Potential Economic Gains from Gsp Plus Status for Pakistan: an Empirical Analysis

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POTENTIAL ECONOMIC GAINS FROM GSP PLUS STATUS FOR PAKISTAN: AN EMPIRICAL ANALYSIS

MUHAMMAD SHAHZAD IQBAL

Registration No: 2012-GCUF-09408

Thesis submitted in partial fulfillment of

the requirements for the degree of

DOCTORATE OF PHILOSOPHY

ECONOMICS

DEPARTMENT OF ECONOMICS

GOVERNMENT COLLEGE UNIVERSITY FAISALABAD

FAISALABAD (PAKISTAN)

August 2016

DEDICATED TO MY PARENTS WHO SACRIFICED THEIR GOLDEN AGE TO EDUCATE ME

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

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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 .

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 ………….

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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

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

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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

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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.

APPENDIX 2 ...... Error! Bookmark not defined. APPENDIX 3 ...... Error! Bookmark not defined. APPENDIX 4 ...... Error! Bookmark not defined. APPENDIX 5 ...... Error! Bookmark not defined.

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

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

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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

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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

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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

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

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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

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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

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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.

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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

1 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 1950- 1960- 1970- 1980- 1990- 2000- 2007- Average Period 1960 1970 1980 1990 2000 2007 2014 Growth 3.50 6.10 4.20 6.60 4.40 6.10 4.53 5.06 Rates 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

2 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

Agri Sector 25%

Services Sector56% Ind. Sector 19%

Agri Sector Ind. Sector Services Sector

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

3 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

Growth Rate Time Period Share in GDP (Percentage)

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

4 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

5 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).

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

11.02 12 9.5 10 8.16 6.13 8 4.3 4.5 6 2.97 4 2 0 1950-60 1960-70 1970-80 1980-90 1990-00 2000-10 2010-15

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 7 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

Wholesale and Retailing Transport Other and Private Communi Services cation services sector

General Finance Administ & ration Insurance

Housing Services

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

8 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

6.73 8 5.93 5.3 4.5 4.55 6 2.8 4 2.8 2 0 1950-60 1960-70 1970-80 1980-90 1990-00 2000-10 2010-15

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.

Table 0.3: Trade Performance of Pakistan (US$ Million)

Trade Years Exports Imports 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) Percentage of total Region(Country)/Year 2011 2012 2013 2014 2015 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 25,369 24,718 24,802 25,078 24,088 Pakistan Source: State Bank of Pakistan, 2015

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. Code Chapter Description 2012 2013 2014 % age No. HS2 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 Articles of leather, animal gut, harness, travel 0.674 0.744 0.742 2.96 6 '42 goods 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 Raw hides and skins (other than furskins) and 0.457 0.530 0.547 2.18 9 '41 leather 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

11 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.

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:

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 14 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. 15

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. 16 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 Share in EU Countries Population S.No Forms of Relationship external Concerned (Millions) Trade (%) EU Member Treaty of Rome/ Treat of 1 …… 455 Countries Accession 2 Mediterranean Association Agreements 10 230 3 ACP Lome Convention 03 580

4 Other Third World Generalised Preferences 25 4000 US, Japan, CIS, 5 Most Favored Nation (MFN) 38 850 etc. 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

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. 18 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. 19

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. 20

(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. 21 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.

 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.

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:

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?

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

26 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.

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

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

29 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)

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

31 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

32 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.

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

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

35 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 CGE models are based on equilibrium microeconomics theories. theory. Econometric techniques are more helpful CGE techniques are more helpful when the when feedback effects to a particular effect of feedback is more in general or for industry. the whole economy. Econometric models are stochastic in CGE models are deterministic in nature. nature. They are mainly based on macroeconomic They are mainly based on macroeconomic formation. structure. Long-term time series data requirements These techniques are able to identify the reduce the chances to identify inter-sectoral linkages between industries and linkages. commodities within the country or between the country and the world. It requires long term cross-sectional data While CGE (comparative static) models which is not easy to find. require only standard year only.

Statistical testing is there in econometric CGE techniques don’t have statistical techniques. 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

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.

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 39 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 neoclassical 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).

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.

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.

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

43 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

44 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

45 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.

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

47 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. 48 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,

49 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.

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

51 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

52 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.

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. 54 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

55 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

56 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. 57 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.

1 Economies of scale means when a certain manufacturer produce a good in abundance, the cost of production minimizes.

58 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.

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

60 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

61 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 interregional 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

62 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. 63

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%

64 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

65 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

66 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.

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. 68

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

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.

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.

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

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.

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.

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 Author’s S. Identification Name and Policy Focus Results No. scheme Date The first ever developed I-O table Preliminary Fei et al for a large-scale industry which 1 Input-Output I-O table 1955 (1962) ultimately helped the researchers to table for industry develop SAM. McCathy & Planning food Land reforms showed maximum 2 Taylor policy SAM 1975-76 redistribution effect. (1980) (subsidies) Decrease in Aggregate demand results into declined wages and Labus Incentives and rent. The enterprises are getting 3 (1988) public sector SAM 1983-84 profits due to incentives, prices are enterprises going down and improvement in the current account balance, exports, and output. If social equality is not considered, Kerosene oil is the best commodity to increase tax revenue but it is Naqvi Energy sector tax least desirable due to the 4 SAM 1983-84 (1998) reforms beneficiary for low-income people and natural gas has least welfare cost so tax revenue can be increased by this.

Depreciation above equilibrium will result incost-push inflation Dutch disease causing a reduction in real income Vos (1998) 5 effect of foreign SAM 1983-84 that ultimately will reduce the

aid aggregate demand in the economy. Similarly, there would be strong Dutch disease effect of foreign aid. The income reduces due to tariff Siddiqui Tariff reduction cuts but the reduction in poor’s 6 and Iqbal and income SAM 1989-90 income is less than the rich (1999) distribution households. Tariff reduction causing an increase in imports and reduction in exports by increasing prices of domestic Tariff and Kemal et products and vice versa. It also 7 income SAM 1989-90 al. (2001) worsens the position of income distribution distribution but it causes an increase in household consumption. New road from Karachi to Network of road Okuda & Peshawar will bring positive Transport and 8 Brohi GTAP 4.0 change in the industrial sector of regional effects (2001) Punjab and NWFP and GDP will in Pakistan increase by 16%. It will bring changes in the Consequences of formation of final products and Martin et quota removal on Pakistan will get benefit from it 9 GTAP 5.4 al (2004) textile and only if it employ its resources clothing efficiently but there is fear of some welfare loss.

Liberalization of Agriculture brings Positive changes in household Gender-based income both in urban and rural Siddiqui impact of 10 SAM 2000 areas. It further improves the (2005) Economic economic growth and redistribution reforms. of income in the short run (negative in the long run). There is a positive relationship Tariff cuts, between tariff cuts and regional exports and disparities and the impact is greater 11 Butt (2006) PAKREG regional during military regimes. Further the disparities tariff cuts across the border causing an increase in real GDP. The external oil price possessed the Ahmad high potential to affect Pakistan External Balance and socio-economic condition and an impact on 12 O’Donogh SAM 2002 increase in foreign saving decrease welfare in ue (2008) poverty in the country and poverty Pakistan is increasing with the increase in import prices. Shaikh SAFTA If SAFTA is fully integrated and and implication on GTAP 4.0 Pakistan gets a tariff cut of 15%, it 13 Rahpoto Pakistan would bring the highest welfare (2009) economy gains for the people. Trade Trade liberalization through the Naqvi Liberalization abolition of the tariffhas apositive 14 SAM 2002 (2010) and Fiscal effect on household welfare and Strictness inequality in Pakistan.

Government revenue increased due Agriculture to agriculture income tax. Rashid 15 income tax and SAM (2002) Manufacturing and imports (2013) economic growth flourished while construction and exports faced decline Any change in water supply from Robinson rivers due to weather changes may 1 and Water Economy SAM 2006 adversely affect the agriculture 6 Gueneau Links (CGE-W) sector but the effects can be (2013) minimized by building Diamer- Basha dam on the Indus. Income inequality in Pakistan is increased by 0.49% from the Agriculture baseline. Medium and large Trade 17 Khan (2015) My GTAP household types are aided, and Liberalization there is nominal increase in the real and Poverty wages of medium and large agricultural labors

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. 79

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. 80 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).

81 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. 82

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.

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. 84 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.

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

86 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.

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 88

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).

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.

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.

“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

92 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

93 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

94 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

95 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.

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

97 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 98

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.

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) 100

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

103 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. 104

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. 107

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. 109

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 TRij which specify the direction of flows from sector i to sector j. For instance, TRhr shows the transfers from the household (h) to rest of the world (r), whereas TRrh shows the transfers from rest of the world to the household.

4.8.5.1 Household Sector

Household income (Yh ), household savings (Ys ), and household expenditures (Ye ) are the main accounts of the household sector. The main source of household income is factor income (Y f ), which is generated within the production activities. Moreover, they also obtain

1 Macroeconomic accounting framework is adopted from Warr & Azis (1997) 112 income through transfers from the government (TRg,h ) and rest of the world (TRr,h ). Income of household can be written as

Yh  Yf TRg,h TRr,h

where Yh = household income,

Y f = household factor income,

TRg,h = transfers from government to household,

TRr,h = 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:

Eh  C TRh,g TRh,r

where TRh,g = transfers from household to the government,

TRh,r = transfers from household to rest of the world, C = consumption of the household. Household saving can be expressed by the following identity

Sh  Yh  Eh

where Sh = saving of household,

= household factor income,

Eh = expenditures of the household. Substituting equation 4.136 and 4.137 in 4.138, we get

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Sh  Yf C  NTRh

where NTRh  (TRg,h TRh,g )  (TR r,h TRh, r )

Where NTRh = 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 (TRg,e ) and rest of the world (TRr,e ) are other sources of income of enterprise. We can express income and expenditure of enterprise as:

Ye Yk,e  Sd TRg,e TRr ,e

where Ye = enterprise income

Yk,e = capital income of enterprise

Sd = consumption of fixed capital (depreciation)

TRg,e = transfer to the enterprise from the government

TRr ,e = transfer to the enterprise from the rest of the world

Ee TRe,h TRe,g TRe,r Where

Ee = expenditure of enterprise

TRe,h = transfer of enterprise to household

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TRe,g = transfer of enterprise to government

TRe,r = transfer of enterprise to rest of the world

Saving account can be obtained by subtracting expenditure from income of enterprise.

Se  Ye  Ee

Substituting equation (4.140) and (1.141) in equation (1.142), we get

Se  Yk ,e  Sd  NTRe Where NTRe  (TRr ,e TRe,r )  (TRg ,e TRe.g ) TRe,h

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” (TRr ,g ). While, government expenditures ( Eg ) consist of transfers to the households (TRg ,h ), transfers to “rest of the world” (TRg ,r ) and public consumptions (G). We will denote total net transfers received through government by NTRg .

Equations for government receipts (Yg ), expenditures ( ) and savings ( S g ) are as follow:

Yg  It TRh,g TRr,g

where = government receipts

I t = indirect taxes

TRh,g = income taxes from households

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TRr,g = transfers from “rest of the world”

Eg  G TRg,h TRg,r where Eg = government outlays G = government consumption expenditure

TRg ,h = transfer to the households from the government

TRg ,r = transfers from government to rest of the world

Sg  Yg  Eg where S g = Government savings By substituting equations (4.143) and (4.144) in (4.145), we obtain

Sg  Yg G  NTRg

where NTRg is net transfers received by governments and can be written mathematically as

NTRg  (TRh,g TRg,h )  (TRr,g TRg,r ) 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 ( Er ), total receipts of foreigners from domestic agents (

Yr ), and foreign savings. Major sources of receipts of foreigners are imports (M), transfers from government (TRg ,r ) and transfer from households (TRh,,r ). While, total expenditure of foreigners ( ) consists of “transfers to households” (TRr,h ), “transfers to government” (

TRr,g ) and exports (E). Total receipts and expenditures of foreigner can be expressed as follows:

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Yr  M TRg ,r TRh,r

where Yr = total receipts of a foreigner from domestic agents M = Imports

TRg ,r = transfer from government to foreigners

TRh,r = transfer from households to foreigners

Er  X TRr ,h TRr ,g

where Er = total payments from foreigners to domestic agents X = total exports

TRr ,h = transfer from foreigners to households

TRr ,g = transfers from foreigners to government We can write identity of foreign savings as

Sr  Yr  Er

where Sr is the foreign savings By substituting equation (4.147) and (4.148) in equation (4.149), we obtain

Sr  M  X  NTRr

where NTRr is net transfers received by foreigners and can be written algebraically as

NTRr  (TRe,g TRr ,g )  (TRh,r TRr ,h )

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

Sh  Sg  Se  Sr  Yf C Yk ,e  Sd  It G  M  E

Rearranging above equation we get

Sh  Sg  Se  Sr  Sd Yf C Yk ,e  It G  M  E

As we know that Gross Domestic Product (GDP) at factor cost can be defined as

YFC  Yf Yk ,e and GDP at market price can be expressed, on the income side, as

Y  YFC  It

Y  Yf Yk ,e  It by substituting GDP at market price(Y) in the equation (4.153), we get the following expression:

Sh  Sg  Se  Sr  Sd Y C G  M  E

Since, we can write the definition of GDP at market price on expenditure side as

Y  C  I G  E  M where I = total value of the gross investment at market price. Arranging above equation for I, we obtain

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I Y C G  M  E

Hence, we can obtain the following equation by substituting value of I into the right-hand side of equation (4.157)

Sh  Sg  Sr  Sd Y C G  M  E 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 120

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

121 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,

123 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.

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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)

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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

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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 Market share Market Wearing Beverag Current Country Textiles Leather (Million Share (% apparel es Status Euro) age) China √ √ GSP 302,049 17.93 Turkey √ Customs 54,374 3.23 India √ √ UnionGSP 37,120 2.2 Brazil √ MFN 30,996 1.84 Vietnam √ tariffGSP 22,189 1.32 Indonesia √ GSP 14,432 0.86 Banglades √ EBA 12,335 0.73 hPakistan √ √ √ √ √ GSP+ 5,510 0.33 Peru √ Free 4,930 0.29 Guatemal √ TradeFree 690 0.04 Source: Pakistan Business Council 2015 a AgreemTrade Agreem 127 ent and GSP+ent 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? 128

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$)

Base Value Post Shock Change Percentage Simulations (Millions US$) Effects in GDP Change GSP Plus status with 213686.2 213956.031 269.828 0.126 Competitors GSP Plus Status with 213686.2 213731.953 45.75 0.021 Quota Restrictions

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.

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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$) Base GSP Plus with GSP Plus with EU Potential EBA Value Competitors Capping (Quota) Status Commodity (Millions Changes Change in Changes Change in Changes Change in US$) in Value Percent in Value Percent in Value 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 4765 1.921 0.04 -0.869 -0.018 1.4 0.029 fibers Crops nec 583 -2.613 -0.45 -0.244 -0.042 -1.906 -0.327 Processed 26562 -0.271 0.00 0.108 0.000 0.167 0.001 rice Oil seeds 4987 -0.112 0.00 0.067 0.001 -0.033 -0.001 Vegetables, 5051 -0.777 -0.02 0.019 0.000 -0.604 -0.012 fruit, nuts Sugar cane, 2958 0.571 0.02 0.277 0.009 0.439 0.015 sugar beet Leather 19420 0.431 0.00 0.23 0.001 0.174 0.001 products Cereal grains 538 -0.616 -0.11 -0.103 -0.019 -0.336 -0.062 nec Food 26297 0.272 0.00 0.234 0.001 0.245 0.001 products nec Wool, silk- worm 125 3.459 2.77 -1.101 -0.881 2.49 1.992 cocoons 276 -1.733 -0.63 -0.223 -0.081 -1.239 -0.449 Coal Wearing 21474 6.15 0.03 0.037 0.000 4.149 0.019 apparel Dairy 27416 0.449 0.00 0.253 0.001 0.299 0.001 products

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Textiles 17662 4.844 0.03 -1.044 -0.006 3.593 0.020 Meat 1332 0.473 0.04 0.269 0.020 0.361 0.027 products nec Animal 1181 0.174 0.01 0.25 0.021 0.079 0.007 products nec 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, 1496 0.248 0.02 0.181 0.012 0.054 0.004 goats, horses 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 2281 -1.318 -0.06 -0.1 -0.004 -1.022 -0.045 products Vegetable 5730 -2.172 -0.04 -0.166 -0.003 -1.749 -0.031 oils and fats Beverages and tobacco 3209 0.817 0.03 0.147 0.005 0.79 0.025 products Petroleum, 8109 -0.175 0.00 0.039 0.000 -0.135 -0.002 coal products Ferrous 874 -5.155 -0.59 -0.819 -0.094 -4.147 -0.474 metals Electronic 4409 -1.471 -0.03 -0.1 -0.002 -1.13 -0.026 equipment Paper products, 4326 -1.08 -0.02 -0.196 -0.005 -0.957 -0.022 publishing 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 3743 -2.34 -0.06 -0.284 -0.008 -1.837 -0.049 products Transport equipment 2751 -2.759 -0.10 -0.418 -0.015 -2.318 -0.084 nec Light 5719 -0.847 -0.01 -0.004 0.000 -0.699 -0.012 Manufactures Chemical, 15527 -3.202 -0.02 -0.611 -0.004 -2.452 -0.016 rubber,

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plastic prods Mineral 3431 -1.473 -0.04 -0.202 -0.006 -1.135 -0.033 products nec Machinery and 10742 -3.102 -0.03 -0.439 -0.004 -2.517 -0.023 equipment nec Manufactures 2720 -5.918 -0.22 -0.763 -0.028 -4.413 -0.162 nec Electricity 41347 0.792 0.00 0.222 0.001 0.607 0.001 Transport and communicati 103519 -0.191 0.00 0.005 0.000 -0.129 0.000 on

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 132 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

133 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)

6 4.791 5 3.692 4 3 2 1.318 0.729 0.907 1 0 -1 GSP Plus with Competitors GSP Plus with Quota Restrictions EBA Status -2 -1.47

Exports Imports

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 134 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,

135 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$)

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 330 -12.421 -3.76 -0.349 -0.11 -9.174 -2.78 fibers Crops nec 115 -9.316 -8.1 -1.169 -1.02 -6.038 -5.25 Processed 1989 -10.532 -0.53 -1.74 -0.09 -3.046 -0.15 rice Oil seeds 22.7 -9.795 -43.15 -1.331 -5.86 -8.103 -35.70 Vegetables, 657 -6.195 -0.94 -0.833 -0.13 -4.677 -0.71 fruit, nuts Sugar cane, - 0.011 -11.788 -1.881 -17100.00 -9.912 -90109.09 sugar beet 107163.64 Leather 632 -3.668 -0.58 -2.002 -0.32 -7.604 -1.20 products Cereal grains 71.3 -5.367 -7.53 -0.786 -1.10 -4.329 -6.07 nec Food 938 -8.906 -0.95 -1.446 -0.15 -5.745 -0.61

136 products nec Wool, silk- worm 2.74 -27.079 -988.28 -2.257 -82.37 -21.698 -791.90 cocoons Coal 0.82 -1.2 -146.34 -0.119 -14.51 -0.593 -72.32 Wearing 3679 32.401 0.88 -1.2 -0.03 21.554 0.59 apparel Dairy 33.9 -17.27 -50.94 -2.849 -8.40 -1.46 -4.31 products Textiles 10760 8.217 0.08 -1.512 -0.01 6.209 0.06 Meat 2.94 -21.69 -737.76 -3.793 -129.01 -17.345 -589.97 products nec Animal 72 -5.461 -7.58 -0.303 -0.42 -4.688 -6.51 products nec 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, 0.394 -8.459 -2146.95 -1.263 -320.56 -7.145 -1813.45 goats, horses 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 - 0.007 -8.489 -1.495 -21357.14 -7.181 -102585.71 Gas 121271.43 Sugar 29.4 -11.743 -39.94 -1.532 -5.21 -4.29 -14.59 Wood 21.8 -16.276 -74.66 -2.013 -9.23 -12.901 -59.18 products Vegetable 29.2 -11.564 -39.6 -1.732 -5.93 -8.052 -27.58 oils and fats Beverages and tobacco 361 2.762 0.77 -0.779 -0.22 3.645 1.01 products Petroleum, 401 -1.322 -0.33 -0.239 -0.06 -1.027 -0.26 coal products Ferrous 234 -9.922 -4.24 -1.616 -0.69 -8.004 -3.42 metals Electronic 71.9 -19.561 -27.21 -2.05 -2.85 -14.773 -20.55 equipment Paper products, 55.5 -13.058 -23.53 -2.171 -3.91 -10.685 -19.25 publishing 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 238 -16.967 -7.13 -2.238 -0.94 -13.304 -5.59 products

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Transport equipment 38.3 -19.124 -49.93 -1.61 -4.20 -15.898 -41.51 nec Light 46 -12.64 -27.48 -1.554 -3.38 -9.976 -21.69 Manufactures Chemical, rubber, 839 -12.926 -1.54 -2.085 -0.25 -9.643 -1.15 plastic prods Mineral 671 -6.517 -0.97 -1.124 -0.17 -5.069 -0.76 products nec Machinery and 628 -18.103 -2.88 -2.467 -0.39 -14.842 -2.36 equipment nec Manufactures 921 -15.814 -1.72 -2.162 -0.23 -11.645 -1.26 nec Electricity 90.8 -8.398 -9.25 -0.965 -1.06 -6.894 -7.59 Transport and communicati 1606 -8.468 -0.53 -0.928 -0.06 -6.884 -0.43 on 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).

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Table 0.7: Aggregate Imports of Pakistan, Constant 2011 Prices (Percent and Millions US$)

Base GSP Plus with GSP Plus with EU Potential EBA Value Competitors Capping (Quota) Status Commodity (Millions Changes Change in Changes Change in Changes Change in US$) in Value Percent in Value Percent in Value 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 354 10.434 2.95 -0.845 -2.95 7.832 2.21 fibers Crops nec 147 1.461 0.99 0.283 0.99 1.294 0.88 Processed 2578 7.49 0.29 1.383 0.29 5.978 0.23 rice Oil seeds 28.8 2.547 8.84 0.464 8.84 2.14 7.43 Vegetables, 938 4.385 0.47 0.759 0.47 3.396 0.36 fruit, nuts Sugar cane, 0.013 2.362 18169.23 0.494 18169.23 2.168 16676.92 sugar beet Leather 689 11.553 1.68 1.914 1.68 8.569 1.24 products Cereal grains 74 2.254 3.05 0.282 3.05 2.036 2.75 nec Food 1117 6.096 0.55 1.169 0.55 4.733 0.42 products nec Wool, silk- worm 2.99 7.017 234.68 0.402 234.68 5.458 182.54 cocoons Coal 0.881 -1.18 -133.94 -0.163 -133.94 -0.963 -109.31 Wearing 4256 9.589 0.23 1.567 0.23 7.304 0.17 apparel Dairy 40.2 11.123 27.67 2.064 27.67 8.902 22.14 products Textiles 12412 9.781 0.08 0.926 0.08 6.995 0.06 Meat 3.6 7.894 219.28 0.784 219.28 6.053 168.14 products nec Animal 77.4 3.165 4.09 0.644 4.09 2.533 3.27 products nec 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

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Cattle, sheep, 0.4 4.857 1214.25 0.876 1214.25 3.91 977.50 goats, horses 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 25.7 9.194 35.77 1.6 35.77 7.164 27.88 products Vegetable 37.6 5.103 13.57 0.985 13.57 4.029 10.72 oils and fats Beverages and tobacco 508 3.495 0.69 0.733 0.69 2.737 0.54 products Petroleum, 434 0.591 0.14 0.195 0.14 0.468 0.11 coal products Ferrous 259 1.878 0.73 0.434 0.73 1.48 0.57 metals Electronic 73.8 10.121 13.71 1.736 13.71 7.773 10.53 equipment Paper products, 66.3 6.923 10.44 1.041 10.44 5.394 8.14 publishing 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 264 8.82 3.34 1.474 3.34 6.806 2.58 products Transport equipment 40.3 8.241 20.45 1.434 20.45 6.575 16.32 nec Light 50.2 6.238 12.43 1.157 12.43 4.909 9.78 Manufactures Chemical, rubber, 941 5.039 0.54 0.732 0.54 3.981 0.42 plastic prods Mineral 1032 4.66 0.45 0.784 0.45 3.455 0.33 products nec Machinery and 675 7.978 1.18 1.344 1.18 6.24 0.92 equipment nec Manufactures 1023 8.014 0.78 1.451 0.78 6.186 0.60

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nec Electricity 90.8 5.922 6.52 0.849 6.52 4.724 5.20 Transport and communicati 1606 5.444 0.34 1.088 0.34 4.386 0.27 on 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.

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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$)

Base Value Change in Real Simulations Post Shock Effects (Millions US$) Investment GSP Plus status with 29000 29002.686 2.686 Competitors GSP Plus Status with 29000 29000.507 0.507 Quota Restrictions

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.

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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 (-

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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) GSP Plus status GSP Plus Status with Commodity EBA Status with Competitors Quota Restrictions 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

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Beverages and tobacco 2.274 0.329 1.889 products 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 1.939 0.327 1.539 prods Mineral products nec 2.499 0.423 1.996 Machinery and equipment 2.64 0.445 2.105 nec Manufactures nec 2.106 0.358 1.668 Electricity 2.329 0.412 1.81 Transport and 2.683 0.45 2.141 communication 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.

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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) GSP Plus status GSP Plus Status with Commodity EBA Status with Competitors Quota Restrictions 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, 2.274 0.329 1.889 horses 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 2.546 0.423 2.052 products Petroleum, coal products 0.332 0.059 0.253

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Ferrous metals 1.939 0.327 1.539 Electronic equipment 2.499 0.423 1.996 Paper products, 2.64 0.445 2.105 publishing 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 2.308 0.384 1.836 prods Mineral products nec 1.611 0.278 1.277 Machinery and 2.584 0.433 2.062 equipment nec Manufactures nec 2.423 0.409 1.932 Electricity 1.937 0.326 1.539 Transport and 2.703 0.463 2.133 communication 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.

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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) GSP Plus status GSP Plus Status with Commodity EBA Status with Competitors Quota Restrictions Paddy rice -0.239 0.004 0.012 Wheat -0.111 0.002 0.006 Plant-based fibers -0.135 0 0.017

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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 -0.038 0 -0.002 cocoons 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, 0.009 0 0.001 horses 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 -0.003 -0.002 -0.008 products 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, -0.018 -0.002 -0.009 publishing 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 -0.044 -0.001 -0.008 prods Mineral products nec -0.106 -0.001 -0.008

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Machinery and -0.032 -0.002 -0.009 equipment nec Manufactures nec -0.067 -0.001 -0.008 Electricity 0.035 -0.002 -0.01 Transport and 0.047 -0.002 -0.01 communication 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.

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Figure 0.2: Term of Trade (TOT) of Pakistan, Constant 2011 Prices (Percent)

2.5

1.937 2

1.5

0.5

0.024 0.018 0 GSP Plus status with Competitors GSP Plus Status with Quota EBA Status Restrictions

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

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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$) Base Value Post Shock Change Percentage Simulations (Millions Effects in GDP Change US$) GSP Plus status with 143169.594 143191.188 21.594 0.015 Quota Restrictions 143169.594 144053.641 884.047 0.617 EBA Status 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

152 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$)

GSP Plus with EU Capping Potential EBA Status Base Value (Quota) Commodity (Millions US$) Changes Change in Changes in Change in in Value Percent Value 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 181.39 -0.02 -0.01 0.46 0.26 nec Vegetables, 7201.54 0.196 0.00 0.17 0.00

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fruit, nuts Oil seeds 365.22 -1.722 -0.47 -0.76 -0.21 Sugar cane, 5895.70 0.087 0.00 -0.11 0.00 sugar beet Plant-based 2953.45 -0.796 -0.03 -1.60 -0.05 fibers Cattle,sheep, 4271.85 0.016 0.00 0.23 0.01 goats,horses Livestock and Meat 20862.25 0.125 0.00 0.25 0.00 Products 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 5029.13 -0.357 -0.01 0.93 0.02 Food Vegetable 3506.79 -0.528 -0.02 -0.21 -0.01 oils and fats Dairy 4373.73 0.073 0.00 0.62 0.01 products Sugar 5333.85 0.199 0.00 0.65 0.01 Beverages and tobacco 3618.37 0.117 0.00 0.67 0.02 products Textiles 23984.30 -1.208 -0.01 -1.11 0.00 Wearing 4404.37 -1.296 -0.03 -0.91 -0.02 apparel Leather 1203.00 -1.777 -0.15 -1.99 -0.17 products Wood 2418.66 -0.628 -0.03 0.28 0.01 products Petroleum, 8120.48 -0.24 0.00 0.53 0.01 coal products Chemical,ru bber,plastic 4289.23 -1.07 -0.02 0.22 0.01 prods Mineral 5498.41 0.119 0.00 0.96 0.02 products nec Metals and 2107.05 -1.875 -0.09 0.19 0.01 Products Motor vehicles and 2733.24 -0.569 -0.02 0.55 0.02 parts Electronic 2672.30 -0.795 -0.03 0.58 0.02

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equipment Machinery and 420.68 -2.117 -0.50 -0.94 -0.22 equipment nec Manufacture 1111.31 -1.151 -0.10 0.28 0.03 s nec other 22303.91 -0.216 0.00 0.66 0.00 utilitities Construction 16914.60 0.356 0.00 1.14 0.01 Trade 21665.48 -0.133 0.00 0.39 0.00 Transport equipment 18768.37 -0.036 0.00 0.64 0.00 nec Communicat 3009.44 0.058 0.00 0.76 0.03 ion 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

155 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)

1.5 1.171 1 0.558 0.5 0 -0.5 GSP Plus with Quota Restrictions EBA Status -1 -1.5 Exports Imports -1.282 -2 -1.79 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.

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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$)

GSP Plus with EU Capping Potential EBA Status Base Value (Quota) Commodity (Millions US$) Changes Change in Changes Change in in Value Percent in Value 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, 215.47 -0.169 -0.08 -1.46 -0.68 nuts Oil seeds 20.63 -3.014 -14.61 -1.211 -5.87 Sugar cane, sugar 54.5 0.645 1.18 -7.488 -13.74 beet Plant-based fibers 45.81 1.651 3.60 -4.195 -9.16 Cattle,sheep,goats,h 1.39 -2.489 -179.06 -8.543 -614.60 orses Livestock and 36.51 -1.819 -4.98 -9.552 -26.16 Meat Products

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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 121.88 -3.058 -2.51 -3.119 -2.56 and fats 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 141.04 -0.992 -0.70 -0.479 -0.34 tobacco products 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 623.81 -0.595 -0.10 -0.37 -0.06 products Chemical,rubber, 374.53 -2.182 -0.58 1.301 0.35 plastic prods Mineral products 306.69 -2.111 -0.69 -2.05 -0.67 nec Metals and 619.85 -3.232 -0.52 -0.638 -0.10 Products Motor vehicles 63.04 -1.606 -2.55 -0.323 -0.51 and parts Electronic 28.06 -2.442 -8.70 -0.867 -3.09 equipment Machinery and 370.78 -2.204 -0.59 -1.124 -0.30 equipment nec 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 1157.89 -0.971 -0.08 -0.36 -0.03 equipment nec 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-

158 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$) GSP Plus with EU Capping Potential EBA Status Base Value (Quota) Commodity (Millions US$) Changes Change in Changes Change in in Value Percent in Value 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 2.82 -0.065 -2.30 1.95 69.29 nec Vegetables, 296.05 0.326 0.11 2.76 0.93 fruit, nuts Oil seeds 7.93 0.125 1.58 -0.06 -0.72 Sugar cane, 64.33 -0.353 -0.55 4.49 6.97 sugar beet Plant-based 48.83 -1.791 -3.67 0.52 1.05 fibers Cattle,sheep, 1.43 1.926 134.69 4.99 348.67 goats,horses Livestock and Meat 39.60 2.435 6.15 6.67 16.85 Products Forestry 6.47 -0.672 -10.39 3.87 59.86

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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 1825.23 0.775 0.04 3.08 0.17 Food Vegetable 139.84 1.294 0.93 1.71 1.22 oils and fats Dairy 35.85 2.449 6.83 3.58 9.98 products Sugar 42.92 1.445 3.37 2.50 5.81 Beverages and tobacco 198.90 0.931 0.47 1.09 0.55 products Textiles 9664.46 0.643 0.01 1.17 0.01 Wearing 3023.97 1.404 0.05 1.85 0.06 apparel Leather 514.35 2.405 0.47 3.80 0.74 products Wood 54.12 0.96 1.77 1.23 2.27 products Petroleum, 689.53 0.109 0.02 0.80 0.12 coal products Chemical,rub ber,plastic 426.52 0.534 0.13 0.68 0.16 prods Mineral 452.14 1.796 0.40 2.64 0.58 products nec Metals and 656.26 0.548 0.08 1.03 0.16 Products Motor vehicles and 68.84 0.623 0.90 1.19 1.73 parts Electronic 29.00 1.754 6.05 1.66 5.71 equipment Machinery and 392.60 0.287 0.07 1.04 0.27 equipment nec Manufacture 527.40 1.831 0.35 1.59 0.30 s nec other 0.52 0.833 160.19 -0.17 -31.92 utilitities 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

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equipment nec Communicati 126.11 1.409 1.12 1.74 1.38 on 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$) 1.3 1.2 1.153 1.1 1 0.9 0.8 0.7 0.6

0.5 0.378 GSP Plus Status with Quota Restrictions EBA Status 0.4 0.3 0.2 0.1 0

Source: Author simulation results using MyGTAP program

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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)

2 1.834 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1 0.9 0.8 0.7 0.6 0.5 0.4 GSP Plus Status with Quota Restrictions EBA Status 0.3 0.2 0.1 0.019 0 Source: Author simulation results using MyGTAP program

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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.

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Figure 0.6: Changes in Households Income in Pakistan, Constant 2007 Prices (Percent)

2.5 2.17

1.5

1 0.74

0.5

0 GSP Plus with Quota Potential EBA Status

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.

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Table 0.16: Changes in Household Income of Large and Medium Farm, Constant 2007 Prices (Percent) Income HH Population GSP Plus Potential Household Types shares Code (millions) with Quota EBA Status (percent) Large and medium farm H-MF1 0.8 1.5 -0.478 1.831 Sindh Large and medium farm H-MF2 2.4 6.1 -0.239 1.613 Punjab Large and medium farm H-MF3 0.6 0.8 0.029 2.347 other 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) Income GSP Plus Potential Population Household Types HH Code Shares with EBA (millions) (percent) Quota 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 H-SF3 5.6 3.3 0.885 2.253 Pakistan Source: Author simulation results using MyGTAP program

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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) Income GSP Plus Potential HH Population Household Types shares with EBA Code (millions) (percent) Quota 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 H-0F3 1.7 0.7 0.954 2.323 Pakistan 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).

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Table 0.19: Changes in Household Income of Rural Agricultural Labor, Constant 2007 Prices (Percent) Income HH Population GSP Plus Potential Household Types shares Code (millions) with Quota EBA Status (percent) 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 H-AGW3 0.4 0.2 1.498 3.944 Pakistan 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) Income Population GSP Plus Potential Household Types HH Code shares (millions) with Quota EBA Status (percent) 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 H-NFOTH 27.7 17.3 0.997 1.356 other Source: Author simulation results using MyGTAP program

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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) Income Potential HH Population GSP Plus Household Types shares EBA Code (millions) with Quota (percent) 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).

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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)

GSP Plus with Potential EBA Factor RF Code Description Quota 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)

GSP Plus with Potential EBA Factor RF Code Description Quota Status

LA-MF1 Labor - agric (own)-med Sindh -1.244 1.184

Labor 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) GSP Plus with Potential EBA Factor RF Code Description Quota Status LA-SF1 Labor - agric (own)-sm Sindh -1.227 1.136 Labor 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) GSP Plus with Potential EBA Factor RF Code Description Quota Status LA-AGW Labor - agric (wage) 1.355 5.366

Labor 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

171 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)

GSP Plus with Potential EBA Factor RF Code Description Quota Status LN-LG1 Land - large- Sindh -1.252 0.464

Land 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) GSP Plus Potential Factor RF Code Description with Quota EBA Status LN-MD1 Land - irrigated - med Sindh -1.228 1 Land 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) GSP Plus with Potential EBA Factor RF Code Description Quota Status LN-SM1 Land - irrigated - sm Sindh -1.215 0.977 Land 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) GSP Plus with Potential EBA Factor RF Code Description Quota Status LN-DR1 Land non-irrig - sm/m Sindh -1.12 0.821

Land 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) GSP Plus with Potential EBA Factor RF Code Description Quota Status K-LVST Capital livestock 1.818 6.24 K-AGR Capital other agriculture -1.106 0.798 Capital 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

176 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

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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.

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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

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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).

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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.

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