0.005 0 0.008 0.001 0.033 0.042 0.077 0.007 0.013 0.003 0.007 0.018

'76 0.114 0.2 0.16 0.135 0.274 0.313 0.28 0.273 0.305 0.3 0.259 0.238

'78 0.026 0.22 0.419 0.825 0.119 0.312 0.402 0.392 0.505 0.208 0.099 0.321

'79 0.001 0.02 0.036 0.027 0.013 0.014 0.024 0.071 0.024 0.058 0.107 0.036

'80 0.019 0.01 0.147 0.104 0.046 0.029 0.021 0.101 0.128 0.156 0.043 0.073

'81 0.027 0.05 0.172 0.243 0.03 0.027 0.08 0.03 0.055 0.016 0.05 0.071

'82 0.751 0.88 0.957 0.91 0.957 0.865 0.693 0.75 0.784 0.882 0.902 0.849

'83 0.094 0.16 0.085 0.115 0.126 0.047 0.043 0.058 0.044 0.051 0.059 0.08

'84 0.07 0.07 0.065 0.091 0.15 0.148 0.155 0.148 0.113 0.111 0.123 0.113

'85 0.12 0.08 0.075 0.074 0.066 0.053 0.112 0.09 0.074 0.082 0.055 0.08

'86 0.008 0.02 0.007 0.042 0.077 0.056 0.029 0.462 0.003 0.003 0.005 0.065

'87 0.093 0.05 0.093 0.067 0.155 0.127 0.12 0.103 0.086 0.113 0.087 0.099

'88 0.177 0.36 0.03 0.133 0.092 0.069 0.169 0.063 0.079 0.07 0.178 0.129

'89 0.991 0.35 0.167 0.857 0.11 0.078 0.163 0.038 0.031 0.025 0.008 0.256

'90 0.865 0.74 0.622 0.681 0.713 0.765 0.75 0.765 0.826 0.872 0.781 0.762

'91 0.107 0.33 0.278 0.036 0.014 0.016 0.018 0.012 0.009 0.142 0.342 0.119

'92 0.057 0.21 0.051 0.104 0.11 0.085 0.101 0.159 0.17 0.168 0.2 0.129

'93 0.864 0.64 0.364 0.188 0.98 0.28 0.739 0.533 0.625 0.389 0.148 0.522

'94 0.247 0.97 0.871 0.679 0.751 0.941 0.797 0.82 0.786 0.793 0.983 0.785

'95 0.122 0.15 0.184 0.241 0.24 0.312 0.358 0.365 0.342 0.329 0.357 0.273

'96 0.768 0.74 0.571 0.514 0.489 0.399 0.298 0.326 0.23 0.297 0.229 0.442

'97 0.438 0.8 0.845 0.263 0.036 0.824 0.009 0.489 0.727 0.602 0.764 0.527

'99 0.142 0.32 0.477 0.649 0.47 0.168 0.261 0.036 0.415 0.055 0.336 0.303

Source: Author’s Calculation of GLI indices of Pakistan from UNCOMTRADE


222

Appendix D: Pakistan’s Trade Intensity Index with China

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 average

'01 282.74 0.00 446.57 3.46 2.08 1.93 3.23 0.00 0.00 0.00 0.00 67.27

'02 4.88 8.62 10.36 4.18 0.51 0.14 0.02 1.92 1.96 1.77 0.88 3.20

'03 39.66 36.85 30.09 40.18 39.96 37.42 40.77 20.29 18.11 12.92 17.22 30.32

'04 11.56 5.18 5.68 4.90 1.47 1.20 1.37 0.09 0.24 1.01 1.53 3.11

'05 20.98 12.99 35.54 31.40 8.17 1.89 2.08 3.06 8.45 7.40 5.99 12.54

'06 20.47 38.40 68.21 109.83 92.27 61.69 45.54 110.51 156.81 99.56 85.00 80.75

'07 13.86 13.39 12.23 18.74 14.98 8.67 8.24 5.72 2.95 2.20 4.35 9.57

'08 0.16 0.22 0.16 1.49 1.20 1.28 1.77 1.87 1.75 2.31 2.63 1.35

'09 14.33 20.23 25.82 27.17 26.70 26.82 21.11 21.00 14.03 11.54 6.18 19.54

'10 0.01 0.03 0.02 0.04 0.05 0.66 0.94 1.77 11.38 5.32 4.44 2.24

'11 0.38 1.50 0.55 0.64 1.37 0.56 4.61 0.39 0.39 2.35 3.88 1.51

'12 0.39 0.23 0.49 0.65 0.38 0.34 0.17 0.17 0.17 0.22 0.16 0.31

'13 69.26 56.67 85.92 123.48 90.35 60.05 56.30 39.36 65.20 52.35 56.84 68.71

'14 18.55 16.79 16.81 124.93 111.20 51.32 156.53 76.29 25.85 43.52 485.77 102.51

'15 0.01 0.01 0.01 0.00 0.00 0.01 0.01 0.01 0.00 0.01 0.02 0.01

'16 0.20 0.54 2.17 2.55 3.61 3.25 3.95 3.72 3.67 6.88 4.48 3.18

'17 4.08 22.84 1.64 7.37 3.40 1.67 0.67 2.67 1.18 0.62 1.06 4.29

'18 56.62 32.21 19.97 17.79 26.59 43.85 38.94 23.42 14.53 8.12 3.70 25.98

'19 1.68 1.16 0.73 2.43 1.01 0.09 0.25 0.22 0.17 0.09 0.07 0.72

'20 2.59 1.62 1.97 1.15 2.89 3.86 3.93 3.44 1.37 1.36 1.25 2.31

'21 2.06 2.03 2.48 3.83 3.77 2.42 5.90 7.47 3.56 4.59 2.77 3.72

'22 0.04 0.03 0.01 0.04 0.03 0.03 0.08 0.28 0.06 0.26 0.02 0.08

'23 4.59 1.85 2.58 3.44 1.88 2.21 3.59 2.52 2.27 2.26 2.44 2.69

'24 1.89 0.61 0.11 0.73 0.01 0.04 0.02 0.01 0.01 0.05 0.46 0.36

'25 0.71 0.48 1.74 0.68 0.24 0.53 0.92 1.29 1.07 1.45 1.10 0.93

'26 0.64 0.50 0.58 0.80 0.65 0.58 0.87 0.65 0.78 0.62 0.63 0.66

'27 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'28 1.32 1.15 1.18 1.29 0.62 1.24 0.96 0.76 1.01 1.13 1.30 1.09

'29 0.13 0.18 0.17 0.15 0.17 0.24 0.20 0.19 0.19 0.19 0.26 0.19

'30 1.24 0.98 0.89 0.58 0.92 0.44 0.37 0.27 0.18 0.12 0.14 0.56

'31 1.00 0.49 0.07 3.54 1.33 0.22 2.42 3.59 3.43 4.35 6.34 2.43

'32 1.76 1.70 1.88 1.98 1.93 2.66 2.54 2.50 2.59 2.37 2.55 2.22

'33 0.70 1.04 1.04 1.57 1.50 1.08 3.14 4.20 3.30 2.19 2.33 2.01

'34 1.38 2.51 1.46 2.03 2.01 1.29 1.17 1.82 1.83 1.71 1.57 1.71

'35 0.76 2.20 2.92 2.85 2.77 3.40 3.04 3.66 2.99 2.99 3.71 2.84

'36 2.05 1.97 1.11 2.26 2.78 5.65 5.89 4.70 1.57 1.81 2.00 2.89

'37 2.44 2.62 3.62 3.60 2.79 2.71 2.94 3.13 2.95 3.14 5.31 3.20

'38 1.62 0.86 0.85 0.76 0.68 0.69 0.63 0.58 0.57 0.62 0.53 0.76

'39 0.05 0.05 0.06 0.06 0.05 0.05 0.06 0.07 0.06 0.07 0.07 0.06

'40 0.73 0.62 0.42 0.50 0.53 0.70 0.59 0.56 0.72 0.80 0.93 0.65

'41 0.41 0.46 0.47 0.56 0.79 0.89 0.79 0.73 0.97 0.70 0.69 0.68

'42 0.03 0.03 0.03 0.04 0.04 0.06 0.05 0.06 0.06 0.05 0.07 0.05

'43 4.15 5.06 13.90 16.33 12.43 3.64 7.57 2.92 2.45 2.89 3.16 6.77

'44 0.14 0.13 0.16 0.21 0.26 0.22 0.23 0.20 0.25 0.18 0.17 0.19

'45 195.92 122.04 134.63 181.80 137.66 240.92 86.26 78.14 92.89 128.28 131.66 139.11

'46 6.15 3.75 4.90 11.16 8.70 9.17 8.84 10.51 3.33 4.78 3.77 6.82

'47 0.23 0.01 0.00 0.00 0.01 0.15 0.02 0.01 0.01 0.02 0.01 0.04

'48 0.19 0.24 0.32 0.36 0.35 0.51 0.41 0.53 0.52 0.60 0.63 0.42

'49 0.22 0.89 1.81 1.56 0.97 1.04 1.00 2.04 0.41 0.11 0.23 0.93


223

'50 17.98 20.20 18.27 25.29 27.80 29.68 26.30 29.31 30.25 33.68 39.41 27.11

'51 0.27 0.37 0.66 0.84 1.23 2.38 1.57 1.58 1.60 1.32 2.31 1.28

'52 0.12 0.15 0.17 0.21 0.21 0.51 0.39 0.36 0.54 0.54 0.60 0.35

'53 0.18 0.40 0.52 0.69 0.61 1.47 1.24 1.11 1.86 2.13 1.39 1.06

'54 0.24 0.58 0.99 1.24 1.54 1.45 1.62 1.66 1.73 1.72 1.79 1.32

'55 0.12 0.09 0.15 0.56 0.68 0.50 0.77 0.83 0.60 0.58 0.79 0.51

'56 1.54 1.66 2.83 3.07 3.24 2.65 2.82 3.82 4.87 4.26 3.04 3.07

'57 0.08 0.12 0.18 0.36 0.40 0.52 0.71 1.04 1.06 0.79 0.93 0.56

'58 0.37 0.62 0.81 0.83 1.66 1.46 2.39 3.17 2.48 2.21 2.34 1.67

'59 1.57 2.01 2.52 2.62 2.47 2.43 2.69 2.87 2.67 2.52 2.73 2.46

'60 0.07 0.36 0.43 0.41 0.38 0.55 0.71 1.55 1.56 1.71 1.76 0.86

'61 0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.01 0.01 0.01 0.00

'62 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

'63 0.01 0.01 0.01 0.02 0.01 0.01 0.02 0.03 0.02 0.02 0.02 0.02

'64 0.16 0.12 0.12 0.21 0.21 0.25 0.35 0.34 0.34 0.27 0.29 0.24

'65 6.12 9.19 3.30 4.44 3.59 4.84 4.80 2.67 5.20 4.43 4.63 4.84

'66 12.00 12.16 13.40 14.62 12.61 7.41 9.81 10.15 10.65 10.52 11.66 11.36

'67 11.30 11.30 9.58 9.93 8.53 8.04 7.84 5.62 7.27 7.18 7.03 8.51

'68 0.84 0.93 1.25 2.65 2.32 0.97 0.98 1.18 0.98 1.01 0.93 1.28

'69 2.30 1.92 2.57 2.89 1.73 1.18 1.59 1.42 1.01 0.83 0.94 1.67

'70 1.64 1.37 1.00 0.70 0.59 0.60 0.55 0.55 0.44 0.38 0.37 0.74

'71 0.00 0.01 0.01 0.03 0.01 0.01 0.01 0.00 0.00 0.00 0.01 0.01

'72 0.01 0.01 0.03 0.05 0.04 0.03 0.06 0.06 0.08 0.08 0.12 0.05

'73 0.08 0.08 0.08 0.09 0.08 0.09 0.09 0.07 0.09 0.14 0.15 0.10

'74 0.06 0.07 0.05 0.06 0.10 0.09 0.08 0.08 0.07 0.08 0.10 0.07

'75 1.43 1.11 0.58 0.41 0.56 0.58 0.40 0.23 0.45 0.57 0.58 0.63

'76 0.08 0.09 0.07 0.10 0.11 0.11 0.12 0.13 0.14 0.17 0.18 0.12

'78 0.24 0.12 0.08 0.02 0.02 0.02 0.11 0.03 0.17 0.25 0.09 0.10

'79 0.55 0.16 0.35 0.98 0.73 0.32 0.17 0.40 0.07 0.13 0.24 0.37

'80 7.49 0.03 0.19 0.14 0.04 0.73 0.14 0.20 0.41 0.10 0.32 0.89

'81 0.64 1.24 1.51 1.47 0.87 1.13 1.30 1.11 0.66 0.86 1.29 1.10

'82 0.12 0.12 0.12 0.10 0.13 0.14 0.12 0.13 0.12 0.11 0.14 0.12

'83 0.69 0.73 1.15 1.29 0.63 0.67 0.82 0.82 0.60 0.53 0.59 0.77

'84 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01

'85 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.01

'86 0.67 0.46 0.54 0.46 0.34 0.29 0.61 0.16 0.64 0.73 0.51 0.49

'87 0.01 0.01 0.01 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

'88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00

'89 0.00 0.03 0.05 0.00 0.00 0.03 0.01 0.02 0.03 0.03 0.03 0.02

'90 0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.00 0.00 0.01 0.00

'91 0.36 0.29 0.24 0.47 0.56 0.32 0.36 0.41 0.22 0.15 0.21 0.33

'92 0.03 0.08 0.04 0.08 0.07 0.07 0.09 0.16 0.25 0.31 0.38 0.14

'93 52.86 8.18 20.13 15.87 5.47 4.03 12.95 15.85 6.56 14.26 8.38 14.96

'94 0.00 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

'95 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.01 0.01 0.01 0.01 0.01

'96 0.08 0.06 0.07 0.08 0.06 0.10 0.10 0.15 0.10 0.09 0.08 0.09

'97 1.48 0.86 0.38 0.65 0.03 4.12 0.02 0.01 0.30 0.03 0.55 0.76

'99 0.03 0.01 0.02 0.03 0.02 0.06 0.01 0.00 0.00 0.00 0.00 0.02

Source: Author’s Calculation of TII indices of Pakistan with China from UNCOMTRADE


224

Appendix E: Pakistan’s Trade Intensity Index with India Code 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Average

'01 366.99 7964.08 0.00 1579.37 66.02 20.60 514.17 0.00 0.00 15.91 0.00 957.01

'02 0.00 297.30 650.83 385.15 150.12 54.71 38.18 32.64 14.31 8.97 10.20 149.31

'03 0.13 12.25 0.30 15.99 5.31 2.34 2.36 1.65 2.45 0.09 0.13 3.91

'04 203.86 275.25 845.42 136.06 80.25 29.45 39.17 21.02 80.30 198.91 460.60 215.48

'05 0.00 601.38 232.75 336.42 357.90 198.55 219.40 103.32 112.82 138.47 116.49 219.77

'06 0.00 3.96 17.49 19.66 91.59 164.83 82.81 74.90 45.43 111.34 16.01 57.09

'07 17.61 94.08 27.37 25.75 43.19 36.76 33.13 32.43 45.39 46.62 45.06 40.67

'08 21.67 19.58 24.14 24.97 23.65 21.66 22.57 21.59 29.08 24.66 15.87 22.68

'09 7.01 13.08 16.97 12.54 23.31 13.67 22.32 15.79 16.90 11.29 17.22 15.46

'10 0.01 0.55 0.20 1.00 0.78 0.41 0.55 0.30 0.13 0.08 0.10 0.37

'11 1.08 0.00 0.23 3.72 2.39 0.07 0.21 0.12 0.07 0.09 10.76 1.70

'12 7.25 16.38 16.75 15.91 14.94 18.06 10.79 8.02 8.45 11.44 10.47 12.59

'13 14.40 31.90 20.82 19.23 16.02 35.56 29.63 4.02 1.20 1.68 4.04 16.23

'14 929.58 723.43 853.73 659.85 608.84 556.70 292.79 383.43 369.03 311.09 167.18 532.33

'15 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.00 0.00 0.01 0.01 0.01

'16 0.00 0.00 0.00 1.53 0.00 9.32 4.75 14.60 35.06 0.00 0.00 5.93

'17 50.19 5.40 84.03 5.18 0.01 0.02 13.93 25.60 0.44 3.89 0.54 17.20

'18 0.00 38.90 0.00 0.00 0.21 0.00 0.00 0.00 0.11 0.07 2.44 3.79

'19 1.34 2.95 4.06 4.75 2.89 2.69 9.36 9.95 2.04 9.08 8.02 5.19

'20 4.48 0.35 15.29 15.21 9.86 16.84 12.90 13.15 7.41 6.16 8.16 9.98

'21 2.03 3.09 1.32 12.15 2.50 2.42 1.72 0.41 1.55 2.02 2.88 2.92

'22 0.08 0.03 6.81 10.58 0.21 6.03 0.77 0.58 1.44 0.63 1.30 2.59

'23 52.62 92.43 72.30 57.40 33.31 32.06 31.46 28.92 29.35 22.20 19.71 42.89

'24 0.07 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.28 0.68 0.09

'25 0.14 0.15 0.17 2.13 3.51 2.11 3.03 3.05 2.16 2.26 2.28 1.91

'26 6.84 5.75 3.39 2.88 2.60 0.83 0.24 0.44 0.03 0.04 0.34 2.13

'27 0.03 0.03 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.01

'28 0.33 0.21 0.25 0.48 0.23 0.82 0.73 0.79 0.84 1.11 1.14 0.63

'29 1.16 0.80 1.14 1.51 1.41 1.19 0.69 0.73 0.59 0.52 0.46 0.93

'30 0.04 0.14 0.25 0.49 0.66 0.15 0.41 0.28 0.15 0.12 0.30 0.27

'31 0.01 0.00 0.00 0.00 0.01 0.00 0.03 0.01 0.00 0.02 0.01 0.01

'32 3.28 3.29 3.73 3.92 3.46 4.17 3.65 3.30 3.47 3.89 5.04 3.75

'33 0.66 0.70 1.21 0.93 1.30 1.63 2.12 1.67 2.88 5.09 6.09 2.21

'34 1.46 1.27 1.16 9.64 9.77 11.82 6.80 3.60 1.88 3.57 3.80 4.98

'35 14.60 7.70 4.08 5.40 4.30 2.26 2.82 4.36 2.57 3.03 6.02 5.19

'36 0.00 0.00 0.00 0.00 0.08 0.52 14.96 17.08 11.41 3.09 2.58 4.52

'37 0.89 1.54 2.51 1.83 1.88 2.20 1.24 1.58 1.53 1.35 2.89 1.77

'38 0.21 0.29 0.35 0.65 0.91 0.88 1.17 0.96 1.07 0.76 0.84 0.74

'39 0.87 0.47 0.73 0.55 0.38 0.19 0.16 0.18 0.22 0.34 0.28 0.40

'40 2.58 2.04 1.50 1.83 1.80 1.60 1.01 0.99 0.82 1.03 1.29 1.50

'41 0.17 0.39 0.33 1.01 1.17 1.10 1.10 0.94 0.74 1.14 1.86 0.90

'42 0.01 0.00 0.01 0.03 0.02 0.00 0.03 0.01 0.03 0.02 0.04 0.02

'43 0.00 0.00 0.00 2956.80 64.73 597.67 0.00 50.05 65.97 107.80 149.97 363.00

'44 0.44 0.04 0.04 0.03 0.05 0.07 0.08 0.02 0.08 0.14 0.09 0.10

'45 2228.53 2863.39 2717.31 809.92 2751.84 709.98 945.63 388.81 801.39 1411.19 118.60 1431.51

'46 0.00 0.00 0.00 29.05 23.41 0.00 0.00 0.00 0.00 0.00 0.00 4.77

'47 0.02 0.00 0.03 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01

Code 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Average

'48 0.01 0.04 0.04 0.04 0.03 0.05 0.03 0.03 0.05 0.05 0.03 0.04

'49 6.31 5.39 4.55 4.63 6.36 5.11 6.20 8.76 2.52 0.39 0.80 4.64


225

'50 0.28 0.00 0.06 0.06 0.06 0.01 0.29 0.92 0.22 0.78 0.59 0.30

'51 6.30 9.02 16.04 21.56 25.79 22.32 35.99 19.74 33.71 56.69 36.98 25.83

'52 0.21 0.18 0.28 0.62 0.92 0.58 0.50 0.37 0.28 0.34 0.50 0.43

'53 0.17 0.07 0.05 0.02 0.04 0.01 13.09 0.36 0.75 2.10 0.75 1.58

'54 0.04 0.06 0.05 0.12 0.02 0.02 0.01 0.01 0.10 0.58 0.82 0.17

'55 0.06 0.44 0.35 0.13 0.18 0.18 0.34 0.33 0.79 0.74 0.77 0.39

'56 0.14 0.18 0.11 1.02 1.17 1.73 1.00 0.43 2.51 3.24 1.88 1.22

'57 0.05 0.07 0.04 0.05 0.11 0.00 0.02 0.03 0.06 0.08 0.08 0.05

'58 0.57 1.90 1.73 1.87 3.00 1.59 3.63 3.36 3.70 2.76 2.77 2.44

'59 0.31 0.85 0.45 0.49 0.59 0.74 0.56 0.49 0.58 0.66 0.65 0.58

'60 0.33 3.24 2.29 3.79 3.13 3.30 0.85 0.48 1.02 0.52 0.42 1.76

'61 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.00 0.00

'62 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00

'63 0.01 0.01 0.01 0.01 0.02 0.01 0.02 0.01 0.02 0.01 0.01 0.01

'64 0.00 0.00 0.05 0.00 0.01 0.00 0.01 0.03 0.03 0.02 0.02 0.01

'65 0.00 0.61 0.00 1.05 0.72 0.52 0.29 0.34 0.00 7.72 8.05 1.75

'66 76.74 37.54 223.40 32.65 0.00 0.00 30.57 91.12 0.00 0.00 36.13 48.01

'67 0.00 0.00 0.00 0.00 0.16 0.00 0.00 0.07 0.00 0.00 0.00 0.02

'68 0.66 0.84 0.87 0.54 0.68 1.40 1.42 1.61 1.50 1.43 1.78 1.16

'69 0.33 0.89 0.39 0.57 0.19 0.39 0.14 0.06 0.11 0.20 0.12 0.31

'70 0.22 0.40 0.43 0.90 0.49 0.76 0.32 0.12 0.48 0.71 1.46 0.57

'71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00

'72 0.04 0.02 0.04 0.03 0.03 0.03 0.02 0.03 0.03 0.03 0.03 0.03

'73 0.01 0.01 0.01 0.06 0.07 0.03 0.06 0.10 0.02 0.01 0.01 0.03

'74 0.02 0.13 0.11 0.18 0.32 0.40 0.27 0.30 0.30 0.49 0.47 0.27

'75 0.06 0.14 0.05 0.09 0.24 0.45 0.21 0.23 0.16 0.34 0.12 0.19

'76 0.57 0.27 0.26 0.25 0.29 0.21 0.15 0.11 0.12 0.14 0.07 0.22

'78 0.00 4.46 9.80 11.87 1.26 1.78 2.44 2.31 2.35 0.61 0.29 3.38

'79 0.00 0.07 0.75 4.38 6.63 2.36 3.54 3.90 0.16 6.18 3.54 2.86

'80 0.00 0.00 1.36 0.82 0.70 0.51 0.00 0.00 0.00 0.00 0.00 0.31

'81 0.07 0.05 1.60 0.00 0.72 0.00 1.15 13.14 20.86 8.85 14.22 5.51

'82 0.01 0.01 0.06 0.02 0.03 0.02 0.02 0.03 0.02 0.04 0.08 0.03

'83 0.05 0.26 0.13 0.16 0.02 0.12 0.24 0.25 0.26 0.22 0.30 0.18

'84 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00

'85 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'86 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'87 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'89 0.00 0.02 0.00 0.00 0.00 0.00 0.03 0.01 0.04 0.01 0.10 0.02

'90 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

'91 0.00 0.00 0.08 0.01 0.03 0.00 0.00 0.00 0.46 0.23 0.18 0.09

'92 0.00 0.00 0.18 0.09 3.15 0.09 0.28 0.00 0.06 0.00 0.00 0.35

'93 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'94 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'95 0.02 0.03 0.03 0.04 0.05 0.04 0.08 0.04 0.03 0.02 0.02 0.04

'96 0.05 0.01 0.03 0.07 0.10 0.15 0.14 0.23 0.20 0.17 0.12 0.11

'97 0.00 1.14 0.01 0.04 0.00 0.08 0.00 0.00 0.15 0.00 0.02 0.13

'99 0.00 0.01 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01

Source: Author’s Calculation of TII indices of Pakistan with India from UNCOMTRADE


226

Appendix F: Pakistan’s Trade Intensity Index with Afghanistan

Code 2008 2009 2010 2011 2012 2013 2014 Average

'01 73381.3 2283734.3 1569459.3 1014663.8 #DIV/0! #DIV/0! 765372.3 #DIV/0!

'02 981.9 967.4 1814.9 1260.9 #DIV/0! #DIV/0! 1673.7 #DIV/0!

'03 #DIV/0! #DIV/0! 130556.6 #DIV/0! #DIV/0! #DIV/0! 2274.9 #DIV/0!

'04 22130.7 25062.5 13763.8 12067.6 #DIV/0! #DIV/0! 5915.2 #DIV/0!

'05 18.5 1811.7 1565.0 2709.7 1345.8 8534.7 818.9 2400.6

'06 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 37472.8 #DIV/0!

'07 2599.8 1808.3 1397.3 3397.9 #DIV/0! #DIV/0! 533.1 #DIV/0!

'08 57.2 181.8 402.0 672.8 1124.6 565.2 335.1 477.0

'09 6.7 103.9 16.2 12.0 59.7 48.4 175.5 60.4

'10 527.0 1005.9 483.7 558.6 #DIV/0! #DIV/0! 118.0 #DIV/0!

'11 197.6 49.8 518.8 1111.2 2252.5 511.1 799.1 777.1

'12 113.0 10.8 14.0 130.0 278.0 92.4 20.7 94.1

'13 0.3 0.0 25.3 1.3 #DIV/0! #DIV/0! 0.1 #DIV/0!


'15 66.2 209.7 55.9 68.8 29.3 51.7 30.1 73.1


'17 1527.2 244.1 44.8 747.4 6915.9 754.3 254.1 1498.2

'18 18.8 0.0 0.6 2.3 #DIV/0! #DIV/0! 251.4 #DIV/0!


'20 15952.9 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 732.9 #DIV/0!


'22 114.5 1820.2 224.4 143.8 #DIV/0! #DIV/0! 37.2 #DIV/0!


'24 0.2 0.1 4.3 3.1 2.4 12.3 24.9 6.8

'25 452.6 31495.1 30587.5 35514.7 #DIV/0! #DIV/0! 199.4 #DIV/0!


'27 73.7 3.5 3.6 1.9 0.2 0.3 2.1 12.2

'28 106.9 2200.0 1227.4 595.2 4771.5 41.6 147.5 1298.6


'30 83.6 315.8 65.6 81.2 168.2 29.7 20.5 109.2

'31 16.6 0.0 0.2 0.5 5.3 0.0 0.0 3.2

'32 601.2 370.1 336.5 309.0 #DIV/0! #DIV/0! 170.1 #DIV/0!


'34 56.6 230.1 100.7 169.5 712.7 333.4 212.2 259.3


'36 2813.5 3638.9 1905.4 7351.7 #DIV/0! #DIV/0! 3126.8 #DIV/0!



'39 163.2 501.2 116.9 109.8 #DIV/0! #DIV/0! 15.3 #DIV/0!

'40 0.8 4.1 2.6 9.3 19.4 44.1 6.5 12.4

'41 7.9 61.0 18.5 12.7 32.0 11.7 29.0 24.7

'42 0.7 1.3 1.0 0.8 #DIV/0! #DIV/0! 0.3 #DIV/0!


'44 117.3 93.2 93.6 196.8 #DIV/0! #DIV/0! 139.9 #DIV/0!




'48 10.0 17.1 20.6 16.2 158.1 77.2 27.5 46.7

'49 #DIV/0! #DIV/0! #DIV/0! 969.1 #DIV/0! #DIV/0! 0.7 #DIV/0!


'51 93.6 179.6 41.8 80.1 145.6 18.8 78.1 91.1

'52 276.3 147.5 19.7 25.0 #DIV/0! #DIV/0! 7.5 #DIV/0!

'53 0.0 1.3 0.0 4.2 63.5 0.0 0.0 9.9


227

Code 2008 2009 2010 2011 2012 2013 2014 Average




'57 1.4 4.1 5.3 15.8 9.6 3.4 10.1 7.1

'58 1.0 0.5 0.0 0.0 0.0 0.0 0.7 0.3



'61 0.8 0.7 0.2 11.8 #DIV/0! #DIV/0! 0.0 #DIV/0!

'62 7.6 1.2 0.3 2.2 0.3 0.3 0.1 1.7

'63 1.2 4.0 1.3 7.3 #DIV/0! #DIV/0! 3.1 #DIV/0!

'64 20.0 10.8 4.7 14.9 29.2 68.4 44.4 27.5




'68 8.3 19.0 5.0 9.9 13.9 7.3 181.7 35.0


'70 166.9 207.0 93.8 89.0 #DIV/0! #DIV/0! 46.5 #DIV/0!

'71 0.4 0.4 0.1 0.4 #DIV/0! #DIV/0! 2.2 #DIV/0!

'72 0.8 1.2 0.8 1.9 1.9 1.0 4.5 1.7

'73 5581.9 3068.7 6078.4 18419.8 #DIV/0! #DIV/0! 15.6 #DIV/0!


'75 0.0 0.0 0.0 0.0 #DIV/0! #DIV/0! 0.0 #DIV/0!

'76 29.5 49.3 35.9 24.9 #DIV/0! #DIV/0! 14.1 #DIV/0!




'81 #DIV/0! #DIV/0! #DIV/0! #DIV/0! 0.6 0.0 0.0 #DIV/0!

'82 4.5 #DIV/0! 5.3 0.1 #DIV/0! #DIV/0! 1.7 #DIV/0!

'83 0.1 0.2 0.9 1.7 #DIV/0! #DIV/0! 5.7 #DIV/0!

'84 0.3 0.8 0.4 0.5 #DIV/0! #DIV/0! 0.5 #DIV/0!

'85 0.2 0.5 0.2 0.2 7.4 231.1 0.2 34.2


'87 0.0 0.1 0.1 0.1 0.1 0.2 0.1 0.1



'90 0.0 0.0 0.0 0.0 #DIV/0! #DIV/0! 0.0 #DIV/0!

'91 18.0 2.0 4.7 3.8 #DIV/0! #DIV/0! 0.8 #DIV/0!




'95 #DIV/0! #DIV/0! 3686.1 23580.0 #DIV/0! #DIV/0! 0.2 #DIV/0!



'99 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Source: Author’s Calculation of TII indices of Pakistan with Afghanistan from

UNCOMTRADE


228

Appendix G: Pakistan’s Trade Intensity Index with Iran


229

2004 2005 2006 2010 2011 2012 2013 2014 average

'01 #DIV/0! #DIV/0! #DIV/0! 0.555494 0.467535 0.512018 #DIV/0! #DIV/0! #DIV/0!

'02 0.14094 0.344541 #DIV/0! 0.138887 0.17086 0.326393 0.449753 0.631645 #DIV/0!

'03 1.815478 3.211896 3.381287 1.20079 #DIV/0! 2.207302 #DIV/0! #DIV/0! #DIV/0!

'04 0.276646 0.336504 0.55182 0.219257 0.253169 0.661211 0.899885 1.181633 0.547516

'05 #DIV/0! #DIV/0! #DIV/0! 0.481586 0.642712 0.667923 0.639313 0.576796 #DIV/0!

'06 #DIV/0! 0.046642 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!

'07 1.506698 2.368813 1.194156 0.752936 1.160304 4.747896 3.806777 4.124599 2.457772

'08 0.174076 0.130444 0.126475 0.128955 0.164053 0.408427 0.490264 0.434514 0.257151

'09 1.702554 1.368817 1.816171 0.816066 0.870821 1.330494 0.954907 1.335852 1.27446

'10 1.159769 1.312924 4.558154 0.970826 1.134123 0.493362 0.488548 0.507579 1.328161

'11 #DIV/0! 20.82702 #DIV/0! 3.171391 13.91382 46.46629 18.14604 #DIV/0! #DIV/0!

'12 0.856251 1.151439 4.467558 1.171067 1.108777 2.659872 1.993494 2.220705 1.953646

'13 1.679795 1.695147 1.42906 0.576491 #DIV/0! 3.396897 2.350091 1.557171 #DIV/0!

'14 #DIV/0! 6.486625 4.485956 2.531905 1.226409 9.284884 #DIV/0! #DIV/0! #DIV/0!

'15 1.196607 1.365737 4.764586 1.583867 1.528304 1.37603 1.133769 #DIV/0! #DIV/0!

'16 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!

'17 1.716948 2.984851 9.026802 0.976521 0.218735 0.473887 1.491031 1.535524 2.303037

'18 0.279297 0.267783 0.351687 #DIV/0! #DIV/0! #DIV/0! #DIV/0! 0.43945 #DIV/0!

'19 0.883915 0.493432 0.399956 0.261033 0.372247 1.339926 2.209065 2.709685 1.083658

'20 0.278807 0.305804 0.267793 0.160062 0.206148 0.648661 0.824192 0.727847 0.427414

'21 0.679656 0.632211 0.880405 0.171531 0.179838 0.538961 0.652991 0.578709 0.539288

'22 0.929692 1.262393 1.441384 3.597193 #DIV/0! #DIV/0! 9.30006 11.63636 #DIV/0!

'23 #DIV/0! #DIV/0! #DIV/0! #DIV/0! 0.27164 #DIV/0! #DIV/0! #DIV/0! #DIV/0!

'24 0.039392 0.042043 0.757876 #DIV/0! 0.236152 #DIV/0! #DIV/0! #DIV/0! #DIV/0!

'25 0.450533 0.567322 0.835492 0.652099 0.701684 1.262564 1.285822 1.203878 0.869924

'26 0.314818 0.590318 0.38202 0.136765 0.146951 0.059513 0.063804 0.056671 0.218857

'27 0.102555 0.107094 0.15692 0.196545 0.137393 0.234853 0.314068 0.30051 0.193742

'28 0.42127 0.538612 0.599947 0.470857 0.478676 0.586071 0.680486 0.643058 0.552372

'29 1.179333 0.923338 1.302199 0.428119 0.475767 0.511931 0.537324 0.59677 0.744348

'30 0.411529 0.46806 1.273076 0.465601 0.417872 0.635097 0.60587 0.73432 0.626428

'31 0.917678 1.72847 7.404611 0.818403 0.748814 0.746794 0.560386 1.163845 1.761125

'32 0.750938 0.933597 1.911545 0.855339 0.791099 1.012159 1.024936 1.055287 1.041862

'33 0.768193 0.777512 1.930499 0.418567 0.455973 0.437434 0.459125 0.540819 0.723515

'34 0.926225 0.907807 0.938793 0.670044 0.609904 1.039563 1.133685 1.053048 0.909883

'35 0.335524 0.314877 0.650171 0.339434 0.31774 0.30275 0.293865 #DIV/0! #DIV/0!

'36 3.734289 #DIV/0! #DIV/0! #DIV/0! #DIV/0! 5.160454 7.277304 #DIV/0! #DIV/0!

'37 #DIV/0! 0.419617 1.893964 0.355674 0.388371 #DIV/0! #DIV/0! #DIV/0! #DIV/0!

'38 0.620282 0.635239 1.235592 0.531299 0.511543 0.873098 0.982008 1.063809 0.806609

'39 0.626763 0.709486 1.329029 0.373784 0.378443 0.426441 0.414434 0.405289 0.582958

'40 0.545783 0.740794 1.663609 0.496244 0.530721 0.601025 0.691424 0.59284 0.732805

'41 3.713113 4.222063 3.413733 3.083404 3.610023 4.791191 5.320087 7.667441 4.477632

'42 44.61858 56.04496 84.35921 21.11901 20.59554 8.816846 2.445237 1.270353 29.90872


'44 0.238802 0.357309 1.215821 0.201197 0.242689 0.232908 0.307932 0.324805 0.390183


'46 #DIV/0! #DIV/0! #DIV/0! #DIV/0! 0.086757 #DIV/0! #DIV/0! #DIV/0! #DIV/0!

'47 0.708236 0.781265 2.988014 0.633432 0.585054 #DIV/0! #DIV/0! #DIV/0! #DIV/0!

'48 0.448421 0.414127 3.334974 0.395763 0.415784 0.534718 0.372074 0.42619 0.792756

'49 0.452118 1.466159 2.326614 1.198484 0.625794 4.341179 23.01241 7.225086 5.08098

'50 3.372727 4.841249 13.3945 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!

'51 0.279284 0.316938 0.382387 0.350477 0.472513 0.447294 0.477135 0.703685 0.428714


230

'52 68.41717 79.39663 247.3416 40.04713 43.02064 85.42094 58.76089 54.59153 84.62457

'53 0.801008 0.788673 7.908939 1.039748 1.068101 3.408559 2.307598 #DIV/0! #DIV/0!

'54 2.457191 2.440232 11.07554 2.582025 2.953112 1.489136 1.00023 1.202113 3.149948

'55 0.728083 0.816997 3.884891 1.885043 1.991268 1.594969 1.57041 1.97924 1.806363

'56 1.778182 2.447783 3.840393 1.089247 1.271325 1.032544 1.003916 1.768521 1.778989

'57 0.415661 0.471678 0.440238 0.168796 0.180567 0.651345 0.72504 0.735364 0.473586

'58 1.738448 1.359888 2.128035 0.859037 0.986497 0.605689 0.455493 0.434669 1.07097

'59 0.416662 0.640068 1.929393 0.818245 0.751646 0.560354 0.509689 0.546878 0.771617

'60 125.4072 33.32413 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!

'61 17.98337 16.01083 21.81982 24.77208 33.4588 7.48668 10.55099 6.927254 17.37623

'62 9.705949 16.72263 27.46323 23.67088 52.97131 11.7244 19.02136 8.619886 21.23746

'63 75.86392 46.15064 41.1987 48.99127 46.71886 41.8828 37.36432 23.11582 45.16079

'64 #DIV/0! 2.285472 1.728472 1.103606 1.133919 2.864314 1.110044 #DIV/0! #DIV/0!

'65 0.372054 1.074421 #DIV/0! #DIV/0! 2.511843 #DIV/0! #DIV/0! #DIV/0! #DIV/0!



'68 0.301423 0.260778 0.365543 0.222729 0.256127 0.391843 0.259753 0.201683 0.282485

'69 0.648203 0.689739 0.762736 0.159941 0.177865 0.419573 0.24065 0.150533 0.406155

'70 0.25467 0.266219 0.41148 0.213299 0.224765 0.286181 0.228908 0.268167 0.269211

'71 6.742499 3.059741 3.532546 1.045163 1.261575 0.283716 #DIV/0! 0.219334 #DIV/0!

'72 0.161898 0.288611 0.741091 0.206899 0.192733 0.4603 0.627909 0.639914 0.414919

'73 0.226243 0.258195 0.619589 0.260906 0.30883 0.275427 0.423205 0.382322 0.34434

'74 0.382699 0.529047 0.337202 0.355017 0.30002 0.607877 1.017519 0.87305 0.550304


'76 0.345221 0.323582 0.704542 0.263956 0.234192 0.340657 0.414941 0.493051 0.390018

'78 1.629866 1.2052 0.80633 0.828484 #DIV/0! 2.216444 #DIV/0! #DIV/0! #DIV/0!

'79 0.599175 0.442397 0.263557 0.263078 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!



'82 0.452795 0.51752 1.593882 0.950529 1.001964 0.681483 #DIV/0! 0.403798 #DIV/0!

'83 0.238535 0.353738 0.66051 0.368228 0.346364 #DIV/0! 0.138692 #DIV/0! #DIV/0!

'84 0.287776 0.331977 1.895346 0.261967 0.2586 0.393618 0.511227 0.510492 0.556375

'85 0.478579 0.923123 3.063934 0.792406 0.607883 0.773733 0.730279 0.637644 1.000948

'86 #DIV/0! #DIV/0! 0.247593 #DIV/0! #DIV/0! #DIV/0! #DIV/0! 1.067907 #DIV/0!

'87 0.3846 1.166795 1.946732 0.453586 0.484796 0.618529 0.823402 0.438451 0.789611

'88 3.606548 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 6.220112 #DIV/0!

'89 4.955193 0.310893 2.490819 9.527213 11.61343 1.12722 3.0769 11.94929 5.63137

'90 0.580016 0.684826 2.096541 0.493466 0.528895 0.498093 0.563985 0.608271 0.756762

'91 #DIV/0! #DIV/0! 0.778538 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!

'92 0.297378 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!


'94 2.939943 0.967595 1.8458 0.607034 0.60298 0.389567 0.209087 0.117133 0.959892

'95 8.897398 6.612688 11.15219 1.822375 2.279784 2.545285 1.212672 0.6486 4.396374

'96 2.018885 3.130227 5.014092 1.998323 1.616341 0.921534 1.023401 1.0409 2.095463

'97 #DIV/0! #DIV/0! #DIV/0! 11.32159 #DIV/0! 0.010857 #DIV/0! #DIV/0! #DIV/0!

'99 0.03126 0.017737 0.001497 0.002742 0.006503 0.30516 0.096953 0.059574 0.065178

Source: Author’s Calculation of TII indices of Pakistan with Iran from UNCOMTRADE


231

Appendix H: Pakistan’s Export Intensity Index with China


232

Code 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

'01 782.22 0.00 18132

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'02 1.76 2.31 0.00 0.00 0.00 0.00 0.02 0.17 0.00 0.00 0.00

'03 109.09 93.59 76.06 96.64 97.28 108.39 124.26 61.05 56.39 41.28 56.14

'04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.25 0.58 0.00 0.47

'05 84.72 69.27 154.59 181.34 54.22 10.73 7.78 15.58 48.75 43.21 34.02

'06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 7.82 28.71 101.98 18.09

'07 1.39 0.77 5.26 1.36 0.00 0.00 0.07 0.25 4.81 0.53 1.16

'08 0.23 0.20 0.31 0.74 2.44 2.64 4.62 3.83 4.04 5.62 6.73

'09 6.43 0.00 0.23 0.84 10.40 0.24 0.06 2.78 0.28 2.60 1.00

'10 0.00 0.04 0.02 0.16 0.11 0.08 0.16 0.89 11.66 5.82 4.41

'11 0.37 3.31 0.32 0.00 0.00 0.00 17.37 0.06 0.22 0.38 0.62

'12 1.55 0.75 4.95 4.77 1.17 1.28 1.13 1.29 0.71 0.61 0.51

'13 163.62 154.43 309.76 475.30 527.63 405.26 448.43 303.33 402.69 339.83 425.21

'14 237.94 233.18 188.50 1117.12 1166.96 476.99 615.18 390.41 179.80 274.10 1348.2

'15 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00

'16 29.34 92.90 302.14 258.49 331.31 325.52 261.12 161.66 149.71 156.47 67.60

'17 0.04 0.15 0.04 0.00 1.37 0.00 0.16 0.14 0.06 0.24 0.06

'18 0.00 0.00 9.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'19 0.00 0.52 0.00 9.84 5.58 0.00 0.27 0.01 0.08 0.07 0.01

'20 54.58 13.25 3.38 2.93 2.45 0.34 0.00 0.20 0.02 0.75 1.17

'21 0.02 1.00 2.61 0.00 2.65 2.95 0.00 0.28 0.06 0.04 0.20

'22 0.00 0.00 0.00 0.03 0.03 0.00 0.04 0.34 0.05 0.37 0.00

'23 0.00 0.00 25.27 0.11 12.53 13.46 23.71 15.64 18.86 19.86 28.71

'24 1.91 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'25 1.12 0.43 0.88 0.97 0.30 0.75 1.46 2.16 1.69 2.42 1.88

'26 2.33 2.17 2.21 1.67 1.40 1.14 0.97 0.90 0.93 0.81 0.80

'27 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.00 0.00

'28 0.12 0.27 0.01 0.03 0.01 0.00 0.05 0.12 0.02 0.05 0.10

'29 0.17 1.05 0.59 1.09 0.39 0.59 0.23 0.67 0.23 0.01 0.00

'30 0.09 0.11 0.09 0.03 0.06 0.02 0.02 0.01 0.01 0.01 0.01

'31 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 #DIV/0!

'32 0.15 0.04 0.24 0.04 0.03 0.03 0.41 0.49 0.08 0.11 0.09

'33 0.04 0.00 0.47 0.55 0.61 0.05 0.34 0.13 0.09 0.05 0.02

'34 0.54 0.00 0.44 0.56 0.13 0.02 0.07 0.07 0.06 0.03 0.05

'35 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.24 0.00 0.00

'36 15.00 9.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.29 0.07 0.00 0.00

'38 1.15 0.12 0.04 0.09 0.05 0.11 0.07 0.08 0.06 0.05 0.08

'39 0.09 0.01 0.03 0.07 0.05 0.05 0.09 0.09 0.06 0.09 0.07

'40 0.06 0.04 0.02 0.02 0.00 0.00 0.00 0.00 0.03 0.00 0.01

'41 0.60 0.75 0.75 0.79 1.08 1.18 0.98 0.92 1.18 0.86 0.82

'42 0.72 0.24 0.33 0.13 0.11 0.12 0.10 0.09 0.08 0.13 0.10

'43 0.00 0.00 0.00 0.00 0.00 0.85 0.00 6.56 0.00 0.52 8.82

'44 0.00 0.00 0.01 0.01 0.03 0.01 0.00 0.01 0.01 0.01 0.02

'45 0.00 #DIV/0! 0.00 0.00 0.00 0.00 #DIV/0! 0.00 0.00 0.00 #DIV/0!

'46 0.00 0.00 0.00 0.00 0.00 230.10 0.00 0.00 0.00 0.00 0.00

'47 0.00 0.00 0.00 0.00 0.01 3.40 0.00 0.00 0.17 0.00 0.00

'48 0.03 0.10 0.01 0.00 0.00 0.01 0.10 0.13 0.02 0.01 0.04

'49 0.31 0.01 0.13 0.14 0.14 0.04 0.11 0.16 0.02 0.05 0.05

'50 0.00 10.23 0.00 10.97 104.49 27.26 45.53 25.32 0.00 25.01 25.30

'51 0.01 0.01 1.58 0.74 1.18 1.30 0.30 0.60 0.94 0.79 0.79

'52 0.27 0.33 0.37 0.55 0.64 1.47 0.99 0.82 1.03 1.20 1.48

'53 0.00 0.35 0.30 0.57 2.67 0.00 0.00 0.31 1.82 0.76 0.00

'54 0.03 0.03 0.07 0.10 0.04 0.45 0.29 0.20 0.22 0.10 0.08

'55 0.05 0.04 0.07 0.05 0.06 0.09 0.12 0.14 0.21 0.11 0.26


233

'56 0.06 0.07 0.15 0.36 0.04 0.04 0.07 0.25 1.32 0.83 0.34

'57 0.15 0.27 0.53 0.37 0.46 0.61 0.67 1.67 2.30 1.45 1.63

'58 0.06 0.08 0.13 0.55 0.54 0.47 0.43 0.35 0.11 0.18 0.19

'59 0.00 1.01 0.08 0.00 0.00 0.00 0.09 0.09 0.13 0.63 0.31

'60 0.09 0.22 0.03 0.07 0.01 0.07 0.12 0.37 0.31 0.55 0.31

'61 0.02 0.02 0.01 0.01 0.00 0.02 0.03 0.04 0.09 0.11 0.10

'62 0.00 0.01 0.01 0.01 0.01 0.02 0.02 0.04 0.06 0.07 0.08

'63 0.38 0.08 0.07 0.13 0.28 0.27 0.49 0.58 0.56 0.60 0.66

'64 0.05 0.05 0.00 0.03 0.04 0.01 0.00 0.01 0.01 0.01 0.02

'65 0.00 0.00 1.82 2.52 8.82 1.42 1.39 0.00 0.78 0.00 0.00

'66 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 16.10

'67 0.00 0.00 0.00 0.00 3.23 37.40 0.00 4.25 12.33 0.00 0.00

'68 1.82 1.42 1.77 1.29 1.68 3.18 2.91 5.20 4.24 6.53 3.87

'69 0.00 0.03 1.15 1.44 0.52 0.98 0.57 0.00 0.01 0.01 0.70

'70 0.23 0.34 0.48 0.04 0.02 0.01 0.00 0.00 0.01 0.10 0.00

'71 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02

'72 0.00 0.05 0.04 0.06 0.43 0.09 0.28 0.38 0.19 0.08 0.06

'73 0.00 0.02 0.02 0.04 0.01 0.00 0.00 0.01 0.00 0.01 0.01

'74 0.01 0.26 0.20 0.09 0.16 0.22 0.12 0.10 0.09 0.10 0.13

'75 0.00 0.00 0.00 0.00 0.32 0.43 0.00 0.00 0.00 0.00 0.00

'76 0.00 0.02 0.06 0.00 0.00 0.02 0.00 0.01 0.00 0.00 0.00

'78 0.00 7.10 0.00 0.32 0.00 0.00 0.28 0.00 0.88 2.76 0.00

'79 0.00 0.00 0.00 0.00 0.00 0.36 0.96 0.00 0.00 0.00 0.00

'80 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'81 0.00 0.00 6.92 10.19 1.39 0.00 3.91 1.25 4.54 1.81 0.35

'82 0.16 0.29 0.21 0.16 0.23 0.17 0.11 0.13 0.09 0.16 0.15

'83 0.00 0.10 0.00 1.72 0.39 0.29 0.11 0.67 0.43 0.65 0.70

'84 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'85 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'86 0.00 0.00 0.00 0.00 0.01 0.62 0.00 0.00 0.05 1.01 0.05

'87 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'88 0.00 0.00 0.01 0.00 0.01 0.00 0.01 0.00 0.00 0.00 0.00

'89 0.00 0.00 0.00 0.00 0.00 0.00 1.18 0.02 0.00 2.56 1.15

'90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'91 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'92 0.00 0.07 0.00 0.00 0.07 0.05 0.00 0.05 0.04 0.08 0.09

'93 0.00 0.00 0.00 0.00 13.19 0.00 10.92 185.67 0.00 0.00 0.00

'94 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.01 0.02

'95 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02

'96 0.02 0.01 0.03 0.05 0.02 0.06 0.10 0.75 0.06 0.03 0.03

'97 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.62 0.08 0.11

'99 0.05 0.00 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00

Source: Author’s Calculation of EII indices of Pakistan with China from UNCOMTRADE


234

Appendix I: Pakistan’s Export Intensity Index with India


235

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

'01 0.00 0.00 0.00 0.00 0.00 0.00 1475.16 0.00 0.00 0.00 0.00

'02 0.00 0.00 145.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 66.98

'03 10.15 922.52 18.69 1040.38 129.63 88.22 93.39 49.56 122.40 10.72 12.18

'04 0.00 689.98 0.00 0.00 104.27 3.06 0.00 1.68 0.00 0.00 38.08

'05 0.00 0.00 0.00 223.41 22.68 0.00 138.75 56.87 22.42 0.00 0.00

'06 0.00 0.00 0.00 209.99 43.07 0.00 0.00 300.80 0.00 0.00 0.00

'07 82.91 348.01 32.18 7.89 0.36 0.26 0.20 3.70 1.15 0.50 0.00

'08 67.57 70.36 86.17 96.78 79.09 65.00 59.26 46.65 68.03 55.85 36.28

'09 24.60 45.34 55.49 78.41 33.29 51.20 32.55 25.82 8.42 10.63 16.15

'10 4.65 64.75 0.87 3.33 0.06 3.70 0.00 0.18 0.00 0.24 0.00

'11 8.67 0.00 0.21 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00

'12 249.01 369.79 205.71 184.30 176.87 249.86 198.41 100.40 88.12 121.25 106.71

'13 34.79 75.73 50.02 2.38 14.49 16.04 11.33 4.35 14.66 10.50 29.13

'14 105.36 0.00 0.00 70.88 52.90 141.24 273.35 274.16 0.00 0.00 0.00

'15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'16 0.00 0.00 0.00 144.03 0.00 544.52 410.07 535.98 841.83 0.00 0.00

'17 74.58 34.75 0.00 0.47 0.05 0.00 0.00 0.00 2.17 11.40 0.17

'18 0.00 0.00 0.00 0.00 9.35 0.00 0.00 0.00 0.00 0.00 0.00

'19 0.00 6.07 15.29 7.93 1.23 2.58 5.82 2.33 2.27 1.29 7.61

'20 36.11 4.68 231.41 167.83 141.58 217.17 123.51 125.79 63.31 58.88 29.85

'21 15.14 10.70 27.08 2.64 18.80 4.00 5.89 1.85 2.51 2.26 9.96

'22 0.12 0.03 11.56 17.93 0.29 9.02 1.35 1.20 3.12 1.36 2.33

'23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.14

'24 0.87 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.95

'25 0.54 0.34 0.48 5.34 6.36 3.85 5.57 5.54 3.54 4.33 4.49

'26 0.04 0.11 0.18 0.20 0.11 0.10 0.13 0.10 0.06 0.06 0.26

'27 0.43 0.30 0.21 0.10 0.08 0.03 0.02 0.00 0.01 0.02 0.01

'28 0.10 0.10 0.01 0.05 0.75 7.02 10.08 8.56 8.90 9.66 11.18

'29 0.53 1.26 5.65 1.27 3.69 4.52 4.36 2.53 2.05 5.04 1.38

'30 0.11 0.09 0.06 0.21 0.02 0.00 0.02 0.00 0.11 0.03 0.04

'31 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 #DIV/0!

'32 0.30 0.00 0.05 0.00 0.00 0.10 0.10 0.47 0.57 0.13 0.44

'33 1.87 0.16 0.00 0.18 0.62 0.61 0.40 0.32 0.40 0.04 0.00

'34 0.00 0.00 1.34 2.70 2.40 0.00 0.25 0.55 0.13 0.08 0.04

'35 8.66 0.41 0.00 11.57 20.82 3.32 13.79 22.03 2.38 0.00 0.08

'36 0.00 0.00 0.00 0.00 0.90 3.48 190.05 154.92 108.22 26.46 24.22

'37 0.00 0.76 0.00 0.00 0.00 0.00 0.00 0.08 0.00 0.00 0.00

'38 0.57 0.32 0.33 0.33 0.12 0.16 2.44 0.19 0.29 0.37 0.16

'39 0.15 0.21 0.14 0.19 0.14 0.19 0.36 0.14 0.14 0.08 0.24

'40 0.03 0.18 0.20 0.05 0.27 0.61 0.49 0.20 0.08 1.55 3.88

'41 0.62 1.45 1.16 3.51 3.93 3.24 3.33 3.25 2.67 4.48 6.25

'42 0.86 0.15 0.23 0.67 0.28 0.06 0.39 0.11 0.27 0.16 0.38

'43 0.00 0.00 0.00 9225.62 0.00 2127

0.00 206.38 57.47 505.10 438.03

'44 0.01 0.00 0.09 0.00 0.01 0.00 0.02 0.02 0.10 0.05 0.04

'45 0.00 #DIV/0! 0.00 0.00 0.00 0.00 #DIV/0! 0.00 0.00 0.00 #DIV/0!

'46 0.00 0.00 0.00 407.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'47 0.00 0.00 0.91 3.25 0.26 0.00 0.00 0.00 0.00 0.00 0.00

'48 0.09 0.32 0.17 0.02 0.01 0.12 0.00 0.03 0.10 0.08 0.01

'49 8.67 0.63 1.79 2.40 8.02 2.47 2.46 2.95 5.75 9.04 4.31

'50 2.97 0.00 1.54 4.10 2.14 2.16 10.48 28.06 13.06 18.90 28.52

'51 26.83 36.60 61.57 79.24 92.69 88.25 102.05 71.18 99.09 139.30 125.53

'52 0.30 0.75 1.14 1.23 0.93 1.41 0.97 0.69 1.12 0.60 0.99

'53 1.08 0.00 0.30 0.00 0.00 0.00 0.00 0.10 9.80 95.81 23.77

'54 0.22 0.29 0.35 1.53 0.29 0.21 0.17 0.56 0.09 0.23 0.14

'55 0.05 0.68 2.43 0.69 0.37 0.04 0.15 0.08 0.04 0.02 0.01


236

'56 0.03 0.32 0.07 2.80 4.62 11.36 6.35 3.08 7.24 3.67 0.45

'57 1.90 1.99 0.51 1.47 1.65 0.01 0.45 0.59 1.47 2.02 1.86

'58 1.75 7.22 5.86 0.42 1.03 1.09 0.39 0.27 0.52 0.27 0.02

'59 0.06 0.99 0.00 0.96 1.40 0.07 0.73 0.00 0.56 0.23 0.48

'60 0.58 5.09 4.84 7.41 6.77 6.95 1.74 1.51 3.07 1.88 1.99

'61 1.15 0.89 0.23 0.11 0.21 0.27 0.30 0.38 0.31 0.13 0.09

'62 0.30 0.10 0.26 0.05 0.13 0.29 0.09 0.07 0.17 0.11 0.10

'63 0.19 0.22 0.19 0.19 0.21 0.08 0.23 0.14 0.22 0.16 0.06

'64 0.04 0.02 0.59 0.05 0.11 0.03 0.06 0.32 0.33 0.17 0.12

'65 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'66 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'67 0.00 0.00 0.00 0.00 28.52 0.00 0.00 108.54 0.00 0.00 0.00

'68 1.77 1.38 1.24 1.20 0.64 0.45 1.46 1.53 1.78 1.55 2.24

'69 0.16 0.15 0.02 0.16 0.04 0.00 0.00 0.39 0.05 0.02 0.03

'70 0.38 0.62 2.36 5.68 2.48 4.14 2.03 0.85 5.02 7.58 12.02

'71 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'72 0.09 0.42 0.18 0.23 0.08 0.00 0.15 0.26 0.55 0.74 0.63

'73 0.01 0.00 0.03 0.04 0.02 0.03 0.03 0.00 0.01 0.02 0.02

'74 0.23 1.85 1.34 0.77 0.82 1.16 1.20 0.80 0.84 1.69 1.84

'75 0.00 21.65 2.62 0.00 0.00 0.00 0.00 0.00 0.00 20.76 0.00

'76 0.00 0.18 0.69 0.50 0.25 0.00 0.11 0.00 0.15 0.69 0.46

'78 0.00 44.33 49.72 30.84 15.56 13.27 14.53 14.58 10.98 5.06 1.92

'79 0.00 0.00 4.08 18.67 38.63 0.00 11.02 19.35 13.51 5.23 7.35

'80 0.00 0.00 30.51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'81 0.00 0.00 11.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'82 0.01 0.05 0.17 0.05 0.10 0.01 0.02 0.03 0.03 0.05 0.11

'83 0.00 3.28 7.27 5.87 0.00 0.00 0.00 0.02 0.04 0.05 0.00

'84 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00

'85 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'86 0.00 0.00 0.00 0.58 0.00 0.00 0.00 0.00 0.00 0.91 0.11

'87 0.00 0.01 0.01 0.02 0.02 0.01 0.00 0.01 0.01 0.00 0.00

'88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'89 0.00 0.00 0.01 0.00 0.13 0.00 0.01 0.00 0.00 0.00 0.00

'90 0.01 0.01 0.01 0.02 0.01 0.01 0.02 0.03 0.02 0.02 0.03

'91 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'92 0.00 0.00 0.29 0.09 5.19 0.05 0.45 0.00 0.11 0.00 0.00

'93 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'94 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'95 0.03 0.02 0.01 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.03

'96 0.26 0.05 0.11 0.07 0.04 0.05 0.07 0.01 0.03 0.02 0.01

'97 0.00 131.93 0.26 2.70 0.00 1.95 0.00 0.02 0.00 0.00 0.33

'99 0.01 0.03 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00

Source: Author’s Calculation of EII indices of Pakistan with India from UNCOMTRADE


237

Appendix J: Pakistan’s Export Intensity Index with Afghanistan


238

Codes 2008 2009 2010 2011 2012 2013 2014 average s.d

'01 10455272 4190971 2909224 2253598 #DIV/0! #DIV/0! 4697812 #DIV/0! #DIV/0!

'02 2916.027 1241.545 2536.769 2199.685 #DIV/0! #DIV/0! 2235.834 #DIV/0! #DIV/0!

'03 #DIV/0! #DIV/0! 160480.9 #DIV/0! #DIV/0! #DIV/0! 3079.926 #DIV/0! #DIV/0!

'04 80599.95 64475.84 47246.91 37121.73 #DIV/0! #DIV/0! 18190.24 #DIV/0! #DIV/0!

'05 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 0 #DIV/0! #DIV/0!

'06 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 108145.8 #DIV/0! #DIV/0!

'07 14008.28 21580.34 16365.5 19373.27 #DIV/0! #DIV/0! 3259.245 #DIV/0! #DIV/0!

'08 21788.82 19671.23 38012.73 64507.47 #DIV/0! #DIV/0! 1440.617 #DIV/0! #DIV/0!

'09 120.5946 2509.191 290.9009 136.5669 917.1966 471.5472 2203.482 949.9257 1001.356

'10 1328.236 1328.964 628.3518 750.7296 #DIV/0! #DIV/0! 156.5442 #DIV/0! #DIV/0!

'11 591.1695 112.661 1413.124 1357.037 2109.076 800.2404 907.5 1041.544 647.9569

'12 25240.91 #DIV/0! 10386.32 28515.6 #DIV/0! #DIV/0! 317.6709 #DIV/0! #DIV/0!



'15 1079.59 3504.243 1332.912 1142.926 350.2823 941.793 811.0684 1308.973 1016.677


'17 2310.812 755.4637 532.5685 2212.117 7208.699 889.682 283.0834 2027.489 2420.389

'18 957.7445 0 386.6208 665.4964 #DIV/0! #DIV/0! 74672.85 #DIV/0! #DIV/0!




'22 369.6606 3141.47 476.8027 326.649 #DIV/0! #DIV/0! 44.29168 #DIV/0! #DIV/0!


'24 1.100057 0.521258 1.488231 3.409727 0 14.98081 1.17388 3.239138 5.286485

'25 1282.941 761597.1 169246.4 174053.9 #DIV/0! #DIV/0! 223.837 #DIV/0! #DIV/0!


'27 1165.497 51.2455 43.84726 26.39238 1.0131 3.716659 42.92412 190.6623 430.3141

'28 9773.072 47176.45 26656.48 12808.52 86659.9 816.1435 3512.214 26771.83 30826.35


'30 692.4672 1847.129 355.0973 414.2254 711.1156 160.0914 107.4464 612.5103 592.4174

'31 19339.56 0 397.0906 2689.522 27999.89 0 #DIV/0! #DIV/0! #DIV/0!

'32 13139.88 6443.349 5343.61 4358.395 #DIV/0! #DIV/0! 2376.104 #DIV/0! #DIV/0!


'34 948.8312 1998.565 1409.953 2084.651 5344.205 2122.844 1676.645 2226.528 1437.847


'36 8058.162 7741.551 4233.427 15047.21 #DIV/0! #DIV/0! 4241.334 #DIV/0! #DIV/0!



'39 967.1129 2147.473 595.9279 598.9645 #DIV/0! #DIV/0! 104.5214 #DIV/0! #DIV/0!

'40 66.54219 270.019 226.0941 642.0394 937.2952 2130.51 331.7678 657.7525 711.7898


'42 2.497999 1.664404 1.386973 1.160573 #DIV/0! #DIV/0! 0.506648 #DIV/0! #DIV/0!


'44 1045.748 686.3934 617.204 984.1082 #DIV/0! #DIV/0! 746.4507 #DIV/0! #DIV/0!




'48 266.4776 435.024 533.4782 382.8304 2211.057 905.9931 305.6128 720.0676 691.0051

'49 #DIV/0! #DIV/0! #DIV/0! 13795.23 #DIV/0! #DIV/0! 74.01306 #DIV/0! #DIV/0!

'50 7158.204 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 0 #DIV/0! #DIV/0!


'52 115.0898 9.344384 2.326081 3.736417 #DIV/0! #DIV/0! 0.910412 #DIV/0! #DIV/0!

'53 0 0 0 113.3344 836.6286 0 0 135.709 311.9491




239


'57 54.11739 44.88067 42.81597 167.7632 #DIV/0! #DIV/0! 16.86246 #DIV/0! #DIV/0!

'58 4.76902 1.148877 0.107384 0.053935 0.016424 0.013962 2.513595 1.231885 1.815385



'61 1.329962 0.909784 0.253562 16.13186 #DIV/0! #DIV/0! 0.019692 #DIV/0! #DIV/0!

'62 11.09942 1.499107 0.369439 3.010147 0.380358 0.390324 0.103067 2.407408 3.96611

'63 1.337164 3.249298 1.356021 7.944046 #DIV/0! #DIV/0! 3.898615 #DIV/0! #DIV/0!

'64 42.30115 22.89461 12.95924 39.02446 69.62644 140.0284 95.19836 60.29039 44.86725




'68 54.43673 52.26288 16.43452 29.0528 35.24012 20.89189 650.5818 122.7001 233.2226


'70 596.8255 612.1255 444.7347 530.2605 #DIV/0! #DIV/0! 196.8523 #DIV/0! #DIV/0!

'71 0.712916 0.426573 0.109573 0.036559 #DIV/0! #DIV/0! 0.037721 #DIV/0! #DIV/0!

'72 12.57642 15.73699 7.767397 12.14655 19.9703 9.681441 34.22743 16.01522 8.966047

'73 55862.28 14804.13 23241.56 50235.13 #DIV/0! #DIV/0! 93.60289 #DIV/0! #DIV/0!


'75 0 0 0 0 #DIV/0! #DIV/0! 0 #DIV/0! #DIV/0!

'76 336.4331 409.015 326.4092 230.6986 #DIV/0! #DIV/0! 121.0382 #DIV/0! #DIV/0!




'81 #DIV/0! #DIV/0! #DIV/0! #DIV/0! 18.10301 0 0 #DIV/0! #DIV/0!

'82 17.55945 #DIV/0! 12.70359 0.307264 #DIV/0! #DIV/0! 3.986184 #DIV/0! #DIV/0!

'83 2.631402 11.33227 32.50021 41.18479 #DIV/0! #DIV/0! 229.0032 #DIV/0! #DIV/0!

'84 3.212334 12.20447 6.200182 8.076365 #DIV/0! #DIV/0! 6.366109 #DIV/0! #DIV/0!

'85 2.444456 8.424441 2.310145 5.636475 261.3522 11611.59 8.005835 1699.967 4371.658


'87 0.441592 1.673076 0.920326 1.71529 1.189875 2.570946 1.775705 1.469544 0.688619



'90 0.217637 0.166525 0.053819 0.079983 #DIV/0! #DIV/0! 0.005587 #DIV/0! #DIV/0!

'91 4974.545 302.1664 905.279 1134.18 #DIV/0! #DIV/0! 5.240545 #DIV/0! #DIV/0!




'95 #DIV/0! #DIV/0! 3499.99 29084.23 #DIV/0! #DIV/0! 0.207104 #DIV/0! #DIV/0!


'97 0 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 0 #DIV/0! #DIV/0!

'99 0.217517 0.188061 0.07251 0.031236 5.22E-05 0.001552 0.012048 0.074711 0.091277

Source: Author’s Calculation of EII indices of Pakistan with Afghanistan from

UNCOMTRADE


240

Appendix K: Pakistan’s Export Intensity Index with Iran Codes 2004 2005 2006 2010 2011 2012 2013 2014 average

'01 0 0 0 18103.06 34830.92 3485.757 0 0 7052.467

'02 6.056945 0 0 28.23044 464.4695 796.9311 174.0283 217.2396 210.8695

'03 82.41032 0 16.76355 0 0 13.02511 0 0 14.02487

'04 0 0 72.48442 0 7.252233 1.388217 48.58612 0 16.21387

'05 0 0 0 1522.028 831.1165 766.3486 681.9425 0 475.1794

'06 0 0 0 0 0 0 0 0 0

'07 4.115352 494.5731 261.8373 77.1031 2.76107 0.980858 0.891308 6.041936 106.038

'08 0.161956 1.949469 110.7683 33.75939 38.51072 72.29401 27.08841 0.375119 35.61342

'09 75.85249 0 120.8649 26.55532 6.024732 3.796 3.461361 0 29.56935

'10 19.09481 20.60927 102.405 8.972102 4.701135 3.406497 0.645995 0.23976 20.00932

'11 0 66.664 0 0 14.39861 10.1707 0 0 11.40416

'12 17.4781 18.0062 93.70615 16.9469 22.38052 53.00854 4.881 0.067931 28.30942

'13 22.59943 0.931367 0 0 0 0 0 0 2.941349

'14 0 633.6879 4802.198 3329.078 1707.82 2468.899 0 0 1617.71

'15 0 0.015977 0.193693 0.869039 0.017162 0.111384 0 0 0.150907

'16 0 0 #DIV/0! 0 0 0 0 0 #DIV/0!

'17 0 0.240954 14.29909 12.96374 0 0.321339 1.550535 1.287501 3.832894

'18 0 0 0 0 0 0 0 0 0

'19 0 6990.229 40682.42 7.100843 4.410509 0 0 3.390986 5960.943

'20 529.8444 67.37654 695.0161 80.61484 32.97127 68.41413 35.81649 14.30887 190.5453

'21 35.56584 0 31.04461 0.472751 0.576298 0 1.614869 0.878091 8.769057

'22 0 0 0 47.1625 0 0 0 0 5.895312

'23 0 0 0 0 0.017438 0 0 0 0.00218

'24 2.016861 3.358577 109.5931 0 0 0 0 0 14.37107

'25 1.269087 7.236365 15.41568 0.902051 0.813633 1.166526 0.366114 0.101433 3.408861

'26 0 0 0 0 0.194544 0 0 0 0.024318

'27 0.015672 0 0.001543 0 0.00068 0 0 0 0.002237

'28 0 0 1.576066 2.650288 3.90725 0 0.110961 0 1.030571

'29 0 1.071672 0 0.413104 0.341455 0 0 0 0.228279

'30 0.125155 0.120811 1.706156 0.145027 0.195703 0.369139 0.044609 0.045875 0.344059

'31 0 0 0 0 0 0 0 #DIV/0! #DIV/0!

'32 0 0.088788 28.20842 0.365367 1.08879 0.350562 0.074561 0 3.772061

'33 0.670449 8.76585 63.35252 0.690766 0.285045 4.477587 5.067652 1.005196 10.53938

'34 8.395098 0 0 0 7.705217 0 0 0 2.012539

'35 123.1824 131.2484 245.2734 184.9884 169.1784 244.9272 35.16572 0 141.7455

'36 152.8146 #DIV/0! #DIV/0! #DIV/0! #DIV/0! 57.66659 129.2584 0 #DIV/0!

'37 0 9.70431 37.9271 0 0 0 0 0 5.953926

'38 0.013363 0.27814 0.49389 1.453553 0.846566 0.529428 0.524126 0.225313 0.545547

'39 0.410294 0.075812 0.302802 0.156644 0.058383 0.108452 0.187872 0.088661 0.173615

'40 0.058775 0.153933 0.393346 0.06848 0 0 0.580643 0 0.156897

'41 43.58893 77.66246 35.04484 1.01661 13.85545 11.26223 19.63984 11.00322 26.6342

'42 6.599012 139.5663 165.4438 1.720985 1.465539 0.306728 0.014015 0.006483 39.39036

'43 0 0 #DIV/0! 0 0 0 0 0 #DIV/0!

'44 1.0972 1.142271 9.788472 0.381266 0.197402 0.11712 7.014725 0.230766 2.496153

'45 0 #DIV/0! 0 #DIV/0! 0 0 0 #DIV/0! #DIV/0!

'46 0 0 0 0 120.3454 0 0 0 15.04318

'47 0 0 22.27568 0 0 0 0 0 2.78446

'48 15.00302 0.560777 46.81551 0.618837 6.195678 12.17879 18.21483 15.85272 14.43002

'49 0 8.001462 0 1.136667 1.030803 2.455426 0 0.645547 1.658738

'50 24.65263 553.8547 0 0 0 0 0 0 72.31341

'51 0 0 0 0 0 0 0 0 0

'52 2.224633 3.338363 6.089878 0.388199 0.533052 0.426018 0.147327 0.052139 1.649951

'53 344.473 311.7801 2323.735 356.9082 437.9064 171.4285 456.6066 0 550.3547


241

'54 1.948469 5.488865 4.559434 0.061761 0.976095 0.536146 0.034023 0 1.700599

'55 15.03227 17.33259 10.30218 0.041517 0.082976 0.039647 0.001726 0.00323 5.354517

'56 0.576277 0.10727 6.245303 22.07687 13.46966 4.903615 1.78931 0.130049 6.162295

'57 39.49902 47.30644 141.134 0.094456 0.110634 0.09833 0 0 28.53036

'58 0.748372 58.10364 4.295886 0.132849 6.901957 2.112746 0.511599 0 9.100882

'59 0.170906 1.3492 11.88826 27.78447 12.76709 2.221971 1.809693 1.43289 7.42806

'60 34.18309 27.08905 0 0 0 0 0 0 7.659016

'61 4.971594 7.26983 16.54844 0.880094 1.027472 0.069146 0.024919 0.004845 3.849543

'62 2.573161 2.646328 9.255303 0.666273 0.242295 0.088121 0.000705 0.002085 1.934284

'63 2.676201 2.325703 8.782219 1.156917 1.234048 0.107348 0.0737 0.007971 2.045513

'64 0 0 0.381163 0.908953 0.519412 0.05752 0 0 0.233381

'65 0 16.81167 0 0 51.64647 0 0 0 8.557268

'66 0 0 0 0 0 0 0 0 0

'67 0 0 0 0 0 0 0 0 0

'68 1.096817 0.765006 0.840659 1.144672 0.050579 0.099014 0.370651 0 0.545925

'69 0 0.045605 0 0.089925 0.299466 0 0.117018 0.079192 0.078901

'70 17.01215 0.358468 0.272832 0 0 0.003939 0 0 2.205924

'71 0.437794 0.198493 0.797718 0.000154 0.001895 2.26E-06 0 0.000677 0.179592

'72 0 0.004874 4.929969 0.004137 0 0.004446 0 0 0.617928

'73 0.009015 0.064269 0.040858 0.011863 0.008511 0.001997 0.003246 0 0.01747

'74 0.025035 0 0.134399 0.137097 0.061197 0.03861 0.170329 0.077717 0.080548

'75 0 0 0 0 0 0 0 0 0

'76 0.095994 0 0 0.018208 0.00633 0.141147 0.035114 0.056119 0.044114

'78 0 82.51448 125.8243 0 0 0 0 0 26.04235

'79 0 0 0 0 0 0 0 0 0

'80 0 0 0 0 0 0 0 0 0

'81 0 0 0 0 0 0 0 0 0

'82 0.050592 0.097645 2.111729 2.255201 3.538702 0.238896 0 0.024908 1.039709

'83 0.498342 0.226218 8.243253 2.499585 4.978899 0 0 0 2.055787

'84 0.005404 0.00321 0.03397 0.002629 0.005326 0.002901 0.002842 0.007611 0.007987

'85 0.000527 0.003625 0.028137 0.00503 0.009812 0.031634 5.59E-05 0.00165 0.010059

'86 0 0 0 0 0 0 0 0 0

'87 0.006912 0.012569 0.000579 0.027999 0.003014 0.00044 0.000675 0 0.006524

'88 0.054896 #DIV/0! #DIV/0! #DIV/0! #DIV/0! 0 0 0.013298 #DIV/0!

'89 0.128698 0.417078 28.52576 9.627714 85.04715 6.954452 1.435565 115.2967 30.92914

'90 0.022999 0.014987 0.061831 0.042242 0.037692 0.029988 0.027743 0.013641 0.03139

'91 0 0 0 0 0 0 0 0 0

'92 8.349232 0 0 0 0 0 0 0 1.043654

'93 0 #DIV/0! #DIV/0! #DIV/0! #DIV/0! 0 0 0 #DIV/0!

'94 0.019239 0.112107 0.151809 0.053252 0.007428 0.001432 0.000959 0.000706 0.043366

'95 0.04577 0.068855 0.029743 0.027126 0.032735 0.015147 0.008332 0.001128 0.028604

'96 1.157283 1.066723 3.407946 0.496339 0.289657 0.034959 0 0.000283 0.806649

'97 0 0 0 413.5166 0 0 0 0 51.68958

'99 0.000211 5.12E-05 0.000521 0 0.001968 0 0 0 0.000344

TOTAL -0.00407 -0.00707 -0.0076 -0.0057 -0.00377 -0.00465 -0.00234 -0.00146 -0.00458

Source: Author’s Calculation of EII indices of Pakistan with Iran from UNCOMTRADE


242

Appendix L: Pakistan’s Import Intensity Index with China


243

Codes 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Average

'01 0.00 0.00 23.55 4.26 2.53 7.15 16.07 0.00 0.00 0.00 0.00 4.87

'02 259.64 102.78 71.75 71.23 28.47 12.23 0.56 190.98 570.85 587.16 224.29 192.72

'03 0.00 0.00 0.21 0.67 3.24 32.43 5.07 0.86 3.79 0.59 0.88 4.34

'04 56.63 23.76 25.21 19.49 6.15 8.48 12.29 0.13 0.37 22.87 26.77 18.38

'05 180.56 26.68 330.46 19.39 3.01 0.42 7.84 6.15 4.01 0.00 8.52 53.37

'06 145.91 162.57 232.99 249.09 313.89 136.99 110.57 327.28 440.21 295.22 212.20 238.81

'07 22.09 24.10 16.78 28.05 19.60 12.66 12.24 10.07 4.70 4.28 7.07 14.70

'08 0.50 0.68 0.34 4.41 2.57 2.73 2.09 3.65 2.10 1.53 1.02 1.97

'09 15.81 22.75 29.52 32.08 31.51 32.31 25.70 26.31 18.78 15.35 8.24 23.49

'10 0.17 0.17 0.14 0.10 0.09 9.75 99.53 143.08 318.98 61.38 102.61 66.91

'11 2.29 2.20 22.50 19.89 6.39 1.63 3.90 9.67 4.94 62.27 128.31 24.00

'12 1.92 1.41 1.84 2.91 3.44 2.95 1.78 1.13 1.71 2.19 1.80 2.10

'13 82.03 97.88 27.32 39.80 17.67 18.07 14.73 21.06 14.88 16.71 11.75 32.90

'14 2.71 10.49 11.61 10.82 5.57 6.33 6.26 8.49 7.26 3.51 5.86 7.17

'15 0.22 0.12 0.14 0.07 0.04 0.14 0.20 0.15 0.07 0.22 0.40 0.16

'16 0.00 0.00 0.00 0.02 0.92 0.19 0.15 5.89 10.71 15.66 9.59 3.92

'17 102.79 58.01 4.35 34.62 45.94 4.21 1.44 11.35 33.29 27.79 25.40 31.75

'18 192.80 96.97 54.83 45.69 70.20 133.92 119.24 68.70 42.36 23.32 10.52 78.05

'19 2.65 2.13 1.37 3.26 0.39 0.25 0.59 0.72 0.57 0.35 0.30 1.14

'20 2.31 3.02 4.26 2.63 6.37 9.27 9.77 10.26 4.69 4.19 3.00 5.43

'21 6.14 4.11 4.15 6.97 6.98 4.27 11.22 14.44 7.87 10.04 5.36 7.41

'22 0.35 0.62 0.43 0.73 0.87 1.56 3.82 4.89 1.57 0.63 2.09 1.60

'23 15.76 7.28 6.99 8.46 3.22 2.97 2.76 3.52 2.09 1.39 1.17 5.06

'24 22.38 2.94 0.39 2.51 0.04 0.11 0.07 0.08 0.06 0.27 2.71 2.87

'25 1.72 2.15 9.04 2.90 1.30 3.25 3.04 2.48 2.45 2.33 1.82 2.95

'26 0.17 0.07 0.11 0.04 0.01 0.46 1.05 0.07 0.40 1.75 3.55 0.70

'27 0.06 0.05 0.06 0.05 0.03 0.00 0.00 0.00 0.00 0.00 0.01 0.02

'28 2.48 2.00 2.21 2.24 1.06 2.38 1.89 1.32 1.85 2.07 2.33 1.98

'29 0.47 0.46 0.48 0.40 0.39 0.60 0.52 0.48 0.49 0.50 0.61 0.49

'30 3.97 3.16 3.05 1.98 3.12 1.56 1.22 1.01 0.72 0.52 0.63 1.90

'31 2.82 2.00 0.20 6.29 2.39 0.39 3.57 5.15 5.33 6.69 8.72 3.96

'32 4.70 4.84 4.42 4.31 4.29 6.20 5.57 5.03 5.17 4.66 4.69 4.90

'33 1.16 1.62 1.61 2.54 2.70 2.07 5.58 7.53 6.10 4.29 4.71 3.63

'34 3.35 6.12 3.48 4.71 4.87 3.40 3.04 4.50 4.65 4.76 3.94 4.26

'35 2.30 7.10 9.62 8.18 6.84 9.05 8.66 10.28 8.66 7.95 9.38 8.00

'36 31.68 26.36 17.89 19.69 19.01 31.42 36.75 46.42 18.37 21.26 17.98 26.07

'37 5.08 5.26 8.93 9.61 7.34 8.58 8.64 10.05 9.25 9.23 15.44 8.85

'38 4.50 2.35 2.38 1.86 1.50 1.69 1.48 1.36 1.39 1.45 1.19 1.92

'39 0.17 0.17 0.17 0.15 0.14 0.14 0.16 0.19 0.16 0.17 0.16 0.16

'40 1.69 1.39 1.13 1.01 1.09 1.43 1.30 1.22 1.44 1.54 1.66 1.35

'41 0.60 0.22 0.10 0.43 0.89 0.67 0.84 2.23 3.62 2.68 3.95 1.48

'42 1.82 1.85 2.05 2.07 2.10 1.80 1.81 1.55 1.57 1.58 1.93 1.83

'43 8.61 5.89 19.50 27.19 19.53 6.77 11.91 4.58 4.71 5.02 5.50 10.84

'44 0.35 0.30 0.32 0.43 0.56 0.54 0.65 0.70 0.76 0.63 0.58 0.53

'45 568.81 317.34 324.95 541.78 410.89 749.07 318.30 329.33 363.24 420.28 407.63 431.96

'46 25.27 24.01 25.89 16.52 11.26 16.64 11.87 26.32 23.47 26.95 25.09 21.21

'47 75.32 1.75 0.49 0.13 0.67 2.28 2.13 0.74 1.32 3.86 1.94 8.24

'48 0.53 0.51 0.58 0.60 0.58 0.81 0.63 0.77 0.75 0.86 0.88 0.68

'49 0.37 1.64 2.81 2.24 1.33 1.65 1.61 3.40 0.62 0.16 0.35 1.47

'50 24.48 23.41 21.75 28.04 31.15 32.38 28.82 31.52 32.59 37.99 42.62 30.43

'51 0.79 1.07 1.22 2.32 3.37 7.25 6.10 5.31 6.03 6.01 8.51 4.36

'52 0.02 0.03 0.04 0.04 0.06 0.04 0.14 0.20 0.38 0.42 0.44 0.17

'53 0.41 0.83 1.01 1.38 1.00 2.61 2.16 1.94 3.11 3.40 2.29 1.83

'54 1.16 1.77 2.26 2.55 2.43 2.48 2.49 2.26 2.33 2.24 2.27 2.20

'55 0.36 0.20 0.39 1.85 1.67 1.27 2.07 2.01 1.39 1.31 1.46 1.27


244

'56 12.12 9.94 12.12 11.04 10.93 6.49 6.88 7.89 8.46 8.07 7.75 9.24

'57 1.97 2.24 2.41 4.23 3.87 4.30 4.87 5.88 6.01 6.98 7.16 4.54

'58 3.47 2.12 2.45 2.33 3.14 3.37 4.59 5.07 4.50 3.77 3.90 3.52

'59 4.21 3.98 4.97 4.84 3.89 3.80 4.07 4.14 4.07 3.80 3.88 4.15

'60 1.64 3.60 3.21 3.20 2.82 3.49 4.08 4.16 3.86 3.54 2.96 3.32

'61 0.32 0.39 0.34 0.37 0.39 0.40 0.37 0.39 0.39 0.36 0.39 0.37

'62 0.40 0.32 0.30 0.39 0.41 0.51 0.38 0.42 0.41 0.37 0.32 0.38

'63 0.07 0.36 0.41 0.46 0.22 0.19 0.28 0.29 0.21 0.18 0.21 0.26

'64 1.06 0.88 0.83 0.92 0.91 1.02 0.91 0.88 0.80 0.74 0.78 0.88

'65 13.90 12.40 6.15 7.83 5.20 8.57 8.65 3.05 8.70 7.17 8.33 8.18

'66 15.06 16.81 16.54 17.77 18.70 14.03 14.24 16.12 16.22 14.31 14.37 15.83

'67 13.27 13.08 11.53 11.95 10.31 8.74 8.49 6.11 7.87 7.62 7.45 9.68

'68 1.97 1.99 2.66 4.06 3.53 1.63 1.55 1.74 1.55 1.37 1.34 2.13

'69 2.99 2.36 3.02 3.42 2.04 1.39 1.88 1.69 1.19 0.97 1.04 2.00

'70 3.28 2.46 1.90 1.33 1.09 1.16 0.99 0.97 0.76 0.65 0.69 1.39

'71 0.01 0.01 0.01 0.10 0.12 0.08 0.06 0.02 0.01 0.00 0.04 0.04

'72 0.04 0.03 0.06 0.08 0.05 0.09 0.11 0.10 0.14 0.13 0.17 0.09

'73 0.14 0.14 0.11 0.14 0.12 0.14 0.15 0.13 0.18 0.22 0.23 0.16

'74 0.38 0.25 0.13 0.34 0.48 0.45 0.56 0.56 0.46 0.49 0.63 0.43

'75 8.71 9.00 4.76 4.27 10.24 5.07 2.32 1.81 2.96 3.17 1.65 4.91

'76 0.17 0.17 0.13 0.16 0.19 0.24 0.22 0.23 0.25 0.29 0.28 0.21

'78 0.30 0.02 0.11 0.00 0.03 0.07 0.19 0.07 0.09 0.26 0.14 0.12

'79 1.43 0.57 0.75 1.91 2.47 2.24 0.84 2.23 0.74 1.11 0.95 1.39

'80 13.28 0.09 0.54 0.31 0.14 5.80 0.57 1.21 4.61 0.46 1.31 2.57

'81 0.80 1.64 1.95 1.75 1.13 1.84 1.94 1.57 0.93 1.28 1.91 1.52

'82 0.33 0.23 0.26 0.24 0.28 0.36 0.38 0.38 0.33 0.26 0.34 0.31

'83 0.87 0.93 1.40 1.56 0.78 0.80 0.97 0.97 0.69 0.60 0.68 0.93

'84 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

'85 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.01 0.01

'86 0.71 0.50 0.64 0.53 0.40 0.45 0.76 0.24 0.70 0.80 0.58 0.57

'87 0.02 0.01 0.01 0.01 0.01 0.02 0.01 0.01 0.01 0.01 0.01 0.01

'88 0.00 0.01 0.00 0.00 0.01 0.00 0.00 0.01 0.01 0.00 0.28 0.03

'89 0.01 0.04 0.06 0.00 0.00 0.03 0.01 0.02 0.03 0.03 0.04 0.02

'90 0.01 0.01 0.01 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

'91 0.57 0.54 0.43 0.76 0.94 0.53 0.63 0.77 0.41 0.27 0.41 0.57

'92 1.10 0.80 1.81 1.74 1.39 1.69 2.08 2.32 3.45 4.19 4.41 2.27

'93 105.24 13.19 25.91 18.13 10.61 5.03 20.94 18.46 10.15 18.24 9.39 23.21

'94 0.04 0.03 0.04 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03

'95 0.09 0.09 0.09 0.08 0.07 0.08 0.08 0.07 0.07 0.07 0.06 0.08

'96 0.15 0.11 0.11 0.12 0.08 0.13 0.13 0.18 0.12 0.12 0.10 0.12

'97 8.23 1.65 1.06 0.84 2.21 8.49 0.03 0.01 0.49 0.04 1.63 2.24

'99 0.07 0.02 0.04 0.11 0.08 0.21 0.16 0.05 0.79 0.09 0.18 0.16

Source: Author’s Calculation of III indices of Pakistan with China from UNCOMTRADE


245

Appendix M: Pakistan’s Import Intensity Index with India Codes 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

'01 4391.87 25369.73 0.00 2369.37 119.34 88.24 396.94 0.00 0.00 47.19 0.00

'02 0.00 2208.48 2350.21 2131.19 2157.55 1436.23 1162.90 830.58 803.10 425.97 436.49

'03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'04 386.53 462.84 1465.78 219.01 142.71 69.37 105.57 60.31 186.49 445.13 823.14

'05 0.00 13254.22 8011.00 5851.20 7756.69 5028.16 4258.36 2179.03 3065.72 2419.85 1630.83

'06 0.00 9.38 38.43 24.04 215.13 282.38 156.23 119.58 102.03 321.47 34.90

'07 14.88 96.88 69.78 93.43 155.85 158.31 120.58 131.67 217.63 183.95 191.59

'08 0.46 1.49 1.19 1.47 0.68 1.19 2.65 15.21 9.91 5.53 6.96

'09 8.44 16.44 20.53 14.68 29.15 17.08 28.26 19.84 22.67 15.52 24.35

'10 0.01 3.65 0.85 0.49 2.06 2.58 18.42 8.91 3.13 0.80 0.91

'11 2.01 0.00 6.63 106.70 10.50 0.17 0.50 2.13 0.67 1.32 182.73

'12 5.43 16.44 18.66 17.50 15.74 17.47 11.51 8.71 9.32 11.52 11.37

'13 88.24 141.92 95.09 124.32 101.01 154.00 136.60 34.59 14.95 16.84 16.23

'14 1159.98 895.92 1129.63 901.33 835.10 788.75 479.08 581.04 540.75 485.64 495.72

'15 0.14 0.16 0.16 0.18 0.11 0.12 0.07 0.05 0.05 0.08 0.07

'16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'17 1.45 2.66 98.49 13.56 0.04 0.34 30.72 44.07 1.11 1.63 8.24

'18 0.00 227.53 0.00 0.00 0.00 0.00 0.00 0.00 0.53 0.21 7.59

'19 2.09 4.62 5.47 6.76 5.14 4.20 14.24 17.21 3.41 19.11 14.07

'20 2.49 0.00 0.00 0.00 0.00 0.00 0.05 0.09 0.85 2.52 15.62

'21 2.55 4.52 0.82 18.94 2.63 3.81 2.37 0.53 2.63 3.38 3.87

'22 0.00 0.00 0.00 0.00 2.66 0.00 0.43 0.00 0.07 0.00 0.00

'23 68.03 110.27 80.06 66.01 37.62 40.89 42.13 36.25 38.29 28.26 26.04

'24 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.80 1.53

'25 0.12 0.26 0.15 0.25 1.05 1.23 1.74 1.41 1.45 3.75 0.72

'26 13.21 9.39 9.49 10.22 8.78 2.67 3.52 3.27 0.01 0.03 5.10

'27 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01

'28 1.24 0.91 1.04 2.38 1.11 2.38 0.89 1.64 1.75 2.74 3.10

'29 2.55 1.73 2.10 3.26 2.98 2.54 1.54 1.58 1.29 1.08 1.12

'30 0.05 0.22 0.40 0.74 0.99 0.23 0.62 0.43 0.22 0.18 0.43

'31 0.62 0.00 0.01 0.07 2.01 0.23 3.98 1.27 0.33 1.28 0.94

'32 5.46 6.76 6.46 6.50 5.86 7.72 6.81 6.34 6.70 6.95 8.70

'33 0.95 1.06 1.82 1.42 2.03 2.61 3.36 2.53 4.43 8.15 9.81

'34 5.05 3.65 3.10 25.43 22.31 30.20 16.64 8.17 3.99 9.25 9.91

'35 29.87 16.47 9.98 8.88 5.41 4.64 3.49 5.87 5.95 6.72 13.98

'36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'37 8.35 14.88 25.82 20.93 19.24 29.67 10.81 16.69 19.97 15.54 28.68

'38 0.50 0.60 0.80 1.45 2.06 2.19 2.83 2.40 2.56 1.66 2.01

'39 1.89 1.18 1.75 1.45 1.13 0.66 0.41 0.52 0.77 1.08 0.94

'40 5.00 3.99 3.41 4.08 3.84 3.72 2.69 2.39 2.05 2.25 2.80

'41 0.11 0.11 0.19 0.04 0.50 0.31 0.37 0.33 0.25 0.48 0.68

'42 0.01 0.00 0.08 0.17 0.01 0.01 0.00 0.01 0.01 0.01 0.14

'43 0.00 0.00 0.00 26071.52 834.28 632.95 0.00 0.00 694.00 0.00 0.00

'44 6.35 0.50 0.32 0.37 0.53 0.88 1.04 0.24 0.89 1.62 0.90

'45 6850.03 10582.73 9418.87 3193.29 9763.71 2596.83 2871.64 1130.86 2681.35 4578.11 438.89

'46 0.00 0.00 0.00 24.05 36.87 0.00 0.00 0.00 0.00 0.00 0.00

'47 6.82 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'48 0.04 0.10 0.13 0.17 0.13 0.18 0.11 0.10 0.15 0.16 0.11

'49 26.13 30.01 21.84 18.92 21.92 16.86 23.32 28.70 7.46 0.95 2.82

'50 0.14 0.01 0.00 0.00 0.01 0.00 0.39 1.76 0.13 0.03 0.00

'51 8.50 9.06 13.54 23.38 25.76 20.31 17.40 10.24 12.74 2.43 2.89

'52 1.00 0.65 1.48 2.51 3.48 3.40 2.56 2.15 1.64 1.82 3.16

'53 0.19 0.14 0.06 0.03 0.07 0.03 20.31 0.65 0.65 0.61 0.89


246

'54 0.01 0.03 0.03 0.01 0.02 0.02 0.01 0.00 0.14 0.79 1.14

'55 0.12 0.67 0.35 0.19 0.34 0.41 0.79 0.76 1.77 1.69 1.56

'56 1.09 0.45 0.44 1.34 0.51 0.18 0.12 0.13 3.85 6.42 5.96

'57 0.00 0.20 0.32 0.01 0.45 0.00 0.00 0.00 0.02 0.01 0.04

'58 0.56 1.03 1.90 6.81 6.81 4.35 8.46 7.08 8.84 5.69 5.87

'59 1.52 4.75 3.11 2.46 3.43 5.19 2.84 3.21 3.03 3.11 2.04

'60 2.13 5.39 0.00 0.00 0.00 0.00 0.15 0.00 0.05 0.11 0.03

'61 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.08 0.05

'62 0.00 0.02 0.00 0.03 0.00 0.00 0.00 0.02 0.04 0.04 0.18

'63 0.00 0.10 0.04 0.01 0.03 0.04 0.09 0.02 0.03 0.01 0.01

'64 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.02

'65 0.00 1.05 0.00 2.24 1.23 1.19 0.81 0.50 0.00 16.11 18.34

'66 466.64 270.96 1520.98 447.75 0.00 0.00 438.19 2540.44 0.00 0.00 534.29

'67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'68 1.43 1.79 1.88 0.79 1.14 3.17 2.78 3.66 3.48 3.43 3.36

'69 0.93 3.01 1.38 1.98 0.71 1.32 0.43 0.12 0.33 0.48 0.24

'70 0.44 0.92 0.42 0.51 0.44 0.38 0.30 0.12 0.10 0.11 0.25

'71 0.00 0.00 0.00 0.00 0.02 0.01 0.01 0.00 0.06 0.00 0.29

'72 0.07 0.05 0.08 0.08 0.07 0.08 0.06 0.06 0.05 0.04 0.05

'73 0.01 0.02 0.02 0.12 0.13 0.05 0.12 0.25 0.05 0.01 0.01

'74 0.03 0.09 0.08 0.23 0.51 0.62 0.33 0.44 0.23 0.16 0.17

'75 2.21 1.74 0.80 2.35 4.88 10.50 4.63 4.78 0.59 0.99 0.31

'76 1.26 0.63 0.59 0.58 0.72 0.62 0.41 0.39 0.35 0.23 0.11

'78 0.00 0.00 2.92 8.71 4.83 0.24 0.14 0.65 0.33 0.91 0.96

'79 0.00 0.55 1.44 8.36 9.89 3.95 5.01 5.18 0.16 10.12 7.48

'80 0.00 0.00 0.00 5.05 3.28 3.41 0.00 0.00 0.00 0.00 0.00

'81 0.49 0.32 4.34 0.00 5.09 0.00 8.40 63.19 98.62 43.44 85.56

'82 0.03 0.01 0.10 0.01 0.02 0.06 0.07 0.09 0.06 0.11 0.22

'83 0.08 0.28 0.00 0.02 0.03 0.25 0.54 0.58 0.64 0.50 0.71

'84 0.00 0.00 0.01 0.01 0.01 0.02 0.02 0.01 0.01 0.01 0.02

'85 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01

'86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'87 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'88 0.00 0.00 0.01 0.02 0.00 0.00 0.00 0.00 0.00 0.01 0.00

'89 0.00 0.09 0.00 0.00 0.00 0.00 0.07 0.01 0.10 0.04 0.20

'90 0.03 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.02 0.02 0.02

'91 0.00 0.00 0.28 0.05 0.12 0.00 0.00 0.00 2.18 1.12 0.96

'92 0.00 0.00 2.88 1.83 0.00 3.09 0.00 0.00 0.00 0.00 0.00

'93 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

'94 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00

'95 0.21 0.57 0.42 0.44 0.72 0.37 1.06 0.40 0.28 0.17 0.12

'96 0.00 0.01 0.03 0.17 0.24 0.34 0.31 0.51 0.40 0.34 0.24

'97 0.00 0.02 0.00 0.00 0.01 0.00 0.00 0.00 0.25 0.00 0.00

'99 0.00 0.03 0.08 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.04

Source: Author’s Calculation of III indices of Pakistan with India from UNCOMTRADE


247

Appendix N: Pakistan’s Import Intensity Index with Afghanistan

Codes 2008 2009 2010 2011 2012 2013 2014 average s.d


'02 #DIV/0! #DIV/0! 0 #DIV/0! #DIV/0! #DIV/0! 0 #DIV/0! #DIV/0!


'04 0 159.8979 74808.41 17733.16 #DIV/0! #DIV/0! 1433.914 #DIV/0! #DIV/0!

'05 447.2258 30936.05 9183.114 55302.93 30713.83 188024.4 11324.46 46561.71 64998.44


'07 8696.344 1294.595 715.8184 1453.142 #DIV/0! #DIV/0! 701.0267 #DIV/0! #DIV/0!

'08 162.9924 234.6234 365.9217 944.4164 1541.482 1261.949 841.0223 764.6295 530.8525

'09 43.87407 7.374272 14.09097 35.78219 0.327812 0.144042 5.410315 15.28624 17.56868

'10 #DIV/0! 46145.49 2071.937 #DIV/0! #DIV/0! #DIV/0! 114.1895 #DIV/0! #DIV/0!


'12 63.85289 6.793093 5.529367 15.69629 8.08474 10.45477 19.56341 18.56779 20.59262

'13 3.425302 0 123.0645 13.28422 #DIV/0! #DIV/0! 0.365641 #DIV/0! #DIV/0!


'15 0 0 0 61.12097 #DIV/0! #DIV/0! 0 #DIV/0! #DIV/0!





'20 10.15932 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 27.49165 #DIV/0! #DIV/0!





'25 #DIV/0! 7290.481 10338.74 22508.42 #DIV/0! #DIV/0! 1389.944 #DIV/0! #DIV/0!


'27 #DIV/0! 96.51057 #DIV/0! 116.7937 #DIV/0! #DIV/0! 9.663856 #DIV/0! #DIV/0!














'41 44.79525 283.3015 140.8942 84.12225 228.7099 103.4466 219.4815 157.8216 87.58048

'42 2.707045 0 0 0 #DIV/0! #DIV/0! 0 #DIV/0! #DIV/0!









'51 95.29807 41.10777 59.38544 76.79362 51.56528 19.4712 171.2558 73.55389 49.48217

'52 29851.67 10370.54 178.0121 335.3184 #DIV/0! #DIV/0! 73.05875 #DIV/0! #DIV/0!



248




'57 6.709321 17.15983 23.11574 41.56648 19.00147 17.9643 136.6059 37.44614 44.96484











'68 0 0 0.251367 0 #DIV/0! #DIV/0! 80.76006 #DIV/0! #DIV/0!



'71 0 848.8681 #DIV/0! #DIV/0! #DIV/0! #DIV/0! 12.26619 #DIV/0! #DIV/0!

























'97 0 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 0 #DIV/0! #DIV/0!

'99 #DIV/0! 0.169852 0.059236 0.006947 0.002417 0.008835 0.022521 #DIV/0! #DIV/0!

Source: Author’s Calculation of III indices of Pakistan with Afghanistan from

UNCOMTRADE


249

Appendix O: Pakistan’s Import Intensity Index with Iran Code

s

2004 2005 2006 2010 2011 2012 2013 2014 average s.d

'01 0 0 0 388.031

2 0 69.6454

1 0 0 57.2095

7

135.875

6 '02 0 447.821

8 0 0 0 129.254

1 0 0 72.1344

9

158.395

5 '03 4660.40

8

2096.56

3

846.329

9

2545.01

5 0 0 0 0 1268.54 1708.58

4 '04 6.50172

9

455.760

4

538.942

4

55.4352

6

34.1403

5

465.241

9

1567.15

6 655.355 472.316

7

509.861

6 '05 0 0 0 434.302

9

43.6580

4

524.298

8

287.132

5

1652.31

2 367.713 560.139

'06 0 28.8424

2 0 0 0 0 0 0 3.60530

2

10.1973

3 '07 420.123

9

176.384

1

36.0310

4

3.53768

3

6.48037

7

37.7028

7

33.9807

9

70.2955

5

98.0670

4

141.193

2 '08 55.2515

2 23.3197 22.7538

5

9.65729

3

12.4596

6

17.6754

2

11.3811

6

16.1736

2

21.0840

3

14.6873

6 '09 6.28303

5

8.71924

2

15.2635

8

10.4653

8

7.82663

9

26.7202

1

18.8037

2

50.2678

8

18.0437

1

14.6826

6 '10 0 352.211

4

145.923

9 0 0 0 71.1849

6

59.8840

4

78.6505

4 122.19

'11 0 13.1025

6 0 1.11962

2 0 0 94.6441

2 0 13.6082

9

33.0561

3 '12 50.0954

1

22.8307

5

18.5498

5

6.63196

8

5.89296

2

3.22868

2

2.69698

4

2.33973

4

14.0332

9

16.4807

5 '13 249.468

7

66.1580

4 99.5642 10.2992

6 0 9.69866

2

3.76017

7

1.31877

4

55.0334

8 86.6117

'14 0 0 0 5.69383

1 0 0 0 0 0.71172

9

2.01307

3 '15 0.86288

3

0.19621

6 0 0 0 0 18.4054

4 0 2.43306

8

6.46070

4 '16 0 0 0 0 0 0 0 0 0 0

'17 15.4515

7

0.22050

9

0.04448

6 0.02235 9.10594

6

5.53219

6

4.77375

9

39.0835

2

9.27929

2

13.1681

1 '18 122.705

1 3.73614 1.39672

9 0 0 0 0 3.04237

3

16.3600

5

42.9958

6 '19 8.86712

1

19.9076

3 5.20765 0.28525

7

0.15640

9

15.1408

3

35.9185

1

0.24341

9

10.7158

5

12.5525

9 '20 43.5800

5

43.4348

2

26.9366

1

4.78334

2

1.96003

1

8.81460

4

5.46232

9

5.11842

1

17.5112

8

17.8032

2 '21 0 10.3724

2 0 9.56769

7

1.41821

2 0.2679 12.4145

3 4.84088 4.86020

4

5.20704

1 '22 0.72271

8

2.85290

2

0.24438

1

203.736

2 0 0 51.5482

3

45.2622

2

38.0458

3

70.3492

9 '23 0 0 0 0 0 0 0 0 0 0

'24 0 0 12.2801

7 0 1.60977

9 0 0 0 1.73624

4

4.29746

9 '25 20.8249

1

47.5356

6

82.5081

9

5.11180

8

11.6064

9

10.5064

5

17.7454

3

21.9466

9 27.2232 25.7456

8 '26 87.638 87.8179 60.5749

6

62.1548

9

116.190

9

18.7404

3

6.19450

8

0.01203

7

54.9154

5

42.5763

3 '27 0.05447

3

0.02956

1

0.03011

4 0.07084 0.00260

2

0.00368

4

0.00986

8

0.01107

8

0.02652

8

0.02500

6 '28 5.00271

1

2.28954

8

0.89360

9

0.11383

2

0.05000

4

0.47639

3

0.17534

9

0.05686

7

1.13228

9

1.73565

3 '29 3.13861

1

2.52507

3 3.46239 2.09752 1.00470

3

0.29016

1

0.09057

1

0.09279

2

1.58772

8

1.39171

1 '30 0.18117

4

0.27066

4

0.82686

4

1.29874

7

1.12428

4

0.64845

5

0.14155

7

1.88928

3

0.79762

8

0.61653

2 '31 5.05926

7

22.9312

3

109.948

3 7.8883 0.19332

4

0.03619

5

0.20320

8

0.08932

9

18.2936

4

37.8438

9 '32 1.47492

4 0.7723 1.38049

2

0.10095

8

0.27676

9

0.40927

2

0.15697

7

0.69996

5

0.65895

7

0.53086

2 '33 0.47892

4 0 0.08734

6

0.57235

9

0.11631

5 0 10.8588

4

9.67163

1

2.72317

6

4.67068

9 '34 13.5435

3 10.1154 5.15749

1

1.13725

5

0.76307

9

3.80797

9

2.26240

9

8.08522

5

5.60904

6

4.59071

7 '35 0.25876

9 0 1.08022

4 0 0 0 0 0 0.16737

4

0.37979

9 '36 0 #DIV/0

!

#DIV/0

!

#DIV/0

!

#DIV/0

! 0 376.816

7 0 #DIV/0

!

#DIV/0

! '37 0 0 0 218.269

3

1152.49

8 0 0 0 171.345

9

403.735

6 '38 0.29158

4

1.02934

8

0.71029

7

3.08283

1

3.21760

7

3.36806

6

0.30878

1 0.13165 1.51752 1.44120

7 '39 5.21118

7

1.98768

3

0.89066

7

1.21523

8

0.86117

4

0.09073

3

0.04455

4

0.04775

9

1.29362

4

1.72145

5 '40 2.10962

4

1.75439

8

1.25906

6

1.43155

5

3.11941

3

10.1390

8 5.11731 7.08382

8

4.00178

4

3.20597

5 '41 16.5906

7

27.2911

2

46.2219

2

31.9522

1

30.9159

5

51.5212

1

145.321

8

99.8294

3

56.2055

4

44.0771

1 '42 0 4.78869

1

8.02553

3 0 0 141.777 0 0 19.3239

1

49.5706

7 '43 0 0 0 0 0 0 0 0 0 0

'44 0 6.40665

4

5.07416

1

0.34729

2 0 0 5.68453

1

0.53148

5

2.25551

5

2.89853

5 '45 0 0 0 0 #DIV/0

! 0 0 0 #DIV/0

!

#DIV/0

! '46 0 0 0 0 0 0 0 0 0 0

'47 1.78889

3

0.24418

1 0 0.05819

1

0.39143

3 0 0 0 0.31033

7

0.61480

5 '48 0.25170

4

0.19183

2

4.50395

4 0.12145 0.70748

7

0.42262

4

0.71146

7

0.55996

3 0.93381 1.46010

7


250

'49 1.31742

5

0.20359

8

0.26405

8 3.33276 4.26369 4.69980

8 0.46173 1.83548

7

2.04731

9

1.82331

7 '50 302.449

1

1560.58

9

295.218

1 0 0 0 0 0 269.782

1

538.756

4 '51 96.0835

6

87.6562

4

25.5892

8

1.91950

9 13.5851 3.68151

1

50.6079

6

19.9831

9

37.3882

9

36.9356

3 '52 56.2701

8

50.9093

8

39.2490

5

1.80570

6

9.54581

7 0 0.65798

8

0.05248

8

19.8113

3 24.6504

'53 0 0 0 0 0 0 0 0 0 0

'54 0.39959

9

0.01583

3 0 0 0 0.91122

5

1.27505

6 1.1218 0.46543

9

0.55309

5 '55 5.98304

8

3.96872

5 8.24394 0 0.24696

8 0 0.14538 0 2.32350

8

3.30373

8 '56 10.0289

7

1.93219

5 1.39615 0.30393

4

1.33923

8 28.8094 33.9568

5

48.2852

3 15.7565 18.6567

7 '57 7.09843

4

5.56710

3

4.61808

5 2.49083 1.99261 15.5721

5

12.4070

8

10.0371

2

7.47292

6

4.82990

7 '58 0 0.15361 0.04155

4

0.03971

4 0 0 0 1.05848

5 0.16167 0.36609

8 '59 3.51810

5

0.46539

9

2.49868

5 0 0 0 0 0 0.81027

3

1.39308

2 '60 0 0 0 0 0 0 0 0 0 0

'61 0 0 0 0 0 0 0 0 0 0

'62 0 0 0 0 0.06851 0 0 0 0.00856

4

0.02422

2 '63 1.30219

4

0.42412

1

0.04210

7

4.08187

4

0.63388

9

0.04795

9

0.11756

2

1.84948

8

1.06239

9

1.38089

3 '64 0 0.01352

8

0.09084

4

0.00503

2 0 0.26056

7

0.24898

1 0 0.07736

9

0.11367

9 '65 185.550

1

20.2086

8 0 0 0 0 0 0 25.7198

5

64.9671

9 '66 0 0 0 0 0 0 0 0 0 0

'67 0 0 0 0 0 0 0 0 0 0

'68 9.01779

3

5.76062

5

1.09637

5

0.67509

9

0.57822

3

0.32238

9

0.44080

8

0.23168

5

2.26537

5

3.29055

1 '69 1.30978

3

0.46190

9 0.59734 0.01153

3

0.02924

2

0.98483

5

3.87570

1

15.9425

8

2.90161

6

5.41279

7 '70 1.44501

6

3.10475

1

1.37596

2

0.63380

5

0.64955

6

2.75306

9

2.61541

7

1.64156

9

1.77739

3

0.94762

8 '71 0 0.00229

3 0 0 0.00236

8

0.17305

2 0 0 0.02221

4

0.06095

7 '72 0.47509

7

0.92989

1

1.33045

6

1.16454

8

0.56971

8

0.64120

9

0.49988

1

0.43789

4

0.75608

7

0.34241

2 '73 0.05462

3

0.17995

7

0.15553

3

0.15081

4

0.11867

9

0.30855

4

1.44033

3

1.41624

9

0.47809

3

0.59080

2 '74 0.99552

8

0.08128

4

0.00745

5

0.37020

4

0.04176

6

0.00736

4

0.00266

8

0.01038

8

0.18958

2

0.34829

6 '75 0 0 0 0 0 0 0 0 0 0

'76 0.64197

2

0.27126

9

0.14467

6

0.04546

7

0.01866

5 0 0 0 0.14025

6

0.22414

5 '78 234.917

2

265.040

4

5.50802

1

2.35372

8 0 0.29076 0 0 63.5137

7

115.385

4 '79 44.4573

1

34.4897

4

15.9285

6

6.78381

8 0 0 0 0 12.7074

3

17.6226

5 '80 0 0 0 0 #DIV/0

! 0 0 0 #DIV/0

!

#DIV/0

! '81 0 0 0 0 0 0 0 0 0 0

'82 0 0 0.12913

7

0.36486

8 0 0 0 0 0.06175

1

0.13054

8 '83 0.79298

9

0.25395

4

0.40921

6

0.12895

9 0 0 2.83294

8 0 0.55225

8

0.96088

4 '84 0.00196

7 0.07022 0.05983

4 0.04939 0.02872 0.01125

9

0.02134

5

0.02339

7

0.03326

7 0.02407

'85 0.04377

5 0.01277 0.04574

7

0.09924

7

0.00786

3

0.03531

1

0.89383

3

1.12713

8 0.28321 0.45403

6 '86 0 0 0.06837

1 0 0 0 0 4.15241

8

0.52759

9

1.46484

4 '87 0.00379 0.02160

2

0.01847

9

0.00101

6

0.00232

6

0.00095

5 0.00133 0.00363

8

0.00664

2

0.00838

3 '88 0 #DIV/0

!

#DIV/0

!

#DIV/0

!

#DIV/0

! 0 0 0 #DIV/0

!

#DIV/0

! '89 0 941.870

6 0 0.25896

3 0.51124 0 7.80447

6 14.245 120.586

3

331.889

8 '90 0.05267 1.33058

9

0.87669

2

0.09712

9

0.59320

8 0.10721 0.04684

9

0.02270

6

0.39088

2

0.49208

8 '91 0 0 0.13409

4 0 0 0 0 0 0.01676

2

0.04740

9 '92 0 0 0 0 0 0 0 0 0 0

'93 0 #DIV/0

!

#DIV/0

!

#DIV/0

!

#DIV/0

! 0 0 0 #DIV/0

!

#DIV/0

! '94 0.02176

2 0 0 0 0 0.26554 0.09080

2

0.00818

9

0.04828

7

0.09307

8 '95 0 0 0 0 0.03124

8 0 0 0 0.00390

6

0.01104

8 '96 0.87439

6

1.02063

2

0.39967

9 4.41293 2.43329

1 0.42162 0.02756

3

0.22794

7

1.22725

7

1.49006

5 '97 0 0 0 0 0 0.76258

7 0 0 0.09532

3

0.26961

5 '99 16.7125

5

1.62174

6

0.01453

2

0.03503

6

0.01255

3

0.00011

5

0.22793

1

2.86596

2

2.68630

3

5.76289

6 Source: Author’s Calculation of III indices of Pakistan with Iran from UNCOMTRADE


251

Appendix P: Average GLI of Pakistan and its Neighbouring Countries


252

Cod

es

Pakist

an

Indi

a

Chin

a

Afga

n

iran

'01 0.517 0.71

3

0.62

8

0.00

7

0.45

2 '02 0.157 0.00

1

0.59

4

0.00

6

0.18

7 '03 0.030 0.04

1

0.72

4

0.02

2

0.54

8 '04 0.789 0.34

9

0.49

9

0.00

6

0.47

2 '05 0.115 0.48

7

0.36

9

0.01

1

0.21

5 '06 0.810 0.23

7

0.75

5

0.00

2

0.46

7 '07 0.462 0.70

6

0.36

4

0.52

6

0.21

1 '08 0.701 0.91

0

0.85

1

0.10

0

0.24

4 '09 0.214 0.29

6

0.15

9

0.55

6

0.58

2 '10 0.265 0.10

9

0.55

3

0.00

3

0.01

7 '11 0.364 0.42

3

0.85

2

0.00

0

0.54

7 '12 0.204 0.26

9

0.18

8

0.12

2

0.13

6 '13 0.321 0.22

3

0.47

3

0.00

0

0.67

7 '14 0.418 0.28

6

0.69

3

0.10

3

0.22

6 '15 0.148 0.21

6

0.10

7

0.00

6

0.18

8 '16 0.295 0.03

8

0.03

0

0.00

0

0.36

8 '17 0.357 0.38

3

0.83

9

0.00

0

0.31

0 '18 0.095 0.46

0

0.72

3

0.00

0

0.47

2 '19 0.636 0.26

6

0.75

2

0.00

0

0.36

9 '20 0.824 0.29

8

0.13

0

0.04

2

0.53

9 '21 0.588 0.32

0

0.61

4

0.00

0

0.46

6 '22 0.105 0.73

6

0.72

2

0.00

1

0.35

7 '23 0.234 0.16

7

0.80

7

0.00

3

0.06

5 '24 0.764 0.09

0

0.89

9

0.00

2

0.08

9 '25 0.402 0.82

0

0.83

4

0.20

9

0.39

9 '26 0.586 0.69

4

0.02

7

0.07

2

0.21

8 '27 0.151 0.46

3

0.28

9

0.04

3

0.07

6 '28 0.098 0.48

3

0.84

3

0.00

2

0.76

9 '29 0.057 0.90

2

0.74

4

0.01

1

0.59

8 '30 0.455 0.29

7

0.71

3

0.00

1

0.17

3 '31 0.008 0.02

0

0.70

1

0.00

2

0.35

4 '32 0.192 0.77

4

0.89

6

0.00

0

0.31

8 '33 0.240 0.55

3

0.71

6

0.01

4

0.29

8 '34 0.201 0.85

3

0.88

8

0.00

0

0.56

3 '35 0.391 0.88

7

0.86

8

0.00

1

0.44

4 '36 0.231 0.27

7

0.14

2

0.00

0

0.25

9 '37 0.096 0.19

8

0.77

1

0.00

1

0.00

4 '38 0.053 0.87

8

0.83

6

0.00

3

0.46

6 '39 0.395 0.78

3

0.77

2

0.00

5

0.67

8 '40 0.109 0.88

5

0.94

4

0.00

1

0.18

4 '41 0.365 0.67

9

0.23

7

0.00

1

0.03

1 '42 0.047 0.13

3

0.08

7

0.14

6

0.38

1 '43 0.461 0.22

0

0.46

9

0.00

9

0.45

0 '44 0.438 0.19

8

0.89

3

0.00

1

0.16

6 '45 0.077 0.61

5

0.66

9

0.00

0

0.20

2 '46 0.497 0.56

9

0.01

2

0.01

4

0.49

0 '47 0.024 0.00

4

0.01

5

0.10

7

0.15

5 '48 0.123 0.57

6

0.66

1

0.00

4

0.08

6 '49 0.210 0.57

8

0.58

8

0.00

6

0.51

4 '50 0.098 0.87

3

0.14

5

0.00

9

0.23

2 '51 0.697 0.56

4

0.88

3

0.03

1

0.78

6 '52 0.318 0.19

9

0.91

6

0.26

0

0.32

1 '53 0.189 0.78

6

0.81

0

0.11

9

0.08

1 '54 0.368 0.51

4

0.57

6

0.00

0

0.13

8 '55 0.855 0.37

8

0.60

1

0.00

0

0.11

8


253

'56 0.747 0.81

2

0.66

7

0.00

3

0.40

9 '57 0.214 0.08

6

0.12

3

0.19

1

0.13

9 '58 0.708 0.66

0

0.33

0

0.00

4

0.32

9 '59 0.264 0.40

7

0.62

1

0.13

0

0.08

7 '60 0.512 0.79

6

0.48

9

0.00

0

0.24

6 '61 0.021 0.02

7

0.03

3

0.00

1

0.07

4 '62 0.026 0.03

3

0.05

9

0.00

1

0.25

1 '63 0.080 0.14

1

0.02

8

0.00

1

0.21

8 '64 0.575 0.21

4

0.06

5

0.00

5

0.14

3 '65 0.744 0.42

8

0.01

9

0.00

4

0.39

3 '66 0.559 0.21

8

0.01

2

0.00

0

0.04

4 '67 0.090 0.08

0

0.14

1

0.00

1

0.09

8 '68 0.812 0.48

8

0.29

5

0.11

7

0.49

5 '69 0.231 0.72

8

0.10

0

0.00

1

0.45

8 '70 0.392 0.91

4

0.65

6

0.00

1

0.52

2 '71 0.491 0.77

7

0.77

0

0.15

1

0.29

9 '72 0.036 0.84

3

0.73

5

0.12

4

0.36

3 '73 0.508 0.72

1

0.37

8

0.00

1

0.24

7 '74 0.626 0.77

8

0.29

8

0.13

9

0.40

6 '75 0.018 0.24

2

0.30

3

0.01

4

0.08

6 '76 0.238 0.81

9

0.75

1

0.00

8

0.77

9 '78 0.321 0.30

6

0.55

2

0.00

0

0.27

0 '79 0.036 0.69

1

0.52

9

0.00

1

0.30

7 '80 0.073 0.33

8

0.56

3

0.00

0

0.05

5 '81 0.071 0.35

0

0.54

9

0.10

2

0.16

7 '82 0.849 0.91

3

0.45

1

0.00

2

0.05

9 '83 0.080 0.85

6

0.26

6

0.02

3

0.22

2 '84 0.113 0.48

5

0.70

8

0.00

5

0.08

4 '85 0.080 0.47

3

0.90

9

0.00

4

0.10

1 '86 0.065 0.49

5

0.26

9

0.00

8

0.01

8 '87 0.099 0.57

3

0.87

5

0.00

1

0.23

1 '88 0.129 0.41

6

0.19

8

0.00

1

0.13

2 '89 0.256 0.68

3

0.15

7

0.00

0

0.26

5 '90 0.762 0.43

9

0.73

3

0.00

3

0.03

0 '91 0.119 0.55

3

0.79

4

0.00

3

0.09

4 '92 0.129 0.80

1

0.26

7

0.08

7

0.05

8 '93 0.522 0.79

7

0.11

3

0.00

1

0.04

8 '94 0.785 0.93

8

0.07

9

0.04

1

0.35

1 '95 0.273 0.77

6

0.07

5

0.00

2

0.21

0 '96 0.442 0.93

1

0.23

0

0.00

0

0.12

9 '97 0.527 0.19

4

0.41

3

0.02

2

0.44

8 '99 0.303 0.66

1

0.47

1

0.10

9

0.14

1

Source: Author’s Calculation from

UNCOMTRADE

254 PAKISTAN’S FREE TRADE POTENTIAL WITH NEIGHBOURS

254

Appendix Q: Average Revealed Comparative Advantage of Pakistan and its

Neighbours


255

Average Revealed Comparative Advantage (2004-14)

Cod

e

Pakist

an

India Chin

a

Afghanist

an

Ira

n '01 0.115 0.032 0.20

2

0.063 1.08

4 '02 1.239 2.270 0.07

2

0.002 0.02

2 '03 2.397 2.812 1.00

1

0.046 0.09

5 '04 0.728 0.293 0.04

8

0.005 0.06

1 '05 2.989 0.643 1.70

8

6.556 1.88

9 '06 0.049 0.199 0.14

8

0.001 0.01

5 '07 1.963 1.013 1.00

1

10.705 0.84

6 '08 3.083 0.923 0.33

1

55.290 3.69

8 '09 1.178 3.386 0.40

6

6.917 0.58

5 '10 14.181 5.022 0.03

0

0.182 0.00

1 '11 8.562 0.960 0.26

1

0.298 0.03

0 '12 0.867 1.260 0.24

7

6.176 0.09

0 '13 6.791 15.60

6

1.19

8

167.673 1.22

5 '14 28.586 3.702 0.94

0

21.312 0.67

2 '15 0.911 0.551 0.05

3

0.251 0.03

3 '16 0.162 0.172 1.42

1

0.000 0.09

4 '17 7.214 1.670 0.26

7

0.097 0.02

3 '18 0.003 0.150 0.07

6

0.000 0.06

5 '19 0.733 0.428 0.18

7

0.004 0.10

2 '20 0.727 0.484 0.99

8

0.865 0.29

2 '21 0.278 0.519 0.32

8

0.013 0.05

3 '22 2.379 0.198 0.11

8

0.030 0.00

8 '23 0.735 1.460 0.31

6

0.016 0.00

8 '24 0.414 1.337 0.24

3

0.073 0.00

6 '25 11.065 2.469 0.65

3

18.149 3.61

8 '26 0.342 0.352 0.01

2

0.051 2.81

8 '27 0.160 1.202 0.08

9

0.641 4.83

7 '28 0.174 0.688 0.98

4

0.006 1.08

2 '29 0.095 1.620 0.82

9

0.016 1.55

8 '30 0.292 1.336 0.10

2

0.045 0.03

3 '31 0.000 0.069 1.07

6

0.011 1.97

3 '32 0.385 2.053 0.64

4

0.006 0.03

7 '33 0.120 0.740 0.29

1

0.191 0.01

6 '34 0.423 0.543 0.46

4

0.010 0.11

6 '35 0.188 0.671 0.71

5

0.001 0.03

3 '36 2.954 1.095 1.65

9

0.000 0.07

0 '37 0.000 0.101 0.59

1

0.003 0.00

1 '38 0.077 0.971 0.59

1

0.012 0.09

3 '39 0.439 0.511 0.85

7

0.052 1.51

4 '40 0.059 0.846 0.98

0

0.045 0.01

9 '41 11.424 2.215 0.12

4

8.176 0.66

1 '42 7.321 1.957 3.16

2

0.039 0.00

3 '43 0.048 0.011 2.62

4

18.454 0.00

1 '44 0.289 0.146 0.81

2

0.077 0.00

4 '45 0.000 0.068 0.08

8

0.000 0.00

7 '46 0.373 0.091 5.43

9

0.244 0.03

2 '47 0.002 0.006 0.02

0

0.060 0.00

1 '48 0.292 0.379 0.82

2

0.041 0.01

7 '49 0.071 0.408 0.67

9

0.047 0.02

5 '50 0.366 2.916 4.26

6

0.140 0.01

0 Average Revealed Comparative Advantage (2004-14)

Cod

e

Pakist

an

India Chin

a

Afghanist

an

Ira

n '51 0.748 0.733 1.40

5

14.329 0.36

6 '52 56.593 8.273 2.05

8

24.478 0.03

2


256

'53 0.209 4.443 2.55

8

0.071 0.00

9 '54 0.501 2.923 2.67

0

0.035 0.06

8 '55 7.486 3.044 2.42

5

0.014 0.15

9 '56 2.621 0.803 1.44

9

0.009 0.03

6 '57 5.820 6.617 1.33

5

14.731 2.80

7 '58 1.230 1.767 3.01

3

0.044 0.02

1 '59 0.299 0.884 2.31

3

1.045 0.00

3 '60 0.806 0.452 3.34

4

0.000 0.02

4 '61 7.599 1.842 3.07

0

0.014 0.00

1 '62 6.391 2.270 2.76

8

0.030 0.00

0 '63 45.408 4.175 3.49

4

0.031 0.03

5 '64 0.694 1.222 3.11

6

0.049 0.00

4 '65 0.176 0.292 3.92

7

0.053 0.00

2 '66 0.051 0.033 6.39

6

0.000 0.00

0 '67 0.003 1.894 6.06

6

0.003 0.00

2 '68 0.472 1.665 1.78

2

0.030 0.20

1 '69 0.153 0.834 3.24

5

0.005 0.19

6 '70 0.299 0.590 1.82

5

0.010 0.26

8 '71 0.157 3.894 0.82

0

0.251 0.00

3 '72 0.102 1.300 1.07

7

4.544 0.44

2 '73 0.475 1.392 1.52

3

0.041 0.04

0 '74 0.691 1.307 0.35

8

0.244 0.52

6 '75 0.001 1.378 0.55

9

0.228 0.00

1 '76 0.159 0.863 1.02

4

0.059 0.30

3 '78 0.539 1.481 0.16

7

0.000 1.03

3 '79 0.076 1.714 0.28

7

0.001 1.97

9 '80 0.012 0.260 0.14

7

0.000 0.00

0 '81 0.015 0.195 1.48

8

0.012 0.02

7 '82 1.130 0.798 1.75

0

0.020 0.00

9 '83 0.025 0.478 2.05

8

0.161 0.00

2 '84 0.091 0.373 1.49

0

0.066 0.00

9 '85 0.030 0.222 1.91

1

0.050 0.00

4 '86 0.009 0.192 2.36

0

0.029 0.00

1 '87 0.032 0.613 0.36

8

0.020 0.00

9 '88 0.047 1.228 0.06

7

0.043 0.00

2 '89 0.013 2.066 1.54

9

0.000 0.06

4 '90 0.482 0.240 1.03

3

0.069 0.00

8 '91 0.049 0.094 0.74

6

0.003 0.01

0 '92 0.395 0.148 2.08

7

0.037 0.00

2 '93 0.092 0.274 0.09

2

0.049 0.00

6 '94 0.301 0.290 3.04

5

0.398 0.00

7 '95 2.106 0.182 3.24

4

0.005 0.00

5 '96 0.602 0.614 2.28

4

0.008 0.00

9 '97 0.005 0.500 0.19

5

1.279 0.17

9 '99 0.004 0.193 0.06

2

5.108 0.01

6 Source: Author’s Calculation from

UNCOMTRADE