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i
POTENTIAL ECONOMIC GAINS FROM GSP PLUS STATUS
FOR PAKISTAN: AN EMPIRICAL ANALYSIS
BY
MUHAMMAD SHAHZAD IQBAL
Registration No: 2012-GCUF-09408
Thesis submitted in partial fulfillment of
the requirements for the degree of
DOCTORATE OF PHILOSOPHY
IN
ECONOMICS
DEPARTMENT OF ECONOMICS
GOVERNMENT COLLEGE UNIVERSITY FAISALABAD
FAISALABAD (PAKISTAN)
August 2016
ii
DEDICATED TO MY PARENTS WHO SACRIFICED THEIR
GOLDEN AGE TO EDUCATE ME
iii
AUTHOR’S DECLARATION
I Muhammad Shahzad Iqbal hereby state that my PhD thesis titled “Potential Economic
Gains From GSP Plus Status For Pakistan: An Empirical Analysis” is my own work and has
not been submitted previously by me for taking any degree from this University
(Government College University Faisalabad) or anywhere else in the country/world.
At any time if my statement is found to be incorrect even after my Graduate the university
has the right to withdraw my PhD degree.
Muhammad Shahzad Iqbal
October 31, 2017
iv
PLAGIARISM UNDERTAKING
I solemnly declare that research work presented in the thesis titled “Potential Economic
Gains From GSP Plus Status For Pakistan: An Empirical Analysis” is solely my research
work with no significant contribution from any other person. Small contribution/help
wherever taken has been duly acknowledged and that complete thesis has been written by
me.
I understand the zero tolerance policy of the HEC and University (Government College
University Faisalabad) towards plagiarism. Therefore I as an Author of the above titled thesis
declare that no portion of my thesis has been plagiarized and any material used as reference
is properly referred/cited.
I undertake that if I am found guilty of any formal plagiarism in the above titled thesis even
after award of PhD degree, the University reserves the rights to withdraw/revoke my PhD
degree and that HEC and the University has the right to publish my name on the
HEC/University Website on which names of students are placed who submitted plagiarized
thesis.
Student /Author Signature:___________________________
Name:. Muhammad Shahzad Iqbal .
Registration No: . 2012-GCUF-09408 .
v
CERTIFICATE BY SUPERVISORY COMMITTEE
We certify that the contents and form of thesis submitted by Mr. Muhammad Shahzad Iqbal,
Registration No. 2012-GCUF-09408 has been found satisfactory and in accordance with the
prescribed format. We recommend it to be processed for the evaluation by the External
Examiner for the award of degree.
Signature of Supervisor ……………………….
Name: ……………………Dr. Sofia Anwar………
Designation with Stamp…professor/Chairman….
Co-Supervisor
Signature ……………………….……………….
Name: …….. Dr. Muhammad Aamir Khan….
Designation with Stamp Assistant Professor,
COMSATS Institute of Information Technology,
Islamabad ………….
Member of Supervisory Committee
Signature ………………….…………..………….
Name: ……Dr. Muhammad Rizwan Yaseen…….
Designation with Stamp.Assistant Professor……..
Chairperson
Signature with Stamp……………………………
Dean / Academic Coordinator
Signature with Stamp……………………………
vi
TABLE OF CONTENTS
AUTHOR’S DECLARATION ................................................................................................ iii
PLAGIARISM UNDERTAKING ........................................................................................... iv
LIST OF FIGURES ................................................................................................................. xi
LIST OF TABLES .................................................................................................................. xii
ACKNOWLEDGEMENT ..................................................................................................... xiv
LIST OF ABBREVIATIONS ................................................................................................. xv
ABSTRACT ........................................................................................................................... xix
CHAPTER 1: INTRODUCTION ............................................................................................. 1
1.1 An Outline of Pakistan Economy ................................................................................. 2
1.1.1 Agriculture Sector ......................................................................................................... 3
1.1.2 Industrial Sector ............................................................................................................ 5
1.1.3 Services Sector .............................................................................................................. 7
1.1.4 Pakistan’s Trade Statistics ............................................................................................ 9
1.2 European Union (EU28) and Position of Pakistan ..................................................... 12
1.2.1 Principles and Objectives ............................................................................................ 13
1.2.2 EU’s Trade Policy: Fan of Instruments ...................................................................... 13
1.2.3 EU’s Trade Policy: Differentiation by Area ............................................................... 16
1.3 GSP plus Status and Pakistan ..................................................................................... 20
1.4 Motivation of the Study .............................................................................................. 20
1.5 Research Problem ....................................................................................................... 23
1.5.1 Objectives of the Study ............................................................................................... 24
1.6 Research Questions ..................................................................................................... 24
1.6.1 The General Questions ................................................................................................ 25
1.6.2 Specific Research Questions ....................................................................................... 25
CHAPTER 2: THEORETICAL FRAMEWORK ................................................................... 26
2.1 International Trade and Growth Theories ................................................................... 27
2.2 Brief History of Modern Trade Agreements ............................................................... 28
2.3 Preferential and Free Trade Agreements of Pakistan ................................................. 31
2.3.1 External Trade Regime of EU and Pakistan ............................................................... 33
2.3.2 The Evolution of the GSP plus Arrangements ............................................................ 35
vii
2.4 Justification for Using CGE Modeling ....................................................................... 35
2.4.1 Econometric Models vs. CGE Models........................................................................ 36
CHAPTER 3: REVIEW OF LITERATURE .......................................................................... 39
3.1 Introduction ................................................................................................................. 39
3.2 International Trade and Economic Growth................................................................. 40
3.3 Exports and Economic Growth Nexus ........................................................................ 42
3.4 Computable General Equilibrium Models and the Economy ..................................... 45
3.5 Computable General Equilibrium Models and Trade Liberalization ......................... 51
3.6 European Union (EU) and Trade Liberalization ......................................................... 56
3.7 Trade Liberalization in the GTAP Framework ........................................................... 59
3.8 History of CGE Models Applied in Pakistan .............................................................. 65
3.9 Drawbacks in Previous Studies ................................................................................... 72
3.9.1 Limited Focus on Trading Blocks and Especially the European Union ..................... 72
3.9.2 Usage of Inadequate Databases .................................................................................. 72
3.9.3 Poor Quality of Limited Number of Studies on Regional Issues................................ 73
3.9.4 Single Model Repetition to Analyze Trade Liberalization ......................................... 74
3.9.5 Contradictory Results of Some Studies on Trade liberalization ................................. 74
3.10 Proposed CGE Study in Light of Past Literature Review ........................................... 74
3.11 Summary of Literature Employed CGE Models in Pakistan ...................................... 75
CHAPTER 4: METHODOLOGICAL FRAMEWORK ......................................................... 79
4.1 Historical Background of the CGE Modelling ........................................................... 80
4.2 Defining the CGE Model ............................................................................................ 84
4.3 Multi-Country Models (GTAP Model) ....................................................................... 88
4.4 Working of GTAP 9 Database .................................................................................... 90
4.5 GTAP Standard Model: Income Expenditure Global Accounts ................................. 90
4.5.5 The Standard GTAP Model and the Accounting Relationships ................................. 91
4.5.6 Distribution of Sales to the Regional Markets ............................................................ 91
4.5.7 Source of Household Purchases in the GTAP Model ................................................. 93
4.5.8 Firm’s Purchase Sources ............................................................................................. 93
4.5.9 Sources of Household (HH) Factors Service Income ................................................. 94
4.5.10 Regional Income and Border Involvement in the GTAP Framework ........................ 94
viii
4.5.11 The GTAP Model and the Global Sectors .................................................................. 95
4.5.12 Equilibrium Condition in the GTAP Model ............................................................... 96
4.5.13 Linearized Representation of Accounting Equations ................................................. 96
4.5.14 Macroeconomic Closures............................................................................................ 97
4.5.15 Data Sources Used in Creating the GTAP Database .................................................. 98
4.6 MyGTAP Database ..................................................................................................... 99
4.6.5 Relationships in MyGTAP Model ............................................................................ 100
4.6.6 Inter-regional Transfers ............................................................................................ 102
4.6.7 Multiple Households and Endowments .................................................................... 103
4.6.8 Expenditures of Private Household .......................................................................... 105
4.6.9 Constant Difference of Elasticity (CDE) .................................................................. 107
4.6.10 LES ........................................................................................................................... 107
4.6.11 Armington Elasticity ................................................................................................. 109
4.6.12 Population ................................................................................................................. 110
4.6.13 Welfare ...................................................................................................................... 110
4.7 MyGTAP Model Closure .......................................................................................... 110
4.8 Social Accounting Matrix (SAM) for MyGTAP ...................................................... 111
4.8.5 Framework of Macroeconomic Accounting ............................................................. 112
4.8.6 The Macro Aggregates .............................................................................................. 118
4.9 Data Sources for SAM 2007-08 ................................................................................ 119
CHAPTER 5: RESULTS & DISCUSSION ......................................................................... 120
5.1 Pakistan-EU Trade Relationships at a Glance .......................................................... 120
5.2 Does GSP Plus is different from Normal GSP? ....................................................... 121
5.3 Opportunities for Pakistan under GSP plus Arrangements ....................................... 122
5.4 Pakistan’s Major Competitors: Challenges vs. Opportunities .................................. 123
5.5 Potential for Pakistani Imports after GSP plus Status .............................................. 126
5.6 Research Simulations Used in this Study ................................................................. 128
5.7 Results of the Simulations with GTAP 09 ................................................................ 129
5.7.1 Changes in GDP and Production of Pakistan ........................................................... 129
5.7.2 Changes in Exports and Imports of Pakistan ............................................................ 133
5.7.3 Impact on Real Investment ....................................................................................... 142
ix
5.7.4 Change in Prices of Goods for Domestic Household ............................................... 143
5.7.5 Changes in the Prices of Commodities Supplied ...................................................... 145
5.7.6 Changes in Prices of Imported Commodities ........................................................... 147
5.7.7 Impact on Pakistan’s Terms of Trade ....................................................................... 150
5.8 Results of the Simulations with MyGTAP ............................................................... 151
5.8.1 Changes in GDP and Production of Pakistan ........................................................... 152
5.8.2 Changes in Exports and Imports of Pakistan ............................................................ 155
5.8.3 Impact on Real Investment ....................................................................................... 161
5.8.4 Impact on Pakistan’s Terms of Trade ....................................................................... 162
5.8.5 Changes in Household Income in Pakistan ............................................................... 163
5.8.6 Household Income of Large and Medium Farm ....................................................... 164
5.8.7 Income of Small Farm Household ............................................................................ 165
5.8.8 Income of Landless Farmer Household .................................................................... 166
5.9 Effects on Real Returns to Factors in Pakistan ......................................................... 168
5.9.1 Wages of Large Agriculture Land Owned Labor ..................................................... 169
5.9.2 Wages of Medium Agriculture Land Owned Labor ................................................. 170
5.9.3 Wages of Small Agriculture Land Owned Labor ..................................................... 170
5.9.4 Wages of Skilled and Unskilled Labor ..................................................................... 171
5.9.5 Real Return to Land of Large Agriculture Farms ..................................................... 172
5.9.6 Real Return to Land of Medium Agriculture Farms ................................................. 173
5.9.7 Real Return to the Land of Small Agriculture Farms ............................................... 173
5.9.8 Real Return to the Land of Non-irrigated Agriculture Farms................................... 174
5.9.9 Real Return to the Capital ......................................................................................... 175
CHAPTER 6: SUMMARY AND CONCLUSION .............................................................. 176
6.1 Introduction ............................................................................................................... 176
6.2 Summary of Research Findings and Policy Implications ......................................... 176
6.3 Limitations of the Study............................................................................................ 184
6.4 Recommendations for Further Research ................................................................... 185
6.5 Concluding Observations .......................................................................................... 186
Bibliography ......................................................................................................................... 188
APPENDIX 1 ........................................................................... Error! Bookmark not defined.
x
APPENDIX 2 ........................................................................... Error! Bookmark not defined.
APPENDIX 3 ........................................................................... Error! Bookmark not defined.
APPENDIX 4 ........................................................................... Error! Bookmark not defined.
APPENDIX 5 ........................................................................... Error! Bookmark not defined.
xi
LIST OF FIGURES
Figure 1.1: Sectoral Share of GDP in Pakistan ......................................................................... 3
Figure 1.2: History of Industrial Sector Growth ....................................................................... 7
Figure 1.3: Components of Services Sector .............................................................................. 8
Figure 1.4: Growth Rate of Services Sector of Pakistan .......................................................... 9
Figure 4.1: The Standard GTAP Model .................................................................................. 91
Figure 4.2: Flows of Income and Expenditures in MyGTAP Model ..................................... 99
Figure 5.1: Merchandise Exports and Imports of Pakistan¸ (Percent) .................................. 134
Figure 5.2: Term of Trade (TOT) of Pakistan, Constant 2011 Prices (Percent) ................... 151
Figure 5.3: Merchandise Exports and Imports of Pakistan (Percent) ................................... 156
Figure 5.4: Changes in Real Investment, Constant 2007 Prices (Million US$) ................... 161
Figure 5.5: Changes in Term of Trade (TOT) of Pakistan, Constant 2007 Prices, (Percent) 162
Figure 5.6: Changes in Households Income in Pakistan, Constant 2007 Prices (Percent) ... 164
xii
LIST OF TABLES
Table 1.1: History of Growth rates of Pakistan Economy (Average Growth) .......................... 2
Table 1.2: Performance of Agriculture Sector of Pakistan ....................................................... 4
Table 1.3: Trade Performance of Pakistan (US$ Million) ...................................................... 10
Table 1.4: Pakistan Top 10 Importing countries (US $ Million) ............................................ 10
Table 1.5: Pakistan’s Top Ten Exports to the World (US $ Millions) ................................... 11
Table 1.6: The Pyramid of the EU Trade Relations................................................................ 17
Table 2.1: Comparison between CGE Models and Econometric Models .............................. 36
Table 3.1: Summary of CGE Models History in Pakistan ...................................................... 75
Table 5.1:Comparison of Imports by the EU (28) with GSP Plus Beneficiaries (US $ Million)
.............................................................................................................................. 125
Table 5.2: Imports from Pakistan into the EU 28 (category wise) (US$ million) ................ 126
Table 5.3: Top 10 Exporters of EU28 ................................................................................... 127
Table 5.4: GDP Quantity Index, Constant 2011 Prices (Percent and Millions US$) ........... 129
Table 5.5: Changes in Pakistan’s Real Out Put, Constant 2011 Prices (Percent and Millions
US$) ..................................................................................................................... 130
Table 5.6: Aggregate Exports of Pakistan, Constant 2011 Prices (Percent and Millions US$)
.............................................................................................................................. 136
Table 5.7: Aggregate Imports of Pakistan, Constant 2011 Prices (Percent and Millions US$)
.............................................................................................................................. 139
Table 5.8: Real Investment, Constant 2011 Prices (Percent and Millions US$) .................. 142
Table 5.9: Changes in Prices of Goods in Domestic Market, Constant 2011 Prices (Percent)
.............................................................................................................................. 144
Table 5.10: Change in the Supply Price of Input, Constant 2011 Prices (Percent) .............. 146
Table 5.11: Changes in Prices of Imported Commodities, Constant 2011 Prices (Percent) 148
Table 5.12: GDP Quantity Index, Constant 2007 Prices (Percent and Millions US$) ......... 152
Table 5.13: Changes in Pakistan’s Real Output, Constant 2007 Prices (Percent and Millions
US$) ..................................................................................................................... 153
Table 5.14: Aggregate Exports of Pakistan, Constant 2007 Prices (Percent and Millions US$)
.............................................................................................................................. 157
xiii
Table 5.15: Aggregate Imports of Pakistan, Constant 2007 Prices (Percent and Millions US$)
.............................................................................................................................. 159
Table 5.16: Changes in Household Income of Large and Medium Farm, Constant 2007 Prices
(Percent) ............................................................................................................... 165
Table 5.17: Changes in Household Income of Small Farmers, Constant 2007 Prices (Percent)
.............................................................................................................................. 165
Table 5.18: Changes in Household Income of Landless Farmers, Constant 2007 Prices
(Percent) ............................................................................................................... 166
Table 5.19: Changes in Household Income of Rural Agricultural Labor, Constant 2007 Prices
(Percent) ............................................................................................................... 167
Table 5.20: Changes in Household Income of Rural Non-farm Household, Constant 2007
Prices (Percent) .................................................................................................... 167
Table 5.21: Changes in Household income of Urban Household, Constant 2007 Prices
(Percent) ............................................................................................................... 168
Table 5.22: Change in Real Wages of Large Agriculture Land Owned Labor (Percent) ..... 169
Table 5.23: Change in Real Wages of Medium Agriculture Land Owned Labor (Percent) 170
Table 5.24: Change in Real Wages of Small Agriculture Land Owned Labor (Percent) ..... 171
Table 5.25: Change in Real Wages of Skilled and Unskilled Labor (Percent) ..................... 171
Table 5.26: Change in Real Return to Land of Large Farms (Percent) ................................ 172
Table 5.27: Change in Real Return to Land of Medium Farms (Percent) ............................ 173
Table 5.28: Change in Real Return to Land of Small Farms (Percent) ................................ 173
Table 5.29: Change in Real Return to Land of Non-irrigated Farms (Percent) .................... 174
Table 5.30: Change in Real Return to Capital (Percent) ...................................................... 175
xiv
ACKNOWLEDGEMENT
I am thankful to Almighty Allah Who granted me the health, energy and courage to
undertake this research and without His countless blessings it was not possible to complete. It
is utmost pleasure for me to extend my sincere gratitude and give due credit to my supervisor
Dr. Sofia Anwar for sparing her precious time in spite of her extremely busy schedule. Her
human-friendly attitude and timely comments enabled me to complete this research work. I
owe special thanks to my Co-supervisor Dr. Muhammad Aamir Khan, for his guidance and
highly valued comments at each stage. His expertise, critical comments and suggestions
made it possible to improve significantly.
I formally acknowledge and thank a number of people and especially Mr. Muhammad
Tayyeb Riaz for his support and company at every challenging time. I am also grateful to Dr.
Hasnain Abbas Naqvi and Dr. Vaqar Ahmed for being a source of inspiration and guidance at
each stage of my research career.
I am indebted to say thanks to my Parents for their moral support and encouragement
rendered during this research work. My wife and children suffered a lot during my Ph.D.,
special thanks for them, for listening to my complaints and frustrations, and for believing in
me.
Muhammad Shahzad Iqbal
xv
LIST OF ABBREVIATIONS
ACP African, Caribbean and Pacific
ACCU African Continental Custom Union
ADB Asian Development Bank
ADF Augmented Dickey and Fuller
AEG Augmented Engle-Granger
AGE Applied General Equilibrium
ASEAN Association of Southeast Asian Nations
ADRL Autoregressive-Distributed Lag
CAP Common Agriculture Policy
CCP Common Commercial Policy
CDE Constant Difference of Elasticity
CER Closer Economic Relation
CET Common External Tariff
CGE Computable General Equilibrium
COMESA Common Market for Eastern and Southern Africa
CPEC China-Pak Economic Corridor
DDA Doha Development Agenda
EAC East African Community
EBS Export Bonus Scheme
EBA Everything But Arms
EBS Export Bonus Scheme
ECOWAS Economic Community of West African States
EEC European Economic Community
EPA Economic Partnerships Agreement
ERP Economic Revival Program
EU European Union
FBS Federal Bureau of Statistics
FDI Foreign Direct Investment
FTA Free Trade Agreement
xvi
FTAA Free Trade Area of the Americas
GAMS General Algebraic Modeling System
GATT General Agreement on Tariffs and Trade
GDP Gross Domestic Product
GEMPACK General Equilibrium Modelling Package
GSP Generalized System of Preferences
GTAP Global Trade Analysis Project
GMP Global Mediterranean Policy
GOP Government of Pakistan
HH Household
HMF Household with Large and Medium Farm
HSF Household with Small Farm
HS Harmonized System
HO Heckscher-Ohlin
IEA International Energy Agency
IFPRI International Food Policy Research Institute
IPTS Institute for Prospective Technological Studies
KPK Khyber Pakhtunkhwa
PSSP Pakistan Strategy Support Program
PTCA Preferential Trading and Cooperation Agreements
PTA Preferential Trade Agreement
IMF International Monetary Fund
IO Input-Out out
ITC International Trade Centre
JMC Joint Ministerial Commission
LDC Least Developed Economies
MATLAB Matrix Laboratory
MINAP Micro Impacts of Macro- economic Adjustment Policies
MFA Multi-Fiber Arrangement
MFN Most Favored Nation
MS Micro-simulation Approach
xvii
NAFTA North American Free Trade Area
NEC Not Elsewhere Classified
NEC National Economic Council
NTB Non-Tariff Barrier
NWFP North West Frontier Province
OCT Overseas Countries and Territories
OECD Organization for Economic Co-operation and Development
OGL Open General License
OIC Organization of Islamic Cooperation
OLS Ordinary Least Square
OMA Orderly Marketing Arrangement
PBS Pakistan Business Council
PIDE Pakistan Institute of Development Economics
PTA Preferential Trade Agreement
PSDP Public Sector Development Programme
R & D Research and Development
RH Representative Households
RHS Right Hand Side
RWSM Regional Water System Model
SAARK South Asian Association for Regional Cooperation
SAM Social Accounting Matrix
SAP Structural Adjustment Programs
SADC South African Development Community
SAFTA South Asian Free Trade Agreement
SAPTA South Asian Preferential Trade Agreement
SBP State Bank of Pakistan
SITC Standard International Trade Classification
SME Small and Medium Enterprises
SPS Sanitary and Phyto-Sanitary
SRO Special Regulatory Orders
STPF Strategic Trade Policy Framework
xviii
TOT Terms Of Trade
TQ Tariff Quota
TC Tariff Ceilings
USA United States of America
VER Voluntary Export Restraint
VAR Vector Autoregressive
WIOD World Input-Output Database
WTO World Trade Organization
xix
ABSTRACT
The importance of trade has been recognized as a vital component of sustainable
development for an economy. To achieve the goal of sustained economic growth, economies
always try to maximize the benefits of trade and especially exports.
The purpose of the study is to investigate the impact of Generalized System of Preferences
(GSP) plus on the economic growth of Pakistan. The European Union, the largest trading
partner of Pakistan granted this status to Pakistan in December 2013. The study attempted to
employ the Computable General Equilibrium (CGE) model in its global version called
Global Trade Analysis Project (GTAP) to measure the economic gains for Pakistan at macro
level under the GSP plus status. The study also used MyGTAP, developed by Minor &
Walmsley (2013) to calculate the impact at the household level. This MyGTAP model uses
the data of the latest available Social Accounting Matrix (SAM) to makes changes in the
standard GTAP by including multiple types of household and labor.
The results of different simulations run by standard GTAP and MyGTAP reveal that there is
an overall increase in the GDP of Pakistan. The results of all simulations by using standard
GTAP 09 suggest a positive change in the real GDP, real investment, merchandise imports
and terms of trade of Pakistan while the merchandise exports of Pakistan show decline in
case of the second simulation. The main findings of the simulations, run under MyGTAP
model also show a positive change in real GDP, merchandise imports, real investment and
terms of trade while the first simulation shows a negative change in merchandise exports.
Similarly, – EBA status of Pakistan in the EU28 show an increase in the household income
with maximum gain by the household of rural Sindh with no agriculture land and a positive
change in real wages of most of the factors. However, the large and medium agricultural
household types show a negative change in household income in case of the first simulation.
Comparatively low improvement over the urban and non-farm household of rural areas of
Pakistan.
Keywords: Economic growth, trade, GSP Plus, European Union, CGE model, real GDP,
terms of trade, real investment, household income etc.
1
CHAPTER 1: INTRODUCTION
International trade theories concern with the gains accruing to trading partners on their
mutual trade if tradable goods are produced according to the principle of comparative
advantage based on their factor endowments. Economies at national or international level pay
special attention towards the production structures while considering the trade policy
instruments. Tariffs and quotas are the instruments of trade policy that affect the relative
prices of the goods in any given economy. The demand for inputs changes when the
economy changes the mix of produced goods and services. Hence, it is difficult to predict
that any given change in trade policy will affect only one sector of the economy. The
backward and forward linkages in the economy bring a change in the sectoral output mix
according to the strength of the linkages. (Karingi, et al., 2005).
In the desire of economic growth expansion, many developing economies have espoused
external economic liberalization policies. It is based on a common fabrication that countries
with less trade restrictions have fast-paced economies and vice-versa. Trade liberalization has
an inherent tendency to raise employment elasticity of economic growth thereby creating a
better impact. However, critics of globalization find a chance to emphasis that growth
benefits might possibly be unevenly spread; as a result, the impingements of distributions
could also affect the poor adversely (Krueger, 1998).
Trade liberalization can effectively be the reason of better economic growth. Benefits of total
factor productivity gained by the economies of scale alongside enhanced efficiency; have a
powerful potential to be transformed in to an immense raise in potential output. The studies
conducted by Freund & Bolaky (2008) and Changa, Kaltanic, & Loayza, (2009) show that
the growth effect of trade openness is significantly positive provided that partner countries
successful in achieving regulatory reforms like business rules, financial developments,
expansion in better education or rule of law, increase in employment opportunities labor
market flexibility, etc. Otherwise, trade is not associated with long-run growth in such
economies. In addition, due to the tendency of attracting Foreign Direct Investment (FDI)
and larger access to regional markets, liberalized trade regime becomes a place of interest for
2
foreign investment prospects. A higher value of Foreign Direct Investment (FDI)
consequently, may also pave the way for a larger-scale technology transfer (Chanda , 1997 )
and inter-industry linkages (Wang, 2011) as well as total factor productivity.
1.1 An Outline of Pakistan Economy
Pakistan came into being existence as a result of the division of the sub-continent on August
14, 1947. It was an agrarian economy at the time of independence with agriculture sector
playing a vital role. The service sector scarcely existed at that time and industrial sector was
at its beginning. Currently, the industrial sector is well established along with moderately
developed services sector in the country and the role of agriculture sector is supportive in the
structure of GDP. Since last decade, the economy of Pakistan has shown a good progress in
all essential sectors.
Pakistan is a developing country and is still struggling to enhance the economic growth. The
progress of the economy for the last sixty-eight years is poor as well as inspiring. It is
inspiring because despite of great population growth rate it has reached fast development rate
resulting a decrease in poverty levels and an increase in per capita income. Due to structural
changes, the economy has changed from an agrarian economy to a more expanded
production structure economy. From country’s total exports, production contributes 80
percent of it. Although country is growing in long run but inconsistant economic growth is
still a problem. The history of economic growth in different decades can be seen from the
table 1.1.
Table 0.1: History of Growth rates of Pakistan Economy (Average Growth)
Time
Period
1950-
1960
1960-
1970
1970-
1980
1980-
1990
1990-
2000
2000-
2007
2007-
2014 Average
Growth
Rates 3.50 6.10 4.20 6.60 4.40 6.10 4.53 5.06
Source:- Pakistan Bureau of Statistics
Economic growth is a generic term, it means progress in all segments of the country’s
economy (Barro & Martin, 2004). Commodity sector and services sectors are major sectors
3
of the economy. The industrial sector, agriculture sector, construction, and power sectors,
quarrying and mining, are the elements of the commodity sector while communication,
transport and storage segments, retail and wholesale trade, public administration, possession
of dwellings and defense are the elements of the service sector.
The backbone of Pakistan economy is agriculture. The contribution of the agriculture sector
to GDP was greater in early years of independence, but now the trend has changed and the
industrial, as well as services sectors, have a major share in overall GDP. If we take a look at
current situation of Pakistan, we can observe a remarkable increase in services sector GDP of
Pakistan. In 2014-15 share of the agriculture sector to GDP was 25% whereas the share of
the industrial sector to GDP was 19% and services sector’s share to GDP was 56 %.
(Pakistan economic survey 2014-15).
Figure 0.1: Sectoral Share of GDP in Pakistan
Source: Pakistan Economic Survey 2014-15
To demonstrate the sectoral importance in the economy of Pakistan a complete overview of
three sectors of the economy is given separately.
1.1.1 Agriculture Sector
This sector is a major contributor to the Pakistan economy since 1947. Its contribution to
GDP in 2015 remained 20.9%. It provides employment chances for 43.5% of total country’s
Agri Sector
25%
Ind. Sector
19%
Services
Sector56%
Agri Sector Ind. Sector Services Sector
4
labor force. Also, 60% of the population in rural areas extracts their livelihood directly or
indirectly from agriculture sector (Government of Pakistan, 2014-15). The agriculture sector
provides raw material to the textile sector. It has been playing an important role in decreasing
the poverty, changing the direction of industrialization, enabling overall economic growth
and ensuring food security. Being a dominant sector of the economy, every government tried
to make the agriculture sector fruitful, gainful, and effective to increase the quality of life and
to expel hunger and malnutrition from the country (Iqbal, 2008).
Table 0.2: Performance of Agriculture Sector of Pakistan
Time Period Growth Rate
(Percentage) Share in GDP
1950-1960 1.8% 47.7%
1960-1970 5.1% 45.8%
1970-180 2.4% 38.9%
1980-1990 5.4% 30.6%
1990-2000 4.4% 25.8%
2000-2010 3.2% 22.1%
2010-2015 2.73% 21%
2015-16 2.76% 20.9%
Source: Federal Bureau of Statistics, Government of Pakistan (2015).
Major crops of the country comprise wheat, cotton, sugarcane, maize, rice and minor crops
comprise mash, mung, onion, masoor, chilies, and potatoes. Fishery, livestock, forestry are
the sub-sectors of agriculture sector of Pakistan. Kharif and Rabi are two main crop seasons
in Pakistan (Sethi, 2007). If we take an expression at the past of Pakistan, it is clear that the
country’s agriculture sector contributed a healthy share in GDP growth. The growth from the
previous sixty-sevenyears can be seen from the table 1.2.
The above table shows that the growth rate of agriculture over the years was volatile. It was
1.8% in 1950-1960; the lowest-most in the history. During the decade of 1960-1970, the
highest growth rate i.e. 5.2% was recorded; credit goes to green revolution (Khan J. , 2012).
The growth rate declined to 2.4% during 1970-1980 again due to lack of implementation of
5
the policy recommendations by five-year plan (Chaudhry & Chaudhry, 1997). Agriculture
sector saw growth of 5.4% in 1980-1990. But, starting from 1990 to 2010, the growth rate
was constantly decreasing from 4.4% in 1990-2000 to 3.2% in 2000-2010 respectively.
While it remained 2.73% on average during the era of 2000-2010. From 2011-2015 it has
been growing at the rate of 2.76%. The declining trend in growth rate is due to physical
changes in the economy, as now a days more and more devotion is given to the services
sector and industrial sector (Khan J. , 2012).
As far as the percentage share of the agriculture sector to GDP is concerned, there is also a
declining trend. It was at its peak in 1950’s and 1960’s, but it started slowing down in
1970’s. Due to structural changes in the economy, industrial sector and services sector shares
increased resulting a decline in the share of the agriculture sector.
1.1.2 Industrial Sector
This sector further can be divided into manufacturing, mining, electricity generation and
construction sub-sectors. In 1947 at the time of independence, out of entire 955 industries
only 34 industries belonged to Pakistan. These industries were not sufficient for a new born
economy to face the industrialized world. By accepting this challenge, Pakistan employed all
of its available resources in the production sector (Hussain, 2005).
The industries operating in Pakistan at the time of independence were cotton ginning
factories, rice husking mills, small sugar mills, canning factories and flour mills. In 1947 it
was suggested in an industrial conference of Pakistan to inaugurate those industries which
employed the locally produced raw materials. In order to strengthen the industrial sector of
the country industrial credit and investment corporation and industrial finance corporation
were formed in 1948. Consequently, the involvement of industrial sector in GDP was 6.9%
in 1950. (Husain, 2005)
Industrial Development Corporation (PIDC) was established in 1950 to fulfill the industrial
needs of the country, especially in the areas where the private sector was reluctant to invest.
Many fresh industries were installed in the country to increase the manufacturing capacity of
6
units like jute, paper, and fertilizers due to availability of local raw material. In 1958 export
bonus scheme was announced which enhanced the exports volume of manufactured items of
the country and export duties were dropped. The growth of industries especially the textile
products and agricultural processing food products were considerable. The contribution of
the industrial sector in GDP of the country improved from 9.7% to 11.9% in 1954-55 (Saeed,
2015).
In 1960 there was a modification in the formation of the industries related to consumer goods
into heavy industries like electrical complex, machine tools, iron and steel and
petrochemical. For the duration of the second five years plan performance of the industry in
terms of productivity, progress and export volume was increased. From 1960 to 1965 the
contribution industrial sector of the country’s GNP was increased to 11.8%.
Industrial performance progress, productivity and export volume was disappointing during
the era of 1971-1977. There were several reasons for the poor performance of the industrial
sector of the country including the separation of East Pakistan (now Bangladesh) in addition
to war against India. Production of heavy industries declined due to damages in the
indigenous market. In addition to that, nationalization of the industry, interruption of foreign
aid, depreciation of the currency to the level of 131%, decrease in exports volume, labor
unrest, nationalization of the industries, floods, the adverse climate for investment that
demotivated the investment and recession in the world trade were also responsible. During
that period, the annual progress rate of the industrial sector of the country cut down to 2.8%
annually (Saeed, 2015).
In order to help the economy to recover, the units of cotton ginning, flour, and rice husking
were denationalized by the government in July 1977 to 1980. Private sector investors started
to invest in large scale industries. In 1989 the annual growth rate in manufacturing industry
was 8.2%. In 1990 the progress rate of large-scale manufacturing industries declined to 4.7%
in the first half and further to 2.5% in second half due to political instability draught in
second half (Husain, 2005).
7
During the period of 2000 to 2010, more attention was paid to the industrial sector, so its
share in GDP increased significantly. Due to the diversification of economy from agriculture
sector to industrial sector its share in GDP increased by 21% on average, while this sector
itself grew by 2% on average. Details of the growth rate of industrial sector during different
decades are given in the figure. It is very clear from the figure 1.2 that during the decade of
1950-1960, there was an appreciable increase as compared to 1947. In 1960-1970, when
industrial reforms were taken into consideration, the industrial sector growth was maximum.
In 1970’s it declined again to 6.13% but again the industrial sector experienced an increasing
trend in 1980’s. It declined during 1990’s but stabilized during the 2001-2015 time period
(Government of Pakistan, Various Issues).
Figure 0.2: History of Industrial Sector Growth
Source: Federal Bureau of Statistics, Government of Pakistan (2015-16)
1.1.3 Services Sector
In the modern world, service sector contributes a lion’s share in the GDP of any economy
and plays a significant role in increasing/establishing the growth rate (Singh, 2010). It
contributes 53.3% of GDP in the economy of Pakistan and 44% of labor force is employed in
this sector. This sector not only provides the services in the form of industry and business but
also provides public services governed by the government. This sector is a symbol of the
development of the human capital and good governance. It not only includes the education or
health services but also the services of transport and communication, law and order, and
environment which are truly based on quality are included. Many financial services on the
other hand, like, financial regulations which are also known as e-governance are also part of
0
2
4
6
8
10
12
1950-60 1960-70 1970-80 1980-90 1990-00 2000-10 2010-15
8.16
11.02
6.13
9.5
2.97 4.3 4.5
8
it. By means of e-governance, the government information is readily available for the people
which not only decrease the time and costs of transactions but also increases the quality of
the governance. Additionally, it helps to clear the working image of the government. The
service sector of Pakistan deals with many fields including public administration and
defense, possession of houses/apartments, wholesale and retail business, finance and trade,
the insurance industry, telecommunication social and personal services (for details, please
see figure 1.3 below).
Figure 0.3: Components of Services Sector
Source: Author’s own design
During the tenure of economy breakdown when most of the revenue generation sectors of the
Pakistan economy faced a huge decline in their growth, the service sector of Pakistan kept on
growing even at that time. The cross-country data analysis reveals different stages of the
structural transformation. This transformation consists of two stages. At the first stage, the
share of industrial sector increased exactly equal to the decrease in agricultural sector in
services sector
Wholesale and
Retailing
Transport and
Communication
Finance &
Insurance
Housing Services
General Administ
ration
Other Private
Services
9
value. At second stage, substitutions were implanted in the service sector and industry
whereas the agriculture sector remained unchanged (Zaidi, 2015).
In case of Pakistan, the transformation is based on only one stage that is from agriculture
sector to services. The service sector of Pakistan grew at a faster rate of 5.46% during 1975 -
76 to 2009-10 whereas the growth rate of industrial sector was 5.7%. It dropped down to
4.1% during 2010-11 and further declined to 2.9% in previous year. Figure 1.3 indicates that
services sector grew constantly since early years of independence. It was at its peak during
1960-70, after reaching its maximum point during next decade it declined to 2.8% on
average. Then again it starts increasing from 1980 and the growth rate is reasonable good.
Figure 0.4: Growth Rate of Services Sector of Pakistan
Source: Pakistan Economic Survey (various issues)
1.1.4 Pakistan’s Trade Statistics
Pakistan stands at 70th
position in the list of export economies. Trade patterns are very
similar throughout the history of the economy. It is firmly believed that exports are the
engine of the economy (Baier & Bergstrand, 2009). The theme of the export-led growth was
followed by the government. The economies with higher export growth have higher growth
rates and vice versa (Tekin, 2012). Exports performance of Pakistan remained impressive in
the past. The above discussion concludes that economic structure of the economy has
changed over the passage of time. If we carefully examine the trade statistics, it is evident
that Pakistan always faced trade deficit (Raana, 2008) owing mainly to inconsistency in trade
policies as well as of political stability. The statistics presented in the table below tells the
story of export performance.
0
2
4
6
8
1950-60 1960-70 1970-80 1980-90 1990-00 2000-10 2010-15
2.8
6.73
2.8
5.93 4.5
5.3 4.55
10
Table 0.3: Trade Performance of Pakistan (US$ Million)
Years Exports Imports Trade
Balance
1985-86 3,070 5,634 -2,564
1990-91 6,131 7,619 -1,488
1995-96 8,707 11,805 -3,098
2000-01 9,202 10,729 -1,527
2005-06 16,451 28,581 -12,130
2010-11 24,810 40,414 -15,604
2014-15 25,369 45,826 -20,457
Source: Pakistan Bureau of Statistics, 2015
Pakistan’s current trade data imitates the spillover effects in the growth of both imports and
exports. Pakistan’s top 10 importing destinations represent 78 percent of the total import
share and if we narrow it to the top 5, the ratio accounted for 70 percent of the total imports.
These figures show the fact that imports are subjected to high vulnerability to external
shocks. Table 1.4 shows Pakistan’s top 10 importing countries and total import value during
the years of 2011 to 2015.
Table 0.4: Pakistan Top 10 Importing countries (US $ Million)
Region(Country)/Year 2011 2012 2013 2014 2015 Percentage of total
Exports (2015)
EU 6,346 5,306 6,273 7,224 7,523 31.23
U. S. A. 4,102 3,949 3,887 4,440 3,960 16.44
China 1,645 2,085 2,699 2,688 2,321 9.63
Afghanistan 1,865 1,380 1,059 1,245 1,696 7.04
United Arab Emirates 1,855 1,947 1,936 1,715 1,295 5.38
Bangladesh 908 663 680 724 689 2.86
Saudi Arabia 426 456 512 502 496 2.06
India 287 333 329 423 415 1.72
Turkey 751 609 414 366 323 1.34
South Korea 415,466 500,906 408,366 379,070 336,423 0.4
Total Exports of
Pakistan 25,369 24,718 24,802 25,078 24,088
Source: State Bank of Pakistan, 2015
11
Pakistan’s top 5 five export commodities account for 60.67 percent of total exports while if
we step up to the top 10 this share is 73 percent of total exports. Due to the sluggish behavior
of the world trading activities in 2012 added with weak global demand, local energy dearth
and a tapered export base underwrite Pakistan’s high trade deficit. The energy crises are
playing a key role in increasing the trade deficit.
Pakistan’s major export destinations are EU, USA, and China with a share of 31.23 percent,
16.44 percent and 9.63 percent in total exports during the fiscal year 2014-15 (see table 1.5).
Tables 1.4 and 1.5 illustrate Pakistan’s top 10 exporting destinations and top 10 exporting
commodities and their contribution is the total exports during different fiscal years with
percentages.
Table 0.5: Pakistan’s Top Ten Exports to the World (US $ Millions)
S.
No.
Code
HS2 Chapter Description 2012 2013 2014 % age
1 '52 Cotton 5.226 5.334 4.731 18.87
2 '63 Other made textile articles, sets, worn clothing etc 3.285 3.686 3.907 15.58
3 '61 Articles of apparel, accessories, knit or crochet 2.006 2.105 2.403 9.58
4 '10 Cereals 2.061 2.181 2.211 8.82
5 '62 Articles of apparel, accessories, not knit or crochet 1.694 1.855 1.985 7.91
6 '42
Articles of leather, animal gut, harness, travel
goods 0.674 0.744 0.742 2.96
7 '25 Salt, sulphur, earth, stone, plaster, lime and cement 0.714 0.723 0.694 2.77
8 '27 Mineral fuels, oils, distillation products, etc 0.331 0.527 0.648 2.58
9 '41
Raw hides and skins (other than furskins) and
leather 0.457 0.530 0.547 2.18
10 '17 Sugars and sugar confectionery 0.254 0.634 0.439 1.75
Source: Pakistan Business Council, (various issues)
Although, the industry is continuously shifting from primary goods to secondary and finished
goods but the progress of shift is very impassive. Pakistan displays a strong comparative
advantage in beverage and tobacco, crude materials, vegetable oil and fats and basic
manufactures and comparative advantage to some extent in food and live animals. On the
12
other side, data shows that Pakistan has a strong comparative disadvantage in all the
categories of capital intensive goods like mineral fuels, chemicals and machine, tools,
transport equipment, miscellaneous manufactured, etc (Zaidi, 2015). Table 1.4 and 1.5
further reveals that Pakistan’s exports are country and commodity concentrated. This lack of
diversity is also threatening the growth of exports.
An important conclusion to be drawn from above discussion is that a country’s pattern of
foreign trade, the composition of exports and the direction of exports depends on both supply
and demand conditions. The determinants of supply and demand are continuously changing
as the resources are ran down or made outdated by technological changes (and changed
factor endowments) elsewhere. Costs fall as output expands and knowledge builds up,
bringing innovations or technological breakthroughs. Changes in the size, age and sex
composition also alter the relationship between labor and physical capital and the stock of
different kinds of human capital. The outcome is that comparative advantage or disadvantage
of a country is ever changing. The analysis of Pakistan’s net composition of foreign trade
clearly points out the changing comparative advantage and comparative disadvantage as well as
its changing pattern of foreign trade. It reveals that the attainment of capital goods and
technology have been enabling Pakistan to decrease its comparative disadvantage in capital
intensive categories of traded goods.
1.2 European Union (EU28) and Position of Pakistan
The European Union has gradually expanded its external trade relations with the passage of
time. In practical terms, this means that EU’s external trade regime has been extended to new
subjects as integration progressed through the stages (Molle & Mourik, 1988). The EU’s
external trade policy regime is highly complex and complicated. The very complexity of
trade regime governing access to EU market can be seen as the number of trade barriers
operative in itself. This section is devoted to identifying the instruments of EU’s external
trade regime operative to regulate trade flows between its trading partners and examine their
application.
13
1.2.1 Principles and Objectives
The external trade policy of EU is centered to various theoretical principles (Brenton, 2003).
The literature on trade relationships indicates that trade openness helps an economy to grow
faster. This openness keeps the domestic firms under the pressure of imports and the
competition of foreign firms which is not possible in a closed economy. The trade regime of
EU has been in line with these theoretical recipes (Yanikkaya, 2003).
The Treaty of Rome explains the importance of Common Commercial Policy (CCP) that
may help to remove the tariffs on international trade. The Treaty (Article 27) gives the
following motives:
The need to stimulate trade between member states as well as non-members.
The possible improvement in the competitive capability of the undertakings.
The avoidance of competitive distortions in finished goods markets, related to
supplies of inputs and secondary products.
The avoidance of serious disturbances in the member states’ economies, while
ensuring the growth of production and consumption within the EU.
The common commercial policy (Article 133) covers not only tariffs but other trade
instruments as well. So, all powers regarding export policy, the achievements of uniform
liberalization, tariff rates changes, anti-dumping or countervailing duties and trade agreement
conclusions etc, are within the competence of EU institution. Nevertheless, the mixed nature
of their economies caused member countries to use independently all sorts of instruments on
the borderline of trade policy. The EU’s external trade regime worked out over the years is
examined as under:
1.2.2 EU’s Trade Policy: Fan of Instruments
This section identifies the instruments of the external trade policy of EU, to be used to
regulate trade flows between the EU and rest of the world. The instruments of the EU’s CCP
can be categorized into tariff and Non-Tariff Barriers (NTBs). A brief detail is as follows:
14
1.2.2.1 Common External Tariff (CET)
The CET of the EU was established for each category as the arithmetic averages of the tariffs
applied by all the member states. Thus, the first CET reflected the whole story of the trade
relations of all member states. The EU has effectively moved towards free trade, as in line
with the guidelines given in the Treaty of Rome. The EU’s trade regime under custom union
will help to improve the common interest areas, to smoothen the world trade, to remove the
trade restrictions gradually and dropping the customs tariffs (Reinisch, 2013).
Some major reductions in customs tariffs have been made in the framework of General
Agreement on Tariff and Trade (GATT). The so called ’Dillion Round’ of 1960-62 and the
subsequent ‘Kennedy Round’ of the mid-1960s cut the tariffs by about half. A further tariff
cut of some 30 per cent of the 1978 level was agreed upon during the so called ‘Tokyo
Round’ of the mid-1970s. The recent Uruguay Round has resulted in further cuts.
Consequently, the general level of tariff protection of the EU is now very low, about 4
percent in the most favored nations (MFN). For many manufactured products applied tariffs
the EU are actually now nil or negligible. Moreover, the dispersion has become very narrow;
only very few tariffs on manufactures exceed 11 percent (Naeem, 2006).
1.2.2.2 Non-Tariff Barriers (NTBs)
Less visible than tariffs but no less effective as instruments of trade policy are the so called
‘non-tariff barriers’ (NTBs) (Molle, 2006). In line with the EU’s policy objectives (internal
obligations set by the Treaty of Rome and external obligations set by international
institutions like the GATT/WTO), the EU has tried over the years to free its external trade
from NTBs. The various types of NTBs implemented by the EU as instruments of its trade
policy are identified and analyzed as follows (European Constitution, 2004):
Quotas: Many quotas applied to imports from non-EU members date from pre-EU times.
Other quotas have been introduced over the last decades with the objective of protecting the
so called ‘sensitive sectors’1. Quantitative restrictions (QRs) are limits put on the volume of
1 Sensitive sectors are composed of low-technology manufacturers, using relatively standardized, labor
intensive production technologies, the very sectors in which LDCs have been gaining increasing comparative
advantage. Paramount amount among them is the textile and clothing sector. Under the Multi-Fiber
15
imports of a certain good allowed into the EU in a certain period (usually for one year),
sometimes expressed in monetary values. A special type is the so called ‘tariff quota or tariff
ceilings’ (TQs/TCs)1. TQs/TCs is the maximum quantity which may be imported at a certain
lower or no tariff ( as under GSP), all quantities beyond that come under normal common
higher tariffs.
Voluntary Export Restraints (VERs) and Orderly Marketing Arrangements (OMAs)2:
VERs/OMAs existed outside the GATT framework, and were, therefore, a form of a political
point of view, more expedient than quotas. They have been widely used to restrict trade
flows. The discipline of VERs was imposed by the EU mostly on textile imports from the
GSP beneficiaries and OMAs from the Mediterranean or associated countries. VERs/OMAs
forced the exporting countries to restrict their exports voluntarily and keep them within the
agreed limits.
Anti-Dumping/Subsidies: GATT/WTO rules allow the importing country to take protective
measures against unfair trade practices such as dumping, subsidies, etc. In this case, countries
are allowed to impose anti-dumping or anti-subsidy duties, as the case may be, level
offsetting the difference between the selling prices the dumping firm charges in its home and
export markets or off the negative effects of subsidies. Such measures are allowed to be taken
if there is a sudden substantial surge in imports; there is a substantial price difference
between home and export prices of the exporter/substantial negative effects of subsidies, and
the imports cause material injury to the home producers.
Arrangement (MFA), negotiated between the EU and the principle textile exporting developing countries, the
latter have agreed to a voluntary restriction of their textile exports to the EU. In practice, within the framework
of MFA, the EU members signed agreement and fixed the quantities of textile products they will import from
each separate exporting country.
1 The difference between these two types of restrictions i.e., tariff quotas and tariff ceiling is a technical/legal
one. Tariff ceilings are like a tariff quotas with the difference that the normal tariffs is not re-imposed
automatically, as in the case of quotas, once the ceilings is exhausted but is subject to negotiation between the
EU member states.
2 The difference between VERs and OMAs is also a technical/legal one. OMAs are the multilateral
arrangements; while the VERs are negotiated bilaterally. Under these arrangements, instead of the importing
country imposing quantitative restrictions (quotas) or raising tariffs, the exporting country ‘voluntarily’ agrees
to restrict its exports up to the agreed limits. VERS are existed mostly between the EU and GSP beneficiaries
and OMAs between the EU and Mediterranean or associated countries.
16
These GATT/WTO rules have inspired the EU to frame anti-dumping/ countervailing
regulation (Regulation 2423/88; 3283/94; 384/96). The procedure is as under:
A complaint is lodged by firms directly concerned; the regulation indicates in detail
what information the EU requires;
Verification by the EU of the information given by the complaining party.
If a dumping margin is found to exist and if the injury has been done, the EC may
either accept the exporter’s offer to adjust prices and / or subsidize, if the adjustment
is insufficient, then impose a duty.
Other Non-Tariff Barriers: It deals with the preferential treatment over imported products
within EU market along with other treatments like safeguard clause1, safety norms, fiscal
treatment, state monopolies or public tenders, legal regulations etc.
1.2.3 EU’s Trade Policy: Differentiation by Area
The above mentioned regulatory instruments of the EU’s trade policy, with the exception of
anti-dumping and countervailing duties, were designed, at the initial stage, to apply to all
imports irrespective of their country of origin or consignment. However, with the passage of
time and with the possibility of associate agreements with non-members (please see article
238 of the Treaty of Rome for further details). The more complex application of the CCP has
been explored. In this case, the EU’s approach has been rationalistic rather than global one
(Bollen, Ville, & Orbie, 2016).
1.2.3.1 Preferential Trading and Cooperation Agreements (PTCA)
The most important regional instrument of the EU’s CCP is the conclusion of ‘preferential
trading and cooperation agreements’. These agreements provide a range of special
advantages to specific groups of countries with which the EU wanted to retain special
1 The EU’s GSP scheme has been governed by a general a ‘safeguard clause’. This clause empower the EU to
suspend tariff preferences and restore the customs duty partially or fully if the quantities or prices of imports are
deemed to be causing serious disruption of the domestic market. This could also be invoked to prevent the
interest of countries enjoying special preferences in the EU market. The normal customs duty is restored, for the
product or origin concerned, by the means of a Commission regulation.
17
economic relations due to economic and political reasons. Such special and differential
treatments now play quite an important role in determining the scope and direction of EU’s
trade relations with the non-members within the regional context (Ford, 2013).
Table 1.6 shows a summary idea of a highly differentiate system of EU’s trade relations that
has been evolved over the years. It is often commented that the EU’s different trade
arrangement with its trading partners adds up to a hierarchy of trade preferences - referred to
as ‘pyramid’ of trade privileges (Stevens, 1981). At the apex of this hierarchy comes intra-
EU trade (trade between the member states of EU) that is free from all sorts of quotas and
tariffs. Next to this are the ‘Association Agreements’ that are signed with Meditatrian
countries under the preferential trade agreements. Next, comes the ACP (African, Caribbean,
and Pacific) countries with unlimited duty-free access for exports of manufactures and
(almost all) agricultural goods not covered by the Common Agriculture Policy (CAP).
Table 0.6: The Pyramid of the EU Trade Relations
S.No Countries
Concerned Forms of Relationship
Share in EU
external
Trade (%)
Population
(Millions)
1 EU Member
Countries
Treaty of Rome/ Treat of
Accession …… 455
2 Mediterranean Association Agreements 10 230
3 ACP Lome Convention 03 580
4 Other Third World Generalised Preferences 25 4000
5 US, Japan, CIS,
etc. Most Favored Nation (MFN) 38 850
Source: Compiled from the European World Yearbook.
The next tier consists of non-ACP developing countries which qualify for GSP treatments.
The EU has concluded a number of non-preferential trade agreements with Asian and Latin
American economies bilaterally (meaning no trade preferences apart from those available
under the GSP). And has also signed some regional framework agreements with ASEAN
(Association of Southeast Asian Nations), Central American and with the Gulf states. Below
the GSP beneficiaries comes the other GATT/WTO signatories which qualify for Most
18
Favored Nations (for details, please see Mishalani et al (1981), Hine (1985), Pomfret (1986),
and Naeem, (1994)).
At the apex of the pyramid are EU’s member countries. The number of EU members has
gradually been increased over the years. At present there are 28 countries (EU28) - members
of the EU and many have applied for membership1. The EU being a customs union - trade
among members is free from all types of barriers.
The trade relation of the EU with the Mediterranean countries has been of special nature due
to historical, political and economic reasons (Shlaim & Yannopoulos, 2008 and Pomfret,
1986). The EU has trade agreements with the Mashreq (Egypt, Lebanon, Jordan, and Syria)
and the Maghreb (Morocco, Algeria and Tunisia) economies. The parts concerned with trade
were in the form of one-way preference scheme, which means that these countries have
tariff-free access for industrial goods but in the case of agriculture goods, they have
preferential access to the EU market. For some sensitive products, the imports into the EU
market were limited by import quotas or import ceilings under OMAs as discussed in the
previous section (Nabli, 1997). The EU has association agreement - leading towards full
membership – with Turkey and Yugoslav Republic of Macedonia. Under these arrangements,
these countries have obtained non-restricted access for manufactured goods to the EU
market. These agreements aspired to a full-fledged customs union, which has recently been
realized between Turkey and the EU. Similarly, the EU has a free-trade agreement, on the
principle of full reciprocity with Israel (Hoekman & Djankov, 1997).
Right from the start, the EU has taken over the responsibility for easy access of products of
the former French colonies in sub-Saharan Africa. After the UK joining the EU in 1973, the
schemes were extended to the former British colonies as well. The EU also has signed an
1 The EEC was created in March 1957 with the signing of Treaty of Rome with six members Italy, West
Germany, France and Benelux States (EU6). The number of member countries of the EU has gradually been
increased from EU6 in 1957 to EU9 in 1973 when the United Kingdom, Denmark and Ireland left the EFTA
and joined EEC. Greece joined in 1981 and Portugal in 1986 (EC12). In 1993 Sweden, Finland and Austria
joined (EC15). In 2004 ten more countries, like, Slovenia, Latvia, Czech Republic, Slovakia, Poland, Hungary,
Estonia, Lithuania, and Malta became the member increasing its membership from EU15 to EU25. In 2007
Bulgaria and Romania joined while in 2013 Croatia joined the camp making it EU28. Turkey and Yugoslav
Republic of Macedonia have also applied for membership.
19
agreement with the ACP (African, Caribbean and Pacific) countries called the Lome
Convention. The economic structure of this agreement resembles with that of the association
agreements. The present Lome Convention applies to some 70 ACP states1 (Archer & Butler,
1996). The main provision of the agreements are:
Tariff preferences are fairly generous for ACP countries; indeed, almost their entire
exports have access to the EU market free from any tariff or quota. In that sense the
ACP countries have a more favorable deal as compared to GSP countries- which are
subject to formal and informal quantitative restrictions as we will discuss below2.
The EU tariffs preferences are non-reciprocal; the agreements stipulates only that the
ACP countries grant imports from the EU the same favorable treatment that is
allowed to the most favored developed countries. The ACP’s agricultural exports to
the EU market coming under the CAP receive, within some quantitative limits, a
reduction of the levies, which the EU puts on many agricultural imports.
The Lome Convention also provides the procedure for stabilizing the export earnings
of the ACP states which are heavily dependent upon primary export products. This
scheme is known as ‘STABEX’. Similarly, a complementary scheme to the STABEX
called SYSMIN’ (also referred to as MINEX) was introduced. SYSMIN was meant to
stabilize the export earnings of the ACP states to maintain their ‘export capacity’ of
exportable minerals like aluminum, uranium, cobalt, etc. (Archer & Butler, 1996).
1 The signatories of Lome Convention (ACP states) are: “Angola, Antigua, Barbuda, Bahamas, Barbados,
Belize, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde Central African Republic, Chad,
Comoros, Congo, Cote d’Ivore, Djibouti, Dominica, Dominican Republic, Equatorial Guinea, Ethiopia, Fiji,
Gabon, Gambia, Ghana, Grenada, Guinea, Guinea Bissau, Guyana, Haiti, Jamaica, Kenya, Kiribati, Lesotho,
Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Papua New
Guinea, Rwanda, St Christopher and Nevis, St Lucia, St Vincent and Grenadines, Sao Tome and Principe,
Senegal, Seychelles, Sierra Leon, Solomon Island, Somalia, Sudan, Suriname, Swaziland, Tanzania, Togo,
Tonga, Trinidad and Tobago, Tuvalu, Uganda, Western Samoa, Vanuatu, Zaire, Zambia, Mozambique”.
2 The EU’s GSP scheme was put into effect in July, 1971 by Council Regulation (OJL, No.142 of 28.06.1971)
which granted non-reciprocal tariff preferences to all finished and non-finished industrial products originating
from the developing countries with focus on the objectives (i) to help the poor economies in increasing their
export earnings (ii) to help their industries to grow, (iii) and to speed up their rate of economic growth. Since
then the scheme has been improved from time to time. The GSP scheme of EU is currently covering some 124
developing countries and 23 dependent territories in Asia, the Far East and Latin America.
20
At the bottom of the pyramid, there is a group of countries that gets treatment of the Most
Favored Nation (MFN) from the EU. This group belongs to all non-European industrialized
countries, like the USA, Japan, Australia, Russia, etc.
1.3 GSP plus Status and Pakistan
A number of changes were approved in the existing Generalized System of Preferences on
31st October 2012 into the European Parliament. Revision to the qualification criteria in the
system generated opportunity for Pakistan to gain GSP plus status that enabled Pakistani
exporters to take the opportunity and have open access to EU markets for GSP-eligible
products. Pakistan had to face a stiff competition to countries gaining benefits from GSP like
China, India, Vietnam, especially in textile and apparel industry. On the other hand, some
LDCs like Bangladesh after gaining the status of EBA1, are also creating problems for
Pakistani exporters (TRTAP, 2013). Hence Pakistan’s inculcation2 in the list of GSP plus
beneficiary makes Pakistan second largest3 textile and garment exporter in the South East
Asian Region (PBC, 2014). Pakistan Business council has forecasted that it is not only the
textiles and garments sector but Pakistan may gain benefits from most of the products listed
in the GSP plus. The European Union (EU) granted Generalized System of Preferences
(GSP) Plus status to Pakistan in December 2013 (Awan, Sarwar, & Siddique, 2015).
1.4 Motivation of the Study
International trade is suffering from the state of instability. This is the primary concern for
many countries and especially the developing ones. Many bilateral and regional trade
liberalization agreements are in an awful situation that promotes the understanding of World
Trade Organization (WTO) being unable to achieve the expected pace of success to promote
the multilateral agreements. Some support the idea that trade should be free for all
1EBA (Everything But Arms) is a status granted to the LDCs to export every kind of products except arms
without any quota or duty restrictions to the EU market (7,140 Tariff lines). 48 LDCs are included into this
category (Kennedy, 2011, European Commission, 2011).
2 “Regulation (EU) No 978/2012 of the European parliament and of the Council of 25 October 2012 applying a
scheme of generalized tariff preferences and repealing Council Regulation (EC) No 732/2008”.
3 Some of the competitors do not qualify for the GSP plus status in the EU (India, Colombia, China, Vietnam
and Thailand). On the other hand India has graduated from textiles section and China has graduated from both
textiles and garments of the standard GSP.
21
(Acharyya, 2011) but others suggest that many countries especially the poor regions are not
prepared to face this competition of liberalization (Hur & Park, 2012) and one should
consider the special conditions for such countries. So the trading rules should be based on
different stages of development (Freres & Mold, 2004) and (Baldwin & Jaimovich, 2012).
Economic integration is a process in which the independent economies gradually unify by
removing trade restrictions and allowing movement of factors of production. Consequently,
the extent to which economic integration occurs in any specific arrangement is determined
principally by the degree to which restrictions on the mobility of goods, services, capital and
labor are removed. While economic integration theory focuses on the economic gains and
losses accruing to countries as barriers to trade and factor mobility are removed. In fact, in
the case of economic integration we discuss trade arrangements that involve preferential
liberalization of trade between limited numbers of countries within the international
community. The nations forming economic integration desire to capture the economic
benefits associated with the dismantlement of barriers between their economies and the
international economy. However, the extent of economic integration is determined
principally by the degree to which restrictions on the mutual trade and movement of factors
of production are eliminated (Ulasan, 2015). The gradual process of reducing barriers to
trade, i.e., progressive economic integration, is one of the most important forces that has
shaped the world economy post 1960s (Brulhart & Mathews, 2007).
European Union (EU) under the umbrella of General Agreement on Tariffs and Trade
(GATT)1, launched the Generalized System of Preferences (GSP) in 1971. It is a unique
system of different trade agreements favorable to developing countries. The basic purpose of
GSP was to promote the efficient usage of resources for production activities in developing
economies. Ultimate purpose was to transfer the international resources from developed
countries to developing countries by using the facilities of international trade. On the other
hand, the preferences given to the developing economies impaired the multilateral
liberalization. The World Bank report of 2003 argued that the non-reciprocal system of
1 Current WTO (World Trade Organization) is modified form of GATT.
22
preferences like GSP are mere a “Faustian bargain” because the damage is greater than the
benefits. (Dowlah, 2008).
With the steps to control imports, the EU also adopted measures aimed to facilitate the
imports from other developing countries not included in the Lome Convention. The most
important step taken in this regard was the provision of special tariff preferences to these
developing countries under the Generalized System of Preferences (GSP). The motive of the
GSP was to help them in solving their economic problems. With this aim, the EU established
preferential trade relations with the Asian and Latin American economies under the GSP
scheme. Under this scheme, the EU waived customs duties on imports of the products from
these developing countries (with the exception of so-called of sensitive products) and the
duties on agriculture and food products were also reduced which do not compete with the
ACP. Some 124 developing countries and 23 dependent territories in Asia, the Far East and
Latin American countries are now covered by the EU’s GSP scheme (Sapir & Langhammer,
1987) and (Naeem, 2006). The GSP system of the EU has the following distinctive features:
The GSP is not a uniform world system, applied in the same way by all the developed
countries; on the contrary, the EU, the USA, Japan and others have created their own
systems, albeit broadly on the same principles. The EU version of GSP is
autonomously granted to a number of beneficiary countries. As it is not an agreement
conducted between two or more parties after negotiations, the EU can unilaterally
decide to change it or even withdraw it completely.
The GSP scheme offers a tariff preference, in general. Exportable goods coming
under GSP are imported into the EU tariff-free, whereas non-GSP countries face the
full CET. There are no reciprocity, EU exports to GSP countries receiving MFN
treatment.
The GSP scheme is confined to semi-manufactured and manufactured goods
excluding agriculture. For ‘sensitive product’ it is used to be limited to sometimes
fairly restricted quotas.
23
The GSP is in principle available to all developing countries, but the EU has signed
bilateral agreements those to which it agree to give GSP status. This scheme is also
run under the safeguard measures and social clause. In practice, some countries
coming under the GSP, such as ACP and Mediterranean countries, prefer another,
more advantageous scheme as discussed earlier; that leaves Latin American and
Asian countries as the most important beneficiaries.
The European Union (EU) is not only the largest single operating market of the world but
also the biggest trading partner of Pakistan. Approximately one-third of Pakistan’s total trade
volume is running with EU. One should keep in mind the two remarks before trying to find
out the position of Pakistan in EU (Khorana, et al., 2012). First, Pakistan is not part of EU’s
rationalistic approach that includes the “Lome Convention” or EU’s policy towards some
regions like Mashreq, Maghreb, and Meditatrian economies. Second, Pakistan appears in the
front line of the EU's global approach (GSP Scheme) (Gillespie, 2013).
The above considerations motivated this study that intends to develop an analytical
framework to incorporate the latest GSP status of Pakistan and to see the effects of Pakistan’s
GSP Plus access in the EU28. The findings of the research may help in policy formulation
for the government of Pakistan to reduce the budget deficit, inequality and unemployment
from Pakistan.
1.5 Research Problem
Joining and signing of any FTA brings changes in the economy by altering the relative prices
and income distribution. These measures cause shifts of resources among sectors in order to
adopt the structure of the economy according to the changes in national and international
economic environments. It has been proved by many studies that in developing countries,
natural resources have been related to unsatisfactory economic growth (Kanji & Barrientos,
2002). International trade is an integral part of the development of an economy that results
efficient use of natural resources in order to compete at international level. In this regard,
trade liberalization produces opportunities as well as challenges for many countries and
Pakistan is no exception. Similarly, economies get benefits through economic integrations.
24
The European Union is the largest export destination for Pakistan. Recently, EU granted GSP
plus status to Pakistan. Our main research problem is to investigate the likely impact of GSP
Plus on Pakistan’s exports to the European Union and hence its resulting effects on economic
development. The impact can be underestimated or overestimated if one tries to capture the
impact of FTA like GSP plus by applying partial equilibrium analysis. As all the sectors of
the economy are interlinked, so any shock in one sector leads to changes in other sectors. So,
the main objective of the study is to find out the impact of GSP plus status of Pakistan in the
EU - on Economic growth of the country. To capture the impact of these measures,
Computable General Equilibrium models are an ideal tool (Naqvi, 2010).
The study will apply the global version of the CGE (GTAP) keeping in mind the sectoral
linkages of the economy to identify and quantify the direction and the magnitude of the short
run implications of this trade opportunity on the household welfare. More precisely, the study
intends to look at the effects of this export opportunity on macro variables, industry level
variables and household level. As we want to investigate the possible outcomes from a series
of different policy experiments, the resulting likelihood of far-reaching economy-wide
implications makes the adoption of a CGE model suitable here. The specific objectives of the
study define as:
1.5.1 Objectives of the Study
1. To develop a global CGE model primarily linked with income inequality in Pakistan.
2. To study the economy-wide impact of European GSP plus status to Pakistan.
3. To identify the policy options to minimize the negative impacts of the GSP Plus
status on the marginalized population in Pakistan.
1.6 Research Questions
Based on the CGE model, this study lays out general and specific question alongside with
experiments to help us respond to these questions. From a general perspective, the current
work is aimed at examining the following:
25
1.6.1 The General Questions
1. What is the interaction between trade with EU and the rest of the economy after GSP
Plus status in terms of change in GDP, trade flows and other macroeconomic
aggregates?
2. How the trade with EU after GSP plus status is likely to affect the domestic market in
the short-run?
3. How can a national trade policy be formulated and implemented to benefit the
country?
4. How can we allocate the export revenue to maximize people’s welfare?
1.6.2 Specific Research Questions
1. Historically, how has the state acted to manage the economy?
2. With respect to trade annexes with the European Union, how is Pakistan
performing?
3. How are the internal shocks in the production process likely to affect the
economy?
4. How are the external shocks in the international market with respect to EU, likely
to affect the economy?
26
2 CHAPTER 2: THEORETICAL FRAMEWORK
International trade theories concerned with the gains accruing to trading partners on their
mutual trade if tradable goods are produced according to the principle of comparative
advantage based on their factor endowments. Economies at national or international level pay
special attention towards the production structures while considering the trade policy
instruments. Tariffs and quotas are the instruments of trade policy that affect the relative
prices of the goods in any given economy. The demand for inputs changes when the
economy changes the mix of produced goods and services. Hence, it is difficult to predict
that any given change in trade policy will affect only one sector of the economy. The
backward and forward linkages in the economy bring a change in the sectoral output mix
according to the strength of the linkages. (Karingi, Lang, Oulmane, Perez, Jallab, &
Hammouda, 2005).
In the desire of economic growth, many developing economies have espoused external
economic liberalization policies. It is based on a common fabrication that countries with
fewer trade restrictions have fast-paced economies and vice-versa. Trade liberalization has an
inherent tendency to raise employment elasticity of economic growth thereby creating a
better impact. However, critics of globalization find a chance to underline that growth
benefits might possibly be unevenly spread; as a result, the impingements of distributions
could also affect the poor adversely (Krueger, 1998).
Trade liberalization can be the reason for better economic growth. Benefits of total factor
productivity gained by the economies of scale alongside enhanced efficiency; have a
powerful potential to be transformed into an immense rise in potential output. The studies
conducted by Freund & Bolaky (2008) and Changa, Kaltanic, & Loayza, (2009) showed that
the growth effect of trade openness is significantly positive provided that partner countries
successful in achieving regulatory reforms like business rules, financial developments,
expansion in better education or rule of law, increase in employment opportunities labor
market flexibility, etc. Otherwise, trade is not associated with long-run growth in such
economies. In addition, due to the tendency of attracting Foreign Direct Investment (FDI)
and larger access to regional markets, liberalized trade becomes a place of interest for foreign
27
investment prospects. A higher value of Foreign Direct Investment (FDI) consequently, may
also pave the way for a large-scale technology transfer (Chanda , 1997 ) and inter-industry
linkages (Wang, 2011) as well as total factor productivity.
2.1 International Trade and Growth Theories
The implications for the association between economic growth and trade can be traced from
the endogenous growth theory which is also known as new growth theory. However, many
other approaches have also used the notation of new growth theory and investigated the same
relationship with a different perspective. Lucas (1988) focused on the perspective of
comparative advantage and learning by experience, to study the relationship between trade
and economic growth. Consequently, a country having comparative advantage in human
capital will specialize in producing such goods where human capital is involved. This
specialization will be reinforced with earned experience. Similarly, Research and
Development (R&D) and innovation were also taken into account while studying the
relationship between economic growth and foreign trade. The R&D and innovation were
considered as the base of economic growth when analyzing the economic growth in open and
closed economies in particular. Another study conducted by Grossman & Helpman (1991)
observed that international trade helps an economy to develop its technology base which
reduces the cost of producing things and ultimately results into economic growth. The new
technology also helps an economy to diversify the production capacity. Significantly, it is
international trade that forces the economies to perform under strong competition that
enables them to innovate and produce at economies of scale (Afonso, 2001).
Furthermore, there is a group of researchers that investigated the role of international trade in
capital accumulation and changes in the investment patterns. International trade helps the
economies to obtain advanced technology and other factors of production from other
countries. The domestic product will become an additional factor of production which due to
accumulated capital and advanced technology will be converted into advanced featured
product (Afonso, 2001). In other words, the international trade helps the economy to utilize
the domestic resources at maximum by widening the availability of capital equipment and
intermediate goods.
28
The import of capital goods from advanced countries also helps the developing countries to
get access to the advanced technology. In addition to this, it is international trade that helps
the domestic producers to produce at economies of scale which helps the producer to achieve
increasing return to scale. In this way, international trade helps to reduce the cost of
production and increase the production and consumption at the domestic level (Hamori &
Razafimahefa, 2003).
Generally, it is believed that it is technology that affects the productivity at maximum which
ultimately results into economic growth of an economy. It means the imported technology is
a very important component of economic growth. The argument is further supported with the
assumption that foreign trade facilitates the developing countries to adopt the advanced
technology which ultimately leads to the growth of TFP (Yapraklı, 2007).
2.2 Brief History of Modern Trade Agreements
The emergence of “General Agreement on Tariffs and Trade (GATT)” in January 1948 is
considered to be the beginning of the modern trade history. The General Agreement on
Tariffs and Trade (GATT) used to play very important role in world trade sphere since it
became effective in January 1948. The Articles of the GATT were originally agreed in 1947
(referred to as GATT 1947) and subsequently, with some revisions, in 1994 (referred to as
GATT 1994) as part of the Uruguay Round negotiations that created the “World Trade
Organization (WTO)” (World Bank, 2000). The principles of the GATT became the basic
rules and regulations of international trade. The main purpose of the GATT was to promote
free trade with the abolition of tariffs and reduction in quota tariffs. GATT promoted the
smooth flow of international trade under its clause of “Most Favored Nation (MFN)” status
(all member countries enjoy equal concessions) (World Trade Organization, 1995).
It was the beginning of the decade of the 1950s when some European countries decided to
establish regional cooperation that ultimately push the Europe to establish a continental
integration. In 1951, coal and steel treaty was signed by the Germany, France, Italy,
Netherland, Belgium and Luxembourg. The agreement was re-negotiated and resulted in the
establishment of “European Economic Community (EEC)” in 1957. It was 1973 when the
29
United Kingdom along with Denmark and Ireland joined the EEC and started working on
political and economic cooperation (Winters A. L., 1994). The name was changed to
European Union (EU) in 1992 and working was started to launch a single currency. The Euro
was launched in 1999 as the common currency for EU countries except the United Kingdom,
Sweden and Denmark. EU has a complex system of preferential and non- preferential trade
agreements (For more details please see chapter 1). The EEC inspired the other regions of the
world to establish PTAs (Preferential Trade Agreements) and FTAs (Free Trade
Agreements). The EU has established different bilateral agreements with developed
countries, Meditatrian countries, African Caribbean and Pacific (ACP) countries and
developing countries (Winters, 2016).
It was the EEC that inspired the developing countries in Africa, Central and South America
and the Pacific to establish their own regional agreements. The most common agreements
were the Central American Common Market and the East African Community that collapsed
by the end of the 1970s (de Melo, Panagariya, & Rodrik, 1993). During the last decade of
20th
century, EU has signed a number of bilateral agreements with countries in the Middle
East (Israel, Jordan, Lebanon and the Palestinian Authority) as well as in North Africa
(Egypt, Tunisia, Algeria and Morocco) aiming to form a free trade area similar to the “North
American Free Trade Area (NAFTA)” (Ojeda, Sherman, & Lewis, 1995).
It was 1985 when the United States shifted its approach of multilateralism to bilateralism by
signing a free trade agreement with Israel and a more comprehensive agreement with Canada
in 1988. This Canada-US free trade agreement then converted to NAFTA in 1994 with the
inclusion of Mexico (Ojeda, Sherman, & Lewis, 1995). Old arrangements in Latin America
i.e. the “Andean Community” and the “Central American Common Market” were re-
established with a broader vision. The most common example is the agreement between the
Argentina, Brazil, Paraguay and Uruguay, known as MERCOSUR which started working as
custom union.
This wave of regionalism also affected the African countries resulting into the establishment
of the “Common Market for Eastern and Southern Africa (COMESA)” with focus to realize
30
the economies as part of Africa, “the East African Community (EAC)” that includes the
Kenya, United Republic of Tanzania and the Uganda with focus to cooperate in
industrialization and economic development, “Economic Community of West African
States (ECOWAS)” with similar objectives and the “Southern African Development
Community (SADC)”, an attempt to integrate the South Africa into regional economies . The
focus of all these agreements was the maximum economic cooperation (Osman R. M., 2011).
It was 1983 when New Zealand and Australia decided to sign a free trade agreement known
as “Closer Economic Relation (CER).” The purpose of this agreement was to deepen the
trade relations between both economies. This agreement helped the New Zealand economy to
access better dairy products from Australia and Australia achieved the goal to maximize the
export of dairy products to New Zealand. There is no tariff or quantitative restriction exists
and the goods and services between both countries can move freely (Scollay, Findlay, &
Kaufmann, 2010).
In Asia, the most prominent example is the Association of Southeast Asian Nations
(ASEAN) which was established in 1967 to cooperate with member countries during crises
and to increase the economic cooperation especially in the fields of agriculture, financial
services, tourism and science and technology. Currently, the ASEAN community consists of
ten member countries including the Indonesia, Thailand, Malaysia, Philippines, Singapore,
Vietnam, Myanmar, Cambodia, Laos and the Brunei and all are working to promote peace,
prosperity and research (Hansakul, 2013).
The second important free trade agreement is known as South Asian Free Trade Area
(SAFTA). SAFTA is a trade agreement among SAARC (South Asian Association for
Regional Cooperation) countries. SAFTA was initially signed in 1993 as “South Asian
Preferential trading Arrangement (SAPTA)” and started working in 1995 with aim to
promote peace and economic cooperation among member states i.e. Afghanistan,
Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka (Srinivasan, Kalaivani,
& Ibrahim, 2011)
31
2.3 Preferential and Free Trade Agreements of Pakistan
The terms PTA (Preferential Trade Agreement) and FTA (Free Trade Agreement) are same
in a sense that they both deal with the ease of international trade. The term FTA is used in
when two or more economies sign an agreement of trade liberalization to facilitate each other
in the flow of goods and services and investment. The economies integrate with each other
by removing trade barriers while the PTA aims to reduce the tariff not completely abolish it
in order to ease the international trade (Baldwin & Freund, 2011).
Free Trade Agreements (FTAs) became famous globally after the failure of WTO in
resolving the issues of international trade flows. Pakistan is a developing economy with the
aim to grow its international trade relationships. For this purpose, the country has signed
different bilateral and multilateral trade agreements with the economies of the South Asian
region as well as in the regions of Europe, Latin American, South East Asia and Asia Pacific.
The purpose of signing different PTAs/FTAs was to facilitate the trade with different
economies around the globe and stimulate the investment opportunities for improvement in
the exports and economic growth (Taniguchi & Yanovic, 2007). Pakistan is also adopting an
export-led development policy for which the market share at international level is crucial and
to get such market entry Pakistan had to establish such preferential and free trade
arrangements.
The FTA between Pakistan and China holds a great significance as it offers a great
opportunity to the goods and services. The FTA further enables the manufacturers in Pakistan
to have access to machinery and chemicals at zero tariff rates. This free trade agreement is
covering the areas of investment and trade in goods and services. The agreement of trade in
goods and investment was signed in 2006 while the agreement on trade in services was
signed in 2009 (Kataria & Naveed, 2014).
China has market access in Pakistan in 11 sectors and 107 sub-sectors while China has given
access to Pakistan in 11 Sectors and 133 sub-sectors. This agreement provides full security to
Chinese investment and China did a lot of investment in Pakistan using Pakistan’s cheap and
32
hardworking labor force. Pakistan also provides a lot of investment opportunities to China
(Kataria & Naveed, 2014).
Afghanistan is Pakistan’s second largest trading partner following USA. So Pakistan is
perusing to have an FTA with Afghanistan, Iran, and Turkey while it already had a PTA with
Iran in 2006, under this agreement Pakistan offered a concession of 338 tariff lines to Iran
and Iran on the other hand offered 309 concession of tariff lines to Pakistan. Pakistan also
signed PTA with Indonesia and Morocco, being a Muslim country the PTA agreement
between Indonesia and Pakistan helped to further strengthened the economic integration and
trade between both countries. This agreement was signed in 2012 (Kataria & Naveed, 2014).
The operation of PTA between Pakistan and Indonesia follows the mutual recognition
agreement on plants and Sanitary and Phyto-Sanitary (SPS) measures. This agreement
considers Pakistan as pest free area for kinnow and allows its entry to Indonesia through the
port of Jakarta which assists Pakistan to increase the market share of its agriculture products
in Indonesia (Kawai & Wignaraja, 2011).
Pakistan and Morocco signed the PTA and FTA negotiations jointly in 2008 in the first
session of Joint Ministerial Commission (JMC) to improve commercial co-operation and
heighten their trade. Pakistan and Singapore free trade agreement was announced in May
2005 and since then three rounds of negotiations have been held. Pakistan-Singapore FTA
helped Pakistan to get access to service sector in Singapore and improved it’s exportation of
manpower and also help to enhance the market share of SME’s (Kawai & Wignaraja, 2011).
Pakistan signed a trade agreement on June 12, 2005, with fellow SAARC nation, Sri Lanka.
According to this agreement, both countries arranged to give preferential access to each
partner’s export goods by giving away tariff concessions. Sri Lanka enjoys the duty-free
access to the Pakistani market for its 206 products including tea, rubber, and coconut. On the
other hand, Sri Lanka has granted duty-free access to 102 Pakistani products including
basmati rice, oranges, and engineering products. This agreement includes the removal of
33
tariffs, para-tariffs, safeguard measures and settlements of disputes etc. (Shaikh & Rahpoto,
2009)
Pakistan and Malaysia also signed free trade agreement on November 8, 2007, at Kuala
Lumpur Malaysia, the first bilateral agreement between the two members of Organization of
Islamic Cooperation (OIC). Pakistan is one of the major importers of palm oil after China
and after this free trade agreement there was 99.5 percent increase in the importation of palm
oil from Malaysia due to lower duty on Malaysia palm oil but it has been found that the trade
balance is in favor of Malaysia, not Pakistan (Butt, 2006).
Pakistan and Mauritius also signed preferential trade agreements on July 2007 in Mauritius in
which Mauritius has given a concession to Pakistan on 102 items and Pakistan in turn gave
concession on130 items and this bilateral agreement was in favor of Pakistan as it can
augment its food exports. The final objective of this preferential trade agreement was to give
a path to free trade agreement which includes all trade, products, and services but this has not
been occurred yet (Butt, 2006).
The most important trade agreement for Pakistan that was signed between Pakistan and
European Union (EU) on December 2013 is GSP Plus. Although Pakistan is enjoying trade
relationships with EU under the umbrella of Generalized System of Preference (GSP) ever
since its emergence in 1971. The current status grants duty-free and quota-free access to most
of the Pakistani products (Naeem, 2006).
2.3.1 External Trade Regime of EU and Pakistan
The European Union has gradually expanded its external trade relations with the passage of
time. In practical terms, this means that EU’s external trade regime has been extended to new
subjects as integration progressed through the stages (Molle & Mourik, 1988). The EU’s
external trade policy regime is highly complex and complicated. The very complexity of
trade regime governing access to EU market can be seen as the number of trade barriers
operative in itself.
34
The rapid increase in trade and economic relations of Pakistan with EU is starting in 1973
when UK becomes the part of EEC. By the analysis of EU’s trade statement, it point outs two
closely related points:
The agreement explains the continuation and extension of trade with
developing countries based on regions. Lome Convention and association
agreements with the countries of Mediterranean and East and West.
To apply the policy at world level, which consists of an instrument like GSP
agreement, provision of financial and technical support, food aid, etc.
Commercial Cooperation Agreement was signed between Pakistan and EU in June 1976 gave
trade benefits to Pakistan. The agreement provided the opportunity to Pakistan to build a
strong trade and economic relationship with EU. This agreement was limited to trade only. In
1986, a new agreement was signed under the name of “Commercial Cooperation and
Economic Development”1.This agreement was in the favor of both as it was broader in vision
and almost covered all the aspects including economic, scientific, technology and financial
cooperation (Naeem, Trade Implications for Pakistan in the European Union Market in the
Milieu of EU Enlargement from EU15 to EU25, 2006). The third agreement between
Pakistan and EU was signed on November 24, 2001 with some reduced financial benefits to
Pakistan but covered more areas including joint declaration of intellectual, industrial and
commercial property and taking the responsibility by Pakistan to arrange re-admission
agreements with the member states of the EU (European Constitution, 2004).
EU is considered as the biggest trading partner of Pakistan. For instance, during 2014-15,
31.23% of the total trade of Pakistan was with EU nations while the USA is left behind with
16.44%. Garments, cotton, textile are considered the major exporting sectors of Pakistan. To
conclude, since 1971, Pakistan is the country that takes most of the benefit of this EU GSP
agreement, and all the instruments written in the agreement has been applied to the Pakistan.
Besides the benefits, the position of Pakistan is not very strong in the hierarchy set by the
1 The agreement was signed by both the countries will automatically be renewed after its expiry. But if any
party in the agreement wants to cancel it, that party have to inform the authorized body
35
EU’s agreements of trade preferences, as compared to the ACP and Mediterranean countries
(Sincai, 2014).
2.3.2 The Evolution of the GSP plus Arrangements
The standard GSP provides generous access to the developing countries into the EU through
partial or full tariff relieves. During October 2012, the European parliament made some
amendments into the standard GSP through the establishment of European Parliament's
Regulation No. 978/2012. New scheme is called GSP plus that allows considerable growth to
that country which is not economecally strong and complies with its binding undertakings.
The beneficiary country has to implement and maintain 27 core international conventions
that are related to human and labour rights, good governance and environment protection.
The countries, already being benefited by this agreement have to prove themselves by
meeting the conditions written on the agreement. That specific country has also to meet the
standards of “rules of origin” which means that if a particular product is made in multiple
stages and the inputs are imported from different countries, it has to meet the rigid
requirements (Cuyvers & Soeng, 2013).
Initially, the countries used to graduate (exclude from the preferential treatment list) from the
beneficiary list after achieving certain diversification in export items. The latest scheme of
GSP plus has abolished this condition of graduating by sections. Moreover, a country may
apply for GSP plus status at any time during a year instead of waiting for every month. The
preference margin between normal GSP and GSP plus is quite significant and rates of
utilization of this margin are quite high in GSP plus scheme (Onguglo, 2010).
2.4 Justification for Using CGE Modeling
By using various modeling methods the effect of tariff reduction on the four regions of
Pakistan can be calculated. Econometric models, I-O model and CGE models, these three
models can be used to calculate the effects of policy options. Dick et al. (1983), Shoven &
Whalley (1984), Wong (1990), Bandara (1991), Baldwin and Venables (1995) and Karingi
(1998) all identified in their studies that all the techniques have their own strengths and
36
weaknesses. In the light of these studies, it is easy to predict which technique is most useful
in a given situation as compared to others.
2.4.1 Econometric Models vs. CGE Models
Econometric models are more useful when results of a policy are affecting a specific industry
(Chow, 1977). Econometric approaches are very simple. However, these models are not
based on neoclassical microeconomic theories so they may not focus on the consumer
behavior, utility, and profit maximization (Lucas R., 1976). Moreover, the econometric
technique is based on large time series, cross-sectional and panel data especially which are
not very easy to find particularly in a less developed country and at micro level. The data
limitations make the econometric models less useful when studying the inter-sectoral
linkages. A brief summary of comparison between CGE models and Econometric Models is
given below.
Table 2.1: Comparison between CGE Models and Econometric Models
Econometric Models CGE Models
They are not based on neoclassical
microeconomics theories.
CGE models are based on equilibrium
theory.
Econometric techniques are more helpful
when feedback effects to a particular
industry.
CGE techniques are more helpful when the
effect of feedback is more in general or for
the whole economy.
Econometric models are stochastic in
nature.
CGE models are deterministic in nature.
They are mainly based on macroeconomic
formation.
They are mainly based on macroeconomic
structure.
Long-term time series data requirements
reduce the chances to identify inter-sectoral
linkages.
These techniques are able to identify the
linkages between industries and
commodities within the country or between
the country and the world.
It requires long term cross-sectional data
which is not easy to find.
While CGE (comparative static) models
require only standard year only.
37
Statistical testing is there in econometric
techniques.
CGE techniques don’t have statistical
testing.
Source: Dick et al. (1984); Shoven & Whalley (1984); Bandara (1991); Baldwin & Venables
(1995); Karingi & Siriwardana (2003); and Butt (2006)
CGE models are based on equilibrium theory which is helpful when a situation arises due to
changes in tax and trade policy. These models can establish the linkages between
commodities, industries in the domestic economy or with the whole world. Bandara (1989, p.
45) uses this argument to find the impact of tariff cuts.
“To analyze the detailed effects of tariffs one must examine quantitatively the
chain of events that take place when tariffs are cut, as follows. A cut in tariffs
alters consumption patterns in a tariff-reducing country. Then, imports rise
and the relative prices of imports and domestic goods change. Consequently,
changes in tariffs cannot be considered in isolation. Their repercussions are
propagated throughout the economy as they affect production, investment and
consumption decisions. Clearly, a partial equilibrium approach cannot fully
capture this chain of events and their interactions.”
CGE model is more reliable than to the econometric model in order to find out the effects of
GSP plus the status of Pakistan in the EU on the economy as a whole. Because CGE model
requires only benchmark year data while econometric model requires long term cross-
sectional data which is very difficult. So there is no doubt in it that CGE model is more
suitable to identify the effects of tariffs cut off EU on Pakistan’s economy as compared to the
econometric model.
Considering the case of Pakistan, there is not known single study which has focused on Free
Trade Agreement (FTA) between Pakistan and any regional block. There is only one study
conducted by Shaikh &Rahpoto (2009) which calculated the impact of SAFTA on Pakistan
economy being part of it. Similarly, the GTAP used by previous studies did not represent
38
Pakistan as a separate country. This study is using the latest version of GTAP (9), to
calculate the impact of existing EU trade agreements on Pakistan economy.
39
3 CHAPTER 3: REVIEW OF LITERATURE
3.1 Introduction
It passed more than two decades that International Monetary Fund (IMF) and World Bank
has engaged the international economic community with the worldwide campaign of
economic reforms through the economic stabilization and structural adjustment programs.
However, the debate shifted its focus to international economic integration through fair
international competition and trade liberalization from structural adjustment and stabilization
(Hassan, 1997).
International economics is considered to be the oldest study branch of the economics
discipline. Economic activities within an economy and between two economies have been
accepted as separate issues. Both types of the economies have differences not only in
customs, population and consumptions patterns but also the trading economies have tax and
currency system for the traded products. The ancient problems not only exist today but also
many other problems joined them (Hogendorn & Brown, 1979).
Nature has distributed the resources in such a way that some goods are available in one
community and other community may have some other goods. For example, every country
needs oil to run the economy but very few countries are self-sufficient, so others have to
import it. These sorts of patterns of trade hardly need explanation. Similarly, many
commodities that are traded among different countries are produced at many places, so the
patterns of trade are unique according to the availability and nature of land and labor. These
factors directly affect the cost of production that provides acompetitive edge on the other
countries (Naqvi, 2010).
Changes in the worldwide economic, political and legal framework not only brought many
opportunities but also restrictions to many economies of the world. Especially the
development during Uruguay Round of negotiations on the General Agreement on Tariffs
and Trade (GATT) that focused more on market orientation rather following the traditional
approach of control and central planning. Similarly, expansion of European Union (EU)
brought many threats and opportunities for the developing economies. Different agreements
40
of the EU are aimed to promote the economic activities in the developing world (Naeem,
2006).
The objective of this chapter is to investigate the literature to come up with a methodology to
analyze the impact of trade agreements on economic development, household welfare, and
inequality. It is suggested by trade-related literature that there exists a relationship between
trade policy and poverty. Various researchers have used different conceptual and empirical
approaches to examine this relationship. These approaches may have their own weaknesses,
which could eventually affect the results. Therefore, the empirical evidence should be viewed
in the light of the strengths and the flaws of the adopted conceptual and empirical approach.
This research is focused on Computable General Equilibrium (CGE) modeling approach.
CGE model is an empirical counterpart of the well-known theoretical general equilibrium
model, which has become the most widely applied counterfactual analytical tool.
3.2 International Trade and Economic Growth
A lot of discussions have been made on the role of trade liberalization in poverty reduction.
Many researchers tried to investigate the potential impacts of trade liberalization on the
development and poverty reduction in developing economies. Similarly, classical and neo-
classical trade theories also forecast that trade liberalization increases the general welfare
level in an economy but these theories failed to explain the links between welfare and trade
liberalization (Michaely, 1977). In this context, a two country, two goods and two factors
theory presented by Heckscher & Ohlin (1919) states that if an economy desires to increase
the exports and production, it has to focus on enhancing the productivity. Even today, the
developing economies have abundant unskilled labor force and that can be compensated by
increasing the trade and price of produced goods (Paudel & Perera, 2009).
Modern theories of trade suggest that trade liberalization brings efficiency through
economies of scale, technological improvements, access to the information and spillover
effects. It is unfortunate that these theories fail to explain the effects of liberalization on non-
tradable and non-homogenous goods along with some explicit factors or segments of the
labor market (Winters, 2002).
41
One can find a large number of studies that have investigated the impact of foreign trade on
the economic growth of an economy. Grossman & Helpman (1991); Frankel & Romer
(1999); Rodriguez & Rodrik (2000); Wacziarg & Welch (2003) and Alcala & Ciccone
(2004) consider the foreign trade as the prime factor of economic growth. Similarly, the work
of Sachs & Warner (1995) concluded that the economies with free trade experienced higher
growth rate as compared to the economies without free trade. Edwards (1998) attempted to
investigate the link between Total Factor Productivity (TFP) and foreign trade in 93 countries
and concluded that the growth rate of TFP is higher in the open economies as compared to
the economies with limited trade openness.
It has been observed that the role of exports in economic growth has been studied possibly
because of the rapidly growing role of export-led growth strategies adopted by many
countries. A group of researchers including Krueger (1998; Chenery (1979); Tyler (1981);
Kavoussi (1984); Balassa (1985); Ram (1985); Fosu (1990); and Salvatore & Hacter (1991)
argues that it is economic growth that enhances the exports while others like Kwan &
Cotsomitis (1990); Ahmad & Kwan (1991); Yaghmaian (1994)suggest that it is export that
results into increased economic growth. The results provided by the empirical study
conducted by Vohra (2001) suggest that export influences the economic growth in an
economy only when it achieves a certain level of economic development. Similarly, Subasat
(2002) suggested that the middle-income country in influenced greater by the export-led
growth strategy than a country with less focus on this strategy. The study further discovered
that the middle-income country grows faster with growth-led strategy than low and high-
income countries.
Reductions of discriminatory tariffs also change the terms of trade not only in the domestic
country but also in the trading partners and other regional group members. Mundell (1964)
investigated the impact of discriminatory tariff reductions on the terms of trade, assuming the
availability of substitutes, discovered that the tariff reduction by a regional member not only
improves the terms of trade in that country but also brings benefits for the other member
countries. He further concluded that higher tariff rates before liberation bring greater gains
for the partners in terms of trade.
42
3.3 Exports and Economic Growth Nexus
The relationship between economic growth and foreign trade is generally studied by keeping
in mind that it is exports that help to grow an economy (Emery, 1967). The assumption that
exports lead to economic growth has been studied by many researchers. There are mainly
two reasons for the development of studies on exports-led growth. The new growth theory is
one of them that help to build a model to calculate the impact of growth factors on the export
performance of an economy. The second reason is the new developments in the econometric
tests such as co-integration and causality. These tests are widely used to calculate the
relationship between trade and economic growth (Lee & Huang, 2002).
Redding & Venables (2004) classified determinants of export into two groups internal and
external determinants in order to investigate them. They measured the effects of geological
area, overseas and domestic market coverage and bureaucratic qualities on export
performance of sub-Saharan Africa. According to this study the geological region of a
country directly affect its trade with the overseas market and trade performance is determined
by overseas coverage of a country. High overseas market coverage results in superior export
performance. According to this study, two factors influence export performance firstly
geological location of the country, secondly population of oversea countries that are adjacent
to your border. Finally bureaucratic abilities or institutional factors also influence trade with
foreign markets.
Din et al. (2009) discussed the decisive factors of firm level Pakistan’s export performance
according to the survey organized on four export segments: “leather products, textile, and
apparel, fisheries and agri-food”. The study applied practical OLS technique. The study
calculated contact of every illustrative inconsistent variable. The findings indicated that level
of investment in client-oriented technology, executive capability and valuable position of
organizations had positive impact on the export act. At the same time lack of certification to
fulfill the supply capacity with process standards and global product quality, proved to be
constraints and had an informal impact. Foreign-owned firms perform better than the
domestic firms due to better managerial skills, improved technology and easy access to
foreign markets for exports.
43
Rehman et. el. (2011) analyzed and estimated Pakistan’s exports trends towards relative
price, exchange rates, and gross domestic products over thirty years. The study determined
pros and cons of export trends and found plus and minus impact on exports and concluded
positive and negative correlations. To ensure stationary, the study examined “Augmented
Dickey and Fuller (ADF)” and found that data wasn’t stationary. To analyze the integration
between variables the study applied “Augmented Engle-Granger (AEG)” approach and found
that variables are co-integrated. The study showed and verified that the export performance
of the country is influenced by GDP, exchange rate, and related prices. Furthermore, the
conclusion of the study indicated that if GDP of developing country like Pakistan increases it
can enhance exports. Further, the findings indicated that high value of related prices results in
positive export outcomes. Besides, GDP and exchange rate were considerable while related
prices were inconsequential.
Shahbaz et al. (2011) analyzed Pakistan - a developing country economy. The study
examined the role of exports in economic growth by using quarterly data of 28 years. The
study by using ADRL approach found that export expansion resulted to overall economic
growth. Furthermore the working capital is found the leading determinant of the economy.
Depreciation of currency enhanced the exports and hence economic growth. The study
suggested that textile and agriculture sectors are correlated and in order to maximize the
exports, government have to focuss on both.
Portugal-Perez & Wilson (2012) choose more than 100 developing countries to check the
influence of soft and hard infrastructure on the export performance for the period of 2004-
2007. The study concluded that in these countries, export performance is positively affected
by the trade facilitations. Further, the study found that in countries with higher per capita
income, marginal effects of improvement in transportation and business environment is
decreasing but in the case of physical infrastructure and information technology it is
increasing.
Ratnaike (2012) studied the OECD countries to check the influence of trade liberalization
along with domestic competitiveness and world demand on the export performance by using
44
panel data approach. The results of the study showed that world demand and domestic
competitiveness strongly influence the export performance while trade policy has
aninsignificant impact on it. The study revealed that increased world demand resulted in
improved export performance. On the other hand, the cost of producing exportable
commodities decreased due to domestic competition which further helped to increase the
exports. It was more interesting to note that domestic demand has significant negative impact
on the export performance of these economies.
Khondoker & Kalirajan (2012) explained wide-ranging factors that influence the export
performance of nations which are in developing phase on products which need a higher
number of labors with the usage of the cross-country panel data. According to the findings of
the study developing nations should focus on their organizational frameworks to build labor-
friendly environment because this can result in industrial growth on all sort of industries even
smaller industries like garments. Although it is necessary for all developing countries to
increase their exports but keeping in mind that these countries have access labor force. So, if
they consider installing new industries with higher labor count this can help them enhancing
employment opportunities and exports.
Auera & Mehrotrab (2014) discovered that the Asian economies with higher trade intensity
and close trade relations experience more close movements of consumer and producer price
inflations. The study further highlighted the importance of supply chain management for the
price spillovers at cross-border sectoral level while using the data set of the “World Input-
Output Database (WIOD)”. It was further discovered by the study that Asia-Pacific region
economies are realizing the increasing importance of imported intermediate inputs and its
impact on the prices of domestically produced goods.
Gulzar & Ghani (2014) analyzed the causal relationship between PSDP (Public Sector
Development Programme) expenditures, trade in service, both public and private investments
and economic development of Pakistan. The study discussed it in the context of the
Keynesian theory of government expenditures, supply-side hypothesis of Neo-classical,
Wagner’s economic growth theory along with high mass consumption demand pull and
45
export-led well-known theory while considering the issues of security, stability, and
governance. Unit root and co-integration methods were applied to check stationary issue and
long run relationships respectively. Vector Auto Regressive (VAR) approach was adopted for
multivariate analysis. The study found that a big push is required in private investment and
trade in service to enhance the economic growth.
3.4 Computable General Equilibrium Models and the Economy
Policy makers usually check the indirect and direct effects through CGE about certain
policies, for instance they check that if a specific policy is formed then which sectors and
how much percent of the benefit we can gain or losses we can bear. The advantage of using
CGE is that it uses general equilibrium, helps in the adjustment of policy issues and after a
micro and macro analysis none of the fields is left untouched. Models effectively portray the
view of an economy as a whole, they are being used for prediction and these predictions are
according to the results obtained. The basic objective of the policy makers is to see the real
picture of the economy through theories and form models according to that for future
prediction. In order to check the authenticity of theories, we run models to judge them and
also come to know about implications of the theories and can address different policy issues.
There were many surveys conducted regarding CGE and every author according to his field
of interest studied different portion of it. Like for example, Pereira & Shoven (1988) studied
the national taxation portion of dynamic CGE, Shoven & Whalley (1984) focused on trade
and taxation portion and De Melo (1988) focused on developing countries, studied the trade
scenario quantification and CGE’s contribution in it. Robinson et al. (1999) did a survey on
CGE model and especially focused on its application side. Bandra (1991) surveyed the
policies in LDC’s through CGE. Kraybill (1993) compared by analyzing the input-output and
regional issues. Through CGE many issues of taxes and public finance, environmental and
energy policies, tariff and other trade policies and developmental policies have been
addressed.
According to the statement of Shoven & Whalley (1992), once the general equilibrium is
formed it then becomes quite easy to check for all the possible policy changes. Now when
46
the concept of liberalization policies got attention at the same time CGE got its importance in
front of developing economies, because they had to check the effects of these policies of
different forms on their economy. CGE incorporates all the interactions that are market-based
and through its results, it shows that which kind of policy would be more appropriate for a
certain economy. The reason why CGE is more appropriate than all its predecessors because
it eliminates the linearity constraint which was an issue in all the previous models. But every
model has a certain implication. CGE models, unlike other models, no doubt solves the
complexity of micro-macro analysis to a greater extent but still this issue is not solved
completely as none of the models can address all the issues in adjustment programs. CGE
models have been classified from most simple to complex depending on the study.
CGE models usually help in a way that we come to know that which policy is affecting the
economy in positive and negative terms and its extent can also be checked. Janvry et.al
(1991) developed a CGE model for Ecuador to study the policies to be adopted and
incorporated using financial portfolio and took inflation and interest rates as endogenous
variables. Through simulation results, it was observed that if the current expenditures are
reduced then it would benefit the economy in the long run. But the contractionary monetary
policy would discourage the private investors due to a rise in interest rates, which would
reduce growth in the present. Different effects of the above-mentioned policies were seen on
sectoral poverty. If we check the rural sector it benefited from this policy of reduced
expenditure but the urban sector is affected badly as it has to face the exchange rate
devaluation, demand contraction and the losses in public goods benefit. The study concluded
at the end that reduced fiscal expenditure benefited the poor sector of Ecuador.
Adelman & Robinson (1988) worked on the rules of macro closure, for this, a CGE model
was formed. The study found that insensitivity occurred regarding the size of distribution
while sensitivity was observed in functional distribution regarding the rule. It concluded that
balance of payment has equal importance as of saving-investment closure rule. Simulation
results in every country vary, if some policies are going well for one country, it is not
necessary that same results would be observed for another country. It is because each country
has a different adjustment pattern of market mechanism and institutional structure.
47
Bourguignon et al. (1989) worked on two economies by developing a macro model. The two
economies were middle income Latin America and low-income African country, the results
they obtained clearly explained that the devaluation of exchange benefits the low-income
people as they are in located export markets while affects the middle income because when
the Government reduces its expenditures uniformly it has fewer effects on low-income
people but greater effects on middle-income as through this inequality accruing of premium
occurs on capital.
Once a financial CGE model had been developed by Bourguignon, Branson, & Melo (1989)
and then was further extended by Fargeix & Sadoulet (1990) as they represented all the
advancement in the field of structural adjustment policies regarding modeling on income
distribution and performance of the economy. Further extension of them was made in such a
way that they incorporated the market for loanable funds. Countries start adopting the
policies of structural adjustment in order to have a sustained growth rate. But some studies
revealed that adoption of these programs affect the poor people a lot so these policies may
not be fully applied.
Diao et al. (1998) attempted to investigate the impact of research and development (R&D)
activities and trade protections on the economic growth of Japanese economy by using the
CGE model. The model used the real data for calibration and estimated the transitional
equilibrium and steady state results. The results of steady state equilibrium were found odd
showing a week effect of tariff imposition on the production of domestic final products. The
study justified the strange results with two reasons: first, it is due to limitations of the model
and second, final good producers did not compete with R&D activities for resources
allocation. The model designed for the study was unable to detect the impacts of technology
improvements accrued from trade. On the other hand, the transitional equilibrium showed a
substantial impact of protection trade policies on the output growth of the economy.
Adam & O’Connell (2004) considered the aid and trade options to investigate and compare
its impact on developing African economies. The study employed CGE model arguing that
48
econometric models were unable to detect the “Dutch disease1” effects on these economies.
The results of the simulations showed that gains from trade were more than the gains from
aid. The study encouraged those transfers (whether through aid or trade) that helped to
accumulate the capital in the economy. This accumulated capital will help to shift the exports
from raw to manufactured goods that ultimately result into increased household welfare. It
was interesting to note that transfers through aid adversely affected the exports and domestic
productivity while the trade helped to increase not only the domestic output but also the
consumption level. Similarly, the distortions attached with aid were found more important
than the trade. It was due to the fact that subsidies given for export promotion will increase
the fiscal burden ultimately.
Siddiqui (2007) employed both static and dynamic CGE to calculate the impact of agriculture
trade liberalization both at domestic and abroad on the economic growth of Pakistan. For this
purpose, the study used SAM for the year of 2002 for Pakistan. The findings of the
simulation revealed a positive impact of agriculture trade liberalization both at domestic and
international level on economic growth of Pakistan. Further, it was explored that the impact
of liberalization at international level was stronger than domestic level liberalization. The
study concluded that this agriculture trade liberalization was more beneficiary for a rural
household in the long run than for urban household while in the case of income distribution,
it revealed positive impact in the short run but the adverse negative impact in long run.
Cockburn et al. (2008) employed CGE model to calculate the impact of trade liberalization
on different economic indicators by giving more attention to the patterns of income,
consumption, trade and production patterns along with basic tariff structure and the role of
relative factor endowments. The study compared seven African and Asian economies to
check the effects. The study found that trade liberalization affected different commodities
and household sectors in different manners. Further, the urban household was found befitting
from liberalization while rural household was losing, similarly agriculture sector seemed
1 “Dutch disease” is a term that is used when an economy faces the negative impact of anything such as
discovery of natural resources (oil, gas etc.), that causes a rapid inflow of foreign currencies which appreciates
the domestic currency resulting into decline in exports of other goods due to increased prices in the foreign
markets.
49
losing its growth rate while manufacturing sector gaining. Overall the trade liberalization
helped to reduce poverty and increase the household welfare. Wages and prices increased and
interestingly, increase in wage rate was more than the increase in price level. Trade
liberalization brought pro-urban effects which were considered due to a major reduction in
the land returns.
Gilbert (2008) applied CGE model to calculate the impact of SAFTA on South Asian
economies in terms of poverty, household welfare, and inequality. The study reviewed all the
economies in the region that make it unique contribution in the existing literature. The study
found that due to similar product mix all economies except Bangladesh will get the benefit at
moderate level due to trade liberalization. This liberalization will increase the household
welfare in general. The study suggested that in the case of Bangladesh, unilateral trade
reforms are a better solution. For India and Bangladesh, the study found that the trade
liberalization under SAFTA will help to increase the income inequality and increase poverty
level.
Panda & Kumar (2009) employed the CGE model by using SAM of 2003-04 of India to
investigate the impacts of trade liberalization on GDP growth. The study showed a negative
impact on the GDP growth of India due to liberalization. It was further explored that it was
only agriculture sector that benefitted from the unilateral and multilateral agreements while
in the case of non-agricultural products, the growth was only possible if the agreement was
unilateral. In both cases, wages and prices increased and interestingly, increase in wage rate
was more than the increase in price level resulting an increase in real income of the
household. The study concluded that lower income group of people either rural or urban,
adversely affected in terms of food intake (calories) while other group improved benefits.
Ahmed & O’Donoghue (2010) employed CGE model to check the effects of external balance
variations on different sectors of Pakistan economy. The economy was aggregated into 33
sectors that measured the impact of changes in external savings and import prices on these
sectors. The results showed that if foreign saving is increased (50%), it causes an increase in
imports and reduction in exports of the economy. The most affected sectors were livestock,
50
cement, textile and leather in export reduction area while the income of unskilled labor (non-
agriculture) and agriculture labor was increased. The reduction in export was the result of an
increased prices of imported inputs especially petroleum prices. The results further explored
that it will also excavate the poverty and income inequality.
Bouet et al. (2010) used MIRAGE which is a global CGE model to investigate the gains and
losses of SAFTA (South Asia from South Asian Free Trade Agreement) to members of and
non-member countries. The study analyzed both the situations of including and excluding the
products of the sensitive list in the process of trade liberalization. The results revealed that if
trade if liberalized at its full strength (including all products); it simply results into trade
diversion effect. This liberalization did not seem to be in favor of LDCs of the region. It was
interesting to note that it was Sri Lanka which obtained maximum benefits from SAFTA, it is
because the country already imposed minimum tariff rates in the region. Further, it was
discovered that this liberalization is increasing the income of unskilled labor at a higher rate.
The study concluded that SAFTA is promising a low tariff income for all member countries.
Naqvi et al. (2011) tried to explore the impact of agricultural income tax on income
inequality and welfare of household in Pakistan. The purpose of the study was to estimate the
possibility and validity of employing an agriculture income tax and estimating the possible
effects occurring at the micro and macro level in the country. The study applied a CGE
model for analyzing the situation of agricultural income tax and reduction in sales tax for
production activities to adjust the budget surplus. The experiment was based on the two
elements. The results suggested the implementation of agricultural income tax was beneficial
for the economy in terms of household welfare at the macro level and very important tool for
the development strategies of future.
Osman (2011) discussed the trade relationship between EU and Southern Africa by using the
comparative static multi-region, multi-sector CGE globe model as a tool for conducting
various simulation scenarios in order to examine the effects of the envisaged EU-SADC
(Southern African Development Community) EPAs (Economic Partnership Agreements) on
individual SADC economies. The modeling work utilized the most recent GTAP database.
51
The simulation results suggested that a comprehensive EPA scenario is essential welfare-
improving for many SADC members. The agreements, however, did not serve as a stumbling
block towards more integration for SADC members into the world markets. The study further
suggested that a comprehensive EPA scenario is the best option vis-à-vis the WTO-
compatible alternatives for SADC non-LDCs.
Ahmed et al. (2013) by using the dynamic CGE model analyzed the impact of public
expenditures (macro-micro) on the economic growth in Pakistan. The study considered two
approaches in the simulation for public investment. Infrastructure investment for financed by
production taxes in the first approach and in the second approach it was financed by foreign
borrowings. It was important to note that the impact of both approaches was same especially
when considering the long run goals of poverty reduction and macroeconomic gains. The
study further discovered that financing by tax puts stress on output in the short run at
industrial level while financing through foreign borrowing have to impact like “Dutch
Disease” in the short run.
Bhatti et al. (2014) used the simple Computable General Equilibrium (CGE) to discuss the
role of fiscal policy in poverty reduction in Pakistan. The CGE model takes into account
market interaction, it creates ripples in the whole economy by showing the outcome effects
of pricing in one market and other markets. Further, the model even shows the quantity
effects of pricing in the original market. The study used SAM 2002 developed by (Dorosh et
al. 2006). The study found that a policy mix of sales tax, income tax and government
expenditure help to reduce income inequality while at the same lessens the economy’s
financial dependency.
3.5 Computable General Equilibrium Models and Trade Liberalization
Some researchers have also used the CGE model to investigate the impacts of trade
liberalization. Trade liberalization can be categorized into unilateral, bilateral or multilateral
trade liberalization. Under the unilateral trade liberalization, economies are assumed to
eliminate/reduce tariffs against rest of the world economies but the later do not need to do so.
The bilateral trade liberalization is possible under the free trade agreement between two
52
economies in which each one is agreed to reduce/eliminate the tariff on its import from its
trade partner. Under the multilateral trade liberalization, every member country of the free
trade agreement (FTA) reduces or eliminates tariff against all the members of the FTA. Some
of the studies based on the trade liberalization are reviewed as follows.
In a broad review about the outcomes of trade liberalization on poverty, authors Hertel &
Reimer, (2005), concluded that both micro and macro methodological approaches should be
considered. The study concluded that to quantify the impact of trade liberalization on
poverty, there are possible four methodological groups namely (a) cross-country regression
analysis, (b) partial equilibrium or cost of living analysis, (c) the general equilibrium analysis
using different simulations and (d) mixed approach (combination of micro-macro analysis), it
is also known as the post simulation analysis of the simulations of general equilibrium. All
the four groups include the traditions of “bottom-up” or “top-down” associated with experts
of trade and poverty analysis. The “bottom-up” approaches based on household expenditure
data in detail while top-down” approaches based on the data of national accounts. The study
concluded that any analysis of trade and poverty needs to be reported by both perspectives
and even many studies did so in “micro-macro” approach. The study suggested that further
research needs to be directed towards the factor markets improvements, taxes, transfer
payments and costs associated with domestic marketing. Further, the household surveys also
need to be reconciled with the data of national accounts.
While conducting a survey on impacts of trade reforms on the poverty Kraev & Akolgo,
(2005) pointed out the need of certain properties in a model while calculating the
distributional impacts of macroeconomic policy reforms. The study stressed that these
properties should be used in all four types of models namely CGE, econometric,
microsumulation and fixed ratio that are commonly used for assessing the impact of such
policy options. The five recommended properties namely; (i) representing specific policy
controls employed by policy packages, (ii) providing pliability in modelling production and
employment nest, (iii) representing connections among macroeconomic variables and
production nest, (iv) representing short term and medium term dynamics instead of long term
and (v) producing procedures of confidence for the model’s output. The study concluded
53
that despite lacking short term analysis and weakness of non-verifiability, the CGE models
still provides the best results among others when considering the said criteria.
The study based on literature review of 16 studies has assessed the impact of world full trade
liberalization using CGE modeling application was conducted by Bouet & Krasniqi (2006).
Despite having found distinctive underlying assumptions in the model and the scenarios
stimulated, it was concluded that trade reform has a positive effect on world welfare.
However, trade liberalization in the agricultural sector might have a negative impact on
welfare for countries that import agricultural products or have preferential access to some
markets. These models were designed to gauge the detailed impact of trade liberalization on
welfare at either global level or at the country level. Nonetheless, they ignored variables such
as changes in income distribution and poverty level at a micro level. Different strategies and
techniques have developed and applied to overcome these shortcomings.
To summarize the contribution of CGE modeling in assessing the impact of trade
liberalization on poverty and welfare, a detailed literature survey was conducted by Cloutier
et al (2008). The literature review shows that different results can be obtained using CGE
modeling depending on the structure of a model and the different policy scenarios simulated
in the model. Policy scenarios such as reducing or eliminating tariffs and quotas to all or
some sectors result in different policy simulations. To overcome revenue lost due to
elimination or reduction of tariffs or quotas, different compensatory mechanisms are adopted
by the countries.
Combining dynamic CGE model to a representative household model in Vietnam Wong
(2008) developed a macro-micro analytical approach. The results showed a significant
increase in economic growth with both gains from liberalizing trade against ASEAN and also
a remarkable boost when expanding it to cover rest of the world. He also concluded that
capital investment and human capital accumulation see a rise as soon as Vietnam expands its
trade with other countries. In addition, although poverty in Vietnam falls due to the
substantial lift in the economic growth but in the rural sector, income inequality increases
tremendously in most vulnerable households when liberalizing trade.
54
To study the trade liberalization impact on poverty, Nahar & Siriwardana (2009) developed a
CGE model with different simulation for Bangladesh. The result indicated that total
elimination of tariffs favors export-oriented sectors in the economy. In the short run, both
rural and urban states experienced in the overall reduction in the short run with a marginal
increase in poverty gap was projected for urban areas. In contrast, the trade liberalization
reduced absolute poverty both in urban and rural areas during long-run.
While studying the changes in distribution of income due to foreign trade, Ahmed &
O'Donoghue (2010) showed that overall consumption increased with increase in the foreign
savings but decreased with increase in the import price of petroleum and industrial raw
materials. The study used the SAM (2002) in which activities were aggregated into three
major groups including agriculture (12 sectors), industrial (16 sectors) and service (6 sectors)
with disaggregation of labor into ten categories based on size, type of employment (agri/non-
agri) and skilled/unskilled. Households were grouped into urban and rural households
disaggregated into total 17 sub-categories based on farm size and poor/non poor (urban and
rural). The policy experiments included (i). 50 % increase in foreign savings (ii) 10%
increase in the import price of petroleum and (iii). 10 % increase in the import price of
industrial raw materials. The results showed that trade deficit increased due to increase in
foreign savings. The findings highlighted that the only losers are the large and medium size
farmers and the small farmers become better off. Poverty decreased and the Gini coefficient
showing a slight decrease. Manufacturing sector stands the worst among all the sectors.
Measuring the poverty and welfare impacts of trade liberalization in the CGE framework
using the SAM (1995-96)1 a study was conducted by Raihan (2010). The activities were
grouped into three groups; agriculture (7), industries (12) and services (6). The experimental
design consisted of (a). Elimination of all types of tariffs accompanied by an increase in
production tax and the imposition of new taxes on the construction sectors. (b). full tariff
elimination accompanied by an increase in income tax (c). Reduction in tariff rate to the
actual level of tariff reduction undertaken by the government. The results showed that the
1 The SAM consisted of 26 production sectors and 7 factors of production (6 types of labor and one capital).
HH were aggregated into 7 groups based on location, sociological and wealth criteria.
55
import volume of many goods that included petroleum, chemical, clothing grains and
machinery increased. Demand for import substitutes decreased and for composite import
increased due to a decrease in import prices. The results of the second simulation are similar
to simulation 1 although they are lower in magnitudes. Exports price decreased which
resulted in anincrease in demand for exports. Labor and capital demand in the protected
sector (petroleum, chemical, and machinery) decreased whereas in the less protected sector
(ready-made garments and commercial) increased. Labor income in all simulations decreased
along with a decline in HH’s income with a larger reduction in the income of rural
households. Overall welfare decreased with greater loss in the welfare of rural households
than the urban and rich households. In the second simulation, the loss in the welfare of rich
households is higher than rural households whereas in the third simulation the overall
consumption of all the households increased leading to welfare gain to all the households.
Poverty level increased under the first simulation and decreased under the 2nd
and 3rd
simulations.
In Argentina with special emphasis on export taxes, Cicowiez, Bonilla, & Bonilla (2010)
studied both poverty and inequality due to trade reforms. The study used a national CGE
model combine it with a global economy-wide CGE model (World Bank LINKAGE Model),
and micro-simulations. They based the national CGE model on Social Accounting Matrix
(SAM) with 24 activities and 26 commodities. The results obtained show that full trade
liberalization of world excluding export taxes, the agricultural and non-agricultural goods,
decreases poverty and income inequality in Argentina. However the effects on poverty and
inequality were even deteriorating somewhat when only the agricultural goods were
considered.
In order to examine the effect of trade reforms and alternative global trade strategies on
poverty as inequality of various households by using the comprehensive micro data of
Brazilian states from 1987-2005, a study was conducted by Castilho et al. (2010). The results
demonstrate that the states directly affected by tariff cuts witnessed a decline in household
poverty and the inequality than the ones which received lesser exposure states. Liberalizing
trade helps to enhance household poverty and inequality in metropolitans and if one can link
56
this into reductions in rural areas as during studies they found no significant effect on the
rural poverty. Added to that they observed that world market integration, import
dissemination will have a similar role to trade liberalization for both urban and rural states of
Brazil. However, increasing export exposure seems to have drastically reduced both useful
measures of household welfare.
In order to better capture the heterogeneous household response to trade Cockburn et al.
(2014) suggested the use of CGE models in conjunction with micro simulation models. The
study suggested that simplest approach to evaluate income & poverty effects using a CGE
model is to disaggregate the total representative households (RH) to get the true picture of
diverse learning patterns. The study further found that higher the number of RH, the more the
problem is minimized. The two techniques widely adopted to evaluate the effects on poverty
and income inequality are the RH approach and the micro simulation approach (MS).
Another detailed literature review was conducted by focusing on the studies that employed
CGE model by OH & Kyophilavong (2015). The study attempted to investigate the
relationship between poverty and trade liberalization through literature surveys. The study
concluded that since last two decades, researchers are more focused on this issue by applying
different methods but the CGE approach remained most successful among others. The study
further investigated different approaches of CGE and discovered that Global Trade Project
(GTAP) model is more sophisticated and popular among policy makers. The literature
concluded that the impacts of trade liberalization on poverty are mixed. It depends on the
type of liberalization (ASEAN integration, Bilateral agreements, and WTO), characteristics
of the economy, price phenomenon and factor markets, patterns of taxes and governments
spending and technological and economic development.
3.6 European Union (EU) and Trade Liberalization
Over the past two decades, the World Bank and International Monetary Fund (IMF) have
engaged the international economic community with the worldwide campaign of economic
reforms through the economic stabilization and structural adjustment programs. However, the
debate shifted its focus to international economic integration through fair international
57
competition and trade liberalization from structural adjustment and stabilization (Hassan,
1997).
According to classical trade theory, trade liberalization is associated with the positive welfare
of the society. The two country, two goods and two factors model suggested by Heckscher-
Ohlin states “an increase in exports and production in the division that focuses more on the
production part of any economy”. Even today the developing nations have abundance
unskilled labors that can be improved with trade through more production of goods.
Although modern trade theories suggest that economic efficiency is the result of economies
of scale, liberalized trade, improved technology, access to information etc. but these theories
fail to answer the effects of trade liberalization on non-tradable goods, the goods that are not
homogenous and segmented labor market (Winters, 2002).
Lewis et al. (2001) attempted to investigate the impact of free trade agreement (FTA)
between South Africa and EU and the unilateral measures taken by EU to open its markets
for some countries of the South African region in 1999 by using the CGE model. The results
of the study revealed that although trade liberalization brought benefits for some countries
and some countries suffered but the overall gain for the region was more than the loss. This
trade creation that resulted due to trade liberalization strongly denied the “beggar thy
neighbor1” policy in the context of FTA. The study concluded that if real GDP growth rate
and real absorption are taken into account, the unilateral access of South African countries
into the EU market brought more benefits than the FTA of EU-SADC (South African
Development Community).
Monteagudo & Watanuki (2003) compared the gains from Mercosur free trade agreement
with Free Trade Area of the Americas (FTAA) and its FTA with European Union (EU) using
the CGE model with aggregation of regions and commodities into 12 regions and 13
commodity sectors. The standard CGE model was extended by incorporating externalities
(sectoral exports externality, import externality, and aggregate exports externality) and
1 It is an economics terminology used when an economy attempts to cure its economic problems in such a way
that it increases the problems of other economies.
58
economies of scale in the manufacturing sectors1. The analysis was based on three types of
policy simulations: (a). Formation of FTAA and elimination of tariff on the intra-FTAA. (b).
FTA between Mercosur and EU with tariff-eliminating on the intra-regional trade. (c).
Simultaneous formation of FTAA and FTA between Mercosur and EU. Simulations showed
that exports of individual economies of Mercosur increased. The findings also revealed that
exports from the United States to Argentina and Brazil increased as a result o Mercosur FTA
with FTAA. Under the first experiment imports to Mercosur from EU and rest of the world
decreased due to the trade diversion effect. The gain to Mercosur in terms of exports
performance is higher in FTA with EU than FTA with FTAA with a more heterogeneous
growth in Mercosur exports in case of EU than the FTAA. However, gains to Mercosur from
the combined FTAs (simultaneously signing FTA with both EU and FTAA) were found to be
greater than the sum of gains from the two individual FTAs. The welfare gain to Mercosur in
terms of GDP growth rate was higher in the case of FTA with EU than the FTAA.
Chishti, Zulfiqar, & Naqvi (2008) employed the CGE model with Globe to calculate the
multi-countries and multi-sectors effects. The study investigated the impact of trade policy of
EU on Pakistan and on other Asian economies. The study found that Pakistan is getting
benefits only in the fields of textile and clothing and similar was true for rest of the Asian
economies. Similarly, EU-India free trade agreement showed negative effects for Pakistani
exports to EU. This is because Pakistan and India have same product basket for EU. In the
case of GSP, Pakistan is likely to get benefit only if other competitors are not benefitting
from the same products under the same scheme.
Osman (2011) discussed the trade relationship between EU and Southern Africa by using the
comparative static multi-region, multi-sector CGE globe model as a tool for conducting
various simulation scenarios in order to examine the effects of the envisaged EU-SADC
(Southern African Development Community) EPAs (Economic Partnership Agreements) on
individual SADC economies. The modeling work utilized the most recent GTAP database.
The simulation results suggested that a comprehensive EPA scenario is essential welfare-
1 Economies of scale means when a certain manufacturer produce a good in abundance, the cost of production
minimizes.
59
improving for many SADC members. The agreements, however, did not serve as a stumbling
block towards more integration for SADC members into the world markets. The study further
suggested that a comprehensive EPA scenario is the best option vis-à-vis the WTO-
compatible alternatives for SADC non-LDCs.
Ahmad & Kalim (2014) examined the export competitiveness of the textile and clothing
sector of Pakistan. The study suggested in order to gain maximum benefits from the current
status of GSP Plus, Pakistan has to focus on the product diversification. By using maximum
likelihood method, the study analyzed the pre and post quota performance by using a time
series data from the period of 1980 to 2011. The study discovered that there was no
significant improvement in the performance of this sector. Several other factors like higher
input cost and price level higher than the competitors, played an important role in the week
performance of this sector.
Pakistan Business Council (PBC (2014)) estimated how much Pakistan can gain at zero
percent import duty in the EU market. The study estimated that maximum US$ 7.7 billion
imports are possible from Pakistan in the EU at the end of 2016 which is higher than the
2013 imports that were US$ 6.0 billion. It was further discovered that 74 items from Pakistan
with 6 digit HS code have more potential in the EU market. These high potential products
include those products that were exported to EU from Pakistan in 2013 with value of more
than US$ 1 million and 6% market share of total imports of EU from rest of the world in
same product lines while for rest of the world, the exports of these products were US$ 10
million in the same year. It is important for exporter from Pakistan to understand that per unit
cost of production is higher in Pakistan than the competitors in the region such as India,
Bangladesh, and China. This higher cost of production may minimize or nullify the zero
tariff advantage of Pakistan in the EU.
3.7 Trade Liberalization in the GTAP Framework
The following studies have relied on examining the impact of the bilateral and multilateral
trade agreements in the CGE framework.
60
To assess the effects of multiregional trade liberalization of markets in 14 countries, Hertel et
al. (2004) used the dynamic CGE framework. The study combined global macro model also
known as GTAP model with a Microsimulation model. The Microsimulation model was
based on country level household surveys that simulated the effects on poverty for Indonesia.
In order to for this methodology to work the data source needed to be compatible with both
models. As shown in many previous studies that very rich households report their capital
income much lower than their actual income (Atkinson 1995 and Mistiaen & Ravaillon
2003). To offset this difference, the author’s adjusted non-agricultural profit-type returns for
richest of the households in the survey, to keep the ratio of agricultural and non-agricultural
income from GTAP database. Furthermore, they also adjusted GTAP database that would
reflect the factor composition of income from the household survey. This micro-simulation
model estimated poverty line which measures poverty as the level of utility as opposed to
identifying a basic bundle of goods within an LES consumption function for households.
To calculate the effect of Vietnam’s trade reforms with GTAP model, Vanzetti & Huong
(2006) developed CGE model. The results of the study revealed that both imports and
exports of all tradable sectors increase with the largest surge in textiles and apparel. While in
the case of unilateral liberalization total welfare gains increase substantially. Unskilled labor
income boosts up as much as 38 percent with the mainstream labor involves in the
manufacturing of textiles, apparel, wood products and telecommunications. However,
Vanzetti & Huong (2006) identified that these results appeared unrealistic and recommended
some kind of trade-off between labor use and wages as this closure will yield better results.
The framework of both partial and general equilibrium analysis was used by Abdelmalki,
Sandretto & Jallab (2007) to examine the potential impacts of FTA between the United Sates
and Morocco by using the GTAP (version. 6). The experimental design was consisted of
‘strongly asymmetric liberalization’, ‘intermediate asymmetric liberalization’ and ‘full
liberalization’. Under the partial equilibrium analysis, only a single scenario was tested, that
is; tariff reduction by Morocco on its imports from the United States. The findings of the 1st
and 2nd
scenarios under the CGE modeling indicate that US received higher gain than its
counterpart in terms of increase in GDP and welfare US continued to receive higher gains in
61
the 3rd
simulation (full liberalization) whereas Morocco received negative gain in terms of
welfare. Rest of the world got suffered under all the three scenarios. Morocco benefitted in
many sectors with textile and clothing sectors are the biggest winners but faced loss in
transport and wheat production while the US stood gainer in wheat production and poultry.
The partial equilibrium analysis’ results indicated that Morocco received potential gains in
terms of increase in consumers’ welfare and increase in exports.
In order to assess the economic impacts of bilateral free trade agreements related to Japan
Abe (2007) employed the GTAP model (version. 6.2) with aggregation of sectors and regions
into 25 sectors and 24 regions. The experiments included simulations for all bilateral FTAs
of Japan and simulations for the regional/multilateral trade agreements related to Japan. The
first analysis was made for Japan’s bilateral FTAs with Malaysia, Singapore, and Mexico in
which Japan followed asymmetry in tariff elimination in terms of commodities while its
partners were assumed to abolish all their tariffs against Japan. The study showed that all the
bilateral FTAs’ partners of Japan gained from FTA in terms of increase in their GDPs and
EVs. Japan gained from FTA with Malaysia and Mexico only and experienced loss in its
welfare from its ‘FTA’ with Singapore. Rest of the world got suffered due to the loss in share
of the market in Japan and its FTA partners but overall world welfare increased. Mexico led
in gains in sectoral production as compared to the other economies. The study also carried
out a number of simulations to examine the static as well as dynamic impacts of all the
possible future bilateral FTAs of Japan as well as regional FTAs in the similar fashion. The
results revealed that Japan’s gain increased with increase in the number bilateral FTAs. Rest
of the world suffered due to lossof welfare gains.
Gilbert (2008) studied the application of SAFTA and its impacts on poverty and income
distribution. The study used GTAP 6 with the base year 2001 database and GAMS to run the
model. The experiment was performed in 20 percent tariff reduction and 10 percent in all
applied tariff. The study involved 10 households and16 commodities were used. The study
found mixed results in the case of poverty and inequality. The regional integration seems
against Bangladesh and India in terms of absolute poverty. The study concluded that the
welfare effect of SAFTA is positive but modest on overall south Asian countries. The study
62
suggested that the gains from trade are always under government controls and the fruits of
liberalization should be distributed to the economy to maximize the welfare impact.
To assess the impacts of FTA between Mercosur and EU in the framework of CGE model
with the aggregation scheme of 33 commodity groups and 21 regions, Boyer & Schuschny
(2010) employed the GTAP (version.6). Two policy experiments were carried out; 1st: “full
liberalization” and 2nd
“partial liberalization”. The simulation results revealed that the inter-
regional as well as intra-regional trade increased along with an increase in GDP, exports and
imports of the Mercosur region with improvement in the terms of trade (TOT). However,
Mercosur experienced a negative impact on its trade balance with differential impacts on
various sectors across the member economies. The GDP of EU decreased while exports and
imports were increased with improvements in the trade balance. Trade flows to rest of the
world decreased for both the regions. The production of agriculture and light manufacturing
sectors increased and of the heavy manufactured decreased for Mercosur. Both the regions
gained as shown by efficiency changes in terms of re-allocation of resources, change in terms
of the TOT and change in saving-investment balance.
To assess the impacts of India’s FTA with ASEAN, Sikdar & Nag (2011) showed that
India’s exports to the AEAN member economies increased significantly. However, an
increase in imports was higher than exports and so India suffered due to a loss in its terms of
trade. This study employed the GTAP model (version07) with the re-aggregation of regions
and sectors into 20 regions and 35 sectors. The findings highlighted that Thailand, Singapore
and Malaysia welfare increased. Total production of the ASEAN region increased along with
an increase in input demand and input prices. The simulations results also pointed out that
rest of the world stood worst due to its loss in the market share in the ASEAN region.
Another study attempted to investigate the impacts of trade liberalization between Pakistan
and the SAFTA member economies by Shaikh et al (2012). The study utilized the modified
version of the GTAP (version.4) database with aggregation of regions into 10 regions and
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commodities into 10 commodity groups1. The policy experiments included ‘unilateral trade
liberalization’, ‘regional trade liberalization’ and simultaneously ‘unilateral and regional
trade liberalization’ with three additional experiments associated with one each to the initial
three experiments. The study also employed the conditional sensitivity analysis (CSSA)
associated with the three policy experiments to check the sensitivity results. The results
indicated that Pakistan received a welfare gain of 1.53 % in terms of increase in GDP.
Overall imports increased and exports decreased (textile exports increased while food,
mining and manufacturing exports decreased) with improvement in its terms of trade (TOT).
On the other hand, due to the regional trade liberalization or equivalently reduction in import
tariff by SAFTA, the volume of trade in the SAFTA region increased with highest welfare
gain to India followed by Pakistan which received relatively less gain. However, rest of the
Asia adversely affected due to the trade diversion effect. The results also showed that the
unilateral and regional trade liberalization simultaneously increased the welfare of both
Pakistan and India with greater improvement in TOT. Rest of the Asia got suffered fromloss
in their terms of trade (TOT) and trade volumes.
A CGE model for Mozambique was developed by Minor & Mureverwi (2013) to analyze the
impact of free trade agreement on poor households. The study employed MyGTAP
developed by Minor & Walmsley (2013) to investigate the distributional consequences of
three trade agreements namely Regional Economic Agreement (REA), Tripartite Free Trade
Agreement (TFTA) and African Continental Custom Union (ACCU). The study includes 21
Regions, 10 households, and 22 commodities. The results of the study found that the
completion of RECs have minimal effect on household’s income and loss of government
revenue. In the case of TFTA, sugar export has been increased and rendered positive effect
on the real income of the agriculture households. In addition to this, in the case of ACCU, the
poor household would suffer. Moreover, the projected income of the country would fall.
1 The 10 regions in GATP were, Pakistan, India, Rest of Asia, ASEAN, and rest of Asia, Japan, European
Union, NAFTA, Middle East and rest of the world. The 10 groups of commodities were agri-groups, mining
and quarrying, processed food, textile, wearing appraisal, petroleum and coal, machinery, transport, services
and others.
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To examined the potential trade effects between Pakistan and India, Raihan & De (2014)
conducted a study by using GTAP. The study discussed the major trade barriers to bilateral
trade as well as for regional trade. The research study involved GTAP analysis to judge the
welfare effect of Pakistan MFN status to India .The simulation has been studied in 10 regions
and 29 commodities. The study presented the comprehensive assessment of the trade
relations between India and Pakistan with detailed analysis of trade modalities. The study
concluded that the welfare effect of MFN status for both countries is higher if it is supported
by improved trade facilitation across the borders. This not only increase the trade volume but
also exports between the two countries. The GTAP simulation further analysed that Pak-
India trade cooperation would have positive affects for other South Asian countries.
To calculate the impact of food and nutrition security on multiple household of Ghana a
study was conducted by Kuiper & Shutes (2014). The study employed the newly developed
MyGTAP database of Minor & Walmsley (2013). The study involved multiple householdsto
study the effects of food policy on the most vulnerable sector of the society helping the
government to design intervention in order to provide relief to the poor segment of the
society. The study embedded 19 commodities and 9 households. In the study, the following
three approaches were used to incorporate multiple household data in GTAP database.
Firstly, user weights have been assigned to household and incorporate in GTAP. Secondly,
the study included household data through national SAM. Thirdly, they directly place
household survey in GTAP analysis. The result suggested that the removal of export subsidy
is useful to the poor people of Ghana.
To examine the impacts of trade liberalization between Pakistan and India another study was
conducted by Pohit & Saini (2015). The study employed the GTAP’ (version 8) with
aggregation of regions and commodities into 13 regions and 20 commodity sectors. The
policy simulations included (i) ‘full liberalization of trade against each other’, (ii)“simulation
1 plus 50% productivity improvement in all modes of transportation services” and
(iii)“simulation 2 plus full liberalization. The analysis revealed that due to trade
liberalization, the welfare of both India and Pakistan increased with a higher benefit to India
under policy experiment 1. Welfare increased for both the economies when a 50%
65
productivity improvement was introduced in the modes of transportation engaged in trade
between Pakistan and India. The welfare for India increased by 4 times when full
liberalization was included in FTA whereas the welfare of Pakistan decreased as compared to
simulation 2. All the three types of policy experiments showed an increase in exports to each
other.
3.8 History of CGE Models Applied in Pakistan
Many authors have observed the literature on CGE models applied to less develop and
developing countries. In this section, we will focus on major CGE studies on Pakistan. The
strengths and weaknesses in these studies are mentioned in a useful manner in order to add
some new dimensions.
Fei (1962) constructed the first ever I-O table in Pakistan with the base year 1955. It was first
general equilibrium analysis with strength. It meant to focus on large-scale industry like
mining, large scale manufacturing sector and input structures of agriculture and ignored rest
of the production areas. The table classified the industry into 6 sectors (agriculture, mining,
industrial, unallocated, wage and foreign trade sector) ignoring the service sector. Since then,
in Pakistan, a large number of I-O tables and Social Accounting Matrices (SAM) have been
constructed. The first SAM was developed in 1962. However, despite the availability of the
I-O tables and SAMs at regular intervals since 1962, the first ever CGE model was developed
after 18 years by McCarthy and Taylor (1980). The latest SAM available for Pakistan is
available with the base year 2007-08 developed by Debowicz et al (2012).
McCathy & Taylor (1980) developed a CGE model by using SAM 1975-76 focusing on food
policy reforms and how they may influence the economic growth of Pakistan. The model was
an open economy with government sector where industrial sector was disaggregated into 11
and household into three sectors keeping in view the socio-economic groups for both urban
and rural. The major focus of the study was to observe the changes in patterns of household
consumption when prices and real income changes. The simulations were performed by
increasing government expenditures, removing subsidies on wheat, increasing subsidy on
66
fertilizer, increasing wages and land reforms. The results revealed the maximum impact of
land reforms on the economic growth as compared to other simulation results.
Labus (1988) developed a behavioral CGE model (comparative static model) for the public
sector of Pakistan aiming to check the influence of state-owned manufacturing activities on
the economy. The model used the SAM 1983-84 and used only government, enterprises, and
household as institutions in order to check the impact of liberalized policy as well as the
policy of price in the public sector. The results of the simulations showed that liberalization
policy brings a positive change in the current account balance, it causes an increase in real
GDP and reduction in prices. Furthermore, the activities relating to exportable commodities
have been increased converting the losses into profits of public owned enterprises. The model
had nothing to do with the welfare impact of the household.
Naqvi (1998) used the SAM 1983-84 for the CGE model developed aiming to analyze the
economy-wide impact of energy policy. The results found by the simulation show that a
change in energy tax have varied influence on different commodities i.e. if distortions are
removed from taxes on petroleum products, it fulfill the objective of social equity while
removal of distortion in taxes of electricity have no impact on the consumption of rural
household while it showed a negative impact on urban household. On the other hand
applying a tax on natural gas brought a negative impact on the real consumption of the
household. It was further discovered that removing distortions not only increase the real GDP
but also bring a positive change in the trade balance. The model was simply a static model
aiming comparative analysis and had nothing to do with the furcating.
Vos (1998) developed financial CGE model for Pakistan by using SAM 1983-84 aiming to
calculate the impact of foreign aid and Dutch disease effect. The study found that during the
era of the 1980s, the economy of Pakistan was not a constraint to foreign aid rather it
generate strong Dutch disease effect by shrinking the exports, reducing commodity
competitiveness and jolting the structural adjustment efforts. The results further found that
additional depreciation of currency not only increase the cost push inflation but also reduce
the real income and aggregate demand. Also, a fiscal cut abroad brings deflation and a shift
67
towards public investment from public consumption may bring a positive change in
economic growth similarly a reduction in debt also seemed positive for economic growth.
Siddiqui & Iqbal (1999) developed first CGE for Pakistan under MIMAP (Micro Impacts of
Macro- economic Adjustment Policies) project. When simulation occurred it was observed
that reduction in tariff also causes a reduction in wages and dividends of households. This
proportion of decline in income was observed through dividends than wages. It was
interesting to find that rich was more affected by the tariff reduction than the poor in the form
of wages because rich was getting profit in the form of dividends while poor was only getting
wages. The study further concluded that this tariff reduction reduces the income disparity
between urban and rural areas.
Kemal et al. (2001) developed a CGE model under MIMAP project to investigate the
changes in household income and other macro aggregates when tariff on industrial imports
are reduced. The results found that reduction in tariff reduces the prices of imported goods
that ultimately reduce the output price and the structure of input prices. It further increases
the gap between poor and rich household. There is an increase in consumption which showed
a positive welfare effect on household but this increase in consumption is greater in rich than
the poor. The study further argued that the government revenue also reduced due to low
investment which ultimately may affect the economic growth adversely.
Okuda & Brohi (2001) developed a CGE model for Pakistan by using GTAP 4 to investigate
the impact of roads and transport infrastructure on the economy. The study proposed a
multiregional CGE model in order to check the impact of new road network between Karachi
and Peshawar. The simulation results discovered that new road will bring positive change in
the industrial sector of Punjab and NWFP (North West Frontier Province, Now KPK (Khyber
Pakhtunkhwa)) provinces of Pakistan. The road network will increase the real income of
household that ultimately will increase the utility level in both provinces. The study further
concluded that this network will bring a positive change of 16% to GDP of the economy.
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Martine et al (2004) used CGE model by using GTAP 5.4 to measure the impact of quota
removal after the end of Multi-Fiber Arrangement (MFA). The study applied global CGE
model to calculate the impact of the Agreement on Textiles and Clothing (ATC) on the
countries like Pakistan, Bangladesh, China, India, Hong Kong, Indonesia, and Taiwan, who
were initially getting quota from USA and EU. The major focus of the study is Pakistan to
calculate the gains and losses after quota removal. The results of the study showed that the
quota removal will bring changes in the formation of final goods as well as in the mix of
intermediate goods. The study concludes that Pakistan will get benefit from it only if it
employs its resources efficiently but there is fear of some welfare loss.
Siddiqui (2005) examined the trade liberalization by employing the CGE model and
adjustment in fiscal policy. The study extended the updated SAM of Siddiqui & Iqbal (1999)
applying the methodology of Fontana and Wood (2000). The study incorporated additional
sectors and sub-actors into the model and it also allowed the intra-household allocation of
resources. The SAM included nine categories of the HH with aggregation into male and
female and four education levels, nine social reproductive sectors and nine leisure sectors1.
The SAM considered two sources of household income: the value of labor used in production
sectors and adjusted income of the own account workers. The SAM also considered the
market and non-market sectors as well as the paid and unpaid sectors of the economy with
disaggregation of labor into male and female with further categorization in terms of four
education levels2. The production accounts were consisted of 4 major categories; the
agriculture (5 sectors), mining (1 sector), manufacturing (8 sectors) and others (7 sectors).
The results found that compensatory trade liberalization increases the employability of male
and female. It reduces the gap between the wages of male and female along with over
burdening the female in Pakistan. It also helps to empower the women with greater pace than
any other activity. The study suggested that with compensatory measures, the impact of
liberalization should also be measured on the household work and leisure along with other
market-based activities.
1 The study also incorporated the time allocation in market and non-market activities.
2 The four education levels are; no education, less than 5 year of education, 5 but less than 10 years and 10 and
above 10 years of education.
69
Butt (2006) utilized the CGE model for Pakistan to calculate the impact of tariff cuts on the
regional disparities, output, employment, and exports by keeping in view different regions of
the country. The study developed a PAKREG database by utilizing the I-O table of 1990-91
developed by Federal Bureau of Statistics (FBS 2001). The study in this way helped the
GTAP to recognize Pakistan as a separate country. The results of the study revealed a
positive impact of trade liberalization on all regions of Pakistan in terms of improvement in
output, exports, and employment. The results further discovered a positive relationship
between trade liberalization and regional disparities during the military regimes and opposite
in the case of democratic governments. The cross-border tariff cuts seemed to help the
increment in real GDP slightly in the short run but significant in the long run.
Ahmed & Donoghue (2008) described the welfare effect of external balances on Pakistan
economy. The study used CGE model to capture the economy-wide impact of policies
simulation. Social accounting Matrix (2002) was used as a database and GAMS software to
run the model. The study encompassed 12 agriculture sector 16 industrial sector and 6
services sector. Households have been distributed in rural and urban. The rural households
have been further distributed into 17 categories. The experiment was performed through
trade liberalization simulations. The simulations were concerned with 50 percent increase in
foreign savings, 10 percent increase in overall import prices and 10 percent increase in the
import prices of petroleum etc. The result of the study suggested that the external oil price
possessed the high potential to affect Pakistan socio-economic condition. Increase in foreign
saving decrease poverty in the country. The analysis suggested that poverty is increasing with
the increase in import prices.
Shaikh & Rahpoto (2009) has studied the SAFTA implication on Pakistan economy using
GTAP model. The GTAP model is unable to examine the vibrant effect of trade liberalization
but it is very effective in the comparative static analysis in the case of any trade reforms. This
study used 10 regions and 10 commodities. The experiments are based on the unilateral trade
liberalization (uniform tariff rate 15 percent), regional trade liberalization, and unilateral
trade liberalization (15 percent) for the rest of the world. The study used GTAP model to
investigate the benefits and costs of granting MFN (Most Favored Nation) status to India and
70
SAFTA. The results highlight the potential industries which are to be expanded or
contracted. Pakistan gained the highest welfare in case of SAFTA with the 15 percent
uniform external tariff. There is high demand in the international trade for Pakistani dates,
leather, and garments etc. The study identified a variety of industries in which a high
potential exists. The SAFTA role is important by giving the opportunity to member countries
to achieve economies of scale, diversify their exports net, improves competitiveness. The
study further explored that if SAFTA is fully integrated and Pakistan gets a tariff cut of 15%,
it would bring the highest welfare gains for the people.
Naqvi (2010) applied CGE model using SAM 2002 to investigate the fiscal strictness and
trade liberalization impact on household welfare and inequality. The study explored that
there are two principal effects of export taxes and tariffs. Firstly, they reduce trade volumes
on both the import and export sides. Secondly, they impose economic costs by inducing
resource misallocation. Therefore, if trade-related taxes are eliminated, an economy can
avoid production and consumption distortions. It is an established fact that free trade leads to
enhanced efficiency The case of efficiency for free trade is the converse process to Tariff’s
cost-benefit analysis. The study further discovered that for a small country like Pakistan,
imposing a tariff does not allow it to influence world prices. However, prices for domestic
consumers and domestic producers do rise as a result. Consequently, imports and
consumption are reduced and the production of import substitute increased.
Bouet et al. (2010) used CGE model to investigate the gains and losses of SAFTA (South
Asia from South Asian Free Trade Agreement) to members and non-member countries. The
study analyzed both the situations of including and excluding the products of the sensitive list
in the process of trade liberalization. The results revealed that if trade if liberalized at its full
strength (including all products), it simply results in trade diversion effect. This liberalization
did not seem to be in favor of LDCs of the region. Further, it was discovered that this
liberalization is increasing the income of unskilled labor at a higher rate. The study
concluded that SAFTA is promising a low tariff income for all member countries.
71
Rashid (2013) utilized CGE models to measure the latest terms of trade for the agriculture
sector comparing it to the industrial sector in Pakistan during the years of 2000-2010 and to
study the impact of agriculture income tax on Pakistan economy by using social accounting
matrix (SAM 2002). The result of the experiment showed a 5% and 10% increase in the
government revenue through the imposition of agriculture tax. The study further elaborated
that manufacturing and imports flourished while construction and exports faced decline. The
labor demand in non-agriculture sector rose whereas the demand for labor in agriculture
sector reduced due to increase in the agriculture income tax.
Robinson and Gueneau (2013) attempted to describe an economy-wide linked CGE model
dynamic in nature and a regional water system model (RWSM). The study used this CGE-W
model to investigate the impact of water stress on the agriculture productivity. The main
focus of the study was to investigate the Indus river water basin water flow and shocks that
ultimately influence the agriculture productivity in Pakistan. The model (RWSM-Pak)
applied on Pakistan is newly developed by the World Bank. The experiments investigated the
changes in water supply due to changes in weather and found that water shocks adversely
affect the agriculture productivity. The study concluded that any change in water supply from
rivers due to weather changes may adversely affect the agriculture sector but the effects can
be minimized by building Diamer-Basha dam on the Indus basin.
Khan et al. (2015) developed a CGE model to investigate the impact of agriculture trade
liberalization (the elimination of import tariff and the removal of export subsidies) on income
inequality of Pakistan. The study adopted the newly developed MyGTAP model developed
by Minor and Walmsley (2013). The model used a two kind of database i.e. GTAP and SAM
(2007-08). This study deeply analyzed the impact of agriculture trade liberalization on
multiple households. The study encompassed 18 households, 12 regions, and 37 sectors. The
result of agriculture trade liberalization suggested that income inequality in Pakistan is
increased by 0.49% from the baseline. Medium and large household types are aided, and
there is a nominal increase in the real wages of medium and large agricultural labors. The
labor intensive crops are replaced by capital intensive and cheap imported products that
ultimately helped to enhance the income inequality in Pakistan.
72
3.9 Drawbacks in Previous Studies
The study tries to find out the weaknesses and strengths of the previous studies with a focus
to build a foundation and to give new dimensions for future CGE studies on Pakistan and
also tries to avoid those drawbacks highlighted in previous studies.
3.9.1 Limited Focus on Trading Blocks and Especially the European Union
Pakistan is a member of World Trade Organizations (WTO) and like other less developed
countries it is restricted to continue trade liberalization. Only one known study conducted by
Shaikh & Rahpoto (2009) focussed on the impact of trade with a regional trading block
SAFTA by using GTAP database. As mentioned above around 9 studies in Pakistan focused
on trade liberalization. Almost 6 studies which were conducted by PIDE professionals have
tried to examine the impact of trade liberalization on poverty and income discrimination
under different situations. The researchers found that trade liberalization in Pakistan reduced
poverty in the country and increased the level of income for households. Another study on
trade liberalization by Martin et al (2004) used global CGE model and found that the
termination of textiles and clothing quotas from the EU, US, and Canada reduced the income
level in developing countries and especially Pakistan. The European Union is the biggest
trading partner of Pakistan but there is not a single known study that specifically focused on
the issue by using CGE models. CGE model used in this study for Pakistan is built with the
aim to tackle this drawback for the existing CGE literature in Pakistan. This model would be
able to measure the impact of the European Union current policies on the Pakistan economy
as a whole.
3.9.2 Usage of Inadequate Databases
About 9 studies of CGE on Pakistan used inadequate databases. 2 groups have been
developed from these 9 studies. The first group consists of 3 studies that use GTAP databases
and remaining 6 studies focused on trade liberalization and income discrimination that have
been conducted by the PIDE professionals.
The studies that have used GTAP database in Pakistan include (Okuda & Brohi, 2001;
Shaikh & Rahpoto 2009; and Martin et al., 2004). It raises some serious concerns if the
73
GTAP database was used to develop an I-O table and to develop a global CGE model for
Pakistan. Okuda & Brohi (2001) used GTAP 4 database in order to deduct I-O table for
Pakistan. However, for a single country like Pakistan data was not available in GTAP 4
database. So in order to find out the data for Pakistan, Bangladesh and Maldives, the residual
database was used. So this data was not able to reflect the true image of Pakistan economy.
Martin (2004) developed a global CGE model for Pakistan by using GTAP 5.4 database.
Again the problem was unavailability of data as countries like Bangladesh, Maldives, Nepal
and Bhutan fell in residual data. So it is not certain that these residual values showed the true
image of Pakistan’s economy. Butt (2006) for the first time made serious efforts by
developing PAKREG to introduce Pakistan as a separate country in future GTAP databases.
Remaining studies which were focusing on trade liberalization were based on SAM ranging
from 1989-1990. Without having an import matrix in I-O framework the analysis of trade
liberalization raises doubts in the researcher about the reliability of these results. Khan (2015)
used MyGTAP to utilize the maximum updated SAM for Pakistan. So in order to cope with
this problem, the study is using latest GTAP 09 first time in Pakistan.
3.9.3 Poor Quality of Limited Number of Studies on Regional Issues
There is only one known study conducted by Okuda & Brohi (2001) that developed
multiregional CGE to analyze the impact of policies at the regional level of Pakistan. The
study has some serious drawbacks that may negatively affect the reliability of it for
policymakers. First of all the quality of the database was not very satisfactory. Secondly,
because the data was much aggregated in terms of macroeconomic and only 8 sectors were
identified so, the results were very limited in value terms. Thirdly the study did not identify
the impact of macroeconomic shocks on the variables like aggregate real investment,
aggregate real consumption, the balance of trade, government real expenditures and the
fluctuations in the stock exchange rates and trade policies. Fourthly, it seemed that long run
closure has been followed, but it did not provide information regarding the closure of the
model itself. So the researcher can conclude that no authentic and reliable study on regional
issues by using CGE has been applied till now. The current study is focusing on this issue
too.
74
3.9.4 Single Model Repetition to Analyze Trade Liberalization
As mentioned above out of 9 studies on trade liberalization of Pakistan 6 were conducted by
the PIDE professionals. These studies were based on SAM but having different production
sectors except Martin at el (2004) used GTAP 5.4, Shaikh (2009) GTAP 07 database in their
study. Only one model cannot be used for analyzing trade liberalization of Pakistan. It was
only Khan (2015) who attempted to perform experiments with new and updated technique in
the absence of latest database with GTAP.
3.9.5 Contradictory Results of Some Studies on Trade liberalization
Siddiqui, et al (1999) from PIDE were the initial researchers who conducted the first study on
trade liberalization in Pakistan. The study found that due to trade liberalization income level
of each household decreased. Siddiqui & Iqbal, (2001) have indicated that as a result of trade
liberalization income level of the household would increase. Siddiqui & Kemal (2002);
Kemal et al (2001); Siddiqui & Kemal (2002) and Kemal et al., (2003) also found that trade
liberalization creates income discrimination but also enhance household welfare.
3.10 Proposed CGE Study in Light of Past Literature Review
In the previous section, many weaknesses have been described in the CGE studies in
Pakistan that create doubts in the minds of policy makers. So in order to remove the
drawbacks of the prior studies, the study is using the updated GTAP 09. There is not even a
single study that has used the impact of FTA between a trading block and Pakistan. This
study is attempting seriously to resolve this issue too. The comparative static CGE model is
more useful for Pakistan in order to find the impact of tariff cuts on Pakistan.
75
3.11 Summary of Literature Employed CGE Models in Pakistan
Table 3.1 summarizes the history of studies that employed CGE model in the case of
Pakistan.
Table 3.1: Summary of CGE Models History in Pakistan
S.
No.
Author’s
Name and
Date
Policy Focus Identification
scheme Results
1 Fei et al
(1962)
Preliminary
Input-Output
table for industry
I-O table 1955
The first ever developed I-O table
for a large-scale industry which
ultimately helped the researchers to
develop SAM.
2
McCathy &
Taylor
(1980)
Planning food
policy
(subsidies)
SAM 1975-76 Land reforms showed maximum
redistribution effect.
3
Labus
(1988)
Incentives and
public sector
enterprises
SAM 1983-84
Decrease in Aggregate demand
results into declined wages and
rent. The enterprises are getting
profits due to incentives, prices are
going down and improvement in
the current account balance,
exports, and output.
4 Naqvi
(1998)
Energy sector tax
reforms SAM 1983-84
If social equality is not considered,
Kerosene oil is the best commodity
to increase tax revenue but it is
least desirable due to the
beneficiary for low-income people
and natural gas has least welfare
cost so tax revenue can be
increased by this.
76
5 Vos (1998)
Dutch disease
effect of foreign
aid
SAM 1983-84
Depreciation above equilibrium
will result incost-push inflation
causing a reduction in real income
that ultimately will reduce the
aggregate demand in the economy.
Similarly, there would be strong
Dutch disease effect of foreign aid.
6
Siddiqui
and Iqbal
(1999)
Tariff reduction
and income
distribution
SAM 1989-90
The income reduces due to tariff
cuts but the reduction in poor’s
income is less than the rich
households.
7 Kemal et
al. (2001)
Tariff and
income
distribution
SAM 1989-90
Tariff reduction causing an increase
in imports and reduction in exports
by increasing prices of domestic
products and vice versa. It also
worsens the position of income
distribution but it causes an
increase in household
consumption.
8
Okuda &
Brohi
(2001)
Network of road
Transport and
regional effects
in Pakistan
GTAP 4.0
New road from Karachi to
Peshawar will bring positive
change in the industrial sector of
Punjab and NWFP and GDP will
increase by 16%.
9 Martin et
al (2004)
Consequences of
quota removal on
textile and
clothing
GTAP 5.4
It will bring changes in the
formation of final products and
Pakistan will get benefit from it
only if it employ its resources
efficiently but there is fear of some
welfare loss.
77
10 Siddiqui
(2005)
Gender-based
impact of
Economic
reforms.
SAM 2000
Liberalization of Agriculture brings
Positive changes in household
income both in urban and rural
areas. It further improves the
economic growth and redistribution
of income in the short run (negative
in the long run).
11 Butt (2006)
Tariff cuts,
exports and
regional
disparities
PAKREG
There is a positive relationship
between tariff cuts and regional
disparities and the impact is greater
during military regimes. Further the
tariff cuts across the border causing
an increase in real GDP.
12
Ahmad
and
O’Donogh
ue (2008)
External Balance
impact on
welfare in
Pakistan
SAM 2002
The external oil price possessed the
high potential to affect Pakistan
socio-economic condition and an
increase in foreign saving decrease
poverty in the country and poverty
is increasing with the increase in
import prices.
13
Shaikh
and
Rahpoto
(2009)
SAFTA
implication on
Pakistan
economy
GTAP 4.0
If SAFTA is fully integrated and
Pakistan gets a tariff cut of 15%, it
would bring the highest welfare
gains for the people.
14 Naqvi
(2010)
Trade
Liberalization
and Fiscal
Strictness
SAM 2002
Trade liberalization through the
abolition of the tariffhas apositive
effect on household welfare and
inequality in Pakistan.
78
15 Rashid
(2013)
Agriculture
income tax and
economic growth
SAM (2002)
Government revenue increased due
to agriculture income tax.
Manufacturing and imports
flourished while construction and
exports faced decline
1
6
Robinson
and
Gueneau
(2013)
Water Economy
Links
SAM 2006
(CGE-W)
Any change in water supply from
rivers due to weather changes may
adversely affect the agriculture
sector but the effects can be
minimized by building Diamer-
Basha dam on the Indus.
17 Khan (2015)
Agriculture
Trade
Liberalization
and Poverty
My GTAP
Income inequality in Pakistan is
increased by 0.49% from the
baseline. Medium and large
household types are aided, and
there is nominal increase in the real
wages of medium and large
agricultural labors
79
CHAPTER 4: METHODOLOGICAL FRAMEWORK
The liberalization of world trade and welfare of household living in developing countries are
the two prominent issues figured in international trade negotiations. The question arises that
will liberalized world trade benefit people who live in poverty? This question has motivated
policy debates, especially since the launched of the Doha Development Agenda (DDA)1 in
2001 (Corong, 2014). While assessments on the relationship between global trade
liberalization and poverty levels have proliferated during the last decade [(Hertel & Winters,
2006); (Harrison, 2007); (Anderson et al 2009)], meanwhile analysis on the differential trade
negotiations on the household welfare and economic development in developing countries
like Pakistan remained oblivious.
Any change in output or price of one commodity may bring changes in the output of other
products, government revenue, and expenditures, exports, imports and employment. To
understand the linkages between all sectors of the economy, Computable General
Equilibrium (CGE) models are an ideal tool that has been studied in detail focusing the
issues related to trade negotiations. These models link the factor and product market to the
macroeconomic linkages of saving and investment (Minor & Mureverwi, 2013). A change in
prices in one market can be linked to changes in other markets. Several types of CGE models
are employed for this purpose: some are dynamic, emphasizing the impacts of investment
and year-on-year growth rates in industry and trade (based on projections); others are static
modeling investment purchases, but not the impacts of investment on productive capacity
growth over time.
This chapter intends to explain the methodology for the research. The purpose of this chapter
is two fold: firstly, to develop an understanding of Computable general equilibrium modeling
by examining different definitions, evolution and historical background of CGE modeling;
secondly, discusses the Global Trade Analysis project (GTAP) along with MyGTAP, its
structure, the accounting relationships and the behavioral equations.
1 It is round of trade negotiation of the World Trade Organization’s (WTO) aiming to facilitate and increase the
world trade through removal of trade barriers.
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4.1 Historical Background of the CGE Modelling
History of CGE modeling starts from Johansen, (1960) when he presented the first model
containing a household sector with utility maximization and twenty industries with cost
minimizing. The model explained that production and consumption decisions are strongly
influenced by the price factor while the model determined the price using the market
equilibrium assumptions. Ultimately he developed a mathematical multi-sectoral model
describing the growth in Norway. The study applied Frisch’s (1959) additive utility model
while using the household price estimates and elasticity of income in the Input and Output (I-
O) data table. The researchers, later on, followed the work of Johansen (1960). The ORANI
model of Australia Dixon et al (1977) and Dixon et al (1982) was the succession of
Johansen’s model that ultimately became the reason of GTAP (Global Trade Analysis
Project) model having global linkages of the economy with rest of the world.
Extensive use of CGE model was witnessed by 1970s and 1980s. The modelers focused on
the analysis of economic development problems of the developing countries. By elaborating
the treatment of income distribution, foreign trade, and several policy instruments, these
models extended the coverage of CGE models. Beyond the concept of Walrasian, several
modelers1 extended the CGE model by adding ‘structuralist’ features in it. Development of
large number of CGE models and their use for analyzing policy is because of appropriate
software development and fast computers. Analysis of every policy like agricultural, major
tax reforms, the amendment in trade policy regimes and economic integration are included in
this application. In developing countries, there are several policy issues and to illuminate
them a great number of CGE models have been designed2.
The most important source of stimulation for CGE modeling was the primal-dual solution to
linear programming models of country wide resource allocation and its competitive general
equilibrium. For the economic policy analysis, there was the extensive usage of linear
programming models during the era of the 1960s and 1970s. A distinctive method of activity
1 For example, (Taylor & Black, 1974); Taylor & Lysy (1979).
2 The work of Devarajan et al (1997) for developing countries.
81
analysis approach to CGE models was developed from linear programming tradition
(Ginsburgh & Waelbroeck, 1981).
Scarf (1969) made a great breakthrough in the history of CGE modeling by introducing an
algorithm for the solution of general equilibrium problems. Detailed and complex general
equilibrium models were developed in the early 1970s and these models could be solved
computationally. Steady progress in the power of computers made possible, how to give
solution and develop large models. After this advancement algorithm was improved and its
refined version was introduced. In mathematical economies, a new research area of
developing simpler and powerful version of general equilibrium was started but its real
essence or heart remains same as it was algorithm presented by Scarf (1969)1.
Shoven & Whalley (1972) presented the first applications of computational general
equilibrium. Computational models are allowed to be more sophisticated and practical
because of the flexibility of giving a solution to the algorithm and it leads to numerical
answering to complicated questions. Policy issues of tax reforms and international trade are
addressed by them and they follow earlier analytical models. Many modelers followed the
Shoven-Whalley version of CGE model and in this context, it mentions three lines of
research. The first one was followed by many representative articles and it included United
States economy’s multi-sector energy model which was developed by Jorgenson et al (1974)
and after that Jorgenson and several associates made amendments in it2.
Two significant
contributions were made by it even it was not similar to the original model of Walrasian, the
first contribution was the introduction of more accurate functional forms, having much
approximation to reality which leads to systematic treatment of technological progress and
other includes the dependency of the model on the economic estimation of various sub
model’s parameters.
The second line of research was started by Manne & Preckel (1983) who developed the
treatment of dynamic issues. By specifying the cost and constraints attached with fractional
1 For more details please see (Todd, 1984)
2 For details please see (Jorgenson & Fraumeni, 1981), (Jorgenson & Slesnick, 1985), Jorgenson (1984).
82
adjustment on the part of economic agents and providing solutions to the model as a
complete inter-temporal optimization. Initially, it was focusing the area of energy policy only
but later it extended to the trade and development areas of the economy. The main
distinctiveness of these models was: a strong focus on dynamic issues, integration at a low
level and simple functional formation are the distinctive features of these models. Manne &
Preckel (1983) presented the model of economic growth with three regions with a simple
structure. This model provides the insight of trade issues between countries producing oil,
less developed, and developed ones.
The work done by Shoven & Whalley (1984) did not attain the same degree of details as
work of Jorgenson & Slesnick (1985) and Manne & Preckel (1983). Manne & Preckel
(1983) emphasized the pedagogical role of the model. It shows the comparatively better way
of presenting the importance of some interactions or feedback instead of attempting to
calculate the impact of policy issues. Furthermore, he tried to highlight the importance of
presenting those interactions or feedbacks that normally are not considered important during
policy debates. In this way, this factor becomes the most significant for the success of CGE
models and to answer why these models are more practical.
The third line of research was developed from the multi-sector planning models being
popularly used by development economists and supported by the World Bank (Blitzer et al
1975). This has contributed significantly to the development of the CGE models. To examine
the structural issues of the developing countries, the economists and policy makers have been
dealing with the disaggregated model. Extensions of the Leontief model complemented
sophisticated models of consumer expenditures. International trade was the basis of the initial
approach. The concept of Social Accounting Matrices (SAM)1 was developed to achieve a
completely consistent framework. It was simple and comprehensive to adopt the general
equilibrium assumptions. It further helped the policy makers to resolve the problems of
economic development in detail. Devarajan et al 1986) worked on the comprehensive
bibliography of this type of work.
1 SAM is a method of describing in detail whole transactions in the economy keeping in view the balance
between income and expenditures.
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CGE approach is very near to practical policy issues. The models are not meant to use as
policy measures for scientific or academic point, rather they provide assistance in the form of
recommendations to design a policy decision made by world bank or governments. These
models mainly address the questions related to international trade- i.e., the introduction of
export subsidies or tariffs in a particular country. The results obtained by these models
provide a better understanding of key factors through certain outcomes on one side may
check the outcomes of alternative options through simulation.
In addition to the three lines of research mentioned above, many researchers have applied
this approach to new problems and countries. Moreover, many modelers adopted the
approach of combining some advantages of three approaches. Goulder & Borges (1984)
made an attempt to combine the Shoven & Whalley (1984) custom with the more modern
adaptable practical structures utilized by Jorgenson & Slesnick (1985) as a part of their USA
energy policy model. They likewise utilized the particular constraints connected with the
presence of consumable assets. This research also provides the results associated with the
impact of higher energy prices along with the impact of taxes on energy prices and hence
economy.
To build the ORANI model a different approach was utilized, for this purposes a large model
with multi-purpose was built to address the large and small issues of the economy. This
model was considered most useful for policy options in Australia (Dixon et al 1982).
A new type of application was introduced by Mohammad & Whalley (1984), which tried to
measure the effects of government interventions and distortions associated with the agents on
the economic performance. This application played an important role in policy decisions.
This approach clearly benefited from the Shoven & Whalley (1984) model. It shows that
“rent seeking” behavior of the economic agent is not to maximize the national output or
income but to increase its own share in the national economy by using national resources and
creating distortions.
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The above discussion although did not cover the history of CGE models comprehensively.
The purpose of this section was to introduce some studies that utilized this approach by
applying different techniques and reasoning, to address different issues associated with
different economies. Hence it fulfilled its purpose of a brief history of CGE modeling.
4.2 Defining the CGE Model
There is no accurate definition of Computable General Equilibrium models1. It is a kind of
economic models that incorporates with economic data to estimate the effects of a policy
change, change in the technology of changes in external variables on the economy. It is a
multi-sector model based on real data from one or many economies. Secondly, the model
explains the explicit information about the behavior of economic agents. Further, it
represents the households as utility maximizers and firms as profit maximizers or cost
minimizers. The role of factor/commodity prices is highlighted through such optimized
assumptions. Decision made by firms and households for production and consumption are
influenced by price level that ultimately explains the mechanism of price setting through
demand and supply forces. At the end, the CGE models produce numerical results. Lastly,
the main advantage of CGE models is that they produce numerical results. The parameters
and coefficients in the related equations are estimated with reference to the numerical
database. The vital part of the data which is the base of the model is generally a set of inputs
and outputs; these are accounts that show the flow of factors and goods among industries for
a specific time period.
The most precise definition available in literature is by Shoven & Whalley (1984) and is as
follows:
“CGE model is one in which all market clear simultaneously”.
Although the definition by (Shoven & Whalley, 1984) has serious flaws it still gives the basic
idea about CGE models. In CGE models unemployment can be allowed, therefore, it does
not necessarily suggest that all markets are clear. The same criticism applies to the definition
1 Sometimes called Applied General Equilibrium (AGE) models. However, international trade theory normally
is considered as an application of CGE models.
85
of Borges (1986) quoted in the subsequent discussion. According to Robinson (1988), if a
model strictly contains four elements then it is called a CGE model. These four elements are
as follows:
a) Well defined economic agents whose analysis of behavior is required.
b) Conditions and rules of their behavior, under which they function, for example, utility
maximization of consumer and profit maximization of producers, are clearly
identified.
c) Identification of the factors that affect the decision-making power of the economic
agents like prices.
d) Recognition of the prevalent structure of the economy such as perfect competition.
Another modeler, Borges (1986) defined the CGE in the following words:
“Based on the Walrasian tradition, applied general equilibrium models describe the
allocation of resources in a market economy as the result of the interaction of supply and
demand, leading to equilibrium prices. The building blocks of these models are equations
representing the behavior of the relevant agents -- consumers, producers, the government,
etc. Each one of these agent demands or supplies goods, services and factors of production,
as a function of their prices. Assuming that market forces will lead to equilibrium between
supply and demand, the general equilibrium model computes the prices that clear all
markets, and determines the allocation of resources and the distribution of incomes that
result from this equilibrium.”
Distinguishing characteristics of CGE model of an economy were described by Shoven &
Whalley (1984) as follows:
a) In an economy, there are n produced commodities in n markets.
b) By assuming that consumers maximize utility subjected to their budget limit, the
demand side of the economy can be derived.
c) Producers maximize their profits and by assuming that production side of the
economy is derived.
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d) Generally, non-increasing or constant return to scale technology is assumed
(Increasing returns to scale and imperfect competition can also be included into CGE
models; (see Harris (1984).
e) The demand for any commodity satisfies Walras’Law and is non-negative,
continuous, homogenous of degree zero and depends on all prices.
On the behalf of above discussion, it can be said that across market subject to instructional
and behavioral constraint CGE model simulates that interaction of different economics
agents. For further details, please see Dixon et al (1982) and Shoven & Whalley (1992).
Since modern version of Walras model of the competitive economy is CGE models. The
unique feature of general equilibrium modeling of considering the economy as a set of agents
is derived from Walrasian general economic equilibrium theory. Under a given set of income
distribution and initial endowment, these agents interact in several markets for an equal
number of commodities and by optimizing its own profit, cost objectives or utility every
agent defines its behavior of demand and supply. Walras Law i.e. “The global identity of
income and expenditure is fulfilled by the set of excess supply functions which is a yield of
their decisions. Supply and demand are hiring in equilibrium by a set of prices under same
general equilibrium conditions and this is proved by Arrow & Debreu (1954).
The real side of the economy is focused on CGE models, so financial assets markets are not
included in them, and this is among one of the several differences between CGE models and
others numerically based models (Ljungqvist & Sargent, 2000). Therefore relative product,
the real exchange rate, and factor prices are determined by CGE model usually, however,
nominal prices and nominal exchange rate cannot be determined by that. In other words,
CGE models do not aim to explain business cycle instead they are aimed at growth paths and
illustrating equilibrium resource allocation. On the relative prices of goods or factors and
equilibrium allocation of resources, the impact of specific policies is aimed by CGE models.
However, some modelers extended CGE model beyond the original Walrasian model to
cover the imperfection of markets. That is why, to highlight the flexibility of the computable
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general equilibrium models, some modelers used the term general equilibrium programming
(Zalai, 1982) or generalized equilibrium modeling (Nesbitt, 1984).
According to Savarad (2003), CGE Models are considered very useful tool if one tries to
investigate the consequences of policy reforms on the public in terms of poverty, inequality
etc. Different sectors of the economy are interlinked in the CGE framework. Contrarily, the
economic theory is unable to furnish such detailed analysis of the policy reforms. Winters et
al (2004) and Harrison et al (2010) confirmed that the economic theory is insufficient if one
tries to investigate the inter-sectoral effects of a government policy reform. Blake (1998)
indicated that in the neoclassical model, consumer intends to maximize its utility level while
the firms/producers are eager to maximize the profit by cost minimizing and adapting the
behavior of average pricing and CGE models follow the same behavior. There is a system of
equations on which the Computable General Equilibrium models are based that link the
different sectors of the economy as explained by Bandara (1991). Similarly, Shaikh et al
(2012) revealed that while capturing the inter-sectoral linkages or the interactions at the
macro level both CGE and AGE models are consistent internally. While Adams et al (1998)
further disclosed that the CGE model not only integrates different sectors but also contains a
lot of behavioral equations. When a change occurs in the price level, the system of equations
integrates to calculate the change in the behavior of consumer and producer. This system of
equations is solved by using different software packages like GAMS, GEMPACK,
MATLAB etc.
CGE models are considered more useful while analyzing the trade policy changes. When
government brings a change in the trade policy, the equations in the model instantly integrate
and calculate the possible outcomes in different sectors of the economy. Although the CGE
models are considered complex and artificial in nature but important to calculate the inter-
sectoral impact within an economy and between other economies (Kehoe & Kehoe, 1994).
Partial equilibrium models on the other hand based on time series data containing limited
endogenous variables.
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4.3 Multi-Country Models (GTAP Model)
Multi-country or global models consist of multiple countries or the total global economy
(Wobst, 2001). These models tend to have fewer sector details and are designed for analysis
of proposed multi-lateral policies such as free-trade agreements. Moreover, these models do
not maintain a single country model assumption of exogenising global or trading partner
effects. Therefore, the implications of these effects - coming from rest of the world or other
countries - have been endogenised. Any effects, transmitted to by means of various channels,
of policy changes in the rest of the world, would have direct in addition to indirect influence.
These models explicitly capture this transmission mechanism. Therefore, these models can
be applied in policy experiments of multilateral trade liberalization. The Global Trade
Analysis Project (GTAP) model is the most widely known modeling system of multi-country
models. GTAP is a multi-sector, multi-region, computable general equilibrium model1 with
perfect competition and returns to scale (McDougall et al 1998). This model is being
employed for a number of applications (international trade, agricultural analysis, labor
markets, etc).
The GTAP model is a linear model built on the neoclassical theories and is comparative
static in nature. In order to perform the analysis at the country level, it uses the common
global database. The model exhibits a utility maximizing and constant return to scale
condition for all households and firms profit and considers that all markets are perfectly
competitive. To solve the model, GEMPACK software is used (Harrison & Pearson, 1996).
In reality, the GTAP model is a multi-region CGE model aimed to deal with trade policy
reforms with the help of comparative static analysis as explained by Adams et al (1998).
The centerpiece of the GTAP model is the internally consistent database with a base year that
is provided by the individuals of the representative economies on input-output table. In
addition to that, the data related to trade, tariffs, quotas etc is provided by the experts. A
single regional household with aggregate utility functions is the governing tool of the GTAP.
This regional household is assumed to receive income from the domestic firm in exchange of
1 there are some applications to partial equilibrium analysis
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its endowment commodities. The regional expenditures are distributed among savings,
personal and government expenditures. To produce the goods for final demand, endowment
commodities are combined with the intermediate commodities by the firms. The produced
goods are assumed to be purchased by the government and private household that also
purchase the capital goods against the household savings. Open economy version further
includes the global bank and the transport and trade activities where global bank deals with
the regional investment and global saving by creating a composite investment good that is
supplied to the regional households to satisfy their saving demands.
The GTAP model includes a non-standard “Constant Difference of Elasticity (CDE)”
expenditure function. The advantages of a CDE function is that it models well a variety of
consumption patterns found at differing income levels. That is to say, it generates classical
"Engels" curves which are characterized by shifting consumption between necessities and
luxury goods. While the CDE provides a good basis for modeling private consumption across
a broad range of households and countries, it is not ideal for modeling extreme situations,
where poverty and subsistence expenditures are dominant. Subsistence expenditures are
defined as a share of expenditure being tied to a specific consumption bundle, which must be
consumed no matter what changes in prices and incomes may arise in the simulation (Minor
& Mureverwi, 2013).
In this study along with standard GTAP model, we will also use MyGTAP linking the
Pakistan economy with rest of the world. This model was developed by Walmsley and Minor
(2013) and is extended version of standard GTAP developed by (Hertel & Tsigas, 1997).
Single regional household along with related distribution parameters are eliminated in
MyGTAP and are replaced by directly linking the expenditures incurred by government and
private household to the income sources. Similarly, in order to analyze the distributional
impacts for policy recommendations, multiple households are placed instead of the single
private household. The extended model also helps the government to calculate the impact of
subsidies on the government budget that help the policy makers to decide the income
treatments. It further incorporates the regional remittances and capital income abroad (Siddig
et al, 2014).
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4.4 Working of GTAP 9 Database
Against the previous GTAP databases, the latest version that released in May 2015, contains
three reference years, 2004, 2007 and 2011. The GTAP 8 contained 2 reference years 2004,
and 2007 while all of the previous years had only one base year. Similarly, the latest database
contains 140 regions with 57 sectors as against of the previous version with 129 regions and
57 sectors. The standard countries have been increased to 244 from 226 and have been
aggregated into 140 regions. It is very important to note that latest version has updated the
database of Pakistan along with 18 other countries only, adjusting the other data with 2011
base year. For further details of the aggregation schemes, please see appendix 1.
The data on GDP, private consumption, government consumption and investment was taken
from the World Bank and was used for updating the input-output tables. Penn World Tables
version 8.0.3 provided the data on the physical stock and depreciation. International Energy
Agency (IEA) provided the energy data and agriculture export subsidy data was taken from
World Trade Organization (WTO), Food and Agriculture Organization (FAO) and
International Food Policy Research Institute (IFPRI). Trade data for the GTAP member
economies was obtained from Comtrade and was combined with same data obtained from
IMF to improve the data quality. Protection data related to output subsidies, input subsidies,
land-based payments, labor-based payments and capital-based payments was obtained from
Institute for Prospective Technological Studies (IPTS).
4.5 GTAP Standard Model: Income Expenditure Global Accounts
A large number of equations are required for the GTAP model. The underlying equation
areof two types of which the first deals with the accounting relationship and its work is to
balance the receipts and expenditure of every agent in the economy, while the second type of
equations deals with the behavior of the optimizing agent.
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Figure 0.1: The Standard GTAP Model
Source: Walmsley & Minor 2013; Based on Brockmeier 1996.
4.5.5 The Standard GTAP Model and the Accounting Relationships
The basic notations and equations of the GTAP model will be discussed in this section along
with the intuition behind the GTAP model and offers a detailed picture of the accounting
relationships. The first segment of the accounting relationships in the GTAP model is the
distribution of the firm’s sale to the regional market. In the open economy version of the
GTAP model, firms combine primary factors (endowment commodities) with intermediate
inputs to produce final goods for sale to the domestic market as well as to the international
market. The model is derived from GTAP source developed by Hertel & Tsigas (2000).
4.5.6 Distribution of Sales to the Regional Markets
Sectors and commodities have one to one relationship in the GTAP model. The only single
output is assumed to be produced by each sector in the model. Firms produce and sell output
to domestic as well as to the regional markets. The value of firm’s output at the agent’s price
is given in the equation 4.1 in appendix 2.
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“VOA(i, r)” is the output value at the agent’s price that shows the payments received by
firms in region ‘r’ in the ith industry. “PS(i, r)” is the price index of ‘i’ in the region ‘r’ and
“QO(i, r)” is the quantity index of ‘i’ in region ‘r’. The model added the producer’s tax
“PTAX (i, r)” to obtain the value of the output at the market prices from the value of output.
The equation 4.2 in appendix 2 represents it where “VOM(i, r)” is the firm’s output value at
the market price which is the sum of the value of domestic sale “VDM(i, r), value of exports
of “i” from region ‘r’ to all the destinations in ‘s’ “VXMD(i, r, s) and sale to international
transport sector “VST(i, r)”. The exports tax (XTAX) in equation 4.3 in appendix 2 is added to
express exports as fob-based value. Similarly, the value “VXWD (i, r, s)” represent the
exports at the fob price of ‘i’ exported from region ‘r’ to ‘s’ and “VXMD(i, r, s)” shows the
of exports at the domestic market price.
4.5.6.1 International Transportation Margin
The international transportation margin is the difference when the value of import is
calculated with CIF (Cost, insurance, and freight) and value of export is calculated at FOB
(Freight on Board) as shown in equation 4.4 in appendix 2. Where “VIWS(i,r,s)” shows the
value of the world imports and “MTAX (i, r, s) represent the import tax that is added in the
“VIMS (i, r, s)” in order to calculate the value of transaction at international commodity in
domestic price. Hence we get equation 4.5 in appendix 2.
A single composite import good and its value are allocated among three sources of import
demand that consist of the imports of commodity ‘i’ to region ‘s’ from three different
sources. That is value of imports of ‘i’ in region ‘s’ imported by ‘private household’
evaluated at the market price “VIPM(i, s)” further the value of imports by firms in ‘s’
“VIFM((i, s)” and the value of imports at the market price by the government “VIGM(i, s)”.
This relationship is represented by equation 4.6 in appendix 2.Where, the value of imports of
industry ‘i’ from region ‘r’ to region‘s’ is represented by “VIM (i, s)”.
In the GTAP model, the accounting relationships also identify the sources of households’
purchases to establish a link between industrial output and household expenditure on that
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output. The following section explains the sources of household purchases in the GTAP
model.
4.5.7 Source of Household Purchases in the GTAP Model
In order to finalize the relationship between household purchases and industrial production in
the GTAP model, it is assumed to distribute the total household expenditures on good ‘i’ in
region ‘s’. Equation 4.7 in appendix 2 represents the value of household (private) purchases
“VPA (i, s)”of commodity ‘i’ from region ‘s’ at agent price. These household purchases
consist of expenditure of household on domestic commodities (VDP) as well as on the
composite imports (VIPA) at the agent price. The value of private purchases at market price
can be calculated very easily from the private purchases. In a similar way, we may model the
government purchases by distributing the government purchases into domestically produced
goods and composite imports.
4.5.8 Firm’s Purchase Sources
In the previous section, we highlighted the way, firm’s sale is allocated between firm’s sale
to the domestic market as well as its sale to the international market. The firm’s sale of ‘i’ of
region ‘r’ to region ‘s’ can also be termed as expenditure on imports in ‘s’ which is
distributed between private household, government and firm’s expenditure. Firms in region
‘s’ purchase primary factors of production as well as intermediate goods from the domestic
market. So total purchases of the firm in region ‘s’ can be decomposed into firm’s
expenditure on domestic inputs and firm’s expenditure on imported inputs.
Equation 4.8 in appendix 2 explains the Value of firm’s Purchase of ‘i’ in ‘s’ evaluated at
agents’ price where “VFA(i,j,s)” stands for the value of firm’s purchase in sector ‘j’ of input
‘i’ in region ‘s’ evaluated at agent’s price. The right hand side of the equation contains the
two terms that represent the two components of the firm’s purchases evaluated at the agent’s
price which can be expressed in terms of the market price by deducting the intermediate
input taxes ‘DFTAX(i,j,s)’ (tax on the purchase of domestic inputs of ‘i’ in sector ‘j’ in region
‘s’ and IFTAX(i,j,s)) (tax on the imports of intermediate and primary inputs from region ‘r’).
The endowment commodities in the GTAP model can also be evaluated both at agent as well
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as the market price which includes the firm’s purchases of non-tradable commodities like
land, labor and capital.
4.5.8.1 Linking the firm’s receipts and purchase and the zero profit condition
The equation 4.9 in the appendix 2 shows the above relation, where looking at the right-hand
side of the equation, the first term represents the firm’s expenditure on tradable commodities
summed over ‘i’ tradable inputs and the second term shows the total expenditure on its
purchase of the endowment commodities evaluated at the agent’s price. The left-hand side of
the equation is the firm’s total receipts by ‘sector ‘j’ in ‘s’ evaluated at the agent’s price from
its sale of output. It is worthy to note here that in GTAP model, all receipts of the firm must
be exhausted on firm’s expenditure in order to satisfy the zero profit condition.
4.5.9 Sources of Household (HH) Factors Service Income
The Endowment Commodities can be grouped into a mobile endowment that earns same
market returns and an immobile endowment that earn differential returns. See equations 4.10,
4.11 and 4.12 in appendix 2 along with brief details.
4.5.10 Regional Income and Border Involvement in the GTAP Framework
Border intervention by exports and border intervention by imports are included in the border
interventions in the GTAP model. In determining the regional income both interventions
have their concerns. When subsidy is given on exports, then the domestic price of exports
“PM(i,r)” is greater than the fob-based price of exports “PFOB(i,r,s)” and regional income
decreases. This border intervention by exports and its impact on regional income is
represented by the equation 4.13 in appendix 2. When tax is levied on exports of ‘i’ from
region ‘r’ to region ‘s’, the international price of exports is higher than the domestic price of
exports. The regional income increases because the government collects positive revenue
from tax on exportable goods.
Similarly, the case of border intervention with imports and regional income is represented by
equation 4.14 in appendix 2. When the market price of importable goods “PMS(i,r,s)” in the
market (s) is higher than the world price of importable goods PCIF(i,r,s), it indicates the
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presence of import tax on imports supplied from region ‘r’ to region ‘s’. In this case, the
import border intervention positively contributes to the regional income. The contribution in
government revenue and regional income is negative in case of subsidy on imports from
region ‘r’, the market price of imports in region ‘s’ supplied from the source region “r” is less
than their price in the source region.
4.5.11 The GTAP Model and the Global Sectors
4.5.11.1 Global Transport Sector
This sector is introduced in the GTAP model to account for the differences between the
values of exports of good ‘i’ supplied from region ‘r’ to region ‘s’ evaluated at the world
price (fob-based value) and the value of imports of the same good supplied from region ‘r’ to
region ‘s’ at the world price (cif-based value). The difference between the two values is
called the international transport margin shown in equation 4.15 in appendix 2, where “VIWS
(i,r,s)” is the value of imports of good ‘i’ from region ‘r’ to region ‘s’ evaluated at the world
price and “VXWD (i,r,s)” is the value of exports from ‘r’ to ‘s’ at the world domestic price.
This sector accumulates the regional exports of transport equipment along with the insurance
services to a make composite transport good that is used to carry the merchandise among the
regions. The individual regional economies export the transport services to the global
transport sector and the appropriate summation of these transport specific goods and all the
routes, yields the total demand for world transport services. Equation 4.16 in appendix 2
summarizes the relationships
4.5.11.2 The Global Bank
The Global Banking sector is required in the GTAP model as an intermediate between global
saving and investment. A composite investment good is produced in the global bank that is
based on net portfolio investments of the respective regions. The global bank offers these
composite investment goods to the regional households at a common price to meet their
saving demands. That is; the regional saver households face a common price of their savings
and by the virtue of Walras’ Law, global savings must equal to global investment to satisfy
the accounting relationships. Equation 4.17 in appendix 2 summarize this relationship and
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equation 4.18 in appendix 2 shows the value of ending capital stock. Further details are
available in the appendix 2.
4.5.12 Equilibrium Condition in the GTAP Model
In order to convert the GTAP model into general equilibrium model, economists have used
the terms of quantities rather using the values. Hertel et al (2010) showed the accounting
relationships in terms of values that have been discussed above, embody the equilibrium
conditions that make the model a general equilibrium model in nature. We can easily convert
these accounting relationships into exhaustive accounting relationship by converting into
quantities by considering a common domestic price. The equilibrium condition for traded
goods is given in the equation 4.19 in appendix 2 and accounting relationships in terms of
quantities by introducing a common price are given in equation 4.20 in appendix 2. Similar,
exercise can also be applied for non-tradable commodities to verify that and all the relations
are exhaustive in nature and satisfy the necessary general equilibrium conditions in the
GTAP Model.
4.5.13 Linearized Representation of Accounting Equations
The above-discussed accounting relationships in the GTAP model are nonlinear in nature.
However, the accounting relationships should be linearized in order to implement the GTAP
model. Hertel & Tsigas (1997) have shown that the linearization of the non-linearized model
involves total differentiation of the equations. The transformed equations are simply alinear
combination of the weighted price and quantity changes. To covert these transformed
equations into value terms, the equations are multiplied by the common price. The first
accounting equation in the GTAP model in this fashion is the equation for the tradable
market clearing condition equation 4.22 in appendix 2.
The variable is indexed over all tradable goods and all regions. The domestic market for
tradable commodities can be decomposed into ‘domestic market for imports from region ‘r’
and ‘domestic market for domestically produced goods’ in region ‘s’ in the GTAP model.
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Similarly, Market equilibrium in the domestic market for imports from region ‘r’ is
summarized in equation 4.23 in appendix 2 and Market equilibrium in the domestic market
for domestically produced goods is represented in equation 4.24 in appendix 2. The usage of
lower case letters in the equation shows the percentages changes in the respective variables
weighted by the values of respective quantities evaluated at the market price. The left-hand
side variable shows the percentage in the quantity of the domestically produced goods
weighted by the value of the domestically goods valued at the market price.
Equation 4.25 in appendix 2 represents the market clearing conditions for non-tradable
endowment commodities. The GTAP model decomposes primary factors (non-tradable
endowments) into mobile and sluggish factors. Further, the accounting relationships and
market clearing conditions in the new version for the two types of endowment commodities
(mobile and immobile) are explained in equation 4.25 and 4.26 in appendix 2.
4.5.14 Macroeconomic Closures
Most of the static AGE models deals with the macroeconomic policies and the issues related
to monetary policies but GTAP model simulates effects of the trade policy and the shocks
relating to the resources and calculates the impact on the international trade and production
patterns at the global level in the medium term. A number of macroeconomic closures are
required to be fixed to operationalize the GATP database and models. This model is neither
considered as an international one (McKibbin & Sachs, 1991) nor it is meant to obtain a
series of equilibrium (Burniaux & Mensbrugghe, 1994). There is nothing to capture the
impact of investment on the productivity in next time periods as well. However, it requires
some attention because keeping in mind the final demand, investment affects productivity
across the regions. Sen (1963) defined it the problem of “Macroeconomic closure” because
we are unable to find an international mechanism that may determine the investment. In
comparative static models, Dewatripont & Michel (1987) brought four possible solutions to
the investment indeterminist problem. The three closures are neoclassical in nature where
investment can be fixed simply and rest of the sources are allowed to adjust while in fourth
closure it is permitted to the investment to adjust but instead of adding an investment
relationship independently, it is adjusted according to the savings. There are some applied
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equilibrium models that have no such closure (Hertel & Tsigas 1997) rather they fix the
current account balance as macroeconomic closure allowing the domestic saving to move in
acircle with the changes in investment. It is important to understand the identity of national
income accounts suggested by Dornbusch (1980).
S - I X + R - M --------------(4.65)
The identity describes the difference between regional saving (S) and investment (I) which is
exactly equal to the current account surplus. ‘X’ represents exports and ‘M’ is for imports.
The term ‘R’ represents the receipts of international transfers. In our GTAP framework, there
is no ‘R’, so we set it equal to zero. If we fix the right-hand side (RHS) of the equation, the
left-hand side will also be fixed.
Equation 4.65 explains the above situation in detail where the RHS of the equation is fixed
on the regional basis. Although there is no global bank that may act as an intermediary to
balance the saving and investment at the global level, but the equality is assured at the global
level in the new equilibrium between the saving and investment. In short, the approach seems
neoclassical closure because investment is forced to change according to the changes in
regional savings as described by Dewatripont & Michel (1987).
In the GTAP framework, the global bank purchases the shares of regional investment goods
in the portfolio using the receipts coming from the homogenous savings commodities that are
assumably sold to the regional household. The global closure is neoclassical in nature in the
model because whenever there is any change in global savings, the portfolio adjusts to
accommodate it. Nevertheless, a little adjustment is permitted in the investment mix on
regional basis by including one more dimension in the model that may determine the change
in investment.
4.5.15 Data Sources Used in Creating the GTAP Database
According to Philip, (2013), two types of data sources are used in the GTAP database. The
regional input-output table received from the GTAP member economies and the data
collected from international organizations such as UN Comtrade database that provide the
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merchandise and service data for GTAP member economies, World Development Indicators
provide data for GDP, data on private consumption, gross fixed capital formation
government consumption, capital stock and depreciation data, data related to tariffs is taken
from International Tariff Commission and International Trade Center (ITC), energy data is
taken from International Energy Agency (IEA) and IMF and local governments provide data
related to income and taxes.
4.6 MyGTAP Database
The study has linked the latest available comprehensive Social Accounting Matrix (SAM)
2007-08 developed by International Food Policy Research Institute (IFPRI) to the latest
extension of the standard GTAP to make it MyGTAP.
Figure 0.2: Flows of Income and Expenditures in MyGTAP Model
Source: Minor & Walmsley 2012.
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The newly developed MyGTAP1 explains that government collects revenue from taxes and
foreign aid to spend on government expenditures, funding foreign governments, providing
subsidies and transferring to households. Budget deficit or surplus is decided on the basis of
the difference between government spending and income. Income sources for private
households, on the other hand, are a function of returns to factor endowments (land, labor
and capital), net rent on foreign capital and foreign remittances, transfer payments made by
the government and other households. The household net income is either spent or save.
4.6.5 Relationships in MyGTAP Model
The study has already explained the source of the database in MyGTAP. As discussed earlier,
the standard GTAP is modified to make it MyGTAP where the single regional household is
replaced with the multiple private households along with a separate government sector for
one region (Walmsley & Minor, 2013). Just like in the case of standard GTAP, the private
households receive income from factors, but in MyGTAP, it also incorporates foreign
remittances and capital, which is further used for consumption and saving purpose. The new
model assumes that the government sector gains income (GOVINC) from taxes (TTAX) and
foreign aid in (AIDI). Government income is consumed on the transfers to the private
household (TRNG) and foreign aid out (AIDO). Moreover, government entertains the
receiving of foreign aid instead of the direct acceptance by the private households (Minor &
Walmsley (2012).
The income received by the government is used to fund government expenditure (equation
4.109) and government savings (equation 4.110).
1 Developed and used by Khan et al, (2015)
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The government faces deficit or saving on the basis of the difference between government
expenditures and income (equation 4.111). One can assume that the share of government
expenditure in income remains constant or can specify alternative assumptions by fixing the
government deficit.
(4.111)
In case of private households, the income is received from factors (EVOAH), then
depreciation (VDEPH) is subtracted, net foreign labor remittances (REMIH and REMOH)
and foreign capital income (FYIH and FYOH) is added, then added the transfers between
households (TRNH) and transfers from the government (TRNG) (Equation 4.112).
Using Cobb Douglas just like in the standard GTAP model, each private household’s income
is allocated to private consumption and savings. In similar way, regional savings are
calculated adding up all the private household savings with government’s savings (equation
4.113) and allocated across investment.
It is to be noted that the value of savings is no longer the same as that in the standard GTAP
database because remittances and other foreign transfers have altered incomes, while
expenditures on commodities remain the same. In order to ensure the balance between
expenditures and income, savings is adjusted (Minor & Walmsley, 2012).
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4.6.6 Inter-regional Transfers
The foreign income flows are not bilateral, instead, flows in and out of a country/region are
provided. The work was initially undertaken by Sonmez et al, (2011) and then utilized by
(Khan et al 2015).
4.6.6.1 Remittances
It is assumed that the remittances that flow out of a country (remoh), change with average
wages of skilled and unskilled labor (psh) and any changes in the endowment of labor (qoh)
(Equation 4.114).1
The remittances that flow in of every country (remih) are then adjusted (equation 4.115) to
ensure that total remittances “coming in” and “going out” are equal (Equations 4.116).
remohh,r (4.116)
Equation 4.116 determines ‘remih’ and all remittances in ‘remih’ change by the average
equation 4.115).2 By altering the closure remittances out can be fixed or remittances in can be
fixed and remittances out adjust to again ensure that total remittances in equal total
remittances out (Minor & Walmsley, 2012).
1 Variables ‘sremoh’ and ‘remavo’ are exogenous and equal to zero in the standard closure. (Minor &
Walmsley, 2012)
2 Variable ‘sremih’ is exogenous and equal to zero in the standard closure.
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4.6.6.2 Foreign Income
In the same way, foreign income in and out is determined, although capital and .rental rates
are used to determine foreign income out rather than wages and labor supply (equations
4.117, 4.118 and 4.119).
4.6.6.3 Foreign Aid
Similarly, the foreign income in and out is determined in the same way, although government
income is used to determine movements in foreign aid out (equations 4.120, 4.121 and
4.122).
4.6.7 Multiple Households and Endowments
The above discussion shows that the equations relating to private household income
(equation 4.112), foreign income and remittances are allocated across households. The
database of standard GTAP does not recognize multiple households, and hence there is only
one private household (again, figure 1 illustrates the regional household in the standard
GTAP model. The income of all factors is accrued and the whole consumption and saving is
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undertaken by that one household (the “Main Household” of this model)1. Using the
MyGTAP data program, multiple households can be added to one country or region of the
standard GTAP database. This program produces a large number of zeroes, for example
when a household or endowment does not exist for a particular region. This can cause some
structural singularity issues in the model (Minor & Walmsley, 2012).
In order to include multiple households, a number of changes are required to the model such
as:
a) To track the supply of household factor and ownership of factor endowments (income) and
possible unemployment of those factors.
b) The additional endowment types are needed to allow.
c) The need to accommodate transfers between households and to the government.
d) The possibility of differential income and commodity taxes.
At this point each household supplies endowments to firms. Hence the aggregate supply of
each endowment is the sum total of all endowments supplied by all households (equation
4.123). The household income is reduced by the appropriate amount of depreciation when we
know the ownership of capital by households (kbh) (equation 4.112).
We also include equations 4.124 and 4.125 to incorporate unemployment closures. ‘emplh
(i,h,r)’ and ‘empl (i,r)’ allow for us to consider employment on labor supplied by particular
households or all households equally. As it’s done in the standard GTAP model, once the
supply of every single endowment (qo(i,r)) is determined this endowment moves easily or
sluggishly between sectors depending on whether the endowment is defined as mobile or
sluggish (Minor & Walmsley, 2012).
1 Note that this Main household is not the Regional Household discussed in Hertel (1997) because it is a private
household. The GTAP regional household collects all income and allocates it to both private household and
government consumption and savings. In contrast, Main household in MyGTAP simply aggregates all private
households, excluding government transactions.
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The MyGTAP data program also allows the user to split endowments, since the existence of
multiple households may also necessitate the need to separate, not just the supply, but also
the demand for endowments. For example rural and urban households both supply unskilled
workers, however, it is unlikely that these are easily substitutable and hence the user may
want to separate the demand for rural unskilled and urban unskilled so as to reduce the
substitutability between them (creating two prices which move independently in the
processes). This is all achieved using the MyGTAP data program. Once the endowments are
split in the data and the endowment set extended, the standard GTAP equations still hold with
no change in the underlying model (Minor & Walmsley, 2012).
In order to accommodate potential income transfer between households and differential taxes,
the model also includes a number of additional variables. Two transfers are included in the
database and model, transfers between households ‘TRNH(k,h,r)’ is the transfer from
household k to household h in region ‘r’; and transfers from household ‘h’ to the government
(TRNG(h,r)). The value of transfers is considered zero in the MyGTAP data program, if it is
not specified by the user. These transfers are assumed to be exogenous in the model. To
allow for the differences in tax rates paid by households, the income taxes (toh) and
commodity taxes (‘tpdh’ and ‘tpmh’) are also included (Minor & Walmsley, 2012).
4.6.8 Expenditures of Private Household
To determine household consumption of each commodity, the model is set up so that the user
can define whether they want to use Constant Difference of Elasticity (CDE)1 or linear
expenditure system (LES). This means that in developed economies where the Frisch
parameter (“The marginal utility of income with respect to income”) is one, the user can opt
1 See Hertel (1997) for an explanation of the CDE function used in GTAP.
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to keep GTAP’s CDE, while imposing the LES in other regions where subsistence is
important.
With the help of a binary parameter (PRIVTYPE), the user defines the use of CDE or LES
that is read in from of (default.prm) in the ‘GTAPPARM’ file. In the MyGTAP data program
this takes the value of 1 for the special country where LES is to be used, and zero in all other
countries1. The user can change this using ‘ViewHAR’ but should be careful not to impose
the LES on countries where the FRISCH parameter is greater than -1.8. The set of regions is
then divided into two subsets:
Set REG_LES # Countries for which the LES system applies#
= (all,r,REG:PRIVTYPE(r)>0);
Set REG_CDE # Countries for which the CDE system applies#
= (all,r,REG: PRIVTYPE(r)=0);
Total private consumption expenditure (yph(h,r)) is determined by a Cobb-Douglas function
regardless of the choice of specification, as private household income is allocated across
private consumption and household savings in a similar way to which it is determined in the
standard GTAP model, albeit at the household level. Equation 4.126 summarizes it as:
It is worth mentioning that with respect to income, the elasticity of private expenditure
(UELASPRIV) is equal to 1 and under the specification of both CDE and LES, it is
endogenous. The elasticity remains unchanged under the specification of LES while the
elasticity changes due to any change in income and its allocation across commodities (with
different levels of income elasticity) under the CDE.
1 This is similar to the parameter SLUG which is used for determining sluggish verses mobile endowments.
(Minor & Walmsley, 2012b).
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4.6.9 Constant Difference of Elasticity (CDE)
The countries that traditionally use the CDE in the standard GTAP model apply the
traditional CDE with two differences (Equation 4.127). First, the equation only applies to the
subset of regions REG_CDE; and second, household private expenditure (yph(h,r)) is being
allocated across commodities only and not the total private expenditure of the regional
household (yp(r) in the standard GTAP).
4.6.10 LES
The ORANI model developed by Dixon et al (1982) is used for the codes to incorporate the
LES for the REG_LES subset of countries.
The MyGTAP model adds the first two parameters in the tab file:
1. The Frisch LES 'parameter' (FRISCH(h,r)) is calibrated from the income elasticity and
household consumption shares1 or read in from the parameters file, if the header exists:
2. Household expenditure elasticities (EPS(i,h,r)) are set equal to the income elasticities also
used in the CDE or read in from the parameters file, if the header exists.
These parameters can then be used to determine the average (equation 4.130) and marginal
(equation 4.131) share of luxury goods in total expenditure:
1 Calibration equations used are based on those taken from the CRUSOE suite developed by Mark Horridge.
“http://www.monash.edu.au/policy/crusoe.htm” and Minor & Walmsley (2012) also include an assertion that all
FRISCH parameters are less than -1.8 for REG_LES countries.
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With the share of luxury goods in total expenditure known from equations (4.130) and
(4.131) and total consumption expenditure determined by Equation (4.126) it is then a matter
of determining how this income will be divided across subsistence ((qph_sub(i,h,r))) and
luxury (qph_lux(i,h,r)) consumption. Total consumption (qph(i,h,r)) then depend on the sum
of these two demands for subsistence and luxury commodities (equation 4.132).
Following the LES methodology, subsistence consumption (qph_sub(i,h,r)) remains constant
and only changes with changes in the population or number of households (poph(h,r)) and
any taste changes (asub(i,h,r)). This is shown in equation 4.134.
Consumption of luxury commodities (qph_lux(i,h,r)) then depends on private expenditure left
over for luxury consumption (yph_lux(h,r)), prices (pph(i,h,r)) and a taste parameter
(alux(i,h,r)): Equation 4.134.
In order to determine how much of private expenditure is left for luxury goods (yph_lux)
after the subsistence, goods have been purchased we simply need to ensure that we are on our
budget constraint (equation 4.135). That is, we need to ensure that total expenditure (yph,
determined by equation 4.126) equals the sum of expenditures on all commodities, which
depends on real consumption (qph, determined by equation 4.132) and prices (pph).
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4.6.11 Armington Elasticity
The implementation region-specific Arminigtonselasticity is used. First ESUBD_R, the
standard GTAP region-generic elasticity is defined and read into the model from the GTAP
Database. Next, a region-specific elasticity is defined. This is initially set equal to the region-
generic, unless an additional header exists (ifheaderexists), “ESDR” containing region-
specific details. The implementation region-specific Arminigtons in the tab file is shown in
the box below1.
Coefficient (parameter)(all,i,TRAD_COMM)
ESUBD_R(i)
# region generic el. of sub. domestic/imported for all agents #;
Read
ESUBD_R fromfile GTAPPARM header"ESBD";
Coefficient (all,i,TRAD_COMM)(all,r,REG)
ESUBD(i,r) # region specific el. of sub. among imports of i in Armington
structure #;
Formula (all,i,TRAD_COMM)(all,r,REG)
ESUBD(i,r) = ESUBD_R(i) ;
Read (ifheaderexists)
ESUBD fromfile GTAPPARM header"ESDR";
In order to obtain region-specificArmingtons, the user can include them themselves directly
in the parameters file or they can modify “flexagg” to aggregate the GTAP elasticities using
region-specific weights. By using region-specific weights, the aggregated elasticities would
differ across regions.2
1 The code is also adjusted in a similar way for the elasticity of substitution between imports from different
regions. 2 There are plans to include this in the GTAPAgg program.
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4.6.12 Population
The percentage change in the population by household (poph(r)) is included in the model as
an exogenous variable. Since only the total population is known from the GTAP Database
and not the populations of each household type, we cannot determine the percentage change
in the total population from the changes by population; hence the percentage change in the
total population (pop(r)) is removed from the model. This means that ug(r) is no longer
defined as a per capita variable and hence we re-label it qgov(r) to show that it is now
defined as real government expenditure. At this stage, we do not have any equations related
to the migration of people between households.
4.6.13 Welfare
Since the regional household has been removed the current welfare decomposition needs to
be revised1. For the time being it has been removed.
4.7 MyGTAP Model Closure
Model closures are the starting point of this model that assumes perfect competition in all
sectors of the economy (Walmsley & Minor, 2013). Capital and labor as factors of
production are considered to be fully mobile among different sectors of the economy and
land along with natural resources is immobile. The economy of Pakistan faces the problem of
high unemployment rate, so we assumed that unskilled labor (LASKU)2 is unemployed.
Similarly, it is assumed that the factors prices influence the foreign income flows in the
respective country. The trade balance is endogenous and expected rate of return determines
the investment as in the case of standard GTAP model and total domestic savings by the
government budget deficit and sum of the private household savings.
Any country that gets GSP Plus status in the EU faces the annual growth capping mechanism
for products with higher growth rates (see results chapter for further details). The study
incorporated the capping mechanism with quota restrictions.
1 This is on the list for future work.
2 swap empl("LASKU","pakistan") = pfactreal("UnSkLab","pakistan") ;
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4.8 Social Accounting Matrix (SAM) for MyGTAP
Pakistan Institute of Development Economics (PIDE) developed the first Social Accounting
Matrix (SAM) for Pakistan in 1985 with the base year 1979. Federal bureau of Statistics
(FBS) under Project “Improvement of National Accounting System (INAS) with
collaboration with the Netherland government developed the second SAM that was limited to
the only single household. Siddiqui & Iqbal (1999) constructed a new SAM for Pakistan with
the base year 1989-90 and aggregated the industrial classification in Input-Output (IO) table
into SAM with five production accounts. Dorosh, Niazi, & Nazli (2006) built a broad SAM
with 34 production accounts and 19 household groups with the base year 2001-02. These
household groups were disaggregated across provincial basis, hence most suited for policy
analysis that targets particular households. Waheed & Ezaki (2008) produced a financial
SAM for the year 1999–00. They disaggregate the workings of the loanable funds market
into disaggregated payments related to physical and financial flows among institutions (Khan
et. al 2015).
The latest available SAM that depends heavily on concomitant National Accounts and
household data was developed by Debowicz et.al (2012) with the base year 2007-08, under
the Pakistan Strategy Support Program (PSSP) funded by USAID aiming to support the
Government of Pakistan with evidence-based policy reform for pro-poor economic growth
and enhanced food security.
To implement a CGE model with an income distribution component, a consistent database is
required. MyGTAP in the study pursues the SAM (2007-08) desegregation of activities,
commodities, factors and institutions. The model follows the framework developed by
Lofgren et al. (2001). This model is a standard static model rather than dynamic CGE model.
Therefore the second-period effects of changes in investment expenditures are not taken into
account. Moreover, the model neither specific about the time horizon of the adjustment nor
how the adjustment is sequenced. Otherwise stated, the model cannot resolve whether
adjustment from the base to a new equilibrium takes place over any particular length of
time, or whether a large part of the adjustment occurs in a particular year (see appendix 4).
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Equations of the model are employed to describe inter-relationship of the macro economy.
SAM provides actual values for the coefficients in these equations through the calibration
process. The model will be solved primarily for equilibrium to make sure that the base year
dataset is reproduced. Afterward, it would be possible to shock the model with a change in
the value of one of the exogenous variables. The model will be resolved for equilibrium and
the changes in the values of the endogenous variables. Moreover, these values will be
compared to those of the base-year equilibrium to establish the impact of the exogenous
shock.
4.8.5 Framework of Macroeconomic Accounting
A SAM is a square matrix which presents monetary flows that reflect the all transaction of
receipts and payments between various agents in the economy. Furthermore, it follows a
framework of macroeconomic accounting which permits us to compute a variety of macro
identities. The study expresses macroeconomic accounting framework1 in the form of
algebraic equations which could be used in computing different macro identities. Further, all
institutions of the economy are divided into four - household (h), government (g), enterprises
(e) and rest of the world (r) - to state the macroeconomic framework.
The study denotes Yi for income in sector i, Si for saving in sector i and Ei for expenditure in
sector i. Moreover, all transactions among sectors are denoted by ijTR which specify the
direction of flows from sector i to sector j. For instance, hrTR shows the transfers from the
household (h) to rest of the world (r), whereas rhTR shows the transfers from rest of the world
to the household.
4.8.5.1 Household Sector
Household income ( hY ), household savings ( sY ), and household expenditures ( eY ) are the
main accounts of the household sector. The main source of household income is factor
income ( fY ), which is generated within the production activities. Moreover, they also obtain
1 Macroeconomic accounting framework is adopted from Warr & Azis (1997)
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income through transfers from the government ( hgTR , ) and rest of the world ( hrTR , ).
Income of household can be written as
hrhgfh TRTRYY ,,
where hY = household income,
fY = household factor income,
hgTR , = transfers from government to household,
hrTR , = transfers to the household from rest of the world.
Income of a household must be equal to the expenditure of household when talking about in
terms of accounting relationships. The income received by the household comprises of the
household factor income, the income that it receives from government and rest of the world.
Therefore, household consumption, transfers to government and rest of the world comprise
household’s total expenditure. The relationship can be expressed as:
rhghh TRTRCE ,,
where ghTR , = transfers from household to the government,
rhTR , = transfers from household to rest of the world,
C = consumption of the household.
Household saving can be expressed by the following identity
hhh EYS
where hS = saving of household,
fY = household factor income,
hE = expenditures of the household.
Substituting equation 4.136 and 4.137 in 4.138, we get
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hfh NTRCYS
where )()( ,,,, rhhrghhgh TRTRTRTRNTR
Where hNTR = total net transfers received by the household sector.
Therefore, three key accounts of the household sector can be expressed by equations (4.136),
(4.137) and (4.139).
4.8.5.2 Enterprise Sector
Resembling the households sector, the enterprise sector also consists of three accounts which
are income, expenditure and saving. The income of enterprise is mainly driven from
operating surplus. That is generated by deducting the consumption of fixed capital from the
total capital income within the production activities. Transfers by the government ( egTR , )
and rest of the world ( erTR , ) are other sources of income of enterprise. We can express
income and expenditure of enterprise as:
eregdeke TRTRSYY ,,,
where eY = enterprise income
ekY , = capital income of enterprise
= consumption of fixed capital (depreciation)
egTR , = transfer to the enterprise from the government
erTR , = transfer to the enterprise from the rest of the world
regehee TRTRTRE ,,,
Where
eE = expenditure of enterprise
heTR , = transfer of enterprise to household
dS
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geTR , = transfer of enterprise to government
reTR , = transfer of enterprise to rest of the world
Saving account can be obtained by subtracting expenditure from income of enterprise.
eee EYS
Substituting equation (4.140) and (1.141) in equation (1.142), we get
edeke NTRSYS ,
Where hegeegreere TRTRTRTRTRNTR ,.,,, )()(
Thus, Enterprise receipts (income), expenditure and saving can be expressed by equations
(4.140), (4.141) and (4.143) respectively.
4.8.5.3 Government Sector
The government sector, similar to household and enterprise sector, consists of three accounts,
i.e., government receipts (Revenues), government expenditure (Outlays) and government
saving. Government receipts include indirect taxes, income taxes from households, and
transfers from “rest of the world” ( grTR , ). While, government expenditures ( gE ) consist of
transfers to the households ( hgTR , ), transfers to “rest of the world” ( rgTR , ) and public
consumptions (G). We will denote total net transfers received through government by gNTR .
Equations for government receipts ( gY ), expenditures ( gE ) and savings ( gS ) are as follow:
grghtg TRTRIY ,,
where gY = government receipts
tI = indirect taxes
ghTR , = income taxes from households
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grTR , = transfers from “rest of the world”
rghgg TRTRGE ,,
where gE = government outlays
G = government consumption expenditure
hgTR , = transfer to the households from the government
rgTR , = transfers from government to rest of the world
ggg EYS
where gS = Government savings
By substituting equations (4.143) and (4.144) in (4.145), we obtain
ggg NTRGYS
where gNTR is net transfers received by governments and can be written mathematically as
)()( ,,,, rggrhgghg TRTRTRTRNTR
Therefore, total government revenues, expenditures and saving can be expressed by equation
(144). (145) and (147).
4.8.5.4 Rest of the World Sector
The sector “rest of the world” shows the supply of imports to and demand for our exports
from the rest of world. This sector consists of three main accounts. These are total payments
from foreigners to domestic agents (rE ), total receipts of foreigners from domestic agents (
rY ), and foreign savings. Major sources of receipts of foreigners are imports (M), transfers
from government ( rgTR , ) and transfer from households ( rhTR ,, ). While, total expenditure of
foreigners (rE ) consists of “transfers to households” ( hrTR , ), “transfers to government” (
grTR , ) and exports (E). Total receipts and expenditures of foreigner can be expressed as
follows:
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rhrgr TRTRMY ,,
where rY = total receipts of a foreigner from domestic agents
M = Imports
rgTR , = transfer from government to foreigners
rhTR , = transfer from households to foreigners
grhrr TRTRXE ,,
where rE = total payments from foreigners to domestic agents
X = total exports
hrTR , = transfer from foreigners to households
grTR , = transfers from foreigners to government
We can write identity of foreign savings as
rrr EYS
where rS is the foreign savings
By substituting equation (4.147) and (4.148) in equation (4.149), we obtain
rr NTRXMS
where rNTR is net transfers received by foreigners and can be written algebraically as
)()( ,,,, hrrhgrger TRTRTRTRNTR
Total foreigners receipts from domestic agents, total foreigners payments to domestic agents
and foreign saving can be expressed by equations (4.148), (4.149) and (4.151) respectively.
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4.8.6 The Macro Aggregates
In this section, we derive GDP at factor cost as well as market price. We estimate these
macro aggregates from the income and expenditure side and also from investment and saving
equilibrium. These aggregates can be obtained if we sum equation (4.139), (4.143), (4.147)
and (4.151) we get
EMGISYCYSSSS tdekfregh ,
Rearranging above equation we get
EMGIYCYSSSSS tekfdregh ,
As we know that Gross Domestic Product (GDP) at factor cost can be defined as
ekf YYYFC ,
and GDP at market price can be expressed, on the income side, as
tIYFCY
Or
tekf IYYY ,
by substituting GDP at market price(Y) in the equation (4.153), we get the following
expression:
EMGCYSSSSS dregh
Since, we can write the definition of GDP at market price on expenditure side as
MEGICY
where I = total value of the gross investment at market price.
Arranging above equation for I, we obtain
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EMGCYI
Hence, we can obtain the following equation by substituting value of I into the right-hand
side of equation (4.157)
EMGCYSSSS drgh
where I = total value of the gross investment at market price.
Therefore above equation shows the investment and saving equilibrium.
4.9 Data Sources for SAM 2007-08
The data was used from the following sources:
2007-08 National Accounts 2007-08
Value added by 15 sectors (Handbook of Statistics)
Macroeconomic Aggregates
1990-91 Input-Output Table (97 sectors)
2007-08 Agricultural Statistics of Pakistan
2007-08 Pakistan Integrated Household Survey
Commodity level trade data from the Ministry of Finance (MOF)
2000-2001 SAM for Pakistan
Please see appendix 3 for the detailed structure of SAM 2007-08.
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CHAPTER 5: RESULTS & DISCUSSION
Multi-country or global models consist of multiple countries or the total global economy.
These models are specifically designed to analyze the trade agreements. Moreover, these
models do not maintain a single country model assumption of exogenising global or trading
partner effects. Therefore, the implications of these effects - coming from rest of the world or
other countries - have been endogenized. Any effects, transmitted to by means of various
channels, of policy changes in the rest of the world, would have direct as well as indirect
influence. These models explicitly capture this transmission mechanism. Therefore, these
models can be applied in policy experiments of multilateral trade liberalization (Wobst,
2001).
The CGE model in its global version is supported by the Global Trade Analysis Project
(GTAP) model as it provides the modeling framework as well as the database to the CGE
model. That is; the main source of data for the global CGE model is the GTAP database. The
model of GTAP is the most commonly used and known software for the multi-country trade
analysis. It is Multi-region, multi-country and multi- sector CGE model which assumes
perfectly competitive markets and return to scale (Burfisher, 2011). GTAP 09 with reference
years 2004, 2007 and 2011, 140 regions, 57 sectors and 244 countries has been used to link
the Pakistan economy with rest of the world in general and European Union (EU28) in
particular.
The study has fully calibrated various policy experiments by varying the related parameters.
This chapter will discuss in detail the simulations performed and results of different scenarios
modeled for Pakistan keeping in view the objectives of the study. The chapter is organized as
follows; first, we will explain an overview of Pakistan trade with EU, then simulation design
of the scenarios carried out. The following section will represent the results. This will be
accompanied by brief discussion of overall results at the end.
5.1 Pakistan-EU Trade Relationships at a Glance
Since many years EU is the largest importer of Pakistani products. Total exports from
Pakistan to the EU during the year 2014 were US$ 8.13 billion which accounted 29 percent
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for the total exports. It was 21.5 percent in 2012 and 24 percent in 2011. Although EU is
considered to be the dominant importer for Pakistan but country always showed a sluggish
export growth, especially in terms of commodity diversification. On the other hand,
penetration into the EU market remained overdue comparing to other competitors (PBC,
2014).
Pakistan is a member of the preferential trading system of EU, ever since its evolution. The
examination evidents that external trade relations of the EU with the developing countries
has been conducted with a number of different channels, principally with the African
Caribbean and Pacific (ACP) states through the Lome Convention, Mediterranean countries
through the Global Mediterranean Policy (GMP) and with rest of Latin American and Asian
developing countries including Pakistan through the GSP scheme. This is evident from the
EU’s complex network of discriminatory tariff through generalized and country-specific or
region-specific trade preferences. While, trade relations with most industrialized countries
have been based on most-favored-nation treatment (Naeem, 2006).
The common commercial policy uses a spread of instruments to regulate trade among the EU
and its trade partners. It covers not only tariff but other trade instruments as well. A
complicated system of trade advantages, differentiated according to specific groups of
countries, has drawn up to a hierarchy of trade preferences called as ‘pyramid’ of trade
preferences. The examination of EU’s trade regime practice worked out over the years point
out that it uses fairly complicated procedures and very elaborate panoply of instruments.
Although the system has some of the economic effects hoped for and has been established for
political reasons; it seems advisable to simplify considerably in order to expand its benefits
largely according to’ trade not aid’ principle (Persson & Wilhelmsson, 2016).
5.2 Does GSP Plus is different from Normal GSP?
The basic and foremost objective of the preferential system known as Generalized System of
Preferences (GSP) is to help the economies to reduce the poverty, promote good governance
and sustainable economic growth. These preferences enable the economies to increase their
role in the international trade and especially the exports to the EU that ultimately help them
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to reduce the poverty and maintain a sustainable development. The GSP of EU covers the
following regimes (European Commission, 2013).
i) The standard/normal GSP that covers more than 6300 tariff lines, transports
preferences to 90 developing countries that have been reduced from 177 in 2013.
ii) GSP Plus that brings special arrangements to promote good governance and
sustained development in addition to offering duty-free access to more goods from
the vulnerable economies including Pakistan. The list includes 25 countries adding
9 more to the previous 16. The beneficiary economies have to implement and ratify
certain international conventions.
iii) The most attractive arrangements for the 50 Least Developed Economies (LDCs)
called Everything But Arms (EBA), provides duty-free and quota-free access to
nearly all commodities.
In addition to the preferential agreements, EU has established trade relations on the basis of
Most Favored Nation (MFN) treatment allowing all industrialized countries outside EU to
trade with.
5.3 Opportunities for Pakistan under GSP plus Arrangements
Pakistan is benefitting from EU (European Commission at that time) since 1976. Pakistan
was already enjoying the traditional status of GSP by paying 20% less duty than the MFNs to
the EU. This concession not only helped the Pakistani products to gain access but also to
sustain its position in the EU market. Despite all this, Pakistani products were facing tough
competition from efficient producers like China, India, Indonesia, Vietnam and Thailand at
one hand and at other hand the countries who gained duty-free and quota-free access through
EBA status were giving tough competition. So, the GSP plus status provided an opportunity
to Pakistani products to compete with others (PBC, 2014). Pakistan has the following
opportunities.
i) India, China, Indonesia, Vietnam, Colombia and Thailand are not eligible for the
GSP plus status.
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ii) In textile and clothing sector, China has already graduated and India has
graduated from the textile sector of the normal GSP providing an opportunity for
the textile and clothing sector of Pakistan.
iii) After Bangladesh, Pakistan will be the second country in the region enjoying
duty-free access in the EU market.
Keeping in view the above points and economic conditions of Pakistan, GSP plus status in
the EU is promising a lot of opportunities for Pakistan in terms of trade, investment,
institutional development, sustainable economic growth and employment generation etc.
5.4 Pakistan’s Major Competitors: Challenges vs. Opportunities
In December 2013, European Union granted GSP plus status and since January 2014,
Pakistan is enjoying this status. It is expected from the very beginning that the exports from
Pakistan are expected to increase in the EU market under GSP plus status. This status will
substantially increase Pakistan’s exports to the EU28, especially in textile, wearing apparel
and leather sectors.
Identification of “high potential” products of Pakistan in the EU after the GSP plus status is
the first step that should be followed by the identification of potential competitors with the
same status or even better. EU have a range of agreements with different countries including
GSP, GSP plus, Every Thing but Arms (EBA), Overseas Countries and Territories (OCT),
Economic Partnerships Agreement (EPA) and some more. While considering the competition
among developing economies, EBA is considered to be more attractive than the GSP plus
status (Carbone & Orbie, 2016).
Although, achieving the status of zero tariff on the export of all products is a huge
opportunity but the exports from Pakistan may not observe abrupt jump. It is because
Pakistan will continue to face a tough competition from countries enjoying the same or better
treatment in the EU market. The countries with GSP plus status will face an annual capping
mechanism while others with EBA status like Bangladesh will enjoy the tariff-free access
throughout the year. In addition to such status in the EU market, the commodity price,
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production capabilities and demand for the products will also play a key role in such
competitive environment. Table 5.1 below summarizes the position of Pakistan and its
competitors with similar status in the EU market.
125
Table 0.1:Comparison of Imports by the EU (28) with GSP Plus Beneficiaries (US $ Million)
S.No Exporters 2006 2007 2008 2009 2010 2011 2012 2013 2014
1 Peru 4535.426 5735.355 5757.745 4606.083 6964.311 9044.237 8245.533 7319.704 6829.214
2 Pakistan 4608.631 5299.239 6068.424 5295.437 5918.299 7485.797 6093.209 6868.222 7220.692
3 Paraguay 381.972 595.263 732.295 509.935 1312.965 1657.424 1225.061 1574.558 1527.568
4 Costa Rica 4385.611 4861.657 5446.408 4516.831 5309.968 6077.34 6790.581 6394.572 6118.975
5 Ecuador 2359.165 2795.452 3569.817 3078.315 3047.403 3712.947 3601 3897.129 4003.406
6 Georgia 644.582 663.565 908.006 604.577 886.863 923.853 762.903 916.273 907.527
7 Armenia 442.26 488.988 478.108 241.345 323.211 443.014 330.801 329.391 335.891
8 Bolivia 192.642 247.807 474.357 420.556 569.521 592.171 603.733 761.402 879.886
9 Mongolia 79.506 111.22 95.385 75.696 135.866 114.874 93.291 105.559 115.773
10 Cabo Verde 39.629 25.548 40.429 39.03 50.536 63.852 68.475 64.404 78.477
Source: International Trade Centre (ICT)
126
5.5 Potential for Pakistani Imports after GSP plus Status
Currently, it seems that tariff-free access of Pakistan into the EU will bring a lot of
opportunities in terms of trade and economic growth. The statistics predict that product
diversification may bring more opportunities for Pakistan.
The products in which Pakistan is already enjoying high market access – import is 6% of the
total EU imports for the particular commodity may not be able to enjoy the zero-tariff status.
In addition to that, annual growth capping mechanism under GSP plus will also restrict the
imports from Pakistan. It is 17.5% for most of the products except textiles and ethanol where
it is 14.5% and 13.5% respectively (PBC, 2015).
Some of the products from Pakistan are already under duty-free access in the EU28 as
normal or general tariff is already zero e.g. rice, sports equipment, surgical goods, meat
products and fruits. It means after the GSP plus status, 90 percent of the products from
Pakistan will lie under the duty-free category (Pakistan Economic Survey 2013-14).
Table 0.2: Imports from Pakistan into the EU 28 (category wise) (US$ million)
Products/Year 2010 2011 2012 2013 2014 2015
Products of Vegetable 55.8 57.5 47.4 49.1 57.7 67.8
Prepared Foodstuffs 6.7 54.2 45.6 89 101.2 115.1
Mineral 3.3 5.1 5.5 7.1 8.4 9.9
Chemical/ Allied 1.2 1.1 1 1.5 1.8 2.1
Plastic Articles 78.9 46.9 32.1 41 48.2 56.6
Raw Hides 33.7 44.4 43.1 61.1 71.8 84.3
Textile Articles 1,466.40 1,920.00 1,816.00 1,569.20 1796.7 2057.2
Footwear 38 45.6 44 45.7 53.7 63.1
Natural Pearls 10.2 9.9 9.2 7.4 8.7 10.3
Base Metals and Articles 0 0 1.7 1.3 1.5 1.8
Miscellaneous Products 58.4 66.3 70.1 66.3 77.9 91.5
Total 1,752.60 2,251.00 2,115.70 1,938.70 2227.6 2559.7
Source: ITC and Authors Calculations
127
There are approximately 74 items with 6 digit HS code that are identified as the potential
beneficiary products. The products included in the list are only those having export value
above 1 million US dollar in 2013. These products have not only less than 6% market share
in the EU market but also the exports in each product to the world market remained more
than 10 million US dollar for the same period. Detailed list of these products can be seen in
appendix 4 and 5.
In order to keep the things simple, the study has further spread the 74 potential products into
11 strategic sectors. Table 5.2 summarizes the story.
According to the study conducted by the Pakistan Business Council (2014 & 2015), there
would be a benefit of more than US$ 1 billion per annum to Pakistan. Duties and tariffs
on most of the products from Pakistan will be reduced to minimum or zero value.
Pakistan is already among the top ten exporters of EU28 having a comparative advantage
in the sectors of textiles, wearing apparel, leather and beverages. Table 5.3 shows that
Pakistan in the only country that have a comparative advantage in all categories. After
attaining the GSP Plus status, some better results are expected.
Table 0.3: Top 10 Exporters of EU28
Country Textiles Wearing
apparel Leather
Beverag
es
Current
Status
Market share
(Million
Euro)
Market
Share (%
age)
China √ √ GSP 302,049 17.93
Turkey √ Customs
Union
54,374 3.23
India √ √ GSP 37,120 2.2
Brazil √ MFN
tariff
30,996 1.84
Vietnam √ GSP 22,189 1.32
Indonesia √ GSP 14,432 0.86
Banglades
h
√
√
EBA 12,335 0.73
Pakistan √ √ √ √ GSP+ 5,510 0.33
Peru √ Free
Trade
Agreem
ent and
GSP+
4,930 0.29
Guatemal
a
√ Free
Trade
Agreem
ent
690 0.04
Source: Pakistan Business Council 2015
128
5.6 Research Simulations Used in this Study
In order to implement any modeling technique, the study has to follow two stages. At the first
stage, the model is separated from the base without any alteration in the exogenous variables
or parameters. In the second stage, the base values are then compared with the simulation
results that are instigated. The exogenous variable is modified to illustrate a change in the
trade policy and the solution is then compared with the base model. In this way, we can
capture the impact of different import concessions provided by the EU28 on the economic
growth of Pakistan (The details of regional and sectoral aggregations are in appendix 1).
The study will run the three simulations using GTAP 09 (Base year 2011-12) and two
simulations with MyGTAP (with latest available SAM 2007-08) to study the impact of GSP
Plus on Gross Domestic Product (GDP) of Pakistan, exports, imports, real investment, terms
of trade, prices of imports and exports and prices at domestic level.
The study incorporates the MyGTAP model to calculate the impact of policy options on the
household income and real wages, as the standard GTAP model is not an appropriate tool for
this purpose. In order to look deep into the objectives of the study, each scenario/shock has a
set of simulations. The study will run the following simulations.
Simulation I: EU-28 GSP Plus status with other competitors: It allows duty-free and quota-
free imports from Pakistan. What would happen by applying tariff rate (based
on 2011-12) on other competitors in the sectors of textile, wearing apparel,
beverages and leather?
Simulation II: EU-28 GSP Plus status with quota restriction: What would happen if quota
restriction is applied on Pakistan to incorporate the capping mechanism of
the EU28?.
Simulation III: Potential EU28-EBA with Competitors: What would happen if Pakistan gets
the Everything But Arms (EBA) status in the EU28 with no Capping
mechanism/Quota restriction, with main competitor Bangladesh that already
enjoying the EBA status?
129
5.7 Results of the Simulations with GTAP 09
The study used GTAP version 09 to obtain the results for all three simulations explained
above. The results of all three simulations are presented below showing a change in baseline
value. Different tables below explain the change in million dollars value as well as in
percentages.
5.7.1 Changes in GDP and Production of Pakistan
It is believed that trade openness and especially increases in exports, leads to increase in real
GDP and economic growth. Table 5.4 explains the impacts of our three simulations on the
real GDP of Pakistan that means, changes in output are measured at base prices. The impact
of all three simulations is positive and encouraging --- showing a positive change in the
baseline value.
Table 0.4: GDP Quantity Index, Constant 2011 Prices (Percent and Millions US$)
Simulations Base Value
(Millions US$)
Post Shock
Effects
Change
in GDP
Percentage
Change
GSP Plus status with
Competitors 213686.2 213956.031 269.828 0.126
GSP Plus Status with
Quota Restrictions 213686.2 213731.953 45.75 0.021
EBA Status 213686.2 213895.25 209.047 0.098
Source: Author’s simulation results using GTAP 09 program
The results of the first simulation revealed maximum gains while simulation two shows
minimum benefits for the GDP of Pakistan. In the first simulation by relaxing Pakistan from
tariffs after GSP Plus status as compared to its competitors in the EU28, GDP of Pakistan
gains benefit of US$269.828 million from the baseline value. While under the same status of
Pakistan in the EU28, when changes are applied by applying quota restrictions to calculate
the impact of EU capping mechanism, the gains are minimum among three simulations. In
this case, the GDP of Pakistan increases by US$45.75 million which is only 0.021 percent
positive change. The third simulation also produces very encouraging results with a positive
change of US$209.047 million in GDP.
130
Changes in real output in different sectors of Pakistan are represented in table 5.5. The
results of all three simulations revealed mixed effects on the real output of commodities. The
results of the simulation when quota restriction is applied on imports from Pakistan into
EU28 have more winning sectors while the simulation where Pakistan is competing with
other rivals under GSP plus status have minimum winning sectors.
Table 0.5: Changes in Pakistan’s Real Out Put, Constant 2011 Prices (Percent and
Millions US$)
Commodity
Base
Value
(Millions
US$)
GSP Plus with
Competitors
GSP Plus with EU
Capping (Quota)
Potential EBA
Status
Changes
in Value
Change in
Percent
Changes
in Value
Change in
Percent
Changes
in Value
Change in
Percent
Paddy rice 5259 -0.829 -0.02 0.073 0.001 0.287 0.005
Wheat 7853 -1.242 -0.02 -0.035 0.000 -1.083 -0.014
Plant-based
fibers 4765 1.921 0.04 -0.869 -0.018 1.4 0.029
Crops nec 583 -2.613 -0.45 -0.244 -0.042 -1.906 -0.327
Processed
rice 26562 -0.271 0.00 0.108 0.000 0.167 0.001
Oil seeds 4987 -0.112 0.00 0.067 0.001 -0.033 -0.001
Vegetables,
fruit, nuts 5051 -0.777 -0.02 0.019 0.000 -0.604 -0.012
Sugar cane,
sugar beet 2958 0.571 0.02 0.277 0.009 0.439 0.015
Leather
products 19420 0.431 0.00 0.23 0.001 0.174 0.001
Cereal grains
nec 538 -0.616 -0.11 -0.103 -0.019 -0.336 -0.062
Food
products nec 26297 0.272 0.00 0.234 0.001 0.245 0.001
Wool, silk-
worm
cocoons
125 3.459 2.77 -1.101 -0.881 2.49 1.992
Coal 276 -1.733 -0.63 -0.223 -0.081 -1.239 -0.449
Wearing
apparel 21474 6.15 0.03 0.037 0.000 4.149 0.019
Dairy
products 27416 0.449 0.00 0.253 0.001 0.299 0.001
131
Textiles 17662 4.844 0.03 -1.044 -0.006 3.593 0.020
Meat
products nec 1332 0.473 0.04 0.269 0.020 0.361 0.027
Animal
products nec 1181 0.174 0.01 0.25 0.021 0.079 0.007
Raw milk 5992 0.388 0.01 0.216 0.004 0.181 0.003
Meat 4133 -0.284 -0.01 0.09 0.002 -0.306 -0.007
Cattle, sheep,
goats, horses 1496 0.248 0.02 0.181 0.012 0.054 0.004
Forestry 599 -1.615 -0.27 -0.2 -0.033 -1.242 -0.207
Fishing 2014 0.439 0.02 0.22 0.011 0.34 0.017
Oil 1979 -1.248 -0.06 -0.194 -0.010 -0.966 -0.049
Gas 1623 -1.271 -0.08 -0.2 -0.012 -0.972 -0.060
Sugar 8353 0.585 0.01 0.293 0.004 0.461 0.006
Wood
products 2281 -1.318 -0.06 -0.1 -0.004 -1.022 -0.045
Vegetable
oils and fats 5730 -2.172 -0.04 -0.166 -0.003 -1.749 -0.031
Beverages
and tobacco
products
3209 0.817 0.03 0.147 0.005 0.79 0.025
Petroleum,
coal products 8109 -0.175 0.00 0.039 0.000 -0.135 -0.002
Ferrous
metals 874 -5.155 -0.59 -0.819 -0.094 -4.147 -0.474
Electronic
equipment 4409 -1.471 -0.03 -0.1 -0.002 -1.13 -0.026
Paper
products,
publishing
4326 -1.08 -0.02 -0.196 -0.005 -0.957 -0.022
Metals nec 805 -11.243 -1.40 -1.788 -0.222 -9.084 -1.128
Minerals nec 5759 -0.917 -0.02 -0.103 -0.002 -0.717 -0.012
Metal
products 3743 -2.34 -0.06 -0.284 -0.008 -1.837 -0.049
Transport
equipment
nec
2751 -2.759 -0.10 -0.418 -0.015 -2.318 -0.084
Light
Manufactures 5719 -0.847 -0.01 -0.004 0.000 -0.699 -0.012
Chemical,
rubber, 15527 -3.202 -0.02 -0.611 -0.004 -2.452 -0.016
132
plastic prods
Mineral
products nec 3431 -1.473 -0.04 -0.202 -0.006 -1.135 -0.033
Machinery
and
equipment
nec
10742 -3.102 -0.03 -0.439 -0.004 -2.517 -0.023
Manufactures
nec 2720 -5.918 -0.22 -0.763 -0.028 -4.413 -0.162
Electricity 41347 0.792 0.00 0.222 0.001 0.607 0.001
Transport and
communicati
on
103519 -0.191 0.00 0.005 0.000 -0.129 0.000
Services 80130 0.243 0.00 -0.011 0.000 0.602 0.001
Source: Author’s simulation results using GTAP 09 program
The case of first simulation i.e. relaxing Pakistan from tariffs as compared to competitors
shows more variations in real output. On a dollar value basis, the maximum gain in real put
is witnessed by the wearing apparel, with an increase of US$ 6.15 million (an increase of
0.03 percent from baseline value) followed by textile sector with US$ 4.844 million (0.03
percent from baseline). The other sectors that showed notable positive trend include wool
and silkworm cocoons US$ 3.45 million, plant-based fibers US$ 1.921 million, dairy
products US$ 0.449 million, sugar cane and sugar beet US$ 0.571 million, leather products
US$ 0.431 million, food product Not Elsewhere Classified (nec) US$ 0.272 million and
beverages and tobacco products US$ 0.817 million, meat product nec US$ 0.473 million,
animal products nec US$ 0.174 million, raw milk US$ 0.388 million, Cattle, sheep, goats,
horses US$ 0.248 million, fishing US$ 0.439 million, sugar US$ 0.585 million and
beverages and tobacco US$ 0.817 million. While all other sectoral output deteriorates. The
maximum decrease was seen in metals nec US$ -11.243 million. Other notable decrease was
seen in manufactures nec US$ -5.918 million, ferrous metals US$ -5.155 million, chemical,
rubber, plastic products US$ -3.202 million, machinery and equipment nec US$ -3.102
million, transport equipment nec US$ -2.759 million and metal products US$ - 2.34 million.
In the case of second simulation i.e. applying quota restrictions on imports from Pakistan to
calculate the impact of capping mechanism applied by EU28 shows more winning sectors as
133
compared to the first simulation. There are twenty winning sectors as compared to the first
simulation, where these were 16 only. The maximum gain was seen in the sectors of sugar
with US$ 0.293 million followed by sugar cane and sugar beets with US$ 0.277 million,
meat products nec US$ 0.269 million, dairy product US$ 0.253 million, animal products
US$ 0.250 million. The other winning sectors include, paddy rice, processed rice, oilseeds,
vegetables, fruits, nuts etc, leather products, food products nec, wearing apparel, raw milk,
cattle, sheep, goat, horse, fishing, beverages and tobacco products, petroleum and coal
products, electricity and transport and communication. While there is a decrease in real out
in rest of the sectors. The prominent sectors with a decrease in output include metal nec with
US$ -1.788 million, wool and silk worm cocoons with US$ -1.0101, textiles with US$ -
1.044 million and plant based fiber with US$ -0.869 million.
The results of the third simulation i.e. if Pakistan gets the status of EBA in the EU28 show
increase in real output in 19 sectors of Pakistan. The results reveal that wearing apparel
sector is winner acquiring the first position with a gain of US$ 4.149 million followed by
textiles sector with a gain of US$ 3.593 million. Wool, silk worm, cocoons sector also shows
impressive performance with a gain of US$ 2.490 million.
The other winning sectors include, paddy rice, plant-based fiber, processed rice, sugar cane
and sugar beet, leather products, food products nec, dairy products, meat products nec,
animal products nec, raw milk, cattle, sheep, goat, horse, fishing, sugar, beverages and
tobacco products, electricity and services. While there is a decrease in real out in rest of the
sectors. The prominent sectors with a decrease in output include ferrous metals with US$ -
4.147 million, chemical, rubber and plastic products with US$ -2.452 million, transport
equipment with US$ -2.318 million, metal nec with US$ -1.749 million, electronic
equipment with US$ -1.130 million and forestry with US$ -1.242 million. Table 5.6
summarizes the story of all three simulations.
5.7.2 Changes in Exports and Imports of Pakistan
Trade balance always plays very important role in the process of economic growth for an
economy. Exports are normally considered the goods and services for which the foreigners
134
pay the price to domestic economy and imports are considered to be the goods and services
for which domestic residents pay the price to the foreign economy (Mankiw, 2007).
Figure 0.1: Merchandise Exports and Imports of Pakistan¸ (Percent)
Source: Author’s simulation results using GTAP 09 program
After gaining tariff free and quota-free entry into the EU28, it is expected that the exports
from Pakistan may rise. Similarly, the flow of imports will also increase due to increased
demand for foreign inputs and resultant higher prices of many goods. Figure 5.1 explains the
results of all three simulations. The results of the first simulation revealed maximum gains
while simulation two shows losing position of Pakistan. In first simulation by relaxing
Pakistan from tariffs after GSP Plus status as compared to its competitors in the EU28, the
merchandise exports of Pakistan gain 1.318% from the baseline value while under the same
status of Pakistan in the EU28, when changes are applied by applying quota restrictions to
calculate the impact of EU capping mechanism, the gains are negative. In this case, the
merchandise exports of Pakistan decrease by -1.47 percent. The results of the third
simulation i.e. if Pakistan achieves EBA status in the EU28 also produces very encouraging
results with a positive change of 0.907 percent in exports.
On the other hand, imports of Pakistan increase in all three simulations. In the first
simulation, the merchandise imports of Pakistan gain 4.791 percent from the baseline value
while the second simulation reveals an increase in imports by 0.729 percent. The results of
1.318
-1.47
0.907
4.791
0.729
3.692
-2
-1
0
1
2
3
4
5
6
GSP Plus with Competitors GSP Plus with Quota Restrictions EBA Status
Exports Imports
135
the third simulation i.e. if Pakistan achieves EBA status in the EU28 also produces a positive
change of 3.692 percent in merchandise imports of Pakistan.
After gaining the tariff free and quota free access in the EU28, the exports of different
products of Pakistan are expected to rise. Similarly, there are equal chances of increase in
prices in Pakistan that ultimately may result to increase the imports. This free access is
expected to bring positive change in the production of many goods along with the enhanced
availability of imported goods. Ultimately, the production of domestic goods may decrease
due to the availability of imported goods at a lesser price. This change in production may
differ across different sectors of the economy.
Tables 5.6 and 5.7 explain the changes in exports and imports of Pakistan after all
simulations. On the export side, table 5.6 shows that there are some positive changes in the
results of two simulations while there is no winning sector in one simulation. The results of
the first simulation when relaxing Pakistan from tariffs and quotas after GSP Plus status as
compared to its competitors in the EU28, shows only 3 sectors with again in exports. The
results in table 5.6 show maximum gains in the wearing apparel sector with US$ 32.401
million, followed by textile sector with US$ 8.212 million. The third sector with positive
gain is beverages and tobacco products that gain US$ 2.762 million from the baseline value.
While rest of the sectors are losers in terms of export performance. The prominent
deterioration has been seen in the sectors of wool, silkworm cocoon (US$ -27.079 million),
meat products nec (US$ -21.690 million), transport equipment (US$ -19.124 million),
machinery and equipment nec (US$ -18.103 million) and manufactures nec (US$ -15.814
million).
The results of the simulation 2 i.e. GSP plus status of Pakistan in the EU28 with quota
restrictions show no positive change in sectoral exports of Pakistan. It is mainly because the
quota (capping) restriction discourges the exports of the commodities where Pakistan has a
comparative advantage while in rest of the sectors, the performance is already poor. The
results of simulation 2 in table 5.6 show all sectors with a decline in exports. Major
deterioration is seen in sectors of meat products nec (US$ -3.793 million) followed by cattle,
136
sheep, goats and horses (US$ -3.533 million), dairy products (US$ -2.849 million) and
metals nec (US$ -2.488 million).
The results of the simulation 3 i.e. if Pakistan achieves the status of EBA in the EU28, show
some winning sectors. Just like simulation 1, the maximum gain is shown in wearing apparel
sector with US$ 21.554 million from baseline followed by textiles sector with US$ 6.209
million. The other winning sectors include paddy rice (US$ 4.71 million) and beverages and
tobacco products that gain US$ 3.645 million. While rest of the sectors are losers in terms of
export performance. The prominent deterioration has been seen in the sectors of all services
(US$ -41.091 million), wool, silkworm cocoon (US$ -21.698 million), meat products nec
(US$ -17.345 million), transport equipment (US$ -15.898 million), machinery and
equipment nec (US$ -14.842 million) and electronic equipment (US$ -14.773 million).
Table 0.6: Aggregate Exports of Pakistan, Constant 2011 Prices (Percent and Millions
US$)
Commodity
Base
Value
(Millions
US$)
GSP Plus with
Competitors
GSP Plus with EU
Capping (Quota)
Potential EBA
Status
Changes
in Value
Change in
Percent
Changes
in Value
Change in
Percent
Changes
in Value
Change in
Percent
Paddy rice 213 -13.864 -6.51 -0.727 -0.34 4.71 2.21
Wheat 839 -13.789 -1.64 -2.16 -0.26 -11.789 -1.41
Plant-based
fibers 330 -12.421 -3.76 -0.349 -0.11 -9.174 -2.78
Crops nec 115 -9.316 -8.1 -1.169 -1.02 -6.038 -5.25
Processed
rice 1989 -10.532 -0.53 -1.74 -0.09 -3.046 -0.15
Oil seeds 22.7 -9.795 -43.15 -1.331 -5.86 -8.103 -35.70
Vegetables,
fruit, nuts 657 -6.195 -0.94 -0.833 -0.13 -4.677 -0.71
Sugar cane,
sugar beet 0.011 -11.788
-
107163.64 -1.881 -17100.00 -9.912 -90109.09
Leather
products 632 -3.668 -0.58 -2.002 -0.32 -7.604 -1.20
Cereal grains
nec 71.3 -5.367 -7.53 -0.786 -1.10 -4.329 -6.07
Food 938 -8.906 -0.95 -1.446 -0.15 -5.745 -0.61
137
products nec
Wool, silk-
worm
cocoons
2.74 -27.079 -988.28 -2.257 -82.37 -21.698 -791.90
Coal 0.82 -1.2 -146.34 -0.119 -14.51 -0.593 -72.32
Wearing
apparel 3679 32.401 0.88 -1.2 -0.03 21.554 0.59
Dairy
products 33.9 -17.27 -50.94 -2.849 -8.40 -1.46 -4.31
Textiles 10760 8.217 0.08 -1.512 -0.01 6.209 0.06
Meat
products nec 2.94 -21.69 -737.76 -3.793 -129.01 -17.345 -589.97
Animal
products nec 72 -5.461 -7.58 -0.303 -0.42 -4.688 -6.51
Raw milk 0.536 -15.613 -2912.87 -1.549 -288.99 -13.078 -2439.93
Meat 104 -18.811 -18.09 -3.533 -3.40 -13.685 -13.16
Cattle, sheep,
goats, horses 0.394 -8.459 -2146.95 -1.263 -320.56 -7.145 -1813.45
Forestry 7.44 -9.278 -124.7 -1.37 -18.41 -7.281 -97.86
Fishing 34.8 -7.69 -22.1 -1.901 -5.46 -5.955 -17.11
Oil 1.01 -2.92 -289.11 -0.621 -61.49 -2.244 -222.18
Gas 0.007 -8.489
-
121271.43 -1.495 -21357.14 -7.181 -102585.71
Sugar 29.4 -11.743 -39.94 -1.532 -5.21 -4.29 -14.59
Wood
products 21.8 -16.276 -74.66 -2.013 -9.23 -12.901 -59.18
Vegetable
oils and fats 29.2 -11.564 -39.6 -1.732 -5.93 -8.052 -27.58
Beverages
and tobacco
products
361 2.762 0.77 -0.779 -0.22 3.645 1.01
Petroleum,
coal products 401 -1.322 -0.33 -0.239 -0.06 -1.027 -0.26
Ferrous
metals 234 -9.922 -4.24 -1.616 -0.69 -8.004 -3.42
Electronic
equipment 71.9 -19.561 -27.21 -2.05 -2.85 -14.773 -20.55
Paper
products,
publishing
55.5 -13.058 -23.53 -2.171 -3.91 -10.685 -19.25
Metals nec 480 -15.681 -3.27 -2.488 -0.52 -12.671 -2.64
Minerals nec 303 -2.9 -0.96 -0.502 -0.17 -2.421 -0.80
Metal
products 238 -16.967 -7.13 -2.238 -0.94 -13.304 -5.59
138
Transport
equipment
nec
38.3 -19.124 -49.93 -1.61 -4.20 -15.898 -41.51
Light
Manufactures 46 -12.64 -27.48 -1.554 -3.38 -9.976 -21.69
Chemical,
rubber,
plastic prods
839 -12.926 -1.54 -2.085 -0.25 -9.643 -1.15
Mineral
products nec 671 -6.517 -0.97 -1.124 -0.17 -5.069 -0.76
Machinery
and
equipment
nec
628 -18.103 -2.88 -2.467 -0.39 -14.842 -2.36
Manufactures
nec 921 -15.814 -1.72 -2.162 -0.23 -11.645 -1.26
Electricity 90.8 -8.398 -9.25 -0.965 -1.06 -6.894 -7.59
Transport and
communicati
on
1606 -8.468 -0.53 -0.928 -0.06 -6.884 -0.43
Services 3076 -9.931 -0.32 -1.371 -0.04 -41.091 -1.3359
Source: Author’s simulation results using GTAP 09 program
Table 5.7 illustrates the simulated changes in Pakistan's imports resulting from the three
simulations. Interestingly, the imports in coal sector deteriorated in all three simulations and
in the case of simulation 2, the imports of plant-based fibers decreased. While in the case of
all other sectors, the imports of Pakistan increased. The results of simulation 1 i.e. GSP Plus
status of Pakistan while maintaining the competitors at their existing positions show that the
only sector where imports decrease is coal where is decreased to US$ -1.18 million while in
rest of the sectors, the imports increased. The major increase is seen in the sectors of cattle,
sheep, goat and horses (US$ 11.763 million), leather products (US$ 11.553 million), dairy
products (US$ 11. 123 million), plant-based fibers (US$ 10.434 million) and electronic
equipment (US$ 10.121 million).
139
Table 0.7: Aggregate Imports of Pakistan, Constant 2011 Prices (Percent and Millions
US$)
Commodity
Base
Value
(Millions
US$)
GSP Plus with
Competitors
GSP Plus with EU
Capping (Quota)
Potential EBA
Status
Changes
in Value
Change in
Percent
Changes
in Value
Change in
Percent
Changes
in Value
Change in
Percent
Paddy rice 248 8.146 3.28 1.223 3.28 6.659 2.69
Wheat 913 9.781 1.07 1.474 1.07 7.61 0.83
Plant-based
fibers 354 10.434 2.95 -0.845 -2.95 7.832 2.21
Crops nec 147 1.461 0.99 0.283 0.99 1.294 0.88
Processed
rice 2578 7.49 0.29 1.383 0.29 5.978 0.23
Oil seeds 28.8 2.547 8.84 0.464 8.84 2.14 7.43
Vegetables,
fruit, nuts 938 4.385 0.47 0.759 0.47 3.396 0.36
Sugar cane,
sugar beet 0.013 2.362 18169.23 0.494 18169.23 2.168 16676.92
Leather
products 689 11.553 1.68 1.914 1.68 8.569 1.24
Cereal grains
nec 74 2.254 3.05 0.282 3.05 2.036 2.75
Food
products nec 1117 6.096 0.55 1.169 0.55 4.733 0.42
Wool, silk-
worm
cocoons
2.99 7.017 234.68 0.402 234.68 5.458 182.54
Coal 0.881 -1.18 -133.94 -0.163 -133.94 -0.963 -109.31
Wearing
apparel 4256 9.589 0.23 1.567 0.23 7.304 0.17
Dairy
products 40.2 11.123 27.67 2.064 27.67 8.902 22.14
Textiles 12412 9.781 0.08 0.926 0.08 6.995 0.06
Meat
products nec 3.6 7.894 219.28 0.784 219.28 6.053 168.14
Animal
products nec 77.4 3.165 4.09 0.644 4.09 2.533 3.27
Raw milk 0.536 9.443 1761.75 1.467 1761.75 7.455 1390.86
Meat 112 11.763 10.50 2.083 10.5 9.012 8.05
140
Cattle, sheep,
goats, horses 0.4 4.857 1214.25 0.876 1214.25 3.91 977.50
Forestry 8.91 3.507 39.36 0.614 39.36 2.662 29.88
Fishing 40 4.562 11.41 1.213 11.41 3.532 8.83
Oil 1.04 0.259 24.90 0.133 24.9 0.201 19.33
Gas 0.007 4.245 60642.86 0.965 60642.86 3.606 51514.29
Sugar 35.4 8.042 22.72 1.443 22.72 6.229 17.60
Wood
products 25.7 9.194 35.77 1.6 35.77 7.164 27.88
Vegetable
oils and fats 37.6 5.103 13.57 0.985 13.57 4.029 10.72
Beverages
and tobacco
products
508 3.495 0.69 0.733 0.69 2.737 0.54
Petroleum,
coal products 434 0.591 0.14 0.195 0.14 0.468 0.11
Ferrous
metals 259 1.878 0.73 0.434 0.73 1.48 0.57
Electronic
equipment 73.8 10.121 13.71 1.736 13.71 7.773 10.53
Paper
products,
publishing
66.3 6.923 10.44 1.041 10.44 5.394 8.14
Metals nec 496 3.026 0.61 0.626 0.61 2.394 0.48
Minerals nec 393 1.824 0.46 0.406 0.46 1.425 0.36
Metal
products 264 8.82 3.34 1.474 3.34 6.806 2.58
Transport
equipment
nec
40.3 8.241 20.45 1.434 20.45 6.575 16.32
Light
Manufactures 50.2 6.238 12.43 1.157 12.43 4.909 9.78
Chemical,
rubber,
plastic prods
941 5.039 0.54 0.732 0.54 3.981 0.42
Mineral
products nec 1032 4.66 0.45 0.784 0.45 3.455 0.33
Machinery
and
equipment
nec
675 7.978 1.18 1.344 1.18 6.24 0.92
Manufactures 1023 8.014 0.78 1.451 0.78 6.186 0.60
141
nec
Electricity 90.8 5.922 6.52 0.849 6.52 4.724 5.20
Transport and
communicati
on
1606 5.444 0.34 1.088 0.34 4.386 0.27
Services 3076 6.511 0.21 0.819 0.21 20.013 0.651
Source: Author’s simulation results using GTAP 09 program
The results of simulation 2 i.e. GSP plus status when quota restriction applied on imports
from Pakistan into the EU28 show that there two sectors where imports decreased. The
results of this simulation are more interesting and somehow better in terms of the trade
balance of Pakistan. In the sector of plant-based fibers, the decrease in imports is US$ -0.845
million and in the sector of coal, it is (US$ -0.163 million. While rest of sectors show a
positive change in the imports. The major gain in imports is shown in the sectors of cattle,
sheep, goat and horses (US$ 2.083 million), dairy products (US$ 2.064 million), leather
products (US$ 1.914 million), electronic equipment (US$ 1.736 million), wearing apparel
(US$ 1.567 million) and wheat (US$ 1.474 million).
The results of simulation 3 i.e. if Pakistan achieves the status of EBA in the EU28 just like
the status of Bangladesh, seem very similar to the simulation 1. Coal is the only sector that
showed deterioration in the imports with US$ -0.963 million. The rest of the sectors of the
economy showed again in the imports. Maximum gain in imports is seen in the sector of all
services where it increased by US$ 20.013 million. The other major sectors with an increase
in imports include dairy products (US$ 8.902 million), leather products (US$ 8.569 million),
plant-based fibers (US$ 7.832 million), electronic equipment (US$ 7.773 million) and wood
products (US$ 7.164 million).
Overall results of the simulations presented in figure 5.1, table 5.6 and table 5.7 show that
although some sectors of the Pakistan economy showed positive growth in exports but this
increase in exports is quite less than the increase in imports. In terms of trade balance of
Pakistan, the situation remained almost same i.e. trade deficit.
142
5.7.3 Impact on Real Investment
Real investment is the money spends to purchase the machinery rather than securities and
financial instruments. The study under consideration designed three simulations using GTAP
version 09 to calculate their impact on the real investment. The results of the three
simulations are presented in table 5.8. All three simulations generated positive results. The
first simulation i.e. GSP plus status of Pakistan in the EU28 while relaxing Pakistan from all
tariffs and quotas as compared to its competitors, show a maximum change in real
investment (US$ 2.686 million). The results of the simulation 2 i.e. GSP plus status of
Pakistan when quota restrictions are applied on Pakistan to justify the capping mechanism in
the EU28 show a minimum positive change in real investment (US$ 0.507 million). The
results of simulation 3 i.e. if Pakistan gets the status of EBA in the EU28, are also positive
and similar to simulation 1. There is a positive change of US$ 2.106 million in the real
investment.
Table 0.8: Real Investment, Constant 2011 Prices (Percent and Millions US$)
Simulations Base Value
(Millions US$) Post Shock Effects
Change in Real
Investment
GSP Plus status with
Competitors 29000 29002.686 2.686
GSP Plus Status with
Quota Restrictions 29000 29000.507 0.507
EBA Status 29000 29002.106 2.106
Source: Author’s simulation results using GTAP 09 program
The positive results of all simulations show that after getting the status of a duty-free and
quota-free entry into the EU28, Pakistan needs to enhance the production capacity that is
only possible with improved real investment. In the case of the second simulation, when the
quota is applied on the imports from Pakistan in the EU28, the production capacity has been
limitised that resulted in less improvement in real investment.
143
5.7.4 Change in Prices of Goods for Domestic Household
A country expects a change in the sectoral prices after sudden change in the trade balance.
The results of three simulations showed an increase in the exports of Pakistan that ultimately
may cause an increase in price level at the domestic market. The increase in exports not only
bring a pressure on the prices of inputs that ultimately result into increased output prices but
also cause an increase in the demand for imports in the neglected production sectors.
The results of the all three simulations are presented in table 5.9. Interestingly the results of
all three simulations show a negative growth in the price of sugar cane and sugar beet sector.
It is because exports from Pakistan in these sectors are already lower than the production
capacity. The maximum deterioration is seen in the simulation 1 i.e. GSP plus status of
Pakistan in the EU28 without restrictions, which is -3.996 percent. The rest of the sectors
showed an increase in the price level for all commodities. The maximum gain in price in
simulation 1 is shown in the sector of ferrous metals (3.514 percent). Other sectors with a
prominent increase in price level include oil seeds (3.301 percent), wheat (3.267 percent),
crops nec (3.208 percent), processed rice (3.129 percent) and minerals nec (2.953 percent).
The minimum increase in the price level is shown in electronic equipment with 0.297
percent.
The results of simulation 2 i.e. GSP plus status of Pakistan in the EU28 with quota
restrictions, showed a minimum increase in the price level among all three simulations. This
is because the quota restrictions control the exports in the EU28 that ultimately reduce the
shortage at domestic level. The sector of sugar cane and sugar beet affected in positive way
for the domestic residents. The price for this sector decreased by (-0.174 percent). Major
increase in prices is shown in the sectors of ferrous metals (0.859 percent), processed rice
(0.533 percent), oil seeds (0.508 percent), wheat (0.529 percent), vegetable oil and fats
(0.497 percent) and plant based fibers (0.493 percent). The minimum increase is seen in the
sector of cattle, sheep, goats and horses (0.019 percent).
The results of the simulation 3 i.e. EBA status of Pakistan in the EU28, are moderate. Just
like in other two simulations, the prices of sugar cane and sugar beet sector deteriorated (-
144
3.17 percent) while there is a positive change in rest of the sectors. Major increase can be
seen in the sectors of ferrous metals (2.756 percent), oil seeds (2.583 percent), wheat (2.512
percent), processed rice (2.424 percent ( and minerals nec (2.327 percent) while just like
simulation 2, minimum increase is seen in the sector of cattle, sheep, goats and horses (0.088
percent).
Table 0.9: Changes in Prices of Goods in Domestic Market, Constant 2011 Prices
(Percent)
Commodity GSP Plus status
with Competitors
GSP Plus Status with
Quota Restrictions EBA Status
Paddy rice 1.72 0.154 1.738
Wheat 3.267 0.529 2.512
Plant-based fibers 2.831 0.493 2.219
Crops nec 3.208 0.333 2.547
Processed rice 3.129 0.533 2.424
Oil seeds 3.301 0.508 2.583
Vegetables, fruit, nuts 3.141 0.548 2.469
Sugar cane, sugar beet -3.996 -0.174 -3.17
Leather products 1.864 0.29 1.74
Cereal grains nec 1.947 0.282 1.623
Food products nec 2.576 0.088 2.104
Wool, silk-worm cocoons 1.648 0.234 1.479
Coal 2.618 0.439 2.125
Wearing apparel 2.309 0.335 1.925
Dairy products 2.187 0.33 1.815
Textiles 2.346 0.354 1.955
Meat products nec 2.666 0.435 2.133
Animal products nec 2.081 0.306 1.726
Raw milk 2.612 0.433 2.096
Meat 2.424 0.22 2.003
Cattle, sheep, goats, horses 0.12 0.019 0.088
Forestry 2.274 0.37 1.852
Fishing 2.864 0.484 2.259
Oil 2.422 0.323 1.948
Gas 2.869 0.49 2.262
Sugar 2.366 0.361 1.967
Wood products 2.33 0.338 1.939
Vegetable oils and fats 2.908 0.497 2.292
145
Beverages and tobacco
products 2.274 0.329 1.889
Petroleum, coal products 2.241 0.412 1.777
Ferrous metals 3.514 0.859 2.756
Electronic equipment 0.297 0.062 0.222
Paper products, publishing 0.254 0.054 0.209
Metals nec 2.619 0.426 2.106
Minerals nec 2.953 0.491 2.327
Metal products 2.152 0.346 1.736
Transport equipment nec 2.546 0.423 2.052
Light Manufactures 0.332 0.059 0.253
Chemical, rubber, plastic
prods 1.939 0.327 1.539
Mineral products nec 2.499 0.423 1.996
Machinery and equipment
nec 2.64 0.445 2.105
Manufactures nec 2.106 0.358 1.668
Electricity 2.329 0.412 1.81
Transport and
communication 2.683 0.45 2.141
Services 2.613 0.435 2.088
Source: Author’s simulation results using GTAP 09 program
5.7.5 Changes in the Prices of Commodities Supplied
It is the price that a producer has to pay for its inputs used. A sudden increase in the price
level is expected after the increase in exports for certain inputs. The results of the simulations
are presented in table 5.10. The maximum percentage increase is observed in the results of
simulation 1 i.e. GSP plus Status of Pakistan in the EU28 with no restriction. The maximum
price that producer will pay for the inputs will be in the sector of fishing (3.514 percent). The
other sectors with prominent increase include wood products (2.953 percent), cattle, sheep,
goats and horses (2.908 percent), meat products (2.869 percent) and dairy products (2.864
percent). The minimum increase is shown in the field of coal (0.12 percent) followed by gas
(0.254 percent) and petroleum and coal products (0.332 percent).
The least increase in seen in the results of simulation 2 i.e. GSP plus status of Pakistan in the
EU28 with quota restriction. It is because the quota restrictions may not allow a smooth
increase in export flows of Pakistan that will limitise the prices to increase for the producers.
146
The maximum in seen in the fishing sector (0.859 percent) followed by sector of cattle,
sheep, goat and horse (0.497 percent) and wood products (0.491 percent). The least increase
is observed in the coal sector (0.019 percent) and then gas (0.054 percent), petroleum and
coal products (0.059 percent) and oil sector (0.062 percent).
Table 0.10: Change in the Supply Price of Input, Constant 2011 Prices (Percent)
Commodity GSP Plus status
with Competitors
GSP Plus Status with
Quota Restrictions EBA Status
Paddy rice 1.864 0.29 1.74
Wheat 1.947 0.282 1.623
Plant-based fibers 2.576 0.088 2.104
Crops nec 1.648 0.234 1.479
Processed rice 2.618 0.439 2.125
Oil seeds 2.309 0.335 1.925
Vegetables, fruit, nuts 2.187 0.33 1.815
Sugar cane, sugar beet 2.346 0.354 1.955
Leather products 2.666 0.435 2.133
Cereal grains nec 2.081 0.306 1.726
Food products nec 2.612 0.433 2.096
Wool, silk-worm cocoons 2.424 0.22 2.003
Coal 0.12 0.019 0.088
Wearing apparel 2.274 0.37 1.852
Dairy products 2.864 0.484 2.259
Textiles 2.422 0.323 1.948
Meat products nec 2.869 0.49 2.262
Animal products nec 2.366 0.361 1.967
Raw milk 2.33 0.338 1.939
Meat 2.908 0.497 2.292
Cattle, sheep, goats,
horses 2.274 0.329 1.889
Forestry 2.241 0.412 1.777
Fishing 3.514 0.859 2.756
Oil 0.297 0.062 0.222
Gas 0.254 0.054 0.209
Sugar 2.619 0.426 2.106
Wood products 2.953 0.491 2.327
Vegetable oils and fats 2.152 0.346 1.736
Beverages and tobacco
products 2.546 0.423 2.052
Petroleum, coal products 0.332 0.059 0.253
147
Ferrous metals 1.939 0.327 1.539
Electronic equipment 2.499 0.423 1.996
Paper products,
publishing 2.64 0.445 2.105
Metals nec 2.106 0.358 1.668
Minerals nec 2.329 0.412 1.81
Metal products 2.683 0.45 2.141
Transport equipment nec 2.613 0.435 2.088
Light Manufactures 2.604 0.439 2.076
Chemical, rubber, plastic
prods 2.308 0.384 1.836
Mineral products nec 1.611 0.278 1.277
Machinery and
equipment nec 2.584 0.433 2.062
Manufactures nec 2.423 0.409 1.932
Electricity 1.937 0.326 1.539
Transport and
communication 2.703 0.463 2.133
Services 2.831 0.475 11.31
Source: Author’s simulation results using GTAP 09 program
The results of the simulation 3 i.e. if Pakistan achieves the status of EBA in the EU28, shows
a moderate increase in prices for producers. Although service sector prices go very high
(11.31%) but rest of the sectors showed moderate increase with the fish sector (2.756
percent), wood products (2.327 percent), cattle, sheep, goat and horse sector (2.292 percent)
and dairy products (2.259 percent). The least increase in observed in the coal sector (0.088
percent) and then oil (0.222 percent) and gas sector (0.209 percent).
5.7.6 Changes in Prices of Imported Commodities
The prices of imports will also be directly affected by increased exports of Pakistan in all
three simulations. Thus, this will affect all other prices due to interconnection that exists in
the domestic economy. Most domestic prices are prone to fall, that will lead to a switch to
export production. Meanwhile, there is a possibility of a switchover to imported
commodities. This offsetting effect will change the production structure of the economy
which in turns alters the incomes of different institutions in the model.
148
As predictable, the prices of most of the traded commodities have dropped under all three
simulations used in this study as shown in table 5.11. Construction prices will converge
into positive values for all scenarios but the increase is very marginal. The maximum
reduction in prices is shown by the simulation 1 which is in the sector of paddy rice (-
0.239 percent) and minimum reduction in beverages and tobacco products (-0.003
percent). No change in price is seen in oil seed sector while there is positive change in
price in the sectors of cattle, sheep, goats and horses (0.009 percent), petroleum and coal
products (0.003 percent), metals nec (0.004 percent), minerals nec (0.023 percent),
electricity (0.035 percent), oil (0.004 percent), transport equipment (0.002 percent),
transport and communication (0.047 percent) and services (0.051 percent).
In the case of simulation 2 (GSP plus with quota restriction), there are 8 sectors where
prices increase with the maximum rise in the sector of paddy rice (0.004 percent). There is
no change seen in the sectors of plant-based fibers, leather products, wool and silkworm
cocoons, wearing apparel, textiles, animal products, raw milk, sugar and vegetable oils and
fats. While there is deterioration in prices in rest of the sectors with a maximum decrease
in price in the sector of coal (-0.003 percent).
In the case of simulation 3 (EBA status of Pakistan), there are ten sectors of the economy that
are shown with an increase in price level with a maximum increase in the services sector
(2.319 percent). There is no change in the price level in the sectors of animal products nec
and raw milk. While a reduction in price level is shown in rest of the sectors. Maximum
deterioration in price is seen in the sector of coal (-0.015 percent) while gas, electricity and
transport and communication deteriorated with -0.01 percent.
Table 0.11: Changes in Prices of Imported Commodities, Constant 2011 Prices
(Percent)
Commodity GSP Plus status
with Competitors
GSP Plus Status with
Quota Restrictions EBA Status
Paddy rice -0.239 0.004 0.012
Wheat -0.111 0.002 0.006
Plant-based fibers -0.135 0 0.017
149
Crops nec -0.083 0.001 0.004
Processed rice -0.01 0.003 0.009
Oil seeds 0 0.001 0.004
Vegetables, fruit, nuts -0.127 0.001 0.003
Sugar cane, sugar beet -0.014 0.001 0.002
Leather products -0.197 0 -0.005
Cereal grains nec -0.069 0.001 0.002
Food products nec -0.076 -0.001 -0.006
Wool, silk-worm
cocoons -0.038 0 -0.002
Coal -0.081 -0.003 -0.015
Wearing apparel -0.127 0 -0.007
Dairy products 0.074 -0.002 -0.008
Textiles -0.207 0 -0.003
Meat products nec -0.04 -0.001 -0.004
Animal products nec -0.022 0 0
Raw milk -0.056 0 0
Meat -0.01 -0.001 -0.004
Cattle, sheep, goats,
horses 0.009 0 0.001
Forestry -0.084 -0.001 -0.008
Fishing -0.051 -0.002 -0.007
Oil 0.004 -0.001 -0.004
Gas -0.005 -0.002 -0.01
Sugar -0.08 0 -0.003
Wood products -0.036 -0.002 -0.008
Vegetable oils and fats -0.028 0 -0.001
Beverages and tobacco
products -0.003 -0.002 -0.008
Petroleum, coal products 0.003 -0.001 -0.005
Ferrous metals -0.008 -0.002 -0.008
Electronic equipment -0.083 -0.002 -0.008
Paper products,
publishing -0.018 -0.002 -0.009
Metals nec 0.004 -0.002 -0.009
Minerals nec 0.023 -0.002 -0.007
Metal products -0.077 -0.001 -0.008
Transport equipment nec 0.002 -0.002 -0.009
Light Manufactures -0.01 -0.002 -0.008
Chemical, rubber, plastic
prods -0.044 -0.001 -0.008
Mineral products nec -0.106 -0.001 -0.008
150
Machinery and
equipment nec -0.032 -0.002 -0.009
Manufactures nec -0.067 -0.001 -0.008
Electricity 0.035 -0.002 -0.01
Transport and
communication 0.047 -0.002 -0.01
Services 0.051 -0.003 2.319
Source: Author’s simulation results using GTAP 09 program
5.7.7 Impact on Pakistan’s Terms of Trade
It is the ratio of prices that a country receives and pays in exchange for its exports and
imports. It is considered important to understand the impact of changes in price on the
welfare of public generally. The current study investigated the impact of three different
simulations on the change in the price of imports and exports. Pakistan has already achieved
the status of GSP plus in the EU28, the restriction free exports of Pakistan in the case of GSP
plus and EBA may increase the export price of Pakistani products. Similarly, applying quota
restriction may increase the price level at a lower rate.
Figure 5.2 explains the effects of different simulations performed on the Pakistan’s terms-of-
trade. The results of all three simulations are positive. In the case of the first simulation the
export prices that Pakistan receives from EU28 are 0.024 percent higher than the import
prices that Pakistan pays to the EU28. The second simulation also produced positive results
but less than the results of simulation 1. When quota restrictions are applied on the imports of
Pakistan in the EU28, Pakistan is better off in terms of trade with 0.018 percent.
Highest gain is seen in the results of simulation 3, assuming that if Pakistan gets the status of
EBA in the EU28 just like Bangladesh. Due to this status, the exports from Pakistan may
increase rapidly resulting an increase in export prices. Hence, the results of this simulation
show that Pakistan would be 1.937 percent better off in terms of trade.
151
Figure 0.2: Term of Trade (TOT) of Pakistan, Constant 2011 Prices (Percent)
Source: Author’s simulation results using GTAP 09 program
The results obtained by using GTAP 09 are very much similar to the previous studies that
conclude that output and exports are inter-related. It is very difficult for an economy to grow
without trade openness. Developing and semi-industrialized economies have to focus on the
efficient use of factors of production. When exports in an economy increase, it not only
increases the output level but also cause an increase in the prices at domestic level. Similarly,
increased production put a pressure on the imports of related inputs in the form of capital and
raw material. On the other hand, increased prices of domestic commodities due to export
pressure also cause an increase in the imports of similar commodities. The studies that
support the above argument include (Esfahani, 1991), (Senguptaa & Espanab, 1994),
(Ekanayake, 1999), (Jung & Marshall, 1985) and (Feasel, Kim, & Smith, 2001).
5.8 Results of the Simulations with MyGTAP
The study also used MyGTAP in order to calculate the impact of two simulations on the real
wage rate and household primarily. The base year used in MyGTAP is 2007, as the latest
available SAM for Pakistan is of the year 2007-08. In addition to calculating the impact of
simulations on household income and real wage rate, the study has also discussed some other
0.024 0.018
1.937
0
0.5
1
1.5
2
2.5
GSP Plus status with Competitors GSP Plus Status with Quota Restrictions
EBA Status
152
areas of the economy. The study has only used the two following simulations for this
purpose.
Simulation I: EU-28 GSP Plus status with quota restriction: What would happen if quota
restriction is applied on Pakistan to incorporate the capping mechanism of
the EU28?
Simulation II: Potential EU28-EBA with Competitors: What would happen if Pakistan gets
the Everything But Arms (EBA) status in the EU28 with no Capping
mechanism/Quota restriction, with main competitor Bangladesh that already
enjoying the EBA status?
The results of these simulations are presented below.
5.8.1 Changes in GDP and Production of Pakistan
The results of the both simulations are presented in table 5.12 which show a positive change
in real GDP of Pakistan. In the case of the first simulation, the GDP of Pakistan increases by
US$21.594 million which is 0.015 percent positive change from the baseline value. The
results of the second simulation also very encouraging with positive change of US$ 884.047
million in GDP.
Table 0.12: GDP Quantity Index, Constant 2007 Prices (Percent and Millions US$)
Simulations
Base Value
(Millions
US$)
Post Shock
Effects
Change
in GDP
Percentage
Change
GSP Plus status with
Quota Restrictions 143169.594 143191.188 21.594 0.015
EBA Status 143169.594 144053.641 884.047 0.617
Source: Author simulation results using MyGTAP program
Similarly, table 5.13 presents the changes in the real output of different sectors of Pakistan
after both simulations. The results of both simulations reveal mixed effects on the real output
153
of commodities. The results of the first simulation show that there are 13 sectors out of 38
where output level is increased with maximum increase in the services sector (US$ 0.383
million). The other major winning sectors are construction US$ 0.356 million, sugar US$
0.199 million, vegetable, fruit and nuts US$ 0.196 million, livestock and meat products US$
0.125 million. While there is decrease in the real out in rest of the sectors. The prominent
sectors with a decrease in output include machinery and equipment US$ -2.117 million,
metals and products US$ -1.875, leather products US$ -1.777 million and oil seeds with US$
-1.722 million.
The results of the second simulation show an increase in real output in 25 sectors of Pakistan.
The results reveal that paddy rice shows amaximum gain with US$ 2.080 million followed
by construction sector (US$ 1.14 million), services sector (US$ 1.26 million), mineral
products nec (US$ 0.96 million) and processed food (US$ 0.93 million). While there is
deterioration in the 11 sectors. The prominent sectors with a decrease in output include
leather products with US$ -1.99 million, plant-based fibers with US$ -1.60 million and
textiles with US$ -1.99 million.
The results of the second simulation are more encouraging than simulation 1 which means
that if Pakistan is allowed to export in the EU28 without any restriction, the real output level
will increase in most of the sectors of the economy. Table 5.13 summarizes the story of all
simulations.
Table 0.13: Changes in Pakistan’s Real Output, Constant 2007 Prices (Percent and
Millions US$)
Commodity Base Value
(Millions US$)
GSP Plus with EU Capping
(Quota) Potential EBA Status
Changes
in Value
Change in
Percent
Changes in
Value
Change in
Percent
Paddy rice 1489.76 -0.205 -0.01 2.08 0.14
Wheat 2616.37 -0.102 0.00 -0.12 0.00
Cereal grains
nec 181.39 -0.02 -0.01 0.46 0.26
Vegetables, 7201.54 0.196 0.00 0.17 0.00
154
fruit, nuts
Oil seeds 365.22 -1.722 -0.47 -0.76 -0.21
Sugar cane,
sugar beet 5895.70 0.087 0.00 -0.11 0.00
Plant-based
fibers 2953.45 -0.796 -0.03 -1.60 -0.05
Cattle,sheep,
goats,horses 4271.85 0.016 0.00 0.23 0.01
Livestock
and Meat
Products
20862.25 0.125 0.00 0.25 0.00
Forestry 291.25 0.027 0.01 -0.07 -0.02
Fishing 879.47 0.076 0.01 0.27 0.03
Minerals 630.01 -0.396 -0.06 0.20 0.03
Oil 1468.00 -0.318 -0.02 -0.23 -0.02
Processed
Food 5029.13 -0.357 -0.01 0.93 0.02
Vegetable
oils and fats 3506.79 -0.528 -0.02 -0.21 -0.01
Dairy
products 4373.73 0.073 0.00 0.62 0.01
Sugar 5333.85 0.199 0.00 0.65 0.01
Beverages
and tobacco
products
3618.37 0.117 0.00 0.67 0.02
Textiles 23984.30 -1.208 -0.01 -1.11 0.00
Wearing
apparel 4404.37 -1.296 -0.03 -0.91 -0.02
Leather
products 1203.00 -1.777 -0.15 -1.99 -0.17
Wood
products 2418.66 -0.628 -0.03 0.28 0.01
Petroleum,
coal products 8120.48 -0.24 0.00 0.53 0.01
Chemical,ru
bber,plastic
prods
4289.23 -1.07 -0.02 0.22 0.01
Mineral
products nec 5498.41 0.119 0.00 0.96 0.02
Metals and
Products 2107.05 -1.875 -0.09 0.19 0.01
Motor
vehicles and
parts
2733.24 -0.569 -0.02 0.55 0.02
Electronic 2672.30 -0.795 -0.03 0.58 0.02
155
equipment
Machinery
and
equipment
nec
420.68 -2.117 -0.50 -0.94 -0.22
Manufacture
s nec 1111.31 -1.151 -0.10 0.28 0.03
other
utilitities 22303.91 -0.216 0.00 0.66 0.00
Construction 16914.60 0.356 0.00 1.14 0.01
Trade 21665.48 -0.133 0.00 0.39 0.00
Transport
equipment
nec
18768.37 -0.036 0.00 0.64 0.00
Communicat
ion 3009.44 0.058 0.00 0.76 0.03
All Services 55321.80 0.383 0.00 1.26 0.00
Source: Author simulation results using MyGTAP program
5.8.2 Changes in Exports and Imports of Pakistan
The duty-free and quota-free entry of Pakistan into the EU28 is expected to bring positive
effects on the exports of Pakistan. Similarly, the flow of imports will also increase due to
increased demand for foreign inputs and resultant higher prices of many goods. Figure 5.3
explains the results of the simulations. The results of both simulations show an increase in
imports and reduction in exports resulting disturbance in the trade balance. The exports of
Pakistan to EU28 reduced by -1.79 percent in the case of the first simulation while in the case
of the second simulation, the reduction is -1.282 percent. This reduction is export is due to
the production capacity of Pakistan in 2007 which was adversely affected by load shedding.
The adverse effects of energy crises increased the production cost in Pakistan resulting into
decline in exports. Due to this reason, the results of the simulations produced negative
impacts.
The results of both simulations show a positive increase in the imports of Pakistan. The
increase is 0.558 percent in the case of the first simulation, while in the case of the second
simulation, the increase is 1.153 percent. This increase in imports is also a result of increased
156
cost of production in Pakistan. Similarly, in order to increase the production, Pakistan would
also require more inputs to import.
Figure 0.3: Merchandise Exports and Imports of Pakistan (Percent)
Source: Author simulation results using MyGTAP program
Figure 5.3 shows that the trade balance is highly deteriorated in case of the first simulation
while in the case of the second simulation, an increase in imports is slightly less than the
decrease in exports. This because the quota restriction will restrict exports to EU28, leaving
more products available for the domestic consumer to consume. Hence imports increased at
a lower rate. While in the case of EBA status, there is no restriction to export resulting into
reduced availability of domestic products. The reduction in availability with increased
demand for imported inputs increased the overall imports of the country.
Looking at the sectoral performance of Pakistan after the tariff free and quota free access in
the EU28, the exports of different products of Pakistan are expected to rise. The increased
exports increase the overall demand for domestic products that ultimately increase the price
level for domestic products. On the other hand, the imports are also expected to rise due to
increased prices of domestic products and demand for imported inputs. It is worth
mentioning that availability of imported goods at a price level lower than domestic goods
decrease the local productivity.
-1.79 -1.282
0.558
1.171
-2
-1.5
-1
-0.5
0
0.5
1
1.5
GSP Plus with Quota Restrictions EBA Status
Exports Imports
157
Tables 5.14 and 5.15 explain the results of both simulations with base year 2007. On the
export side, table 5.14 explains the results of changes in exports at sectoral level for both
simulations. There are only 4 winning sectors in case of the first simulation while in the case
of second simulation, there are 5 winning sectors. Rest of the sectors face deterioration in
exports. The maximum gain is shown in the sectors of paddy rice (US$ 26.521 million) and
sugar (US$ 11.162 million) both in the case of second simulation while the minimum gain is
seen in the sector of cereal grain nec (US$ 0.034 million) which is in the case of simulation
one.
On the other hand, in case of the first simulation, maximum deterioration is seen in the sector
of dairy products (US$ -3.871 million) followed by the sector of cattle, sheep, goats, horses
(US$ -2.489 million) while minimum deterioration is seen in the sector of wheat (US$ -0.036
million). In the case of the second simulation, maximum deterioration is observed by the
sector of wheat (US$ -9.486 million) followed by livestock and meat products (US$ -9.552
million) and minimum deterioration is seen in the sector of utilities (US$ -0.22 million).
Table 0.14: Aggregate Exports of Pakistan, Constant 2007 Prices (Percent and Millions
US$)
Commodity
Base Value
(Millions
US$)
GSP Plus with EU Capping
(Quota) Potential EBA Status
Changes
in Value
Change in
Percent
Changes
in Value
Change in
Percent
Paddy rice 75.77 0.363 0.48 26.521 35.00
Wheat 137.31 -0.036 -0.03 -9.486 -6.91
Cereal grains nec 2.58 0.034 1.32 -3.257 -126.24
Vegetables, fruit,
nuts 215.47 -0.169 -0.08 -1.46 -0.68
Oil seeds 20.63 -3.014 -14.61 -1.211 -5.87
Sugar cane, sugar
beet 54.5 0.645 1.18 -7.488 -13.74
Plant-based fibers 45.81 1.651 3.60 -4.195 -9.16
Cattle,sheep,goats,h
orses 1.39 -2.489 -179.06 -8.543 -614.60
Livestock and
Meat Products 36.51 -1.819 -4.98 -9.552 -26.16
158
Forestry 5.36 0.785 14.65 -6.361 -118.68
Fishing 32.96 -2.093 -6.35 -3.799 -11.53
Minerals 106.24 -0.621 -0.58 -1.38 -1.30
Oil 0.52 -0.195 -37.50 -1.892 -363.85
Processed Food 1421.44 -1.234 -0.09 2.577 0.18
Vegetable oils
and fats 121.88 -3.058 -2.51 -3.119 -2.56
Dairy products 29.63 -3.871 -13.06 -4.244 -14.32
Sugar 30.39 -0.672 -2.21 11.162 36.73
Beverages and
tobacco products 141.04 -0.992 -0.70 -0.479 -0.34
Textiles 8367.7 -1.866 -0.02 -2.143 -0.03
Wearing apparel 2613.35 -1.838 -0.07 -1.629 -0.06
Leather products 468.46 -3.425 -0.73 -4.507 -0.96
Wood products 45.26 -2.547 -5.63 -1.953 -4.32
Petroleum, coal
products 623.81 -0.595 -0.10 -0.37 -0.06
Chemical,rubber,
plastic prods 374.53 -2.182 -0.58 1.301 0.35
Mineral products
nec 306.69 -2.111 -0.69 -2.05 -0.67
Metals and
Products 619.85 -3.232 -0.52 -0.638 -0.10
Motor vehicles
and parts 63.04 -1.606 -2.55 -0.323 -0.51
Electronic
equipment 28.06 -2.442 -8.70 -0.867 -3.09
Machinery and
equipment nec 370.78 -2.204 -0.59 -1.124 -0.30
Manufactures nec 472.9 -2.332 -0.49 -0.393 -0.08
other utilitities 0.52 -1.758 -338.08 -0.22 -42.31
Construction 66.98 -1.431 -2.14 -0.883 -1.32
Trade 51.44 -1.827 -3.55 0.601 1.17
Transport
equipment nec 1157.89 -0.971 -0.08 -0.36 -0.03
Communication 126.11 -1.166 -0.92 -1.799 -1.43
All Services 2483.34 -2.041 -0.08 -1.085 -0.04
Source: Author simulation results using MyGTAP program
Table 5.15 shows the results of the change in imports produced by both simulations. In the
case of the first simulation, there are only 6 sectors where imports deteriorated while rest of
the sectors show positive indication. The sectors where imports deteriorated include plant-
159
based fibers (US$ -1.791 million), forestry (US$ -0.672 million), paddy rice (US$ -0.462
million), sugar cane, sugar beet (US$ -0.353 million), oil (US$ -0.215 million) and cereal
grain nec (US$ -065 million). While rest of the sectors showed positive indication with
maximum gain in imports in the sector of dairy products (US$ 2.449 million) followed by
livestock and meat products (US$ 2.435 million) and the minimum gain was seen in the
sector of wheat (US$ 0.026 million).
The results of simulation 2 shown in table 5.15 indicate that there are only 2 sectors that
show a reduction in imports which are oil seeds (US$ -0.06 million) and other utilities (US$ -
0.17 million) while in the case of rest of the sectors, the imports increased. The maximum
increase in imports in seen in the sector of paddy rice (US$ 8.74 million) followed by
livestock and meat products (US$ 6.67 million) while minimum progress was shown in the
sector of construction trade (US$ 0.09 million).
Table 0.15: Aggregate Imports of Pakistan, Constant 2007 Prices (Percent and Millions
US$)
Commodity Base Value
(Millions US$)
GSP Plus with EU Capping
(Quota) Potential EBA Status
Changes
in Value
Change in
Percent
Changes
in Value
Change in
Percent
Paddy rice 90.12 -0.462 -0.51 8.74 9.70
Wheat 171.68 0.026 0.02 4.75 2.77
Cereal grains
nec 2.82 -0.065 -2.30 1.95 69.29
Vegetables,
fruit, nuts 296.05 0.326 0.11 2.76 0.93
Oil seeds 7.93 0.125 1.58 -0.06 -0.72
Sugar cane,
sugar beet 64.33 -0.353 -0.55 4.49 6.97
Plant-based
fibers 48.83 -1.791 -3.67 0.52 1.05
Cattle,sheep,
goats,horses 1.43 1.926 134.69 4.99 348.67
Livestock
and Meat
Products
39.60 2.435 6.15 6.67 16.85
Forestry 6.47 -0.672 -10.39 3.87 59.86
160
Fishing 38.71 1.388 3.59 2.73 7.04
Minerals 136.61 0.204 0.15 1.34 0.98
Oil 0.55 -0.215 -39.09 0.77 140.73
Processed
Food 1825.23 0.775 0.04 3.08 0.17
Vegetable
oils and fats 139.84 1.294 0.93 1.71 1.22
Dairy
products 35.85 2.449 6.83 3.58 9.98
Sugar 42.92 1.445 3.37 2.50 5.81
Beverages
and tobacco
products
198.90 0.931 0.47 1.09 0.55
Textiles 9664.46 0.643 0.01 1.17 0.01
Wearing
apparel 3023.97 1.404 0.05 1.85 0.06
Leather
products 514.35 2.405 0.47 3.80 0.74
Wood
products 54.12 0.96 1.77 1.23 2.27
Petroleum,
coal products 689.53 0.109 0.02 0.80 0.12
Chemical,rub
ber,plastic
prods
426.52 0.534 0.13 0.68 0.16
Mineral
products nec 452.14 1.796 0.40 2.64 0.58
Metals and
Products 656.26 0.548 0.08 1.03 0.16
Motor
vehicles and
parts
68.84 0.623 0.90 1.19 1.73
Electronic
equipment 29.00 1.754 6.05 1.66 5.71
Machinery
and
equipment
nec
392.60 0.287 0.07 1.04 0.27
Manufacture
s nec 527.40 1.831 0.35 1.59 0.30
other
utilitities 0.52 0.833 160.19 -0.17 -31.92
Construction 66.98 1.142 1.70 1.18 1.76
Trade 51.44 1.415 2.75 0.09 0.18
Transport 1157.89 1.058 0.09 1.10 0.09
161
equipment
nec
Communicati
on 126.11 1.409 1.12 1.74 1.38
All Services 2483.34 1.532 0.06 1.61 0.06
Source: Author simulation results using MyGTAP program
5.8.3 Impact on Real Investment
It is the amount of money spends to purchase of machinery rather than securities and
financial instruments. The study under consideration designed two simulations using
MyGTAP with the base year 2007 to calculate their impact on the real investment.
The results of the both simulations are positive and presented in figure 5.4. The results of the
simulation 1 show a positive change in real investment (US$ 0.378 million). The results of
simulation 2 are also positive and better than simulation 1. There is a positive change of US$
1.153 million in the real investment.
Figure 0.4: Changes in Real Investment, Constant 2007 Prices (Million US$)
Source: Author simulation results using MyGTAP program
0.378
1.153
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
GSP Plus Status with Quota Restrictions EBA Status
162
The positive results of both simulations show that after getting the status of a duty-free and
quota-free entry into the EU28, Pakistan needs to enhance the production capacity that is
only possible with improved real investment.
5.8.4 Impact on Pakistan’s Terms of Trade
Terms of trade is defined as the ratio of prices that a country receives and pays in exchange
of its exports and imports. It is considered important to understand the impact of the change
in price on the welfare of public generally. The current study investigated the impact of two
different simulations on the change in the price of imports and exports. Pakistan has already
achieved the status of GSP plus in the EU28, the restriction free exports of Pakistan in the
case of GSP plus and EBA may increase the export price of Pakistani products. Similarly,
applying quota restriction may increase the price level at a lower rate.
Figure 5.5 explains the effects of different simulations performed on the Pakistan’s terms of
trade. The results of both simulations are positive but very different. In the case of first
simulation the export prices that Pakistan receives from EU28 are 0.019 percent higher than
the import prices that Pakistan pays to the EU28.
Figure 0.5: Changes in Term of Trade (TOT) of Pakistan, Constant 2007 Prices,
(Percent)
Source: Author simulation results using MyGTAP program
0.019
1.834
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9
2
GSP Plus Status with Quota Restrictions EBA Status
163
Highest gain is seen in the results of simulation 2, assuming that if Pakistan gets the status of
EBA in the EU28 just like Bangladesh. Due to this status, the exports from Pakistan may
increase rapidly resulting an increase in export prices. Hence, the results of this simulation
show that Pakistan is receiving 1.834 percent higher export price than it is paying for its
imports from EU28.
5.8.5 Changes in Household Income in Pakistan
The study has discussed the impact of three simulations by using GTAP 09 and two
simulations by using MyGTAP by focusing on the issues of trade, GDP, output and prices. A
unique feature of the MyGTAP model used in this study is the capability to disaggregate the
regional household into both private and government entities (Minor & Mureverwi, 2013).
The study disaggregated the regional household of standard GTAP model into 18 types to
conduct a detailed analysis. The simulations used in the study will calculate the effects on
household income distribution and expenditures. The data and weights required were
obtained from the latest comprehensive Pakistani Social Accounting Matrix (SAM) 2007-08
developed by International Food Policy Research Institute (IFPRI) under Pakistan Strategy
Support Program (PSSP) project, Pakistan.
While conducting the welfare analysis, the studies that employ CGE models, normally show
all households are equally affected due to any change in the trade policy. In the case of
MyGTAP, the households are distributed into categories aiming to calculate the impact on
the marginalized population before designing a trade policy. Any change in the wage rate is
considered as change in the household income. Poverty is calculated on the basis of per-
capita income which is a key determinant of the economic status of a household. The
household income consists of income coming from different factors, so any change in the
income of factors means change in the income of households.
164
Figure 0.6: Changes in Households Income in Pakistan, Constant 2007 Prices (Percent)
Source: Author simulation results using MyGTAP program
The results of both simulations are summarized in figure 5.6 which show a positive change in
the overall household income. There is a change of 0.74 percent in the case of the first
simulation but in the case of the second, the change is 2.17 percent which means that if
Pakistan is allowed to export duty-free and quota-free into the EU28, the household income
in Pakistan will rise.
The results below show a change in all 18 categories of households. The regional household
is divided into three categories, household in Punjab, a household in Sindh and household in
rest of the Pakistan. The results further reveal that every household is not equally affected.
There are some households better off and vice versa.
5.8.6 Household Income of Large and Medium Farm
Table 5.16 represents the results of both simulations in order to check their impact on the
income of a large and medium household of Pakistan. The results reveal that in the case of
the first simulation, the household other than Sindh and Punjab have a positive change of
0.029 percent in its income while the income of the household in Sindh is reduced by -0.478
percent and in Punjab by -0.239 percent. On the other hand, the income of all households
increases with maximum increase of 2.347 percent in rest of the Pakistan.
0.74
2.17
0
0.5
1
1.5
2
2.5
GSP Plus with Quota Potential EBA Status
165
Table 0.16: Changes in Household Income of Large and Medium Farm, Constant 2007
Prices (Percent)
Household Types HH
Code
Population
(millions)
Income
shares
(percent)
GSP Plus
with Quota
Potential
EBA Status
Large and medium farm
Sindh H-MF1 0.8 1.5 -0.478 1.831
Large and medium farm
Punjab H-MF2 2.4 6.1 -0.239 1.613
Large and medium farm
other H-MF3 0.6 0.8 0.029 2.347
Source: Author simulation results using MyGTAP program
5.8.7 Income of Small Farm Household
The results of the both simulations are presented in table 5.17 which show the impact on the
small household living in Pakistan. The results reveal that small farmer living anywhere in
Pakistan is befitted in both cases. The results further reveal that the farmer living in parts
other than Sindh and Punjab is benefitting maximum in case of both simulations while the
small farm household in Sindh is getting minimum benefits in both cases. The maximum
benefit that small farm household of other Pakistan getting is 2.253 percent which is in case
of simulation 2 and small farm household of Sindh is getting minimum benefit of 0.331
percent in case of first simulation.
Table 0.17: Changes in Household Income of Small Farmers, Constant 2007 Prices
(Percent)
Household Types HH Code Population
(millions)
Income
Shares
(percent)
GSP Plus
with
Quota
Potential
EBA
Status
Small farm Sindh H-SF1 3.1 1.8 0.331 2.146
Small farm Punjab H-SF2 16 11.5 0.487 2.179
Small farm other
Pakistan H-SF3 5.6 3.3 0.885 2.253
Source: Author simulation results using MyGTAP program
166
5.8.8 Income of Landless Farmer Household
In this section, we will discuss the rural household that is a farmer but does not own a piece
of agriculture land in any area of Pakistan. The results of both simulations are presented in
table 5.18 that show a positive change in income of all rural households that are landless but
are farmers. In the case of a first simulation landless farmer of Sindh is gaining minimum
(0.405 percent) while the landless farmer of rest of the Pakistan is gaining maximum (0.954
percent). The results of the second simulation show that landless farmer of Punjab is gaining
maximum (2.452 percent) while the landless farmer of Sindh is gaining minimum (2.08
percent).
Table 0.18: Changes in Household Income of Landless Farmers, Constant 2007 Prices
(Percent)
Household Types HH
Code
Population
(millions)
Income
shares
(percent)
GSP Plus
with
Quota
Potential
EBA
Status
Landless farmers Sindh H-0F1 2.5 1.4 0.405 2.08
Landless farmers Punjab H-0F2 3.6 1.8 0.68 2.452
Landless farmers other
Pakistan H-0F3 1.7 0.7 0.954 2.323
Source: Author simulation results using MyGTAP program
5.8.8.1 Income of Landless Labor
The household living in rural area of Pakistan working in agriculture farms as a laborer and
having no land is included in landless labor. The results showing the impact of both
simulations on the household income of landless agriculture labor of Pakistan are presented
in table 5.19. The results show a maximum gain for the landless labor of whole Pakistan in
both simulations as compared to any other household. In the case of the first simulation,
maximum gain is shown for the landless agriculture labor of Sindh (1.563 percent) while the
landless agriculture labor of Punjab is getting minimum gain (1.432 percent). In the case of
the second simulation, the results are quite similar with maximum gain again in case of
landless agriculture labor of Sindh (4.136 percent) while the landless agriculture labor of
Punjab is getting minimum gain (3.512 percent).
167
Table 0.19: Changes in Household Income of Rural Agricultural Labor, Constant 2007
Prices (Percent)
Household Types HH
Code
Population
(millions)
Income
shares
(percent)
GSP Plus
with Quota
Potential
EBA Status
Landless agri. Lab Sindh H-AGW1 3 1.5 1.563 4.136
Landless agri. Lab Punjab H-AGW2 3.3 1.4 1.432 3.512
Landless agri. Lab other
Pakistan H-AGW3 0.4 0.2 1.498 3.944
Source: Author simulation results using MyGTAP program
5.8.8.2 Income of Rural Non-farm Household
In rural areas of Pakistan, there are households that have no direct connection with
agriculture farming. The results shown in table 5.20 reveal the impact of both simulations on
the income of non-farm rural households. The rural non-farm household of Sindh gets
minimum gain (0.994 percent) in the case of the first simulation while rest of the rural non-
farm households in Pakistan gain similar amount (0.997 percent). Similarly, in the case of the
second simulation, rural non-farm households of Sindh get maximum gain (1.569 percent)
and minimum gain (1.356 percent) in the case of rural non-farm households of rest of the
Pakistan.
Table 0.20: Changes in Household Income of Rural Non-farm Household, Constant
2007 Prices (Percent)
Household Types HH Code Population
(millions)
Income
shares
(percent)
GSP Plus
with Quota
Potential
EBA Status
Rural non-farm quintile 1 H-NFQ1 8.2 2.8 0.994 1.569
Rural non-form quintile 2 H-NFQ2 8.9 3.3 0.997 1.539
Rural non-farm quintile
other H-NFOTH 27.7 17.3 0.997 1.356
Source: Author simulation results using MyGTAP program
168
5.8.8.3 Income of Urban Household
Table 5.21 shows the impact of both simulations on the urban household of Pakistan. The
urban household of Sindh is showing maximum gain in both simulations (0.935 percent and
1.342 percent respectively) while the minimum gain is seen in the income of an urban
household of rest of the Pakistan in the case of both simulations (0.86 percent and 1.162
percent respectively).
Table 0.21: Changes in Household income of Urban Household, Constant 2007 Prices
(Percent)
Household Types HH
Code
Population
(millions)
Income
shares
(percent)
GSP Plus
with Quota
Potential
EBA
Status
Urban quintile 1 H-UQ1 8.6 2.6 0.935 1.342
Urban quintile 2 H-UQ2 8.6 3.4 0.927 1.265
Urban other H-UOTH 25.7 38.7 0.86 1.162
Source: Author simulation results using MyGTAP program
Overall, factor income remains positive for almost all households. If there is no tariff and
quota restriction from EU28, the income of every household type will increase. Primarily, it
is because the increased exports of Pakistan will definitely increase the economic activities in
the economy and the backward and forward linkages of the industry will bring positive
change in the income of every household.
5.9 Effects on Real Returns to Factors in Pakistan
Increased trade and especially exports increase the rate of return to factors. According to the
Heckscher-Ohlin model (although it is workable only in the economies where the amount of
goods and the number of production factors is equal (Suranovic S. , 2010)), the products are
assumed to be homogeneous, despite many markets in the world are represented in a better
way with differentiated products. In the case of Pakistan, the model suggests that Pakistan
faces a capital deficit as compared to its competitors and this deficit decreases the return to
capital (Khan et al, 2015).
169
The model used for the calculation of effects on real returns to factors is the extension of
standard GTAP model and uses the Armington assumption that categories the products on
the basis of country of origin. Furthermore, assimilation could disturb the rate of return on
capital by virtue of the prices of transitional and capital goods.
The labor force of Pakistan is more than 65 million. The unemployment rate in Pakistan is
about 6 percent (GOP, 2015). Despite tremendous government efforts for ensuring minimum
wages in Pakistan “Minimum Wages Ordinance 1961, the Punjab Minimum wages for
unskilled Workers Ordinance 1969, Minimum wages Board,” etc ensure that government is
dedicated to support the low-income groups.
The results of both simulations show the change in factor prices with regards to the price
index for private consumption expenditure. However, it fails to consider the impact of
changes in government’s revenue, and government’s capacity to redistribute tax income to
individuals, whether it is through transfer payments or provision of public goods (Khan et al,
2015).
5.9.1 Wages of Large Agriculture Land Owned Labor
The results of both simulations are presented in the table 5.22 showing changes in the wages
of household that owns the large agricultural land. The results reveal that in the case of the
first simulation, the wage of labor decrease by -1.45 percent while in the case of the second
simulation, it increased by 0.283 percent.
Table 0.22: Change in Real Wages of Large Agriculture Land Owned Labor (Percent)
Factor RF Code Description GSP Plus with
Quota
Potential EBA
Status
Labor LA-AGL Labor - agric (own)-large -1.45 0.283
Source: Author simulation results using MyGTAP program
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5.9.2 Wages of Medium Agriculture Land Owned Labor
The results in table 5.23 show the changes in the wages of labor that own medium size piece
of agriculture land, after performing both simulations.
Table 0.23: Change in Real Wages of Medium Agriculture Land Owned Labor
(Percent)
Factor RF Code Description GSP Plus with
Quota
Potential EBA
Status
Labor
LA-MF1 Labor - agric (own)-med Sindh -1.244 1.184
LA-MF2 Labor - agric (own)-med Punjab -1.335 0.285
LA-MF3 Labor - agric (own)-med OPak -0.956 1.487
Source: Author simulation results using MyGTAP program
The results show that in the case of the first simulation, the wage of labor with medium sized
agriculture land decreased by -1.335 percent in the region of Punjab. The results of the
second simulation show that wage of all households with medium sized agriculture land
increased with maximum increase in the case of labor living in parts of Pakistan other than
Punjab and Sindh and that is 1.487 percent.
5.9.3 Wages of Small Agriculture Land Owned Labor
In the case of farmers having a small area of agriculture land in all areas of Pakistan, the
results seem quite similar to the case of farmers having medium sized agriculture land. The
results of both simulations are shown in table 5.24. According to the results, the wages of the
labor with small size agriculture farm decreased everywhere in Pakistan when the country
faces quota restrictions in the EU while a positive change can be seen in the case of
simulation 2 i.e. if Pakistan gets the status of EBA in the EU28.
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Table 0.24: Change in Real Wages of Small Agriculture Land Owned Labor (Percent)
Factor RF Code Description GSP Plus with
Quota
Potential EBA
Status
Labor
LA-SF1 Labor - agric (own)-sm Sindh -1.227 1.136
LA-SF2 Labor - agric (own)-sm Punjab -1.017 0.851
LA-SF3 Labor - agric (own)-sm OPak -0.454 1.577
Source: Author simulation results using MyGTAP program
5.9.4 Wages of Skilled and Unskilled Labor
One additional simulation is added in the study by assuming unskilled labor is unemployed in
the model and then performing both simulations to check what would be the impact on real
wages of other types of labors. The impact of both simulations on the wage of skilled and
unskilled labor is shown in the table 5.25. The results show that in the case of the first
simulation, the wage of agriculture labor, in general, is increased by 1.355 percent, in the
case of unskilled non-agriculture labor, the wage increased by 0.286 percent and in the case
of skilled non-agriculture labor, it is increased by 0.304 percent.
The results of the second simulation are quite different where the wage of non-agriculture
unskilled labor is decreased by -1.851 percent while a maximum increase is seen in the
agriculture labor (5.366 percent)
Table 0.25: Change in Real Wages of Skilled and Unskilled Labor (Percent)
Factor RF Code Description GSP Plus with
Quota
Potential EBA
Status
Labor
LA-AGW Labor - agric (wage) 1.355 5.366
LA-SKU Labor - non-ag (unsk) 0.286 -1.851
LA-SK Labor - non-ag (skilled) 0.304 0.075
Source: Author simulation results using MyGTAP program
It is worthy to note that the supply of labor is fixed in the agriculture sector, so any decrease
in demand may decrease the wage of the agriculture labor. The results of the simulations
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show that the increase in the wage of skilled labor is greater than the increase in the wage of
unskilled labor. Similarly, the supply of production labor is also fixed which results into
increase in wage rate as the production demand increases. The results of the simulations
show that increased exports will increase the wage rate which opposes the theory that
liberalized trade may reduce the wage rate. For further information, please see (Stiglitz,
1970), (Davis, 1996), (Feenstra & Hanson, 1997), (Topalova, 2007), (Harrison A. , 2007).
The majority of the exports from Pakistan are textile and agricultural products so increased
volume of exports due to GSP plus and EBA status in the EU28 might shift labor from the
agriculture to industry. This is primarily due to the fact that land is sector specific but labor is
inter-sectorally mobile. So this offset effect might lead industrialization in Pakistan.
5.9.5 Real Return to Land of Large Agriculture Farms
After labor, land is another factor of production, the rent paid to land is also affected by
certain changes in the trade. The source land is fixed, so any change in demand for the
production of goods requiring more land may result into achange in return to land. The
results shown is table 5.26 reveal the effects of both simulations on the large land farms. The
results of the first simulation show a negative change in the real return to land in case of large
farms everywhere in Pakistan but in case of simulation 2, the large land farm of Punjab is
losing in return while a positive change is seen in the farms of Sindh (0.464 percent) and rest
of the Pakistan (2.053 percent) which is maximum gain.
Table 0.26: Change in Real Return to Land of Large Farms (Percent)
Factor RF Code Description GSP Plus with
Quota
Potential EBA
Status
Land
LN-LG1 Land - large- Sindh -1.252 0.464
LN-LG2 Land - large- Punjab -1.639 -0.175
LN-LG3 Land - large - OthPak -1.085 2.053
Source: Author simulation results using MyGTAP program
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5.9.6 Real Return to Land of Medium Agriculture Farms
The case of a change in areal return to the land of medium farms is a little bit different than
the previous case. The results of both simulations are presented in the table 5.27 that show a
reduction in return to the land of medium farms in the case of first simulation.
Table 0.27: Change in Real Return to Land of Medium Farms (Percent)
Factor RF Code Description GSP Plus
with Quota
Potential
EBA Status
Land
LN-MD1 Land - irrigated - med Sindh -1.228 1
LN-MD2 Land - irrigated - med Punjab -1.338 0.359
LN-MD3 Land - irrigated - med OthPak -0.951 1.467
Source: Author simulation results using MyGTAP program
The maximum reduction in return is seen in the province of Punjab (-1.338 percent). While
in the case of the second simulation, the results are quite encouraging. There is gain in return
to the land of medium farms in all areas of Pakistan with maximum gain in return of 1.467
percent in the region other than Punjab and Sindh.
5.9.7 Real Return to the Land of Small Agriculture Farms
The results of both simulations relating to change in return to the land of small farms are
presented in table 5.28 which are quite similar to the results of medium size farms. The gain
is negative in case of the first simulation for all regions of Pakistan with a maximum loss in
return in the province of Sindh (-1.215 percent). The results of the second simulation are
opposite to the first simulation that shows gain in return to the land of small farms in all areas
of Pakistan. The maximum gain is seen in the areas other than provinces of Punjab and Sindh
that is 1.613 percent.
Table 0.28: Change in Real Return to Land of Small Farms (Percent)
Factor RF Code Description GSP Plus with
Quota
Potential EBA
Status
Land
LN-SM1 Land - irrigated - sm Sindh -1.215 0.977
LN-SM2 Land - irrigated - sm Punjab -1.022 0.942
LN-SM3 Land - irrigated - sm OthPak -0.399 1.613
Source: Author simulation results using MyGTAP program
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5.9.8 Real Return to the Land of Non-irrigated Agriculture Farms
There are certain areas in Pakistan where traditional canal system is not activated to irrigate
the agriculture land. Those farms are called non-irrigated farms. The results of both
simulations are presented in table 5.29 to show the return to the land of non-irrigated farms.
Interestingly the results are very similar to the results of previous two cases (land of medium
and small farms). In the case of the first simulation, there is a negative gain in all areas of
Pakistan where non-irrigated farms exist with the loss of -1.12 percent in the province of
Sindh while in rest of the Pakistan the non-irrigated land suffered from the loss of -1.337
percent in return. The case of the second simulation shows gain in return of the non-irrigated
land with maximum gain in the Sindh province (0.821 percent) whereas in rest of the
Pakistan, the non-irrigated land gains 0.708 percent in return.
Table 0.29: Change in Real Return to Land of Non-irrigated Farms (Percent)
Factor RF Code Description GSP Plus with
Quota
Potential EBA
Status
Land
LN-DR1 Land non-irrig - sm/m Sindh -1.12 0.821
LN-DR2 Land non-irrig - sm/m Punjab -1.337 0.708
LN-DR3 Land non-irrig - sm/m OthPak -1.337 0.708
Source: Author simulation results using MyGTAP program
It is interesting to note that the results of the first simulation suggest a negative gain in return
to land which also support the results of Magee (1972) while working on USA economy. The
results are contradictory with the findings of Hong (1993) which suggested that protection
increases the return as resources shift towards agricultural productivity. The gain is not
possible without technology whether the economy is facing restrictions or not (Ghosh, 2003).
The Stolper-Samuelson Theorem which is based on the famous Heckscher-Ohlin model also
supports the results that liberalized trade lead to increase in return to land (Leamer, 1995).
Similarly, a large number of researchers support the results that return on land increases if
there are no restrictions on the exports of an economy, for example, see [(Runge & Halbach,
1990) and (Chang, 1979)].
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5.9.9 Real Return to the Capital
We have already discussed the impact of both simulations on the return to labor and land. In
this section, we will focus on the factor of capital. The study has divided the capital into four
categories and results along with categories are presented in table 5.30. The results of both
simulations show gain in return to capital in most of the types except capital other than
agriculture (-1.106 percent in the case of quota restriction on Pakistan) and capital formation
(-0.054 percent in case if Pakistan gets EBA status in the EU28). In the case of both
simulations, the maximum gain is seen in capital livestock, 1.818 percent when the quota is
applied on exports from Pakistan to justify the capping mechanism of EU28 and 6.24 percent
gain in return if Pakistan gets the status of EBA in the EU28.
Table 0.30: Change in Real Return to Capital (Percent)
Factor RF Code Description GSP Plus with
Quota
Potential EBA
Status
Capital
K-LVST Capital livestock 1.818 6.24
K-AGR Capital other agriculture -1.106 0.798
KFORM Capital formal 0.105 0.105
KINF Capital informal 0.28 -0.054
Source: Author simulation results using MyGTAP program
The results of both simulations suggest that there is gain in return to capital in most of the
cases which support the work of researchers like (Hong, 1993), (Chang, 1979) and
(Thompson, 2016). Minor & Mureverwi (2013) and Khan et al (2015) also produced the
same results while studying the Mozambique and Pakistan economy respectively. Although
Ghosh, (2003) also support this but adds that gain to return to the capital can be maximized
with continuous improvement in the technology.
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CHAPTER 6: SUMMARY AND CONCLUSION
6.1 Introduction
The study attempted to examine the development experience and the changing character of
Pakistan’s economy over the years in brief. In earlier years, a significant proportion of its
GDP was accounted for by its agriculture sector. But the structure of Pakistan’s economy has
shown a remarkable transformation in its economic structure and now has become more
industrialized. The relative importance of manufactured exports increased substantially as
opposed to traditional ones. The manufacturing sector of Pakistan in the case of exportable
commodities is not diversified.
The evolution of Pakistan’s trade relations with the EU dates back with the establishment of
diplomatic relations in 1962. Currently, the EU is by far the country’s single largest trading
partner absorbing approximately one-third of its total exports; the principle supplier of capital
goods and the leading donor of foreign capital assistance. Historically, the export
performance of Pakistan in the EU market showed encouraging trends. In comparison,
Pakistan export performance (measured in terms of growth and market share) has been far
better in contrast to many GSP, the ACP and Mediterranean countries.
The Generalized System of Preferences (GSP) plus status of Pakistan in the EU28 remained
the main focus of the study. Among the EU’s system of preferences, GSP plus status is
considered a vital opportunity for any economy engaged in trade with EU. Pakistan is among
those countries that are enjoying this status. Although, most of the commodities are
exportable to the EU28 without any tariff or quota under the system of GSP plus but still it is
behind the Everything But Arms (EBA) status of EU granted to Bangladesh.
6.2 Summary of Research Findings and Policy Implications
This section intends to summarize the major findings of the results. The study analyzed the
impact of GSP plus status of Pakistan in the EU through a number of simulation experiments.
The study employed standard GTAP to analyze the impact of GSP plus at the macro level.
Most of the studies conducted previously have found modest and mixed impacts resulting
177
from tariff elimination on a continental wide basis (for more details please see Mold &
Mukwaya (2015), Alam (2015) and Mevel & Karingi (2012)). Aggregated welfare impacts
were calculated for a single "regional" household representative of the government, private
households, and investors. Recognizing that aggregate or "regional" welfare analysis may not
illustrate the impact of different simulations on households, this research also employs an
alternative CGE model called MyGTAP initially developed by Minor & Walmsley (2012)
and then Khan (2015) for Pakistan that disaggregates the regional household into separate
entities.
The standard GTAP examines the impact of trade policies at the macro level, so the study
introduced the MyGTAP model to calculate the effects at household and regional level. It
pays special attention to the labor market according to the skills and region of the labor.
There are numerous studies that employed GTAP model with the assumption of perfect
competition and constant return to scale (Hertel, 1998), in this study, the simulations were
run by using standard GTAP and MyGTAP. The summarized results of standard GTAP are:
The results of all simulations show a positive change in the real GDP ranging from
US$45.75 million (0.021 percent from baseline) in the case of the second simulation.
Maximum positive change is US$269.828 million (0.126 percent) in second
simulation. In the case of the third simulation, the real GDP increases with
US$209.047 million (0.098 percent).
The results of all three simulations revealed mixed effects on the real output of
commodities. On a dollar value basis, Textile, Wearing apparel and wool, silk-worm
cocoons are impacted the most, with a US$ 6.15 million increase (0.03 percent from
baseline) in output for wearing apparel sector when Pakistan achieved GSP plus
status and compared with competitors. Under same status textiles sector improves
with US$ 4.844 million and wool, silk-worm cocoons with US$ 3.459 million. The
notable decrease was seen in the output of metals nec and ferrous metals. The
maximum decline is seen in metals nec (-1.40 percent) under GSP plus status when
comparing with other competitors and -1.128 percent if Pakistan gets EBA status.
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Similarly, in the case of ferrous metals, the decline is -0.59 percent under GSP plus
with competitors and -0.474 percent under EBA status. The maximum decrease under
GSP plus with quota mechanism is seen in wool and silk-worm cocoons which is -
0.881 percent from baseline.
A positive change is seen in real investment under all three simulations. The first
simulation i.e. GSP plus status of Pakistan in the EU28 while relaxing Pakistan from
all tariffs and quotas as compared to its competitors, show a maximum change in real
investment (US$ 2.686 million). The results of the simulation 2 i.e. GSP plus status of
Pakistan when quota restrictions are applied on Pakistan to justify the capping
mechanism in the EU28 show a minimum positive change in real investment (US$
0.507 million). The results of simulation 3 i.e. if Pakistan gets the status of EBA in
the EU28, are also positive and similar to simulation 1. There is a positive change of
US$ 2.106 million in the real investment.
The merchandise imports of Pakistan increase under all simulations with 4.791
percent under the first simulation, 0.729 percent under second and 3.692 percent
positive change from baseline in case of the third simulation. Similarly, an increase in
imports is seen in all sectors except plant base fiber and coal. The maximum decrease
is seen in coal sector (-133.94 percent) under both cases of GSP status. It decreased
by -109.94 percent under EBA status. A decrease of -2.95 percent is seen in the sector
of plant base fiber under quota restrictions in the EU after GSP plus. GSP Plus status
of Pakistan while maintaining the competitors at their existing positions show that the
major increase is seen in the sectors of cattle, sheep, goat and horses (US$ 11.763
million), leather products (US$ 11.553 million) and dairy products (US$ 11. 123
million. The GSP plus status when quota restriction applied on imports from Pakistan
into the EU28 shows amajor gain in imports in the sectors of cattle, sheep, goat and
horses (US$ 2.083 million), dairy products (US$ 2.064 million) and leather products
(US$ 1.914 million). Similarly, if Pakistan gets EBA status in the EU28 just like the
status of Bangladesh, the major gain in imports is seen in the sector of all services
where it increased by US$ 20.013 million. The other major sectors with an increase in
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imports include dairy products (US$ 8.902 million), leather products (US$ 8.569
million) and plant based fibers (US$ 7.832 million).
A decrease of -1.47 percent in merchandise exports is seen when quota restriction is
applied in the EU, while exports increase by 1.318 percent when comparing with
competitors in the EU and 0.907 percent increase if Pakistan gets EBA status. A
positive change is seen in the sectors of textiles, wearing apparel, beverage &tobacco
and paddy rice while rest of the sectors experience a decline. The maximum gains are
seen in the wearing apparel sector with US$ 32.401 million in case of simulation 2.
There is no winning sector under GSP plus the status of Pakistan in the EU28 with
quota restrictions. The results of the simulation 3 show some winning sectors with
maximum gain in wearing apparel sector with US$ 21.554 million from baseline
followed by textiles sector with US$ 6.209 million. The maximum decrease is seen in
the service sector (US$ -41.091 percent) if Pakistan gets EBA status in the EU.
A positive change in terms of trade is seen in all three simulations. In the case of the
first simulation the export prices that Pakistan receives from EU28 are 0.024 percent
higher than the import prices that Pakistan pays to the EU28. When quota restrictions
are applied on the imports of Pakistan in the EU28, Pakistan is better off in terms of
trade with 0.018 percent. While thehighest gain is seen in the results of simulation 3,
assuming that if Pakistan gets the status of EBA in the EU28 just like Bangladesh.
The results of this simulation show that Pakistan would 1.937 percent better off.
A sudden increase in the price level is seen in the prices of inputs that producer have
to pay. The maximum percentage increase is observed in the results of simulation 1.
The maximum price that producer will pay for the inputs will be in the sector of
fishing (3.514 percent). The least increase in seen in the results of simulation. The
maximum in seen in the fishing sector (0.859 percent). The results of the simulation
3, show a moderate increase in prices for producers. Although service sector prices
go very high (11.31%) but rest of the sectors showed moderate increase.
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The prices of most of the traded (imported) commodities have dropped under all
three simulations used in this study. Construction prices will converge into positive
values for all scenarios but the increase very marginal. The maximum reduction in
prices is shown by the simulation 1 which is in the sector of paddy rice (-0.239
percent) and minimum reduction in beverages and tobacco products (-0.003
percent). No change in price is seen oilseed sector. While in the case of simulation
2 (GSP plus with quota restriction), there is a maximum rise in the sector of paddy
rice (0.004 percent). There is no change seen in the sectors of plant-based fibers,
leather products, wool and silkworm cocoons, wearing apparel, textiles, animal
products, raw milk, sugar and vegetable oils and fats. While there is deterioration
in prices in rest of the sectors with a maximum decrease in price in the sector of
coal (-0.003 percent). In case Pakistan gets EBA status in the EU, there is an increase
in price level with a maximum increase in the services sector (2.319 percent). There
is no change in the price level in the sectors of animal products nec and raw milk.
While rest of the sectors are shown with a decrease in the price level. Maximum
deterioration in price is seen in the sector of coal (-0.015 percent) while gas,
electricity and transport and communication deteriorated with -0.01 percent.
The summarized results with MyGTAP are as follow:
The results of the both simulations show a positive change in real GDP of Pakistan.
The GDP of Pakistan increases by US$21.594 million (0.015 percent) when quota
restrictions are applied on imports from Pakistan in the EU under GSP plus status
while a positive change of US$ 884.047 million (0.617 percent) in real GDP in seen
if Pakistan achieves the status of EBA in the EU.
The results of both simulations show mixed effects in the sectoral output. The results
of the second simulation show better performance than the first. The maximum
increase in theservices sector (US$ 0.383 million) when quota restrictions are applied
on imports from Pakistan in the EU. On the other hand, under same simulation
maximum decrease in real output is seen in the sector of machinery and equipment
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US$ -2.117 million. The results of the simulation if Pakistan gets the EBA status
reveal that paddy rice shows amaximum gain with US$ 2.080 million. The prominent
sectors with a decrease in output include leather products with US$ -1.99 million,
plant-based fibers with US$ -1.60 million and textiles with US$ -1.99 million.
The results of both simulations show an increase in merchandise imports and
reduction in merchandise exports resulting disturbance in the trade balance. The
exports of Pakistan to EU28 reduced by -1.79 percent in the case of the first
simulation while in the case of the second simulation, the reduction is -1.282 percent.
This reduction is export is due to the production capacity of Pakistan in 2007 which
was adversely affected by load shedding which increased the production cost in
Pakistan resulting in adecline in exports. The results of both simulations show a
positive increase in the imports of Pakistan. The increase is 0.558 percent in the case
of the first simulation, while in the case of the second simulation, the increase is
1.153 percent. This increase in imports is also a result of increased cost of production
in Pakistan. Similarly, in order to increase the production, Pakistan would also require
more inputs to import.
The sectoral change in exports is also not encouraging in both cases. The results of
the simulation when quota restrictions are applied on Pakistani imports in the EU
show only four winning sectors with maximum gain in exports in the sector of plant-
based fiber (US$ 1.651 million). The maximum deterioration is seen in the sector of
dairy products (US$ -3.871 million). The results of the second simulation, if Pakistan
gets the EBA status in the EU, show only 5 winning sectors with maximum gain in
the sector of paddy rice (US$ 26.521 million). The maximum deterioration is
observed by the sector of wheat (US$ -9.486 million) and minimum deterioration is
seen in the sector of utilities (US$ -0.22 million).
When quota restrictions are applied on the Pakistani imports in the EU, the results
show that there are only 6 sectors where imports deteriorated that include plant-based
fibers (US$ -1.791 million), forestry (US$ -0.672 million), paddy rice (US$ -0.462
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million), sugar cane, sugar beet (US$ -0.353 million), oil (US$ -0.215 million) and
cereal grain nec (US$ -065 million). While rest of the sectors showed positive
indication with maximum gain in imports in the sector of dairy products (US$ 2.449
million). The results of the simulation, when EBA status is given to Pakistan indicate
that there are only 2 sectors that show reduction in imports which are oil seeds (US$ -
0.06 million) and other utilities (US$ -0.17 million) while in case of rest of the
sectors, the imports increased with maximum increase in the sector of paddy rice
(US$ 8.74 million).
The results of both simulations show gain in terms of trade with maximum gain in
case of second simulation i.e. if Pakistan gets the status of EBA in the EU28 just like
Bangladesh, show that Pakistan is receiving 1.834 percent higher export price than it
is paying for its imports from EU28. In the case of the first simulation the export
prices that Pakistan receives from EU28 are 0.019 percent higher than the import
prices that Pakistan pays to the EU28.
The results of both simulations show an increase in real investment. The results of the
simulation 1 show positive change in real investment (US$ 0.378 million) and the
results of simulation 2 are also positive and better than simulation 1. There is a
positive change of US$ 1.153 million in the real investment.
The results of both simulations show a positive change in overall household income.
There is a change of 0.74 percent in case of first simulation, but in case of second
simulation, the change is 2.17 percent which means that if Pakistan is allowed to
export duty free and quota free into the EU28, the household income in Pakistan will
rise. In the case of the first simulation, the household income of medium and a large
farm of Sindh is reduced by -0.478 percent and in Punjab by -0.239 percent, while the
household income of all other households increases under both simulations. The
maximum gain is seen in the income of landless agriculture labor of Pakistan. The
landless farmer is better off than the small farm owners of Pakistan under both
simulations. Similarly, the results of both simulations show that income of small farm
183
owners is better than the income of medium and large farm owners. Interestingly, the
household income of non-agriculture rural and urban population is increased
maximum under the first simulation and under the second simulation, the maximum
gain in household income is seen by the landless agriculture labor.
Factor income of in most of the cases decreased in case of the first simulation while it
is opposite in the case of the second simulation. In the case of first simulation, the
wages of labor increase having no agriculture land, skilled and unskilled non-
agriculture labor. Also the income of livestock capital and formal capital increased.
The results of the simulation 2 show opposite trend. The income of non-agriculture
unskilled labor, land of large agriculture farms in Punjab and informal capital
decreases while rest of the sectors show an increase in the income. The maximum
gain is seen in the wages of agriculture labor (5.366 percent), the income of large
farm land increases by 2.053 percent in the areas of Pakistan other than Punjab and
Sindh and the income of livestock capital rises by 6.24 percent. The overall increase
in income of all factors in the case of the second simulation is due to increased output
in Pakistan.
The policy implications for the study are straight forward and related to trade policy which is
also the main concern of the thesis. Any attempt to improve competitiveness in view of
increased competition after the GSP plus implementation will have to start from one basic
acknowledgment: it is the firms themselves that have the key to success in their hands. Only
if they adopt the right strategies, based on a clear vision of what and how they are producing,
for whom they are producing and in which target market they intend to export and why do
they have a chance of succeeding? Therefore, those firms/enterprises wishing to be proactive
require a supportive environment daring firms to compete within a tougher competition
environment to come. Public authorities have an important role to play in restoring hope
through creating and monitoring a safe and healthy business environment where Pakistan’s
firms can effectively compete domestically and then in the world market.
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The production activities of the country are concentrated towards the sectors of textiles,
wearing apparel, beverages and tobacco and leather products that need to be diversified with
cost effective methods. Similarly, China is the largest competitor of Pakistan in the EU28.
Along with China, India, Turkey and Bangladesh are also major competitors for Pakistan. A
country cannot supply every kind of the products to the world market. While looking at the
weaknesses of its competitors Pakistan can provide better products to the importers.
Moreover, Pakistan can focus on those markets where its competitors have a small share.
Bangladesh is already enjoying the status of EBA in the EU market, China is enjoying least
cost production techniques and Turkey along with modern technology is benefitting from
transportation cost. The results of different simulations revealed that there are many sectors
of the economy where Pakistan is not performing well. It is time to redesign the policies and
educate the industry to invest in those areas where country is lacking behind. The fruits of
GSP Plus or EBA status can only be enjoyed if the industry realizes the hidden potential in
various sectors of the economies.
6.3 Limitations of the Study
Parallel to many empirical studies, this study was constrained by a variety of factors which
could be considered as limitations. The first and the most important limitation is the database,
similar to most other studies which adopt CGE models. The current study used the database
of standard GTAP v9.0 with the base year 2011-12 to calculate the effects of GSP plus status
at the macro level. In order to calculate the effects of GSP plus at the household level, the
study used GTAP v9.0 and Social Accounting Matrix with the base year 2007-08. This
extension of standard GTAP model is known as MyGTAP which provided the parameters
related to trade elasticity but these parameters are not estimated econometrically. Although a
reasonable level of confidence can be attached to the conclusions of the model simulations,
as the results are robust with different Armington parameter values. It is noted that household
welfare results are sensitive to parameter values assumed in the model. Likewise, a superior
understanding of implications at household level could have been achieved if we had been
able to use more disaggregated data at the household level.
185
Another limitation of the study was that the model could only be simulated for comparative
static results rather than the dynamics ones. This could be used to understand the path that
changes the income and expenditure of households over time. It would have been ideal to use
a recursive dynamic model to track the policy implications, given the nature of the
fundamental research problem. Construction of a recursive dynamic CGE of Pakistan model
was severely constrained by relevant data such as capital stock at the industry level and other
time series forecasts for exogenous variables.
Regional disparities play an important role to determine the potential for growth in Pakistan.
It can be seen from the nature of opportunities available in the country by keeping in mind
the regional disparities and look into the regional development aspects with respect to trade
opportunities. It would have been ideal if we had evaluated the policy issues using a regional
CGE model. However, availability of reliable data at the regional level is a major constraint
in constructing a regional CGE model for Pakistan.
Despite the above mentioned limitations, the global CGE model (standard GTAP) generated
reasonable good results at macro level for the country and the MyGTAP model with the most
latest constructed SAM (2007-08) for Pakistan and the other database, generated plausible
empirical results in analyzing the impact of GSP plus status (and EBA) on household welfare
within the context of Pakistan.
6.4 Recommendations for Further Research
The recommendations for further research are directly or indirectly inspired by the above-
mentioned limitations of the current study. Some recommendations for further extension of
the study are as followed:
a) The data available in standard GTAP and SAM is not updated regularly, it would be useful
to spend more time, effort and resources into developing an inclusive database for a more
recent base year. Furthermore, the database should include some of the key features, for
instance, regional level industry and macro data – regional Input-Out out (IO) tables, industry
186
level capital stocks data and time series forecasts for different exogenous variables in the
present model.
b) The GTAP can be enlarged for Pakistan to include features, such as regional extensions in
tracking regional disparities, recursive dynamics in making conditional forecasts, and to
include features of imperfect competition in some of the markets - in order to better capture
the ground realities in Pakistan markets. Introducing imperfection feature of markets will
ensure more realistic simulation results with respect to trade concessions provided by the EU
in terms of implications in the long run within the Pakistan context.
c) Developing an econometrically estimated household level micro-simulation model and
linking it with the CGE model would be an ideal way to obtain welfare impacts of the GSP
plus and other trade opportunities.
6.5 Concluding Observations
The study has attempted to calculate whether the GSP plus status of Pakistan in the EU28
produce positive change in the economic growth in the presence of other competitors with
same or different product mix. The study attempted to calculate the effects of different
potential and current opportunities for Pakistan by using standard GTAP. It further used the
MyGTAP model that is an extension of standard GTAP model, developed by Khan (2015)
for Pakistan that helped to calculate the effects of policy shocks not only at the aggregate
level but also at household income and real wages. The latest standard GTAP used the base
year 2011 while MyGTAP employed the latest available Social Accounting Matrix (SAM)
with the base year 2007-08. The study used GTAP for three simulation experiments (GSP
plus status of Pakistan with respect to its competitors, GSP Plus of Pakistan in the EU28 with
quota restrictions to justify the capping mechanism and if Pakistan gets EBA status in the
EU28) and MyGTAP for two simulation experiments (GSP Plus of Pakistan in the EU28
with quota restrictions to justify the capping mechanism and if Pakistan gets EBA status in
the EU28).
187
The descriptive analysis of the results of different simulations using both standard GTAP and
MyGTAP reveal that there is an overall increase in the GDP of Pakistan. The incentive to
export in the EU28 will increase the production level in the Pakistan. Similarly, the
improvement in production also increases the real wages and household income. Despite
some limitations, the Global CGE model developed in this study produces plausible results
that would help to shed some light on the current debate about the GSP plus effects on
production, exports and household in Pakistan. The results of all simulations by using
standard GTAP 09 suggest a positive change in the real GDP, real investment, merchandise
imports and terms of trade of Pakistan. The merchandise exports of Pakistan increase in first
and third simulation but in case of the second simulation, it shows a decline in merchandise
exports. Further, a positive change in the output of many commodities is seen in the case of
all three simulations.
The main findings of the both simulations, run under MyGTAP model also show a positive
change in real GDP, merchandise imports, real investment and terms of trade while the first
simulation shows a negative change in merchandise exports. Similarly, – EBA status of
Pakistan in the EU28 show an increase in the household income with maximum gain by the
household of rural Sindh with no agriculture land and a positive change in real wages of
most of the factors. However, the large and medium agricultural household types show a
negative change in household income in case of the first simulation. Comparatively low
improvement in the urban and non-farm household of rural areas. Therefore, empirical
evidence in terms of the household welfare from this study supports the overall view that
Pakistan can gradually gain from GSP plus status.
188
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